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 .
Acknowledgements
This communication is in response to amendment filed on 1/21/2026.
Claims 1, 30 are amended.
Claims 4, 11-12, 18, 23-29 are canceled.
Claims 1-3, 5-10, 13-17, 19-22, 30 are currently pending and have been examined.
Claims 1-3, 5-10, 13-17, 19-22, 30 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/12/2023 and 4/29/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1, 30 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant specification and disclosure does not disclose:
“train a first patient identification machine-learning model by adjusting parameters of the first patient identification machine-learning model based on the physical characteristic data, the health characteristic data, and the first threshold values;
train a second patient identification machine-learning model by adjusting parameters of the second patient identification machine-learning model based on the behavioral characteristic data and the second threshold values;”
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 1-3, 5-10, 13-17, 19-22, 30 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, 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a system and method for notifying someone that individuals are preferred individuals for sleep apnea treatment.
The limitations of
providing patient data […], the patient data including physical, health, and behavioral data corresponding to identifiable individuals; and […]:
receive, […], training patient data comprising physical characteristic data, health characteristic data, and behavioral characteristic data associated with identifiable training individuals;
determine or receive first threshold values for the identifiable training individuals associated with a determined likelihood of obstructive sleep apnea, the first threshold values based on patient data of training individuals known to have obstructive sleep apnea;
determine or receive second threshold values for the identifiable training individuals associated with a determined likelihood of long-term adherence to obstructive sleep apnea treatment, the second threshold values based on patient data of training individuals known to have long-term adherence to obstructive sleep apnea treatment
train a first patient identification […] model by adjusting parameters of the first patient identification […] model based on the physical characteristic data, the health characteristic data, and the first threshold values;
train a second patient identification […] model by adjusting parameters of the second patient identification […] model based on the behavioral characteristic data and the second threshold values;
apply the first patient identification […] model that is trained to process at least a portion of the patient data […] to identify and output an initial group of individuals associated with select physical and health characteristics, the identification of the initial group of individuals based on a determined likelihood of obstructive sleep apnea for identifiable individuals meeting or exceeding a first threshold criteria;
apply the second patient identification […] model that is trained to process at least a portion of the patient data associated with the initial group of individuals and configured to use at least a subset of the behavioral data to identify and output a narrower subgroup of individuals associated with select behavioral characteristics, the identification of the narrower group of individuals based on a determined likelihood of long-term adherence to obstructive sleep apnea treatment for individuals in the narrower subgroup meeting or exceeding a second threshold criteria; wherein the second patient identification […] model is applied only to the patient data associated with the initial group of individuals identified by the first patient identification […] model;
generate patient identifiable information from the patient data associated with individuals in the narrower subgroup; generate, based on the patient identifiable information, an indication to one or more designated entities that one or more of the individuals in the narrower subgroup are preferred individuals likely to adhere to long-term obstructive sleep apnea treatment;
generate a personalized treatment pathway for one or more of the preferred individuals in the narrow subgroup likely to adhere to long-term obstructive sleep apnea treatment; and
[…] associated with the one or more designated entities a notification comprising the indication of one or more preferred individuals likely to adhere to long-term obstructive sleep apnea treatment and the personalized treatment pathway for the one or more of the preferred individuals, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by one or more processors, a memory, and a data storage (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the one or more processors, a memory, and a data repository, this claim encompasses a person looking at patient data, determining who has a likelihood of sleep apnea, determining a likelihood of adherence to treatment and then notifying an entity that the narrower group is preferred for treatment in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of (claim 30) one or more processors, a memory, and (Claim 1, 30) a data repository that implements the identified abstract idea. The processor, memory, and database are not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a terminal device that implements the identified abstract idea. The terminal device is not described by the applicant and is recited at a high-level of generality (i.e., a generic terminal device performing a generic device functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional elements of using a network, transmitting a notification. This transmitting step is recited at a high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, which is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim further recites the additional element of using a first machine learning model to output an initial group and a second machine learning model to identify and output a narrower subgroup. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim further recites “train a first patient identification machine-learning model by adjusting parameters of the first patient identification machine-learning model” and “train a second patient identification machine-learning model by adjusting parameters of the second patient identification machine-learning model”. Examiner notes Example 39 (Negative recitation of Mathematical calculations) and Example 47 (Positive recitation of Mathematical calculations). Examiner notes Applicant specification/drawings does not disclose any details on the training of the first/second model.
Step 2B
The claim does 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 element of using one or more processors, a memory, and a data repository to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
The claim does 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 element of using a terminal device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a network, transmitting a notification was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the first and second trained machine learning model was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2-3, 5-10, 13-17, and 19-22 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. Claim 2 merely describes the designated entities. Claim 3, 6, 9 merely describes the treatment pathways. Claim 4 merely describes transmitting the treatment pathway. Claim 5 merely describes the notification. Claim 7 merely describes the improved health outcomes. Claim 8 merely describes transmitting the alert. Claim 13, 14, 15, 16 merely describes select physical and health characteristics. Claim 17, 19 merely describes behavioral characteristics. Claim 20 merely describes inputting of select health, behavioral, or health data. Claim 21, 22 merely describes the notification.
