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 Status
Claims 1-20 are currently pending and under examination herein.
Priority
According to Filling Receipt dated 06/08/2021, the present application does not claim priority to any applications. Accordingly, the effective filing date of the claimed invention is 05/28/2021.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/03/2025 has been entered.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 10/03/2025.
The objection to claims 1 and 11 in the office action filed 04/03/2025 has been withdrawn in view of amendments received 10/03/2025 specifically by correcting the informalities.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
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 an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1 and 11 being representative) is directed to a system and a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1-20 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claims 1 and 11 recite training, iteratively, the probabilistic machine learning model using the probabilistic training data; the limitation training the model is considered a mathematical calculation, as discloses in instant specification Training using a regression algorithm [0039], algorithms used to produce calibration function may include linear discriminant analysis, … [0040], “expected loss” algorithm [0049]. As such, said limitation falls within mathematical concepts groupings of abstract ideas.
Claims 1 and 11 further recite generating the probability of an emergent physiological state; the limitation generating a probability is considered a mathematical calculation, and as such, falls within mathematical concepts groupings of abstract ideas.
Claims 1 and 11 further recite classifying the probability of an emergent physiological state to an intervention, wherein the intervention comprises changing a flight control, utilizing a classifier which comprises: inputting the probability of an emergent physiological state to the classifier; and training the classifier by inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of parameters correlated to candidate interventions; classifying the probability of an emergent physiological state to the intervention as a function of the classifier; the limitations classifying is considered a mathematical calculations, as it involves training a classifier (as recited in claims 1 and 11).
Claims 5 and 15 recite classifying the probability of an emergent physiological state to the intervention; the limitations classifying is considered mathematical calculations, as it involves training a classifier (as recited in claims 1 and 11).
Claims 6 and 18 recite training the classifier; the limitation training the classifier is considered a mathematical calculation when given its broadest reasonable interpretation in light of the specification (specification [0039] : "training may be performed using a regression algorithm and produce a regression mode”; [0048] suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes), and as such falls within mathematical concepts groupings of abstract ideas.
Claims 8 and 17 recite generating the at least a confidence metric; the limitation generating a confidence metric is considered a mathematical calculation (specification [0028] “a "confidence metric," is a quantitative measure of confidence associated with a classification of an intervention”, for example, a percent probability), and as such, falls within mathematical concepts groupings of abstract ideas.
Claims 9 and 19 recite training the probabilistic machine learning model as a function of the machine learning algorithm; the limitation training the probabilistic model is considered a mathematical calculation (specification [0039] : "training may be performed using a regression algorithm and produce a regression mode”; [0048] suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes).
Claims 10 and 20 recite determining a respiration rate as a function of the plurality of inhalation parameters; the limitation determining a respiration rate is considered a mathematical calculation, and as such, falls within mathematical concepts groupings of abstract ideas.
Claims 1 and 20 further recite determining a respiration volumetric flow rate; the limitation determining a volumetric flow rate is considered a mathematical calculation, and as such, falls within mathematical concepts groupings of abstract ideas.
The identified claim limitations fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the following reasons. Therefore, claims 1-20 recite an abstract idea. [Step 2A, Prong 1: YES]
Response to Applicant’s Arguments
Applicant's arguments filed 10/03/2025 have been fully considered but they are not persuasive.
Applicant states:
The fact that a step may "involve" performing a mathematical calculation does not mean that the step "recites" a mathematical calculation. Id. For instance, construction of a tree structure based on spatial relationships between symbols in a textual formula and use thereof in a "local positioning" algorithm to govern display thereof has been found not to recite a mathematical calculation because they "do not recite process steps which are themselves mathematical calculations, formulae, or equations." In re Freeman, 573 F.2d 1237, 1239-1240 and 1246 (C.C.P.A. 1978) (emphasis added). Further, "[w]hen determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017) (determining that the claims to a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform did not merely recite "the abstract idea of using 'mathematical equations for determining the relative position of a moving object to a moving reference frame'."). For example, a limitation that is merely based on or involves a mathematical concept described in the specification may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim. MPEP § 2106.04(a)(2). I.
Applicant respectfully contends that the claims do not recite a mathematical concept because the claim as a whole is not "only based on or involves a mathematical concept."
Applicant respectfully contends that the claims do not recite a mathematical concept because the claim as a whole is not "only based on or involves a mathematical concept." Claim 1 recites a flight mask comprising a fluid channel configured for inhalation by a user; an inhalation sensor module... associated with the user's inhalation; an environmental sensor module... indicative of the user's physiological status; and a processor that...transmits an audible, tactile, or visual signal to the user to decrease the probability of the emergent physiological state. None of these limitations "recite" mathematical relationships, formulas/equations, or calculations.
Rather, they claim a specific configuration of head-worn hardware and sensor modules that acquire real-world inspirate and environmental data that can be used to determine a user's psychological state and actively assist the user. The claimed invention is a physical, head-worn product, not an abstract algorithm. Any internal computations are merely part of operating this concrete, wearable apparatus. Moreover, the mere fact that internal processing may involve calculations (e.g., probabilistic modeling, classifier training) does not convert the claim into one that recites a mathematical concept. As indicated above "a claim does not "recite" a mathematical concept if it is only based on or involves a mathematical concept."
