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 Amendments
The Amendment filed 1/28/2026 has been entered. Claims 1-4, 6-9, 12, 17, and 19-20 were amended. Thus, claims 1-20 are pending in the application.
Claim Objections
Claims 1-2, 9, and 19 are objected to because of the following informalities:
Claim 1 recites “the interface” in line 11, and is suggested to read --the selected interface-- in order to more clearly reference how the limitation was originally claimed.
Claim 2 recites “the interface worn on faces” in line 3, and is suggested to read --the plurality of interfaces worn on faces-- in order to more clearly reference how the limitation was originally claimed.
Claim 9 recites “the plurality of interfaces” in lines 2 and 3, and is suggested to read --the plurality of interfaces used by the user population-- in order to more clearly reference how the limitation was originally claimed.
Claim 19 recites “the interface” in the last line, and is suggested to read --the selected interface-- in order to more clearly reference how the limitation was originally claimed.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the limitations “a plurality of interfaces” in line 6, “respiratory therapy devices” in line 8, and “one or more databases” in line 10 are confusing, as it is unclear as to whether these limitations are meant to include “an selected interface” in line 1, “a respiratory therapy device” in line 2, and “one or more databases” in line 7, respectively. Moreover, the limitation “the interface worn by the user population” in lines 13-14 is confusing, as it is unclear whether the “selected interface…of a user” in lines 1-2 or one of the “plurality of interfaces used by the user population” in line 6 is being referenced.
Claim 3 recites the limitations “the dimensional data of the interface worn on faces of the user population” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 4, the limitation “operational data” in line 2 is confusing, as it is unclear as to whether this is meant to be the same as “operational data” in claim 1 or a new limitation.
Regarding claim 9, the limitation “a face of a user” in line 2 is confusing, as it is unclear as to whether this limitations is meant to include “a face of a user” in claim 1.
Regarding claim 19, the limitations “a plurality of interfaces” in line 6 and “respiratory therapy devices” in line 8 are confusing, as it is unclear as to whether these limitations are meant to include “a selected interface” in line 1 and “a respiratory therapy device” in lines 1-2, respectively.
Regarding claim 20, the limitations “a plurality of interfaces” in line 3, “users” in line 4, “faces” in line 11, and “users” in line 18 are confusing, as it is unclear as to whether these limitations are meant to include “an interface” in line 1, “a user” in line 2, “a plurality of faces” in line 4, and “users” in line 4, respectively. Furthermore, claim 20 recites the limitations “the simulation” in line 16. There is insufficient antecedent basis for this limitation in the claim.
Any remaining claims are rejected based on their dependency on a rejected base claim.
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.
Claims 1-4, 7, 9, 11-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (US 2018/0117272 A1) in view of Gugino (US 2021/0322701 A1), Webb et al. (US 2022/0134137 A1), and Richard et al. (US 7,827,038 B2).
Regarding claim 1, as best understood, Fu discloses a method to evaluate a selected interface to be worn on a face of a user of a respiratory therapy device (method for the selection of a patient interface based on a comparison between a user’s facial features and sizing information of the patient interface; the patient interface is used with respiratory equipment) (abstract; para. [0022]), the method comprising:
storing a facial image of the user in a storage device (stored data 354 in memory/data storage 350 can include captured image data from sensor 340; sensor 340 can be a camera) (Fig. 5; para. [0144]; para. [0150]);
determining facial features of the user based on the facial image (captured images are processed to detect or identify facial features/landmarks and measure distances therebetween) (Fig. 6C; para. [0164]);
storing a corresponding plurality of interface dimensional data from a plurality of interfaces in one or more databases (data record includes patient interface sizes corresponding to a range of facial feature distances/values, and there can be multiple lookup tables for particular forms or models of patient interface) (para. [0176]);
determining a comfort score for the interface to be worn on the face of the user via an evaluation tool, the evaluation tool determining the comfort score based on the determined facial features of the user (processor 310 determines a best fit for a patient interface size based on the facial measurements) (Figs. 6C-6D; para. [0177]);
and a display (display interface 320) (Fig. 5; para. [0177]).
Fu does not disclose storing a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population in one or more databases; storing operational data generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces in one or more databases; the evaluation tool determining the comfort score based on the operational data generated by respiratory therapy devices used by the user population.
