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 .
Information Disclosure Statement
The information disclosure statement(s) filed on 10/16/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claims
This office action is in response to the preliminary amendment filed on 08/25/2023. As directed by the preliminary amendments, claims 3-4, 6, 8, 14, 16-17, 19, 21, 67, 69-70, 75, 79, 81-83, and 91-92 have been amended and claims 2, 5, 7, 9-13, 15, 18, 20, 22-66, 68, 71-74, 76-78, 80, 84-90 and 93-100 have been cancelled. As such, claims 1, 3-4, 6, 8, 14, 16-17, 19, 21, 67, 69-70, 75, 79, 81-83, and 91-92 are being examined in this application.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 6, 14, 16-17, 19, 21, 67, 69, 75, 79 and 91 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sodhi (WO 2020118311 A1).
Regarding claim 1, Sodhi teaches a system for selecting an interface suited to a user's face for respiratory therapy (sleep apnea system 100, see Fig. 1; Sodhi teaches a system that provides assistance to a patient for using a respiratory therapy device and a mask for treatment of mask types as seen in Fig. 1 and [0011], wherein the management server 110 comprises a mask recommendation module 212 to generate a patient-specific recommendation for a type and size of mask 140 as seen in Figs. 2 and 12 and [0038] and [0044]), the system comprising:
a storage device for storing a facial image of the user (Sodhi teaches a patient profile database 250 to store various information about a patient (see Fig. 2 and [0045]) and a mask recommendation module 212 to obtain images of the patient and perform a facial analysis as seen in [0044]. Sodhi further teaches using the facial features of the patient captured in the input image is used in a machine-learning model 1204 to generate a mask size recommendation 1206 and a mask type recommendation 1208 as seen in Fig. 12 and [0092]. As such, Sodhi teaches storing the facial image (from Figs. 6A-6B and [0080]-[0081]) to allow the mask recommendation module 212 to perform the analysis);
a facial profile engine (facial feature analysis and measurement module 1222, see Fig. 12) operable to determine facial features of the user based on the facial image (“…a facial feature analysis and measurement module 1222 to analyze and measure facial features of the patient captured in the input image…” see [0092] and [0044]);
one or more databases (equipment database 254, see Fig. 2) for storing:
a plurality of facial features from a user population and a corresponding plurality of interfaces used by the user population (Sodhi teaches “the system collects facial data from a primary patient and also RPT use and other data from a larger population of patients to assist in the selection of an optimal mask for the primary patient (see [0010]).” Sodhi further teaches an equipment database storing data relating to a plurality of device types and a plurality of mask types (see [0011]) and feedback sent to the management server 110 may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience (see [0088]). Therefore, Sodhi teaches a database with stored facial data from a larger population of patients and corresponding interfaces from the feedback in the management server 110); and
operational data of respiratory therapy devices used by the user population with the plurality of corresponding interfaces (Sodhi teaches an equipment database storing data relating to a plurality of device types and a plurality of mask types (see [0011]). Sodhi further teaches management server 110 to obtain data associated with patients, their device 130 and masks 140 to generate content to assist the patient in setting up and using device 130 and mask 140, including feedback data and sensor data as seen in Figs. 3C and [0037], [0074] and [0088]); and
a selection engine (application server 202, device recognition module 204, mask recognition module 206, mask positioning module 208, mask leak detection module 210, and mask recommendation module 212, see Fig. 12) coupled to the one or more databases (see Fig. 2 and [0038]), wherein the selection engine is operable to select an interface for the user from the plurality of corresponding interfaces based on a desired outcome from the stored operational data and the determined facial features (Sodhi teaches sleep apnea system 10 to use a management server 110 comprising an equipment database 254 and mask recommendation module 212 as seen in Fig. 2 and [0038]. The equipment database 254 stores data relating to a plurality of device types and a plurality of mask types (see [0011]) and the machine-learning model is based on learned correlations between image features and device models from different device type data stored in the equipment database 254 as seen in [0040]. Sodhi further teaches mask recommendation module 212 to recommend a particular mask type and size for a particular patient using a facial feature analysis and measurement module 1222 inputted into one or more machine-learning models 1204 as seen in Fig. 12 and [0092], wherein feedback sent to the management server 110 may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience (see [0088]). Therefore, Sodhi teaches mask recommendation module 212 to select a mask for the patient based on the data in the management server and the determined features from facial feature analysis and measurement module 1222).
