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 . 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 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.
Response to Amendment
This Office Action is responsive to the amendment filed 04/22/2026 (“Amendment”). Claims 1, 2, and 4-14 are currently under consideration. The Office acknowledges the amendment to claim 13. Claims 3 and 15-20 remain withdrawn.
The objection(s) to the drawings, specification, and/or claims, the interpretation(s) under 35 USC 112(f), and/or the rejection(s) under 35 USC 101 and/or 35 USC 112 not reproduced below has/have been withdrawn in view of the corresponding amendments.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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, 2, and 4-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1, 2, and 4-14 are directed to a “method,” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claim 1 recites (“sets forth” or “describes”) the abstract idea of a mathematical concept, substantially as follows:
applying data from portions of a real-time sensor signal to an artificial neural network trained to identify a plurality of motifs associated with the real-time sensor signal based upon corresponding output signals of the artificial neural network, the corresponding output signals providing an indication of correlation of the real-time sensor signal to each of the plurality of motifs; detecting a transition from a first motif of the plurality of motifs to a second motif of the plurality of motifs based upon changes in a first output signal corresponding to the first motif and a second output signal corresponding to the second motif; and identifying a change in an environmental condition based upon the transition between the first and second motifs.
The applying, detecting, and identifying steps involve the mathematical concepts of neural network processing, comparison, and classification. These steps correspond to “[w]ords used in a claim operating on data to solve a problem [that] can serve the same purpose as a formula.” See MPEP 2106.04(a)(2)(I).
Prong Two: Claim 1 does not include additional elements that integrate the mathematical concept into a practical application. Therefore, the claims are “directed to” the mathematical concept. The additional elements merely:
recite the words “apply it” (or an equivalent) with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. an artificial neural network as a processing black box).
As a whole, the additional elements merely generically implement a part of the abstract idea on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the identified change is not outputted in any way such that a diagnostic benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claim 1 does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Dependent Claims
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
introduce extra-solution activity (or the structure used for such activity) (e.g. how the neural network was trained (claims 4-9), etc.), and
describe field-of-use context (e.g. the type of neural network (claim 2), the type of sensor signal (claims 10-14), etc.).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves another technology or technical field, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-6, and 10-14 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by US Patent Application Publication 2021/0027891 (“Rajput”).
Regarding claim 1, Rajput discloses [a] method for anomaly detection in sensor data, comprising (a method for measuring abnormality (anomaly) in patients; paragraph [0086]): applying data from portions of a real-time sensor signal to an artificial neural network trained to identify a plurality of motifs associated with the real-time sensor signal based upon corresponding output signals of the artificial neural network, the corresponding output signals providing an indication of correlation of the real-time sensor signal to each of the plurality of motifs (real-time physiological sensor data is analyzed by a trained neural network (artificial) to indicate a physiological signature 250 (indication of correlation) related to different heart rates and respiration rates (plurality of motifs): paragraph [0067], [0075], [0080]-[0082], [0088]); detecting a transition from a first motif of the plurality of motifs to a second motif of the plurality of motifs based upon changes in a first output signal corresponding to the first motif and a second output signal corresponding to the second motif (data segmentation module 220 identifies changes (transitions) in heart rate data over a start (first signal) and end (second signal) time; paragraph [0082]-[0083]); and identifying a change in an environmental condition based upon the transition between the first and second motifs (data segmentation module 220 identifies time points associated with contextual (environmental) data associated with physiological data; paragraph [0080], [0083]).
Regarding claim 2, Rajput discloses all the features with respect to claim 1, as outlined above. Rajput further discloses, wherein the artificial neural network is a multilayer feedforward network, a deep learning network, a convolutional neural network, or a recursive neural network (neural network may include various deep learning methods; paragraph [0115]).
Regarding claim 4, Rajput discloses all the features with respect to claim 1, as outlined above. Rajput further discloses wherein the artificial neural network is trained using a stochastic training approach (a hidden Markov model is generated (trained) with various transition probabilities (stochastic); paragraph [0082]; NOTE: a hidden Markov model used random (stochastic) hidden vales in the transition layers for filtering data).