In addition, similar to Thales, the amended claim 1 is directed to a particular arrangement of physical sensors integrated with head-worn hardware (here, a flight mask with a fluid channel that routes inspirate to an inhalation sensor module and an environmental sensor module with a processor that uses those real-world measurements to classify an intervention and transmit a signal to the user to decrease the probability of an emergent physiological state). Similar to Thales, the recited improvement flows from how the sensors are configured and used (i.e., the concrete placement in a flight mask fluid path and sensed communication with the cabin environment) to produce actionable control and user signaling, not from claiming any mathematical relationship itself. Thus, even if internal computations are involved, that does not render the claim a mathematical concept or ineligible under § 101. The claimed subject matter is the integrated sensor-hardware system and its concrete application, not the math.
It is respectfully submitted that this is not persuasive. The Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception.
With regards to applicant referring to Thales Visionix, Inc. v. United States for claims that do not recite a mathematical concept, if the are only based on or involves a mathematical concept, Examiner submits that MPEP 2105.04(a) in Thales Visionix, Inc. v. United States, 850 F.3d 1343, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017) states: ("That a mathematical equation is required to complete the claimed method and system does not doom the claims to abstraction."), emphasis added. The claims of Visionix are directed to unconventional /particular configuration of inertial sensors (unconventional additional element) and a particular method of using raw data from the sensors to perform their calculations, as a result, it renders the claims subject matter eligible. See MPEP 2106(a)(2) I. On the other hand, the present claimed invention is directed to obtaining data from sensors of a flight mask (conventional additional element) to calculate a probability, and therefore, not subject matter eligible.
Claims 1 and 11 specifically recites “generating a probability”, which is a mathematical calculation. It is noted that a mathematical relationship may be expressed in words and there is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation" (MPEP 2106.04(a)(2)).
Additionally, claim limitations are analyzed given their broadest reasonable interpretation in light of the specification (MPEP 21006.04 (a)(2) C). The broadest reasonable interpretation of claims 1 and 11 in light of the specification encompasses mathematical concepts and metal processes for at least the steps of generating, training, and classifying.
Furthermore, with regard to Applicant stating “The claimed invention is a physical, head-worn product, not an abstract algorithm. Any internal computations are merely part of operating this concrete, wearable apparatus.” and referring to Thales, Examiner states that the claimed invention is directed to obtaining data (insignificant extra solution activity) from sensors of the flight mask (convention additional elements, see above), performing mental and/or mathematical concepts to generate a probability (mathematical calculation) using a mathematical algorithm (mental and/or mathematical concepts/abstract ideas), and transmitting a signal to user (additional element that amounts to insignificant extra solution activity).
Further, with regards to Applicant referring to “In re Freeman, 573 F.2d 1237, 1239-1240 and 1246 (C.C.P.A. 1978)”, Examiner states that claims are examined according to MPEP guidance.
Applicant further states:
Further, examples of claims that do not recite a mathematical concept issued with or after the 2019 PEG are found at least in Example 39 (Method for Training a Neural Network for Facial Detection)" October Update, p. 7. Further details are provided in the Memorandum entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" dated August 4 2025.
Analogous to those of Example 39 because, like Example 39 that utilizes a first training set and a second training set based on the first one to iteratively train a neural network, steps recited in claim 1 disclose, for example, "training, iteratively, the probabilistic machine learning model using the probabilistic training data; and generating the probability of an emergent physiological state as a function of the trained probabilistic machine learning model;" Thus, analogous to Example 39, claim 1 recites iterative training of a machine learning model through the use of successive training sets derived from prior model outputs, which cannot be solely identified as mathematical concept.
It is respectfully submitted that this is not persuasive. Example 39 is directed to training a neural network for facial detection. In contrast, the instant claims do not recite a neural network nor do the claims include any analogous steps directed to analyzing facial images. Further, instant claims 1 and 11 specifically recites “generating a probability”, which is a mathematical calculation. Furthermore, the broadest reasonable interpretation of claims 1 and 11 in light of the specification encompasses mathematical concepts and metal processes for at least the steps of generating, training, and classifying.
Therefore, claims 1-20 recite abstract idea(s).
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claim(s) 1-20 include the following. The remaining claims do not recite any additional elements.
Claims 1 and 11 recite a flight mask comprising a fluid channel to be in fluidic communication an inspirate; an inhalation sensor module, an environmental sensor module, a processor, and receiving plurality of parameters; transmit an audible, tactile, or visual signal to the user.
Claims 2 and 12 recite a gas concentration sensor and an inspirate pressure sensor.
Claims 3 and 13 recites a light source and a light detector.
Claims 4 and 14 recites a cabin pressure sensor and a positional sensor.
Claims 5 and 15 recites inputting the probability of an emergent physiological state to a classifier.
Claims 6 and 18 recite inputting training data into the machine learning model.
Claims 7 and 16 recite a user signaling device to transmit the audible, tactile, or visual signal to the user.
Claims 8 and 17 recite inputting the intervention class to the machine learning model.
Claims 9 and 19 recite inputting training data to machine learning model.