However, Gugino teaches a method for performing respirator mask fit testing (Gugino; abstract) including storing a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population in one or more databases (user data profile data, which must be stored in a database for reference, includes facial feature data from multiple users corresponding to respiratory models/sizes) (Gugino; Tables 7-10, 18-19; paras. [0197-0198]; para. [0201]; para. [0206]); storing operational data for the plurality of interfaces, when used by the user population with the plurality of interfaces in one or more databases (the pass/fail result of a fit test for each respiratory model/size interface on a user is stored) (Gugino; Tables 7-9, 18-19; para. [0201]; para. [0206]); the evaluation tool determining the comfort score based on the operational data used by the user population (machine learning algorithm determines whether a respirator model/size interface would pass or fail using the recorded pass/fail data from users; the pass/fail indication is determined from a fit score calculated by the algorithm) (Gugino; para. [0085]; para. [0092]; para. [0157]; para. [0201]; para. [0206]). Furthermore, Webb teaches a system with a respiratory that determines whether or not a fit test is satisfied (Webb; abstract) including wherein operational data is generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces in one or more databases; determining the comfort score based on the operational data generated by respiratory therapy devices used by the user population (PPE includes supplied air respirators, which would supply pressurized air to a respirator mask with using a blower; fit test data is stored in a database system and used to perform population-level analytics across multiple customers; fit tests can determine if there are leaks while a respirator is in operation by a user) (Webb; Fig. 11; para. [0047]; para. [0052]; para. [0086]; para. [0090]; para. [0136]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu method to include storing a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population in one or more databases; storing operational data of respiratory therapy devices used by the user population with the plurality of interfaces in one or more databases; the evaluation tool determining the comfort score based on the operational data used by the user population, as taught by Gugino, for the purpose of enabling the determination of whether a mask fit for a user will pass or fail to be made by a trained machine learning algorithm (Gugino; paras. [0200-0201]). Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the Fu method such that the Gugino operational data is generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces in one or more databases; determining the comfort score based on the operational data generated by respiratory therapy devices used by the user population, as taught by Webb, for the purpose of enabling the device to determine if a fit has a leak that occurs in response to certain actions by a user (Webb; para. [0026]; para. [0053]).
Fu does not disclose the evaluation tool determining the comfort score based on an output of a simulator simulating the interface worn by the user population based on the plurality of facial feature data; displaying the comfort score on a display.
However, Richard teaches a mask fitting system (Richard; abstract) including the evaluation tool determining the comfort score based on an output of a simulator simulating the interface worn by the user population based on the plurality of facial feature data (three-dimensional modeling technique is used to determine the mask that will best fit by electronically “placing” the mask model on the 3D image of the patient’s face; system can be used on numerous patients) (Richard; col. 11, lines 58-67; col. 12, lines 1-12; col. 13, lines 9-12); displaying the comfort score on a display (weighted score of likelihood of suitability of fit each identified mask is displayed) (Richard; claim 38).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu evaluation tool such that the evaluation tool is for determining the comfort score based on an output of a simulator simulating the interface worn by the user population on the plurality of facial feature data; displaying the comfort score on a display, as taught by Richards, for the purpose of allowing the method to perform an analysis for leaks and pressure at certain points around the cushion to determine the mask size for maximum comfort (Richard; col. 12, lines 1-6).
Regarding claim 2, the modified Fu teaches wherein the evaluation tool includes a machine learning model outputting the comfort score based on the facial features of the user and dimensional data of the interface to be worn by the user (machine learning algorithm determines whether a respirator model/size interface would pass or fail using the data; the pass/fail indication is determined from a fit score calculated by the algorithm) (Gugino; Tables 7-10, 17-19; para. [0085]; para. [0092]; para. [0157]; para. [0201]; para. [0206]).
Regarding claim 3, as best understood, the modified Fu teaches further comprising training the machine learning model by comparing comfort scores determined from the simulator simulating the plurality of interfaces based on the dimensional data of the interface worn on faces of the user population based on the plurality of facial feature data, with comfort scores provided from the user population (Gugino machine learning algorithm compares its outputs from using training data of respirator models/sizes and subject user data profiles to desired/known outcomes of fit tests; Richard three-dimensional modeling technique is used to determine the mask that will best fit and grading the best fit output) (Gugino, Tables 10, 19, paras. [0200-0202], para. [0206]; Richard, col. 5 lines 13-35, col. 11 lines 58-67, col. 12 lines 1-12).
