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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 3/02/2026, claims 1-2, 6, 8-10, 14, 16-20 have been amended.
Claims 1-20 are pending and have been examined.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/432,102 filed 12/13/2022.
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent Application No. EP23154488.3 filed 02/01/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8 and 16 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Eligibility Analysis Step 1 (YES): Claims 1, 8 and 16 fall into at least one of the statutory categories (i.e., machine or process).
Eligibility Analysis Step 2A1 (YES): The claims recite an abstract idea. The identified abstract idea is for a screening system for an undiagnosed medical condition (e.g., obstructive sleep apnea) comprising at least one controller, as underlined (claim 1 being representative):
query a plurality of selected data sources for medical records to extract medical status information (MSI) of patients in a patient cohort;
form patient cohort data (PCD) comprising patient case records, each of the patient case records comprising the extracted MSI of the patients in the patient cohort;
exclude patient case records from the PCD that are determined to be disqualified according to exclusion rules (ERS), and update the PCD accordingly;
determine risk factors that are present for each patient case record of the updated PCD in accordance with a comparison of the MSI for corresponding patient case records with risk factors for obstructive sleep apnea (OSA);
determine a total risk score for each of the corresponding patient case records by tallying at least one of assigned points and weights, corresponding to the determined risk factors that are determined to be present for the corresponding patient case records, as identified from the MSI, including at least one of diagnosis codes, procedure codes, medication codes, symptoms, and signs;
determine a predicted probability of suspected OSA based upon a logistic function of the determined total risk score for each of the corresponding patient case records;
assign each of the corresponding patient case records to a risk category in accordance with risk thresholds applied to the predicted probability of suspected OSA; and
determine a follow-up procedure for each of the corresponding patient case records in accordance with the assigned risk category for the corresponding patient case records and a system capacity limit.
The identified claim elements, as drafted, is a process that under the broadest reasonable interpretation (BRI) covers a method of organizing human activity (i.e., managing personal behavior or relationships or interactions between people including following rules or instructions) but for the recitation of generic computer component language (discussed below in 2A2). That is, other than reciting the generic computer component language, the claimed invention amounts to screening patients for an undiagnosed medical condition, e.g., obstructive sleep apnea, which is a method of managing personal behavior or relationships or interactions between people. For example, but for the generic computer component language, the claims encompass a person querying medical records, forming patient cohort data, excluding data from the patient cohort data (and updating the patient cohort data accordingly), determining risk factors in each patient’s data, determining a total risk score for each patient, determining a predicted probability of the suspected medical condition for each patient, assigning each patient to a risk category, and/or determining a follow-up procedure for each patient, all 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 relationships or interactions between people but for the recitation of generic computer component language, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. See additionally MPEP § 2106. Accordingly, the claims recite an abstract idea.
Eligibility Analysis Step 2A2 (NO):
The judicial exception, the above-identified abstract idea, is not integrated into a practical application. In particular, the claims recite the additional element(s) of at least one controller (claims 1 and 8) that implement(s) the identified abstract idea. The additional element(s) aforementioned is/are not described by the applicant and is/are recited at a high-level of generality (i.e., each as a generic computer or computer component performing a generic computer or computer component function that facilitates the identified abstract idea) such that this/these amount no more than mere instructions to apply the exception using generic computer component(s) (see Specification e.g., at para. 0035-0036, 0038). See MPEP § 2106.04(d)(I). Accordingly, alone or in combination, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it/they does/do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims further recite the additional element(s) of a plurality of selected data sources (claims 1, 8 and 16) as collecting, transmitting or outputting data. The additional element(s) is/are recited at a high-level of generality (i.e., each as a general means of collecting, transmitting or outputting data) and each amounts to a location from which data is received or to which data is transmitted or outputted, each of which represents an extra-solution activity (e.g., mere data gathering and data output). MPEP § 2106.04(d)(I) indicates that extra-solution data gathering and data output activity cannot provide a practical application. Accordingly, even in combination, this/these additional element(s) does/do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Eligibility Analysis Step 2B (NO):
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 element(s) of at least one controller (claims 1 and 8) to perform the method (represented by claim 1) amount(s) no more than mere instructions to apply the exception using a generic computer or generic computer component. Mere instructions to apply an exception using generic computer(s) and/or generic computer component(s) cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f).
Also discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of a plurality of selected data sources (i.e., each a device that collects, transmits or outputs data) is/are each 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 2106.05(d)(II) indicates that receiving, transmitting or outputting data over a network has been held by the courts to be well-understood, routine, conventional activity (WURC) (citing TLI Communications, Symantec, OIP Techs., and buySAFE). MPEP 2106.05(d)(II) also indicates that storing and retrieving information in memory has been held by the courts to be WURC (citing Versata Dev. Group, Inc. and OIP Techs.) See also MPEP 2106.05(g) (citing Cybersource, Mayo, OIP Techs.) Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims are not patent eligible.
Dependent claims 2-7, 9-15 and 17-20, when analyzed as a whole, are similarly rejected under 35 U.S.C. §101 because the additional limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims, when considered alone or as an ordered combination, either (1) merely further define the abstract idea, (2) do not further limit the claim to a practical application, or (3) do not provide an inventive concept such that the claims are subject matter eligible.
Claim(s) 2, 10 and 18 merely further describe the additional element of a rendering device of the system (e.g., rendering the determined follow-up procedure under the control of the at least one controller), which amounts no more than mere instructions to apply the judicial exception (e.g., rendering data) using a generic computer(s) and/or generic computer components (e.g., the rendering device and the at least one controller housed within the mobile station MS 104). See Applicant’s disclosure at Fig. 1 and para. 0032, 0070. See analysis, supra.
Claim(s) 3, 11 merely further describe(s) the additional element(s) of the at least one controller and the rendering device, which amount no more than mere instructions to apply the judicial exception to render data using generic computer components. See analysis, supra.
Claims 4-7, 9, 12-15 merely further describe the additional element of the at least one controller (e.g., determining an estimated risk as at least one of… probability based upon a logistic function… for each of the corresponding patient case records, determining a system capacity limit, communicating data, determining a cumulative capacity of the at least one SMU to conduct sleep tests and basing the system capacity limit in accordance with a sum of the determined cumulative capacity, render a diagnostic follow-up, etc.) See analysis, supra.