The additional element of a processor is considered as generic computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional elements of receiving data, inputting data, plurality of sensors, plurality of sensor modules, a light source, a light detector, and a user signaling device serve to collect the information for use by the abstract idea. See MPEP 2106.05(g).
Furthermore, the additional element of receiving data, inputting data, and transmitting a signal to user amount to necessary data gathering and outputting, as such, is insignificant extra-solution activity and do not integrate the recited exception into a practical application in Step 2A Prong 2 of 101 analysis (see MPEP 2106.04(d)(2)). The courts have identified limitations that merely gather data or stores data as insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-20 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Response to Applicant’s Arguments
Applicant states:
Applicant respectfully asserts that, at least as amended, representative claim 1
incorporates the alleged abstract idea into a practical application.
Consistent with the Office's Example 45, claim 2 analysis, the present claims use
calculated results to drive real-world control/assistance, integrating any judicial exception into a practical application. In Example 45, temperature measurements and cure-percentage calculations were applied by a controller to open the mold and eject the part at the target cure, thereby avoiding under-cure and over-cure and improving the industrial process. As reason by the Office, this "technical advantage" improved upon previous controllers in the field. Here, the computed probability of an emergent physiological state is likewise applied then acted on by transmitting a user signal (via helmet/earcup/vehicular displays) and/or commanding flight controls (e.g., enabling autopilot, adjusting altitude or G-loads), to decrease the probability of the emergent state in a time-critical cockpit environment. As indicated in the Applicant's specification this technical advantage not only allows for operational enhancements such as the detection of faults or leaks but also allow for intervention procedures which would improve previous flight masks in the field. (See at least Para [0009], [0031]). Similar to the Office's reasoning for Example 45, claim 2 in which the added limitations "in combination with the other claim limitations, reflects the technical advantages described in the specification" the added limitations here reflect the technical advantages described in the specification by improving upon previous flight mask in the field by providing operational enhancements and intervention procedures.
It is respectfully submitted that this is not persuasive. In Example 45, which was directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78, 209 USPQ at 4., the Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. See MPEP 2106.05(e). in contrast to example 45, none of the additionally recited elements in the instant claims integrate the judicial exception(s) into a practical application. Specifically instant claims recite the additional elements of a flight mask comprising a fluid channel, an inhalation sensor module, a gas concentration sensor and an inspirate pressure sensor, a light source and a light detector, an environmental sensor module, a cabin pressure sensor and a positional sensor, a processor, and receiving plurality of parameters; transmit an audible, tactile, or visual signal to the user, inputting data, receiving plurality of parameters, and transmit an audible, tactile, or visual signal to the user, which serve to collect the information for use by the abstract idea and amount to mere data gathering and outputting, as such, is insignificant extra-solution activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claim(s) 1-20 include the following.
Claims 1 and 11 recite a flight mask comprising a fluid channel to be in fluidic communication an inspirate; an inhalation sensor module, an environmental sensor module, a processor, and receiving plurality of parameters; transmit an audible, tactile, or visual signal to the user.
Claims 2 and 12 recite a gas concentration sensor and an inspirate pressure sensor.
Claims 3 and 13 recites a light source and a light detector.
Claims 4 and 14 recites a cabin pressure sensor and a positional sensor.
Claims 5 and 15 recites inputting the probability of an emergent physiological state to a classifier.
Claims 6 and 18 recite inputting training data into the machine learning model.
Claims 7 and 16 recite a user signaling device to transmit the audible, tactile, or visual signal to the user.
Claims 8 and 17 recite inputting the intervention class to the machine learning model.
Claims 9 and 19 recite inputting training data to machine learning model.
The additional element of a processor is conventional computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional element of receiving data, inputting data, and transmitting a signal to user amount to necessary data gathering and outputting, as such, is insignificant extra-solution activity and do not amount to significantly more in Step 2B of 101 analysis (see MPEP 2106.04(d)(2)). The courts have identified limitations that merely gather data or stores data as insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Furthermore, the additional elements of receiving data, inputting data, plurality of sensors, plurality of sensor modules, a light source, a light detector, and a user signaling device amount to conventional, well-understood, and routine methods and systems for inspirate sensing and analysis. This position is supported by Opperman et al. (US10786693B1). Opperman discloses plurality of subject-mounted sensors (sensor modules) and plurality of sensors (inhalation and environmental), an outlet channel or port (a fluid channel), a processor, inputting and outputting data, a light source, a photodiode (light detector) (claim 1). Furthermore, the additional elements of receiving data, inputting data amounts to necessary data gathering and outputting, and as such, considered insignificant extra-solution activity.
Opperman further discloses a system for alerting the subject/wearer of the device or a third party of such dangerous breathing or other health conditions; col. 9, Ls. 55-64.