Regarding claim 4, as best understood, the modified Fu teaches wherein the comfort scores provided from the user population are determined based on at least one of operational data of the respiratory therapy devices used by the user population, or the plurality of facial feature data (in Gugino, a machine learning algorithm determines whether a respirator model/size interface would pass or fail using the recorded pass/fail fit data and the user’s facial features data; the pass/fail indication is determined from a fit score calculated by the algorithm; in Webb, the fit tests are determined based on if there are leaks occurring during operation when actions are performed by a user) (Gugino, Tables 7-10, 17-19, para. [0085], para. [0092], para. [0157], para. [0200-0201], para. [0206]; Webb, para. [0026], para. [0053]).
Regarding claim 7, the modified Fu teaches wherein the simulator simulates pushing the interfaces into simulated faces determined from the plurality of facial feature data from the user population until a seal is between the simulated faces and the simulated pushing of the interfaces is obtained, a pressurization of the simulated interfaces, and a resulting gap between the simulated interfaces and the simulated faces (3-D modeling technique where a model of a mask is electronically “placed” on a model of the patient’s face such that there is a minimized gap between them, i.e. a seal; this modeling technique is used to determine which mask interface would have minimized gaps on the user’s face and perform an analysis for pressures at certain points around the interface cushion; system can be used on numerous patients) (Richard; col. 11, lines 58-67; col. 12, lines 1-12; col. 13, lines 9-12).
Regarding claim 9, as best understood, the modified Fu teaches wherein the selected interface to be worn on a face of a user is one of the plurality of interfaces and one of a plurality of sizes of each of the plurality of interfaces (patient interface models and sizes are compared to the user’s facial feature measurements to determine the best fit) (Fu; paras. [0176-0177]).
Regarding claim 11, the modified Fu teaches wherein the evaluation tool accepts demographic data of the user to determine the comfort score (demographic parameters of an individual can be used in the generation of a pass/fail mask fit prediction using a weighted score) (Gugino; page 29, sections 30-31).
Regarding claim 12, as best understood, the modified Fu teaches wherein the operational data from the respiratory therapy devices used by the user population includes data to determine leaks in operation of the respiratory therapy devices used by the user population (in Richard, there is software to perform an analysis for leaks between a interface cushion on the user’s face during the 3D modeling technique; in Webb, the fit tests are determined based on if there are leaks occurring during operation when actions are performed by a user) (Richard, col. 11 lines 58-67, col. 12 lines 1-12; Webb, para. [0026], para. [0053]).
Regarding claim 13, the modified Fu teaches further comprising scanning the face of the user via a mobile device including a camera to provide the facial image (sensor 340 can be a mobile device’s camera, and is used to captured a facial image of the user) (Fu; Fig. 5; para. [0144]; para. [0150]).
Regarding claim 17, the modified Fu teaches wherein the facial image includes landmarks relating to at least one facial dimension including at least one of face height and nose width (facial feature measurements use landmarks to determine values such as face height and nose width) (Fu; Figs. 12, 14; para. [0191]; para. [0197]).
Regarding claim 18, the modified Fu teaches further comprising determining a predicted leak of the interface via the evaluation tool (software to perform an analysis for leaks with a interface cushion during the 3D modeling technique) (Richard; col. 11, lines 58-67; col. 12, lines 1-12).
Regarding claim 19, as best understood, Fu discloses a system for evaluating a selected interface worn by a user using a respiratory therapy device (method for the selection of a patient interface based on a comparison between a user’s facial features and sizing information of the patient interface; the patient interface is used with respiratory equipment) (abstract; para. [0022]), the system comprising:
a storage device for storing facial image data of the user (stored data 354 in memory/data storage 350 can include captured image data from sensor 340; sensor 340 can be a camera) (Fig. 5; para. [0144]; para. [0150]);
one or more databases for storing: a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population (data record includes patient interface sizes corresponding to a range of facial feature distances/values, and there can be multiple lookup tables for particular forms or models of patient interface) (para. [0176]);
a facial comfort interface evaluation tool coupled to the storage device, the facial comfort interface evaluation tool operable to: output a comfort score of the selected interface worn by the user based on analysis of the facial image data of the user (processor 310 uses the data stored in memory/data storage 350 in its determination of a best fit for a patient interface size based on the facial measurements) (Figs. 5-6D; paras. [0176-0177]);
and a display (display interface 320) (Fig. 5; para. [0177]).
