DETAILED ACTION
This office action is based on the claims filed on 07/21/2026.
Claims 1, 4, 6, 8-9, 11, and 14 have been amended.
Claims 2, 5, 7, 10, 12, and 15 have been cancelled.
Claims 1, 3-4, 6, 8-9, 11, and 13-14 are currently pending and have been examined.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/21/2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1, 3-4, 6, 8-9, 11, and 13-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 3-4 are drawn to a method, Claims 6 and 8-9 are drawn to a system, and Claims 11 and 13-14 are directed to an art of manufacturer, and each of which is within the four statutory categories (i.e., a machine and a process). Claims 1, 3-4, 6, 8-9, 11, and 13-14 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for deriving clinical data trusting levels for medical decision. Obtain subject data and model(s) to determine best fit model to associate information are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“driving a processor to read a parameter dataset, a machine learning model, a model explainable program and a plurality of clinical index range values from a database, wherein the parameter dataset comprises a plurality of parameters, and the clinical index range values correspond respectively to the parameters, wherein the clinical index range values are determined based on clinical trial or clinical practice experience and are stored in the database prior to execution
driving the processor to input the parameter dataset into the machine learning model to generate a predicting result;
driving the processor to execute the model explainable program to the machine learning model to analyze the predicting result generated by the machine learning model and calculate for each of the parameters, (i) each of a plurality of important values representing a relevance and an impact magnitude of each of the parameters to the predicting result, and (ii) each of a plurality of risk indexes generated by the model explainable program and representing a parameter-specific critical value at which the predicting result changes
driving the processor to determine whether a value one of the parameters is outside one of the clinical index range values corresponding to the one of the parameters;
driving the processor to compare the value of the one of the parameters with one of the risk indexes corresponding to the one of the parameters, to generate a risk information indicating whether the value of the one of the parameters be increased or decreased relative to the one of the risk indexes;
driving the processor to compare the parameters and the clinical index range values, and assign each of the parameters to one of a plurality of trusting levels according to a clinical consistency evaluation result between the parameters and the clinical index range values, wherein the one of the parameters corresponds to one of the trusting levels, and the trusting levels comprise:
a first level is assigned to the one of the parameters when a result of the predicting result being high risk or low risk and a result of the value of the one of the parameters exceeding the one of the clinical index range values or not is consistent;
a second level is assigned to the one of the parameters when, after incorporating an auxiliary judgment feature associated with the one of the parameters into an evaluation, the result of the predicting result being high risk or low risk and the result of the value of the one of the parameters exceeding the one of the clinical index range values is inconsistent, wherein the auxiliary judgment feature comprises at least one of a disease history associated with the one of the parameters, a related biomarker value corresponding to the one of the parameters, and a medication record corresponding to the one of the parameters, and the auxiliary judgment feature is retrieved from the parameter dataset; and
a third level is assigned to the one of the parameters when the result of the predicting result being high risk or low risk and the value of the one of the parameters exceeding the one of the clinical index range values or not is inconsistent;
driving the processor to integrate the parameters, the important values corresponding to the parameters, the risk information and the trusting levels corresponding to the parameters into a visualization information that presents the clinical consistency evaluation result for each of the parameters according to the trusting levels
driving the processor to determine whether the predicting result is consistent with a clinical medicine experience to generate a determining result according to the one of the parameters and the one of the trusting levels corresponding to the one of the parameters,
wherein the trusting levels correspond to a reliability of the predicting result, the first level is corresponding to a high reliability, the second level corresponding to a medium reliability, and the third level corresponding to a low reliability;
wherein the model explainable program is one of a SHapley Additive exPlanation (SHAP), a Local Interpretable Model-Agnostic Explanations (LIME) and an Individual Conditional Expectation (ICE).”
Independent Claim 6 recites similar steps as in Claim 1 including:
“a database configured to access a parameter dataset, a machine learning model, a model explainable program and a plurality of clinical index range values, wherein the parameter dataset comprises a plurality of parameters, and the clinical index range values correspond respectively to the parameters; and
a processor signally connected to the database, configured to read the parameter dataset, the machine learning model, the model explainable program and the clinical index range values from the database, wherein the parameter dataset comprises a plurality of parameters, and the clinical index range values correspond respectively to the parameters, and are stored in the database prior to execution, wherein the clinical index range values are determined based on clinical trial or clinical practice experience;...
