DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 21 is amended. Claim 22 is cancelled. Claims 1-17 and 30-31 were previously cancelled. Claims 38-40 are new. Claims 18-21, 23-29 and 32-40 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/10/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 38 and 39 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 38 recites “based on the prediction, administering a supplement or a pharmaceutical to the individual” and claim 39 recites “herein administering the supplement or the pharmaceutical entails a more aggressive remedy than the treatment.” However, the disclosure does not provide support for these limitations. The instant disclosure simply provides for analyzing of data using machine learning models, where the method is carried out via computers (Paragraphs [0006] and [0030]). Applicant cites to
Paragraph [0097] states:
Current treatments for portal hypertension either lack effectiveness for many patients or are invasive. Thus, it is desirable to have a framework for evaluating whether a particular patient is likely to progress to one or more of these conditions, as well as a prediction of the time frame of progression. With such a framework at hand, patients who are likely to progress faster toward an undesirable condition can be identified early in their progression. These patients can then be considered for more aggressive treatment in order to slow their progression. In other situations, patients with any predicted progression speed may be selected for clinical trials of new treatments (e.g., diet, supplements, and/or pharmaceuticals). The embodiments herein may include software that analyzes a database of patients, for example, and provides a ranking of these patients for inclusion in a clinical trial in order of risk of progression (trials with patients having higher risks of progression can shorten the length of the trial and reduce the placebo effect). Thus, the embodiments herein can potentially lead to improved lifespans and improved quality of life for portal hypertension patients.
Paragraph [0122] states:
Machine learning model 806 as trained can then be validated on clinical data to determine to what extent it accurately predicts the progression of portal hypertension in patients. Once validated, machine learning model 806 can be applied to identify patients in hospitals or using hospital services that are candidates for further testing, treatment, or inclusion in clinical trials.
These paragraphs include statements such as “patients with any predicted progression speed may be selected for clinical trials of new treatments (e.g., diet, supplements, and/or pharmaceuticals)” and “machine learning model 806 can be applied to identify patients in hospitals or using hospital services that are candidates for further testing, treatment, or inclusion in clinical trials.” This is not administering a treatment to a patient as claimed, and therefore, the specification does not have support for the newly added claims. Entering a patient into a clinical trial on a computer-based system is not the same as physically administering a treatment to a patient. Recommending a treatment or clinical trial for a patient is merely indicating how the claimed invention might be used as it does not require the treatment actually be administered to the patient.
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 18-21, 23-29 and 32-40 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 18-21, 23-29, 32-35 and 38-40 are drawn to a method for predicting patient disease progression, which is within the four statutory categories (i.e. process). Claim 36 is drawn to a non-transitory medium for predicting patient disease progression, which is within the four statutory categories (i.e. manufacture). Claim 37 is drawn to a system for predicant patient disease progression, which is within the four statutory categories (i.e. machine).
Claims 18-21, 23-29, 32-35 and 38-40 (Group I) recite a method comprising:
obtaining, by a computing system (MPEP §2106.05(f), apply it), an observation of demographic values of an individual, comorbidity values of the individual, vital sign values of the individual, or blood test values of the individual, wherein the individual was diagnosed with portal hypertension or cirrhosis;
applying, by the computing system (MPEP §2106.05(f), apply it), a machine learning model to the observation, wherein the machine learning model was trained with a training data set, wherein the training data set contains observations of corresponding demographic values, comorbidity values, vital sign values, blood test values, or disease progression values for a plurality of individuals diagnosed with portal hypertension or cirrhosis, wherein values within the training data set are 20%-60% unpopulated, wherein the machine learning model is configured to provide a prediction of:
a hazard ratio of whether the individual is expected to exhibit progression to a condition related to portal hypertension or cirrhosis, or
a period of time between that of the observation of the demographic values and a further diagnosis of progression to the condition related to portal hypertension or cirrhosis, and wherein the machine learning model is based on gradient boosting; and
providing, by the computing system (MPEP §2106.05(f), apply it), the prediction based on the observation.
The bolded limitations, given the broadest reasonable interpretation, recite mathematical formulas or concepts (the machine learning aspects of the claim) and/or a certain method of organizing human activity because it recites fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people (determining risk of disease progression using a prediction when caring for a patient by using rules). Any limitations not identified above as part of the abstract idea are underlined and deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 36 and 37 is identical as the abstract idea for Claim 18 (Group I), because the only difference between is the claims are directed towards different statutory categories. Claim 37 further includes the additional elements of one or more processors (MPEP §2106.05(f), apply it) and memory containing program instructions that, upon execution by the one or more processors, cause the computing system to perform operations (MPEP §2106.05(f), apply it).