Claims 6, 14 further describes an additional element of at least one sleep monitoring unit (SMU) (102) performing function(s) at a high-level of generality (e.g., high-level data communication), which amounts no more than mere instructions to apply the judicial exception using generic computer(s) (e.g., SMU 102) and/or generic computer component(s) (e.g., controller 120). See Applicant’s disclosure at Fig. 1 and para. 0056 (“For example, there may be 5 SMUs with a capacity of 2 patient cases/per day (e.g., a 24 hour period). Accordingly, the diagnostic capacity limit may be set to 2x5=10 patient cases/day”.) See analysis, supra.
Additionally, and for completeness, the additional element of the at least one SMU is alternately considered insignificant extra-solution activity as it amounts to a location from which data is received (e.g., by the mobile station 104 controller 190) or to which data is transmitted or outputted. See analysis, supra.
Claim 17 merely further describes the abstract idea (e.g., an act of determining a follow-up procedure for each patient case record).
Claim 19 merely further describes the additional element of a storage device, which amounts no more than mere instructions to apply the judicial exception (e.g., an act of storing the annotated patient cohort data) using a generic computer(s) and/or generic computer components. See Applicant’s disclosure at Fig. 1 and para. 0035, 0036, 0038-0039. See analysis, supra.
Claim 20 merely further describes the abstract idea (e.g., determining a cumulative capacity of a plurality of monitoring units, determining a follow-up procedure for each patient case record). In addition, and for completeness, claim 20 recites “a plurality of monitoring devices (102)” to describe the determined cumulative capacity-to-conduct-tests data (see Applicant’s disclosure at para. 0056); as such, the “plurality of monitoring units” is not an additional element.
Claim Rejections - 35 USC § 103
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 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 1-5, 7, 9-11, 15, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shukla et al. (US 2021/0193320 A1; “Shukla” herein) (see IDS document) in view of Moussavi et al. (CA 3,089,395 filed 8-07-2020; “Moussavi” herein), Merkin (US 2014/0164017 A1) and Wallach et al. (US 2002/0013906 A1; “Wallach” herein).
Re. Claim 1, Shukla teaches a screening system (see Abstract; also, [0104] and Figs. 4-7), comprising: at least one controller ([0008] teaches one or more processors in a distributed computing environment) configured to: query a plurality of selected data sources for medical records to extract medical status information (MSI) of patients in a patient cohort; form patient cohort data (PCD) comprising patient case records, (Abstract, [0008] teach obtaining data set(s) (medical records) related to a patient population diagnosed with a medical condition from one or more databases (necessarily queried). Figs. 2, 8 and [0042], [0047], [0064] teach data integration 210 includes patient definition … generating a patient definition (form PCD) by analyzing the electronic records over time and across multiple de-identified data sets, the electronic records including clinical and natural history data, expert input, and drug and diagnosis codes (MSI) 210, 810… In the patient definition process, the program code identifies the cohort of patients / the training set (forms PCD comprising patient case records). The Applicant’s disclosure at para. 0049 describes MSI as including demographics, medical history on diagnoses, procedures, symptoms, and/or signs.), each of the patient case records comprising the extracted MSI of the patients in the patient cohort (Fig. 2, 8 and [0047], [0064] teach subsequently performing pattern extraction 220, 820 in which one or more programs extract features and create a disease model by refining the patient profile… the internal algorithms applied by the program code include 1) mutual information to inform or refine the patient definition / cohort and/or 2) various datamining techniques including histograms to capture procedures, drugs, diagnosis codes, specialty types, geographic location, patient demographics such as age and gender, and co-morbidities (each of the patient case records comprising the extracted MSI).); exclude patient case records from the PCD that are determined to be disqualified according to exclusion rules (ERS), and update the PCD accordingly (Fig. 1, [0052] teach the program code utilizes the feature selection techniques to identify the mutual information in the data set (patient case records) that characterizes the given condition… and utilizes this mutual information as an inclusion/exclusion index (exclusion rules)… Thus, removing data that does not include the top features 130 (exclude patient case records from the PCD according to exclusion rules). Fig. 8, [0091] teaches after the program code determines the mutual information of individual features or their combination, the program code begins feature selection 830… resulting in an enhanced patient definition (updating the PCD accordingly).); determine risk factors that are present for each patient case record of the updated PCD in accordance with a comparison of the MSI for corresponding patient case records with risk factors […] (see previous citations. Fig. 1, [0052] also teaches… the program code identifies the codes and the combination of codes (risk factors) that separate individuals (each patient case record) with the given condition from a general population… utilizing the feature selection techniques to identify the mutual information in the data set that characterizes the given condition (in accordance with a comparison of the MSI for corresponding patient case records) … the codes selected through mutual information provide the inclusion criteria… most clearly defining the characteristics of a patient with a given disease. [0065]-[0066], [0068] teach constructing the machine learning algorithm, which can be understood as a classifier, as it classifies records (which may represent individuals) … utilizing the frequency of occurrences of features (comparison with risk factors) in the mutual information… the result of applying the classifier is a sorted list of individuals suspected of having the disease (the updated PCD).); determine a total risk score for each of the corresponding patient case records […] corresponding to the determined risk factors that are determined to be present for the corresponding patient case records, as identified from the MSI, including at least one of diagnosis codes, procedure codes, medication codes, symptoms, and signs ([0063], [0066] teach feeding the previously identified feature set into a classifier (as identified from the MSI) and utilizes the classifier to classify records of individuals based on the presence or absence of the given condition… indicating a probability of a given condition with a rating on a scale (determining a total risk score for each of the corresponding patient case records). [0093] teaches in order to score patients, the program code computes the features for every patient in the data set not in the set of gold HAE patients... Each patient's features / characteristics were input by the program code to the HAE computer model and the program code produced a numerical score (corresponding to the determined risk factors)… This numerical score is the likelihood that the patient is an undiagnosed HAE patient… The numerical score can be used to rank patients from those who are most likely to be undiagnosed with HAE to those that are least likely to have HAE. [0053] teaches the program code applies frequency ranking and mutual information procedures to identify the distinguishing features that include diagnoses, procedures, drugs, providers, and locations (the MSI including at least one of diagnosis codes, procedure codes, medication codes) later used to determine predictors of the HAE.); determine a predicted probability of suspected […] based upon a logistic function of the determined total risk score for each of the corresponding patient case records ([0063], [0066] teach constructing and tuning a classifier / machine learning algorithm, which can be utilized with one or more of an RF grouping algorithm and a log regression (a logistic function) … such that the algorithm can distinguish data comprising the disease event from data that does not comprise the disease 160… may assign probabilities to various records (for each of the corresponding patient case records) … when classifying an individual with a given condition utilizing the classifier, the program code may indicate a probability of a given condition (determine a predicted probability of suspected condition) with a rating on a scale (the determined total risk score), for example, between 0 and 1, where 1 would indicate a definitive presence.); assign each of the corresponding patient case records to […] in accordance with risk threshold[…] applied to the predicted probability of suspected […] ([0063] teaches assigning probabilities to various records. [0102]-[0103] teach applying the enhanced patient definition determined during model creation (e.g., Fig. 9, 920) to the remaining population… setting the prediction classifier set to a detection probability (e.g., probability >0.8) (risk threshold) and applying the model to the remaining population. [0098], [0109] teach thus, the one or more programs may score a patient as having a likelihood of a disease within a certain threshold (applied to the predicted probability of a suspected condition), the one or more programs may electronically notify the provider of this result.); and […].