Regarding claim 3, Sodhi teaches the system of claim 1, and further teaches wherein the interface is a mask (Sodhi teaches the sleep apnea device 130 is attached to a face mask 140 as seen in Fig. 1 and [0011] and [0031]) and the respiratory therapy device is configured to provide one or more of a Positive Airway Pressure (PAP) or a Non-invasive ventilation (NIV) (“The sleep apnea device 130 may comprise, for example, a continuous positive airway pressure (CPAP) device…” see [0033]).
Regarding claim 6, Sodhi teaches the system of claim 1, and further teaches wherein the selection engine selects the interface based on demographic data of the user compared with demographic data of a population of users stored in the one or more databases (Sodhi teaches “the system collects facial data from a primary patient and also RPT use and other data from a larger population of patients to assist in the selection of an optimal mask for the primary patient (see [0010]).” Sodhi further teaches mask recommendation module 212 to recommend a particular mask type using a gender recognition module 1226, an age estimation module 1228 and an ethnicity recognition module 1230, wherein the extracted features are inputted to one or more machine-learning models 1204 that are each trained to detect suitability of a patient’s face to different sizes and types of masks 140 as seen in [0092]).
Regarding claim 14, Sodhi teaches the system of claim 1, and further teaches wherein the desired outcome is one of a seal between the interface and a facial surface to prevent leaks; or compliance with using the respiratory therapy device (Sodhi teaches a mask leak detection module 210 detects whether a mask 140 is properly sealed around a patient’s face as seen in [0043] and further teaches the client application 125 presenting the patient with information relevant to preparing for the first night of therapy including a checklist as seen in [0086]. The checklist interface is a mask checklist screen 920 that provides checks for the mask placement such as whether the mask is connected properly, fits comfortably and there are no leaks as seen in Fig. 9a and [0086]. Therefore, Sodhi teaches the seal between the interface and a facial surface to prevent leaks as a desired outcome as it teaches both a mask leak detection module 210 and a leak detected interface 730 as seen in [0083]).
Regarding claim 16, Sodhi teaches the system of claim 1, and further teaches further comprising a mobile device operable to collect subjective data input from a user, and wherein the selection of the interface is based partly on the subjective data (Sodhi teaches a client computing device 120 which is a mobile device to execute a client application 125 as seen in [0036], wherein the client application 125 is use to obtain feedback from the patient such as quality of the patient’s sleep overnight and discomfort as seen in Figs. 10A-10C and [0088]).
Regarding claim 17, Sodhi teaches the system of claim 1, and further teaches further comprising a machine learning module operable to determine types of operational data correlated with interfaces achieving the desired outcome (Sodhi teaches management server 110 to obtain data associated with patients, their device 130 and masks 140 to generate content to assist the patient in setting up and using device 130 and mask 140, including feedback data and sensor data as seen in Figs. 3C and [0137], [0074] and [0088]. Sodhi further teaches “…the management server 110 may collect various usage data associated with sleep apnea treatment in order to generate recommendations to patients that are tailored to their particular characteristics (see [0037])” and “…feedback may furthermore be sent to the management server 110 and may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience (see [0088]).” Sodhi also teaches mask recommendation module 212 to recommend a particular mask type and size for a particular patient using a facial feature analysis and measurement module 1222 inputted into one or more machine-learning models 1204 as seen in Fig. 12 and [0092]. Therefore, the one or more machine-learning models will use the usage data and feedback from management server 110 to aid in recommending an interface).
Regarding claim 19, Sodhi teaches the system of claim 1, and further teaches wherein the selection engine is further operable to receive feedback from the user based on operating the selected interface (Sodhi teaches client application 125 to obtain feedback from the patient following the first night of therapy as seen in Figs. 10A-10C and [0088]) and based on an undesirable result, select another interface based on the desired outcome, wherein the undesirable result is one of low user compliance with therapy, a high leak, or an unsatisfactory subjective result data (Sodhi teaches the client application 125 to solicit feedback including the quality of patient’s sleep overnight, mask discomfort and noise, wherein the feedback is sent to management server 110 and may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience as seen in [0088]. Sodhi further teaches mask recommendation module 212 to recommend a particular mask type and size for a particular patient using a facial feature analysis and measurement module 1222 inputted into one or more machine-learning models 1204 as seen in Fig. 12 and [0092]. Therefore, Sodhi teaches receiving feedback from the user, generating recommendations for the patient and having a mask recommendation module 212 to aid in selecting another interface using one or more machine-learning models 1204 that are each trained to detect suitability of a patient’s face to different sizes and types of masks 140).