Additionally, Dashevsky (US10561863B1) discloses a wearable device for comprehensive bio-monitoring of physiologic metrics to determine metabolic, pulmonary and cardiac function and oxygen saturation measurements from breathing mask apparatuses. The device non-invasively monitors the physiologic profile of the subject, and is capable of detecting physiologic changes, predicting onset of symptoms, and alerting the wearer or another person or system. The device comprises both a wearable sensor suite and a portable gas composition and flow analysis system. It comprises a miniaturized non-invasive sensor suite for detecting physiologic changes to detect dangerous breathing or other health conditions. The acquired physiologic profile is used to generate alarms or warnings based on detectable physiological changes, to adjust gas delivery to the subject, alter mission profiles, or to transfer control of the craft or other duties away from a debilitated subject (abstract, FIG. 1, claims 1-20).
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Applicant’s Arguments
Applicant states:
limitations of claim 1 are anchored in a specific, head-worn flight-mask architecture whose measurements are fused by a processor that (i) generates a probability of an emergent physiological state using a probabilistic machine-learning model trained on both inhalation and environmental parameters, (ii) classifies that probability to a concrete intervention, and (iii) applies the result to transmit user signaling and/or change flight control to decrease the probability of the emergent state. This is not a generic "collect-analyze- display" implementation or an "apply it on a computer" recitation. Rather, it is a closed-loop, real-time safety system implemented in a wearable apparatus that measures, decides, and acts. Applicant respectfully submits that no court cases, literature, or references are of record indicating that the above-described limitations are "well-understood, routine, [and] conventional," and furthermore asserts that neither the instant application nor the prosecution history in this matter contains any admission thereof. Accordingly, Applicant respectfully
submits that the above-described limitations of claim 1 are not "well-understood, routine, [and] conventional," and thus amount to an inventive concept.
Additionally, Applicant respectfully submits that claim 1 recites an inventive concept, at least because claims 1 contains limitation amounting to a non-conventional and non-generic arrangement of process steps. See BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility, LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016). "Examiners should keep in mind that the courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic." May 4th USPTO Memorandum at p. 4; see also DDR Holdings, 773 F.3d at 1257. Moreover, "an inventive concept may be found in the non-conventional and non- generic arrangement" even of generic computer operations on a generic computing device. Bascom, 827 F.3d at 1350.
Without conceding that any limitation of claims 1 is generic or conventional, Applicant respectfully asserts that, taken as a whole, limitations to claims 1 amount to a non-conventional and non-generic arrangement of computer and functions and other technical limitations, because the instant Application does not contain any information to suggest that the elements and/or the combination thereof are conventional. In addition, no evidence has been provided to indicate that claimed elements would be considered conventional. Applicant therefore respectfully submits that claim 1 recites limitations amounting to an inventive concept, and thus to significantly more than the abstract idea to which claims 1 is allegedly drawn. At least for these additional reasons, Applicant respectfully submits claim 1 recites patent eligible subject matter.
It is respectfully submitted that these are not persuasive. The Applicant remarks are directed to Step 2B of 101 analyses, specifically evaluating additional elements to determine whether they amount to an inventive concept by considering them both individually and in combination/as a whole to ensure that they amount to significantly more than the judicial exception itself.
With regards to Applicant stating “no court cases, literature, or references are of record indicating that the above-described limitations are "well-understood, routine, [and] conventional”, Examiner respectfully submits that the question of whether a particular claimed invention is novel or obvious is "fully apart" from the question of whether it is eligible. Moreover, in step 2B analysis, when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. It is important to note that the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). See MPEP 2106.05 I.
As stated above, the additional elements of receiving plurality of parameters; transmit an audible, tactile, or visual signal to the user, inputting data are activities incidental to the primary process or product (all uses of the judicial exception require such data gathering or data output) that are merely a nominal or tangential addition to the claim and they amount to necessary data gathering and outputting. These additional elements are considered insignificant extra-solution activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept. See MPEP 2106.05(g)(3).
Moreover, the additional element of a processor is a conventional computer component and/or process and does not provide significantly more.
In addition, as stated above, the additional elements of a flight mask comprising a fluid channel, an inhalation sensor module, a gas concentration sensor and an inspirate pressure sensor, a light source and a light detector, an environmental sensor module, a cabin pressure sensor and a positional sensor are well-understood, conventional, and routine. Therefore, the additional elements taken individually and as a whole, do not amount to significantly more.
Therefore, the additionally recites elements viewed individually or in combination do not amount to significantly more.
With regards to Applicant referring to BASCOM, Examiner submits that in BASCOM, the claims recited a "specific method of filtering Internet content" requiring "the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user." BASCOM, 827 F.3d at 1345--46, 1350. The installation of a filtering tool at a specific location, remote from the end users, with customizable filtering features specific to each end user, provided an inventive concept in that it gave the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on the ISP server. Id. at 1350. Applicant fails to explain sufficiently and persuasively how instant claims are analogous to this case. We find no analogous non-conventional, non- generic arrangement of known, conventional physical elements.
As such, the rejection of claims 1-20 under U.S.C. 101 is maintained.
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 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.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Opperman et al. (US10786693B1) in view of Neumann (US20210057099A1).
Regarding claims 1 and 11, Opperman discloses a system for inspirate sensing (a breathing mask or system; col. 11, Ls.29-30) to determine a probability of an emergent physiological state (calculating and the like of the signal and data into information related to the subject's physiological condition; col. 11, Ls. 51-55).