Shukla may not teach
risk factors for obstructive sleep apnea (OSA),
determine a total risk score… by tallying at least one of assigned points and weights, corresponding to the determined risk factors,
determining a predicted probability of suspected OSA,
assign each of the corresponding patient case records to a risk category in accordance with risk thresholds (i.e., plural) applied to the predicted probability, or
the assigned risk category for the corresponding patient case records.
Moussavi teaches
risk factors for obstructive sleep apnea (OSA) (pg. 11, para. 1 teaches information collected from each test subject is added to the datastore… includes anthropometric data 201 indicative of OSA risk factors (ex. BMI, age, etc.). Also, pg. 21 at lines 12-21 teaches using the anthropometric information and other data to identify OSA individuals in need of treatment.),
determine a total risk score… by tallying at least one of assigned points and weights, corresponding to the determined risk factors (pg. 1, lines 11-19 teaches determining apnea/hypopnea index (AHI) (total risk score) … classification into non-OSA AHI or OSA AHI depends on the total AHI score. Pg. 14, lines 16-24 and pg. 15, lines 9-21 teach Eq. 1, a polynomial model, created for each possible AHI feature combination… the anthropometric and sound features are denoted by Xi (points) and weighted by ai; and using one of these models to calculate a predicted AHI value (tallying assigned points). See also pg. 15, lines 3-6. Additionally, pg. 19, lines 20-30 teach the classification decision of OSA or non-OSA is evaluated for each patient-matched anthropometric model using the predicted AHI value therefrom, which is then followed by calculation (tallying) of a weighted voting average (total risk score) of the classification results (points) from the different patient-matched anthropometric models.),
determining a predicted probability of suspected OSA (claim 1 and pg. 21 at lines 10-21 teaches classifying said patients as either OSA or non-OSA… using anthropometric information and other data to identify OSA individuals in need of treatment.),
assign each of the corresponding patient case records to a risk category (pg. 36 at lines 19-25 teaches weighted classification decisions of each individual were averaged to result in the final classification decision; any value >0 or <0 was classified to OSA or non-OSA groups (risk category), respectively) in accordance with risk thresholds applied to the predicted probability (pg. 36, lines 5-11 teaches in order to have multiple thresholds for each feature and thereby overcome the complexity and heterogeneity, a Random Forest classifier is preferably used.), and
the assigned risk category for the corresponding patient case records (see previous citations).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla to perform data collection, data processing and machine learning tasks and to use this information as part of systems and methods for screening obstructive sleep apnea as taught by Moussavi, with the motivation of improving techniques and technology for obstructive sleep apnea (OSA) patient screening (see Moussavi at pg. 3, lines 1-9; pg. 31, lines 21-30; and pg. 37, lines 7-15).
Shukla/Moussavi does not teach determine a follow-up procedure for each of the corresponding patient case records in accordance with the assigned risk category for the corresponding patient case records and a system capacity limit.
Merkin teaches
determine a follow-up procedure for each of the corresponding patient case records (Abstract teaches through risk and disease stratification, member profiling, inter disciplinary team follow-up and patient education… formulating and executing individualized care plans. Fig. 1A, [0056]-[0058] teach remote patient screening 50 that takes into consideration the aggregated healthcare data compiled in step 30 (patient case record). Fig. 1A, [0059], [0062], [0065] teach determining that treatment is warranted… further determining and providing suitable home-based care/treatment 90 or authorizing and scheduling treatment at a “brick-and-mortar” facility 110. Further, [0084]-[0087] teach the appropriate care program is recommended based on a point system and total point calculation, as follows: a. Self-Management (Score<=12), b. Complex Case Management (Score 13-16), c. Palliative/Hospice (Score=> 17) (in accordance with the assigned risk category).)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi to perform treatment recommendation for one or more patients based on their aggregated healthcare data and to use this information as part of a healthcare administration method for complex case and disease management as taught by Merkin, with the motivation of improving healthcare delivery efficiency, efficacy, quality and coordination, e.g., for patients with Obstructive Sleep Apnea (see Merkin at Abstract and para. 0095, 0131, 0138).
Shukla/Moussavi/Merkin does not teach determine a system capacity.
Wallach teaches
determine a system capacity limit ([0027] teaches patients are provided with the means to allow the test to be ordered and delivered, e.g., a home sleep apnea test device. [0061] teaches a check of an inventory count database 395 is made to determine whether inventory is available… If the device cannot be shipped immediately due to a lack of inventory (determining a system capacity limit), the order is placed in the backlog ship queue 314.)