Regarding claim 21, Sodhi teaches a method to select a suitable interface to the face of a user for respiratory therapy (Sodhi teaches a system and method that provides assistance to a patient for using a respiratory therapy device and a mask for treatment of mask types as seen in Fig. 1 and [0011]-[0012], wherein the management server 110 comprises a mask recommendation module 212 to generate a patient-specific recommendation for a type and size of mask 140 as seen in Figs. 2 and 12 and [0038] and [0044]), the method comprising:
storing a facial image of the user in a storage device (Sodhi teaches a patient profile database 250 to store various information about a patient (see Fig. 2 and [0045]) and a mask recommendation module 212 to obtain images of the patient and perform a facial analysis as seen in [0044]. Sodhi further teaches using the facial features of the patient captured in the input image is used in a machine-learning model 1204 to generate a mask size recommendation 1206 and a mask type recommendation 1208 as seen in Fig. 12 and [0092]. As such, Sodhi teaches storing the facial image (from Figs. 6A-6B and [0080]-[0081]) to allow the mask recommendation module 212 to perform the analysis);
determining facial features based on the facial images (“…a facial feature analysis and measurement module 1222 to analyze and measure facial features of the patient captured in the input image…” see [0092] and [0044]);
storing a plurality of facial features from a user population and a corresponding plurality of interfaces in one or more databases (Sodhi teaches “the system collects facial data from a primary patient and also RPT use and other data from a larger population of patients to assist in the selection of an optimal mask for the primary patient (see [0010]).” Sodhi further teaches an equipment database storing data relating to a plurality of device types and a plurality of mask types (see [0011]) and feedback sent to the management server 110 may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience (see [0088]). Therefore, Sodhi teaches a database with stored facial data from a larger population of patients and corresponding interfaces from the feedback in the management server 110);
storing operational data of respiratory therapy devices used by a user population with the plurality of corresponding interfaces in one or more databases (Sodhi teaches an equipment database storing data relating to a plurality of device types and a plurality of mask types (see [0011]). Sodhi further teaches management server 110 to obtain data associated with patients, their device 130 and masks 140 to generate content to assist the patient in setting up and using device 130 and mask 140, including feedback data and sensor data as seen in Figs. 3C and [0037], [0074] and [0088]); and
selecting an interface for the user from the plurality of corresponding interfaces based on a desired outcome from the stored operational data and the determined facial features (Sodhi teaches sleep apnea system 10 to use a management server 110 comprising an equipment database 254 and mask recommendation module 212 as seen in Fig. 2 and [0038]. The equipment database 254 stores data relating to a plurality of device types and a plurality of mask types (see [0011]) and the machine-learning model is based on learned correlations between image features and device models from different device type data stored in the equipment database 254 as seen in [0040]. Sodhi further teaches mask recommendation module 212 to recommend a particular mask type and size for a particular patient using a facial feature analysis and measurement module 1222 inputted into one or more machine-learning models 1204 as seen in Fig. 12 and [0092], wherein feedback sent to the management server 110 may be used to generate recommendation for the patient (or for a population of patients) to help improve the experience (see [0088]). Therefore, Sodhi teaches mask recommendation module 212 to select a mask for the patient based on the data in the management server and the determined features from facial feature analysis and measurement module 1222).
Regarding claim 67, Sodhi teaches a method comprising:
receiving sensor data associated with a current fit of an interface on a face of a user, the sensor data collected by one or more sensors of a mobile device (“If the patient selects an option for assistance on the interface 620, the client application 125 may capture video of the user’s face using a user-facing camera of the client device 110…” see [0081] and Fig. 6B; Sodhi teaches a client computing device 120 which is a mobile device (see [0036]) with a camera (see [0042] and a microphone (see [0083]);
generating a face mapping using the sensor data, wherein the face mapping is indicative of one or more features of the face of the user (Sodhi teaches using the camera of client device 120 (see [0036]) to capture a video of the user’s face to present an augmented reality view on the display that includes the captured video and overlays markers on the patient’s face that indicate proper mask placement as seen in Fig. 6B and [0081]. Sodhi further teaches a mask outline 642 with location markers 646 to assist in alignment with facial features as seen in Fig. 6B and [0081]).
