DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Foreign priority has been acknowledged.
The previous claim objection has been overcome.
The previous amendment to the title of the invention has been accepted.
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 03/02/2026, the following has occurred: claims 1, 3, 11-12, and 14-15 have been amended; claim 2 has been canceled; claims 4-10 and 13 remain unchanged; and no new claims have been added.
Claims 1 and 3-15 are pending.
Effective Filing Date: 09/01/2023
Response to Arguments
Specification Objection:
Examiner withdraws the previous specification objection for lack of a descriptive title.
Claim Objection:
Examiner withdraws the previous claim objection in view of the amendments to claim 11.
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant disagrees that the claims recite an abstract idea categorized under mathematical concepts. Applicant states while the claims may involve mathematical operations internal to the model, they are not claimed as free-standing mathematical formulas. The claims do not need to recite specific formulas in order to be considered as containing an abstract idea categorized under mathematical concepts.
Applicant also states that the present claims are similar to those in Thales. The claims of Thales recite a unique arrangement of inertial sensors that are used to track the location of a moving item based on a moving reference frame and the unique arrangement not routine and conventional in the art and thus provided a practical application. The present claims however recite receiving initial score information in a manner dissimilar to Thales. There is no unique arrangement of sensors in the claims.
Applicant further argues that the vector derivatives over high-dimensional score functions and iterative sampling from probability distributions cannot be practically performed within the human mind, and therefore it does not fall within the “mental processes” subgrouping. Examiner however did not direct the abstract idea in the claims towards mental processes, only mathematical concepts.
Step 2A, Prong Two:
Applicant argues that the amended claims are confined to multi-modal physiological signals. Applicant more specifically speaks with respect to claims 14 and 15 and states that real world therapy is being delivered to a patient. Examiner believes that claim 14 does not presently recite providing a specific treatment for a specific affliction. Claim 14 is close though to reciting an improvement to the functioning of an airflow control system if the claim were to convey that the operation of that system is improving. It is not clear in the present language if the system here is autonomously taking in output data and then correcting the operation of the device to provide a certain airflow.
Step 2B:
Applicant argues that the WURC analysis does not address the full ordered combination of features recited in amended claims 1 and 12. Examiner however respectfully disagrees. The present construction of the claims do not recite significantly more than the abstract idea.
35 U.S.C. 103 Rejections:
Applicant argues with respect to the updated independent claims which combine the previous independent claims with previously-claimed claim 2. Applicant explains that the previous citation from the Shouldice reference does not teach the limitation which was added to the independent claims. The limitation is question is:
“wherein the input data comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject”
Applicant states that the citation in Shouldice at paragraph [0048] falls well short of meeting this limitation because Shouldice broadly uses sensor output to derive a “sleep-wake signal” and the sleep-related parameters for insomnia screening and management. Applicant states that the claims state a specific correlation requirement and a specific selection of physiological signal sets directed to sleep stages and sleep-disordered breathing, which is absent in Shouldice’s general list of possible sensors and signals. Examiner however respectfully disagrees. The claims state that the input data comprises two or more sets of signals known to correlate to certain things. For example, the received sleep-wake signal in the form of both audio (one set of signals) and physiological (another set of signals) correlates with a sleep stage of the subject (potentially defining the end of sleep, but also including stage information). The data that is being received is known to correlate with a sleep stage as this sensor data can then be processed in order to generate this sleep stage data. The claims only require that the received data is known to correlate to a sleep stage, it does not require that data be processed or to be more particular than being able to have a known correlation. The correlation for this limitation is passive, not active. Therefore the claim limitations are met by the previous citations. If Applicant is trying to establish a step of association/correlation or is trying to receive a more specific set of data, this detail would need to be included in the claims.
Applicant further argues that there is no processing using multiple independently trained score-based neural networks to generate initial score in Shouldice. Additionally, Applicant states that Shouldice does not teach the initial scores nor combined score architecture in claims 1 and 12. Examiner however does not state that Shouldice is being used to teach this.
Applicant further argues the total amount of references used in combination to reject the independent claims. Examiner however respectfully would like to point out that there is no limit to the combination of references which could be used in a rejection, as long as the combination is reasonable.
