DETAILED ACTION
Status of Claims
Claim(s) 1, 3-8, 10-15, and 17-23 are pending and are examined herein.
Claim(s) 1, 3-8, 10-15, and 17-23 have been Amended. Claim(s) 2, 9, and 16 have been previously Cancelled.
Claim(s) 1, 3-8, 10-15, and 17-23 remain rejected under 35 U.S.C. § 101.
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 .
Response to Amendment
The amendment filed on January 02, 2026 has been entered. Claims 1, 3-8, 10-15, and 17-23 are pending in the application. Applicant’s amendments to the claims have overcome the rejections under 35 U.S.C. § § 112 and 103 set forth in the previous Non-Final Office Action mailed on October 01, 2025. Applicant’s amendments to the claims have been fully considered and are addressed in the rejections below.
Response to Arguments
Applicant's arguments with respect to the rejection under 35 U.S.C. § 103, presented in the remarks filed on 01/02/2026 (see Pp. 13-17), have been fully considered.
The Examiner notes that the amendments to the claims submitted by Applicant render the claimed combination patentably distinct over the cited prior art references. Accordingly, the claims as currently amended overcome the prior art, and the rejection under 35 U.S.C. § 103 is withdrawn.
Applicant's arguments with respect to the rejection under 35 U.S.C. § 101 filed on 01/02/2026 have been fully considered but they are not persuasive.
Applicant’s argument (Pp.18-19 of the remarks):
Applicant argues that the amended claims “recite techniques for improving efficiency of a machine learning model, which is not an abstract idea and-even if it is found to be an abstract idea-encompasses an improvement in machine learning capabilities such that the alleged judicial exception is integrated into a practical application. For example, the claims recite techniques to "improve the computational efficiency of performing risk score generation predictive data analysis by reducing the need for continuous generation of predicted risk scores." See Specification as filed, paragraph [0018].”
Examiner's response: The examiner respectfully disagrees with the applicant’s assertion that the amended claims “recite techniques for improving the efficiency of a machine learning model.” The claimed invention is primarily directed to the abstract idea of determining when to optimally recalculate predicted risk scores for a monitored entity (e.g., a patient) associated with a target risk category (e.g., a condition or disease). As evidenced by Applicant’s own example and according to paragraph [0018], the claimed embodiments reduce the number of times predicted risk scores are recalculated in order to avoid the need for some computational operations configured to recalculate predicted risk scores. Accordingly, the improvement is in when to perform calculations and not in the alleged “efficiency of a machine learning model.” This reduction reflects an improvement of when calculation is performed, rather than an improvement to the function of a computer or to the machine learning model itself. In other words, the claims do not improve the underlying technology or computer functionality, but instead provide an improvement to the abstract idea of risk assessment determination, which does not integrate the judicial exception into a practical application. It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. See MPEP § 2106.05(a).
Applicant’s argument (Pp.18-19 of the remarks):
Applicant argues that the Office Action fails to consider the USPTO 2024 Guidance Update on Patent Subject Matter Eligibility for AI-related and relevant court precedent. Applicant further contends that the rejection is inconsistent with the precedential decision Ex Parte Desjardins, where the Appeals Review Panel emphasized that 35 U.S.C. §§ 102, 103, & 112, are the proper tools to limit patent scope and noted that the claims should not be evaluated at such a high level of generality. Applicant notes that the claims recite recalculating techniques that “vastly improve[s] the computational efficiency of performing risk score generation predictive data analysis by reducing the need for continuous generation of predicted risk scores” (as described in para. [0018]), which is similar to the improvement in the Ex Parte Desjardins claimed training technique improved the storage capacity of a computer. Thus, the present claims are directed to patent eligible subject matter under 3 5 U.S.C. § 101 and the rejection should be withdrawn.
Examiner's response: The Examiner finds Applicant’s arguments unpersuasive. The Examiner notes that the claims were separately evaluated under each applicable patent statute in accordance with the requirements set forth therein. With respect to the 101 subject matter eligibility, the Examiner evaluated the claims under the USPTO’s subject matter eligibility guidance, including MPEP § 2106, and determined that the claims are directed to an abstract idea without significantly more, and therefore are not patent-eligible subject matter.
