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 .
Response to Amendment
This Office Action is in response to the amendment filed on 12/03/2025. Per the amendment, claims 1, 3-5, and 8 are as currently amended, and claims 2 and 6-7 are as previously presented. As such, claims 1-8 are pending in the instant application.
All claim objections and 35 U.S.C. 112(b) rejections are withdrawn in light of the filed amendments.
Claim Objections
Claims 5 and 8 are objected to because of the following informalities:
Claim 5, lines 2-3: “the at least one neural network” should read “the at least one pre-trained neural network” for clarity and consistency.
Claim 8, line 2: “the at least one neural network” should read “the at least one pre-trained neural network” for clarity and consistency.
Claim 8, line 4: “the wearer” should read “a wearer” to establish antecedent basis.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-8 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.
Claim 1 recites the limitation “a pressure regulator housed within a rigid enclosure” in lines 2-3. The specification as filed on 08/18/2025 does not explicitly disclose a pressure regulator and a rigid enclosure in which the pressure regulator is housed therein as claim 1 discloses. The specification filed on 08/18/2025 discloses various pressure sensors ([0032]-[0033]), low-pressure zones generated by an air compressor ([0041]), positive pressure feed in crossflow filtration ([0046]), pressure differences caused by the air compressor ([0052]), increasing air pressure via directing the air through a progressively narrowing cap ([0071]), negative air pressure created when a user inhales and vents are closed ([0072]), and a pressure swing absorption device ([0108]), but never a pressure regulator.
Additionally, claim 1 recites the limitation “generate one or more control signals using at least one pre-trained neural network, to select and activate at least one or more combinations of selectable state activation filters based on the data received from the one or more external environment sensors, one or more module signature data signals generated by the at least one selectable state activation filters, and historical biometric patterns” in lines 15-19. The specification filed on 08/18/2025 does not explicitly disclose the generation of one or more control signals using at least one pre-trained neural network to select and activate at least one or more combinations of selectable state activation filters based on data received from historical biometric patterns. The specification filed on 08/18/2025 does the neural network can be trained using training data sets of normal and abnormal breathing, as well as exhalation patterns that are correlated to biometric data ([0083]). Such pretrained neural networks and neural networks utilizing measured biometric conditions of the user allow the neural network to control the opening and closing of outlet valves ([0032] and [0083]), but never to generate one or more control signals to select and activate at least one or more combinations of selectable state activation filters.
Similarly, claim 8 recites the limitation “wherein the at least one neural network is configured to select from one of a plurality of selectable filter modules to activate based on at least… a biometric condition of the wearer” in lines 1-4. It is best understood the recited “the at least one neural network” is a recitation of the at least one pre-trained neural network disclosed in claim 1 (see lines 15-16). The specification as filed on 08/18/2025 does not explicitly disclose the at least one neural network using a biometric condition of the wearer to select and activate from one of a plurality of filter modules as claim 8 discloses. The specification as filed on 08/18/2025 does disclose the neural network can be trained using training data sets of normal and abnormal breathing, as well as exhalation patterns that are correlated to biometric data ([0083]). Such pretrained neural networks and neural networks utilizing measured biometric conditions of the user allow the neural network to control the opening and closing of outlet valves ([0032] and [0083]), but never to select from one of a plurality of selectable filter modules to activate.
Claims 2-7 are rejected due to dependency on a rejected claim.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 6 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 6 recites the limitation “the at least one neural network is a pre-trained neural network” in lines 1-2, where it is best understood by the Examiner that the at least one neural network disclosed in claim 6 is a recitation of the at least one pre-trained neural network disclosed in claim 1 (see line 15). Therefore, claim 6 fails to further limit the subject matter of claim 1 as claim 1 has already disclosed the at least one neural network is a pre-trained neural network.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Schuller (US 20180104517 A1) in view of Piesinger (US 6772762 B2), and in further view of Kayyali et al. (US 8545416 B1), hereinafter Kayyali.