See Applicant’s disclosure at 0056 (“The capacity limit is based on logistics and resources of being able to reach out and monitor patients… the diagnostic capacity limit (or confirmation target) is expressed as a rate indicating the number of patients that can be referred to follow up diagnostics (e.g., a diagnostic… Sleep Study using, for example, the SMU 102) in a given amount of time… For example, there may be 5 SMUs with a capacity of 2 patient cases/per day”).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi/Merkin to check an inventory count of home sleep apnea test devices and determine that inventory is not available due to a lack of inventory (i.e., a determined system capacity limit) and to use this information as part of a secure medical test and result delivery system as taught by Wallach, with the motivation of improving medical test device ordering, delivery, and retrieval (see Wallach at Abstract), securing data management (see Wallach at para. 0002), and improving sleep testing including cost reduction and promoting continuity of patient care by performing at home testing (see Wallach at para. 0005-0009).
Re. Claim 2, Shukla/Moussavi/Merkin/Wallach teaches the screening system of claim 1, wherein the at least one controller is further configured to (see claim 1 prior art rejection. See also Shukla [0136]) render the determined follow-up procedure on a rendering device of the system under the control of the at least one controller (Shukla Fig. 4, [0081], [0141] teaches utilizing the workstation 310 to connect to a layered, distributed computing environment 320… includes a visualization layer 330 responsible for delivery of comprehensive results on the displays. Merkin Fig. 1A, [0062], [0065] teaches determining the appropriate treatment.)
Re. Claim 3, Shukla/Moussavi/Merkin/Wallach teaches the screening system of claim 2, wherein the at least one controller is further configured to (see claim 1 prior art rejection. See also Shukla [0136]) render one or more of the total risk score, the predicted probability of suspected OSA, and the determined risk category on the rendering device (see claim 1 prior art rejection. Shukla Fig. 4, [0081], [0141] teaches utilizing the workstation 310 to connect to a layered, distributed computing environment 320… includes a visualization layer 330 responsible for delivery of comprehensive results on the displays.)
Re. Claim 4, Shukla/Moussavi/Merkin/Wallach teaches the screening system of claim 1, wherein the at least one controller is further configured to determine an estimated risk (see claim 1 prior art rejection) as at least one of odds, log odds, and probability based upon a logistic function of the determined total risk score for each of the corresponding patient case records (Shukla [0063], [0066] teaches constructing and tuning a classifier / machine learning algorithm, which can be utilized with one or more of an RF grouping algorithm and a log regression (a logistic function) … such that the algorithm can distinguish data comprising the disease event from data that does not comprise the disease 160… may assign probabilities to various records (for each of the corresponding patient case records) … when classifying an individual with a given condition utilizing the classifier, the program code may indicate a probability of a given condition with a rating on a scale (the determined total risk score), for example, between 0 and 1, where 1 would indicate a definitive presence.)
Re. Claim 5, Shukla/Moussavi/Merkin teaches the screening system of claim 1, wherein the at least one controller is further configured to […] (see claim 1 prior art rejection).
Shukla/Moussavi/Merkin does not teach determine a system capacity limit.
Wallach teaches
determine a system capacity limit ([0027] teaches patients are provided with the means to allow the test to be ordered and delivered, e.g., a home sleep apnea test device. [0061] teaches a check of an inventory count database 395 is made to determine whether inventory is available… If the device cannot be shipped immediately due to a lack of inventory (determining a system capacity limit), the order is placed in the backlog ship queue 314.)
See Applicant’s disclosure at 0056 (“The capacity limit is based on logistics and resources of being able to reach out and monitor patients… the diagnostic capacity limit (or confirmation target) is expressed as a rate indicating the number of patients that can be referred to follow up diagnostics (e.g., a diagnostic… Sleep Study using, for example, the SMU 102) in a given amount of time… For example, there may be 5 SMUs with a capacity of 2 patient cases/per day”).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi/Merkin to check an inventory count of home sleep apnea test devices and determine that inventory is not available due to a lack of inventory (i.e., a determined system capacity limit) and to use this information as part of a secure medical test and result delivery system as taught by Wallach, with the motivation of improving medical test device ordering, delivery, and retrieval (see Wallach at Abstract), securing data management (see Wallach at para. 0002), and improving sleep testing including cost reduction and promoting continuity of patient care by performing at home testing (see Wallach at para. 0005-0009).
Re. Claim 7, Shukla/Moussavi/Merkin/Wallach teaches the screening system of claim 1, wherein the at least one controller is further configured to (see claim 1 prior art rejection. See also Shukla [0136]) render a diagnostic follow-up based on the assigned category risk (Merkin Fig. 1A, [0059], [0062], [0065] teach determining that treatment is warranted… further determining and providing suitable home-based care/treatment 90 or authorizing and scheduling treatment at a “brick-and-mortar” facility 110 (rendering a diagnostic follow-up). Moussavi pg. 36 at lines 19-25 teaches… weighted classification decisions of each individual were averaged to result in the final classification decision; any value >0 or <0 (classification risk) was classified to OSA or non-OSA groups, respectively.)
Re. Claim 9, the subject matter of claim 9 is essentially defined in terms of a system, which is technically corresponding to system claim 1. Since claim 9 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Re. Claim 10, the subject matter of claim 10 is essentially defined in terms of a system, which is technically corresponding to system claim 2. Since claim 10 is analogous to claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Re. Claim 11, the subject matter of claim 11 is essentially defined in terms of a system, which is technically corresponding to system claim 3. Since claim 11 is analogous to claim 3, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 3.
Re. Claim 15, the subject matter of claim 15 is essentially defined in terms of a system, which is technically corresponding to system claim 7. Since claim 15 is analogous to claim 7, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 7.
Re. Claim 17, the subject matter of claim 17 is essentially defined in terms of a method, which is technically corresponding to system claim 1. Since claim 17 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Re. Claim 18, the subject matter of claim 18 is essentially defined in terms of a method, which is technically corresponding to system claim 2. Since claim 18 is analogous to claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 2.
