DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 103 is 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.
In relation to claim 103, this claim is rejected for lacking antecedent basis for the term "the conduit." The claim introduces "conduit identification information" but fails to first introduce "a conduit." This leaves the term "the conduit" without a clear reference. The claim should be amended to
introduce "a conduit" before referring to "the conduit" to resolve the indefiniteness.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 10, 18, 44, 47, 48, 50, 51, 124, and 127 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ho et al. (WO 2011/077274A1; hereinafter “Philips”).
Claim 1 recites a method comprising:
generating airflow through a user interface, wherein generating the airflow includes generating the airflow using a flow generator;
measuring one or more airflow parameters associated with the generated airflow, wherein the one or more airflow parameters include at least one of a flow signal of the generated airflow and a pressure signal of the generated airflow;
identifying user interface identification information based on the measured one or more airflow parameters, wherein the user interface identification information is usable to identify a characteristic of the user interfaces receiving existing user interface identification information associated with the flow generator;
determining that the identified user interface identification information is different than the existing user interface identification information; and
generating a notification in response to determining that the identified user interface identification information is different than the existing user interface identification information.
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In relation to independent claim 1, Philips discloses a pressure support system comprising:
generating airflow through a user interface, wherein generating the airflow includes generating the airflow using a flow generator;
Philips teaches a gas flow generator (52) that provides a pressurized flow of breathing gas
to a patient interface (58) (paragraphs [0022]-[0025]; figure 2).
measuring one or more airflow parameters associated with the generated airflow, wherein the one or more airflow parameters include at least one of a flow signal of the generated airflow and a pressure signal of the generated airflow;
The system includes a flow sensor (62) and a pressure sensor (68) to measure airflow parameters (paragraphs [0026]-[0027]).
identifying user interface identification information based on the measured one or more airflow parameters, wherein the user interface identification information is usable to identify a characteristic of the user interfaces receiving existing user interface identification information associated with the flow generator;
The controller (64) automatically identifies the patient interface device by detecting changes in exhaust flow, which corresponds to identifying user interface identification information (paragraphs [0010], [0033]-[0036] and [0037…method of automatically identifying patient interface device 58]).
determining that the identified user interface identification information is different than the existing user interface identification information; and
The system compares measured flow and pressure to predetermined values in a lookup table (118), which constitutes receiving existing user interface identification information (paragraphs [0040], [0043…Curves]). The detection of a change in exhaust flow implies a determination that the identified information is different from existing information (paragraphs [0035], [0038]).
generating a notification in response to determining that the identified user interface identification information is different than the existing user interface identification information.
Finally, Philips discloses that if automatic detection fails, the user is instructed to manually input the mask type via an input/output device (66), which serves as a notification (paragraph [0035]).
In relation to claim 3, Philips anticipates the limitations of claim 3. Philips discloses the use of a lookup table (118) containing predetermined flow rates and pressure points for different mask types. These predetermined values function as template curves. The controller compares the measured data points with these stored curves to identify the mask type (paragraphs [0040], [0043]). This process is equivalent to accessing template curves and generating a comparison to identify the user interface.
In relation to claim 10, Philips anticipates the limitations of claim 10. Philips discloses different types and styles of patient interface devices, such as nasal masks, nasal/oral masks, and full-face masks (paragraphs [0004], [0043]). These different styles inherently correspond to different models from various manufacturers, each with unique exhaust flow characteristics. Therefore, the identification of the mask type as taught by Philips is equivalent to identifying the style or model of the user interface.
In relation to claim 18, Philips teaches that if the automatic identification of the patient interface fails, the system prompts the user to manually input the mask type via an input/output device (66) (paragraphs [0035], [0038]). This process of prompting for and receiving user input is a direct example of determining a need for additional data, generating a prompt, and receiving a response to a question about the user interface, as recited in sub-part (iii) of the claim.