Applicant’s reliance on Ex Parte Desjardins is unpersuasive. In Ex Parte Desjardins, the claims were directed to a specific technique for training a machine learning model that reduced computational complexity and storage requirements, thereby improving the machine learning performance and functioning of a computer itself. In contrast, the present claims are primarily directed to the process of performing risk score calculation analysis to determine when to recalculate a predicted risk score for a monitored entity (e.g., an individual) associated with a target risk category (e.g., a disease or condition). See specification e.g., para. [0026]. Such subject matter falls within the abstract ideas (e.g., mental processes and/or mathematical concepts).
As previously discussed above, the alleged improvement reflects an optimization of the abstract idea itself (risk assessment determination) rather than an improvement to the functioning of a computer, a machine learning model, or other technology (see MPEP § 2106.05(a)). Accordingly, the claims differ in scope and context from those in Ex Parte Desjardins, and Applicant’s analogy is not persuasive.
Applicant’s argument (Pp. 20-22 of the remarks):
Applicant argues that the amended claims are not directed to a mental process because claim 1 recites a computer-implemented method for recalculation determination that optimize execution of a machine learning model to recalculate predicted risk scores, which cannot be practically performed in the human mind. Applicant further contends that, when evaluated as a whole, the claims encompass risk score generation predictive data analysis, and therefore are not directed to a judicial exception under Step 2A, Prong One.
Examiner's response: The Examiner respectfully disagrees. As discussed in details in the rejection below under 3 5 U.S.C. § 101, the amended claims, when evaluated under Step 2A, Prong One, are directed to an abstract idea. The core of the claimed invention is directed to determining when to recalculate a predicted risk score for a monitored entity based on confidence scores and event timing calculations and analysis, which constitutes mental processes and/or mathematical concepts. Specifically, the claims recite steps such as determining delay values, calculating delay-based confidence scores, determining event-based confidence scores, applying weighting scheme based on temporal distance and decay factors, determining a hybrid confidence scores based on these factors, and comparing the hybrid confidence score to a threshold. These steps define mathematical calculations, evaluations, and decision-making processes that can be performed by a human using a pen and paper.
While the claims recite execution “by one or more processors” and using the “machine learning model” to automate these steps, mere automation of these calculations and analysis does not remove them from the mental process or mathematical concept grouping. Further, the Examiner disagrees with Applicant’s assertion that the claims optimize execution of a machine learning model. The claims and the specification describes the process of determining when to recalculate predicted risk scores to reduce the need for continuous recalculation of risk scores. However, they do not recite or disclose a specific machine learning training technique, model execution process, or model structure modification to the operation of a machine learning model that reduce or optimize execution of the machine learning model itself, as alleged by Applicant. Accordingly, the claims do not recite an improvement to the computer or other technology, but rather an optimization to the abstract idea of risk assessment determination to avoid continuous calculation analysis. Therefore, the claims remain directed to a judicial exception under Step 2A, Prong One.
Applicant’s argument (Pp. 23-24 of the remarks):
Applicant argues that, even if the claims are found to be directed to an abstract idea, claim 1 recites combination of additional elements that improve a technical field such that the claim as a whole integrates any alleged abstract idea into a practical application that is patent eligible under Step 2A, Prong Two of 35 U.S.C. § 101. Applicant asserts that the claimed features related to risk score generation predictive data analysis improve the functioning of a machine learning model by reducing the number of times predicted risk scores are recalculated, thereby "the computational efficiency of performing risk score generation predictive data analysis." Applicant relies on Specification paragraph [0018] to contend that refraining from recalculating predicted risk scores until a positive recalculation determination is generated constitutes a technological improvement that integrates the alleged abstract idea into a practical application under Step 2A, Prong Two.
Examiner's response: The Examiner respectfully disagrees and finds Applicant’s arguments under Step 2A, Prong Two unpersuasive. As discussed above, the claims recite a judicial exception in the form of determining whether and when to recalculate a predicted risk score, which represents a mental process and/or mathematical concept. The Examiner further finds that the additional elements recited in the claims do not integrate the judicial exception into a practical application.