Claim 10 describes the additional element of a network server. The server is not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does 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 element of using a server to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Claim 10 merely describes providing the patient information on a server.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 3, 6, 10, 13-17, 19, 21, 30 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487)
CLAIM 1, 30
Boute teaches A method comprising: (Boute para 17 teach a method and para 20 teaches a processor and a memory storing instructions to be executed on a processor)
providing patient data stored in at least one data repository, the patient data including physical, health, and behavioral data corresponding to identifiable individuals; and (Boute Para 25 teaches patient data is stored in memory. Para 49 teaches a clinician reviewing sensor data for a given patient (i.e., identifiable individual). Para 27 teaches the data is electrical, mechanical, chemical or optical information that contains physiological information about the patient)
using a processor, executing machine-readable instructions stored in memory to: (Boute para 17 teach a method and para 20 teaches a processor and a memory storing instructions to be executed on a processor)
receive, from the at least one data repository, training patient data comprising physical characteristic data, health characteristic data, and behavioral characteristic data associated with identifiable training individuals; (Boute para 49 teaches automatic learning algorithms may be implemented based on composite result of all sensor signals and will require sleep apnea episodes. )
determine or receive first threshold values for the identifiable training individuals associated with a determined likelihood of obstructive sleep apnea, the first threshold values based on patient data of training individuals known to have obstructive sleep apnea; (Boute para 50 teaches predetermined sleep apnea detection threshold. Para 56 teaches programming of threshold)
train a first patient identification machine-learning model by adjusting parameters of the first patient identification machine-learning model based on the physical characteristic data, the health characteristic data, and the first threshold values; (Boute para 49 teaches automatically adjusting coefficients based on composite result of sensor signals and sleep apnea episodes)
apply the first patient identification machine-learned model that is trained to process at least a portion of the patient data accessed from the at least one data repository to identify and output an initial […] of individuals associated with select physical and health characteristics, the identification of the initial […] of individuals based on a determined likelihood of obstructive sleep apnea for identifiable individuals meeting or exceeding a first threshold criteria; […]the patient data associated with the initial […] of individuals identified by the first patient identification machine-learning model
(Boute para 49 teaches automatic learning algorithms may be implemented for adjusting coefficients of sensor data used to determine sleep apnea probability of a patient to identify if the patient has sleep apnea. Para 50 teaches determining if this probability exceeds a threshold. Para 49 further teaches data may be from storage of individual data. Boute para 14 teaches transmitting an alert to a clinician or medical facility via wireless or wired communications network. Para 54-56 teaches the alert is related to sleep apnea treatment. Para 35 teaches an external device of a clinician receiving the alert including a display and processing circuitry.)
generate patient identifiable information from the patient data […]; generate, based on the patient identifiable information, an indication to one or more designated entities that one or more of the individuals […] are preferred individuals […] to long-term obstructive sleep apnea treatment. (Boute Para 34 teaches generating an alert to a designated recipient which may include information about sleep apnea episode detections such as time, date, duration, severity, and physiological data. Para 35 teaches alerting a caregiver with recommended actions to be taken.)
generate a personalized treatment pathway for one or more preferred individuals […] to long term obstructive sleep apnea treatment; and (Boute Para 14 teaches detecting sleep apnea and in response delivering a sleep apnea therapy in the form of cardiac overdrive pacing or neuromuscular stimulation such as pectoral stimulation, phrenic nerve stimulation, or stimulation of excitable tissue in the neck or throat. Examiner uses the broadest reasonable interpretation of “personalized” which includes the response taught by Boute because the sleep apnea therapy is used only for that patient which is analogous to personalized to that particular patient)
using a network, transmit to a terminal device associated with the one or more designated entities a notification comprising the indication of one or more preferred individuals […] long-term obstructive sleep apnea treatment and the personalized treatment pathway for the one or more preferred individuals. (Boute para 14 teaches transmitting an alert to a clinician or medical facility via wireless or wired communications network. Para 54-56 teaches the alert is related to sleep apnea treatment. Para 35 teaches an external device of a clinician receiving the alert including a display and processing circuitry.)
