DETAILED ACTION
The present office action represents the final action on the merits.
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 .
Priority
This application claims priority filing date of Australia Application AU2021902284 of 7/26/2021.
Status of Claims
Claims 1, 6, and 18 are amended. Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-5 are drawn to a method for predicting user mouth leak using computer modeling, which is within the four statutory categories (i.e., process). Claims 6-17 are drawn to a method for predicting user mouth leak using computer modeling, which is within the four statutory categories (i.e., process). Claims 18-20 are drawn to a non-transitory computer-readable medium comprising computer-executable instructions for predicting user mouth leak using computer modeling, which is within the four statutory categories (i.e., machine).
Claim 1 recites a method, comprising:
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy;
determining a mouth leak measure of the patient during the PAP therapy, wherein the mouth leak measure indicates whether air leaked from a mouth of the patient during the PAP therapy;
extracting a set of features from the patient data, wherein the set of features comprises an image of a nose of the patient;
generating a predicted mouth leak measure by processing the set of features using a leak model, wherein the predicted mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy; and
refining the leak model based on a difference between the mouth leak measure and the predicted mouth leak measure.
Claim 6 recites a method, comprising:
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy;
extracting a set of features from the patient data, wherein the set of features comprises an image of a nose of the patient;
generating a first predicted mouth leak measure by processing the set of features using a leak model, wherein the predicted mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy; and
in response to determining that the first predicted mouth leak measure satisfies defined criteria, facilitating provisioning of a first PAP apparatus for the patient.
Claim 18 recites a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising:
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy;
extracting a set of features from the patient data, wherein the set of features comprises an image of a nose of the patient;
generating a first predicted mouth leak measure by processing the set of features using a leak model, wherein the predicted mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy; and
in response to determining that the first predicted mouth leak measure satisfies defined criteria, facilitating provisioning of a first PAP apparatus for the patient.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people (e.g., predicting user mouth leak) and mathematical concepts (e.g., mouth leak model). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-5, 7-17, and 19-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Specifically, claim 2 recites wherein extracting the set of features comprises determining facial information comprising: a size and a shape of a nose of the patient; and a predicted measure of nasal resistance to airflow for the patient, wherein the predicted measure of nasal resistance is determined using acoustic impedance measurement, claim 3 recites wherein the patient data comprises at least one of an image of the patient or a three-dimensional scan of the patient, claim 4 recites wherein the leak model comprises a convolutional neural network machine learning model, claim 5 recites wherein extracting the set of features comprises estimating a gravitational force on a mandible of the patient, comprising: estimating a spatial coordinate of a fulcrum of the mandible; estimating spatial coordinates of one or more connection points of one or more organs of the patient to the mandible; determining one or more attributes of the patient based on the patient data; and estimating a mass of the one or more organs based on fitting the one or more attributes to an organ model, claim 7 recites wherein: the patient is engaged in the PAP therapy using a second PAP apparatus; and the first predicted mouth leak measure is further generated by processing an indication of the second PAP apparatus using the leak model, claim 8 recites wherein facilitating provisioning of the first PAP apparatus comprises: generating a plurality of predicted mouth leak measures by, for each respective PAP apparatus of a plurality of PAP apparatuses, processing a respective indication of the respective PAP apparatus and the set of features using the leak model; and determining, based on the plurality of predicted mouth leak measures, that the first PAP apparatus is least likely to cause mouth leak, claim 9 recites wherein extracting the set of features comprises determining facial information indicating a size and a shape of a nose of the patient, claim 10 recites wherein the facial information further comprises a predicted measure of nasal resistance to airflow for the patient, wherein the predicted measure of nasal resistance is determined using acoustic impedance measurement, claim 11 recites wherein the patient data comprises at least one of an image of the patient or a three-dimensional scan of the patient, claim 12 recites wherein the leak model comprises a convolutional neural network machine learning model, claim 13 recites wherein extracting the set of features comprises segmenting the patient data based on a nose of the patient, claim 14 recites wherein extracting the set of features comprises estimating a gravitational force on a mandible of the patient, claim 15 recites wherein estimating the gravitational force comprises: estimating a spatial coordinate of a fulcrum of the mandible; and estimating spatial coordinates of one or more connection points of one or more organs of the patient to the mandible, claim 16 recites wherein estimating the gravitational force further comprises: determining one or more attributes of the patient based on the patient data; and estimating a mass of the one or more organs based on fitting the one or more attributes to an organ model, claim 17 recites wherein estimating the gravitational force further comprises determining a sleeping position of the patient, claim 19 recites wherein: the patient data comprises at least one of an image of the patient or a three-dimensional scan of the patient, and the leak model comprises a convolutional neural network machine learning model, claim 20 recites wherein extracting the set of features comprises estimating a gravitational force on a mandible of the patient, comprising: estimating a spatial coordinate of a fulcrum of the mandible; estimating spatial coordinates of one or more connection points of one or more organs of the patient to the mandible; determining one or more attributes of the patient based on the patient data; and estimating a mass of the one or more organs based on fitting the one or more attributes to an organ model, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 6, and 18.