identifying, using the sensor data and the face mapping, a first characteristic associated with the current fit, wherein the first characteristic is indicative of a quality of the current fit, and wherein the first characteristic is associated with a characteristic location on the face mapping (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]. The mask positioning module 208 is used to capture information from the client device 110 on how the client device 110 is positioned (see [0042]) and the information is used to help determine the location on the patient’s face that leak is occurring as seen in [0043]);
generating output feedback based on the identified first characteristic and the characteristic location (Sodhi teaches client application 125 will either alert the user the test for the mask leak detection process is completed or will alert the patient to reposition the mask 140 since there is a leak detected as seen in [0083]- [0084]).
Regarding claim 69, Sodhi teaches the method of claim 67, and further teaches wherein generating the output feedback comprises:
determining a suggested action, which if implemented, would affect the first characteristic to improve the current fit; and
presenting the suggested action using an electronic interface of the mobile device (Sodhi teaches client application 125 (application on client device 120 which is a mobile device as seen in [0035]) will alert the patient to reposition the mask 140 since there is a leak detected as seen in Figs. 7A-7C and [0083]- [0084]).
Regarding claim 75, Sodhi teaches the method of claim 67, and further teaches wherein the first characteristic includes:
i) a localized temperature on the face of the user;
ii) a localized change in temperature on the face of the user;
iii) a localized color on the face of the user;
iv) a localized change in color on the face of the user;
v) a localized contour on the face of the user;
vi) a localized change in contour on the face of the user;
vii) a localized contour on the interface;
viii) a localized change on the interface;
ix) a localized temperature on the interface; or
x) any combination of i through ix; and
wherein the characteristic location includes:
i) a vertical position of the interface with respect to the one or more features of the face of the user;
ii) a horizontal position of the interface with respect to the one or more features of the face of the user;
iii) a rotational orientation of the interface with respect to the one or more features of the face of the user;
iv) a distance between an identified feature of the interface with respect to the one or more features of the face of the user; or
v) any combination of i through iv (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]. The mask positioning module 208 is used to capture information from the client device 110 on how the client device 110 is positioned (see [0042]) and the information is used to help determine the location on the patient’s face that leak is occurring as seen in [0043]. As such, the leak includes the position of the interface with respect to one or more features of the face of the user).
Regarding claim 79, Sodhi teaches the method of claim 67, and further teaches further comprising generating an interface mapping using the sensor data, wherein the interface mapping is indicative of a relative position of one or more features of the interface with respect to the face mapping (Sodhi teaches a client computing device 120 which is a mobile device to execute a client application 125 as seen in [0036], wherein the client application 125 can assist a patient with mask placement using mask outline 642 and location markers 646 to assist in alignment with facial features as seen in Fig. 6B and [0081]-[0082]), and wherein identifying the first characteristic includes using the interface mapping (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]. The mask positioning module 208 is used to capture information from the client device 110 on how the client device 110 is positioned (see [0042]) and the information is used to help determine the location on the patient’s face that leak is occurring as seen in Figs. 6A-6B and [0043]. Therefore, Sodhi teaches identifying leakage using the interface mapping).
Regarding claim 91, Sodhi teaches the method of claim 67, and further teaches wherein the output feedback is usable to improve the current fit (Sodhi teaches client application 125 will alert the patient to reposition the mask 140 since there is a leak detected as seen in [0083]- [0084])), the method further comprising:
generating an initial score based on the current fit using the sensor data (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]. The mask positioning module 208 is used to capture information from the client device 110 on how the client device 110 is positioned (see [0042]) and the information is used to help determine the location on the patient’s face that leak is occurring as seen in [0043]. Sodhi further teaches client application 125 to alert the patient if a proper seal is not detected as seen in [0084] and therefore giving an initial score of not properly having a seal (e.g. seal is less than 100%));
receiving subsequent sensor data associated with a subsequent fit of the interface on the face of the user, wherein the subsequent fit is based on the current fit after implementation of the output feedback (Sodhi teaches the patient repositioning the mask 140 and trying again accessing the interfaces shown in FIGs. 6A-6C above as seen in [0084]);
generating a subsequent score based on the subsequent fit using the subsequent sensor data; and
evaluating the subsequent score, wherein the subsequent score is indicative of an improvement in quality over the initial score (Sodhi teaches the patient can access the interfaces shown in Figs. 6A-6C again until a proper seal is detected, even accessing aid from a live representative as seen in [0084]. Sodhi further teaches the patient ordering a different size or model of mask 140 if the issue cannot be resolved as seen in [0084]. As such, Sodhi teaches given a subsequent score based on the subsequent positioning, wherein the subsequent score is indicative of an of an improvement in quality when the client application 125 alerts the patient the testing is complete upon detecting a proper seal (e.g. seal is at 100%)).