Specifically, the limitations reciting generating a negative recalculation determination for a predicted risk score at a first temporal unit and refraining from recalculating the predicted risk score until a positive recalculation determination is generated at a second temporal unit define a decision-making process based on mathematical calculations and comparison. These steps involve determining delay values, calculating confidence scores, applying weighting schemes based on temporal distance, generating a hybrid confidence score, and comparing the score to a threshold to decide whether recalculation should occur. Such steps constitute mathematical calculations, evaluations, and judgment that can be practically performed in the human mind, or with the aid of pen and paper.
The additional elements recited in the claim, including the use of generic computer components such as one or more processors and a machine learning model, constitute mere instructions to apply the judicial exception on a computer. The use of these elements merely to automate the abstract processes do not add any meaningful limitations beyond the abstract idea itself. As set forth in MPEP § 2106.05(f), merely implementing a judicial exception on a computer, using a computer as a tool to perform an idea, or adding generic computer components amount to no more than reciting the words “apply it” with the judicial exception and does not integrate the exception into a practical application.
Further, although Applicant asserts that the claims improve the functioning of a machine learning model, the claims do not recite any technical implementation details regarding the training, execution, or operations of the machine learning model itself. Rather, the claims are focused on determining when to invoke the model based on confidence metric and event timing to perform risk score generation. As such the claimed subject matter reflect an optimization of the abstract idea i.e., determining whether to recalculation a risk score for risk assessment analysis, rather than a technological improvement to the machine learning model or to computer functionality.
Additionally, the Examiner notes that the alleged improvement described by the applicant’s specification comes from the judicial exception itself, specifically the recalculation determination and comparison process. As stated in the MPEP 2106.04(a), the judicial exception alone cannot provide the improvement. The improvement to computer functionality or another technology can be provided by additional elements that integrate the exception into a practical application. In this case, the additional elements are recited at a high level of generality and do not, individually or in combination, provide such an integration.
Accordingly, when considered as a whole, the claims remain directed to the abstract idea of determining whether and when to recalculate a predicted risk score, which constitutes a mental process and/or mathematical concept, and the additional elements do not integrate the judicial exception into a practical application or amount to significantly more than the abstract idea itself. Thus, the claims do not satisfy Step 2A, Prong 2 and Step 2B of the subject matter eligibility analysis. See MPEP 2106.
In view of the above, the rejection under 35 U.S.C. § 101 is maintained. For more details on the subject matter eligibility analysis of the claims, the Examiner respectfully refers to the detailed provided in the rejection under 35 U.S.C. § 101.
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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1, 3-7, and 21 recite computer-implemented method (representing a process);
Claim 8, 10-14, and 22 recite a system (representing a machine); and
Claims 15, 17-20, and 23 recite a One or more non-transitory computer-readable media (representing an article of manufacture).
Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claims
1, 3-8, 10-15, and 17-23 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, and hence is not patent-eligible subject matter.
Regarding Amended Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
generating, by one or more processors, a negative recalculation determination for a predicted risk score at a first temporal unit, wherein the predicted risk score is indicative of a predicted risk that a monitored entity is associated with a target risk category, (The “generating” step is an abstract idea of “a mental process” and/or “a mathematical concept.” Examiner’s note: the “generating” step, as drafted and under the broadest reasonable interpretation (BRI), covers a mental process that can be performed in the human mind. But for the recitation of “by one or more processors” that is nothing other using a computer component to perform the abstract idea. In light of the specification, the limitation reciting “generating a negative recalculation determination” represents an indication that a predicted risk score should not be recalculated at a particular time for a given risk category because a confidence-based threshold is not met. This limitation constitutes a decision-making process in which the predicted risk score should not be recalculated at the time and falls under the mental process category of abstract ideas. See [0027].)
in response to generating the negative recalculation determination, refraining, by the one or more processors, from recalculating the predicted risk score via a machine learning model until a positive recalculation determination is generated at a second temporal unit, (That is part of the abstract idea of “a negative recalculation determination.” This limitation represents a conditional decision-making step. It directs that, if a prior determination indicates the predicted risk score should not be recalculated, the recalculation is deferred until a later positive determination is made. This step defines an evaluation and judgment process that can be performed in the human mind. Therefore this falls under the mental process category of abstract idea. The recitation of “via a machine learning model” represents the use of computer instructions to carry out the abstract idea of deciding whether to recalculate a predicted risk score based on the recalculation determination (e.g., negative or positive). The step itself making the determination based on confidence scores and temporal factors, which is directed to a mental process and/or mathematical concept that could be performed in the human mind , and the machine learning model is merely the computer software that execute the abstract idea of risk score generation/analysis.)