Re. Claim 20, Shukla/Moussavi teaches the method of claim 16, comprising acts of:
[…]; and
[…] in accordance with the assigned risk category or risk level for the corresponding patient case records (Moussavi pg. 36 at lines 19-25 teaches weighted classification decisions of each individual were averaged to result in the final classification decision; any value >0 or <0 was classified to OSA or non-OSA groups (risk category), respectively) and […].
Shukla/Moussavi does not teach
determining a cumulative capacity of a plurality of monitoring units to conduct tests,
determining a follow-up procedure for each patient case record in accordance with the assigned risk category or risk level for the corresponding patient case records and in accordance with the determined cumulative capacity of the plurality of monitoring units.
Merkin teaches
determining a follow-up procedure for each patient case record in accordance with the assigned risk category or risk level for the corresponding patient case records (Abstract teaches through risk and disease stratification, member profiling, inter disciplinary team follow-up and patient education… formulating and executing individualized care plans. Fig. 1A, [0056]-[0058] teach remote patient screening 50 that takes into consideration the aggregated healthcare data compiled in step 30 (patient case record). Fig. 1A, [0059], [0062], [0065] teach determining that treatment is warranted… further determining and providing suitable home-based care/treatment 90 or authorizing and scheduling treatment at a “brick-and-mortar” facility 110. Further, [0084]-[0087] teach the appropriate care program is recommended based on a point system and total point calculation, as follows: a. Self-Management (Score<=12), b. Complex Case Management (Score 13-16), c. Palliative/Hospice (Score=> 17) (in accordance with the assigned risk category or risk level).)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi to perform treatment recommendation for one or more patients based on their aggregated healthcare data and a risk calculation and to use this information as part of a healthcare administration method for complex case and disease management as taught by Merkin, with the motivation of improving healthcare delivery efficiency, efficacy, quality and coordination, e.g., for patients with Obstructive Sleep Apnea (see Merkin at Abstract and para. 0095, 0131, 0138).
Shukla/Moussavi/Merkin does not teach
determining a cumulative capacity of a plurality of monitoring units (102) to conduct tests, or in accordance with the determined cumulative capacity of the plurality of monitoring units (102).
Wallach teaches
determining a cumulative capacity of a plurality of monitoring units (102) to conduct tests ([0027] teaches patients are provided with the means to allow the test to be ordered and delivered, e.g., a home sleep apnea test device. Fig. 3A, [0058], [0061], [0074], [0081] teach a check of an inventory count database 395 is made to determine whether inventory is available… and updating inventory counts of test devices available when a device is returned (determine a cumulative capacity of a plurality of monitoring units to conduct tests).), or in accordance with the determined cumulative capacity of the plurality of monitoring units (102) (see previous citations).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi/Merkin to check and update inventory counts of home sleep apnea test devices and to use this information as part of a secure medical test and result delivery system as taught by Wallach, with the motivation of improving medical test device ordering, delivery, and retrieval (see Wallach at Abstract), securing data management (see Wallach at para. 0002), and improving sleep testing including cost reduction and promoting continuity of patient care by performing at home testing (see Wallach at para. 0005-0009).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Shukla in view of Moussavi, Merkin, Wallach and Liu et al. (WO 2020/139845 A1; “Liu” herein) (see IDS document).
Re. Claim 6, Shukla/Moussavi/Merkin/Wallach teaches the screening system of claim 5, wherein the at least one controller is further configured to communicate with (Shukla [0130] teaches the remote computer may be connected to the user’s computer through any type of network) […] and to determine a cumulative capacity of the at least one SMU to conduct sleep tests (Wallach Fig. 3A teaches determining that the device can be shipped immediately (YES) 316) and base the system capacity limit (Wallach Fig. 3A, [0061] teaches a check of an inventory count database 395 is made to determine whether inventory is available… and determining there is no inventory available 314 (base the system capacity limit)) in accordance with a sum of the determined cumulative capacity (Wallach Fig. 3A, [0081] teaches updating inventory counts in the delayed ship queue and backlog (determine the sum of the determined cumulative capacity).)
Shukla/Moussavi/Merkin/Wallach does not explicitly teach communicate with at least one sleep monitoring unit (SMU).
Liu teaches
communicate with at least one sleep monitoring unit (SMU) (Abstract, [0016] teach continuous positive airway pressure (CPAP) therapy has been used to treat OSA with devices used to provide such therapy. Fig. 4C, [0306] teach the device 4000 communicates with the user computing device 7050.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi/Merkin/Wallach to demonstrate communication capabilities of a CPAP device and to use this information as part of systems and methods for prediction of usage or compliance to various devices and services, with the motivation of improving patient usage or compliance with a respiratory therapy device and improving comfort, cost, efficacy, and ease of use and manufacturability of the device (see Abstract and para. 0017, 0019, 0071).
Claims 8, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shukla in view of Moussavi.
Re. Claim 8, the subject matter of claim 8 is essentially defined in terms of a system, which is technically corresponding to the narrower system claim 1. Since claim 8 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Re. Claim 12, the subject matter of claim 12 is essentially defined in terms of a system, which is technically corresponding to system claim 4. Since claim 12 is analogous to claim 4, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Re. Claim 16, the subject matter of claim 16 is essentially defined in terms of a method, which is technically corresponding to system claims 1 and 8. Since claim 16 is analogous to claims 1 and 8, it is similarly analyzed and rejected in a manner consistent with the rejection of claims 1 and 8.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Shukla in view of Moussavi and Wallach.
Re. Claim 13, the subject matter of claim 13 is essentially defined in terms of a system, which is technically corresponding to system claim 5. Since claim 13 is analogous to claim 5, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shukla in view of Moussavi, Wallach and Liu.
Re. Claim 14, the subject matter of claim 14 is essentially defined in terms of a system, which is technically corresponding to system claim 6. Since claim 14 is analogous to claim 6, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Shukla in view of Moussavi and Merkin.