Fu does not disclose a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population; operational data generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces; the evaluation tool operable to: output a comfort score of the selected interface worn by the user based on the operational data generated by the respiratory therapy devices used by the user population.
However, Gugino teaches a method for performing respirator mask fit testing (Gugino; abstract) including a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population (user data profile data includes facial feature data from multiple users corresponding to used respiratory models/sizes) (Gugino; Tables 7-10, 18-19; paras. [0197-0198]; para. [0201]; para. [0206]); operational data of respiratory therapy devices used by the user population with the plurality of interfaces (the pass/fail result of a fit test for each respiratory model/size on a user is stored) (Gugino; Tables 7-9, 18-19; para. [0201]; para. [0206]); the evaluation tool operable to: output a comfort score of the selected interface based on the operational data (machine learning algorithm determines whether a respirator model/size would pass or fail using the recorded pass/fail data from users; the pass/fail indication is determined from a fit score calculated by the algorithm) (Gugino; para. [0085]; para. [0092]; para. [0157]; para. [0201]; para. [0206]). Furthermore, Webb teaches a system with a respiratory that determines whether or not a fit test is satisfied (Webb; abstract) including wherein operational data is generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces; output a comfort score based on the operational data generated by the respiratory therapy devices used by the user population (PPE includes supplied air respirators, which would supply pressurized air to a respirator mask with using a blower; fit test data is stored in a database system and used to perform population-level analytics across multiple customers; fit tests can determine if there are leaks while a respirator is in operation by a user) (Webb; Fig. 11; para. [0047]; para. [0052]; para. [0086]; para. [0090]; para. [0136]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu system to include a plurality of facial feature data from a user population and a corresponding plurality of interface dimensional data from a plurality of interfaces used by the user population; operational data of respiratory therapy devices used by the user population with the plurality of interfaces; the evaluation tool operable to: output a comfort score of the selected interface based on the operational data, as taught by Gugino, for the purpose of enabling the determination of whether a mask fit for a user will pass or fail to be made by a trained machine learning algorithm (Gugino; paras. [0200-0201]). Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the Fu system such that the Gugino operational data is generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the user population with the plurality of interfaces; output a comfort score based on the operational data generated by the respiratory therapy devices used by the user population, as taught by Webb, for the purpose of enabling the device to determine if a fit has a leak that occurs in response to certain actions by a user (Webb; para. [0026]; para. [0053]).
Fu does not disclose the evaluation tool operable to: output a comfort score of the selected interface based on an output of a simulator simulating the interface on simulated faces of the user population based on the plurality of facial feature data; and to display the comfort score of the interface.
However, Richard teaches a mask fitting system (Richard; abstract) wherein the evaluation tool is operable to: output a comfort score of the selected interface based on an output of a simulator simulating the interface on simulated faces of the user population based on the plurality of facial feature data (three-dimensional modeling technique is used to determine the mask that will best fit by electronically “placing” the mask model on the 3D image of the patient’s face; system can be used on numerous patients) (Richard; col. 11, lines 58-67; col. 12, lines 1-12; col. 13, lines 9-12); and to display the comfort score of the interface (weighted score of likelihood of suitability of fit each identified mask is displayed) (Richard; claim 38).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu evaluation tool such that the evaluation tool is operable to: output a comfort score of the selected interface based on an output of a simulator simulating the interface on the plurality of facial feature data; and to display the comfort score of the interface, as taught by Richards, for the purpose of allowing the method to perform an analysis for leaks and pressure at certain points around the cushion to determine the mask size for maximum comfort (Richard; col. 12, lines 1-6).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino and Richard as applied to claim 2 above, and further in view of Grashow (US 2016/0354571 A1).
Regarding claim 5, the modified Fu teaches the invention as previously claimed, but does not teach wherein the simulator models the plurality of interfaces worn on faces of the user population with finite element analysis.