determining, by the one or more processors, a recalculation delay value for a recalculation delay period associated with the predicted risk score based on a calculation temporal unit timestamp for the predicted risk score and a target temporal unit timestamp for a target temporal unit; (The “determining” step is an abstract idea of “a mental process” and/or “a mathematical concept.” Examiner’s note: the claim recites determining a delay value by calculating the difference between timestamps. Under the broadest reasonable interpretation (BRI), this covers a mental process that could be performed in the human mind but for the recitation of “by one or more processors.” A human analyst could manually calculate time difference between timestamps using basic arithmetic. This encompasses the mental process and/or mathematical concept, see MPEP § 2106.04(a)(2)(I) and (III). The descriptive “wherein” clause that provides context about the type of data being used to perform the claimed step but does not add substantive process. That is part of the abstract idea. Under the broadest reasonable interpretation (BRI) in light of the specification, the predicted risk score defines the patient’s likelihood of developing a specific medical condition, the monitored entity represents the individual patient being assessed, and the target risk category represents the specific disease or health outcome being determined, see e.g., [0026]. Thus, an individual healthcare professional can manually determine a patient’s predicted risk of developing or behavioral risk factors and assigning a risk assessment score –concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
determining, by the one or more processors, a delay-based confidence score for the target temporal unit based on the recalculation delay value and a delay-based confidence scoring reduction scheme for the target risk category; (The “determining” step is an abstract idea of “a mental process.” This limitation recites determining a confidence score based on delay value and a scoring scheme. Under its broadest reasonable interpretation, this covers a mental process that could be performed in the human mind but for the “by one or more processors” language. That is nothing in the claim element precludes the step from practically being performed in the human mind. A human could evaluate confidence in risk score based on timing delays using scoring scheme (i.e., predefined rules). This covers mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
determining, by the one or more processors, based a recalculation delay period event associated with the recalculation delay period, an event-based confidence score for the target temporal unit; (The “determining” step is an abstract idea of “a mental process.” This limitation recites determining another confidence score based on events. Under its broadest reasonable interpretation in light of the spec, the delay-based confidence score represents “an estimated/calculated credibility score that describes a degree of reduction in the credibility of risk score that is resulting from passage of time. For example, a healthcare professional could mentally assess that “a one-month recalculation delay value will cause a 0.1 reduction in the confidence score” for a particular disease. Thus, an individual can manually determine that a patient’s 2-month-old disease risk assessment has reduced credibility due to time passage and should receive a lower confidence score. This step cover performance in the human mind including an observation, evaluation, judgment, or opinion, see MPEP § 2106.04(a)(2)(III).)
determining, by the one or more processors, an event weight for the recalculation delay period event based on (i) an event weighting scheme for the target risk category and (ii) a temporal distance between (1) the target temporal unit timestamp for the target temporal unit and (2) a recalculation delay period event timestamp for the recalculation delay period event; (The “determining” step is an abstract idea of “a mental process.” This limitation recites determining weights for events based on predefined rules (i.e., scheme) and timestamps. The determining limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by one or more processors,” nothing in the claim precludes the determining step from practically being performed in the human mind. The event weighting scheme describes an estimated significance of occurrences, where occurrence of an event may be deemed more significant to specific disease/condition. A healthcare professional could mentally assign importance weights – for instance, determining that a patient’s recent smoking event should receive higher significance weight when assessing lung cancer risk vs. diabetes risk, and further adjusting that weight based on how recently the event (i.e., smoking) occurred. This process represent judgment of weighing risk factors that medical professional can manually perform. Therefore, this step encompass the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
determining, by the one or more processors and based on the event weight and the event-based confidence score, an adjusted event-based confidence score for the target temporal unit; (The “determining” step is an abstract idea of “a mental process.” This limitation recites adjusting confidence scores based on weights. Under its broadest reasonable interpretation, this is a mental process that can be practically performed in the human mind. A human can make adjustment to confidence assessments based on weighted factors. For instance, a healthcare professional could mentally perform this adjustment by taking a baseline confidence score for a patient risk assessment and mathematically adjusting it based on weighted factors like recent events and medication. This step covers performance in the human mind that can be derived using basic mathematical calculation and applying predetermined weights to modify confidence assessments. See MPEP § 2106.04(a)(2)(III).)
