Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In the reply filed 12/08/2025, the following has occurred: no claims have been amended, added, or canceled. Now, claim 1 remains pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claim 1 recites receiving a set of patient data corresponding to a first patient, the set of patient data including at least one physiological input based on results of at least one physiological test administered to the first patient; determining, based on the set of patient data, whether a set of one or more data-correlation criteria are satisfied; in accordance with a determination that the set of one or more data-correlation criteria are satisfied: determining a first indication of whether the first patient has one or more respiratory conditions selected from a group consisting of asthma and chronic obstructive pulmonary disease (COPD) based on an application of a first diagnostic model to the set of patient data; and outputting the first indication; in accordance with a determination that the set of one or more data-correlation criteria are not satisfied: determining a second indication of whether the first patient has one or more respiratory conditions selected from a group consisting of asthma and chronic obstructive pulmonary disease (COPD) based on an application of a second diagnostic model to the set of patient data; and outputting the second indication.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the recitation of generic computer components, the claims encompass a user manually acquiring physiological patient data, determining if data correlations from the patient data are satisfied, determining and outputting an indication of whether the patient has asthma and/or COPD. This could encompass a doctor following rules or instructions to diagnose a patient in a hospital.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with generally linking the abstract idea to a particular technological environment.
Claim 1 recites the following generic computer components configured to implement the abstract idea: “one or more processors; one or more input elements; memory; and one or more programs stored in the memory, the one or more programs including instructions,” “one or more input elements.” The written description discloses that the recited computer components encompass generic components including “a client system (e.g., 102) communicates with a server-side application (e.g., the service) on a cloud computing system (e.g., 112) using an application programming interface” (see paragraph 0030). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-26, directly or indirectly, recite the following additional elements that generally link the abstract idea to a particular technological environment: “unsupervised machine learning algorithm,” first and second “supervised machine learning algorithm.” These elements are recited at a high degree of generality and merely function to link the abstract idea to a particular technological environment.
Claims 1-26: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abeyratne, US Patent Application Publication No. 2021/0076977 in view of An, US Patent Application Publication No. 2013/0116578 and further in view of Cohen, US Patent Application Publication No. 2018/0068083.
As per claim 1, Abeyratne teaches a system, comprising: one or more processors; one or more input elements; memory; and one or more programs stored in the memory, the one or more programs including instructions (see paragraph 0035; each block implemented using a programed computer) for: receiving, via the one or more input elements, a set of patient data corresponding to a first patient, the set of patient data including at least one physiological input based on results of at least one physiological test administered to the first patient (see paragraphs 0006-0010; patient is tested for respiratory disease); determining a first indication of whether the first patient has one or more respiratory conditions selected from a group consisting of asthma and chronic obstructive pulmonary disease (COPD) based on an application of a first diagnostic model to the set of patient data (see paragraph 0181; clinical signs are applied to first model to diagnose respiratory disease of patient, including COPD or asthma (paragraph 0178)); and outputting the first indication (see paragraph 0042; diagnosis output by display); determining a second indication of whether the first patient has one or more respiratory conditions selected from a group consisting of asthma and chronic obstructive pulmonary disease (COPD) based on an application of a second diagnostic model to the set of patient data (see paragraph 0185; different models for diagnosing condition, including COPD or asthma (paragraph 0178)); and outputting the second indication (see paragraph 0042; diagnosis output by display).
Abeyratne does not explicitly teach determining, based on the set of patient data, whether a set of one or more data-correlation criteria are satisfied, wherein the set of one or more data-correlation criteria are based on an application of an unsupervised machine learning algorithm to a first historical set of patient data that includes data from a first plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more respiratory conditions; in accordance with a determination that the set of one or more data-correlation criteria are satisfied: wherein the first diagnostic model is based on an application of a first supervised machine learning algorithm to a second historical set of patient data that includes data from a second plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more respiratory conditions; in accordance with a determination that the set of one or more data-correlation criteria are not satisfied; wherein the second diagnostic model is based on an application of a second supervised machine learning algorithm to a third historical set of patient data that includes data from a third plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more respiratory conditions, and wherein the third historical set of patient data is different from the second historical set of patient data.
An teaches determining, based on a set of patient data, whether a set of one or more data-correlation criteria are satisfied, wherein the set of one or more data-correlation criteria are based on an application of an algorithm to a first historical set of patient data that includes data from a first plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more conditions (see paragraph 0198; a certain algorithm is selected for implementation based on whether a risk analysis score (data-correlation criteria) is in a first range (criteria are satisfied), risk analysis may be based on patient population characteristics (paragraph 0098)); in accordance with a determination that the set of one or more data-correlation criteria are satisfied: applying a first diagnostic model, wherein the first diagnostic model is based on an application of a first algorithm to a second historical set of patient data that includes data from a second plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more conditions (see paragraph 0198; a first algorithm, such as logistic regression model, applied based on risk analysis score in first range; paragraph 0098; algorithm applied to patient population); in accordance with a determination that the set of one or more data-correlation criteria are not satisfied: applying a second diagnostic model, wherein the second diagnostic model is based on an application of a second algorithm to a third historical set of patient data that includes data from a third plurality of patients having one or more phenotypic differences, the phenotypic differences including at least data regarding one or more conditions, and wherein the third historical set of patient data is different from the second historical set of patient data (see paragraph 0198; second algorithm such as neural network model, applied based on risk analysis score in in a second range (not satisfying criteria); paragraph 0098; algorithm applied to patient population). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply different models based on a criteria being satisfied in the modeling system of Abeyratne with the motivation of providing improved management of patient-related data (see paragraph 0005 of An).