Regarding claim 5, Rajput discloses all the features with respect to claim 1, as outlined above. Rajput further discloses wherein the artificial neural network is trained using data generated from previously extracted motifs associated with the real-time sensor signal (neural networks estimate the correlation between how the observed data (real-time) differs from previous (extracted) physiological data; paragraph [0086]).
Regarding claim 6, Rajput discloses all the features with respect to claim 5, as outlined above. Rajput further discloses wherein the generated data comprises transitions between two of the previously extracted motifs (physiological signature determined from previous heart rate data; paragraph [0080], [0083], [0095]).
Regarding claim 10, Rajput discloses all the features with respect to claim 1, as outlined above. Rajput further discloses wherein the real-time sensor signal is a biotic signal (real time physiological data is collected by electrocardiogram (biotic) "ECG" sensor; paragraph [0086]).
Regarding claim 11, Rajput discloses all the features with respect to claim 10, as outlined above. Rajput further discloses wherein the biotic signal is a vital sign of an individual exposed to the environmental condition (real time physiological data is collected by ECG sensor and correlates to heart rate (vital sign) in relation to contextual data; paragraph [0068], [0086]).
Regarding claim 12, Rajput discloses all the features with respect to claim 11, as outlined above. Rajput further discloses wherein the biotic signal is an electrocardiogram (ECG) signal (real time physiological data is collected by electrocardiogram (biotic) “ECG” sensor; paragraph [0086]).
Regarding claim 13, Rajput discloses all the features with respect to claim 11, as outlined above. Rajput further discloses wherein the biotic signal is blood oxygenation, EEGs (electroencephalograms), temperature, ocular structure changes, or visual field changes (physiological data includes monitoring the oxygen saturation (blood oxygenation) level; paragraph [0067]).
Regarding claim 14, Rajput discloses all the features with respect to claim 11, as outlined above. Rajput further discloses wherein the environmental condition is associated with a habitat of the individual (contextual data 12 includes location temperature, altitude and air quality, all generally related to living conditions (habitat); paragraph [0068], [0083]).
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 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Rajput in view of US Patent Application Publication 2020/0342328 (“Revaud”).
Regarding claim 7, Rajput teaches all the features with respect to claim 6, as outlined above. Rajput does not appear to explicitly teach wherein the generated data comprise superimposed noise.
Revaud teaches wherein the generated data comprise superimposed noise (generate data with added noise (superimposed) on top of training data; paragraph [0094]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network of Rajput to include the generated data comprise superimposed noise, as taught by Revaud, in order to provide the advantages of a more robust model that is more resilient to noise generated during data collection (Revaud: ¶ 0094, augmentation).
Regarding claim 8, Rajput teaches all the features with respect to claim 5, as outlined above. Rajput does not appear to explicitly teach wherein at least one of the previously extracted motifs comprises superimposed noise.
Revaud teaches wherein the generated data comprise superimposed noise (generate data with added noise (superimposed) on top of training data; paragraph [0094]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network of Rajput to include the generated data comprise superimposed noise, as taught by Revaud, in order to provide the advantages of a more robust model that is more resilient to noise generated during data collection (Revaud: ¶ 0094, augmentation).
Regarding claim 9, Rajput teaches all the features with respect to claim 5, as outlined above. Rajput further teaches the generating previously extracted motifs (physiological signature determined off previous heart rate data; paragraph [0080], [0083], [0095]) and raw sensor signal data (raw sensor data related to physiological parameters; paragraph [0071]) but fails to teach using averaging data and binning into a histogram.
Revaud teaches using averaging data and binning into a histogram (calculates a metric based on averaging data using histogram binning; paragraph [0110]-[0111]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network of Rajput to include using averaging data and binning into a histogram, as taught by Revaud, in order to provide the advantages of creating easily graphable data related to probabilities of various distributions used in the model (Revaud: ¶¶s 0110, 0111).
