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 4 is objected to because of the following informalities: Claim 4 depends on claim 4. It is understood that claim 4 was intended to depend on claim 3. Appropriate correction is required.
Response to Arguments
Applicant’s arguments in combination with amendments, see Remarks and Claims, filed 02/03/2026, with respect to rejections of claims under 35 USC 101 have been fully considered but they are not persuasive.
Beginning on page 5 the applicant argues that the limitations cannot practically be performed in the human mind. This argument is fully considered but is not persuasive. A physician can view the data and make judgment, opinion, etc. The claim does not provide any details regarding the number of data points or a specific rate to be processed. Therefore, as long a physician or one of skill in the art could view and make the same determination, the limitation is understood to be capable of being performed in the human mind.
The applicant argues that the claim requires specific sensors that cannot be performed in human mind. This argument is fully considered but is not persuasive. The mentioned sensors are mere extra-solution activity (pre-solution activity) to gather data. See analysis under 2A-prong 2. Furthermore, using the mentioned sensors are simply appending well-understood, routine and conventional activities previously known in the industry. See analysis under 2B.
The applicant argues that the claim requires extracting features that cannot be performed in human mind. This argument is fully considered but is unpersuasive. The claim does not provide any details regarding the number of features and any specific time-frame to calculate the features. Therefore, a physician could view the acquired data and use random features (i.e., peaks, trough, average, etc.) to make their judgment.
The applicant argues that the machine learning limitation does not recite a mathematical concept. This argument is fully considered but is unpersuasive. Claim 4 recites training the machine learning data using the data collected. Training machine learning data falls under mathematical calculation.
The applicant argues that detecting a physiological condition is not an abstract idea. This argument is fully considered but is unpersuasive. Concepts performed in the human mind include observation, evaluation, judgment, opinion.
Beginning on page 8 the applicant argues the improvement, particular way to achieve the result, etc. These arguments are fully considered but are not convincing. In order to have a technological improvement, the additional elements, either solely or in combination, need to reflect an improvement. Here, the alleged improvement is in the determination step.
On page 9, the applicant argues that the claim recites significantly more than any alleged exception. This argument is fully considered but is not persuasive. The mentioned limitations as recited are well-understood, routine and conventional to provide various vital signs of the user. The claims do not provide any specific technological improvements to any of the mentioned sensors.
Applicant’s arguments in combination with amendments, see Remarks and Claims, filed 02/03/2026, with respect to rejections of claims under 35 USC 103 have been fully considered but they are not persuasive.
The applicant argues that the claims as amended require the extracted features are not derived from any blood pressure measurement. This argument is fully considered but is not persuasive. As written, the claim requires measuring skin nerve activity (skNA), galvanic skin response (GSR), heart rate, and skin temperature and extracting features from the measurements [] wherein the features are not derived from any blood pressure measurement. This is disclosed by Suresh. The claim as written further states “comprising” and not “consisting of”. As such other measurements could later be taken and features from those could also be added.
The applicant argues that the onset of AD is identified prior to a blood pressure increase associated with AD. This argument is fully considered but is not persuasive. Initially, the amended limitations are not shown to be properly supported by the specification as originally presented. Furthermore, Suresh discloses measuring the same vital parameters, drawing features and detecting AD from the measured vital parameters. Therefore, Suresh discloses identifying AD from both the non-invasive vital parameters as well as from the gold standard blood pressure.
The applicant argues that Suresh does not teach identifying the onset of AD prior to a blood pressure increase. Under its broadest reasonable interpretation, the blood pressure can increase while the AD is being detected by the teachings of Suresh. Here, Suresh discloses a system for continuous monitoring the onset of AD before symptoms become extreme and potentially dangerous. Therefore, it is understood that such system would monitor and identify the onset of AD prior to a blood pressure increase or at least during a portion of the blood pressure increase associated with AD.
On page 11, the applicant argues that Suresh’s classification is performed on retrospectively labeled data, where AD has already been identified by blood pressure thresholds. This argument is fully considered but is not persuasive. The claims as written do not recite using “only” the features drawn and as recited, the list of sensed parameters are not an exclusive list (i.e., “consisting of”) but rather use an open ended phrase (i.e., consisting of) – see MPEP 2111.03. Furthermore, Suresh discloses an ongoing method/system to identify AD. Therefore, it would have been obvious to one of ordinary skill in the art to continue operation of the method as taught by Suresh, because doing so would allow for the machine learning to continue learning, updating, and becoming more accurate over time while performing the desired functions of a continuous monitoring for AD for a user.
Claim Rejections - 35 USC § 112
Claims 1-7, 13-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The independent claims 1 and 13 have been amended to recite “wherein the features are not derived from any blood pressure measurement” and “identify the onset of autonomic dysreflexia (AD) in the subject prior to a blood-pressure increase associated with AD”. The applicant has not pointed to any specific portion of the drawing, specification, etc., where support for these limitations could be found. Upon a review of the application as originally presented, such limitations are found to lack proper support. The applicant must either point to where support for these limitations could be found or cancel the limitations which are deemed to be new matter.
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 1-7 and 13-19 are rejected under 35 U.S.C. 101 because of the following analysis:
1 – statutory category: Claim 1-7 recite a series of steps and therefore, falls under the statutory category of being a process. See MPEP 2106.03. Claims 13-19 recite a system, and therefore, falls under the statutory category of being a thing or products. See MPEP 2106.03.