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.
Foreign Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 and 3-15 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, 3-11, 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 and 3-15 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 two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing 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); 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 (y) is representative of a medical condition of 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 (y) is representative of a medical condition of the subject. These steps correspond to Mathematical Concepts. Independent claim 12 recites similar limitations and is also directed to an abstract idea under the same analysis.
Depending claims 3-11 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 3, 6-10, 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 3-11 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) an airflow control system (in claim 14) to perform the claimed steps.
The claims include the additional element step 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 two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing 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.
The a) two sensors, b) airflow control system, and the step of 1) receiving a plurality of initial scores in these steps adds insignificant extra-solution activity to the abstract idea (such as recitation of the a) two sensors and 1) receiving a plurality of initial scores amounts to mere data gathering and recitation of the b) airflow control system 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) an airflow control system to perform the claimed steps and the additional element step 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 two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing 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 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) airflow control system in these steps and the step of 1) receive a plurality of initial scores 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 the data to a system over a network, for example the Internet.
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 and 3-15 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, 3-5, 7-8, 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. 2022/0347412 to Shouldice.
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)
--wherein the output data (y) is representative of a medical condition of the subject (see: 712 of FIG. 7 and 360 of FIG. 3B where there is output data representative of medical condition for the subject).
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 comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing 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).
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.).
Shouldice teaches:
2) --wherein the input data comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject (see: paragraph [0048] where there is input data comprising sleep stage data).
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 comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject as taught by Shouldice in the system as taught by Bui et al. and Clark et al. in combination with the motivation(s) of directing a user towards an appropriate treatment (see: paragraph [0003] of Shouldice).
As per claim 3, Bui et al., Clark et al., and Shouldice in combination teaches the system of claim 1, see discussion of claim 1. Shouldice further teaches wherein the physiological signals comprise one or more of:
--an electroencephalography signal, an electrooculography signal, an electromyography signal, an electrocardiogram signal, a ballistocardiography signal, a seismocardiography signal, a pulse oximetry signal, and/or a respiratory signal (see: paragraph [0046] where there is an EEG and an EMG).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 4, Bui et al., Clark et al., and Shouldice 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 Shouldice 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 7, Bui et al., Clark et al., and Shouldice in combination teaches the system of claim 1, see discussion of claim 1. Shouldice further teaches wherein the input data (xi) is representative of a signal responsive to a sleep stage of the subject during a sleep session (see: paragraphs [0082] and [0090] where there is input data of a sleep stage).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 8, Bui et al., Clark et al., and Shouldice in combination teaches the system of claim 7, see discussion of claim 7. Shouldice further teaches wherein the output data of the generative model is a time series, (see: paragraphs [0083] and [0088] where there is a time series of data of a timeline)
--wherein the time series is a hypnogram or a hypnodensity graph (see: paragraph [0083] where there is a hypnogram).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 12, Bui et al. teaches a computer-implemented method, comprising:
--receiving 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
--processing 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)
--wherein the output data (y) is representative of a medical condition of the subject (see: 712 of FIG. 7 and 360 of FIG. 3B where there is output data representative of medical condition for the subject).
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 comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject,
3) --wherein each score-based neural network (N1-NN) has been independently trained for a different set of training data; and
4) --using 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).
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 for 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) --using 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 for 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 method as taught by Bui et al. with the motivation(s) of improving the accuracy of the system (see: paragraph [0039] of Clark et al.).
Shouldice teaches:
2) --wherein the input data comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject (see: paragraph [0048] where there is input data comprising sleep stage data).
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 comprises two or more sets of one or more physiological signals known to correlate to a sleep stage of the subject or to a sleep-disordered breathing of the subject as taught by Shouldice in the method as taught by Bui et al. and Clark et al. in combination with the motivation(s) of directing a user towards an appropriate treatment (see: paragraph [0003] of Shouldice).
As per claim 13, Bui et al., Clark et al., and Shouldice 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 15, Bui et al., Clark et al., and Shouldice 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).