The additional elements from claim 6 include:
a first PAP apparatus for the patient (apply it, MPEP 2106.05(f)).
The additional elements from claim 7 include:
using a second PAP apparatus (apply it, MPEP 2106.05(f)).
The additional elements from claim 8 include:
the first PAP apparatus comprises (apply it, MPEP 2106.05(f)).
for each respective PAP apparatus of a plurality of PAP apparatuses (apply it, MPEP 2106.05(f)).
The additional elements from claim 18 include:
non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of, “non-transitory computer-readable medium”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Paragraphs [0038], [0042], [0044], and [0045]. (See MPEP 2106.05(f));
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification Paragraphs [0142]-[0144], (i.e., computer, memory, processor) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, analyzing patient data);
Relevant court decisions: The following are examples of court decisions demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving data over a network using the Internet to gather data, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention receives patient data and predicts a user mouth leak.
Dependent claims 2-5, 7-17, and 19-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than receiving data over a system (e.g., predict user mouth leak claims 1-20.).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity, using a system to access and review patient data. The inventive concept is reviewing patient data and predicting if the patient will have a mouth leak using an apparatus, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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, 3, 6-9, 11, 13, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaigler (U.S. Pub. No. 2022/0215959 A1) in view of Aylsworth (U.S. Pub. No. 2010/00186741 A1), Dodds (U.S. Pub. No. 20210370005 A1), and Liu (U.S. Pub. No. 2014/0053840 A1).
Regarding claim 1, Kaigler discloses a method, comprising:
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy (Paragraphs [0061]-[0063] and [0091] discuss obtain from a database system including one or more databases patient characterization data metrics and clinical outcome data metrics for each of a plurality of patients associated with sleep therapy and the selection of interface equipment, a PAP mask system.);
determining a leak measure of the patient during the PAP therapy, (Paragraph [0106] discusses data is taken or determined from metrics output by the PAP device and average or maximum mask leak is measured by the PAP device.);
extracting a set of features from the patient data (Paragraphs [0063], [0094] and [0123] discuss obtain patient characterization data and real-time haptic, audio, or visual feedback/clues may be provided by the system to guide the patient in providing the optimal picture for the extraction of the desired metrics and information about the patient and the patient’s use of the mask.);
generating a predicted measure by processing the set of features using a model, wherein the predicted value indicates a predicted probability that the value will occur during the PAP therapy (Paragraphs [0009]-[0011], [0026], [0045], [0123], and FIGS. 3B, discuss determining a management option for sleep therapy for a person via execution of the algorithm based at least in part upon an optimization of at least one outcome parameter for the person predicted by at least one model of the algorithm based at least in part upon characterization data of the person input into the database system, training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter and the management option may include determination of sleep therapy option for us by the patient.); and
refining the model based on a difference between the measure and the predicted measure (Paragraphs [0045], [0108], [0115], and FIG. 7B discuss training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter, the machine learning models are evaluated against each other based on a weighted formula of the collected metrics and the prediction together with the associated parameters is compared to collected data for the patient.).
Kaigler does not explicitly disclose:
wherein the mouth leak measure indicates whether air leaked from a mouth of the patient during the PAP therapy;
wherein the set of features comprises an image of a nose of the patient;
a mouth leak measure by processing the set of features using a leak model;
the leak model;
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy.
Aylsworth teaches:
wherein the mouth leak measure indicates whether air leaked from a mouth of the patient during the PAP therapy (Paragraphs [0004]-[0005], [0016] discuss CPAP systems and measuring and detecting a mouth leak during treatment.).
mouth leak measure (Paragraphs [0016], [0038], and FIG.4 discuss mouth leak and amount of leak in a patient breathing circuit (i.e. the higher the null voltage, the greater the amount of air escaping through the patient's mouth).).
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy (Paragraphs [0008]-[0011] and [0016] discuss a mouth leak by a patient during use of a PAP machine.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include wherein the mouth leak measure indicates whether air leaked from a mouth of the patient during the PAP therapy, mouth leak measure, and mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy, as taught by Aylsworth, in order to address the reduction or elimination of mouth leaks during CPAP treatment. (Aylsworth Paragraph [0008].).
