DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/26/2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 05/18/2026, the following has occurred: claims 1, 12, and 14-15 have been amended; claims 2-3 and 6-10 have been canceled; claims 4-5, 11, and 13 remain unchanged; and no new claims have been added.
Claims 1, 4-5, and 11-15 are pending.
Effective Filing Date: 09/01/2023
Response to Arguments
35 U.S.C. 101 Rejections:
Applicant argues that the claims now include an affirmative step in which the output data includes a command to alter a pressure or a flow rate of gas delivered to the subject. Examiner however respectfully disagrees that there is a practical application as the claims recite a passive alteration of a gas flow generation system.
35 U.S.C. 103 Rejections:
Applicant argues that the amended claims overcome the art rejections. Examiner has updated the 103 rejection section to account for these amendments using the Stahmann et al. reference.
Lastly, Applicant states that the remaining claims should overcome the 103 rejections based on the independent claims overcoming this rejection. Examiner however respectfully disagrees as the independent claims remain rejected under 35 U.S.C. 103.
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, 4-5, and 11-15 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, 4-5, 11, and 14-15 are drawn to a system and claims 12-13 are drawn to a method, each of which is within the four statutory categories. Claims 1, 4-5, and 11-15 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a processing system configured to:
1) receive a plurality of initial scores (IS1-ISN),
wherein each initial score (IS1-ISN) is an output of a different score-based neural network (N1-NN) based on input data (I1-IN) relating to a subject,
wherein the input data (I1-IN) comprises sleep stage data of the subject and sleep-disordered breathing data of the subject,
wherein the plurality of initial scores (IS1-ISN) is for use in a generative model,
wherein each initial score (IS1-ISN) defines a probability distribution for sampling output data (y),
wherein each score-based neural network (N1-NN) has been independently trained using a different set of training data;
2) use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN);
3) process the combined score (CS), using a sampling technique (ST), to produce the output data (y) of the generative model, wherein the output data includes a command to alter a pressure or flow rate of gas delivered to the subject; and
4) providing the command to a gas flow generating system to cause the gas flow generating system to alter the pressure or flow rate of gas delivered to the subject.
Claim 1 recites, in part, performing the steps of 2) use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN) and 3) process the combined score (CS), using a sampling technique (ST), to produce the output data (y) of the generative model, wherein the output data includes a command to alter a pressure or flow rate of gas delivered to the subject. These steps correspond to Mathematical Concepts.
Claim 1 also recites, in part, performing the steps of 1) receive a plurality of initial scores (IS1-ISN), wherein each initial score (IS1-ISN) is an output of a different score-based neural network (N1-NN) based on input data (I1-IN) relating to a subject, wherein the input data (I1-IN) comprises sleep stage data of the subject and sleep-disordered breathing data of the subject, wherein the plurality of initial scores (IS1-ISN) is for use in a generative model, wherein each initial score (IS1-ISN) defines a probability distribution for sampling output data (y), wherein each score-based neural network (N1-NN) has been independently trained using a different set of training data, 2) use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN), 3) process the combined score (CS), using a sampling technique (ST), to produce the output data (y) of the generative model, wherein the output data includes a command to alter a pressure or flow rate of gas delivered to the subject, and 4) providing the command to a gas flow generating system to cause the gas flow generating system to alter the pressure or flow rate of gas delivered to the subject. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, an individual can process data and then make an adjustment to a system.
Going forward, the abstract concepts above will be considered as a singular abstract idea for further analysis. Independent claim 12 recites similar limitations and is also directed to an abstract idea under the same analysis.