determining, by the one or more processors, a hybrid confidence score for the predicted risk score based on the delay-based confidence score and the adjusted event-based confidence score; (The “determining” step is an abstract idea of “a mental process”. This limitation recites combining confidence scores into a hybrid score. Examiner’s note: Under the broadest reasonable interpretation (BRI), this covers a mental process that could be performed in the human mind – combining multiple confidence assessment into an overall assessment. For example, a healthcare professional can mentally synthesize factors, for example, combining assessment that a 3-month old risk score has reduced credibility due to time (delay-based factor) with the evaluation of recent patient events (event-based factor) to reach to an overall hybrid confidence score assessment. Accordingly, this process covers concept that can be practically performed in the human mind including an observation, evaluation, judgment, or opinion. see MPEP § 2106.04(a)(2)(III).)
generating, by the one or more processors and based on whether the hybrid confidence score satisfies a hybrid confidence score threshold, the positive recalculation determination for the predicted risk score; (The “generating” step is an abstract idea of a “Mental Process.” This limitation recites making a determination by comparing a score to a threshold. The recitation of “positive recalculation determination” generally refers to an indication that a determination has been made that a predicted score meets a threshold for further risk score update/recalculation. Under the broadest reasonable interpretation, this covers a mental process that can be practically performed in the human mind, comparing values and making decisions based on threshold criteria. For example, a healthcare professional could mentally perform this comparison and decision-making, for example, determining that a patient’s hybrid confidence score falls below a predetermined threshold, and decide to perform risk score assessment. This step is an act of evaluating information, which falls under the mental process category, including observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2)(III). The implementation “by the one or more processors” merely automates this abstract mental process.)
optimizing, by the one or more processors and based on the recalculation determination, timing for ... recalculating the predicted risk score. (The “optimizing” step is part of the abstract idea of a mental process. This step appears to recite optimizing timing based on a determination. Under the broadest reasonable interpretation, this covers a mental process that can be performed by a human – deciding optimal timing for recalculation based on prior determinations. An individual can mentally determine optimal recalculation timing. For example, deciding the best time to update the risk score assessment for a particular patient condition. The “optimizing” language is at a high level of generality without technical specificity about how the optimization process is performed.)
recalculating, by the one or more processors and using the machine learning model, the predicted risk score based on the positive recalculation determination. (The “recalculating” step falls under the mental process and/or mathematical concept. Examiner’s Note: the “recalculating” step, as drafted and under its broadest reasonable interpretation, is simply computing a value based on a prior determination, which constitutes a mathematical calculation and decision-making process. This step can be performed mentally or with pen and paper. For example, a human could take an existing score, apply a formula or calculation, and update the score based on a prior assessment. Accordingly, if falls within the mental process and mathematical concept categories of abstract ideas. See MPEP § 2106.04(a)(2)(I) & (III). The recitation of “by the one or more processors and using the machine learning model” merely implements this abstract idea using computer components and does not recite an improvement to the machine learning model itself.)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim as whole merely recites the additional element “by one or more processors.” The recitation of “one or more processors” being configured to perform the aforementioned abstract ideas i.e., it is the tool that is used to perform the determining steps. But the processor is recited so generically (no details whatsoever are provided other than that it is a “processor”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f).
The claim further recites the limitation “wherein the predicted risk score is based on a set of entity data objects stored in a database.” This descriptive limitation merely describes conventional data storage without adding substantive process step. The database is recited at a high level of generality as merely storing data object. This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: This is conventional data storage in conjunction with an abstract idea.
The recitation of “via a machine learning model” and “using the machine learning model” amount to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The use of a machine learning model represents the use of computer instructions to carry out the abstract idea of deciding whether to recalculate a predicted risk score based on the recalculation determination (e.g., negative or positive). The step itself making the determination based on confidence scores and temporal factors, which is directed to a mental process and/or mathematical concept that could be performed in the human mind , and the machine learning model is merely the computer software that execute the abstract idea of risk score generation/analysis.)