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Holley (WO 2010091462 A1).
Regarding claim 4, Sodhi teaches the system of claim 1, and further teaches wherein at least one of the respiratory therapy devices includes an audio sensor to collect audio data during the operation of the at least one of the respiratory therapy devices (Sodhi teaches “mask leak detection module 210 detects whether a mask 140 is properly sealed around a patient’s face based on captured audio of the patient’s breathing while the sleep apnea device is delivering therapy/pressure to the patient (see [0043]).” Sodhi further teaches using a microphone (taken as audio sensor) of the client device 110 to capture audio after the patient places on the mask 140 and the captured audio is sent to the management server 110 and used by mask leak detection module 210 as seen in [0043])
But is unclear on wherein the selection engine is operable to analyze the audio data to determine a type of the corresponding interface of the at least one of the respiratory therapy devices based on matching the audio data to an acoustic signature of a known interface as Sodhi teaches “The mask leak detection module 210 detects the presence or absence of audio features indicative of a leak (see [0043])” indicating the module analyzes the audio data and recognizes the mask’s audio features/acoustic signature to detect if there is a presence or absence of audio features indicative of a leak.
However, Holley teaches wherein the selection engine is operable to analyze the audio data to determine a type of the corresponding interface of the at least one of the respiratory therapy devices based on matching the audio data to an acoustic signature of a known interface (“…the "acoustic signature" of a tested mask may be separated, identified and filtered from the noisy measurement. This acoustic reflection data may be compared to that of previous or predetermined acoustic reflection data from known masks stored as a data template of the apparatus. One way of doing this is to calculate the cross correlation between the current measurement, and previously taken measurements for all known masks or data templates.” See [0123] and [0131]; Holley teaches a database of mask reflections (see [0108] and [0122]) and a detection controller 106 used to determine and analyze sound data from the user as seen in [0073]).
Sodhi teaches mask leak detection module 210 to detect the presence or absence of audio features indicative of a leak (see [0043]). Holley teaches using a controller to detect whether the patient is currently wearing the patient interface based on the nature of the acoustic reflection and can be implemented to determine whether there is a technical problem with the patient interface including leaks and/or kinks in the system (see [0128]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the database and controller taught by Holley to aid in detecting leaks (see [0135]) and obstructions (see [0099]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Tran (US 10282914 B1).
Regarding claim 8, Sodhi teaches the system of claim 1, and further teaches wherein the facial image is determined from a scan from a mobile device including a camera (“If the patient selects an option for assistance on the interface 620, the client application 125 may capture video of the user’s face using a user-facing camera of the client device 110…” see [0081] and Fig. 6B)
but does not teach wherein the mobile device includes a depth sensor, and wherein the camera is a 3D camera, and wherein the facial features are three-dimensional features derived from a meshed surface derived from the facial image.
However, Tran teaches wherein the mobile device includes a depth sensor, and wherein the camera is a 3D camera (Tran teaches a 3D camera can provide skeletal and depth tracking and may gather spatial data that describes objects located in the physical environment external to the depth sensor, wherein the skeletal and depth tracking can be implemented into a mobile device as seen in Col. 9, lines 26-49), and wherein the facial features are three-dimensional features derived from a meshed surface derived from the facial image (Fig. 2 shows taking multiple images or video of the body and applying photogrammetric techniques to create a 3D model of the body as seen in Col. 48, lines 38-45. Tran further teaches the 3D camera to be used for facial recognition as seen in Col. 9, lines 26-49. Therefore, Tran teaches using a 3D camera to take photos of a face to create a 3D model using photogrammetric techniques by using a processor (see Col. 9, line 26 to Col. 10 line 19)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the 3D camera and processor as taught by Tran as having a 3D model is more convenient and saves time (see Col. 2, lines 39-45).