In relation to claim 36, Philips does not explicitly teach separating the airflow parameters into high, non-high, middle, and low frequency components relative to a baseline respiration rate. However, it is a fundamental principle in digital signal processing that a signal can be decomposed into its constituent frequency components for analysis. Different physical phenomena (breathing, snoring, leak) manifest in different frequency bands. Therefore, a person of ordinary skill would have been motivated to separate the signal into different frequency bands to isolate different physical phenomena and create more discriminative features for the machine learning model.
In relation to claim 44, the controller (64) in Philips is operatively coupled to the pressure generator, which includes the flow generator (52), and receives signals from the flow and pressure sensors (paragraphs [0026]-[0028]). The entire identification process is based on the operation of the flow generator and the resulting flow and pressure signals, which are inherently flow generator parameters. The controller's operation is based on these parameters to identify the mask.
In relation to claim 47, Philips describes a process where the controller detects a change of exhaust flow across a predetermined pressure gradient (paragraphs [0010], [0033]). This involves adjusting the pressure (and thus the flow) to different levels and measuring the corresponding flow rates to identify the mask. This process of systematically varying the pressure to measure flow changes is equivalent to generating a known adjustment to the airflow and measuring the parameters to identify the interface.
In relation to claim 48, Philips anticipates the limitations of claim 48. The automatic identification process in Philips is initiated to detect the patient interface in use (paragraphs [0010]). This identification naturally occurs when a new interface is connected or when the system is started, which are transient events that include donning the user interface. The system is designed to identify the interface upon connection, which is inherently a transient event.
In relation to claim 50, Philips anticipates the limitations of claim 50. Philips explicitly teaches that the controller controls the pressure generator to provide pressure and flow to the mask based upon the identified mask type (paragraph [0034], claim 20). This is a direct teaching of updating a setting of the flow generator based on the identified user interface.
In relation to claim 51, Philips anticipates the limitations of claim 51. The entire process in Philips is based on analyzing the flow and pressure signals generated by the pressure generator (which
includes the flow generator) to identify the connected mask. The characteristics of the flow and pressure signals are directly tied to the performance and type of the flow generator itself. Therefore, identifying the mask based on these signals inherently involves identifying the operational characteristics of the flow generator system.
In relation to claims 124 and 127, these claims are directed to a system and a computer program product for carrying out the method of claim 1. As claim 1 is anticipated by Philips, and Philips discloses a system with a controller (64) (i.e., a processor) and memory for executing the identification
Method [paragraph [0028], these claims are also anticipated. The controller in Philips is a hardware implementation of the method, and the instructions it executes are inherent to its function. Therefore, claims 124 and 127 are not patentably distinct from claim 1.
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 13, 25, and 129 are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (WO 2011/077274A1; hereinafter “Philips”) in view of Behbehani et al. (US 5,953,713; hereinafter “University of Texas”).
In relation to claim 13, Philips teaches a method of identifying a user interface by measuring flow and pressure while the mask is worn by the user ([0032], step 104). Philips does not explicitly teach capturing flow data before the user interface is worn (i.e., "first flow data captured while the user interface is not worn") and using both the pre-wear and post-wear data for identification. It would have been obvious to one of ordinary skill in the art to modify the method of Philips to include this differential measurement. It is a fundamental and well-established engineering and scientific principle to measure a system in a baseline or unloaded state (not worn) and then measure it again in its operational or loaded state (worn) to accurately characterize the system's response and isolate the effects of the load. A person of ordinary skill would have been motivated to capture the "not worn" flow data to establish a zero-flow or baseline system response, which would then be compared against the "worn" data to more accurately and reliably determine the specific characteristics of the user interface being used, by canceling out any sensor drift or system biases. This is a predictable design choice to improve the accuracy of the identification system.
In relation to claim 25, as discussed above, Philips discloses the elements of claim 1, including determining user interface identification by comparing measured flow and pressure data to predetermined values, which are features of the airflow parameters. Philips does not explicitly disclose a machine learning model. However, University of Texas (US 5,953,713 A) teaches an apparatus for treating sleep disorder breathing that uses an artificial neural network to recognize patterns in respiration-related variables (Abstract). It discloses obtaining a frequency spectrum from pressure data and inputting the frequency components into a neural network to detect sleep disorder breathing (col. 2, 41-47; FIG. 3, col. 4, lines 1-13). Accordingly, a person of ordinary skill would have been motivated to replace the static lookup table in Philips with a machine learning model as taught by University of Texas to create a more robust and adaptive system for classifying patient interfaces. The neural network in University of Texas, a type of machine learning model, demonstrates the conventionality of using of such models for analyzing respiratory data, making its application to mask identification a predictable development.
