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 .
Notice to Applicant
Receipt of Applicant’s Amendment filed September 29, 2025 is acknowledged.
Response to Amendment
Claims 1, 6, and 11 have been amended. Claims 2-5, 7-10, and 12-15 have been cancelled. Claims 1, 6, and 11 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed on September 29, 2025 have been considered but are not persuasive. Response has been provided below.
Applicant argues 35 U.S.C. §112 Rejections, pg. 9 of Remarks:
Examiner acknowledges Applicant’s amendments and withdraws the 112 rejection.
Applicant argues 35 U.S.C. §101 Rejections, pg. 9 of Remarks:
Regarding Step 2A Prong One, Applicant argues that the claims do not recite methods of organizing human activity and is beyond mere recitation of mathematical processes. Examiner respectfully disagrees.
Applicant first states that the “Claimed invention evaluate[s] competing models for automated clinical screening through direct diagnosis principle of risk aversion where the penalty of wrong diagnosis is monotonically more than missed diagnosis than overdiagnosis along with the related degree of false predictions”, and thus provides a metric for comparing diagnostic performance of models. Examiner notes that establishment for a metric using penalties, as claimed, is directed to an improvement in the abstract idea of the mathematical processes used to perform the evaluation of models. The independent claims recite wherein several equations are used to calculate the first and second penalties, respectively.
Applicant further argues that “the claimed step is not mere mathematical step or organizing human activity, but provides technical effect”. Examiner notes that any claimed technical improvements are evaluated under Step 2A Prong Two. Prong One only determines if an abstract idea is present in the claims and is not a rejection of the claims in and of itself.
Applicant further argues that “It must be noted that the claimed step is not diagnosis a patient’s condition”. Examiner disagrees, as claims 1, 6, and 11 each recite “predicting, …, a diagnosis corresponding to each of the plurality of diagnostic samples”. Despite usage of a multi-label multi-class computational diagnostic model to predict labels and outputs, diagnosis is still performed by the system. Furthermore, the invention itself is directed towards evaluating clinical efficacy of models to ensure said models have high diagnostic performance, which is analogous to evaluating doctor performance and their efficacy in correctly diagnosing patients; see [0002]-[0004] of Applicant specification.
Applicant further argues that “by calculating the first penalty in [using the equations recited in the claims], strict monotonicity of the first penalty is maintained by assigning highest first penalty to wrong diagnosis followed by missed diagnosis and over diagnosis thereby imbibing risk aversion principle of clinical diagnosis.” Examiner notes that this is directed to an improvement to the abstract idea of mathematical processes, as improving the mathematical equations used to classify diagnosis leads to the improvement recited.
Applicant further states that “The contradiction matrix is responsible for penalizing impossible or mutually exclusive diagnostic conditions. This ensures that the predictions are not only accurate but are logically consistent.” Examiner notes that a contradiction matrix is a mathematical process. In addition, defining the matrix with the help of domain experts is considered abstract for managing the personal behaviors of said experts. Furthermore, reciting “wherein the contradiction matrix is responsible for penalizing impossible or mutually exclusive diagnostic conditions and ensures the predictions are accurate and logically consistent” is an intended effect of performing the claimed step of defining the contradiction matrix and does not have patentable weight; see MPEP 2111.04(I).
Applicant further argues that the claims are similar to Example 39 and do not fall under mental processes and are not mere mathematical processes, as the claims are directed towards evaluating competing models for automated clinical screening through direct diagnosis principle of risk aversion where the penalty of wrong diagnosis is monotonically more than missed diagnosis than overdiagnosis along with the related degree of false predictions. Examiner first notes that claims were never characterized as being under mental processes but instead organizing human behavior and mathematical processes. Furthermore, the instant application is dissimilar from Example 39 as the instant application recites abstract ideas of organizing human activities (via diagnosis) and mathematical processes (statistical analysis via the various equations, calculation of penalties, and usage of a contradiction matrix). The steps of Example 39, such as “applying one or more transformations to each digital facial image” are not abstract as they cannot be performed through human means and are not directed towards mathematics as, even though the background describes the transformations being mathematical in nature, the mathematical concepts themselves are not claimed. Lastly, the improvement described by Applicant is directed towards an improvement in statistical analysis, which is directed to an improvement in the abstract idea of mathematical processes.
Regarding Step 2A Prong 2, Applicant argues that the additional elements integrate the judicial exception into a practical application by “providing a new metric and method of evaluating multi-label multi-class clinical diagnosis model”. Specifically, by using a metric that considers context dependent clinical principle, the clinical diagnosis analysis is improved. However, evaluation of diagnosis models in the manner claimed is directed to the improvement in the abstract idea of diagnosis using improved mathematical processes. Though the claimed invention may have industrial applicability, they do not recite a practical application as defined by MPEP 2106.04(d) as there is no specific, technical improvement to the additional elements (hardware processor, multi-label multi-class computational diagnostic models), taken both individually, or in combination.
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, 6, and 11 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a method and a machine (claims 1, 6, and 11). Accordingly, claims 1, 6, and 11 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claim 1 as the claims that represent the claimed invention for analysis, and are similar to system claim 6 and product claim 11.