However, Grashow teaches an adjustment determination unit to receive patient interface device information (Grashow; abstract) wherein the simulator models the plurality of interfaces worn on faces of the user population with finite element analysis (series of finite element analysis used for a set of patient interface devices to be worn by a user) (Grashow; para. [0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified Fu simulator such that the simulator models the plurality of interfaces worn on faces of the user population with finite element analysis, as taught by Grashow, for the purpose of determining patient interface device adjustments to help provide an optimal fit (Grashow; para. [0052]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino, Webb, and Richard as applied to claim 2 above, and further in view of Hendriks et al. (US 2022/0344026 A1).
Regarding claim 6, the modified Fu teaches the invention as previously claimed, but does not teach wherein the dimensional data of the plurality of interfaces worn on faces of the user population is computer aided design (CAD) data.
However, Hendriks teaches a system for monitoring the interaction of a mask with a patient’s face (Hendriks; abstract) wherein the dimensional data of the plurality of interfaces worn on faces of the user population is computer aided design (CAD) data (geometries of masks can be obtained from a CAD file) (Hendriks; para. [0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu dimensional data such that the dimensional data of the plurality of interfaces worn on faces of the user population is computer aided design (CAD) data, as taught by Hendriks, for the purpose of providing specific suitable source for obtaining the geometry of the masks (Hendriks; para. [0039]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino, Webb, and Richard as applied to claim 2 above, and further in view of Vlutters et al. (US 2016/0155017 A1).
Regarding claim 8, the modified Fu teaches the invention as previously claimed, including wherein the simulator outputs contact gaps between skin of simulated faces determined from the plurality of facial feature data from the user population and cushions of the interfaces, contact pressure on skin of the simulated faces (three-dimensional modeling technique determines minimized gaps between an interface cushion and the user’s face, and is used to perform an analysis of pressure at certain points around the cushion for maximizing comfort) (Richard; col. 11, lines 58-67; col. 12, lines 1-12), but does not teach wherein the simulator outputs interface deformation, contact pressure or shear on skin of the simulated faces, skin deformation of the simulated faces, and stress or strain in the cushions of the interfaces.
However, Vlutters teaches a system with a geometric fit score database for 3-D models of patient interface devices (Vlutters; abstract) wherein the simulator outputs interface deformation, contact pressure on skin of the simulated faces, skin deformation of the simulated faces, and stress or strain in the cushions of the interfaces (3-D modeling takes into account deformation of the respective patient interface and the patient’s face, as well as indication the amounts of contact pressure or stress the interface exerts on the patient’s face at different points; as one of ordinary skill in the art would know strain is the measure of deformation in response to stress, the modeling output of deformation and contact pressure would thus involve the stress/strain of the patient interface) (Vlutters; para. [0048]; para. [0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified Fu simulator outputs such that the simulator outputs interface deformation, contact pressure on skin of the simulated faces, skin deformation of the simulated faces, and stress or strain in the cushions of the interfaces, as taught by Vlutters, for the purpose of helping to improve the realism of the 3D modeling of the patient interface on a patient’s face.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino, Webb, and Richard as applied to claim 9 above, and further in view of Van Bree et al. (US 2016/0078687 A1).
Regarding claim 10, the modified Fu teaches the invention as previously claimed, but does not teach wherein the displaying includes displaying a subset of interfaces selected from the plurality of interfaces that fit the face of the user and associated comfort scores.
However, Van Bree teaches an electronic display of a 3-D model of a patient’s face and patient interface device (Van Bree; abstract) wherein the displaying includes displaying a subset of interfaces selected from the plurality of interfaces that fit the face of the user and associated comfort scores (multiple patient interface devices determined as suitable options to fit a user’s face with their overall fit scores 222 are displayed) (Van Bree; Fig. 7; para. [0064]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu display to include displaying a subset of interfaces selected from the plurality of interfaces that fit the face of the user and associated comfort scores, as taught by Van Bree, for the purpose of allowing a user to choose between a number of similarly suitable mask options depending upon stock availability (Van Bree; para. [0064]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino, Webb, and Richard as applied to claim 13 above, and further in view of Viner et al. (US 2020/0230444 A1).
