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 .
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.
Notice to Applicant
In the amendment dated 9/8/2025, the following has occurred: Claims 1,6, 8, 9, 14, and 17 have been amended; Claims 3, 4, 11, 12, 19, and 20 have been canceled; Claims 21 – 26 have been added.
Claims 1, 2, 5 – 10, 13 – 18, and 21 – 26 are 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.
Claims 1, 2, 5 – 10, 13 – 18, and 21 – 26 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.
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1, 5 – 8, 21, and 24), machine (claims 9, 10, 13 – 16, 22, and 25), and manufacture (claims 17, 18, and 26) which recite the abstract idea steps of
receiving:
a probability score associated with a profile of a patient being examined for a medical condition,
a plurality of features associated with the profile, the plurality of features comprising at least 500 features, and
a contribution value for each of the plurality of features that represents each feature's contribution to the probability score;
transforming the plurality of features to a reduced number of features medically relevant to the medical condition based at least on the contribution value of each of the plurality of features, the reduced number of features comprising no more than 50 features;
generating at least one cluster of a portion of a population using patterns across contribution values for a plurality of features associated with the portion of the population,
grouping the profile in the generated cluster based on the profile contribution values and the contribution values of the portion of the population; and
visually presenting to a user a separate visual indicator for each of the reduced number of features, the visual indicators comparing values for the reduced number of features to values for the plurality of features based on percentage rank, wherein the visual indicators are used to affect a medical treatment plan for the patient being examined for the medical condition.
The Examiner understands the claimed invention as a whole in light of the Specification as a whole. The Examiner includes entire paragraphs to better explain context. The Examiner splits this rejection upon how it operates (mathematics) and how the result is used (human activity).
These steps of claims 1, 2, 5 – 10, 13 – 18, and 21 – 26, as drafted, under the broadest reasonable interpretation, includes mathematical concepts. The claim includes multiple mathematical operations including a machine learning model, transforming, clustering, and grouping. The dependent claims add graphing.
[0006] A need exists, therefore, for improved systems and methods for explaining machine learning model outputs.
[0038] One of the ways to explain a patient cohort is to present the patient in relation to a "prototype cluster." This is often done using test results, such that a patient is grouped in a cluster with other patients that have similar test results. However, using test results only does not provide a thorough, comprehensive, or otherwise meaningful understanding of a patient's health.
[0039] Machine learning models may highlight patterns that are unnoticed by a user using only test results. However, the mapping of inputs to outputs in machine learning pipelines is often not explainable. A user such as a reviewing clinician may not fully understand the reasoning behind a model output. For example, a user may not fully understand which features or medical parameters associated with a patient contributed to a phenotype prediction or the extent of any contribution.
[0042] The embodiments described herein provide novel techniques for explaining an output from a machine learning model. Although the present application largely discusses the embodiments in conjunction with healthcare and for patient treatment, the features of the embodiments herein may be implemented in any other applications reliant on machine learning models to generate one or more outputs based on inputted features. For example, companies or marketing agencies may analyze features associated with prospective customers or target demographics to better understand why a particular customer is predicted to respond favorably or unfavorably to a product, service, or advertisement campaign.
These steps of claims 1, 2, 5 – 10, 13 – 18, and 21 – 26, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. While the invention operates via math, the result is a comparison provided to a user such as a clinician.
[0005] This can be particularly true in reviewing the results from machine learning models. While machine learning models may uncover patterns unnoticed by a human reviewer, the model's mapping of inputs to outputs is often not explainable. Explaining the values outputted by a machine learning model can therefore be difficult, and a user may not fully understand the reasoning behind an output. This is particularly true in healthcare settings, in which it is desirable to have a thorough understanding of a patient's health. However, explaining values outputted by machine learning models can be difficult in any other type of application.
[0006] A need exists, therefore, for improved systems and methods for explaining machine learning model outputs.