In relation to claim 129, Philips teaches identifying a user interface based on airflow parameters. Philips does not explicitly disclose determining spectral components of the airflow parameters to determine shape or size features. University of Texas (US 5,953,713 A) teaches obtaining a frequency spectrum from measured pressure data and using the frequency components to detect
sleep disorder breathing (col. 2, 41-47; FIG. 3, col. 4, lines 1-13). This establishes that analyzing the spectral components of respiratory signals was a known technique. Different user interfaces (masks) have different shapes and sizes, which will affect the airflow and pressure dynamics in distinct ways. These differences will be reflected in the spectral components of the measured airflow signals. Therefore, it would have been obvious to one of ordinary skill in the art to analyze the spectral components of the airflow signals in the Philips system to extract features corresponding to the shape and size of the user interface. This would be a predictable way to enhance the identification process, as the spectral characteristics of the airflow are directly influenced by the physical properties (shape and size) of the user interface through which the air is flowing. The motivation would be to obtain a more detailed and accurate identification of the user interface by analyzing its fundamental acoustic and fluid dynamic properties as revealed in the frequency domain.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (WO 2011/077274A1; hereinafter “Philips”) in view of Behbehani et al. (US 5,953,713; hereinafter “University of Texas”), as discussed above, and in further view of the publication by Efrat et al. (Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves; hereinafter “Efrat”) and the publication/Editorial by Lu et al., (Distance metric learning for pattern recognition; hereinafter “Lu”).
In relation to claim 27, as discussed above, Philips, as modified by University of Texas in the rejection of claim 25, teaches comparing measured flow and pressure data points to a set of predetermined template curves stored in a lookup table to identify a mask type. Philips does not explicitly disclose the specific mathematical formulas for "calculating an identification distance," such as minimum distance, flow-based distance, or pressure-based distance. However, at the time of the invention, the use of distance metrics to quantify the similarity or dissimilarity between a measured data point and a set of template curves was a universally known technique in the field of pattern recognition. As evidenced by numerous publications on the topic, such as "Distance metric learning for pattern recognition" and "Curve matching, time warping, and light fields", the concept of calculating a distance to find the best match between a point and a curve or between two curves was well-known in the art at the time of filing. A person of ordinary skill in the art, when faced with the task of implementing the comparison step in Philips, would have found it obvious to use standard distance calculation methods. The motivation would be to implement the comparison in a mathematically rigorous and computationally efficient manner. Calculating a minimum distance, or distances based on the individual flow and pressure components, are the most predictable ways to implement such a comparison. Therefore, since these techniques were well-known in the art at the time of filing, their implementation in the invention would have been considered an obvious alternative in the design of the system.
Claims 32 and 103 are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (WO 2011/077274A1; hereinafter “Philips”) in view of Behbehani et al. (US 5,953,713; hereinafter “University of Texas”), as discussed above, and in further view of Wickham (US 7,987,847B2; hereinafter “ResMed”).
In relation to claim 32, as discussed above, Philips, as modified by University of Texas in the rejection of claim 25, teaches identifying a user interface based on features derived from airflow parameters. Philips discusses unintentional leak in the background (paragraph [0007]). Philips does not explicitly teach using an unintentional leak signal, a nasal-oral breathing signal, or a volumetric breathing signal as features for identification. However, ResMed (US 7,987,847 B2) teaches methods for characterizing mask systems, which includes estimating unintentional leak. ResMed discusses the importance of accounting for unintentional leak and provides methods for its estimation (ResMed; column 2, lines 8-23). Accordingly, it would have been obvious to one of ordinary skill in the art to use an estimate of the unintentional leak as a feature for identifying the user interface in the Philips system. Different masks, due to their design and fit, will have different typical unintentional leak profiles. A person of ordinary skill would be motivated to use this information as an additional feature to improve the accuracy and robustness of the identification system. The combination of Philips' identification method with the known technique of estimating unintentional leak as taught by ResMed would have been a predictable step to enhance the system's performance.