Regarding claim 1, Schuller discloses a portable air purification apparatus (Fig. 7; [0015], line 1) comprising:
an air supply module (103; Fig. 7) comprising a compressor (103 is an air compression unit, see [0052]) housed within an enclosure ([0052]), the air supply module (103; Fig. 7) configured to provide a stream of compressed air ([0015], lines 1-3);
a facial mask (202; Fig. 1A) having at least one selectively openable outflow valve (mask 202 equipped with one or more openable outlet valves that are electronically activated, [0063]);
an air supply conduit (air supply conduit 107; Fig. 2), where the air supply conduit comprises at least one integrated membrane filter device (one or more filtering devices 120; Fig. 2; [0036], lines 2-3), the at least one integrated membrane filter device having a first interface (air intake interface 110A; Fig. 5) and a second interface (air outflow interface 110B; Fig. 5), the first interface (air intake interface 110A; Fig. 5) configured to be selectively coupled to the air supply module (air compressor 103; Fig. 2, where air intake interface 110A is positioned at one end of the filtering device 120; [0041], lines 6-9) and the second interface (air outflow interface 110B; Fig. 5) configured to be selectively coupled to the facial mask (202; coupled via air supply conduit 107; Fig. 5; [0041], lines 3-5), a flexible housing ([0035], lines 3-5) disposed between the first interface (air intake interface 110A) and the second interface (air outflow interface 110B; [0046], lines 3-5), the flexible housing ([0035], lines 3-5) containing one or more filtering materials suitable for filtering one or more contaminants from the stream of compressed air (Fig. 3; [0036], lines 2-3);
one or more external environment sensors ([0033], line 8) configured to detect one or more conditions of an ambient environment external to the facial mask (202; processor 104 configured to evaluate if present ambient environment is toxic or contaminated via one or more connectable sensors, [0072], lines 1-4); and
at least one processor (104) configured to receive at least data from the one or more external environment sensors ([0033], line 8; processor configured to communicate with air quality sensors, [0098], lines 1-2, where the air quality sensors are a type of environment sensor; processor configured to exchange data with one or more environmental sensors, [0033], lines 6-8).
Schuller does not explicitly disclose the air supply module (103; Fig. 7) being housed within a rigid enclosure.
However, Schuller does disclose the air supply module (103; Fig. 7) being housed withing a housing ([0052]), where the housing is made of durable materials and housing elements such as steel ([0055]), where it is well-understood by one or ordinary skill in the art that steel is a considered a rigid material.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the housing of the air supply module (103; Fig. 7) as taught by Schuller such that the housing of the air supply module is rigid and made of steel to increase durability and longevity of the air supply module.
Schuller does not explicitly disclose the air supply module (103; Fig. 7) further comprising a pressure regulator.
However, Piesinger teaches an analogous personal air filtration system with a pressure regulator valve in a standard flow regulator system (Col. 9, line 52).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the air supply module (103; Fig. 7) as taught by Schuller with the pressure regulator valve taught by Piesinger such that air supply module (103; Fig. 7) further comprising a pressure regulator (Piesinger Col. 9, line 52) to improve the safety and comfort of the user wearing the device, and providing a consistent flow of breathable air.
Schuller as modified fails to explicitly disclose the at least one processor (104) configured to generate one or more control signals using at least one pre-trained neural network, to select and activate at least one or more combinations of selectable state activation filters based on the data received from the one or more external environment sensors, one or more module signature data signals generated by the at least one selectable state activation filters, and historical biometric patterns.
Schuller does teach at least one selectively engageable filter module (120; filter module is selectively engageable, [0078], line 1) which can be activated in based on data received from one or more air quality sensors ([0098]) and one or more module signature data signals ([0072], lines 5-7) generated by the at least one selectively engageable filter module ([0071], lines 1-3). Schuller further teaches the processor (104) exchanges data with biological sensors ([0033]), where measured biological conditions are used to control and activate a valve ([0063]). Additionally, Kayyali teaches an analogous therapeutic system to provide compressed air, where the system includes a processor capable of creating and training a neural network with collected data (processor creates command signal to instruct device how to appropriately adjust treatment settings, Col. 29, lines 14-21, where the processor is capable of using neural networks to conduct this process, Col. 29, lines 35-39; Col. 30, lines 40-42) and stored data (Col. 28, lines 27-34), such that the trained neural network can actively predict biometric patterns of the user and adjust one or more or a combination of operational settings to provide an optimal treatment to the user (Col. 3, lines 51-54; Col. 54, lines 5-14). Furthermore, Kayyali teaches the neural network is a pre-trained neural network (Col. 4, line 33; Col. 30; lines 40-42).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the processor (104) taught by Schuller to include the pre-trained neural network taught by Kayyali such that, the at least one processor (104) configured to generate one or more control signals using at least one pre-trained neural network (Kayyali: processor creates command signal to instruct device how to appropriately adjust treatment settings, Col. 29, lines 14-21, where the processor is capable of using neural networks to conduct this process, Col. 29, lines 35-39; Col. 30, lines 40-42; neural network taught by Kayyali is a pre-trained neural network, see Col. 4, line 33; Col. 30; lines 40-42), to select and activate at least one or more combinations of selectable state activation filters (Kayyali pre-trained neural network selects at least one or more combinations of selectable operational parameters to activate and adjust, hence the pre-trained neural network taught by Kayyali would be capable of selecting and activating at least one or more selectable state activation filters, see Kayyali Col. 3, lines 51-54 and Col. 54, lines 5-14, where the at least one or more selectable state activation filters are Schuller 120; Schuller [0078], line 1; Examiner’s Note: where a combination can be interpreted as a combination of a single element and no elements) based on the data received from the one or more external environment sensors ([0098]), one or more module signature data signals ([0072], lines 5-7) generated by the at least one selectable state activation filters ([0071], lines 1-3), and historical biometric patterns (Kayyali Col. 30, lines 40-42 and Col. 28, lines 27-34) to more accurately control the device (Kayyali Col. 28, lines 27-33) and personalize the device control and treatment provided (Kayyali Col. 30, lines 40-45).
