DETAILED ACTION
This office action is based on the claim set filed on 03/25/2026.
Claims 1, 6, and 11 have been amended.
Claims 5, 10, and 15 have canceled.
Claims 1-4, 6-9, and 11-14 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 .
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 1-4, 6-9, and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-4 are drawn to a method, Claims 6-9 are drawn to a system, and Claims 11-14 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 1-4, 6-9, and 11-14 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 are corresponding to the parameters, respectively;
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 calculate a plurality of important values correspond to the parameters and a plurality of risk indexes;
driving the processor to determine whether one of the parameters being out of one of the clinical index range values corresponding to the one of the parameters;
driving the processor to compare the one of the parameters and one of the risk indexes to generate a risk information;
driving the processor to compare the parameters and the clinical index range values, and divide the parameters into a plurality of trusting levels according to a consistency between the parameters and the clinical index range values, wherein the one of the parameters corresponds to one of the trusting levels;
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
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, and determine whether to adjust a medical decision according to the determining result
wherein each of the risk indexes is a critical value of each of the parameters, which changes the predicting result of the machine learning model analyzed by the model explainable program;
wherein the trusting levels comprises a first level, a second level and a third level, the first level represents a result of the predicting result being high risk or low risk and a result of a value of the one of the parameters exceeding the one of the clinical index range values or not is consistent, the second level represents after incorporating an auxiliary judgment feature for evaluation, the result of the predicting result generated by the machine learning model 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, the third level represents 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”
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 are corresponding to the parameters, respectively; and
a processor signally connected to the database, reading the parameter dataset, the machine learning model, the model explainable program and the clinical index range values and configured to perform an explainable artificial intelligence method applied to clinical medicine comprising:
Independent Claim 11 recites similar steps as in Claim 1 including:
A non-transitory computer readable recording medium storing a program for a processor capable of generating a visualization information, to execute an explainable artificial intelligence method applied to clinical medicine 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 computer 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", 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, 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” 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 artificial intelligence (AI) and machine learning”, 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 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 2-4, 7-9, and 12-14 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 2, 7, and 12, 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 with a computing system, or 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. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
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 theses 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").
Claims Free of Prior Art
Claims 1-4, 6-9, and 11-14 have been found by the examiner to be free of prior art. A thorough search of the prior art was conducted and the examiner could not find a single reference or combination of references with adequate rationale to combine that would teach the claimed invention.
Regarding independent claim 1, and similarly claims 6 and 11, none of the prior art teach or fairly suggests the limitation of “wherein each of the risk indexes is a critical value of each of the parameters, which changes the predicting result of the machine learning model analyzed by the model explainable program; wherein the trusting levels comprises a first level, a second level and a third level, the first level represents a result of the predicting result being high risk or low risk and a result of a value of the one of the parameters exceeding the one of the clinical index range values or not is consistent, the second level represents after incorporating an auxiliary judgment feature for evaluation, the result of the predicting result generated by the machine learning model 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, the third level represents 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”. The closest prior art of record is/are:
- Tamanas et al. (US 2023/0274835 A1) teaches detection and prediction models for Alzheimer's disease to the identification of prodromal Parkinson's disease using explainable artificial intelligence by detecting a novel digital biomarker signature from existing datasets using digital biomarker data.
- Mesinovic et al. (“Explainable AI for Clinical Risk Prediction: A Survey of Concepts, Methods, and Modalities”) teaches a system comprising explainable AI models that is/are aligned with guidelines and medical knowledge for predication of clinical risks where the system evaluate transparency and trust or trustworthiness confidence for the clinical risk predication and output results.
- Feng et al. (“Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare”) teaches visualizing monitoring physiological variables displaying control limits that indicates a normal range values and when changes from normal range value, an alarm is triggered indicating exceeding the control limits.
- Chao et al. (US 2024/0394605 A1) discloses obtaining an extubation prediction model and key features used by the extubation prediction model through training and/or verification of a machine learning model, and analyzing key feature data of a patient in real time through the extubation prediction model in order to obtain a possibility of extubation of the patient and its related explanation.
- Albahri et al. – “A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion” teaches a systematic science mapping analysis to address the issues of trustworthiness and objectivity providing decisive evidence for the trustworthiness of AI in health care.
However, no prior art was found teaching individually, or suggesting in combination, all of the features of the applicants' invention, as disclosed in the claimed invention.
Response to Amendment
Applicant's arguments filed 03/25/2026 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 11-13.
On page 12-13 of the remarks, Applicant argues “As shown in paragraph [0026] of the original specification of the present application, the judicial exception ... is integrated into the practical application (e.g., comparing the parameters and the risk indexes to generate a risk information), and each of the amended claims 1, 6, 11 analyzed as a whole is significantly more (generate a determining result according to the parameters, the trusting levels, and determine whether to adjust a medical decision according to the determining result) than the abstract idea implemented using the central processing unit... the present disclosure provides a verifiable technical foundation that substantially improves system reliability and enables more transparent human-machine collaborative decision-making”, Examiner respectfully disagree. Examiner asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim(s) as amended, under BRI, recite a process for collecting and analyzing clinical data parameters and risk index values to predict results and comparing to a clinical range values and generating risk information and trust levels corresponding to each parameter(s) analyzed and present out comes for decision making, while implemented on a computer explainable program and machine learning, to perform the steps, which recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. “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).
Furthermore, the Applicant argues that the judicial exception is integrated into a practical application as described by the Applicant “(e.g., comparing the parameters and the risk indexes to generate a risk information)”, where the comparing parameters and risk index to generate risk information is part of the identified abstract idea steps and not as alleged providing a practical application. Similarly, the Applicant argues that the claims as a whole is significantly more as described by the Applicant “(generate a determining result according to the parameters, the trusting levels, and determine whether to adjust a medical decision according to the determining result)”, where the adjusting a medical decision based on results is a step of the identified attract idea. As described in the rejection above, the claim does not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., processor, machine learning model”, to perform the steps abstract idea such as obtaining, analyzing, comparing, and visualizing/displaying information through leveraging computing technology in a well understood manner however improving upon an abstract idea does not make the abstract idea any less abstract.
In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to decision making, which is solving a health facility and administrative problem, using computers. However, improving upon an abstract idea does not make the abstract idea any less abstract. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept).
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.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 13-16.
In response to the Applicant claim(s) amendment and argument/remarks, Examiner withdraws the prior art rejection, see “Claims Free of Prior Art” above.
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 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.
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.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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