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 .
Notice to Applicant
This communication is in response to the amendment filed 07/29/2025. Claims 1, 7, 14, 18 have been amended. Claims 6, 8 have been canceled. Claims 21-22 have been added. Claims 1-5, 7, 9-22 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-5, 7, 9-22 are allowable over prior art. However, the claims are still rejected under 112 and 101.
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 1-5, 7, 9-22 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.
Claims 1, 14, 18 recites “wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy.” However, the specification does not mention “prediction accuracy” of any models or “[modifying]…weights,” let alone describe retraining by “[modifying] one or more weights of the machine learning model based on prediction accuracy.” With regards to “weights” related to models, the specification only describes: “Learned parameters may also be assessed to determine which subject attributes are associated with high weights (e.g., indicating relatively that they are relatively influential in generating output predictions)” (¶ 00178). Because no additional information is given, the disclosure fails to sufficiently describe “wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy.” As such, it constitutes new matter.
Claim(s) 2-5, 7, 9-13, 21-22 is/are rejected as being dependent on claim 1.
Claim(s) 15-17 is/are rejected as being dependent on claim 14.
Claim(s) 19-20 is/are rejected as being dependent on claim 18.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 22 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “high” in claim 22 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What weight is considered “high”? Appropriate clarification is requested for the proper interpretation of the claim limitations, as the ambiguity renders the metes and bounds of the claim unclear.
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 1-5, 7, 9-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter, wherein the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 14 is drawn to a system which is within the four statutory categories (i.e., machine). Claim 18 is drawn to a computer-program product tangibly embodied in a non-transitory machine-readable storage medium which is within the four statutory categories (i.e., manufacture).
Independent claim 14 (which is representative of independent claims 1, 18) recites…receiving…a query that identifies a treatment of multiple sclerosis; querying…using an identifier of the treatment…; receiving, in response to the query, a set of subject identifiers, wherein each subject identifier in the set of subject identifiers indicates that a subject corresponding to the subject identifier received the treatment; for each subject identifier of the set of subject identifiers: determining, based on data…, a time at which the subject corresponding to the subject identifier initiated the treatment; generating a time-series event chain identifying time periods treatment initiation, duration, and outcomes, wherein the time-series event chain is generated based one or more clinical assessments performed during treatment time periods and one or more subject reports or self-evaluations during the treatment time periods; and extracting, from one or more records associated with the subject identifier: one or more metrics indicative of an outcome of the treatment; and one or more subject attributes, wherein the extraction of the one or more metrics is based at least in part on the time at which the treatment was initiated, and wherein each of the one or more subject attributes reflects a characteristic of a record-corresponding subject or a result of a medical test; identifying, based on the generated time-series event chains, a dynamically determined subset of subjects, wherein each subject in the subset of subjects is similar to another subject not in the subset of subjects, and wherein similarity is determined based on a distance between array representations of records associated with the subject and the other subject; generating a predicted responsiveness of the other subject to the treatment by applying a…model to predict treatment responsiveness…; and outputting a result corresponding to the predicted responsiveness.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting a “cloud-based application server” (claims 1, 14, 18), “data processors” (claims 14, 18), the claim encompasses rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patients. 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 components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 1 further recites… wherein training of the machine learning model uses the extracted metrics and the extracted subject attributes, wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy. Under the broadest reasonable interpretation in light of the disclosure, the limitations noted above, as drafted, encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., cloud-based application server; a data store… having been populating based at least in part on input received from a distributed set of care-provider entities; a trained machine learning model). Claim 14 recites additional elements (i.e., A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions; cloud-based application server; a data store… having been populating based at least in part on input received from a distributed set of care-provider entities; a trained machine learning model). Claim 18 recites additional elements (i.e., A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions; one or more data processors; cloud-based application server; a data store… having been populating based at least in part on input received from a distributed set of care-provider entities; a trained machine learning model). Looking to the specifications, a computing system having one or more data processors, a non-transitory computer readable storage medium containing instructions, a cloud-based application server, a data store is described at a high level of generality (¶ 0012-0014; ¶ 00193), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, the claims add “populating [the data store] based at least in part on input received from a distributed set of care-provider entities,” which only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Furthermore, “a trained machine learning model” is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computing system having one or more data processors, a non-transitory computer readable storage medium containing instructions, a cloud-based application server, a data store) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, the limitations of “populating [the data store] based at least in part on input received from a distributed set of care-provider entities” is determined to constitute well-understood, routine, and conventional elements/functions; as recognized by the courts, receiving or transmitting data over a network, electronic recordkeeping, and storing and retrieving information in memory are well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Furthermore, “a trained machine learning model” is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-5, 7, 9-13, 15-17, 19-20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-5, 7, 9-12, 15-17, 19-20 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 13 further recites the additional elements of “treating the other subject with the treatment,” which does not provide any information as to how the patient is to be treated or what the treatment is, but instead covers any possible treatment that a medical professional decides to administer to the patient. As such, there are no meaningful constraints on the administering step such that the particular treatment or prophylaxis consideration would apply because it is not limited to any particular manner or type of treatment. See MPEP 2106.04(d)(2). Thus, the claims amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 07/29/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 07/29/2025.