Dodds teaches:
wherein the set of features comprises an image of a nose of the patient (Paragraphs [0025]-[0026] discuss capturing images of patient’s face to generate a model that includes the patient’s nose and personalizing a face-engaging surface to a patient’s facial contour and to form a seal.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include wherein the set of features comprises an image of a nose of the patient, as taught by Dodds, in order to provide a structure that can be comfortable for a patient. (Dodds Paragraph [0008].).
Liu teaches:
using a leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.) (Examiner notes that mouth leak is not referenced, however, gas leakage of the mask is interpreted as mouth leak.).
the leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include using a leak model and the leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Regarding claims 3 and 11, Kaigler discloses wherein the patient data comprises at least one of an image of the patient or a three-dimensional scan of the patient (Paragraphs [0005]-[0007], [0092] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data, including the nose, anatomical data may be determined from a two-dimensional or three-dimensional image, photo, video, or model.).
Regarding claim 6, Kaigler discloses method, comprising:
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy (Paragraphs [0061]-[0063] and [0091] discuss obtain from a database system including one or more databases patient characterization data metrics and clinical outcome data metrics for each of a plurality of patients associated with sleep therapy and the selection of interface equipment, a PAP mask system.);
extracting a set of features from the patient data (Paragraphs [0063], [0094] and [0123] discuss obtain patient characterization data and real-time haptic, audio, or visual feedback/clues may be provided by the system to guide the patient in providing the optimal picture for the extraction of the desired metrics and information about the patient and the patient’s use of the mask.);
generating a predicted measure by processing the set of features using a model, wherein the predicted value indicates a predicted probability that the value will occur during the PAP therapy (Paragraphs [0009]-[0011], [0026], [0045], [0123], and FIGS. 3B, discuss determining a management option for sleep therapy for a person via execution of the algorithm based at least in part upon an optimization of at least one outcome parameter for the person predicted by at least one model of the algorithm based at least in part upon characterization data of the person input into the database system, training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter and the management option may include determination of sleep therapy option for us by the patient.); and
in response to determining that the first predicted measure satisfies defined criteria, facilitating provisioning of a first PAP apparatus for the patient (Paragraph [0107] and FIG. 6 discuss using an algorithm to select a mask system for patient based on predicted optimization of one or more outcome parameters.).
Kaigler does not explicitly disclose:
wherein the set of features comprises an image of a nose of the patient
mouth leak measure;
a mouth leak measure by processing the set of features using a leak model;
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy.
Aylsworth teaches:
mouth leak measure (Paragraphs [0016], [0038], and FIG.4 discuss mouth leak and amount of leak in a patient breathing circuit (i.e. the higher the null voltage, the greater the amount of air escaping through the patient's mouth).).
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy (Paragraphs [0008]-[0011] and [0016] discuss a mouth leak by a patient during use of a PAP machine.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a mouth leak measure and mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy, as taught by Aylsworth, in order to address the reduction or elimination of mouth leaks during CPAP treatment. (Aylsworth Paragraph [0008].).
Dodds teaches:
wherein the set of features comprises an image of a nose of the patient (Paragraphs [0025]-[0026] discuss capturing images of patient’s face to generate a model that includes the patient’s nose and personalizing a face-engaging surface to a patient’s facial contour and to form a seal.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include wherein the set of features comprises an image of a nose of the patient, as taught by Dodds, in order to provide a structure that can be comfortable for a patient. (Dodds Paragraph [0008].).
Liu teaches:
using a leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include using a leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Regarding claim 7, Kaigler discloses wherein:
the patient is engaged in the PAP therapy using a second PAP apparatus (Examiner is interpreting “second PAP apparatus” pursuant to Specification Paragraph [0154] such that the patient tests multiple PAP apparatus during the PAP therapy.) (Paragraphs [0015], [0061], [0098] and [0115] discuss existing sleep therapy patient changing an interface system (PAP device) from a plurality of different interface systems and monitoring patients while continuing sleep treatment.); and
the first predicted measure is further generated by processing an indication of the second PAP apparatus (Paragraphs [0020] and [0123] discuss change or adjustment in sleep therapy option based on whether patient is using the interface system incorrectly or non-optimally and recommend to change the interface system.).
Kaigler does not explicitly disclose:
mouth leak measure is further generated by processing an indication of the second PAP apparatus using the leak model.