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As noted above, the claim merely recite the additional element of using one or more processors to perform the determining steps. This merely recite a generic computer component that is used as a tool to perform the abstract process of determining steps. The recited database to store data object represents a generic computer function that is recited at a high level of generality. This amount to mere data gathering in conjunction with the abstract idea. The addition of the data gathering steps cannot provide an inventive concept because it merely represents well‐understood, routine, and conventional function. The courts have recognized computer functions such as “storing and retrieving information in memory” and/or “receiving or transmitting data over a network” as well‐understood, routine, and conventional functions. See MPEP § 2106.05(d).
Accordingly, when viewed as a whole, the claim is primarily directed to the abstract idea of determining a recalculation determination based on a hybrid confidence score derived from time-based and event-based factors to perform risk score assessment. The additional elements, whether considered individually or in combination with the judicial exception, do not integrate the judicial exception into a practical application or provide an inventive concept that amount to significantly more than the abstract idea itself.
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Amended Claim 3,
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 1, recites further limitation such as:
determining an event graph data object for the recalculation delay period; determining, based on the event graph data object ..., one or more graph-based events for the recalculation delay period; and determining the recalculation delay period event based on the one or more graph-based events. (That is part of the abstract idea recited in claim 1 (identifying events and determining event-based confidence score). The claim merely introduce the concept of creating a graph-based representation of events within a specific time window (i.e., delay period) and using this graph to identify important events that occurred during that period. These steps are recited at high level of generality such that they can be practically performed in the human mind with the aid of pen and paper, see MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The recitation of “using a graph-based machine learning model” to determine graph-based events for the recalculation delay period amount to no more than mere instructions to apply the abstract idea on a computer. Merely using a computer (i.e., machine learning model) as a tool to perform the concept of determining the significance of events that occurred during the time window is not sufficient to integrate the abstract idea into a practical application. See MPEP § 2106.05(f).
Step 2B: The additional element does not amount to significantly more than the judicial exception.
As noted above, merely using a machine learning model to determine a graph-based events for the recalculation delay period amount to no more than invoking a computer as a tool to perform the abstract idea. Mere instructions to apply the abstract idea on a computer cannot provide an inventive concept.
Therefore, claim 3 is ineligible.
Regarding Amended Claim 4,
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, recites further limitation such as:
determining an event history data object for the recalculation delay period; determining, based on the event history data object, one or more history-based events for the recalculation delay period; and determining the recalculation delay period event based on the one or more history-based events. (That is part of the abstract idea recited in claim 1 (identifying events and determining event-based confidence score). The claim merely introduce the concept of using data object (e.g., array, vector, or matrix) to represent historical events (i.e., patient data) during a specific time window and using that historical data to identify significant events. These steps are recited at high level of generality such that they could be performed in the human mind and/or with the aid of pen and paper, see MPEP § 2106.04(a)(2)(III). The claim does not define the technical implementation of the process.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 4 is ineligible.
Regarding Amended Claim 5,
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 1, recites further limitation such as:
determining an initial event weight for the recalculation delay period event based on the event weighting scheme for the target risk category; determining an event weight adjustment value for the recalculation delay period event based on the recalculation delay period event timestamp for the recalculation delay period event; and determining the event weight based on the initial event weight and the event weight adjustment value. (That is part of the abstract idea recited in claim 1 (determining an event weight and adjusting the event-based confidence score). The weighing scheme could be a mathematical function to reduce the risk score of a particular event over time, and thus, this step would encompass the mathematical concepts. Therefore, adjusting the weight of an event based on when the event occurred during the delay period is a process that could be performed in the human mind or a human using pen and paper to perform the claimed step. It is important to note that the use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. See MPEP § 2106.04(a)(2)(III). The claim does not detail on the technical implementation of the weighting process, and therefore, this weighting scheme is covering a process that can be performed in the human mind. Accordingly, the steps of claim 5 are recited at high level of generality, and they cover the mental process and/or mathematical concept.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 5 is ineligible.