[0045] FIG. 1 illustrates a system 100 for explaining an output from a machine learning model in accordance with one embodiment. The system 100 may include a user device 102 executing a user interface 104 accessible by a user 106. The user 106 may include healthcare personnel such as a treating clinician, for example.
[0088] Step 704 involves transforming the plurality of features to a reduced number of features based at least on the contribution value of each of the plurality of features. There may a prohibitively high number of features such that a user (e.g., a treating clinician) would have a difficult time in reviewing all features. Accordingly, a dimension reduction service or module such as the dimension reduction module 130 of FIG. 1 may reduce the number of features to a reduced number of features or dimensions. The reduction may be based on the contribution values of features which, in some embodiments, may be or be based on SHAP values. As discussed previously, a single dimension can correspond to several features, or may correspond to only a single feature.
Regarding MPEP 2106.04(a)(2)II includes
Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.
Fundamental Economic Practices or Principles
The courts have used the phrases “fundamental economic practices” or “fundamental economic principles” to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks.
The Examiner notes that a benefit of better treatment is the reduction of risks. Healthcare is also a business.
C. Managing Personal Behavior or Relationships or Interactions Between People
The sub-grouping “managing personal behavior or relationships or interactions between people” include social activities, teaching, and following rules or instructions.
The Examiner notes that user is receiving instructions from the computer on how to treat patients.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2, 5 – 8, 10, 13 – 16, 18, and 21 – 26, reciting particular aspects of how comparing data may be performed but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of when executing on one or more processors amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving from a… amounts to mere data gathering, recitation of visually presenting amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 5 – 8, 10, 13 – 16, 18, and 21 – 26, additional limitations which amount to invoking computers as a tool to perform the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as claims 1, 2, 5 – 10, 13 – 18, and 21 – 26; transforming, generating a cluster, grouping, and graphing, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii))
Additional Elements – Paragraph 95 begins with, “[0095] Specific details are given in the description to provide a thorough understanding of example configurations (including implementations). However, configurations may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail to avoid obscuring the configurations.”
Machine learning model – although not positively recited, paragraph 58 The embodiments described herein may execute models such as those based on linear regression, random forest decision trees, support vector machines (SVMs ), or any other type of model whether available now or formulated hereafter.
Computer – paragraph 34 The present disclosure also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer.
Devices with displays – paragraph 46 The user device 102 may be any sort of device that can visually present data. For example, in some embodiments, the user device may be a personal computer (PC), laptop, tablet, smartphone, television, smartwatch, virtual- or augmented-reality display, etc. This list of devices is exemplary, and other devices whether available now or invented hereafter may be used in conjunction with the embodiments herein.
Storage medium - Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs ), or any type of media suitable for storing electronic instructions, and each may be coupled to a computer system bus.
Network – paragraph 50 The network(s) 114 may be comprised of, or may interface to, any one or more of the Internet, an intranet, a Personal Area Network (PAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN),…
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 5 – 8, 10, 13 – 16, 18, and 21 – 26, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, graphing, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Response to Arguments
Applicant’s arguments, see The Claims, as Amended, Comply with the Written Description Requirement, filed 9/8/2025, with respect to claims 1 - 20 have been fully considered and are persuasive. The 35 U.S.C. 112 rejection of claims 1 – 20 has been withdrawn.
Applicant's arguments filed 9/8/2025 have been fully considered but they are not persuasive.
The Claims, as Amended, Recite Statutory Subject Matter
The Applicant states, “Under Prong One of revised Step 2A, "claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." See MPEP at §2106.04(a)(2).” The argued sentence, found in 2106.04(a)(2)(III)(A) is part of a paragraph.
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because “the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims”); CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699 (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that ‘‘could not, as a practical matter, be performed entirely in a human’s mind’’).