Boute does not teach
apply a first patient identification algorithm that is trained to process at least a portion of the patient data accessed from the at least one data repository to identify and output an initial group of individuals associated with select physical and health characteristics, the identification of the initial group of individuals […] of obstructive sleep apnea for identifiable individuals meeting or exceeding a first threshold criteria;
Causevic does teach
apply a first patient identification algorithm that is trained to process at least a portion of the patient data accessed from the at least one data repository to identify and output an initial group of individuals associated with select physical and health characteristics, the identification of the initial group of individuals […] of obstructive sleep apnea for identifiable individuals meeting or exceeding a first threshold criteria; (Causevic para 59 teaches identifying a group of patients having a similar disease or condition)
[…] the patient data associated with the initial group of individuals identified by the first patient identification machine-learning model (Causevic para 59 teaches identifying a group of patients having a similar disease or condition)
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Boute with teaching of Causevic since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of identifying a group as disclosed by the secondary reference for the identification of identifying one patient as taught by the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Boute in view of Causevic does not teach:
train a second patient identification machine-learning model by adjusting parameters of the second patient identification machine-learning model based on the behavioral characteristic data and the second threshold values;
determine or receive second threshold values for the identifiable training individuals associated with a determined likelihood of long-term adherence to obstructive sleep apnea treatment, the second threshold values based on patient data of training individuals known to have long-term adherence to obstructive sleep apnea treatment
apply the second patient identification machine-learned model that is trained to process at least a portion of the patient data associated with the initial group of individuals and configured to use at least a subset of behavioral data to identify and output a narrower subgroup of individuals associated with select behavioral characteristics, the identification of the narrower group of individuals based on a determined likelihood of long-term adherence to obstructive sleep apnea treatment for individuals in the narrower subgroup meeting or exceeding a second threshold criteria, wherein the second patient identification machine-learning model […]
generating patient identifiable information from the patient data associated with individuals in the narrower subgroup; generate, based on the patient identifiable information, an indication to one or more designated entities that one or more of the individuals in the narrower subgroup are preferred individuals likely to adhere to long-term obstructive sleep apnea treatment
using a network, transmit to a terminal device associated with the one or more designated entities a notification comprising the indication of one or more preferred individuals likely to adhere to long-term obstructive sleep apnea treatment and the personalized treatment pathway for the one or more preferred individuals.
Duckworth does teach:
train a second patient identification machine-learning model by adjusting parameters of the second patient identification machine-learning model based on the behavioral characteristic data and the second threshold values; (Duckworth para 122-123 teaches weighting of questions contribution to overall decision of determining adherence profile. para 123 teaches identifying patients who fall into 3 groups of risk of non-adherence based on a threshold and weighting of responses. Examiner notes that the process of comparing weighted responses to a threshold is an algorithm. Examiner also notes that categorizing patients into 3 groups that are mutually exclusive is identifying a narrower group. Para 129 teaches weightings from a clinical trial for predicting and improving adherence. Examiner notes the claim is so broad as to include training that includes assignment of weights by a clinical trial.)
determine or receive second threshold values for the identifiable training individuals associated with a determined likelihood of long-term adherence to obstructive sleep apnea treatment, the second threshold values based on patient data of training individuals known to have long-term adherence to obstructive sleep apnea treatment (Duckworth para 122-123 teaches weighting of questions contribution to overall decision of determining adherence profile. para 123 teaches identifying patients who fall into 3 groups of risk of non-adherence based on a threshold and weighting of responses. Examiner notes that the process of comparing weighted responses to a threshold is an algorithm. Examiner also notes that categorizing patients into 3 groups that are mutually exclusive is identifying a narrower group. Para 129 teaches weightings from a clinical trial for predicting and improving adherence)
apply the second patient identification machine-learned model that is trained to process at least a portion of the patient data associated with the initial group of individuals and configured to use at least a subset of behavioral data to identify and output a narrower subgroup of individuals associated with select behavioral characteristics, the identification of the narrower group of individuals based on a determined likelihood of long-term adherence to obstructive sleep apnea treatment for individuals in the narrower subgroup meeting or exceeding a second threshold criteria, wherein the second patient identification machine-learning model […]l (Duckworth para 122-123 teaches weighting of questions contribution to overall decision of determining adherence profile. para 123 teaches identifying patients who fall into 3 groups of risk of non-adherence based on a threshold and weighting of responses. Examiner notes that the process of comparing weighted responses to a threshold is an algorithm. Examiner also notes that categorizing patients into 3 groups that are mutually exclusive is identifying a narrower group. Para 84-112 teaches questions and behavioral data )
generating patient identifiable information from the patient data associated with individuals in the narrower subgroup; generate, based on the patient identifiable information, an indication to one or more designated entities that one or more of the individuals in the narrower subgroup are preferred individuals likely to adhere to long-term obstructive sleep apnea treatment (Duckworth para 123 teaches categorizing patients into 3 groups. Examiner also notes that categorizing patients into 3 groups that are mutually exclusive is identifying a narrower group. Duckworth para 122 teaches determining adherence risk profile of a patient into low-risk, moderate risk, and high risk. Para 124 teaches this risk profile may be communicated to a healthcare professional))
generate a personalized treatment pathway for one or more preferred individuals in the narrow subgroup likely to adhere to long term obstructive sleep apnea treatment; and (Duckworth para 123 teaches categorizing patients into 3 groups. Examiner also notes that categorizing patients into 3 groups that are mutually exclusive is identifying a narrower group. Duckworth para 122 teaches determining adherence risk profile of a patient into low-risk, moderate risk, and high risk. Para 124 teaches this risk profile may be communicated to a healthcare professional))
using a network, transmit to a terminal device associated with the one or more designated entities a notification comprising the indication of one or more preferred individuals likely to adhere to long-term obstructive sleep apnea treatment and the personalized treatment pathway for the one or more preferred individuals. (Duckworth para 122 teaches determining adherence risk profile of a patient into low-risk, moderate risk, and high risk. Para 124 teaches this risk profile may be communicated to a healthcare professional)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the group of individuals as taught by Boute in view of Causevic with the group of patients who are most likely to adhere to treatment and indication of preferred individuals likely to adhere to treatment as taught by Duckworth. It would be beneficial to understand how likely a patient is to adhere to treatment because patients do not often adhere to treatment as taught by Duckworth para 6.