Claim(s) 70 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Smith (US 20120245962 A1) and Lawrenson (US 20170203071 A1).
Regarding claim 70, Sodhi teaches the method of claim 67, but does not teach wherein the sensor data includes infrared data from i) a passive thermal sensor; ii) an active thermal sensor; or iii) both i and ii; and wherein the sensor data includes distance data, wherein the distance data is indicative of one or more distances between the one or more sensors of the mobile device and the face of the user.
However, Smith teaches wherein the sensor data includes infrared data from i) a passive thermal sensor; ii) an active thermal sensor; or iii) both i and ii (“…facial scanning module 32 further includes a thermal camera for capturing thermal images of the patient's face and the software provided in processing unit 8 is adapted to automatically evaluate the likelihood of whether a particular respiratory patient interface device will lead to undesirable tissue conditions, such as pressure sores or discomfort, during use based on captured thermal images of the patient's face before an after wearing a particular device.” See [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the processor and facial scanning module with thermal camera as taught by Smith to evaluate the likelihood of whether a particular respiratory patient interface device will lead to undesirable tissue conditions (see [0070]).
However, Lawrenson teaches wherein the sensor data includes distance data, wherein the distance data is indicative of one or more distances between the one or more sensors of the mobile device and the face of the user (Lawrenson teaches a single apparatus 10 realized as a mobile device (see [0093]) comprising a distance sensor 46 to observe the patient 12 as seen in Figs. 1-2 and [0097]. The distance sensor 46 can be used to determine the distance between the microphone 44 and the patient’s head during movement as seen in [0102]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi in view of Smith to include the distance sensor as taught by Lawrenson as an additional observational sensor to provide more information to the patient (see [0097] and [0102]).
Claim(s) 81 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Smith (US 20120245962 A1).
Regarding claim 81, Sodhi teaches the method of claim 67, but does not teach further comprising:
identifying, using the sensor data and the face mapping, a second characteristic, the second characteristic being associated with a possible future failure of the interface; and
generating output feedback based on the identified second characteristic, the output feedback being usable to reduce a likelihood that the possible future failure will occur or to delay occurrence of the possible future failure.
However, Smith teaches identifying, using the sensor data and the face mapping, a second characteristic, the second characteristic being associated with a possible future failure of the interface (“…facial scanning module 32 further includes a thermal camera for capturing thermal images of the patient's face and the software provided in processing unit 8 is adapted to automatically evaluate the likelihood of whether a particular respiratory patient interface device will lead to undesirable tissue conditions, such as pressure sores or discomfort, during use based on captured thermal images of the patient's face before an after wearing a particular device.” See [0032]); and
generating output feedback based on the identified second characteristic, the output feedback being usable to reduce a likelihood that the possible future failure will occur or to delay occurrence of the possible future failure (Smith teaches based on the results of the thermal scanning when processing unit 8 evaluate the likelihood of whether a particular respiratory patient interface device will lead to undesirable tissue conditions (see [0032]), the patient may return the respiratory patient interface device product 24 to the sleep management vending kiosk 2 until a satisfactory respiratory patient interface device product 24 is obtained as seen in [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the processor and facial scanning module with thermal camera as taught by Smith to evaluate the likelihood of whether a particular respiratory patient interface device will lead to undesirable tissue conditions (see [0070]).
Claim(s) 82 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Benson (US 20150126894 A1)
Regarding claim 82, Sodhi teaches the method of claim 67, but does not teach further comprising accessing historical sensor data associated with one or more historical fits of the interface on the face of the user prior to receiving the sensor data, wherein identifying the first characteristic further uses the historical sensor data.