Depending claims 4-5, 11, and 13-15 include all of the limitations of claims 1 and 12, and therefore likewise incorporate the above described abstract idea. Depending claim 4 adds the additional step of “use the generative model to process the input data (I1-IN) using the different score-based neural networks (N1-NN) to generate the plurality of initial scores (IS1-ISN)”; claim 5 adds the additional steps of “receive a plurality of sets of input data, each set of input data containing input data for a respective one of the plurality of different data types” and “provide each set of input data to a score-based neural network trained using a set of training data of the same type to generate the plurality of initial scores”; and claim 11 adds the additional steps of “generate a plurality of samples by iteratively performing a sampling on the initial score” and “process the plurality of samples to generate a measure of uncertainty of the initial score”. Claim 14 recites that there is an airflow control system that is configured to control a property of the airflow based on an output, but this claim lacks enough detail to indicate that the airflow system is being improved. Additionally, the limitations of depending claims 13 and 15 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 4-5, 11, and 13-15 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 12 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) two sensors (in claims 14 and 15) and b) a gas flow generating system (in claim 14) to perform the claimed steps.
The a) two sensors and b) gas flow generating system in these steps adds insignificant extra-solution activity to the abstract idea (such as recitation of the a) two sensors which amounts to mere data gathering and recitation of the b) airflow control system which amounts to insignificant application, see MPEP 2106.05(g)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) two sensors and b) a gas flow generating system to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) that does not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mathematical steps. Specifically, MPEP 2106.05(d) recites that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
The a) two sensors and b) gas flow generating system in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives sensor data and a plurality of initial scores, and transmits command data to a gas flow generating system.
Mere instructions to apply an exception using insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1, 4-5, and 11-15 are therefore 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 12-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0339217 to Bui et al. in view of U.S. 2022/0230276 to Clark et al. and further in view of U.S. 2005/0115561 to Stahmann et al.
As per claim 1, Bui et al. teaches a processing system configured to:
--receive a plurality of initial scores (IS1-ISN), (see: paragraph [0006] where there is reception of a plurality of initial scores in the form of images)
--wherein the plurality of initial scores (IS1-ISN) is for use in a generative model, (see: FIG. 3B and paragraph [0094] where the images (initial scores) are used in a generative model)
--wherein each initial score (IS1-ISN) defines a probability distribution for sampling output data (y), (see: paragraph [0214] where there is an initial score defines a probability distribution for sampling output data) and
--process the combined score (CS), using a sampling technique (ST), to produce the output data (y) of the generative model (see: 358 of FIG. 3B and 710 of FIG. 7 where there is processing of a combined score (all images) to produce output data of a condition classification).
Bui et al. may not further, specifically teach:
1) --wherein each initial score (IS1-ISN) is an output of a different score-based neural network (N1-NN) based on input data (I1-IN) relating to a subject,
2) --wherein the input data (I1-IN) comprises sleep stage data of the subject and sleep-disordered breathing data of the subject,
3) --wherein each score-based neural network (N1-NN) has been independently trained using a different set of training data; and
4) --use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN);
5) --wherein the output data includes a command to alter a pressure or a flow rate of gas delivered to the subject; and
6) –providing the command to a gas flow generating system to cause the gas flow generating system to alter the pressure or flow rate of gas delivered to the subject.
Clark et al. teaches:
1) --wherein each initial score (IS1-ISN) is an output of a different score-based neural network (N1-NN) based on input data (I1-IN) relating to a subject, (see: paragraphs [0010] – [0011] where there are scores for the input data. The input data being related to a subject was already taught in the Bui et al. reference)
3) --wherein each score-based neural network (N1-NN) has been independently trained using a different set of training data; and (see: paragraph [0070] where there are neural networks which are trained. Also see: paragraph [0018] where the networks are individually trained using different data)
4) --use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN) (see: paragraphs [0028] and [0032] where there is generation of a combined score by performing a vector derivative with respect to the output data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) wherein each initial score (IS1-ISN) is an output of a different score-based neural network (N1-NN) based on input data (I1-IN) relating to a subject, 3) wherein each score-based neural network (N1-NN) has been independently trained using a different set of training data, and 4) use the generative model to generate a combined score (CS) by performing a vector derivative with respect to the output data (y) on the plurality of initial scores (IS1-ISN) as taught by Clark et al. in the system as taught by Bui et al. with the motivation(s) of improving the accuracy of the system (see: paragraph [0039] of Clark et al.).