Aylsworth teaches:
mouth leak measure (Paragraphs [0016], [0038], and FIG.4 discuss mouth leak and amount of leak in a patient breathing circuit (i.e. the higher the null voltage, the greater the amount of air escaping through the patient's mouth).).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a mouth leak measure, as taught by Aylsworth, in order to address the reduction or elimination of mouth leaks during CPAP treatment. (Aylsworth Paragraph [0008].).
Liu teaches:
using the leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include using the leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Regarding claim 8, Kaigler discloses wherein facilitating provisioning of the first PAP apparatus comprises:
generating a plurality of predicted measures by, for each respective PAP apparatus of a plurality of PAP apparatuses, processing a respective indication of the respective PAP apparatus and the set of features using the model (Paragraphs [0029]-[0031], [0061], and [0100] discuss data or metrics for patients may be collected/determined using monitoring and surveying devices and associated software to provide initial optimization in, for example, selecting and fitting sleep therapy equipment and/or for a long-term optimization loop for each of a plurality of patients, and one or more patient outcome parameters are predictively optimized and training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter for an interface system.); and
determining, based on the plurality of predicted measures, that the first PAP apparatus is least likely to cause issues (Paragraphs [0061] and [0102] discuss determining/recommending sleep therapy management options in, for example, the selection of optimal interface equipment (for example, a PAP mask system) for patients, and data available for the new patient is entered into the database and the model or models resulting from machine learning algorithm(s) identify, select or predict an interface system (and/or other sleep therapy management option(s)) determined on the basis of the interface system (and/or other sleep therapy management option(s)) being mostly likely to provide a maximized or optimal result for the one or more predefined outcome parameters..
Kaigler does not explicitly disclose:
mouth leak measures by, for each respective PAP apparatus of a plurality of PAP apparatuses, processing a respective indication of the respective PAP apparatus and the set of features using the leak model;
mouth leak measures, that the first PAP apparatus is least likely to cause mouth leak.
Aylsworth teaches:
mouth leak measure (Paragraphs [0016], [0038], and FIG.4 discuss mouth leak and amount of leak in a patient breathing circuit (i.e. the higher the null voltage, the greater the amount of air escaping through the patient's mouth).).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a mouth leak measure, as taught by Aylsworth, in order to address the reduction or elimination of mouth leaks during CPAP treatment. (Aylsworth Paragraph [0008].).
Liu teaches:
using the leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include using the leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Regarding claim 9, Kaigler discloses wherein extracting the set of features comprises determining facial information indicating a size and a shape of a nose of the patient (Paragraphs [0005]-[0007], [0094], and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data, and FIG.1 illustrates a representative embodiment of a screen capture in which software hereof is used to determined dimensions of a patient's head (including dimensions of the patient's face), including the nose.).
Regarding claim 13, Kaigler discloses wherein extracting the set of features comprises segmenting the patient data based on a nose of the patient (Paragraphs [0005]-[0007], [0094], and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data, and FIG.1 illustrates a representative embodiment of a screen capture in which software hereof is used to determined dimensions of a patient's head (including dimensions of the patient's face), including the nose and evaluate image in real time to ensure dimensions are collected and ensuring alignment and angles are correct and visual feedback/clues are provided to guide the patient in providing the optimal picture for the extraction of the desired metrics.).
Regarding claim 18, Kaigler discloses a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising (Paragraph [0032] discusses a non-transitory computer readable storage medium having instructions stored thereon, that when executed by a processor, perform actions.):
accessing patient data for a patient associated with a positive airway pressure (PAP) therapy (Paragraphs [0061]-[0063] and [0091] discuss obtain from a database system including one or more databases patient characterization data metrics and clinical outcome data metrics for each of a plurality of patients associated with sleep therapy and the selection of interface equipment, a PAP mask system.);
extracting a set of features from the patient data (Paragraphs [0063], [0094] and [0123] discuss obtain patient characterization data and real-time haptic, audio, or visual feedback/clues may be provided by the system to guide the patient in providing the optimal picture for the extraction of the desired metrics and information about the patient and the patient’s use of the mask.);
generating a first predicted measure by processing the set of features using a model, wherein the predicted value indicates a predicted probability that the value will occur during the PAP therapy (Paragraphs [0009]-[0011], [0026], [0045], [0123], and FIGS. 3B, discuss determining a management option for sleep therapy for a person via execution of the algorithm based at least in part upon an optimization of at least one outcome parameter for the person predicted by at least one model of the algorithm based at least in part upon characterization data of the person input into the database system, training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter and the management option may include determination of sleep therapy option for us by the patient.); and
in response to determining that the first predicted measure satisfies defined criteria, facilitating provisioning of a first PAP apparatus for the patient (Paragraph [0107] and FIG. 6 discuss using an algorithm to select a mask system for patient based on predicted optimization of one or more outcome parameters.).