However, Benson teaches “The cloud-based data analysis and storage platform has a database, centrally organizes and stores the output data from the sensor platform in the database, maintains a history of each procedure of the sensor platform in the database, analyzes the stored output data and the history to define stored output data of a successful CMAP outcome, predicts an outcome of the current procedure by comparing at least a portion of the output data in the current procedure with output data in the database to determine if the portion of the current output data correlates with the stored output data of the successful CMAP outcome, and communicates in real time the outcome of the current procedure (see [0038]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the cloud-based data analysis and storage platform as taught by Benson to store historical sensor information within a database that can be accessed to aid in predicting an outcome of the current procedure (see [0038]) for enhanced decision making. Sodhi in view of Benson teaches comprising accessing historical sensor data associated with one or more historical fits of the interface on the face of the user prior to receiving the sensor data, wherein identifying the first characteristic further uses the historical sensor data (Sodhi in view of Benson teaches a cloud-based data analysis and storage platform (taught by Benson) used to store previous sensor data from the camera and microphone of Sodhi associated with capturing audio of the patient’ s breathing and seeing how the mask is positioned to determine a leak. The cloud-based data analysis and storage platform can predict an outcome of the current procedure by comparing at least a portion of the output data in the current procedure with output data in the database (see [0038] of Sodhi). Therefore, the historical sensor data associated with one or more historical fits can be accessed through the database and identifying a leak can use historical sensor data).
Claim(s) 83 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Vlutters (US 20160092645 A1).
Regarding claim 83, Sodhi teaches the method of claim 67, but does not teach further comprising generating a current fit score using the sensor data and the face mapping, wherein the output feedback is generated to improve a subsequent fit score.
However, Vlutters teaches generating a current fit score using the sensor data and the face mapping, wherein the output feedback is generated to improve a subsequent fit score (Vlutters teaches a face scanning unit 10 comprising a camera and a processing unit 40 with a geometric fit score determining unit 42 as seen in Fig. 1 and [0034]. Vlutters further teaches receiving a 3D model of the patient’s face in operation S1, receiving a 3D model of the patient interface device in S2, choosing a distinctive feature on the patient’s face and aligning the patient interface with the feature as seen in Fig. 2 and [0044]-[0046]. A geometric fit score is then calculated based on the interaction between the 3-D model of the respective patient interface device and the 3-D model of the patient's face as seen in [0047]-[0048]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the processing unit taught by Vlutters for a calculated fit score based on how well the interface will fit a user’s face that will aid a user in choosing whether or not the interface is right for them (see Fig. 5 and [0057]).
Claim(s) 92 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sodhi (WO 2020118311 A1) in view of Hendriks (US 20220344026 A1).
Regarding claim 92, Sodhi teaches the method of claim 67, and further teaches wherein identifying the first characteristic associated with the current fit includes:
determining a breathing pattern of the user based on the received sensor data (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]);
determining a leak characteristic using the breathing pattern (Sodhi teaches a microphone of the client device is used to capture audio of the patient’ s breathing and the mask leak detection module 210 detects the presence or absence of audio features indicative of a leak as seen in [0043]).
But does not teach wherein identifying the first characteristic associated with the current fit includes:
determining a thermal pattern associated with the face of the user based on the received sensor data and the face mapping; and
determining a leak characteristic using the breathing pattern and the thermal pattern, wherein the leak characteristic is indicative of a balance between intentional vent leak and unintentional seal leak.
However, Hendriks teaches determining a thermal pattern associated with the face of the user based on the received sensor data and the face mapping (Hendriks teaches sensing arrangement 24 comprising a number of thermal sensors for leak detection, by placing thermistors on specific spots around and on mask 14 as seen in Fig. 1 and [0052]. Therefore, the sensing arrangement 24 can provide for leak detection but can also provide a specific location for the leak as seen in [0052]); and
determining a leak characteristic using the breathing pattern and the thermal pattern (Hendriks teaches sensing arrangement 24 comprising a number of thermal sensors for leak detection, by placing thermistors on specific spots around and on mask 14 as seen in Fig. 1 and [0052]. Hendriks further teaches a stereo microphone or multiple microphones are used to localize the sound created by the air turbulences of leaking air, wherein the loudness of the leak can be related to the leak rate as seen in [0052]), wherein the leak characteristic is indicative of a balance between intentional vent leak and unintentional seal leak (Hendriks teaches the total leak rate to be the intentional leak of the exhalation ports plus the unintentional leak, wherein when the unintentional leak is above a threshold, the sensors are to look for the leak location as seen in [0050]-[0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system taught by Sodhi to include the sensing arrangement and computing arrangement taught by Hendriks for another sensing arrangement to check for seal leakage to assure the unintentional leak rate is below a threshold (see [0050]-[0051]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Salmon (US 20180008794 A1) teaches gender based full-face mask sizes.
Martins (US 20160310730 A1) teaches comparing current sensor data with previous sensor data.
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/TINA ZHANG/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785
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