Opperman further discloses that the breathing mask is a flight mask commonly used in the art today, whether it be military, combat, commercial, freight, recreational, personal flight, or otherwise (Col. 57, para. 3, FIGs. 1-3).
Opperman further discloses a fluid channel configured to be in fluidic communication with at least an inspirate (a breathing mask with one or more sensors (a gas intake port tube; col. 11, L. 40; Sensors or sensor banks located on or within the plenum provide a measure of the
oxygen concentration of the reserve gas stored in the plenum and provides a checkpoint along the breathing gas pathway (col. 76, L. 2-7); in various other plenum chambers, mounted to the pilot, contained or integrated into the pilot's breathing system, mask, or tubes or hoses, and the like …a sensor bank where by numerous sensors are housed together in a given location and sampling tubes or ports (for example, Fluid channel) are extended from the sensor bank to the various locations of the environment, system or components thereof to have access to the breathing gas and be able to measure the particular condition at those locations (col. 20, first para.) (Specification [0013] “a "fluid channel" is any pathway for a flow of fluid; for instance, without limitation, a fluid channel may include any of a manifold, a plenum, a hose, a tube, a conduit, and the like” …. “"fluidic communication" is a relationship between two or more things between which fluid, which may include without limitation a liquid or a gas, may pass”)); an inhalation sensor module, in fluidic communication with the fluidic channel (a breathing mask or system with one or more sensors, such as gas sensors, organic compound (volatile or non-volatile) sensors, flow sensors incorporated into the mask; col. 11, L. 29-40; configured to sense and transmit a plurality of inhalation parameters as a function of the at least an inspirate (The sensor signals are transmitted through an appropriate link to an electronic data acquisition or controls box; col. 11, Ls. 44-47).
Opperman further discloses an environmental sensor module, in sensed communication with an environment substantially outside of the fluid channel, configured to sense and transmit a plurality of environmental parameters indicative of the physiological status of the user as a function of the environment (a breathing mask or system with one or more sensors, such as gas sensors, organic compound (volatile or non-volatile) sensors, flow sensors temperature sensors, heat flux sensors, respiration sensors, pressure sensors incorporated into the mask; col. 11, L. 29-40).
Opperman further discloses that measurements and signals from the sensors described herein are further used to calculate other environmental and physiological conditions of and surrounding the subject. The sensor measurements and subsequent algorithmic calculations are used to monitor the subject's overall condition, to detect or predict the onset of dangerous breathing or other health conditions, to mitigate the onset or severity of those dangerous breathing or other health conditions and their symptoms (col. 13, para. 3).
Opperman further discloses a processor, in communication with the inhalation sensor module and the environmental sensor module, wherein the processor is further configured to: receive the plurality of inhalation parameters and the plurality of environmental parameters (The sensor signals are transmitted through an appropriate link to an electronic data acquisition or controls box or other subsystem that might in certain embodiments contain either a small on-board processor and/or other electronic components for not only receiving the sensor(s) signal, but also for possibly filtering, digitizing, converting, calculating and the like; col. 11, Ls. 45-52).
Opperman further discloses generating a probability of an emergent physiological state utilizes a probabilistic machine learning model, and further comprises: receiving at least an environmental parameter of the plurality of environmental parameters and at least an inhalation parameter of the plurality of inhalation parameters correlated to a probabilistic machine learning outcome; training, iteratively, the probabilistic machine learning model using the probabilistic machine learning data; and generating the probability of an emergent physiological state as a function of the trained probabilistic machine learning model (The processor of the current system contains at least one algorithm for substantially identifying or predicting dangerous health conditions based on the signals received from the connected sensors of the sensor system. Respiratory and gas exchange patterns that are reflective of healthy or dangerous conditions are analyzed via a machine learning classifier. The classifier algorithm, which is trained on these data and directly measured blood gas data. The classifier then uses the respiratory gas calculations to predict subsequent blood gas values. Respiratory and gas exchange patterns that are reflective of healthy or dangerous conditions may be analyzed and classified via a linear lung model or, more preferably, via a machine learning classifier. The machine learning classifier may be based off of a “strong learning” method, such as an artificial neural network, a support vector machine, or a Bayes classifier (a probabilistic model), which may apply training data from a multitude of individuals to any user (col. 46, Ls. 16-67).
Opperman further discloses that the system comprises a machine learning classifier algorithm that uses sensor data to predict if the subject soon thereafter experiences exertional
hypoxemia based on the increased likelihood of such onset (col. 54, Ln. 40-50).
Opperman further discloses employing the systems, upon receiving an alert or warning from the biometric monitoring system, automatically take control of the subject's vessel or equipment allowing an auto-pilot feature to keep the aircraft aloft while the subject is restored to capacity (and other interventions, such as activating an automatic safety mechanism) and that the processor calculates biometric data (The machine learning Bayes classifier (for example, probabilistic machine learning model) trained on sensor data and measured data using the calculations to classifying respiratory patterns and dangerous conditions and predicts the likelihood of a dangerous condition and determines, based on the resultant biometric data, whether the subject is experiencing a dangerous condition; see col. 46 and 54) and sends a warning or alert based on the measured and calculated values (Cols 69-70, FIG. 8); reading on limitations of classifying the probability of an emergent physiological state to an intervention, wherein the intervention comprises changing a flight control.