Boute in view of Causevic in view of Duckworth does not teach:
generate a personalized treatment pathway for one or more preferred individuals in the narrow subgroup likely to adhere to long term obstructive sleep apnea treatment; and
Nease does teach:
generate a personalized treatment pathway for one or more preferred individuals in the narrow subgroup likely to adhere to long term obstructive sleep apnea treatment; and (Nease col 11, line 48-59 teach identifying a therapy program based on the likelihood of therapy adherence)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the treatment pathway as taught by Boute in view of Causevic in view of Duckworth with the plan for patients likely to adhere to the treatment pathway as taught by Nease. It would be beneficial to take into consideration non-compliance when structuring a therapy program as taught by Nease col 2 line 38-52.
Boute in view of Causevic in view of Duckworth in view of Nease does not teach:
wherein the second patient identification machine-learning model is applied only to the patient data associated with the initial group of individuals identified by the first patient identification machine-learning model;
Pandya does teach:
wherein the second patient identification machine-learning model is applied only to the patient data associated with the initial group of individuals identified by the first patient identification machine-learning model; (Pandya para 24 teaches a first model trained on data and output from the first model and then a second model to identify data within the output from the first model)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the models as taught by Boute in view of Causevic in view of Duckworth in view of Nease with the second model only applied to output of first model as taught by Pandya. It would be beneficial to have dynamic processing techniques that improve performance and improve likelihood of identifying data as taught by Pandya para 7.
CLAIM 2
Boute teaches wherein the one or more designated entities include a health care provider (Boute Para 14 teaches the alert may be to a clinician), an integrated delivery network, a health care payor, an administrator, at least one of the one or more individuals, or any combinations thereof. (Examiner notes additional limitations interpreted as optional due to claim language “or any combinations thereof.)
Examiner also notes Boute may not explicitly teach “an integrate delivery network, a health care payor, an administrator, at least one of the one or more individuals.” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute teaches that data containing labels is stored and transmitted, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable.
CLAIM 3
Boute in view of Causevic further in view of Duckworth teaches generating the personalized treatment pathway for the one or more of the preferred individuals based at least in part on the physical, health, and behavioral data corresponding to each. (Boute Para 14 teaches detecting sleep apnea and in response delivering a sleep apnea therapy in the form of cardiac overdrive pacing or neuromuscular stimulation such as pectoral stimulation, phrenic nerve stimulation, or stimulation of excitable tissue in the neck or throat. Para 19 teaches examples of sensors that detect physical, health and behavioral data that is used to detect sleep apnea or respiratory disturbances including a pacemaker, defibrillator, electrocardiogram monitor, blood pressure monitor, drug pump, insulin monitor, or neurostimulator. Examiner uses the broadest reasonable interpretation of “personalized” which includes the response taught by Boute because the sleep apnea therapy is used only for that patient which is analogous to personalized to that particular patient)
CLAIM 6
Boute teaches wherein the personal treatment pathway […]. (Boute Para 35 teaches alerting a caregiver with recommended actions to be taken. Boute Para 14 teaches detecting sleep apnea and in response delivering a sleep apnea therapy in the form of cardiac overdrive pacing or neuromuscular stimulation such as pectoral stimulation, phrenic nerve stimulation, or stimulation of excitable tissue in the neck or throat.)
Boute in view of Causevic further in view of Duckworth do not teach wherein the personal treatment pathway further includes an analysis of improved health outcomes by treating obstructive sleep apnea.
Duckworth does teach wherein the generated personal treatment pathway further includes an analysis of improved health outcomes by treating obstructive sleep apnea. (Duckworth para 249 teaches encouraging a patient by telling the patient that they will feel better the more they treating the sleep apnea by increasing CPAP use)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Duckworth with the teaching of Boute in view of Causevic further in view of Duckworth since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Boute in view of Causevic further in view of Duckworth in view of Nease or Duckworth. Including an analysis of improved health outcomes by treating sleep apnea as taught by Duckworth does not change or affect the normal treatment pathway. The generation of a treatment pathway would be performed the same way even with the addition of including an analysis of improved health outcomes by treating sleep apnea. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth in view of Nease and Duckworth do not interfere with each other, the results of the combination would be predictable.