Claim 1:
A processor implemented method comprising:
receiving, via one or more hardware processors, a dataset comprising a plurality of diagnostic samples and corresponding ground truth;
predicting, via the one or more hardware processors, a diagnosis corresponding to each of the plurality of diagnostic samples using a multi-label multi-class computational diagnostic model, wherein the predicted diagnosis comprises one or more diagnostic conditions, wherein the multi-label multi-class computational diagnostic model is a classifier, wherein for a given diagnostic sample, the classifier predicts corresponding label and output of the classifier is in the form of scores which are correlated to probabilities of a diagnostic condition being present and a thresholding protocol, and the predicted diagnosis is a set of all diagnostic conditions that satisfy a prediction threshold, wherein the multi-label multi-class computational diagnostic model is implemented using machine learning techniques;
classifying, via the one or more hardware processors, the predicted diagnosis in a class among a plurality of classes comprising: (i) a wrong diagnosis, (ii) a missed diagnosis, (iii) an over diagnosis and (iv) a right diagnosis, wherein the predicted diagnosis for a diagnostic sample from among the plurality of diagnostic samples is classified as (i) the wrong diagnosis if the predicted diagnosis and ground truth corresponding to the diagnostic sample are disjoint, (ii) the missed diagnosis if the predicted diagnosis is a proper subset of the ground truth corresponding to the diagnostic sample, (iii) the over diagnosis if the ground truth corresponding to the diagnostic sample is a proper subset of the predicted diagnosis, or (iv) the right diagnosis otherwise;
calculating, via the one or more hardware processors, a first penalty for the diagnosis corresponding to each of the plurality of diagnostic samples based on the class of the predicted diagnosis, wherein the first penalty is calculated by one of: (i)
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if the predicted diagnosis is a right diagnosis, (ii)
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if the predicted diagnosis is an over diagnosis, and (iv) 0 if the predicted diagnosis is a wrong diagnosis, wherein
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is a weight matrix comprising cost of misclassification, and wherein strict monotonicity of the first penalty is maintained by assigning highest first penalty to wrong diagnosis followed by missed diagnosis and over diagnosis thereby imbibing risk aversion principle of clinical diagnosis;
calculating, via the one or more hardware processors, a second penalty for the diagnosis corresponding to each of the plurality of diagnostic samples based on a contradiction matrix, wherein the second penalty is calculated as (i)
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is an entry in the contradiction matrix, wherein the contradiction matrix provides contradictory and non-contradictory pairs of diagnostic conditions, wherein the contradiction matrix is defined with the help of domain experts, wherein the contradiction matrix is responsible for penalizing impossible or mutually exclusive diagnostic conditions and ensure the predictions are accurate and logically consistent;
computing, via the one or more hardware processors, a pre-score for each of the plurality of diagnostic samples based on the corresponding first penalty and second penalty;
obtaining, via the one or more hardware processors, a score corresponding to the multi-label multi-class computational diagnostic model by summing up the pre-score of each of the plurality of diagnostic samples; and
evaluating, via the one or more hardware processors, the multi- label multi-class computational diagnostic model with a metric that is based on (i) the score corresponding to the multi-label multi-class computational diagnostic model, (ii) a pre-computed score of a perfect multi-label multi-class computational diagnostic model whose predictions always belong to the right diagnosis class and (iii) a pre- computed score of a null multi-label multi-class computational diagnostic model which predicts null or 0 only.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mathematical processes. The claim elements are directed towards calculating penalties using the claimed equations and contradiction matrix, computing pre-scores, obtaining scores by summing pre-scores, and evaluating the diagnostic model, which describes statistical analysis of the model output data.
These claims further recite: certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards “predicting, via the one or more hardware processors, a diagnosis corresponding to each of the plurality of diagnostic samples using a multi-label multi-class computational diagnostic model, wherein the predicted diagnosis comprises one or more diagnostic conditions”. Diagnosing a patient condition falls under the abstract concept of managing personal behaviors of people, as it is a human activity regularly performed by medical care providers for their patients. The claim further recites defining the contradiction matrix “with the help of domain experts”, which is managing the personal behaviors of said experts. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that MPEP 2106.04(a)(2)IIC states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Accordingly, the claim recites at least one abstract idea.
Claims 6 and 11 are abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the Claims:
one or more hardware processors, a multi-label multi-class computational diagnostic model, a classifier (1); a memory, one or more communication interfaces, one or more communication interfaces (6); one or more non-transitory computer-readable storage mediums (11)
The independent claims teach an insignificant extra-solution activity of receiving data. See MPEP 2106.05(g).
Any computing devices and their associated components (one or more hardware processors) that would be able to perform the method are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0029] of Applicant specification recites: “The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.” No specific, technical improvements are being made to computing devices as any generic computing device is applied to perform the abstract idea.
Multi-label multi-class computational diagnostic models are also taught at a high level of generality. [0019] of Applicant specification recites: “Often, the multi-label multi-class computational diagnostic models are classifiers implemented using machine learning techniques.” No specific, technical improvements are being made to diagnostic models as any generic multi-label multi-classification model is used to perform the mathematical abstract idea of evaluating the efficacy of the model.
The storage devices (memory, non-transitory computer readable medium) are also taught at a high level of generality. [0031] of Applicant specification recites: “The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.” No specific, technical improvements are being made to storage devices as they are simply used to perform the insignificant extra-solution activity of storing data.
Interfaces are also taught at a high level of generality. [0030] of Applicant specification recites: “The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) 106 receives a dataset comprising a plurality of diagnostic samples and their corresponding ground truth as input and gives score of the multi-label multi-class computational diagnostic model as output.” No specific, technical improvements are being made to interface devices as they are simply used to perform the insignificant extra-solution activities of receiving and outputting data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims 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 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 activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Therefore, whether taken individually or as an ordered combination, claims 1, 6, and 11 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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