Opperman further discloses that the sensor measurements and subsequent algorithmic calculations are used to monitor the subject's overall condition, to detect or predict the onset of dangerous breathing or other health conditions, to mitigate the onset or severity of those dangerous breathing or other health conditions and their symptoms, and to activate an alert or warning system which notifies the subject or a third party who may then initiate action to further prevent, mitigate, or treat the dangerous conditions and symptoms (col. 13, para. 3).
Further regarding claim 1 and 11, Opperman does not expressly disclose classifying the probability of an emergent physiological state an intervention, wherein the intervention comprises utilizing a classifier.
Neumann discloses methods and systems to generate a descriptor trail using artificial intelligence comprising receiving biological data and outputs a function of a biological data and a server to generate an ameliorative output as a function of a prognostic output (abstract). Opperman further discloses that generating prognostic outputs as a function of ranked prognostic probability score (claim 6).
Neumann further discloses a first training set including physiological state data and a correlated first prognostic label [0046], where physiological data includes evaluation of sensor [0051] to generate prognostic output using selected machine learning process [0044]. Neumann further discloses that the prognostic label calculates probability of developing a condition (for example, generating a probability of an emergent physiological state) [0053].
Neumann further discloses a second training set correlated with second prognostic label and generating an ameliorative output using machine learning model, where the model uses data from first training set as an input to determine relationships between elements of physiological data and ameliorative labels [0069]. Neumann further discloses that the ameliorative process label learner selects a machine-learning model as a function of inputs and outputs utilized by ameliorative process label to generate ameliorative model [0072].
Neumann further discloses a descriptor generator module designed to generate a descriptor trail that includes an element of diagnostic data (for example, an intervention) [0079]; reading on limitations of inputting the probability of an emergent physiological state to the classifier; and training the classifier by inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of parameters correlated to candidate interventions; classifying the probability of an emergent physiological state to the intervention as a function of the classifier.
Regarding claims 2 and 12, Opperman discloses that the inhalation sensor module further comprises: at least a gas concentration sensor, configured to sense and transmit at least an inspirate gas concentration parameter as a function of a gas concentration within the at least an inspirate (Various embodiments of the system of the present invention use a small or portable sensor unit or units (sometimes referred to as Portable Digital Analysis Unit(s) or PDAUs) capable of providing time based measurements of a subject's ventilation, inhaled breath (e.g., flow, gas concentrations, and the like), oxygen uptake (O2), carbon dioxide (CO2) output; col. 11, Ls. 8-15).
and at least an inspirate pressure sensor, configured to sense and transmit at least an inspirate pressure parameter as a function of a pressure of the at least an inspirate (a breathing mask or system with one or more sensors, such as gas sensors, organic compound (volatile or non-volatile) sensors, flow sensors temperature sensors, heat flux sensors, respiration sensors, pressure sensors incorporated into the mask; col. 11, L. 29-40).
Regarding claim 3 and 13, Opperman discloses that the at least a gas concentration sensor comprises: a light source configured to illuminate a portion of the at least an inspirate; and a light detector configured to detect a light, wherein the light is either originated from the light source or excited by light from the light source (at least one subject-mounted sensor, the sensor adapted to measure partial pressure of oxygen within the mask in real time and to produce a signal corresponding to said partial pressure of oxygen, the sensor comprising a light source comprising at least one inlet channel or port adapted to allow gas to enter the at least one sensor and at least one outlet channel or port adapted to allow gas to exit the at least one sensor and a reflective interior surface adapted to reflect and direct light from the light source to the surface coated in fluorescent dye and/or light reflected from the surface coated in fluorescent dye to the photodiode (light detector); claim 1).
Regarding claim 4 and 14, Opperman discloses that the environmental sensor module further comprises: at least a cabin pressure sensor, configured to sense and transmit at least a cabin pressure parameter as a function of a pressure of the environment (to measure breathing conditions and predict the onset of various physical conditions sensors are required to measure to detect surrounding conditions. Sensors for detection of other surrounding conditions may be included as well, such as for ambient (cabin) pressure, ambient pressure (non-enclosed environments such as divers, man-mounted systems, ground applications, and the like; col. 12, Ls. 44-55).
and at least a positional sensor, configured to sense and transmit at least a movement parameter as a function of a movement (Integrated mask embodiments may further include other sensors within the mask as well. Some embodiments may include at least one accelerometer and one gyroscope in the sensor chamber. The accelerometer and/or gyroscope allow the system to track the subject's head position which can be used to determine and monitor the subject's level of consciousness; col. 40, Ls. 44-50).
Regarding claim 5 and 15, Opperman discloses that the processor is further configured to (see processor 335 of FIG. 8): classify the probability of an emergent physiological state to an intervention, wherein classifying further comprises: inputting the probability of an emergent physiological state to a classifier; and classifying the probability of an emergent physiological state to the intervention as a function of the classifier. (The sensor data is transmitted to a processor which calculates using bayes classifier Bayes classifier (a probabilistic model; col. 46, Ls. 16-67) using that information or data to control the delivery of gases, medication, and/or other physical stimulation to the subject; col. 11, Ls. 50-55).