Examples of claims that do not recite mental processes because they cannot be practically performed in the human mind include:
a claim to a method for calculating an absolute position of a GPS receiver and an absolute time of reception of satellite signals, where the claimed GPS receiver calculated pseudoranges that estimated the distance from the GPS receiver to a plurality of satellites, SiRF Tech., 601 F.3d at 1331-33, 94 USPQ2d at 1616-17;
a claim to detecting suspicious activity by using network monitors and analyzing network packets, SRI Int’l, 930 F.3d at 1304;
a claim to a specific data encryption method for computer communication involving a several-step manipulation of data, Synopsys., 839 F.3d at 1148, 120 USPQ2d at 1481 (distinguishing the claims in TQP Development, LLC v. Intuit Inc., 2014 WL 651935 (E.D. Tex. 2014)); and
a claim to a method for rendering a halftone image of a digital image by comparing, pixel by pixel, the digital image against a blue noise mask, where the method required the manipulation of computer data structures (e.g., the pixels of a digital image and a two-dimensional array known as a mask) and the output of a modified computer data structure (a halftoned digital image), Research Corp. Techs., 627 F.3d at 868, 97 USPQ2d at 1280.
The Examiner includes the entire section to show that the “cannot be practically” regards the task itself and not the complexity of the task.
The Applicant states, “Applicant submits that the above receiving and transforming steps do not recite an abstract idea at least because these steps cannot be practically performed in the human mind. The claimed steps include transforming a plurality of features comprising at least 500 features to a reduced number of features comprising no more than 50 features. The human mind would not be able to practically process at least 500 features and transform the features to no more than 50 features.” The Applicant’s discussion of task difficulty does not match the MPEP guidance. Further, the Applicant has not shown proof for the Applicant’s conclusion that the claimed invention cannot be practically be performed.
The Applicant further states, “Additionally, the reduction of the number of features analyzed reduces the computational burden on the processing components of the present application. This in turn improves their performance, as they are not required to process as many (e.g., over 500) features.” The Applicant’s opinion regarding potentially improving the computer performance is not based upon facts. There is nothing within the Specification that describes this feature. Regardless, the invention is not directed towards improving the functioning of a computer. The result could be, if actually disclosed, be the result of applying the abstract idea to a computer to achieve the benefits of applying the abstract idea to a computer.
The Applicant states, “The application as filed describes the unique technical challenges of explaining outputs of machine learning models, as the model's mapping of inputs to outputs is often not explainable. See appl. as filed at [0004] and [0005].” Paragraphs 4 and 5 do not include language describing “unique technical challenges of explaining outputs of machine learning models.” The challenges are not technical but as described in paragraphs 4 and 5, they are related to human perception. Therefore, the Applicant has applied this abstract idea to technology to achieve the benefits of that technology.
The Applicant further states, “A user such as a reviewing clinician may not fully understand the reasoning behind a model output. See id. at [0039].” And here is an example of how the invention is directed towards organizing human activity.
The Applicant states, “The visual presentation step recited above contributes to the technical improvement by presenting a comparison of the outputs of a machine learning model in a manner that allows further insight into a patient's health or actionable knowledge for medical personnel. For example, the visual indicators provide the outputs and reasons behind the outputs in an easy-to-understand manner. See id. at [0084] and [0085].” The Specification does not describes the invention as a technical improvement. The invention uses technology to achieve all the benefits of applying that technology to the abstract idea. Further, “in an easy-to-understand manner” is the part of the method of organizing human activity.
The Applicant further states, “The visual presentation step is integral to the practical applicability of the machine learning model and thus goes beyond insignificant post-solution activity.” The outputting of results from an algorithm allows the user to see the results of that output. The form of the output uses technology and provides data that has a potential application. Until a user sees the data and acts upon the data, the data remains data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Donovan et al. Pub. No.: US 2013/0080134 Embodiments of the present invention relate to methods and systems for predicting the occurrence of a medical condition such as, for example, the presence, indolence, recurrence, or progression of disease (e.g., cancer), responsiveness or unresponsiveness to a treatment for the medical condition, or other outcome with respect to the medical condition.
Drake et al., Pub. No.: US 2021/01749 Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
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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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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626