Response to Arguments
Applicant’s arguments filed 04/22/2026 have been fully considered.
In response to the arguments regarding the rejections under 35 USC 101, they are not persuasive. The claims are directed to a mathematical concept because they recite steps that identify motifs and compare the motifs over time to arrive at a classification. There is no requirement for a “mathematical concept” to explicitly recite formulas. The claims can also involve mathematical relationships and mathematical calculations, which includes algorithms. Such is the case here. For example, the “applying” step performs a calculation to indicate correlation and thereby identify a motif, the “detecting” step performs a comparison calculation, and the “identifying” step performs a classification calculation.
Applicant’s citations are distinguishable. For example in Thales, the unique arrangement of sensors is what was critical. Such an arrangement is not found here. In Example 39, the claim did not “set forth or describe” a judicial exception. Here, the language identified above does set forth and describe the mathematical concepts.
The claims are also not directed to a practical application of the abstract idea. Any alleged “technical solution to a technical problem” is not provided by “additional elements” (i.e., elements that are not part of the abstract idea). An improved abstract idea is still an abstract idea. Further, Applicant’s citations to various technical solutions from the specification include important unclaimed features, such that any improvements from the specification are not adequately reflected in the claims. For example, at least claim 1 says nothing of a space habitat, biometric changes, integrated contribution to a PHM, log files, etc.
It is unclear why Applicant thinks the Examiner has categorically excluded AI innovations from patent protection. Applicant recites the artificial neural network as a generic processing element. They do not recite any particular layers, structure, or other details of the neural network. Thus, there is no “AI innovation.”
All claims remain directed to ineligible subject matter.
In response to the arguments regarding the rejections under 35 USC 102, they are not persuasive. Applicant highlights every element of e.g. claim 1, saying that Rajput fails to disclose any of the claim. It appears that Applicant has failed to fully consider the Office’s citations and the disclosure of the reference itself. ¶ 0080 teaches that the classifier is an artificial neural network (explicitly stated), trained to identify motifs. “Motif” is a broad term that may simply refer to e.g. a pattern, morphology, signature, profile, etc. The “junk data” is identified because it has a particular “motif.” Further, ¶ 0080 is not the only one that was cited. For example, ¶¶s 0082 and 0088 describe a physiological signature 250, which affects e.g. heart and respiration rates. The signature indicates correlation with heart and respiration rates because it affects any determined rates. The phrase “indication of correlation” is broad, and need not be construed as narrowly as argued by Applicant.
Detecting a transition between motifs is simply detecting a change from one pattern, morphology, signature, profile, etc., to another. The cited data segmentation module 220 identifies these changes/transitions. ¶ 0083 specifically mentions detecting a change in e.g. heart rate (based on a change in motif). The identified change in environmental condition is the change in contextual data. If Applicant is further arguing based on the understanding of claim 14 (that “environment” e.g. relates to habitat), the Office action notes that contextual data can include location temperature, altitude, and air quality, etc.
Regarding claim 4, it is maintained that ¶ 0082 discloses training using a stochastic training approach. Generating a Hidden Markov Model meets this claim language. In such a model, the transition between states, which is hidden, is a Markov process with transition probabilities. Probabilistic models are stochastic. See e.g. https://en.wikipedia.org/wiki/Hidden_Markov_model. Further, the different sleep stages can correspond to different motifs, and ¶ 0082 describes detecting transitions between stages, thus identifying a change in environmental condition (e.g. sleep condition).
Regarding claims 5 and 6, Applicant ignores the disclosure in e.g. ¶ 0086 of how previous data is used in the training. The previous data includes motifs that help define the classifications made by the model/classifier.
Regarding claims 7-9, it is maintained that the art teaches what it was cited for. Revaud teaches superimposing noise into training data. Applicant does not address this, and does not address the motivation for combination.
All claims remain rejected in light of the prior art.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREY SHOSTAK whose telephone number is (408) 918-7617. The examiner can normally be reached Monday-Friday, 7am-3pm PT.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson, can be reached at telephone number (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791
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