Re. Claim 19, Shukla/Moussavi teaches the method of claim 16, comprising acts of:
[…] one or more of the total risk score (the rating on the scale indicating the probability of the given condition), the estimated risk predicted probability of the undiagnosed medical condition (the probability of the given condition), and the determined risk category or risk level (the respectively classified OSA or non-OSA group) (see analogous claim 1 prior art rejection. The Examiner notes only one of these is required for the claim to be met.); and
storing […] on a storage device ([0136] teaches the system can operate in a peer-to-peer mode where certain system resources, including one or more databases, are shared, but the program code executable by one or more processors is loaded locally on each computer/workstation. [0139] teaches the host computer includes a memory to store instructions and data… Execution may include loading data into a register from memory or storing data back to memory from a register.)
Shukla/Moussavi may not teach annotating the updated PCD with one or more of the total risk score, the estimated risk predicted probability of the undiagnosed medical condition, and the determined risk category or risk level, or the annotated PCD.
Merkin teaches
annotating the updated PCD with one or more of the total risk score, the estimated risk predicted probability of the undiagnosed medical condition, and the determined risk category or risk level, and the annotated PCD ([0011] teaches each patient’s membership/enrollee data is updated on a periodic basis. Fig. 2, [0131], [0134] teaches an enrollment roster for obstructive sleep apnea (OSA) (PCD)… identifies the list of potential patients to be enrolled to each disease registry… The list for each disease registry is generated using patient's conditions, laboratory data and medication information whereby the patient's conditions, laboratory data and medication information is associated with a point system… The criteria and points related to each criteria (the total risk score) are displayed on the enrollment roster (annotation)… The enrollment roster also includes the tier (the determined risk category or risk level). Additionally, Fig. 27D teaches a user counts may enter the total score (annotating the updated PCD with one or more of the total risk score).)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the method, computer program product, and system identifying a probability of a medical condition in a patient of Shukla/Moussavi to update enrollee data on a periodic basis for one or more patients, enter enrollee data such as a total score, and generate and display an enrollment roster for obstructive sleep apnea / a list of potential patients and to use this information as part of a healthcare administration method for complex case and disease management as taught by Merkin, with the motivation of improving healthcare delivery efficiency, efficacy, quality and coordination, e.g., for patients with Obstructive Sleep Apnea (see Merkin at Abstract and para. 0095, 0131, 0138).
Response to Arguments
Application Data Sheet
Regarding the Title of the application, the Applicant has submitted a corrected ADS. The Examiner has filed a bibliographic data sheet reflecting the change to the Title of the Application. The objection is withdrawn.
Rejections under 35 U.S.C. §112(b)
Regarding the rejections, the Applicant has amended the claims to overcome the previous issues of indefiniteness. All 112(b) rejections are withdrawn.
Rejections under 35 U.S.C. §101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
A1. “Examiner argues that the alleged abstract idea as "a screening system for an undiagnosed medical condition (e.g., obstructive sleep apnea)" in which, ''but for the recitation of generic computer component language," a person could query medical records, form and update patient cohort data, exclude disqualified records, determine risk factors, determine a total risk score and predicted probability, assign each patient to a risk category, and determine a follow-up procedure.” (Remarks, pgs. 9-10).
Re. argument A1: The Examiner respectfully submits that the identified abstract idea is the underlined portions, i.e., a screening system for an undiagnosed medical condition (for example, OSA), as underlined. A clinician could follow rules (a protocol or procedure) for querying records to be pulled by a nurse in a records room, form and update patient cohort data, exclude disqualified records, determine risk factors, determine a total risk score, predict probability, assign each patient to a risk category, and determine a follow-up procedure (i.e., managing personal behavior including rule following) in the manner underlined in the identified abstract idea. Of course, this abstract idea may be applied on a generic computer using records stored thereon or on a generic computer network.
A2. “what remains is not a business method, legal interaction, or social-behavior rule set; it is a technical workflow for processing and analyzing multi-source electronic medical data (MSI), computing disease-specific risk scores and probabilities, and coordinating diagnostic use of finite monitoring resources” (Remarks, pg. 10).
Re. argument A2: The Examiner respectfully submits that the underlined portions encompass the judicial exception and there is nothing self-evident that would remove the claims from being directed to a judicial exception. For example, the steps are not steps describing the technical details of a new machine learning model. We must now look to discussion of the additional elements for a practical application or an inventive concept.
A3. “Independent claim 1, for example, recites a specific screening system in which at least one controller undertaking multi-source data querying, MSI-based risk-factor detection, point/weight tallying, probabilistic modeling, and capacity-limited follow-up is a concrete, technology-based pipeline for operating on electronic medical data and allocating diagnostic resources; it is not a fundamental economic principle, not a commercial or legal interaction, and not a generic rule for managing personal behavior or interpersonal relationships” (Remarks, pg. 10).
Re. argument A3: The Examiner respectfully submits the basis of rejection as necessitated by amendment. Also, a person (e.g., a clinical investigator) would not be limited in ability to generic rule following. People involved in meaningful scientific and technologic work follow highly detailed rules or instructions.
A4. “First, the claims recite a specific improvement in a technical field. Specifically, computer-implemented screening and triage systems for undiagnosed medical conditions, particularly obstructive sleep apnea (OSA) … Here, a multi-source MSI screening and diagnostic scheduling system is improved. (see, e.g., SPEC [0049]-[0051], [0057]-[0058], [0068])” (remarks, pg. 12).
Re. argument A4: The Specification does not appear to disclose how any of the additional elements may be improved and the claims fail to recite any improvement to these additional elements. Para. 0049 describes extracting, querying, organizing, storing, updating functions at a high level of generality. One or more of these types of functions are recited. Assuming arguendo that one of these functions is purported as a technical solution to a technical problem, these functions are not of sufficiently technical detail. Para. 0050-0051 describe high level selecting and linking functions that are not recited in the claims. Para. 0057-0058 describes high level data computation and reporting. Para. 0068 describes patient risk stratification without describing how it could be done in a way to improve an additional element, such as the recited controller of claims 1 and 8. The Examiner does not see an alleged improvement to the additional elements of the data source system(s) or the screening system controller. The Examiner notes that claim 16 only contains the additional element of at least one data source. The Examiner also cannot find an improvement to the system capacity (see specification at para. 0030) at least because the limitation of the number of tests an SMU can run in a day is not a technical problem, it is an administrative problem due to the human constraint of the number of hours a person sleeps in a day, which limits the testing device turnover rate and the cumulative capacity.