Regarding Amended Claim 6,
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 1, recites further limitation such as:
determining a delay-based confidence score weight and an event-based confidence score weight for the target risk category; determining an adjusted delay-based confidence score based on the delay-based confidence score and the delay-based confidence score weight; and determining the hybrid confidence score based on the adjusted delay-based confidence score and the adjusted event-based confidence score. (That is part of the abstract idea recited in claim 1 (determining confidence scores). The claim merely introduces the concept of adjusting confidence scores without any detail on the technical implementations of these steps. Thus, weighting and adjusting the confidence score are steps of which could be derived manually. Accordingly, these steps are recited at a high-level of generality such that they could be performed in the human mind and/or with the aid of pen and paper, see MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 6 is ineligible.
Regarding Amended Claim 7,
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 1, recites further limitation such as:
in response to generating the positive recalculation determination, determining a recalculated predicted risk score for the target risk category. (That is part of the abstract idea recited in claim 1 (recalculation determination). The claim further defines the decision-making process with respect to the positive recalculation determination (i.e., affirmative recalculation determination). In other words, the idea of determining when recalculation is deemed necessary based on the risk assessment analysis using the determined hybrid confidence score compared to a threshold. This is also part of the abstract idea of a “mental process”. Therefore, this step falls under the Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, or opinion). See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 7 is ineligible.
Regarding Amended Claim 8,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 8. The only difference is that claim 1 is drawn to a method, and claim 8 is drawn to a system. The recitation of “A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations …,” which is directed to the applying of mere instructions (on a generic computer) to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instruction to apply the abstract idea using a generic computer component. Therefore, the additional elements do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, claim 8 is ineligible.
Regarding Amended Claim 10,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 10.
Therefore, claim 10 is ineligible.
Regarding Amended Claim 11,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 11.
Therefore, claim 11 is ineligible.
Regarding Amended Claim 12,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 12.
Therefore, claim 12 is ineligible.
Regarding Amended Claim 13,
The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 13.
Therefore, claim 13 is ineligible.
Regarding Amended Claim 14,
The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding Amended Claim 15,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 15. The only difference is that claim 1 is drawn to a method, and claim 15 is drawn to one or more non-transitory computer-readable. The recitation of “One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors…,” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instruction to apply the abstract idea using a generic computer component. Therefore, the additional elements do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, claim 15 is ineligible.
Regarding Amended Claim 17,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Amended Claim 18,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Amended Claim 19,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding Amended Claim 20,
The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
Regarding Amended Claim 21,
Step 2A Prong 1: Claim 21, which incorporates the rejection of claim 1, recites further limitation such as:
optimizing timing for the recalculating of the predicted risk score via the machine learning model to determine an updated version of the predicted risk score. (That is part of the abstract idea recited in claim 1. The claim limitation is broadly interpreted in light of the specification as determining when to optimally recalculate predicted risk scores by using hybrid confidence scores for predicted risk scores based at least in part on delay-based confidence scores for the predicted risk scores and event-based confidence scores for the predicted risk scores. The recalculation determination to update the risk score analysis is a process that can be performed in the human mind. For example, an individual (i.e., a doctor) can determine the risk score for a particular patient needs to be updated according to the risk score analysis based on time and event factors. See MPEP § 2106.04(a)(2)(III). The “machine learning model” recitation represents a computer component to perform the updated risk score generation.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The recitation of “via the machine learning model” amounts to no more than merely using a computer component (i.e., machine learning model) as a tool to perform the concept of risk score calculation or generation, which is not sufficient to integrate the abstract idea into a practical application. See MPEP § 2106.05(f).
Step 2B: The additional element does not amount to significantly more than the judicial exception.
As noted above, merely using a machine learning model to determine when to recalculate an updated risk score or to determine an updated risk score amounts to no more than invoking a computer as a tool to perform the abstract idea. Mere instructions to apply the abstract idea on a computer cannot provide an inventive concept.
Therefore, claim 21 is ineligible.
Regarding Amended Claim 22,
The claim recites similar limitations as corresponding claim 21. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 21, as described above, is equally applicable to claim 22.
Therefore, claim 22 is ineligible.
Regarding Amended Claim 23,
The claim recites similar limitations as corresponding claim 21. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 21, as described above, is equally applicable to claim 23.
Therefore, claim 23 is ineligible.
Conclusion
THIS ACTION IS MADE FINAL. 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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/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121