Kaigler does not explicitly disclose:
wherein the set of features comprises an image of a nose of the patient
mouth leak measure;
a mouth leak measure by processing the set of features using a leak model;
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy.
Aylsworth teaches:
mouth leak measure (Paragraphs [0016], [0038], and FIG.4 discuss mouth leak and amount of leak in a patient breathing circuit (i.e. the higher the null voltage, the greater the amount of air escaping through the patient's mouth).).
mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy (Paragraphs [0008]-[0011] and [0016] discuss a mouth leak by a patient during use of a PAP machine.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a mouth leak measure and mouth leak indicates a predicted probability that air will leak from the mouth of the patient during the PAP therapy, as taught by Aylsworth, in order to address the reduction or elimination of mouth leaks during CPAP treatment. (Aylsworth Paragraph [0008].).
Dodds teaches:
wherein the set of features comprises an image of a nose of the patient (Paragraphs [0025]-[0026] discuss capturing images of patient’s face to generate a model that includes the patient’s nose and personalizing a face-engaging surface to a patient’s facial contour and to form a seal.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include wherein the set of features comprises an image of a nose of the patient, as taught by Dodds, in order to provide a structure that can be comfortable for a patient. (Dodds Paragraph [0008].).
Liu teaches:
using a leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include using a leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Regarding claim 19, Kaigler discloses wherein:
the patient data comprises at least one of an image of the patient or a three-dimensional scan of the patient, and the model comprises a convolutional neural network machine learning model (Paragraphs [0005]-[0007], [0092] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data, including the nose, anatomical data may be determined from a two-dimensional or three-dimensional image, photo, video, or model.).
Kaigler does not explicitly disclose:
the leak model.
Liu teaches:
the leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kaigler in view of Aylsworth and Liu, and in further view of Joseph (U.S. Pub. No. 2021/0251520 A1).
Regarding claim 2, Kaigler discloses wherein extracting the set of features comprises determining facial information comprising:
a size and a shape of a nose of the patient (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data, and FIG.1 illustrates a representative embodiment of a screen capture in which software hereof is used to determined dimensions of a patient's head (including dimensions of the patient's face), including the nose.); and
a predicted measure of data for the patient, wherein the predicted measure of data is determined using a measurement (Paragraphs [0045], [0061]-[0063] and [0091] discuss obtain from a database system including one or more databases patient characterization data metrics and clinical outcome data metrics for each of a plurality of patients associated with sleep therapy and training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter.).
Kaigler does not explicitly disclose:
a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement.
Joseph teaches:
a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement (Paragraph [0007 discusses monitoring respiration and nasal airflow with an acoustic measurement device.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement, as taught by Joseph, in order to continuously quantify and analyze the pattern of a person’s physiological conditions. (Joseph Paragraph [0002].).
Regarding claim 10, Kaigler discloses wherein the facial information further comprises a predicted measure of data for the patient, wherein the predicted measure of data is determined using a measurement (Paragraphs [0045], [0061]-[0063] and [0091] discuss obtain from a database system including one or more databases patient characterization data metrics and clinical outcome data metrics for each of a plurality of patients associated with sleep therapy and training one or more machine learning algorithms to develop a model for determining a predicted value or values of one or more metrics resulting from a predicted optimization of the outcome parameter.).
Kaigler does not explicitly disclose:
a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement.
Joseph teaches:
a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement (Paragraph [0007 discusses monitoring respiration and nasal airflow with an acoustic measurement device.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include a measure of nasal resistance to airflow for the patient, wherein the measure of nasal resistance is determined using acoustic impedance measurement, as taught by Joseph, in order to continuously quantify and analyze the pattern of a person’s physiological conditions. (Joseph Paragraph [0002].).
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kaigler in view of Aylsworth and Liu and in further view of Neumann (U.S. Pub. No. 2021/0241872 A1).
Regarding claims 4 and 12, Kaigler discloses wherein the model comprises a neural network machine learning model (Paragraph [0108] discusses a machine learning algorithms to obtain predictive performance include neural networks.).
Kaigler does not explicitly disclose:
the leak model;
convolutional neural network.