Regarding claim 6 and 18, Opperman discloses that the processor is further configured to (see processor 335 of FIG. 8): train the classifier, wherein training the classifier further comprises: inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of parameters; and training the classifier as a function of the machine learning algorithm (raw signal traces are sliced to represent individual breaths, and each breath is reduced, via numerical integration and multiplication, to the gases produced and consumed. These breath-by-breath values are read into the buffer of a classifier algorithm, which is trained on these data …The machine learning classifier is be based off of a Bayes classifier, which may apply training data from a multitude of individuals to any user; col. 46, Ls. 51-67).
Regarding claims 7 and 16, Opperman discloses a user-signaling device communicative with the processor and configured to transmit a signal to a user as a function of the intervention (the present invention includes a system for alerting the subject/wearer of the device or a third party of such dangerous breathing or other health conditions, and/or implementing an automated or semi-automated system for closed-loop or semi-closed loop control of the breathing mix of gases. A further embodiment of the present invention will monitor, and/or alert individuals to the operation, performance and condition of the life support hardware; col. 9, Ls. 55-64).
Regarding claims 8 and 17, Opperman discloses that the classifier may be a “lazy learner” that continuously compares a user's respiratory and gas exchange patterns with the measured blood oxygenation levels, creating a unique algorithm for that particular user that improves classification accuracy with continued use. Opperman does not expressly disclose generating at least a confidence metric, wherein generating the at least a confidence metric further comprises: inputting the intervention class to a machine learning model; and generating the at least a confidence metric as a function of the machine learning model.
Neumann discloses that any machine-learning model utilized to generate a prognostic output and/or ameliorative output, a plurality of prognostic outputs, a plurality of ameliorative outputs, any regression models, weighted variables, confidence levels, error functions, datasets, models, and/or calculations utilized to generate a prognostic output and/or ameliorative output [0079].
Regarding claims 9 and 19, Opperman the processor is further configured to: train the probabilistic machine learning model, wherein training the probabilistic machine learning model further comprises: inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of parameters correlated to a probabilistic outcome; and training the probabilistic machine learning model as a function of the machine learning algorithm (raw signal traces are sliced to represent individual breaths, and each breath is reduced, via numerical integration and multiplication, to the gases produced and consumed. These breath-by-breath values are read into the buffer of a classifier algorithm, which is trained on these data …The machine learning classifier is be based off of a Bayes classifier, which may apply training data from a multitude of individuals to any user; col. 46, Ls. 51-67; calculating with the processor to produce and algorithm an output comprising a blood oxygen concentration response (SpO2 Response) value corresponding to a real-time measurement of changes in the subject's blood oxygen concentration measured based at least in part on the signal. Generating, with the processor and algorithm, a continuous blood oxygenation profile for the subject over time and identifying or predicting dangerous breathing or other health conditions of the subject based at least in part on the continuous blood oxygenation profile for the subject; claims 1-13).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Opperman and Neumann, the examiner concludes that the combination of Opperman and Neumann represents the use of known techniques to improve similar methods. Both Opperman and Neumann are directed to predicting probability of an emergent physiological state employing machine learning algorithms. Opperman only disclosed a system for inspirate sensing including a fluid channel, a sensor module, a processor, generating a probability of an emergent physiological state by training a probabilistic machine learning model, and classifying the physiological state to plurality of interventions such as changing a flight control. In the same field of research, Neumann provided the classifier feedback and input aggregation to aggregate inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks [0028]. Combining the inspirate sensing system and probabilistic prediction of Opperman with classifier feedback and input aggregation of Neumann would have allowed for better learning and more stable representation of the system’s state (less noise as oppose to raw sensor data) and consequently more accurate predictive results. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Opperman and Neumann. This combination would have been expected to have provided a more accurate determination of probability of an emergent physiological state. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Response to Applicant’s Arguments
Applicant's arguments filed 10/03/2025 have been fully considered but they are not persuasive.
Applicant states:
Applicant respectfully submits that Opperman does not teach, suggest or motivate "a flight mask comprising a fluid channel configured for inhalation by a user, wherein the fluid channel is configured to be in fluidic communication with at least an inspirate, an inhalation sensor module... configured to sense and transmit a plurality of inhalation parameters associated with the inhalation of the user... an environmental sensor module... configured to sense and transmit a plurality of environmental parameters indicative of a physiological status of the user... and...transmit an audible, tactile, or visual signal to the user as a function of the intervention to decrease a probability of an emergent physiological state associated with the user" as recited in part by amended claim 1. Accordingly, applicant respectfully submits that claim 1 is patently distinguishable from Opperman for at least the reasons described above.