Regarding claim 2, Schuller as modified teaches the portable air purification apparatus of claim 1, wherein the one or more membrane filtering materials (Fig. 3; [0036], lines 2-3) comprise one or more membrane filtering fibers (112; [0039], lines 3-4), the one or more membrane filtering fibers (112) having an exterior surface (fibers are hollow, lines 1-2; [0040], lines 12-14, where hollow fibers have an interior surface a respective exterior surface must be present to create the hollow shape of the fibers) and an interior surface ([0040], lines 12-14), the interior surface ([0040], lines 12-14) defining a central lumen ([0040], lines 12-14) running the length of each of the one or more membrane filtering fibers (112; [0040], lines 2-4), and an enclosing sheath (122; Fig. 3) disposed over the exterior of the one or more membrane filtering fibers (Fig. 3; [0039], lines 5-6); wherein the enclosing sheath (122; Fig. 3) encloses a volume (114; Fig. 3) in communication with the second interface (air outflow interface 110B; [0045], lines 4-5) and the central lumen ([0040], lines 12-14) is in communication with the first interface (air intake interface 110A; [0041], lines 1-3).
Regarding claim 3, Schuller as modified teaches the portable air purification apparatus of claim 1, further comprising a plurality of sensors ([0049], lines 1-2) configured to measure at least an internal pressure level of the one or more selectable state activation filters ([0049], lines 1-3) and communicate this data with the at least one processor ([0098], lines 1-2.
Regarding claim 4, Schuller as modified teaches the portable air purification apparatus of claim 1, further comprising at least one biometric sensor (biological sensors, [0033], lines 7-8; [0063], lines 7-8) configured to measure a biometric parameter of a wearer of the facial mask ([0063], lines 7-10), wherein the biometric sensor (biological sensors, [0033], lines 7-8; [0063], lines 7-8) is configured to provide data to the at least one processor (104; [0033], lines 6-8).
Regarding claim 5, Schuller as modified teaches the portable air purification apparatus of claim 4, but fails to explicitly teach wherein the data from the biometric sensor (biological sensors, [0033], lines 7-8; [0063], lines 7-8) is provided to the at least one neural network, the at least one neural network further configured to generate a control signal to activate the at least one selectively openable outflow valve in response to the data provided by the at least one biometric sensor.
However, Kayyali further teaches data from at least one sensor being provided to the at least one neural network (sensor data is related to neural network, Col. 28, lines 27-29), the at least one neural network (Col. 28, line 27) configured to generate a control signal in response to data provided by at least one biometric sensor (a closed loop [automatic] control system uses data and signals from biosensors to actuate a treatment system for the subject, Col. 3, lines 44-54, where the neural network is a control mechanism of the control system, Col. 28, lines 27-28).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Schuller with Kayyali such that the data from the biometric sensor (Schuller: biological sensors, [0033], lines 7-8; [0063], lines 7-8) is provided to the at least one neural network (Kayyali: Col. 28, line 27; sensor data is related to neural network, Col. 28, lines 27-29), the at least one neural network (Kayyali: Col. 28, line 27) further configured to generate a control signal to activate the at least one selectively openable outflow valve in response to the data provided by the at least one biometric sensor (Kayyali: a closed loop [automatic] control system uses data and signals from biosensors to actuate a treatment system for the subject, Col. 3, lines 44-54, where the neural network is a control mechanism of the control system, Col. 28, lines 27-28; Schuler: outflow valve is activated in response to biometric data obtained by one or more biometric sensors, [0063], lines 8-10) where the inclusion of a neural network allows for more accurate control of the device, leading to more effective and personalized performance of the device (Kayyali: Col. 28, lines 27-33).
Regarding claim 6, Schuller as modified teaches the portable air purification apparatus of claim 5, but fails to explicitly teach wherein the at least one neural network (Kayyali: Col. 28, line 27) is a pre-trained neural network.
However, Kayyali further teaches the at least one neural network (Col. 28, line 27) is a pre-trained neural network (Col. 4, line 33; Col. 30; lines 40-42). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Schuller with Kayyali such that the at least one neural network (Kayyali: Col. 28, line 27) is a pre-trained neural network (Kayyali: Col. 4, line 33; Col. 30, lines 40-42) where the function of the device is improved as it can be personalized to a user's specific needs through training of the neural network (Kayyali: Col. 35, lines 15-19).