CLAIM 10
Boute teaches further comprising providing the patient identifiable information on a network server accessible to a third-party. (Boute Para 3 teaches transmitting data over a network to a patient management center to process data and retrieve relevant information for distribution to a clinician or medical center (i.e., a third party) . Examiner notes that transmitting information over a network is done by through the use of a server which send information and a client which receives information. )
CLAIM 13
Boute teaches wherein one or more of the select physical and health characteristics are indirectly attributable to or intensified by obstructive sleep apnea. (Boute Para 45 teaches oxygen saturation, specifically if it is low, as an example of a parameter that is intensified by sleep apnea)
CLAIM 14
Boute teaches wherein the select physical characteristics include neck circumference, weight, gender, blood pressure (Boute Para 24 teaches signals include blood pressure), age, body mass index, or any combinations thereof. (Examiner notes additional limitations interpreted as optional due to claim language “or any combinations thereof.)
Boute may not explicitly teach “neck circumference, weight, gender, age, body mass index.” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute teaches that data containing labels is stored, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable. The Examiner notes that the physical characteristic are only recited to include the listed types of data and there is no indication that these types of data are actually used in the claim, thus they represent non-functional labels.
CLAIM 15
Boute teaches wherein one or more of the select physical and health characteristics include information provided by the identifiable individuals. (Para 27 teaches physiological signals which are provided by the identifiable individual)
CLAIM 16
Boute teaches wherein the select health characteristics include snoring history, heart conditions, (Boute para 16 teaches monitoring heart failure using sensors. Para 29 teaches a heart wall motion sensor, heart rate or heart signal from an EGM/ECG source may be determined. ) history of tiredness, observed apnea, diabetes, or any combinations thereof. (Examiner notes additional limitations interpreted as optional due to claim language “or”.)
Boute may not explicitly teach “snoring history, history of tiredness, observed apnea, diabetes.” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute teaches that data containing labels is stored, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable.
CLAIM 17
Boute teaches wherein the behavioral characteristics […] (Boute Para 27 teaches physiological information about the patient including electrical, mechanical, chemical or optical information)
Boute in view of Causevic further in view of Duckworth does not teach wherein the behavioral characteristics include demographic information, wherein the demographic information includes education, employment, place of residence, marital status, or any combinations thereof.
Duckworth does teach wherein the behavioral characteristics include demographic information, wherein the demographic information includes education, employment, place of residence, marital status, or any combinations thereof. (Duckworth para 78 teaches demographics information which include education, employment status, marital status.)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Duckworth with teaching of Boute in view of Causevic further in view of Duckworth since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Boute in view of Causevic further in view of Duckworth or Duckworth. Including demographic data in behavioral characteristics as taught by Duckworth does not change or affect the normal behavioral characteristics. Behavioral characteristics would be performed the same way even with the addition of demographics data. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth and Duckworth do not interfere with each other, the results of the combination would be predictable.
Boute in view of Causevic further in view of Duckworth may not explicitly teach “a place of residence.” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute in view of Causevic further in view of Duckworth teaches that data containing labels is stored, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable
CLAIM 19
Boute teaches wherein the behavioral characteristics includes motivation, fitness level (Para 28 teaches an activity sensor that can be used in detecting a rest or sleep state (i.e., fitness level)), exercise routine, adherence to prescribed medication protocols, adherence to prior doctor recommendations, or any combinations thereof. (Examiner notes additional limitations interpreted as optional due to claim language “or”.)
Boute in view of Causevic further in view of Duckworth may not explicitly teach “motivation, exercise routine, adherence to prescribed medication protocols, adherence to prior doctor recommendations” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute teaches that data containing labels is stored, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore would have been prima facie obvious to one of ordinary skill in the art at the time of time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable.
CLAIM 21
Boute teaches wherein the notification comprises a notification to an identified individual that includes a direct message or an email message delivered through a health portal. (Boute para 34 teaches an alert to a designated recipient over a network which may be a telephone network or local area network and para 36 teaches the wireless message may be on a programmer/monitor device which advises the patient. (i.e., direct message on a health portal))
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) in view of Bilir (US 10628555)
CLAIM 5
Boute teaches wherein the notification […] (Boute Para 34 teaches generating an alert to a designated recipient which may include information about sleep apnea episode detections such as time, date, duration, severity, and physiological data. Para 35 teaches alerting a caregiver with recommended actions to be taken.))
Boute in view of Causevic further in view of Duckworth in view of Nease does not teach wherein the notification includes an analysis of potential healthcare cost savings by treating potential obstructive sleep apnea.
Bilir does teach wherein the notification includes an analysis of potential healthcare cost savings by treating potential obstructive sleep apnea. (Column 4, lines 17-25 teaches showing determining cost savings achieving by complying with a treatment)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the notification as taught by Boute in view of Causevic in view of Duckworth with the cost analysis of potential healthcare cost savings by complying with the treatment as taught by Bilir. It would be beneficial for patients to have a realistic assessment of treatment outcome as taught by Bilir col 2, line 3-14.
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) in view of Laschet (US 20200005940) referred to hereinafter as Laschet
CLAIM 7
Boute in view of Causevic further in view of Duckworth in view of Nease teaches The method of claim 6, wherein the improved health outcomes […]. (Duckworth para 249 teaches encouraging a patient by telling the patient that they will feel better the more they treating the sleep apnea by increasing CPAP use)
Boute in view of Causevic further in view of Duckworth in view of Nease does not teach wherein the improved health outcomes include decreased mortality rate, readmits, hospital time, or any combinations thereof.