Liu teaches:
the leak model (Paragraphs [0032], [0035], [0041], [0045]-[0047] discuss leak between the mask and patient’s face and gas leakage flow vale of the mask using leakage estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the leak model, as taught by Liu, in order to improve the human-machine synchrony and comfort and tolerance of the patient. (Liu Paragraph [0003].).
Neumann teaches:
convolutional neural network (Paragraph [0064] discusses machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include convolutional neural network, as taught by Neumann, in order to inform a user that a therapeutic provision is suitable based on a user's own unique physiological information and for an intended medical purpose. (Neumann Paragraph [0002].).
Claims 5, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaigler in view of Aylsworth and Liu and in further view of Florman (U.S. Pub. No. 2017/0224443 A1) and Neumann (U.S. Pub. No. 2021/0241872 A1).
Regarding claim 5, Kaigler discloses wherein extracting the set of features comprises estimating a mandible of the patient, comprising:
estimating a spatial coordinate of a face (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.);
estimating spatial coordinates of one or more connection points of one or more organs of the patient (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.);
determining one or more attributes of the patient based on the patient data (Paragraph [0063] discusses obtain patient characterization data, including patient weight.); and
estimating information of the one or more organs based on fitting the one or more attributes to an organ model (Paragraphs [0045, [0061], [0097], and FIG. 3B discuss databases includes patient characterization data metrics and clinical outcome data metrics and anatomical data/metrics for each of a plurality of patients, including the ear, nose, mouth, and one or more machine learning and or other algorithms are used with the data to provide a model to determine optimized values for sleep therapy for a patient.).
Kaigler does not explicitly disclose:
the feature is a gravitational force on a mandible;
the coordinate of a fulcrum of the mandible;
the coordinates to the mandible;
information is a mass of the one or more organs.
Florman teaches:
the feature is a gravitational force on a mandible (Paragraph [0081] discusses forces on the mandible.).
the coordinate of a fulcrum of the mandible (Paragraph [0082] discusses the fulcrum of the mandible.).
the coordinates to the mandible (Paragraphs [0081]-[0082] discuss the position of the mandible in relation to teeth, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the feature is a gravitational force on a mandible, the coordinate of a fulcrum of the mandible, and the coordinates to the mandible, as taught by Florman, in order to ensure that when using a device the parameters that are affected by the device are controlled accurately. (Florman Paragraph [0002].).
Neumann teaches:
information is a mass of the one or more organs (Paragraph [0022] discusses physiological state data may include measures of organ muscle mass.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include information is a mass of the one or more organs, as taught by Neumann, in order to ensure accurate selection and utilizing of a therapeutic provision and inform a user that a therapeutic provision is suitable based on a user’s own unique physiological information. (Neumann Paragraph [0002].).
Regarding claim 16, Kaigler discloses wherein estimating further comprises:
determining one or more attributes of the patient based on the patient data (Paragraph [0063] discusses obtain patient characterization data, including patient weight.); and
estimating information of the one or more organs based on fitting the one or more attributes to an organ model (Paragraphs [0045, [0061], [0097], and FIG. 3B discuss databases includes patient characterization data metrics and clinical outcome data metrics and anatomical data/metrics for each of a plurality of patients, including the ear, nose, mouth, and one or more machine learning and or other algorithms are used with the data to provide a model to determine optimized values for sleep therapy for a patient.).
Kaigler does not explicitly disclose:
estimating a gravitational force;
information is a mass of the one or more organs.
Florman teaches:
estimating a gravitational force (Paragraph [0081] discusses forces on the mandible.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include estimating a gravitational force, as taught by Florman, in order to ensure that when using a device the parameters that are affected by the device are controlled accurately. (Florman Paragraph [0002].).
Neumann teaches:
information is a mass of the one or more organs (Paragraph [0022] discusses physiological state data may include measures of organ muscle mass.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include information is a mass of the one or more organs, as taught by Neumann, in order to ensure accurate selection and utilizing of a therapeutic provision and inform a user that a therapeutic provision is suitable based on a user’s own unique physiological information. (Neumann Paragraph [0002].)
Regarding claim 20, Kaigler discloses wherein extracting the set of features comprises estimating information of the patient, comprising:
estimating a spatial coordinate of a face (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.);
estimating spatial coordinates of one or more connection points of one or more organs of the patient (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.);
determining one or more attributes of the patient based on the patient data (Paragraph [0063] discusses obtain patient characterization data, including patient weight.); and
estimating information of the one or more organs based on fitting the one or more attributes to an organ model (Paragraphs [0045, [0061], [0097], and FIG. 3B discuss databases includes patient characterization data metrics and clinical outcome data metrics and anatomical data/metrics for each of a plurality of patients, including the ear, nose, mouth, and one or more machine learning and or other algorithms are used with the data to provide a model to determine optimized values for sleep therapy for a patient.).