A5. “Second, the claimed controller and data sources are not incidental or post-solution activities…” (remarks, pg. 12).
Re. argument A5: The Examiner respectfully submits the basis of rejection as necessitated by amendment. Given the BRI, the additional elements do not provide a practical application or significantly more. The interaction between the controller and the plurality of selected data sources is recited at a high level of generality. The involvement of the plurality of selected data sources amounts to locations to which data (e.g., a query) is received. Technical detail of the querying function is omitted such that it is not clear how querying data would improve the controller within the meaning of the word. This querying of the additional element amounts to mere data gathering (and a pre-solution activity).
The Examiner respectfully asserts again that there is no alleged improvement to the additional elements of the data source system(s) or the screening system controller, such that there is no recited improvement in the claims. One way a case may recite a technical improvement is by reciting a technical solution to a technical problem supplied by the Applicant’s disclosure. See response to argument A4. Para. 0043, 0047-0051, 0056, 0068 do not describe a technical solution let alone a technical problem related to any recited computer implementation by any recited additional element. Thus, the claims recite generic computer implementation along with the extra-solution activity.
A6. “Lastly, the claims do more than gather and display data; they transform raw, heterogeneous MSI into a different kind of information…” (remarks, pg. 12).
Re. argument A6: See response to arguments A4-A5. Para. 0068-0071 do not describe a technical solution let alone a technical problem. The Examiner respectfully submits that the controller is recited at a high level of generality and is a generic computer component. The use of the controller for querying, forming patient cohort data, disqualifying data, updating patient cohort data, determining risk factors, determining a total risk score (identifying data, tallying data), determining a predicted probability, assigning a risk category and/or determining a follow-up procedure, as drafted, does not provide an improvement within the meaning of that word; the controller is not made to physically run faster, utilize fewer resources, or run more efficiently. Utilizing a computer or tool to perform an abstract idea in a faster or more accurate manner is utilizing a computer as designed and is insufficient to provide a practical application or significantly more. See Alice Corp. Also, this is not a practical application by any measure provided for in the 2019 Patent Eligibility Guidance. Mere instructions to apply the abstract idea on a generic computer cannot provide a practical application or an inventive concept. MPEP 2106.05(f).
The Examiner notes the courts have held that a transformation of data is not a “transformation” sufficient to render a claim subject matter eligible (“For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”) See MPEP 2106.05(c). This “machine or transformation test” is replaced by the most recent version of the MPEP including 2106.05(f).
Re. “a concrete clinical scheduling problem”, this described problem is not a technical problem caused by the technological environment to which the claims are confined (a well-known, general-purpose computer). It is, at best, an administrative problem. Difficulty in sharing, manipulating, and handling data (e.g., for scheduling) between one or more healthcare entities existed well-prior to the advent of clinical information systems. Difficulty in scheduling resource-based appointments (e.g., resource limited medical testing) existed well-prior to the advent of clinical information systems. The description of the scheduling of patients by one or more cooperating healthcare entities being “difficult” does not indicate how or why this is a technical problem. It is also unclear if the Applicant’s claimed invention actually solves this purported problem or increases it; for example, there is no nexus between the querying of data from any of the selected sources by the controller being difficult, slow, or not visible and the claimed invention. As another example, the availability of testing resources for sleep testing is limited by the need to conduct sleep tests on humans whose sleep requirements control the length of SMU testing.
A7. “The amended claims recite a specific, non-generic architecture for: converting heterogeneous claims/EMR data into interpretable risk-factor tallies; mapping those tallies to probabilistic risk estimates for an undiagnosed condition; and allocating limited diagnostic capacity based on both risk and system capacity. The Previous Action does not identify any prior-art teaching of this particular combination, nor does it provide any evidence that such MSI-based risk-factor tallying, logistic-probability computation, and capacity-limited triage were well-understood, routine, and conventional in screening systems at the time of the invention…” (remarks, pg. 13-14).
Re. argument A7: The Examiner respectfully submits that the additional elements are recited at a high-level of generality and as performing conventional computer implementation. Considering the additional elements, alone or in combination, there is no specific limitation other than what is well-understood, routine and conventional activity in the field, such that no unconventional steps confine the claim to a particular useful application. See MPEP § 2106.05(I)(A). Rather, the limitations simply append well-understood, routine and conventional activities, specified at a high-level of generality, to the judicial exception. See again MPEP § 2106.05(I)(A). Further, while the process may be detailed (i.e., improved), only additional elements can provide a practical application or an inventive concept (“significantly more”).
Also, the Examiner respectfully submits that the additional elements neither provide a practical application nor significantly more alone or in combination. Further… “[a]s 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." MPEP 2016.05(I) (internal quotations omitted).
Re. WURC analysis: The Examiner respectfully submits that only the additional element of the “plurality of selected data sources” required (and received) WURC analysis at Step 2B, since only this additional element has been identified as reciting well-understood, routine, and conventional activity. See MPEP 2106.05(II). Therefore, the argument is not persuasive. Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two.
Regarding the rejection of Claims 2-20, the Applicant has not offered any or any additional arguments with respect to these claims other than to reiterate the argument(s) present for the claim(s) from which they depend or are analogous to. As such, the rejection of these claims is also respectfully maintained.
Rejection under 35 U.S.C. §103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
B1. “The Previous Action maps the "total risk score" limitation primarily to Shukla at [0052], [0063], [0066], and [0093], and treats the numerical probability output of Shukla’s classifier as the claimed "total risk score." (See Previous Action at pp. 14-15.) Applicant respectfully submits that, even in view of the secondary references, the cited art does not teach or suggest the specific scoring and capacity-aware follow-up now recited in claims 1, 8, and 16.” (Remarks, pg. 15-16).