Laschet does teach wherein the improved health outcomes include decreased mortality rate, readmits, hospital time, or any combinations thereof. (Laschet para 17 teaches enhanced patient outcome such as survival rate (i.e., decreased mortality rate) Examiner notes additional limitations interpreted as optional due to claim language “or”.)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Laschet with teachings of Boute in view of Causevic further in view of Duckworth further in view of Nease since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Boute in view of Causevic further in view of Duckworth further in view of Nease or Laschet. Including decreased mortality rate as taught by Laschet does not change or affect the normal analysis of patient outcomes. Analysis of patient outcomes would be performed the same way even with the addition of including decreased mortality rate. Since the functionalities of the elements in Laschet and Boute in view of Causevic further in view of Duckworth further in view of Nease do not interfere with each other, the results of the combination would be predictable.
Examiner notes Boute in view of Causevic further in view of Duckworth may not explicitly teach “improved health outcomes include decreased … readmits, hospital time …” However, the limitation claims labels that do not result in a manipulative difference between the labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the labels was substituted with nothing. Because Boute in view of Causevic further in view of Nease teaches that data containing labels is stored, substituting the labels of the claimed invention for the labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of time of filing to have substituted the labels applied to the stored data of the prior art with any other labels because the results would have been predictable.
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) in view of Berger (US 8200510).
CLAIM 8
Boute teaches transmitting an alert directly to a corresponding individual […] (Boute para 35 teaches an alert may be sent to a patient or clinician)
Boute in view of Causevic further in view of Duckworth does not teach transmitting an alert directly to a corresponding individual to inquire about a sleep test with their healthcare provider.
Berger does teach transmitting an alert directly to a corresponding individual to inquire about a sleep test with their healthcare provider. (Berger column 4, line 57-60 teaches directing operators with high predicative values for sleep apnea to go to a physician to obtain a prescription for a sleep test. Examiner notes “to inquire about a sleep test” is non-functional descriptive information.)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Berger with teaching of Boute in view of Causevic further in view of Duckworth since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Boute in view of Causevic further in view of Duckworth in view of Nease or Berger. Inquiring about a sleep test as taught by Berger does not change or affect the normal transmitting of an alert. Transmitting of an alert would be performed the same way even with the addition of inquiring about a sleep test. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth in view of Nease and Berger do not interfere with each other, the results of the combination would be predictable.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) in view of non-patent literature drganent.com (Feb 1, 2020, “I think I have sleep apnoea – Which type of sleep study is suitable for me?”, https://drganent.com/blog/which-type-of-sleep-study-is-suitable-for-me/)
CLAIM 9
Boute in view of Causevic further in view of Duckworth the personal treatment pathway […] (Boute para 14 teaches transmitting an alert to a clinician or medical facility via wireless or wired communications network. Boute Para 14 teaches detecting sleep apnea and in response delivering a sleep apnea therapy in the form of cardiac overdrive pacing or neuromuscular stimulation such as pectoral stimulation, phrenic nerve stimulation, or stimulation of excitable tissue in the neck or throat. Para 35 teaches alerting a caregiver with recommended actions to be taken. Para 54-56 teaches the alert is related to sleep apnea treatment. Para 35 teaches an external device of a clinician receiving the alert including a display and processing circuitry.)
Boute in view of Causevic further in view of Duckworth do not teach the generated personal treatment pathway includes a recommended sleep test method.
Drganent does teach the generated personal treatment pathway includes a recommended sleep test method. (Drganent heading 6 “Who should undergo a level 1 or 2 sleep study” teaches that there are different test options for patients that may be better for certain patients)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of drganent with teaching of Boute in view of Causevic further in view of Duckworth since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either drganent or Boute in view of Causevic further in view of Duckworth in view of Nease. Including a recommended sleep test method as taught by drganent does not change or affect the normal generation of a personal treatment pathway. Generating a personal treatment pathway would be performed the same way even with the addition of recommending a sleep test. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth further in view of Nease and drganent do not interfere with each other, the results of the combination would be predictable.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) in view of Cashman (US 20140330579) referred to hereinafter as Cashman.
CLAIM 20
Boute teaches wherein any of the select health, behavioral, or demographic information […]. (Boute Para 27 teaches the patient data is electrical, mechanical, chemical or optical information that contains physiological information about the patient. Examiner notes “demographic information” is interpreted as optional due to claim language “any of the select … or …”.)
Boute in view of Causevic further in view of Duckworth does not teach wherein any of the select health, behavioral, or demographic information is data input by a healthcare provider during one or more previous patient encounters.