Kaigler does not explicitly disclose:
the feature is a gravitational force on a mandible;
the coordinate of a fulcrum of the mandible;
the coordinates to the mandible;
information is a mass of the one or more organs.
Florman teaches:
the feature is a gravitational force on a mandible (Paragraph [0081] discusses forces on the mandible.).
the coordinate of a fulcrum of the mandible (Paragraph [0082] discusses the fulcrum of the mandible.).
the coordinates to the mandible (Paragraphs [0081]-[0082] discuss the position of the mandible in relation to teeth, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the feature is a gravitational force on a mandible, the coordinate of a fulcrum of the mandible, and the coordinates to the mandible, as taught by Florman, in order to ensure that when using a device the parameters that are affected by the device are controlled accurately. (Florman Paragraph [0002].).
Neumann teaches:
information is a mass of the one or more organs (Paragraph [0022] discusses physiological state data may include measures of organ muscle mass.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include information is a mass of the one or more organs, as taught by Neumann, in order to ensure accurate selection and utilizing of a therapeutic provision and inform a user that a therapeutic provision is suitable based on a user’s own unique physiological information. (Neumann Paragraph [0002].).
Claims 14-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kaigler in view of Aylsworth, Dodds, and Liu and in further view of Florman (U.S. Pub. No. 2017/0224443 A1).
Regarding claim 14, Kaigler discloses wherein extracting the set of features comprises estimating information of the patient(Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.).
Kaigler does not explicitly disclose:
the feature is a gravitational force on a mandible.
Florman teaches:
the feature is a gravitational force on a mandible (Paragraph [0081] discusses forces on the mandible.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the feature is a gravitational force on a mandible, as taught by Florman, in order to ensure that when using a device the parameters that are affected by the device are controlled accurately. (Florman Paragraph [0002].).
Regarding claim 15, Kaigler discloses wherein estimating the information comprises:
estimating a spatial coordinate of a face (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.); and
estimating spatial coordinates of one or more connection points of one or more organs of the patient (Paragraphs [0005]-[0007] and FIG. 1 discuss measuring anatomical feature of patient’s face using image or video data.);
determining one or more attributes of the patient based on the patient data (Paragraph [0063] discusses obtain patient characterization data, including patient weight.); and
estimating information of the one or more organs based on fitting the one or more attributes to an organ model (Paragraphs [0045, [0061], [0097], and FIG. 3B discuss databases includes patient characterization data metrics and clinical outcome data metrics and anatomical data/metrics for each of a plurality of patients, including the ear, nose, mouth, and one or more machine learning and or other algorithms are used with the data to provide a model to determine optimized values for sleep therapy for a patient.).
Kaigler does not explicitly disclose:
the feature is a gravitational force on a mandible;
the coordinate of a fulcrum of the mandible;
the coordinates to the mandible.
Florman teaches:
the feature is a gravitational force on a mandible (Paragraph [0081] discusses forces on the mandible.).
the coordinate of a fulcrum of the mandible (Paragraph [0082] discusses the fulcrum of the mandible.).
the coordinates to the mandible (Paragraphs [0081]-[0082] discuss the position of the mandible in relation to teeth, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kaigler to include the feature is a gravitational force on a mandible, the coordinate of a fulcrum of the mandible, and the coordinates to the mandible, as taught by Florman, in order to ensure that when using a device the parameters that are affected by the device are controlled accurately. (Florman Paragraph [0002].).
Regarding claim 17, Kaigler discloses wherein estimating the information further comprises determining a sleeping position of the patient (Paragraph [0063] discusses patient characterization data includes patient sleep position data.).