Independent Claim 11 recites similar steps as in Claim 1 including:
“A non-transitory computer readable recording medium comprising a program for an explainable artificial intelligence method applied to clinical medicine, wherein the program, when executed by a processor, is configure to preform steps comprising:...”
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process along with Mathematical Calculations and relationships that constitute Mathematical Concepts but for the recitation of generic computing components. For example, the limitations encompass a user to obtain clinical parameters and values to calculate their importance and range of the parameters to compare to risk index and determine its trusted level and present the results into visual or graphic information for medical decision, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper and mathematical concepts, but other than the mere nominal recitation of "processor, artificial intelligence, machine learning model, SHapley Additive exPlanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE)", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper but for the recitation of generic computing components and algorithm(s), see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, artificial intelligence, machine learning model, non-transitory computer readable recording medium, SHapley Additive exPlanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE)” are computing components that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions and are recited at a high level of generality, for example, the machine learning is recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific process how these elements are implemented to perform a task as such applying an algorithm(s) to create an integrate parameters and it risk value and trusted level using known data is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these 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. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). 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 mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 3-4, 8-9, and 13-14 include all of the limitations of claim(s) 1, 6, and 11, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 3-4, 8-9, and 13-14, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. The claims recite additional elements “processor, artificial intelligence”. In particular, the claims recite the additional elements that implement the identified abstract idea. These computing components are recited at a high level of generality, for example, the artificial intelligence (AI), are recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific process how these elements are implemented to perform a task as such applying an AI to create an integrate parameters and it risk value and trusted level using known data is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent). Similarly, as mentioned above Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “e.g. AI, processor” to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Response to Amendment
Applicant's arguments filed 07/21/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 16-26.
On page 23 of the remarks, Applicant argues “Under Step 2A, Prong One, the amended claims do not recite a mental process ... Rather, the risk index is generated only by executing the trained machine-learning model together with the model explainable program... A human could not practically perform in the mind the claimed operations of executing the trained model, applying SHAP, LIME, or ICE to that model...”, Examiner respectfully disagree. The claims, given their broadest reasonable interpretation, recite an abstract idea which have been analyzed under Step 2A, Prong One reciting a medical decision process for obtain clinical parameters and values to calculate their importance and range of the parameters to compare to risk index and determine its trusted level and present the results into visual or graphic information of the medical decision, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Mental Process.
Furthermore, the claim(s) recite additional element(s) “e.g., machine learning model, non-transitory computer readable recording medium, SHapley Additive exPlanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE)” to implement the identified abstract idea where the use of algorithms such as SHapley Additive exPlanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE) is/are interpreted as certain mathematical formula(s) that are implemented on a machine learning model without describing how such algorithms are used to process results, see Applicant [0021] “the machine learning model 112 can include a model trained by any of a Transformed Fuzzy Neural Network (TFNN),... The model explainable program 113 can be one of a SHapley Additive explanation (SHAP), a Local Interpretable Model-Agnostic Explanations (LIME) and an Individual Conditional Expectation (ICE), but the present disclosure is not limited thereto”. Under Step 2A, Prong One, claims can recite a mental process even if they are claimed to be performed on a computer while such algorithms are plugged on machine learning model without describing how the model works and generates output of explainable program. “The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." see MPEP § 2106.04(a)(2)(III)(C).
The Examiner notes that in McRO the as-filed disclosure explicitly described that computers could not previously be programmed to perform the particular type of animation described and that only human animators were previously capable of such animation. Because the claimed invention solved this particular problem, the court found that the claimed invention was an improvement to computer technology. There is no such problem described in Applicant's disclosure.