Stahmann et al. teaches:
2) --wherein the input data (I1-IN) comprises sleep stage data of the subject and sleep-disordered breathing data of the subject; (see: paragraphs [0706] and [1550] where the input data comprises sleep data and the breathing data and it is used to modify respiratory therapy)
5) --wherein the output data includes a command to alter a pressure or a flow rate of gas delivered to the subject; (see: paragraphs [0706] and [1550] where the input data comprises sleep data and the breathing data and it is used to modify respiratory therapy. The output data of the respiratory controller of paragraph [1331] is used to modify a gas flow) and
6) –providing the command to a gas flow generating system to cause the gas flow generating system to alter the pressure or flow rate of gas delivered to the subject (see: paragraph [1331] where there is a command to modify a gas flow).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 2) wherein the input data (I1-IN) comprises sleep stage data of the subject and sleep-disordered breathing data of the subject, have 5) wherein the output data includes a command to alter a pressure or a flow rate of gas delivered to the subject, and 6) provide the command to a gas flow generating system to cause the gas flow generating system to alter the pressure or flow rate of gas delivered to the subject as taught by Stahmann et al. in the system as taught by Bui et al. and Clark et al. in combination with the motivation(s) of (see: paragraph [0003] of Stahmann et al.).
As per claim 4, Bui et al., Clark et al., and Stahmann et al. in combination teaches the system of claim 1, see discussion of claim 1. Bui et al. further teaches:
--use the generative model to process the input data (I1-IN) using the different score-based neural networks (N1-NN) to generate the plurality of initial scores (IS1-ISN) (see: paragraph [0018] where the networks are individually trained using different data. Data is being processed by the generative model using the different networks).
As per claim 5, Bui et al., Clark et al., and Stahmann et al. in combination teaches the system of claim 4, see discussion of claim 4. Bui et al. further teaches wherein:
--each set of training data comprises training data for a respective one of a plurality of different data types; (see: paragraph [0156] where there is training of models using the received data) and
--the processing system is further configured to:
--receive a plurality of sets of input data, each set of input data containing input data for a respective one of the plurality of different data types; (see: paragraph [0140] where the input data can be one type or many different types of data. Input data is being received here) and
--provide each set of input data to a score-based neural network trained using a set of training data of the same type to generate the plurality of initial scores (see: paragraph [0140] where the input data can be one type or many different types of data. This data is being provided to a trained network here).
As per claim 12, claim 12 is similar to claim 1 and is therefore rejected in a similar manner to claim 1 using the Bui et al., Clark et al., and Stahmann et al. references in combination.
As per claim 13, Bui et al., Clark et al., and Stahmann et al. in combination teaches the method of claim 12, see discussion of claim 12. Bui et al. further teaches a computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to claim 12 (see: paragraph [0141] where there is such a code).
As per claim 14, Bui et al., Clark et al., and Stahmann et al. in combination teaches the system of claim 1, see discussion of claim 1. Bui et al. further teaches a respiratory support system for providing an airflow to a subject, the respiratory support system comprising:
--the processing system according to claim 1; (see: claim 1)
--two sensors; (see: paragraph [0147] where there are sensors) and
--wherein each of the two sensors is adapted to generate a different physiological signal of the subject, (see: paragraph [0147] where the sensors here are configured to generate different biological/physiological signals) wherein the input data comprises the different physiological signals of the subject, (see: paragraph [0147] where there is received data (input data) of the different physiological signals) wherein each score-based neural network is trained using a respective instance of training data for a same type of data as the respective one of the different physiological signals (see: paragraph [0156] and [0157] where there is training on models based on different types of data. Clark teaches of using multiple different, trained networks).