Additionally, Abeyratne and An does not explicitly identify unsupervised machine learning and supervised machine learning models as the models that are applied. Cohen teaches applying unsupervised machine learning and supervised machine learning models to respiratory data to diagnose COPD and asthma (see paragraphs 0264 and 0360). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply supervised and unsupervised machine learning models in the system of Abeyratne and An with the motivation of taking advantage of different aspects of training models (see paragraph 0281-0282 of Cohen).
Response to Arguments
In the remarks filed 18/667,421, Applicant argues (1) the broadest reasonable interpretation of claim 1 does not encompass Certain Methods of Organizing Human Activity; (2) the rejection does not identify the specific limitations that recite the abstract idea; (3) the claim requires determining whether a set of one or more data-correlation criteria are satisfied, and based on the determination, determining a first indication based on a first diagnostic model or determining a second indication based on a second diagnostic model as part of the abstract idea, which is not an abstract idea; (4) the rejections only address the additional elements individually, not in combination; (5) page 5 highlights limitations that provide and improvement in the technical field of computational disease modeling and prediction; (6) the additional elements are not well-understood, routine, and conventional and this is not addressed in the rejection, similar to published Example 29; (7) An does not teach the datasets that the models are based on and a third historical set of patient data is different from the second historical set of patient data; (8) the 103 rejections do not provide adequate reasoning for combining the reference teachings absent hindsight reasoning.
In response to argument (1), the examiner respectfully maintains that claim 1 recites limitations that encompass Certain Methods of Organizing Human Activity. The examiner acknowledges that the claims recite computer implementation of the steps in the form of a processor and instructions for carrying out the steps. However, simply adding a generic computer processor to carry out the abstract idea does not change the identification of an abstract idea. Furthermore, the additional elements, including the generic computer components, have not been identified as part of the abstract idea, but instead as additional elements, subsequently addressed in the rejection.
In response to argument (2), the examiner respectfully disagrees. As shown at paragraph 5 above, the limitations reciting the abstract idea are identified. While, the majority of the limitations are identified as reciting the abstract idea, the rejection does not identify all of the limitations. Specifically, additional elements, including the generic computer components and machine learning models are not included, but instead are identified as additional elements, subsequently addressed in the rejection. Furthermore, the identification of these recitations as an abstract idea is further discussed at paragraph 6.
In response to argument (3), these argued recitations are identified as part of the abstract idea. As explained in the above rejections, these steps could be carried out manually by an individual following rules or instructions. Again, the examiner acknowledges that the claim recites these steps being carried out by a computer. However, as explained in the rejections, this generic computer components merely acts as a tool to implement the abstract idea.
In response to argument (4), the rejections address the additional elements in combination with the other recitations by identifying the additional elements as generic computer components used as a tool to implement the abstract idea. As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Furthermore, the three recitations of “machine learning algorithm” are identified as generally link the abstract idea to a particular technological environment. The claims do no include any additional limitations regarding these algorithms other than these algorithms being applied to certain data and the results of that application. There are, for example, no recitations regarding how or if any of the machine learning models are trained or if they have any particular data structure. Based on these broad recitations, in combination with the steps of the abstract idea, these additional elements have been found to generally link the abstract idea to a particular technological environment (i.e. generic machine learning).
In response to argument (5), while the claims may provide an improvement in differentiating between COPD and asthma, such an improvement is not a technical improvement. Applicant highlights the use of three machine learning algorithms. However, as explained in the responses above, these algorithms are recited at such a high level of generality, they provide no more technical limitation other than generally linking the abstract idea to that technical field. Rather, accurately differentiating asthma and COPD patients provides a business improvement in medical care, but does not provide a technical improvement to any technical implementation.
In response to argument (6), none of the additional elements have been identified as well-understood, routine, and conventional. In published Example 29, additional elements included obtaining a plasma sample from a human patient and detecting whether JUL-1 is present in the sample by contacting the sample with an anti-Jul-1 antibody. Pending claim 1 does not include any type of similar additional elements. As explained above, the additional elements are only limited to generic computer components and broadly recited machine learning algorithms. Therefore, the comparison to Example 29 is not found to be persuasive.
In response to argument (7), An is relied on to teach applying a first model based on application of an algorithm to historical patient data from a plurality of patients. An describes selecting a regression model to be applied based on the results of a risk analysis (paragraph 0198). As further described at paragraph 0098, risk scores as based on comparison of a patient to a patient population. Therefore, the selection and application of the linear regression model is based on historic patient data. The second model, neural network model, is similarly selected and applied. Additionally, paragraph 0198 describes the different models being selected based on the risk analysis resulting in different risk score ranges. Since, as described at paragraph 0098, the risk analysis is based on historical patient data, different resulting ranges would indicate different input patient data.
In response to argument (8), it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Further, 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). The rejection identifies that applying different models based on a criteria being satisfied, as in An, in the modeling system of Abeyratne with the motivation of providing improved management of patient-related data (see paragraph 0005 of An). While it is true that Abeyratne is also concerned with improved management of patient-related data, one of ordinary skill in the art would have looked to similar teachings, such as An, for the best selection and application of models. Similarly, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply supervised and unsupervised machine learning models in the system of Abeyratne and An with the motivation of taking advantage of different aspects of training models (see paragraph 0281-0282 of Cohen). Again, although Abeyratne and An are also concerned with data modeling, this would not prevent one of skill in the art from looking to similar data modeling schemes for selecting the best available at the time of the effective filing date.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wolz, US Patent Application Publication No. 2017/0351829, discloses selecting disease models based on historic data related to patients with similar symptoms, treatment, and progression of a disease under consideration.
Badnjevic et al., An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings, discloses a diagnostic system, modeling patient data, to differentiate among patients with asthma and COPD.
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 C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683