Shouldice further teaches:
--wherein the output data is representative of one or more sleep stages of the subject (see: paragraph [0082] where the hypnogram includes sleep stage information. Also see: paragraph [0083] where there is output of the hypnogram).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Claim 6 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. further in view of U.S. 2022/0347412 to Shouldice as applied to claim 1, and further in view of U.S. 2016/0157725 to Munoz.
As per claim 6, Bui et al., Clark et al., and Shouldice in combination teaches the system of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the generative model is a clinical assessment tool,
--wherein the input data comprises physiological signals of the subject,
--wherein the clinical assessment tool comprises a model for predicting whether or not one or more pathologies are present by processing the physiological signals.
Munoz teaches:
--wherein the generative model is a clinical assessment tool, (see: paragraph [0163] where there is such a clinical assessment tool. The tool being a model was taught in the rejection of claim 1)
--wherein the input data comprises physiological signals of the subject, (see: paragraph [0102] where clinical input is being received. Also see: paragraph [0110] where there is different sensors and data for those sensors)
--wherein the clinical assessment tool comprises a model for predicting whether or not one or more pathologies are present by processing the physiological signals (see: paragraph [0109] where there is assessment and detection of pathologies. There is a prediction/determination of whether there is are pathologies or not).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the generative model is a clinical assessment tool, wherein the input data comprises physiological signals of the subject, wherein the clinical assessment tool comprises a model for predicting whether or not one or more pathologies are present by processing the physiological signals as taught by Munoz in the system as taught by Bui et al., Clark et al., and Shouldice in combination with the motivation(s) of improving clinical analysis and assessment of diseases (see: paragraph [0022] of Munoz).
Claims 9-10 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. further in view of U.S. 2022/0347412 to Shouldice as applied to claim 1, and further in view of U.S. 2024/0207615 to Mech.
As per claim 9, Bui et al., Clark et al., and Shouldice in combination teaches the system of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the input data (xi) is representative of a signal response to a disordered breathing of the subject during a sleep session.
Mech teaches:
--wherein the input data (xi) is representative of a signal response to a disordered breathing of the subject during a sleep session (see: paragraph [0029] where there is a determination of a disordered breathing during sleep. Also see: paragraph [0051] where there is received input data which is used).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the input data (xi) is representative of a signal response to a disordered breathing of the subject during a sleep session as taught by Mech in the system as taught by Bui et al., Clark et al., and Shouldice in combination with the motivation(s) of improving outcomes for the individual (see: paragraph [0085] of Mech).
As per claim 10, Bui et al., Clark et al., Shouldice, and Mech in combination teaches the system of claim 9, see discussion of claim 9. Mech further teaches wherein the output data of the generative model is a time series, wherein the time series is representative of a sleep event probability over time (see: paragraph [0051] where the data is a time series collected over time and this data includes probability data for events).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 9, 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. further in view of U.S. 2022/0347412 to Shouldice 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 Shouldice 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 Shouldice in combination with the motivation(s) of improving confidence that the model is making accurate predictions (see: paragraph [0051] of Mahishi et al.).
Claim 14 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. further in view of U.S. 2022/0347412 to Shouldice as applied to claim 1, and further in view of U.S. 2022/0023561 to Hanafialamdari et al.
As per claim 14, Bui et al., Clark et al., and Shouldice 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).
Bui et al., Clark et al., and Shouldice in combination may not further, specifically teach:
1) --an airflow control system, and
2) --wherein the airflow control system is configured to control a property of the airflow to the subject based on the output data of the generative model.
Hanafialamdari et al. teaches:
1) --an airflow control system, (see: paragraph [0023] where there is a breathing assistance device) and
2) --wherein the airflow control system is configured to control a property of the airflow to the subject based on the output data of the generative model (see: paragraph [0023] where there is adjustment of a breathing assistance device using a model. The model being a generative model was taught in the rejection of claim 1).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) use an airflow control system and have 2) wherein the airflow control system is configured to control a property of the airflow to the subject based on the output data of the generative model have wherein the output data is representative of one or more sleep stages of the subject as taught by Hanafialamdari et al. in the system as taught by Bui et al., Clark et al., and Shouldice in combination with the motivation(s) of helping patients breathe (see: paragraph [0137] of Hanafialamdari).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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