Dependent Claims 19-29, 32-35 and 38-40 include other limitations, for example Claim 19, recites a applying, by the computing system, a second machine learning model to the observation, wherein the second machine learning model was trained with at least part of the training data set, and wherein the second machine learning model is configured to provide a second prediction of: a second hazard ratio of whether the individual is expected to exhibit progression to a second condition related to portal hypertension or cirrhosis, or (ii) a second period of time between that of the observation and a second further diagnosis of the second condition; and providing, by the computing system, the second prediction based on the observation, Claim 20 recites applying, by the computing system, a further machine learning model to the observation, wherein the further machine learning model was trained with at least part of the training data set, and wherein the further machine learning model is configured to provide a further prediction of: a further hazard ratio of whether the individual is expected to exhibit progression to any condition related to portal hypertension or cirrhosis, and
(ii) a further period of time between that of the observation and a further diagnosis of any condition related to portal hypertension or cirrhosis; and providing, by the computing system, the further prediction based on the observation, Claim 21 recites wherein providing the prediction comprises displaying the prediction on a graphical user interface (MPEP §2106.05(f), apply it), or wherein obtaining the observation comprises receiving the observation from a client device in communication with the computing system over a network, and wherein providing the prediction comprises transmitting the prediction to the client device (MPEP § 2106.05(g), insignificant extra-solution activity and MPEP §2106.05(f), apply it), Claim 22 recites wherein obtaining the observation comprises receiving the observation from a client device in communication with the computing system over a network (MPEP §2106.05(f), apply it), and wherein providing the prediction comprises transmitting the prediction to the client device (MPEP §2106.05(f), apply it), Claim 23 recites wherein the disease progression values for a particular individual of the plurality of individuals includes an index date and one or more outcomes, and wherein each of the one or more outcomes indicates a particular condition and an observed period of time between its index date and when the particular condition was diagnosed, Claim 24 recites wherein the disease progression values also include one or more additional outcomes, and wherein each of the one or more additional outcomes indicates an unknown condition and an additional observed period of time between the index date and when the unknown condition was identified, Claim 25 recites wherein there is at least six months of vital sign values or blood test values prior to the index date in the disease progression values for the plurality of individuals, Claim 26 recites wherein the particular condition is one of varices, variceal hemorrhages, recurrent variceal hemorrhages, ascites, refractory ascites, hepatic encephalopathy, recurrent hepatic encephalopathy, portosystemic shunts, or jaundice, Claim 27 recites wherein the demographic values include ages, genders, races, or ethnicities of the plurality of individuals, Claim 28 recites wherein the vital sign values include body mass indices, blood pressure readings, or heart rates of the plurality of individuals, Claim 29 recites wherein the comorbidity values include indications of diabetes or obesity, Claim 32 recites wherein the machine learning model is based on gradient boosting and survival time analysis, Claim 33 recites wherein the training data set includes at least 10,000 observations gathered from medical claim records or electronic health records, Claim 24 recites wherein the hazard ratio is provided as a Boolean indication of progression to the respective condition, Claim 35 recites wherein the observations in the training data set also include indications of medications, prescriptions, or treatments relating to the plurality of individuals, and wherein the observation also includes indications of medications, prescriptions, or treatments relating to the individual, Claim 38 recites based on the prediction, administering a supplement or a pharmaceutical to the individual (MPEP §2106.05(f), apply it, MPEP § 2106.05(h), generally linking), Claim 39 recites wherein the individual is under a treatment for portal hypertension or cirrhosis, wherein the prediction indicates that the period of time is shorter than a threshold value, and wherein administering the supplement or the pharmaceutical entails a more aggressive remedy than the treatment (MPEP §2106.05(f), apply it, MPEP § 2106.05(h), generally linking), and Claim 40 recites based the prediction, including the individual in a clinical trial for a supplement or a pharmaceutical, but these only serve to further limit the abstract idea (Examiner notes that the limitations underlined are considered additional elements and are addressed below.), and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 36 and 37.