Regarding claim 7, Schuller as modified teaches the portable air purification apparatus of claim 1, wherein the one or more module signature data signals ([0072], lines 5-7) generated by the at least one selectable state activation filters ([0071], lines 1-3) is broadcast to the at least one processor (104) through one of a DC based powerline communication, Wi-Fi, Bluetooth, or near field RF communication ([0071], lines 6-9).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Schuller in view of Piesinger in view of Kayyali as applied to claim 1 above, and further in view of Perusse et al. (US 20150296917 A1), hereinafter Perusse.
Regarding claim 8, Schuller as modified teaches the portable air purification apparatus of claim 7, where the at least one pre-trained neural network (Kayyali: Col. 28, line 27) is used to activate at least one or more combinations of selectable state activation filters based on data received from the one or more module signature data signals generated by the at least one selectable state activation filters and historical biometric patterns (see claim 1 above). Schuller as modified is silent to wherein the at least one neural network (Kayyali: Col. 28, line 27) is configured to select from one of a plurality of selectable filter modules (120; filter module is selectively engageable, [0078], line 1) to activate based on at least the one or more module signature data signals ([0072], lines 5-7), and a biometric condition of the wearer (Kayyali Col. 30, lines 40-42 and Col. 28, lines 27-34).
However, Perusse et al. teaches a plurality of filter cartridges, where each filter cartridge has a different filtration, air flow, weight, and balance characteristics to allow a user to select an air filter that is appropriate for the work conditions and environmental conditions being performed ([0031], lines 20-24). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Schuller as modified with Perusse et al., such that the at least one neural network (Kayyali: Col. 28, line 27) is configured to select from one of a plurality of selectable filter modules (120; filter module is selectively engageable, [0078], line 1; Perusse et al.: [0031], lines 20-24) to activate based on at least the one or more module signature data signals ([0072], lines 5-7), and a biometric condition of the wearer (Kayyali Col. 30, lines 40-42 and Col. 28, lines 27-34) to effectively filter our harmful contaminants and toxins present in the air inhaled by the user (Perusse et al.: [0031], lines 4-5).
Response to Arguments
Applicant's arguments with respect to claim 1 filed on 12/03/2025 have been fully considered but they are not persuasive.
On page 5 of the Remarks (filed 12/03/2025), Applicant argues Schuller and Kayyali do not teach or suggest the claimed functionality of the claimed invention. Applicant states the Examiner invoked improper hindsight in combining the teachings of Kayyali (the use of a neural network to adjust the operation of a medical device) with the teachings of Schuller to arrive at the solution of utilizing a neural network to adjust the utilization of filters as described in the present application. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On page 5 of the Remarks, Applicant argues the cited references, whether considered individually or in combination, fail to provide teaching, suggestion, or motivation to suggest the desirability to combine Schuller and Kayyali. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the teaching, suggestion, or motivation for the rejection is found in Kayyali Col. 30, lines 40-45 and Col. 28, lines 27-34, and in the knowledge generally available to one of ordinary skill in the art.
On pages 5-6 of the Remarks, Applicant argues Schuller and Kayyali fail to teach or suggest the amended claims (filed on 12/03/2025), specifically amended claim 1. Applicant’s arguments have been considered but are moot because of the new ground(s) of rejection necessitated by the amendment. Schuller in combination with Kayyali and Piesinger does teach the amended limitations of claim 1 (see 103 rejection of claim 1 above).
On page 6 of the Remarks, Applicant argues Kayyali does not teach or suggest using a neural network to select different operative components, such as filter combinations, based on encountered environmental conditions and historical biometric trends. While the examiner agrees Kayyali does not teach or suggest using a neural network to select different operative components based on encountered environmental conditions, Schuller does teach selecting and activating filter modules based on one or more air quality sensors ([0098], see claim 1 above). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Furthermore, Kayyali does teach controlling and adjusting operational parameters of a device based on stored data and real-time data using a neural network to provide a user with an optimal treatment of breathable compressed air (Kayyali Col. 28, lines 27-34; Col. 30, lines 5-14; Col. 3, lines 51-54; and Col. 54, lines 5-14). Hence, it would be obvious to one of ordinary skill in the art that the neural network of Kayyali would be capable of adjusting operative components based on received data and stored data to provide the user with an optimal treatment of filtered compressed air (see 103 rejection of claim 1 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wilson & Wilson (WO 2017074954 A1): Regarding a system to protect a user from potentially hazardous environmental conditions and utilizes machine learning to maintain a filtered and breathable air supply to a user based on measured external pollution levels and contaminants detected in the external air.
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 ABIGAYLE DALE whose telephone number is (571)272-1080. The examiner can normally be reached Monday-Friday from 8:45am to 5:45pm ET.
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/ABIGAYLE DALE/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785