On page 23-24 of the remarks, Applicant argues “Under Step 2A, Prong Two, the amended claims integrate any alleged judicial exception into a practical application.... The focus of the claims is therefore the particular manner in which the computing system evaluates and exposes the clinical reliability of a model prediction, not the use of a computer merely as a tool for carrying out an otherwise abstract evaluation. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016)”, Examiner respectfully disagree. In Enfish, the claim(s) provided an improvement to a computer function and/or technical field (self-pointing database) reciting a self-referential table for a computer database providing a particular improvement in the computer's functionality that improves the way a computer stores and retrieves data in memory whereas the instant claim(s) and specifications do not recite an improvement to technology, as in Enfish, but to performance of an abstract idea such as collecting, analyzing, and outputting data while using well-known computer system and components. The claims at issue do not require any nonconventional computer, network, or other components, or even a non-conventional and non-generic arrangement of known, conventional pieces (e.g., ML model using data inputs), but merely call for performance of the claimed functions on a set of generic computer components.
The thrust of Applicant's invention is to improve the abstract idea of determining insurance risk through leveraging computing technology and machine learning in a well understood manner. However, improving upon an abstract idea does not make the abstract idea any less abstract. Furthermore, the machine-learning analytics model is not configured in a manner other than what any off-the-shelf, commercially available processor is capable of being programmed to do, see (Applicant [0021]). The fact that machine-learning is applied in the area of determining insurance risk does not change this.
On page 25 of the remarks, Applicant argues “The amended claims also recite significantly more under Step 2B ... Considered as a whole, these limitations impose meaningful constraints on how the machine-learning prediction is technically evaluated and presented and provide a specific solution ...”, Examiner respectfully disagree. The fact that the judicial exception, identified in the rejection above, relies upon gathering data and analyzing clinical data does not impart an improvement to any existing computer, or any other technology or technical field. At best, this gathering and analyzing data to be used by the general machine-learning analytics model may improve the abstract idea of determining risk. However, improving upon an abstract idea does not make the abstract idea any less abstract. Furthermore, the machine-learning analytics model is not configured in a manner other than what any off-the-shelf, commercially available processor is capable of being programmed to do. The fact that machine-learning is applied in the area of determining insurance risk does not change this.
The elements of the instant process, when taken alone, each execute in a manner conventionally expected of these elements. The elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone.
According to the USPTO guidelines of April 19, 2018 incorporating the Berkheimer memo (Berkheimer memo, hereinafter),
In step 2B analysis, an additional element (or combination of elements) is not wellunderstood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
1. A citation to an express statement in the specification or to a statement made by
an applicant during prosecution that demonstrates the well-understood, routine,
conventional nature of the additional element( s ).
2. A citation to one or more of the court decisions discussed in MPEP §
2106.05(d)(Il) as noting the well-understood, routine, conventional nature of the additional
element(s).
3. A citation to a publication that demonstrates the well-understood, routine,
conventional nature of the additional element( s ).
4. A statement that the examiner is taking official notice of the well-understood,
routine, conventional nature of the additional elements). This option should
be used only when the examiner is certain, based upon his or her personal knowledge,
that the additional elements) represents well-understood, routine, conventional activity engaged
in by those in the relevant art, in that the additional elements are widely prevalent or in common
use in the relevant field, comparable to the types of activity or elements that are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S. C. §112(a).
The fact that a generic computing system, such as described above, can be suitably
programmed to perform the claimed method without requiring any nonconventional computer,
network, or other computing components, or even a "non-conventional and non-generic
arrangement of known, conventional pieces" but instead merely call for performance of
the claimed functions on a set of generic computer components, satisfies the Berkheimer memo requirement that the additional elements are conventional elements (as outlined in criterion 1 of the Berkheimer memo).
Moreover, Examiner asserts that all of the requirements for patentability as provided in 35 U.S.C. 101, 102, 103, and 112 must be met before a claim is allowed, see MPEP 706(I).
Therefore, the Applicant argument is found to be unpersuasive and Examiner remains the 101 rejections of claims which have been updated to address Applicant's argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAAELDIN ELSHAER whose telephone number is (571)272-8284. The examiner can normally be reached M-Th 8:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MAMON OBEID can be reached at Mamon.Obeid@USPTO.GOV. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
Prosecution Insights