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“This integrated machine learning approach addresses the technological problem of using heterogeneous medical data, which is collected at different times and in different formats and is thus unstructured and not comparable across patients, leading to computational difficulty in identifying which patients might respond similarly to treatments. The machine learning techniques are integrated into the technological solution of applying trained models to predict treatment responsiveness for patient, where the trained models improve their predictive accuracy through feedback-driven weight adjustment…the amended claims recite specific machine learning integration techniques that constitute technological solutions to technological problems in medical data analysis. The claimed model application, training, and retraining with weight modification integrate any alleged abstract idea into a practical technological application that improves the functioning of computer technology and solves real-world technological challenges”; “"applying a trained machine learning model to predict treatment responsiveness, wherein the training uses the extracted metrics and the extracted subject attributes, wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy" is not a method of organizing human activity (as alleged on page 3 of the Office Action). Training the machine learning model such that the trained model is usable in "predict[ing] treatment responsiveness" is a specific technical solution to the problem of the use of unstructured, incomparable medical data for identifying treatment responsiveness”;
“the trained machine learning model is integrated into a practical application, namely "generating a predicted responsiveness of the other subject to the treatment". This is a specific practical application, where generating the predicted responsiveness can be used to treat patients based on medical data analysis”;
“the claims, as amended, recite the use of specific training data ("the extracted metrics and the extracted subject attributes") and a specific training methodology ("wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy") to train and retrain a machine learning model for a specific purpose (generating predicted treatment responsiveness)”;
“Claims 21 and 22 further establish the patent eligibility of the claimed invention by reciting specific technological implementations of supervised machine learning training and model analysis that constitute concrete improvements to computer functionality…newly added claim 21 and claim 22 constitute specific technological processes that integrate the alleged abstract idea into a concrete machine learning implementation that improves computer functionality through specialized computational techniques for medical data analysis.”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 101 rejections,
“This integrated machine learning approach addresses the technological problem of using heterogeneous medical data, which is collected at different times and in different formats and is thus unstructured and not comparable across patients, leading to computational difficulty in identifying which patients might respond similarly to treatments. The machine learning techniques are integrated into the technological solution of applying trained models to predict treatment responsiveness for patient, where the trained models improve their predictive accuracy through feedback-driven weight adjustment…the amended claims recite specific machine learning integration techniques that constitute technological solutions to technological problems in medical data analysis. The claimed model application, training, and retraining with weight modification integrate any alleged abstract idea into a practical technological application that improves the functioning of computer technology and solves real-world technological challenges”; “"applying a trained machine learning model to predict treatment responsiveness, wherein the training uses the extracted metrics and the extracted subject attributes, wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy" is not a method of organizing human activity (as alleged on page 3 of the Office Action). Training the machine learning model such that the trained model is usable in "predict[ing] treatment responsiveness" is a specific technical solution to the problem of the use of unstructured, incomparable medical data for identifying treatment responsiveness”:
It is respectfully submitted that per broadest reasonable interpretation of the claim in light of the specification according to a person of ordinary skill in the art, the claims of the present invention encompass the activity of (to paraphrase) rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patients, which covers the sub-grouping of managing personal behavior or relationships or interactions between people in the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Put another way, the claimed invention amounts to a series of rules or steps that a user (i.e., doctor) would follow to analyze patient medical data and provide an assessment for a specific patient’s response to treatment accordingly. This is an abstract idea.
Applicant argues “This integrated machine learning approach addresses the technological problem of using heterogeneous medical data, which is collected at different times and in different formats and is thus unstructured and not comparable across patients, leading to computational difficulty in identifying which patients might respond similarly to treatments.” However, “using heterogeneous medical data, which is collected at different times and in different formats and is thus unstructured and not comparable across patients, leading to computational difficulty in identifying which patients might respond similarly to treatments” only addresses administrative problems, and not a technical problem to any specific devices, or computers for that matter. Even if the claims provide the alleged improvements of “identifying which patients might respond similarly to treatments,” these alleged benefits of the invention are at best, an improvement to the abstract idea (i.e., rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patient). However, an improved abstract idea is still an abstract idea.
The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. Applicant’s claims do not recite the invention of improvements to computer functionality, technology, or any other technological field, but the use of generic computer components (i.e., a computing system having one or more data processors, a non-transitory computer readable storage medium containing instructions, a cloud-based application server, a data store) to predict a patient’s responsiveness to a treatment based on similar patients, which is an abstract idea, but for the recitation of generic computer components. Examiner cannot find and Appellant has not identified any problem caused by the technological environment to which the claims are confined (i.e., a well-known, general purpose computer). While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement to computer technology, a physical improvement to the computer, or any other technical improvement. See MPEP § 2106.04(d)(1) and 2106.05(a).