Clark et al. teaches:
--wherein the generative model comprises the plurality of score-based neural networks, (see: paragraph [0070] where the generative model comprises of neural networks) and wherein each score-based neural network is configured to generate an initial score by processing a respective one of the different physiological signals (see: paragraphs [0094] – [0095] where there are scores being generated from each network in the form of discriminator scores).
Stahmann et al. further teaches of:
--a gas flow generation system (see: paragraph [0021] where there is a respiratory device).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 15, Bui et al., Clark et al., and Stahmann et al. in combination teaches the system of claim 1, see discussion of claim 1. Bui et al. further teaches a sleep stage determination system for determining sleep stages of a subject, the sleep stage determination system comprising:
--the processing system according to claim 1; (see: claim 1)
--two sensors; (see: paragraph [0147] where there are sensors) and
--wherein each of the two sensors is adapted to generate a different physiological signal of the subject, (see: paragraph [0147] where the sensors here are configured to generate different biological/physiological signals) wherein the input data comprises the different physiological signals of the subject, (see: paragraph [0147] where there is received data (input data) of the different physiological signals) wherein each score-based neural network is trained using a respective instance of training data for a same type of data as the respective one of the different physiological signals (see: paragraph [0156] and [0157] where there is training on models based on different types of data. Clark teaches of using multiple different, trained networks).
Clark et al. further teaches:
--wherein the generative model comprises the plurality of score-based neural networks, (see: paragraph [0070] where the generative model comprises of neural networks) wherein each score-based neural network is configured to generate an initial score by processing a respective one of the different physiological signals (see: paragraphs [0094] – [0095] where there are scores being generated from each network in the form of discriminator scores).
Stahmann et al. further teaches:
--wherein the output data further comprising data representative of one or more sleep stages of the subject (see: paragraph [0225] where there is sleep stage data. The output data of the respiratory controller of paragraph [1331] is used to modify a gas flow, and this data is representative of the sleep data).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0339217 to Bui et al. in view of U.S. 2022/0230276 to Clark et al. and further in view of U.S. 2005/0115561 to Stahmann et al. as applied to claim 1, and further in view of U.S. 2025/0307694 to Mahishi et al.
As per claim 11, Bui et al., Clark et al., and Stahmann et al. in combination teaches the system of claim 1, see discussion of claim 1. Bui et al. and Clark et al. in combination may not further, specifically teach, for each initial score:
--generate a plurality of samples by iteratively performing a sampling on the initial score;
--process the plurality of samples to generate a measure of uncertainty of the initial score; and
--process the initial scores to generate a combined score by performing a process comprising combining only those initial scores whose measure of uncertainty meets one or more predetermined conditions.
Mahishi et al. teaches:
--for each initial score:
--generate a plurality of samples by iteratively performing a sampling on the initial score; (see: paragraph [0140] where there is generation of continuous data. Also see: paragraph [0058] where there is sampling and collection services to collect sample data)
--process the plurality of samples to generate a measure of uncertainty of the initial score; (see: paragraph [0051] where the samples are being re-evaluated to determine an aggregate accuracy. Also see: paragraph [0077] where there is processing of samples to generate uncertainty) and
--process the initial scores to generate a combined score by performing a process comprising combining only those initial scores whose measure of uncertainty meets one or more predetermined conditions (see: paragraphs [0077] and [0100] where there is processing of samples to generate an aggregate score (combined score) which needs to meet an accuracy threshold).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to for each initial score: generate a plurality of samples by iteratively performing a sampling on the initial score, process the plurality of samples to generate a measure of uncertainty of the initial score, and process the initial scores to generate a combined score by performing a process comprising combining only those initial scores whose measure of uncertainty meets one or more predetermined conditions as taught by Mahishi et al. in the system as taught by Bui et al., Clark et al., and Stahmann et al. in combination with the motivation(s) of improving confidence that the model is making accurate predictions (see: paragraph [0051] of Mahishi et al.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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