Re. argument B1: The Examiner respectfully submits the basis of rejection as necessitated by amendment. Given the BRI, Shukla, Moussavi, Merkin and Wallach render obvious the recited feature in the claims as drafted. Shukla teaches utilizing a classifier to classify (i.e., categorize) records of individuals based on the presence or absence of the given condition (i.e., risk) indicating a probability of a given condition (the recited “determining a total risk score for each of the patient case records”). Shukla teaches this determination depends on input of the patient features (“factors”) including distinguishing features (“risk factors”) in their data set to a HAE computer model and subsequent output of the likelihood for the patient having an undiagnosed condition (the recited “determining a total risk score… corresponding to the determined risk factors”, “as identified from the MSI”, “including at least one of diagnosis codes”). Moussavi renders obvious determining the risk by tallying at least one of assigned points and weights, corresponding to the determined risk factors (see at least pg. 1 at lines 11-19; pg. 14 at lines 16-24; pg. 15 at lines 9-21; pg. 15 at lines 3-6); and specifically assigning the patient case records to a risk category based on the total risk score (see pg. 36 at lines 19-25) by applying risk thresholds to the predicted probability (pg. 36 at lines 5-11). Merkin renders obvious determining a follow-up procedure for each of the corresponding patient case records (see basis of rejection, supra). This determination is weakly associated by the claim language to Moussavi’s final classification result for each individual as OSA or non-OSA (“the assigned risk category for the corresponding patient case records”) and Wallach’s inventory check and backlog mechanism (“system capacity limit”). Through risk and disease stratification, member profiling, and inter disciplinary team follow-up (see Merkin at abstract), the patient care plan can be formulated and executed according to a patient’s risk category and the inventory check as these information factor into a human decision.
Re. “[T]his stands in contrast to…” and “the tallying of assigned points / weights over MSI-defined risk factors” (emphasis omitted), the Examiner respectfully asserts that the applied combination of references (as necessitated by amendment) teaches or renders obvious the claimed limitations given the BRI in light of the specification, as explained above and in the basis of rejection.
B2. “the Previous Action also does not identify any teaching in Shukla, Moussavi, or Merkin that the follow-up procedure is determined "in accordance with ... a system capacity limit" as now recited in claims 1, 9, and 17” (remarks, pg. 16)
Re. argument B2: In addition, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been prima facie obvious to one of ordinary skill in the art to have modified the primary reference of Shukla to assign the risk value for each individual to a classification group (see Moussavi), with the motivation of improving OSA patient screening (see Moussavi at least pg. 3, lines 1-9); to determine the current number of test devices in inventory (see Wallach), with the motivation of improving medical test device ordering (see Wallach at least para. 0002); and to determine a follow-up procedure for each of the corresponding patient case records (see Merkin), with the motivation of improving such healthcare delivery for patients who need it the most (see Merkin at least at Abstract).
B3. “Finally, Moussavi and Merkin do not cure the deficiencies of Shukla. Moussavi is focused on classification of OSA based on wakeful tracheal breathing sounds and anthropometrics using Random Forest models and voting across subgroups (Moussa vi Abstract; Description, "Systems and Processes" and "Experimental Support - Experiment A," pp. 20-39). It does not use MSI (diagnosis codes, procedure codes, medication codes, symptoms, signs) as patient-level risk factors, does not assign per-factor points/weights, and does not tally such points/weights into a total risk score” (original emphasis omitted) (emphasis added) (remarks, pg. 16).
Re. argument B3: Shukla teaches analyzing an identified feature set, for example, feeding the identified feature set into a classifier to classify records of individuals. The identified feature set for this classification task includes distinguishing features such as diagnosis codes, procedures, drugs, providers, and locations (“MSI”). This identified combination of codes (“risk factors”), mutual information of some individuals characterizing the given condition, is selected/identified (“determine risk factors in accordance with comparison of the MSI for corresponding patient case records with risk factors”) for use in separating individuals with the given condition from the general population… and also in producing a numerical score for each individual (“determine a total risk score… corresponding to the determined risk factors… as identified from the MSI…”)
Moussavi renders obvious the feature of determining a total risk score “by tallying at least one of assigned points and weights, corresponding to the determined risk factors” for OSA. See at least pg. 1 at lines 11-19, pg. 14 at lines 16-24, pg. 15 at lines 9-21 and pg. 15 at lines 3-6.
Merkin is not relied upon for this feature.
The Examiner notes that one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
B4. “Finally, Applicant respectfully submits that independent claim 1 is directed to a screening system for obstructive sleep apnea (OSA). Independent claim 8, in combination with claim 9, and independent claim 16, in combination with claim 17, recite substantially similar limitations but in the context of a screening system and method, respectively, for a given undiagnosed medical condition rather than OSA specifically. For at least these reasons, the combination of Shukla, Moussavi, and Merkin fails to teach or suggest the amended limitations of claims 1, 8, and 16” (remarks, pg. 17).
Re. argument B4: The Examiner respectfully submits that the prior art references applied to the narrower claim 1 still teach the claimed features of the broader claims 8-9 and 16-17. That the references teach additional features (narrower features) is immaterial to the facts. Undiagnosed (by a doctor) OSA still reads on the broadly claimed “undiagnosed medical condition” recited in the broader claims. Further, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Regarding the rejection of Claims 2-20, the Applicant has not offered any or any additional arguments with respect to these claims other than to reiterate the argument(s) present for the claim(s) from which they depend or are analogous to. As such, the rejection of these claims is also respectfully maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Razavian et al. (US 2017/0308981 A1) for teaching patient condition identification and treatment.
Lewis (US 2018/0166174 A1) for teaching disease mitigation and elimination health learning engine.
Laschet et al. (US 2020/0005940 A1) for teaching system and method for generating a care services combination for a user.
Masters et al. (WO 2020/081866 A1) for teaching methods for targeted assessment and treatment of chronic obstructive pulmonary disease and acute events and mortality associated therewith.
Lau et al. (US 10,512,429 B2) for teaching discrimination of Cheyne-stokes breathing patterns by use of oximetry signals.
Den Teuling et al. (US 2019/0088369 A1) for teaching determining patient status based on measurable medical characteristics.
Shugg et al. (US 2021/0007659 A1) for teaching system and method for sleep disorders: screening, testing and management.
Tiron et al. (US 2022/0007965 A1) for teaching methods and apparatus for detection of disordered breathing.
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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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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