Cashman does teach wherein any of the select health, behavioral, or demographic information is data input by a healthcare provider during one or more previous patient encounters. (Cashman para 23 teaches vitals section includes data input by a provider during a previous visit)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Cashman with teaching of Boute in view of Causevic further in view of Duckworth since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Cashman or Boute in view of Causevic further in view of Duckworth. Including data input by a provider during one or more previous visits as taught by Cashman does not change or affect the normal data. Data would be stored and used the same way even with the addition of including data input by a provider during one or more previous visits. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth and Cashman do not interfere with each other, the results of the combination would be predictable.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708) in view of Causevic (US 20110144518) in view of Duckworth (US 20150154380) in view of Nease (US 8666926) in view of Pandya (US 20200184487) further in view of Acharya (US 8965986)
CLAIM 22
Boute teaches wherein the notification comprises a notification to a healthcare provider or administrator associated with the preferred individuals that […] . (Boute Para 34 teaches generating an alert to designated recipient which may include information about sleep apnea episode detections such as time, date, duration, severity, and physiological data. Para 35 teaches alerting a caregiver with recommended actions to be taken.)
Boute does not teach wherein the notification comprises a notification to a healthcare provider or administrator associated with the preferred individuals that includes an indication of a communication method most likely to result in patient follow up.
Acharya does teach wherein the notification comprises a notification to a healthcare provider or administrator associated with the preferred individuals that includes an indication of a communication method most likely to result in patient follow up. (Acharya claim 6 teaches identifying which message method is most likely to elicit a response)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Acharya with teaching of Boute in view of Causevic further in view of Duckworth since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A); see MPEP 2143(I)(A)). It can be seen that each element claimed is present in either Acharya or Boute in view of Causevic further in view of Duckworth in view of Nease. Including an indication of the communication method mostly likely to result in patient follow up as taught by Acharya does not change or affect the normal notification. Generating a notification would be performed the same way even with the addition of including an indication of the method most likely to result in patient follow up. Since the functionalities of the elements in Boute in view of Causevic further in view of Duckworth in view of Nease and Acharya do not interfere with each other, the results of the combination would be predictable.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cole, Cluster-then-predict for classification tasks, Feb 10, 2020
Teaches clustering first then later classifying on a cluster
Response to Arguments Regarding U.S.C. 101
Applicant argues pg. 20:
Under the Office's guidance, operations that configure and train models using large, heterogeneous datasets and explicit parameter-adjustment routines are not reasonably characterized as activities that can be performed in the human mind or as mere mathematical formulas recited at a level of abstraction divorced from an implementation. See MPEP § 2106.04(a)-(b) and the 2019 Revised Guidance (mental processes must be practically performable in the human mind); see also the 2024 AI examination guidance and the 2025 eligibility reminders clarifying that detailed model-training steps that cannot practically be executed mentally should not be grouped as "mental processes."
Examiner responds:
Examiner has not characterized the Abstract idea as mental process.
Applicant argues pg. 20-21:
To the extent the Office previously characterized the claim as reciting certain methods of organizing human activity or a result-oriented "data analysis," that characterization no longer matches the amended claim's specificity.. §2106.04(a)(2)(II). Rather, it recites, inter alia,
"determine or receive first threshold values ... based on patient data of training individuals known to have obstructive sleep apnea,"
"determine or receive second threshold values ... based on patient data of training individuals known to have long-term adherence to obstructive sleep apnea treatment,"
"train a first patient identification machine-learning model ... based on the physical characteristic data, the health characteristic data, and the first threshold values," and "train a second patient identification machine-learning model ... based on the behavioral characteristic data and the second threshold values,"
“apply the trained first patient identification machine-learning model" and
"apply the trained second patient identification machine-learning model," wherein the second model is applied only to the patient data associated with the initial group of individuals identified by the
first patient identification machine-learning model."
These are particular model-configuration and pipeline constraints on a computer-implemented system that, as set forth in MPEP § 2106.04(d)(l) and§ 2106.05(a), reflect a specific way of achieving improved computer and model functionality, not a mere recital of a desired result.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to for notifying someone that individuals are preferred individuals for sleep apnea treatment. Applicant points exactly to limitations in the claims that fall with this characterization. "determine or receive first threshold values ... based on patient data of training individuals known to have obstructive sleep apnea," "determine or receive second threshold values ... based on patient data of training individuals known to have long-term adherence to obstructive sleep apnea treatment," "train a first patient identification […] model ... based on the physical characteristic data, the health characteristic data, and the first threshold values," and "train a second patient identification […] model ... based on the behavioral characteristic data and the second threshold values," “apply the trained first patient identification […] model" and "apply the trained second patient identification […] model," wherein the second model is applied only to the patient data associated with the initial group of individuals identified by the first patient identification […] model." Because the claim elements fall under a series of rules or instructions that a person or persons would follow to notifying someone that individuals are preferred individuals for sleep apnea treatment, the claimed invention is directed to an abstract idea. The additional elements of “machine-learned” is addressed in Step 2A2 analysis above and has not been found to integrate the abstract idea into a practical application.