In relation to claim 103, Philips teaches a method of identifying a user interface by measuring pressure and flow data, generating data points, and comparing them to curves (paragraphs [0040], [0043]). Philips does not explicitly teach identifying conduit identification information. ResMed (US 7,987,847 B2) teaches a method for characterizing different mask systems, including both masks and hoses (conduits). ResMed explicitly discloses: “[a] method and a CPAP apparatus for characterizing different mask systems, e.g., masks and hoses, are provided" (Abstract). ResMed further teaches a method for determining the air flow characteristics of the air delivery hose (conduit) using flow and pressure measurements. Therefore, it would have been obvious to one of ordinary skill in the art at
the time the invention was made to combine the teachings of Philips and ResMed. A person of ordinary skill would have been motivated to incorporate the conduit characterization method of ResMed into the system of Philips to improve the accuracy of the overall respiratory therapy system. Since the conduit's characteristics (e.g., length, diameter, resistance) affect the pressure and flow delivered to the patient, identifying these characteristics would allow for more precise control and therapy. Therefore, it would have been a predictable and logical step to extend the identification process in Philips to include
the conduit, as taught by ResMed.
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (WO 2011/077274A1; hereinafter “Philips”) in view of Behbehani et al. (US 5,953,713; hereinafter “University of Texas”), as discussed above, and in further view of Migliori (WO1992/005439A1).
Philips, as modified by University of Texas, teaches a method of identifying a user interface by analyzing features derived from airflow parameters. Philips does not explicitly disclose using a resonant frequency signal as one of those features. Migliori (WO 1992/005439 A1) teaches a method of characterizing an object using resonant ultrasound spectroscopy. This method involves applying acoustic waves to an object over a range of frequencies and determining the resonant spectrum of the object to form a unique signature (Abstract). Migliori explain that this technique provides a unique characterization of an object that can be used to distinguish similar objects based on their physical differences (Abstract). Therefore, it would have been obvious to one of ordinary skill in the art to
apply the resonant frequency analysis taught by Migliori to the user interface identification problem in Philips. A person of ordinary skill would have been motivated to use the resonant frequency signature of the user interface as a feature for identification because, as taught by Migliori, it would have provided a unique and robust characterization of the object. By exciting the user interface with the airflow from the flow generator (which would contain a spectrum of frequencies) and measuring the resulting pressure and flow signals, one could determine the resonant frequencies of the user interface and use this information to identify it. This would be a predictable application of a known characterization technique to a known problem.
Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (WO 2011/077274A1; hereinafter “Philips”) in view of Behbehani et al. (US 5,953,713; hereinafter “University of Texas”), as discussed above, and in further view of the publication by Dey et al., ("ObSA: Automated detection of obstructive sleep apnea from ECG signals using CNN"; hereinafter “Dey”).
Philips provides the foundational method for identifying a user interface based on flow and pressure. Philips does not teach the use of a spectrogram or a deep neural network. Dey teaches an automated method for OSA detection using a convolutional neural network (CNN), a type of deep neural network, based on ECG signals (Abstract). This publication establishes that using deep learning for analyzing physiological signals for respiratory classification was well-known in the art at the time of filing. Given that spectrograms were a well-established method for representing signals for neural network input, it would have been obvious to one of ordinary skill in the art to apply this technique to the flow and pressure signals from Philips. The motivation would have been to leverage the advanced feature extraction capabilities of deep neural networks to improve the accuracy and robustness of mask identification over the simpler lookup table method. Therefore, the combination of Philips and the state of the art, as exemplified by Dey, renders claim 46 obvious.
Conclusion
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Respectfully submitted,
/MANUEL A MENDEZ/ Primary Examiner, Art Unit 3783