Regarding claim 14, the modified Fu teaches the invention as previously claimed, including wherein the camera is a 3D camera (stereoscopic camera used to obtain three-dimensional image) (Fu; para. [0201]), and wherein the facial features are three-dimensional features derived from a meshed surface derived from the facial image (mesh used to determine face volume; mesh generated based on a three-dimensional facial image, wherein the mesh overlays the surface of the three-dimensional image of the user’s face) (Gugino; Figs. 17A-17B; para. [0125]), but does not teach wherein the mobile device includes a depth sensor.
However, the modified Fu does teach determining facial features such as the depth of the patient’s nose (Richard; col. 15, lines 13-16). Moreover, Viner teaches a method of fit testing a respirator (Viner; abstract) including a depth sensor (imaging sensor can include a depth sensor) (Viner; para. [0116]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified Fu mobile device to include a depth sensor, as taught by Viner, for the purpose of providing a specific suitable means for measuring facial depths which one of ordinary skill in the art could feasibly expect to perform reasonably well.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Gugino, Webb, and Richard as applied to claim 1 above, and further in view of Znamenskiy et al. (US 2015/0262422 A1).
Regarding claim 15, the modified Fu teaches the invention as previously claimed, including wherein the facial image is a two-dimensional image including landmarks (captured two-dimensional image includes the identified locations of certain facial features such as the borders of the eyes and mouth) (Fu; para. [0165]), but does not teach wherein the facial features are three-dimensional features derived from the landmarks.
However, Znamenskiy teaches a system for collecting data of a face of a subject (Znamenskiy; abstract) wherein the facial features are three-dimensional features derived from the landmarks (3D data is generated from 2D images of the face, the generate a 3D model of the face which would include facial features) (Znamenskiy; para. [0048]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Fu facial features such that the facial features are three-dimensional features derived from the landmarks, as taught by Znamenskiy, for the purpose of helping to realize or test a model of a patient interface on a 3D model of the user’s face, thereby helping to provide the most suitable mask (Znamenskiy; para. [0048]).
Regarding claim 16, the modified Fu teaches wherein the facial image is one of a plurality of two-dimensional facial images (two-dimensional images of the patient’s face) (Fu; para. [0146]; para. [0150]), and wherein the facial features are three-dimensional features derived from a 3D morphable model adapted to match the facial images (the 3D model of the subject’s face which matches the 2D images would include 3D facial features) (Znamenskiy; para. [0048]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Gugino in view of Webb and Richard.
Regarding claim 20, as best understood, Gugino discloses a method of training a machine learning model to output a comfort score for an interface worn by a user (machine learning algorithm is trained to determine a prediction of a respirator model/size fitting a user) (paras. [0200-0201]; para. [0206]), method comprising:
collecting dimensional data for a plurality of interfaces for a respiratory therapy device (dimensional data of three masks for each respirator is collected) (Table 10; para. [0133]);
collecting facial data from a plurality of faces of a population of users wearing the plurality of interfaces (user data profile data including facial feature data from multiple users) (Tables 17, 19; paras. [0196-0198]);
determining a comfort score for each of the plurality of interfaces worn by the population of users (the pass/fail result for a fit test for each respiratory model/size on a user is predicted) (Tables 18-19; para. [0201]; para. [0206]);
creating a training data set of the dimensional data of the plurality of interfaces and the facial dimension data (known data of respirator models/sizes and facial feature dimensions of users is used to train the machine learning algorithm) (Tables 10, 19; paras. [0200-0201]; para. [0206]);
and adjusting the machine learning model by providing the training data set to predict a predicted comfort score for each of the plurality of faces and each of the plurality of interfaces worn by users (machine learning algorithm uses the training data to predict the pass/fail outcome of fit tests of respirator models/sizes on users) (Tables 10, 19; paras. [0200-0201]; para. [0206]), and comparing the predicted comfort score with the associated comfort score determined for each of the plurality of interfaces worn by the population of users (the remaining or new data is used to test the machine learning algorithm, wherein the outputs of the machine learning algorithm are compared to desired/known outcomes, and the machine learning algorithm uses these comparisons to learn and adjust) (paras. [0201-0202]).
Gugino does not disclose collecting operational data generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the population of users with the plurality of interfaces; simulating the plurality of interfaces worn on faces of the user population based on the operational data generated by respiratory therapy devices.