Applicant argues pg. 21:
The present claim is directed to a specific improvement in the functioning of computer-implemented machine-learning systems and in the technical field of computerized patient-identification pipelines. The specification explains that the invention receives, partitions, and utilizes distinct training datasets and label sources to train two different models and then constrains inference such that the second model is applied only to a previously identified initial cohort, thereby improving precision of identification, reducing unnecessary computation over broad populations, and focusing downstream processes only on those individuals who satisfy both a disease-likelihood and an adherence-likelihood threshold.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the computer. The problem of focusing only on individuals who satisfy both disease likelihood and adherence likelihood was not a problem cause by the computer, it is a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a management / training / personnel / business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant argues pg. 21-22:
Applicant submits that the amended independent claims recite a particular improvement in how the computer and models operate, rather than an instruction to apply an abstract idea using generic technology. The Office's 2019 Revised Guidance and MPEP § 2106.05(a) make clear that claims are not directed to an abstract idea where they recite a specific improvement in the functioning of a computer or another technology or technical field. The Office's AI-focused guidance and the 2024 and 2025 eligibility reminders further explain that claims reciting particularized model-training structures, parameter-adjustment techniques, or architectural constraints that yield improved model or system performance should be evaluated as practical applications and improvements, not as generic "apply it" instructions. The present claim, like the eligible examples addressing technical improvements to model-based processing (see, e.g., Subject Matter Eligibility Examples 47-49), recites a particular training and inference pipeline that improves the computer-implemented identification system itself, not merely an abstract end result.
It is also important that the claim does not merely append field-of-use language or insignificant extra-solution activity. The training-time limitations specify how the models are built and configured, including the nature and provenance of the labels and the division of features, by reciting "train a first patient identification machine-learning model ... based on the physical characteristic data, the health characteristic data, and the first threshold values" and "train a second patient identification machine-learning model ... based on the behavioral characteristic data and the second threshold values." The inference-time limitations specify how the trained models are used in a constrained sequence that narrows the population and thereby improves computational efficiency and precision, by reciting "apply a trained first patient identification machine-learning model ... to identify and output an initial group" and "apply a trained second patient identification machine-learning model ... to identify and output a narrower subgroup." These are the types of concrete, model-centric operations that MPEP § 2106.04(d)(l) and § 2106.0S(a) identify as integrating an alleged abstract idea into a practical application by improving the operation of the computer and ML system.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem, model based processing, is not a technological problem caused by the computer. The problem of narrowing the population was not a problem cause by the computer, it is a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a management / training / personnel / business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant argues pg. 21-22:
Should analysis proceed to Step 2B, claim 1 still recites significantly more than any alleged abstract idea. The ordered combination of the additional elements is not shown to be well-understood, routine, and conventional (WURC) in the field, and the Office bears the burden to support a WURC finding with appropriate evidentiary support. […] The Office Action has not identified evidence demonstrating that the features similarly recited by Applicant's amended independent claims were well-understood, routine, and conventional in the field. Absent such evidence, and given the detailed training and inference structures recited in the claim, the additional elements and their ordered combination supply the requisite inventive concept.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis as no improvement was present. The technological environment to which the claims are confined to ( a general-purpose computer performing generic computer functions) and is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.). The additional elements of using a network, transmitting a notification that were found to represent extra-solution activity were analyzed and determined to represent well-understood, routine, conventional activities in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). As such, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea and the claims are not subject matter eligible.
Applicant argues pg. 24:
Further, and contrary to the Office's own examination guidelines, the Office fails to analyze the dependent claims under each step of the 2019 Guidance, and instead improperly supports the rejection of these dependent claims with mere conclusory statements asserting that these dependent claims "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."
Examiner responds:
The Examiner respectfully disagrees. The dependent claims have been analyzed and found to 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. See 101 rejection above.
Response to Arguments Regarding U.S.C. 103 Rejection
Applicant argues pg. 10:
Applicant respectfully submits that Boute, Causevic, Duckworth, and Nease fail to teach or even suggest at least the above-recited elements similarly recited by amended independent claim I and 30. As amended, claim I requires, inter alia, a supervised two-model training and sequential inference architecture having the following concrete operations: receiving training patient data comprising physical, health, and behavioral characteristic data; determining or receiving first threshold values based on training individuals known to have OSA and second threshold values based on training individuals known to have long-term adherence to OSA treatment; training a first patient-identification machine-learning model using the physical and health characteristic training data and the first threshold values; training a second patient identification machine-learning model using the behavioral characteristic training data and the second threshold values; applying the trained first machine-learning model to patient repository data to identify an initial group with OSA likelihood meeting or exceeding a first threshold; and applying the trained second machine-learning model only to the initial group's data to identify a narrower subgroup with long-term adherence likelihood meeting or exceeding a second threshold.
Boute, however, neither teaches nor suggests: receiving training patient data separated into physical, health, and behavioral categories; determining or receiving first threshold values derived from individuals known to have OSA and second threshold values derived from individuals known to have long-term adherence to OSA treatment; training two distinct patient identification machine-learning models tied to disjoint feature spaces; applying those distinct trained models in sequence; or constraining the second model to be "applied only to" the data associated with the initial group output from the first model.
Examiner responds:
Examiner has cited new art to teach the limitation “constraining the second model to be "applied only to" the data associated with the initial group output from the first model.” See Claim 1, 30 rejection above for other limitations.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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