Applicant argues “The machine learning techniques are integrated into the technological solution of applying trained models to predict treatment responsiveness for patient, where the trained models improve their predictive accuracy through feedback-driven weight adjustment…the amended claims recite specific machine learning integration techniques that constitute technological solutions to technological problems in medical data analysis. The claimed model application, training, and retraining with weight modification integrate any alleged abstract idea into a practical technological application that improves the functioning of computer technology and solves real-world technological challenges.” However, Applicant fails to specify what the “technological problems” or “computer technology and…real-world technological challenges” are and how the claims of the present invention provide solutions for the aforementioned problems. Furthermore, the claim limitations to which Applicant seem to refer (i.e., “wherein training of the machine learning model uses the extracted metrics and the extracted subject attributes, wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy”) are interpreted as the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas. Furthermore, “a trained machine learning model” is interpreted as an additional element to be interpreted in Step 2A, Prong Two, which is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “Training the machine learning model such that the trained model is usable in "predict[ing] treatment responsiveness" is a specific technical solution to the problem of the use of unstructured, incomparable medical data for identifying treatment responsiveness.” However, Applicant fails to specify how “Training the machine learning model such that the trained model is usable in "predict[ing] treatment responsiveness" is a specific technical solution to the problem of the use of unstructured, incomparable medical data for identifying treatment responsiveness.” Furthermore, as stated previously above, “identifying treatment responsiveness” only addresses administrative problems, and not a technical problem to any specific devices, or computers for that matter. Even if the claims provide the alleged improvements of “identifying treatment responsiveness,” these alleged benefits of the invention are at best, an improvement to the abstract idea (i.e., rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patient). However, an improved abstract idea is still an abstract idea. Furthermore, the claim limitations to which Applicant seem to refer as “Training the machine learning model such that the trained model is usable in "predict[ing] treatment responsiveness"” are interpreted as the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas. Furthermore, “a trained machine learning model” is interpreted as an additional element to be interpreted in Step 2A, Prong Two, which is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claims are directed to an abstract idea.
“the trained machine learning model is integrated into a practical application, namely "generating a predicted responsiveness of the other subject to the treatment". This is a specific practical application, where generating the predicted responsiveness can be used to treat patients based on medical data analysis”:
Applicant argues “a practical application, namely "generating a predicted responsiveness of the other subject to the treatment."” However, Applicant fails to specify how “generating a predicted responsiveness of the other subject to the treatment” constitutes a “practical application.” Furthermore, the claim limitations to which Applicant seem to refer as “generating a predicted responsiveness of the other subject to the treatment” are interpreted as rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patients, which is part of the abstract idea. Even if the claims provide improvements of “generating the predicted responsiveness can be used to treat patients based on medical data analysis,” these alleged benefits of the invention are at best, an improvement to the abstract idea (i.e., rules or instructions followed to predict a patient’s responsiveness to a treatment based on similar patient). However, an improved abstract idea is still an abstract idea.
Furthermore, as stated previously above, “a trained machine learning model” is interpreted as an additional element to be interpreted in Step 2A, Prong Two, which is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“the claims, as amended, recite the use of specific training data ("the extracted metrics and the extracted subject attributes") and a specific training methodology ("wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy") to train and retrain a machine learning model for a specific purpose (generating predicted treatment responsiveness)”:
Applicant argues “the use of specific training data ("the extracted metrics and the extracted subject attributes") and a specific training methodology ("wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy") to train and retrain a machine learning model for a specific purpose (generating predicted treatment responsiveness).” However, Applicant fails to specify how “the use of specific training data ("the extracted metrics and the extracted subject attributes") and a specific training methodology ("wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy") to train and retrain a machine learning model for a specific purpose (generating predicted treatment responsiveness)” provide “significantly more.” Regardless, the claim limitations to which Applicant refer (i.e., “wherein training of the machine learning model uses the extracted metrics and the extracted subject attributes, wherein the trained machine learning model is retrained using feedback data from one or more additional subjects, and wherein the retraining modifies one or more weights of the machine learning model based on prediction accuracy”) are interpreted as the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which is part of the abstract idea, and not additional elements to be interpreted in Step 2B. Furthermore, “a trained machine learning model” is is described at a high level of generality, such that it is only used to generally apply the abstract idea without placing any limits on how the machine learning functions and only recite the outcome of the abstract idea and does not include details about how “generating a predicted responsiveness” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
“Claims 21 and 22 further establish the patent eligibility of the claimed invention by reciting specific technological implementations of supervised machine learning training and model analysis that constitute concrete improvements to computer functionality…newly added claim 21 and claim 22 constitute specific technological processes that integrate the alleged abstract idea into a concrete machine learning implementation that improves computer functionality through specialized computational techniques for medical data analysis”:
Applicant argues “specific technological implementations of supervised machine learning training and model analysis that constitute concrete improvements to computer functionality.” However, it is noted that the features upon which applicant relies (i.e., “supervised machine learning training and model analysis”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Furthermore, Applicant fails to specify how “specific technological implementations of supervised machine learning training and model analysis…constitute concrete improvements to computer functionality.” Regardless, the claim limitations to which Applicant refer (i.e., claims 21-22) are interpreted as the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers the abstract idea of mathematical relationships, and not additional elements to be interpreted in Step 2A, Prong Two or Step 2B.
Thus, Examiner maintains the 101 rejections of claims 1-5, 7, 9-22, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
Conclusion
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 extension fee 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 date of this final action.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683