2A – Prong 1: The independent claims 1 and 13 recite a judicial exception by reciting the limitations of “measuring, extracting and classifying [] to identify the onset of autonomic dysreflexia (AD) in the subject”. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in mind or by a person using a pen and paper. Therefore, an abstract idea is involved.
2A – Prong 2: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The independent claims 1 and 13 recite the additional limitations of “outputting”, etc. The mentioned limitations are recited at a high level of generality and are considered to be data gathering/processing which are mere extra-solution activity. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
2B: The emphasized elements cited above do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’I, 110 USPQ2d 1976 (2014)).
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, 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 a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claims 2-7 and 14-19 depend on claims 1 and 13. The mentioned dependent claims recite the same abstract idea as the independent claims. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the dependent claim recites the limitations “a plurality of non-invasive sensors attached to a subject, the sensors comprising an ECG sensor, a GSR sensor, a heart rate monitor, and a skin temperature sensor”, “sampling resolution”, “”memory”, “network”, “display”, etc., are recited at a high level of generality and are mere extra-solution activity, and recited as performing generic computer functions. i.e., data processing. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)).
It is further noted that the act of training a machine learning model falls under the judicial exception of mathematical calculations.
Additionally or alternatively, the training of the learning model by inputting training data, and adjusting the model accordingly additionally represents mathematical calculations or mental observations or evaluation to iteratively adjust the model.
The additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, 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 a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Thus, claims 1-7 and 13-19 are directed to an abstract idea and are therefore rejected.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-7, 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over single reference NPL titled “Detection Of Dysautonomia In Spinal Cord Injury Through Non-Invasive Multi-Modal Sensing And Machine Learning” by Suresh et al. (hereinafter “Suresh” – published December 2020 – provided on IS).
Regarding claims 1 and 13. (Currently Amended) Suresh discloses a method/system (e.g., page 28, 78 “determining the onset of AD through non-invasive wearable sensing techniques”, page 86), comprising: measuring skin nerve activity (skNA), galvanic skin response (GSR), heart rate, and skin temperature of a subject (e.g., page 28 “measure skin temperature, heart rate, galvanic skin response (GSR)”, page 38, “skNA”); extracting, a plurality of features from the measurements, the features comprising medianNN, average iskNA, number of bursts, RMSSD, pNN5, or a combination thereof (e.g., page 45 discussing various features), wherein the features are not derived from any blood pressure measurement (the method does not use blood pressure measurements); classifying, based on a machine learning model (e.g., beginning on page 47 employing machine learning techniques, page 77), the plurality of features to identify the onset of autonomic dysreflexia (AD) in the subject (e.g., page 70, 72, 75 (beginning on last paragraphs), characterizing AD); and outputting, in response to the onset of AD, a message indicative of the onset of AD (page 28, page 77-79, 86 “a cloud-based universal classifying machine learning model to automatically recognize AD in real-time”).
Suresh fails to explicitly disclose “prior to a blood-pressure increase associated with AD. However, Suresh discloses a system for continuous monitoring the onset of AD before symptoms become extreme and potentially dangerous. Therefore, it is understood that such system would monitor and identify the onset of AD prior to a blood pressure increase or at least during a portion of the blood pressure increase associated with AD.
Regarding claims 2 and 14. (Original) Suresh discloses the method of claim 1 and system of claim 13, wherein measuring skin nerve activity (skNA), galvanic skin response (GSR), heart rate, and skin temperature of a subject further comprises receiving signals from a plurality of non-invasive sensors attached to a subject, the sensors comprising an ECG sensor, a GSR sensor, a heart rate monitor, and a skin temperature sensor (page 28 “The system continuously measured skin temperature, heart rate and galvanic skin response (GSR) (sweating) through sensors wrist-worn smart watch”, page 36, 38-44, 75-76).
Regarding claims 3 and 15. (Original) Suresh discloses the method of claim 1 and system of claim 13, wherein the machine learning model comprises a neural network (page 51 Neural network).
Regarding claims 4 and 16. (Original) Suresh discloses the method of claim 4 and system of claim 15, wherein the neural network comprises a multilayer perceptron model or a convolutional neural network which can be trained using data collected (page 10, 51-52, 84, 5 layer, training data, etc.).
Regarding claims 5 and 17 (Original) Suresh discloses the method of claim 1 and system of claim 13, wherein the machine learning model is trained based on a plurality of training data comprising labeled features derived from time series data from skin nerve activity (skNA) data, GSR data, and a skin temperature data (page 10, 48, 51-52, 84 “labelled class in the training set”).
Regarding claims 6 and 18. (Original) Suresh discloses the method of claim 1 and system of claim 13, wherein the skin nerve activity (skNA), galvanic skin response (GSR), heart rate, and skin temperature each have a sampling resolution less than 30 seconds (page 18, 47 “datasets were created with window lengths of 5, 10, 15, 30 seconds”).
Regarding claims 7 and 19. (Original) Suresh discloses the method of claim 1 and system of claim 13, wherein outputting, in response to the onset of AD, the message indicative of the onset of AD further comprises, storing the message in a memory, communicating the message over a network, causing the message to be displayed, or a combination thereof (page 22, 28 “wearable sensing technologies” would include a memory, display, etc.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. NPL titled “automated Detection of Symptomatic Autonomic Dysreflexia Through Multimodal Sensing” to Suresh et al.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANA SAHAND whose telephone number is (571)272-6842. The examiner can normally be reached M-Th 8:30 am -5:30 pm; F 9 am-3 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer S McDonald can be reached at (571) 270- 3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANA SAHAND/Examiner, Art Unit 3796
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