Furthermore, Claims 18-29 and 32-37 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computing system, graphical user interface, memory, client device, network, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0009], [0027-0029], [0157] of the present Specification, see MPEP 2106.05(f);
add insignificant extra-solution activity to the abstract idea – for example, the recitation of receiving observation data, which amounts to mere data gathering and/or the recitation of transmitting the prediction data, which amounts to an insignificant application, see MPEP 2106.05(g); and
generally link the abstract idea to a particular technological environment or field of use – for example, the recitation of administering a supplement or pharmaceutical to the individual and wherein administering the supplement or the pharmaceutical entails a more aggressive remedy than the treatment, which amounts to limiting the abstract idea to a particular field of use, see MPEP 2106.05(h)).
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs [0009], [0027-0029] and [0157] of the Specification discloses that the additional elements (i.e. a computing system, graphical user interface, memory, client device, network) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receiving and transmitting data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare).
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives prediction data, and transmits the data to a client device over a network, for example the Internet.
Dependent Claims 19-21, 23-29, 32-35 and 38-40 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than additional elements recited at the “apply it” level (e.g., the client device and network of claims 21). Furthermore, with respect to claims 38-39, as the limitations do not actually provide a treatment or prophylaxis as they are merely an intended use of the claimed invention or a field of use limitation, and therefore, cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. See MPEP § 2106.04(d)(2).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 18-29 and 32-37 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03/10/2026 have been fully considered.
Response to Allegations of New Matter
Examiner notes that as the claim amendments filed after final dated 02/02/2025 were not entered, there were no new matter rejections actual made by Examiner. Examiner, however, in response to the current claims, has made a new matter rejection for claims for claims 38 and 39 as the instant disclosure does not provide support for actually administering any drugs to the individual.
§ 101 Rejections
Applicant argues that he claimed invention provides technical improvements as described in the specification and that “Applicant's claims and those of Desjardins are both directed to training machine learning models (Remarks, page 10).” Examiner disagrees as the instant claims are using an already trained model, and not actively training a model as in Desjardins. Claim 1 recites “applying, by the computing system, a machine learning model to the observation….” The machine learning model is not being generated, rather the claim recites “wherein the machine learning model was trained with a training data set, wherein the training data set contains observations of corresponding demographic values, comorbidity values, vital sign values, blood test values, or disease progression values for a plurality of individuals diagnosed with portal hypertension or cirrhosis.” The claimed invention uses an already trained machine learning model. There is no improvement to the model itself that results from the claimed invention.
Any improvements resulting from the claimed invention are to the abstract idea itself, which is the prediction related to portal hypertension or cirrhosis or a related condition. This is not technical in nature and therefore, not analogous to Dejardins.
Regarding Step 2A, Part One, Applicant alleges that the Examiner did not identify an abstract idea.
Claim 1 recites a method comprising:
obtaining, by a computing system (MPEP §2106.05(f), apply it), an observation of demographic values of an individual, comorbidity values of the individual, vital sign values of the individual, or blood test values of the individual, wherein the individual was diagnosed with portal hypertension or cirrhosis;
applying, by the computing system (MPEP §2106.05(f), apply it), a machine learning model to the observation, wherein the machine learning model was trained with a training data set, wherein the training data set contains observations of corresponding demographic values, comorbidity values, vital sign values, blood test values, or disease progression values for a plurality of individuals diagnosed with portal hypertension or cirrhosis, wherein values within the training data set are 20%-60% unpopulated, wherein the machine learning model is configured to provide a prediction of:
a hazard ratio of whether the individual is expected to exhibit progression to a condition related to portal hypertension or cirrhosis, or
a period of time between that of the observation of the demographic values and a further diagnosis of progression to the condition related to portal hypertension or cirrhosis, and wherein the machine learning model is based on gradient boosting; and
providing, by the computing system (MPEP §2106.05(f), apply it), the prediction based on the observation.
All of the bolded limitations are considered organizing human activity. As indicated in the above rejection, the machine learning limitations are also considered to recite mathematical concepts, which would apply to the limitation beginning with “applying, by the computing system (MPEP §2106.05(f), apply it), a machine learning model to the observation….”
Regarding Step 2A, Part Two, Applicant asserts that the Examiner’s analysis is conclusory. It is unclear how this is interpreted as conclusory when each additional element is identified and analyzed in the rejection. Examiner followed the proper steps as required in MPEP 2106. See above rejection. Each part of the analysis is labeled so Applicant’s argument that the analysis was not complete is not persuasive. Applicant’s further argument regarding a non-precedential PTAB is not persuasive as the Examiner has fully addressed the analysis as required by MPEP § 2106.
Therefore, the claims remain rejected as being directed towards an abstract idea without a practical application or significantly more than the abstract idea itself.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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