However, Webb teaches a system with a respiratory that determines whether or not a fit test is satisfied (Webb; abstract) including collecting operational data generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the population of users with the plurality of interfaces; simulating the plurality of interfaces worn on faces of the user population based on the operational data generated by respiratory therapy devices (PPE includes supplied air respirators, which would supply pressurized air to a respirator mask with using a blower; fit test data is stored in a database system and used to perform population-level analytics across multiple customers; fit tests can determine if there are leaks while a respirator is in operation by a user; an analytics engine uses historical data and models to predict occurrences of safety events across a population) (Webb; Fig. 11; para. [0047]; para. [0052]; para. [0086]; para. [0090]; para. [0100]; para. [0105]; para. [0136]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the Gugino method such that It includes collecting operational data generated by respiratory therapy devices operable to supply pressurized air to the plurality of interfaces, when the respiratory therapy devices are used by the population of users with the plurality of interfaces; simulating the plurality of interfaces worn on faces of the user population based on the operational data generated by respiratory therapy devices, as taught by Webb, for the purpose of enabling the device to determine if a fit has a leak that occurs in response to certain actions by a user (Webb; para. [0026]; para. [0053]).
Gugino does not disclose simulating the plurality of interfaces worn on faces of the user population based on dimensional data of the plurality of interfaces and facial dimensional data derived from the facial data of the plurality of faces; providing the simulation to predict a predicted comfort score.
However, Richard teaches a mask fitting system (Richard; abstract) including simulating the plurality of interfaces worn on faces of the user population based on dimensional data of the plurality of interfaces and facial dimensional data derived from the facial data of the plurality of faces (three-dimensional modeling technique is used to determine the mask that will best fit by electronically “placing” the mask model on the 3D image of the patient’s face, this simulation having the dimensional data of both interface and user’s face via the models/images; system can be used on numerous patients) (Richard; col. 11, lines 58-67; col. 12, lines 1-12; col. 13, lines 9-12); providing the simulation to predict a predicted comfort score (the three-dimensional modeling is used to determine the mask that will best fit a user, this determination involving grading criteria based on mask and user data) (Richard; col. 5, lines 5-35; col. 11, lines 58-67; col. 12, lines 1-12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gugino method by including simulating the plurality of interfaces worn on faces of the user population based on dimensional data of the plurality of interfaces and facial dimensional data derived from the facial data of the plurality of faces; providing the simulation to predict a predicted comfort score, as taught by Richards, for the purpose of allowing the method to perform an analysis for leaks and pressure at certain points around the cushion to determine the mask size for maximum comfort (Richard; col. 12, lines 1-6).
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
On page 6 in the “Claim objections” section of the Applicant’s remarks, the Applicant argues that the claims have been amended to overcome the claim objections of the previous office action. The Examiner agrees, and has thus withdrawn those claim objections. However, the newly amended claims have raised new claim objections as detailed above.
On page 6 in the “Rejections based on 35 U.S.C. 112” section of the Applicant’s remarks, the Applicant argues that the claims have been amended to overcome the 35 U.S.C. 112(b) rejections of the previous office action. The Examiner partially agrees, and has thus withdrawn those 35 U.S.C. 112(b) rejections which were addressed. However, the unaddressed 35 U.S.C. 112(b) rejections are being maintained as detailed above, along with new 35 U.S.C. 112(b) rejections raised by the newly amended claims. With particular regards to the 35 U.S.C. 112(b) rejections relating to a user with a selected interface and a user population with a plurality of interfaces, the Applicant argues that these are separate limitations, and so not indefinite. However, this is not clearly set forth in the claims (for example, by using claim language such as --a plurality of interfaces used by the user population, not including the selected interface to be worn on the face of the user,--), and so the 35 U.S.C. 112(b) rejections are being maintained. Moreover, should these limitations be claimed as separate non-overlapping groups, there would still be confusion and indefiniteness because of dependent claim 9, which asserts the selected interface is one of the plurality of interfaces. Thus, the claims remain rejected under 35 U.S.C. 112(b) rejections as detailed above
Applicant’s arguments on page 8 in the third paragraph to page 9 in the first paragraph of the Applicant’s remarks with respect to the independent claims have been considered but are moot in view of new grounds of rejection with new additional Webb reference being used in the current rejection as discussed above.
Conclusion
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.
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/JACQUELINE M PINDERSKI/Examiner, Art Unit 3785
/RACHEL T SIPPEL/Primary Examiner, Art Unit 3785