It is respectfully submitted that this is not persuasive. Opperman discloses flight mask with multiple sensors used for identification or prediction of dangerous breathing or other health conditions (FIG. 1-3). Opperman further discloses that a standalone breathing mask containing the sensor suite and processor, which can be integrated into any existing system (col. 57, para. 3); Opperman further discloses that the flow sensor may be adapted to fit inside the breathing tube (for example, a fluid channel, see specification [0012]: an "inspirate" is fluid, for example air; [0013]: a fluid channel may include any of a manifold, a plenum, a hose, a tube, a conduit, and the lik) (col. 42, para. 1). Opperman further discloses an inhalation sensor module that include more than just flow sensors, such as also providing oxygen sensor(s), temperature sensors, pressure sensors, particulate and contaminate sensors, and the like (col. 42, para. 1). Opperman further discloses that measurements and signals from the sensors described herein are further used to calculate other environmental and physiological conditions of and surrounding the subject. The sensor measurements and subsequent algorithmic calculations are used to monitor the subject's overall condition, to detect or predict the onset of dangerous breathing or other health conditions, to mitigate the onset or severity of those dangerous breathing or other health conditions and their symptoms, and to activate an alert or warning system which notifies the subject or a third party who may then initiate action to further prevent, mitigate, or treat the dangerous conditions and symptoms (col. 13, para.3). further Neumann discloses classifying the probability of an emergent physiological state an intervention, wherein the intervention comprises utilizing a classifier. As such, the combination of Opperman and Neumann teach all limitations of claims 1 and 11.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Opperman et al. (US10786693B1) in view of Neumann (US20210057099A1), as applied to claims 1-9 and 11-19 above, and further in view of Ciancone (WO2020014326A1).
Regarding claims 10 and 20, Opperman discloses that the processor is further configured to (see processor 335 of FIG. 8): determine a respiration rate as a function of the plurality of inhalation parameters (The classifier may rely on a feature space selected by known metabolic metrics. Features for a given breath may include the inspiratory volume, the expiratory volume, the CO2 produced (by mass or analog), the O2 consumed (by mass or analog), the respiration rate, the breath duration; col. 47, Ls. 5-10);
Further regarding claim 10, Opperman does not expressly disclose determining a respiration volumetric flow rate as a function of the plurality of inhalation parameters.
However, Ciancone discloses an inhalation interface may monitor and analyze flow rate in an inhalation device that includes a processor and collect usage data including the air flow rate; and provide feedback based on the usage data (claim 1). Ciancone further discloses sensors to measure environmental factors and gives active feedback including a signal sent to the user (claim 3). Ciancone further discloses that the trend module may use machine learning to determine an optimum treatment plan for the patient.
Ciancone further discloses During the inhalation, the indicator will indicate the volumetric flow rate, peak flow, duration, or any other inhalation measurement metric [0053].
Ciancone further discloses processing of the pressure signal into volumetric flow rate (Q) with an algorithm performed on the integrated microcontroller or processed remotely on a server ([0020] and [0052]).
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 have modified the method of Opperman and Neumann to have used determining a respiration volumetric flow rate, as shown by Ciancone [0242] to quantify how much fluid is flowing through a system, based on the finding that Ciancone contained a known technique that is applicable to the base method of Opperman and Neumann. There would be a reasonable expectation of success in combining the technique of Ciancone to the method of Opperman and Neumann because all use machine learning algorithms to perform sensor data analytics related to biological data and providing feedback to user.
Response to Applicant’s Arguments
Applicant's arguments filed 10/03/2025 have been fully considered but they are not persuasive.
Applicant states:
Claims 10 and 20 depend, directly or indirectly, on claims 1 and 11. As noted above, claims 1 and 11 are patentably distinguishable over Opperman and Neumann, alone or in combination, for at least the reasons discussed above. As such, Applicant submits that claims 10 and 20 are patentably distinguishable over Opperman and Neumann, alone or in combination, for at least the reasons discussed above.
Ciancone fails to cure the deficiencies of Opperman and Neumann. The Office has not asserted that Ciancone teaches, suggests or motivates "a flight mask comprising a fluid channel configured for inhalation by a user, wherein the fluid channel is configured to be in fluidic communication with at least an inspirate, an inhalation sensor module... configured to sense and transmit a plurality of inhalation parameters associated with the inhalation of the user... an environmental sensor module... configured to sense and transmit a plurality of environmental parameters indicative of a physiological status of the user... and...transmit an audible, tactile, or visual signal to the user as a function of the intervention to decrease a probability of an emergent physiological state associated with the user" as recited in part by amended claim 1. Applicant respectfully submits that Ciancone does not teach, suggest or motivate "a flight mask comprising a fluid channel configured for inhalation by a user, wherein the fluid channel is configured to be in fluidic communication with at least an inspirate, an inhalation sensor module... configured to sense and transmit a plurality of inhalation parameters associated with the inhalation of the user...an environmental sensor module... configured to sense and transmit a plurality of environmental parameters indicative of a physiological status of the user... and...transmit an audible, tactile, or visual signal to the user as a function of the intervention to decrease a probability of an emergent physiological state associated with the user" as recited in part by amended claim 1. Accordingly amended claim 1 is patently distinguishable over Opperman, Neumann and Ciancone, alone or in combination.
It is respectfully submitted that this is not persuasive. As stated above, the combination of Opperman and Neumann teach all limitations of claims 1 and 11. As such, the U.S.C. 103 rejection of claims 10 and 20 is maintained.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHAZAL SABOUR whose telephone number is (703)756-1289. The examiner can normally be reached M-F 7:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D. Riggs can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.S./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686