Response to Arguments
Applicant’s arguments filed 11/6/2024 have been fully considered.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant states, “the present claims, which relate to techniques for training and using machine learning models to predict mouth leak and improve respiratory therapy, do no recite or relate to any method of organizing human activity and are not confined to mathematical concepts.” (Remarks, page 8). Applicant further states, “the present claims, which are directed to training and using machine learning models, are not similar to any of these examples, and plainly do not relate to organizing human activity. For example, nothing in the present claims involves social activity, instructing a user how to proceed, and the like. Example 42 of the Subject Matter Eligibility Examples provided by the Office relates to a "method for transmission of notifications when medical records are updated." This example explains that the claim recites a method of organizing human activity because the claimed invention "allows for users to access patients' medical records and receive updated patient information" which "is a method of managing interactions between people." However, Applicant notes that unlike the claims in Example 42, the present claims do not recite any such interactions or notifications. Instead, the present claims relate to training and using machine learning to predict mouth leak, but do not recite any form of controlling how users interact or act based on this data.” (Remarks, page 9). Applicant states, “Applicant submits that, with respect to subject matter eligibility, the present claims are substantially similar to Example 39 of the Office's Subject Matter Eligibility Examples on Abstract Ideas. As Example 39 explains, a method for training a neural network "does not recite any of the judicial exceptions." For example, "the claim does not recite any mathematical relationships, formulas, or calculations," although "some of the limitations may be based on mathematical concepts." That is, as the guidelines explain, training a machine learning model "does not recite any mathematical relationships, formulas, or calculations," even if some elements "may be based on mathematical concepts." Stated differently, Example 39 clearly explains that training a machine learning model is not a method of organizing human activity, is not directed to any mathematical concepts, is not a mental process, and is not abstract.” (Remarks, page 10). Examiner Respectfully disagrees. Accessing patient PAP data, determining a mouth leak measure and predicting a mouth leak, is not a technical problem rooted in the technology. The use of artificial intelligence, machine learning, or probabilistic modeling to generate reports and enable the comparison of human-generated reports to machine-generated reports is directed to the abstract idea. The amended application is organizing human activity, directed to the abstract idea of gathering data, using artificial intelligence to analyze and compare data, then delivering the information. See Example 47 (Example 47 discusses the eligibility of claims that recite limitations directed to artificial intelligence, the use of a neural network to identify or detect anomalies, the claim is ineligible because it recites an abstract idea and the claim as a whole does not integrate the exception into a practical application and the claim does not provide significantly more than the exception.).
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, here, claim 1 fails to integrate the recited judicial exception into a practical application. Applicant states, “even if the amended claims are found to recite a judicial exception (which they do not), they are eligible because they "reflect[] an improvement in the functioning of a computer, or an improvement to other technology or technical field," and further "integrate" the alleged "judicial exception into a practical application of the exception."” (Remarks, page 11). Examiner respectfully disagrees. The additional elements, the “PAP apparatus” or “processor”, do not result in a practical application as it is recited at an apply it level, as stated above. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. For the reasons stated above, claims 6 and 18 similarly fail to overcome the 35 U.S.C. 101 rejection. Here, there is no improvement to the apparatus or processor or any of the devices. Applicant’s claims are directed to gathering mouth leak data, predicting a mouth leak, and delivering the information.
Applicant further states, “the present claims clearly recite elements that enable various improvements. For example, as explained in the specification, the present claims "facilitate[] provisioning of improved therapy devices for specific patients, thereby improving the therapy results and outcomes," which allows "patients [to] receive improved results." [0061]. As the specification further explains, these targeted improvements can further "improve therapy compliance, reduce adverse side effects, and generally improve patient outcomes" in respiratory therapy. Id. For example, in some embodiments, the model can be used to "determine or identify if there is a threshold therapy pressure at which a threshold mouth leak flowrate is likely to be exceeded. The system may then alert a user, or automatically modify therapy settings or prescriptions. For example, a maximum therapy pressure may be automatically set to a value that limits the likelihood of an undesirable level of mouth leak flow." [0063].” (Remarks, page 12). The amended claims recite additional elements that represent well-understood, routine, conventional activity. MPEP 2106.04(d). Here, the claims are not directed to an improvement in the functioning of the PAP apparatus or the processor. As in Alice, the claims “did not improve the technical capture of information” to predict a mouth leak.
Rejections under 35 U.S.C. 103:
With respect to claim 1 and the 35 U.S.C. 103 rejection, Applicant’s amendment overcomes the previous rejection. Applicant states, “Kaigler contemplates detecting "mask leak" based on "metrics output by the PAP device," but fails to teach or suggest a "mouth leak" measure. Non-Final Office Action, p. 9. Similarly, Liu describes detecting "leak between the mask and the patient's face," but does not teach or suggest "mouth leak." Id. at 10. Additionally, though Aylsworth discusses "mouth leak," Aylsworth contemplates "detecting the presence of mouth leak during ventilation" and, in response to "the detection of a mouth leak, reducing the applied pressure." Abstract. However, Aylsworth is silent with respect to any sort of prediction of mouth leak, and instead focuses solely on detecting mouth leak.” (Remarks, page 14). Examiner respectfully disagrees. The references in combination support an obviousness rejection. Applicant’s arguments with respect to claim 1 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim 1, 6, and 18 amendments.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights