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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 5, 2026 has been entered.
Response to Amendment
The following Office action in response to communications received January 5, 2026. Claims 1 and 11 have been amended. Therefore, claims 1-20 are pending and addressed below.
Applicant’s amendments to the claims are not sufficient to overcome the 35 USC § 101 rejection set forth in the previous office action dated July 3, 2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 11 are directed to an abstract idea for preventing loss of longevity. The claims in the same way recite “receive a longevity parameter of a user; generate a decline threshold by receiving training data comprising a plurality of longevity parameters correlated to a plurality of decline thresholds by deriving the correlations using a data structure comprising an array indexing operation configured to map input values to output values; and outputting the decline threshold based on the longevity parameter; compare the longevity parameter to a decline threshold; identify a longevity decline driver as a function of the comparison, wherein the longevity decliner driver comprises a driver weight, and wherein identifying the longevity decline driver comprises: generating a driver model; receiving driver training data comprising a plurality of comparisons of longevity parameters and decline thresholds correlated to longevity decline drivers, wherein receiving the driver training data comprises processing the training data using a training data classifier; and determining the longevity decline driver; classify the longevity decline driver to a longevity decline stage; and generate a longevity plan as a function of the longevity decline stage using a lookup table comprising relationships between a plurality of longevity decline stages and a plurality of longevity plans.”
The limitations of “receive a longevity parameter of a user; generate a decline threshold by receiving training data comprising a plurality of longevity parameters correlated to a plurality of decline thresholds by deriving the correlations using a data structure comprising an array indexing operation configured to map input values to output values; and outputting the decline threshold based on the longevity parameter; compare the longevity parameter to a decline threshold; identify a longevity decline driver as a function of the comparison, wherein the longevity decliner driver comprises a driver weight, and wherein identifying the longevity decline driver comprises: generating a driver model; receiving driver training data comprising a plurality of comparisons of longevity parameters and decline thresholds correlated to longevity decline drivers, wherein receiving the driver training data comprises processing the training data using a training data classifier; and determining the longevity decline driver; classify the longevity decline driver to a longevity decline stage; and generate a longevity plan as a function of the longevity decline stage using a lookup table comprising relationships between a plurality of longevity decline stages and a plurality of longevity plans,” as drafted, is a process that, under its broadest reasonable interpretation, covers the performance of a “Certain Methods Of Organizing Human Activity” which are concepts performed by managing personal behavior, relationships or interactions between people (including social activities, teaching, and following rules or instructions) and “Mathematical Concepts” which are concepts performed by encompassing mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components. That is, other than reciting “processor, device comprising a biosensor, memory and a machine learning model,” nothing in the claim element precludes the step from practically being performed by managing personal behavior. For example, but for the “processor” language, “receiving” in the context of this claim encompasses the user manually retrieving the parameters for longevity. Similarly, the comparing the parameters to a decline threshold, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation being performed by personal behavior management and encompassing mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “processor, memory, device comprising a biosensor and a machine learning model” to perform all of the “receiving, comparing, identify, generating, determining and classifying” steps. The “processor, memory and a machine learning model” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 has additional limitations (i.e., processor, memory, wearable device comprising a biosensor and a machine learning model). Claim 11 has additional limitations (i.e., processor, wearable device comprising a biosensor and a machine learning model). Looking to the specification, these components are described at a high level of generality (¶ 58; It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-10 and 12-20). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Subject Matter Eligible over the Prior Art
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
The reasons why the claimed limitations overcome the prior art is because Park et al. and Galkin fail to explicitly teach the limitation comprising generate a decline threshold by: selecting training data comprising a plurality of longevity parameters correlated to a plurality of decline thresholds, wherein the correlations are derived from a data structure comprising an array indexing operation configured to maps input values to output values in order to optimize a runtime of the processor; receiving driver training data comprising a plurality of comparisons of longevity parameters and decline thresholds correlated to longevity decline drivers, wherein receiving the driver training data comprises processing the training data using a training data classifier; training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model; and determining the longevity decline driver using the trained driver machine learning model; classify the longevity decline driver to a longevity decline stage.
Response to Arguments
Applicant’s arguments filed January 5, 2026 have been fully considered but they are not persuasive. In the remarks applicant argues:
(1) Claims 1-20 stand rejected under 35 U.S.C. § 101 for allegedly being "directed to an abstract idea " (Office Action p. 2). Specifically, the Examiner argues the claims "cover the performance of a 'Certain Methods of Organizing Human Activity' ... and 'Mathematical Concepts"' (Office Action p. 3-4). Applicant respectfully traverses.
Under the January 2019 Guidance, now incorporated in the June 2020 revision of Manual of Patent Examining Procedure ("MPEP"), the first step of the Alice Corp. Pty. Ltd/Mayo Collaborative Services test was revised. MPEP 2106.04 [incorporating 2019 Revised Patent Subject Matter Eligibility Guidance, 84, Fed. Reg. 50, 52 (January 7, 2019)]. Revised Step 2A now focuses on (1) Whether the claim recites a judicial exception and (2) whether a recited judicial exception is integrated into a practical application. Id. Only if a claim recites a judicial exception and fails to integrate the exception into a practical application, further analysis pursuant to the second step (2B) in the Alice Corp. Pty. Ltd/Mayo Collaborative Services test is needed. Id.
Additionally, the July 2024 Subject Matter Eligibility Examples should be used in
conjunction with the USPTO guidance on subject matter eligibility, which is incorporated into MPEP 2106. These examples are discussed in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence. The update includes examples 47-49, which provide insights into the application of the guidance, particularly for emerging technologies like artificial intelligence.
Solely in the interest of advancing prosecution and while not acquiescing to the Examiner's position, Applicant has amended claim 1 to recite:
An apparatus for preventing loss of longevity, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing
instructions configuring the at least a processor to:
receive, from a wearable device, a longevity parameter of a user, wherein the wearable device comprises a biosensor, the biosensor configured to: measure a physiological data; and transmit, using a network interface device, the physiological data to the at least a processor;
receive, from a user interface communicatively coupled to the at least a processor, input comprising information associated with-a longevity of the user; generate a decline threshold by:
selecting training data comprising a plurality of longevity parameters correlated to a plurality of decline thresholds, wherein the correlations are derived from a data structure comprising an array indexing operation configured to map input values to output values in order to optimize a runtime of the processor; training a threshold machine-learning model using the training data, wherein the threshold machine-learning model is stored in the memory; and
outputting the decline threshold; compare the longevity parameter to the decline threshold;
identify a longevity decline driver as a function of the comparison, wherein identifying the longevity decline driver comprises: generating a driver machine learning model, wherein the driver machine learning model is stored in the memory; receiving driver training data comprising a plurality of comparisons of longevity parameters and decline thresholds correlated to longevity decline drivers, wherein receiving the driver training data comprises processing the training data using a training data classifier;
training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model; and determining the longevity decline driver using the trained driver machine learning model;
classify the longevity decline driver to a longevity decline stage; and
generate a longevity plan as a function of the longevity decline stage using a lookup table comprising relationships between a plurality of longevity decline stages and a plurality of longevity plans. Applicant submits that, according to MPEP 2106.04, independent claims 1 and 11 and their dependent claims 2-10 and 12-20, recite allowable subject matter under Step 2A and/or 2B of the eligibility analysis, as discussed further below in this paper. Step 2A, Prong one
According to the MPEP, "[i]n Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim" MPEP § 2106.04 (II)(A)(1). "The Supreme Court has held that Section 101 contains an implicit exception for '[1]aws of nature, natural phenomena, and abstract ideas,' which are 'the basic tools of scientific and technological work"' Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (citing Mayo, 566 US at 71, 101 USPQ2d at 1965). However, the Supreme Court has clarified that "[a]t some level, all inventions embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas" Id. The Supreme Court has cautioned "to tread carefully in construing this exclusionary principle lest it swallow all of patent law" Id.
Under Step 2A, Prong One, the Examiner alleges that the limitations of Applicant's claim
1 "is a process that, under its broadest reasonable interpretation, covers the performance of a 'Certain Methods Of Organizing Human Activity' ... and 'Mathematical Concepts"' (Office Action p. 3). Applicant respectfully traverses this rejection for at least the following reasons.
Methods for Organizing Human Activity
The MPEP states that not all methods of organizing human activity are considered abstract ideas. According to MPEP §2106.04(a)(2)(II), the phrase "methods of organizing human activity" is used to describe concepts relating to fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations), and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
MPEP guidance makes clear the necessary existence of boundaries for determination of a judicial exception under a certain method of organizing human activity classification. The MPEP guidance states:
The term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. First, not all methods of organizing human activity are abstract ideas (e.g., "a defined set of steps for combining particular ingredients to create a drug formulation" is not a certain "method of organizing human activity"), In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1160-61, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP§2106.04(a)(3).
(MPEP 2106.04(a)(2)(II), emphasis added).
Claim 1, as amended, includes limitations such as "receive, from a wearable device, a longevity parameter of a user, wherein the wearable device comprises a biosensor, the biosensor configured to: measure a physiological data and transmit, using a network interface device, the physiological data to the at least a processor," "the driver machine learning model is stored in the memory," and "the driver machine learning model is stored in the memory." These limitations recite specialized biosensor hardware integrated with network transmission capabilities for capturing physiological measurements and machine learning models persistently stored in memory for computational processing. The claim recites technical components comprising biosensor hardware, network interface devices, and optimized data structures, together with technical operations comprising physiological data measurement and transmission and machine-learning model storage and execution. Accordingly, the claim cannot be fairly characterized as reciting a method of organizing human activity.
Mathematical Concepts
"The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations." MPEP § 2106.04(a)(2)(I)."A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept." Id. For instance, construction of a tree structure based on spatial relationships between symbols in a textual formula and use thereof in a "local positioning" algorithm to govern display thereof has been found not to recite a mathematical calculation because they "do not recite process steps which are themselves mathematical calculations, formulae, or equations." In re Freeman, 573 F.2d 1237, 1239-1240 and 1246 (C.C.P.A. 1978) (emphasis added).
The USPTO recently clarified the difference between claims that "recite" an abstract idea and claims that just "involve" an abstract idea in a Memorandum to Technology Center 3600. USPTO, Memorandum to Tech. Centers 2100, 2600, and 3600 (August 4, 2025). The USPTO drew a distinction between examples 39 and 47 of the Subject Matter Eligibility Examples. Id. at p. 3. Example 39 "illustrates claim limitations that merely involve an abstract idea" whereas Example 47 "shows limitations that recite an abstract idea." Id. The recitation of "training the neural network in a first stage using the first training set" in Example 39 does not recite a judicial exception, regardless of whether it "may involve or rely upon mathematical concepts." Id.
This can be distinguished from Example 47 of the Subject Matter Eligibility Examples, which the USPTO characterizes as reciting an abstract idea. Id. Claim 2 of Example 47 recites: "training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm" Subject Matter Eligibility Examples, Example 47. The USPTO Memo distinguishes this claim in Example 47 from the claim in Example 39 on the basis that claim 2 of Example 47 "requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea." USPTO Memo, p. 3.
Claim 1, as amended, includes limitations such as "a data structure comprising an array indexing operation configured map input values to output values in order to optimize a runtime of the processor, “training a threshold machine-learning model using the training data, wherein the threshold machine-learning model is stored in the memory," and "training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model." The claim recites technological applications of computational operations: array indexing operations to achieve processor runtime optimization through constant-time data access; machine learning model training to enable automated threshold generation and driver identification from biosensor data streams; and recursive training architecture to create iterative model refinement that improves classification accuracy over time. The claim integrates any mathematical operations into a practical application by improving the functioning of the processor through runtime optimization, enabling processing of physiological data, and implementing a self-improving classification system through recursive training feedback. Similar to Example 39, Claim 1, as amended, recites limitations that merely involve mathematical concepts but do not recite a judicial exception, because the claim does not specify any particular mathematical formula, named algorithm, or mathematical technique, and instead recites high-level training and data- processing operations as part of a concrete technological implementation. Accordingly, the claim cannot be fairly characterized as directed to mathematical concepts in the abstract. Step 2A, Prong two
Under Step 2A, Prong Two, examiners must "evaluate whether the claim as a whole integrates the judicial exception into a practical application of that exception." MPEP §2106.04(d). A "claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." This is the case where the claim reflects an improvement in technology. Id. In determining patent eligibility, examiners should consider whether the claim "purport(s) to improve the functioning of the computer itself" or "any other technology or technical field." Alice Corp. Pty. Ltd. v. CLSBank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1976, 1984 (2014). For example, in Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the Federal Circuit held claims patent-eligible where the claims to virus scanning as they were found to be an improvement in computer technology and it was found that they were not directed to an abstract idea. Similarly, in Core Wireless Licensing S.A.R.L. v. LG Elecs., Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018), the Federal Circuit found claims patent-eligible where the claims recited "[a]n improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application" MPEP 2106.05(a)(I). In Enfish, LLC v. Microsoft Corp., the Federal Circuit held that the claims were eligible under Step 2A, Prong Two because they were directed to a self-referential database table that improved the way computers store and retrieve data.
The USPTO has recently provided a precedential ruling that applies subject matter eligibility jurisprudence to artificial intelligence inventions. Ex parte Desjardins et al., Appeal No. 2024-000567, Application 16/319,040, ARP Decision on Request for Rehearing (P.T.A.B. Sept. 26, 2025) (Precedential, Nov. 4, 2025); see also SEC v. Chenery Corp., 332 U.S. 194 (1947) ("Chenery II") (holding that agencies are free to create agency-wide rules through the common law method of adjudicative rulemaking).
In Desjardins, the USPTO Appeals Review Panel (ARP) held claims directed to training a machine learning model subject matter eligible under Step 2A Prong Two of the MPEP prescribed analysis process. Desjardins, p. 7. The USPTO interpreted its own manual and Federal Circuit jurisprudence, articulating the rule that "claims directed to an improvement in the functioning of a computer or an improvement to other technology or technical field are patent eligible." Id., p. 8 (citing MPEP §§ 2106.04(d)(1); 2106.05(a) (citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir 2016) and McRo, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016))). To support its holding, the USPTO found that:
1. Desjardins's specification described an improvement in training the machine learning model itself:
At [0021] of the Specification, "allow[ing] artificial intelligence (AI) systems to "use less of their storage capacity" and enable[ing] "reduced system complexity," Desjardin, p. 9;
and
2. Desjardins's claims reflected the improvement from the specification:
claim 1 . . . reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task, id.
As in ex parte Desjardins, the Present Application represents an improvement in technology and is subject matter eligible under Step 2A Prong Two.
Just as Desjardins's specification taught improvements to AI that "use less . .. storage capacity" and "enable reduced system complexity," the Present Application's specification teaches improvements to AI including:
"use of a dedicated threshold machine-learning model configured to calculate a decline threshold from longevity parameters, enabling automated, model-driven threshold generation rather than static or manually defined limits, Specification [0021];
"training the threshold machine-learning model with data that correlates longevity parameters to decline threshold outputs, improving the accuracy and personalization of threshold determination across different users and physiological metrics, Specification [0021];
"using a driver machine learning model that determines a longevity decline driver and/or driver weight based on a structured comparison between a longevity parameter and a decline threshold, thereby improving automated identification and quantification of decline factors in a longevity monitoring system, Specification [0026];
"training the driver machine learning model with driver training data that explicitly correlates differences between measured longevity parameters and predefined decline thresholds to corresponding longevity decline drivers and driver weights, enabling more accurate, data-driven mapping between physiological changes and decline causation, Specification [0026];
Similarly, like Desjardins, the claims of the Present Application reflect the AI improvements with limitations including:
"training a threshold machine-learning model using the training data, wherein the threshold machine-learning model is stored in the memory;
"generating a driver machine learning model, wherein the driver machine learning model is stored in the memory;
"receiving driver training data comprising a plurality of comparisons of longevity parameters and decline thresholds correlated to longevity decline drivers, wherein receiving the driver training data comprises processing the training data using a training data classifier;
"training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model; and
"determining the longevity decline driver using the trained driver machine learning model;
Here, Claim 1 as amended recites "a data structure comprising an array indexing operation configured [to] map input values to output values in order to optimize a runtime of the processor, “a wearable device comprises a biosensor, the biosensor configured to: measure a physiological data and transmit, using a network interface device, the physiological data to the at least a processor," and "training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model." These limitations integrate any judicial exception into a practical application by improving the functioning of the processor through runtime optimization achieved via constant-time array indexing operations, enabling processing of physiological biosensor data streams, and implementing a recursive training architecture that creates iterative model refinement to improve classification accuracy over time. Like the behavior-based virus scanning in Finjan, the claimed array indexing data structure enables the processor to achieve optimized runtime performance for biosensor data processing. Like the improved user interface in Core Wireless, the claimed combination of optimized data structures, biosensor hardware integration, and recursive machine learning training constitutes an improvement to the functioning of wearable health monitoring systems by enabling real-time decline driver identification and classification. Amended claim 1 recites improvements to processor runtime performance, biosensor data processing capability, and machine learning classification accuracy. Additionally, like Enfish, amended Claim 1 recites an apparatus with a processor and memory configured to receive physiological data from a wearable biosensor, process that data using machine-learning models stored in memory, and generate outputs through defined computational mechanisms. Like the self-referential table in Enfish, the claim is rooted in how the computer operates, not merely in what information it displays. Amended Claim 1 requires generating a decline threshold using training data correlated through a data structure comprising an array indexing operation configured to map input values to output values in order to optimize runtime of the processor, which reflects a technical improvement in data access and execution efficiency analogous to the improved storage and retrieval techniques recognized as patent-eligible in Enfish.
Accordingly, and for at least the above-described reasons, Applicant respectfully submits that claim 1 as amended recites patentable subject matter. Applicant respectfully requests that this rejection be withdrawn.
Sten 2B
Although the 2B analysis by the Office is moot in light of the amendments to claim 1 and the arguments above. The office alleges that "the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use" (Office Action p. 5). This conclusion is not supported by the record or consistent with the applicable legal framework.
"The second step of the Alice test is satisfied when the claim limitations" involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.' "Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S.Ct. at 2359). “MPEP guidance specifies how an examiner is permitted to conclude something is "well- understood, routine, [and] conventional." In BASCOM the court confirms that a combination of known elements arranged in a non-conventional and non-generic manner can amount to an inventive concept. Bascom Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016). In Berkheimer the court held that whether something is well-understood, routine, and conventional is a question of fact requiring evidence. Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018).
Amended claim 1 recites a specific combination of technical elements that work together in a non-conventional manner to achieve processor runtime optimization and biosensor data processing. The additional elements include:
""a wearable device comprises a biosensor, the biosensor configured to: measure a physiological data and transmit, using a network interface device, the physiological data to the at least a processor"
""a data structure comprising an array indexing operation configured [to] map input values to output values in order to optimize a runtime of the processor"
""training a threshold machine-learning model using the training data, wherein the threshold machine-learning model is stored in the memory"
""training the driver machine learning model as a function of the processed driver training data, wherein the driver training data comprises previous outputs from the driver machine learning model"
""processing the training data using a training data classifier"
These additional elements are neither insignificant extra-solution activity nor merely token post-solution steps. Similar to Bascom, recites a non-conventional and non-generic arrangement of biosensor hardware, optimized data structures, and recursive machine learning training that work together to solve to create an apparatus for preventing loss of longevity. The ordered combination of the elements in claim 1, as amended, include: (1) biosensor measurement and network transmission of physiological data, (2) array indexing-based threshold generation optimized for processor runtime, (3) training data classification preprocessing, (4) recursive driver model training using previous model outputs, and (5) automated longevity plan generation, which represents an inventive concept that transforms the claim into patent-eligible subject matter by enabling real-time processing capabilities. Therefore, the amended claims, viewed as a whole, amount to significantly more than the purported abstract idea and satisfy the requirements of 35 U.S.C. § 101.
As such, Applicant submits that claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above.
Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Applicant submits that the rejection to claim 11 has been overcome for the same reasons as to claim 1. Applicant respectfully requests reconsideration and withdrawal of the rejection. Claims 2-10 and 12-20, directly or indirectly, depend on claim 1 or claim 11 and thus recite all the same elements as claim 1 and claim 11. Applicant, therefore, submits that Claims 2-10 and 12-20 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11.
In response to argument (1), the Examiner has carefully considered Applicant's detailed arguments, the amended claim, and the cited authorities including Ex parte Desjardins, Enfish, Finjan, and the USPTO's recent guidance. However, for the reasons set forth below, the rejection under 35 U.S.C. § 101 is maintained.
Step 2A, Prong One: The Claim Recites a Judicial Exception
Applicant argues that the claim does not recite a method of organizing human activity or mathematical concepts. The Examiner disagrees. The claim is directed to the abstract idea of monitoring health data, predicting decline, and generating a wellness plan—a process that falls squarely within the "certain methods of organizing human activity" grouping. At its core, the claim describes a conceptual framework for: (1) collecting personal health information, (2) analyzing that information to identify risks, (3) categorizing those risks into stages, and (4) generating a plan based on those stages. This is fundamentally a process of managing personal health behavior, which is an enumerated abstract idea under MPEP § 2106.04(a)(2)(II) ("managing personal behavior or relationships or interactions between people"). The mere addition of technical terminology such as "machine-learning model," "array indexing operation," and "biosensor" does not alter the fundamental character of what is being claimed—a series of mental steps that could conceptually be performed by a human health coach: observing health data, recognizing patterns, identifying causes of decline, categorizing severity, and recommending interventions.
Regarding mathematical concepts, the claim recites multiple mathematical operations including "training a threshold machine-learning model," "training the driver machine learning model," and "processing the training data using a training data classifier." These are processes that inherently involve mathematical calculations, algorithms, and statistical operations. Unlike Example 39 cited by Applicant, where the claims merely "involved" mathematical concepts without reciting them, this claim affirmatively recites the construction and application of machine learning models—which are fundamentally mathematical in nature. The claim does not merely "involve" mathematics; it requires the performance of mathematical operations as essential claim steps.
Step 2A, Prong Two: The Claim Does Not Integrate the Exception Into a Practical Application
Applicant contends that the claim improves technology through runtime optimization, biosensor integration, and recursive training. The Examiner finds these arguments unpersuasive. The alleged "improvements" are recited at a high level of generality without any detail showing how they actually improve computer functionality. The "array indexing operation configured to map input values to output values in order to optimize a runtime of the processor" is a generic data structure operation—array indexing is a fundamental computer science concept that has existed for decades. The claim does not specify any novel or unconventional indexing technique; it merely states an intended result (optimizing runtime) without describing how the indexing operation achieves this optimization in a non-conventional way.
Similarly, the machine learning elements are recited functionally without any specific implementation details. The claim says "training a threshold machine-learning model" and "training the driver machine learning model" but provides no information about the architecture, algorithms, or techniques used. This is precisely the kind of functional claiming that the courts have found insufficient to confer eligibility. As the Federal Circuit stated in FairWarning IP, LLC v. Iatric Systems, Inc., reciting "a computer with a processor and memory" and "conventional computer components" programmed to perform "generic computer functions" does not transform an abstract idea into a practical application.
The comparison to Ex parte Desjardins is inapt. In Desjardins, the specification specifically described how the claimed invention allowed AI systems to "use less storage capacity" and "enable reduced system complexity," and the claims reflected those specific improvements. Here, it merely describes desired outcomes but does not disclose any specific technical implementation that achieves those outcomes in a non-conventional way. The claim's reference to "optimizing runtime" is a statement of intent, not a description of how the computer's operation is actually improved.
Step 2B: The Claim Lacks an Inventive Concept
Even if the claim were analyzed under Step 2B, it fails to recite significantly more than the abstract idea. The additional elements—biosensor, processor, memory, network interface, machine learning models, data structures—are all well-understood, routine, and conventional components performing their expected functions. Applicant argues that the combination of these elements is non-conventional under Bascom. However, the claim simply arranges generic components to perform their ordinary functions: a biosensor measures data, a processor executes machine learning algorithms stored in memory, and a network transmits information. This is not a "non-conventional and non-generic arrangement" but rather the standard way such systems are built. The claim does not specify any unconventional interaction between these components or any novel ordering that produces a new technical effect.
The Berkheimer framework requires factual support for findings of conventionality. The Examiner relies on the common knowledge in the art that: (1) biosensors for measuring physiological data are conventional, (2) machine learning model training is a well-established technique, (3) array indexing is a fundamental data structure operation, and (4) generating wellness plans from health data is a conventional application. These are matters of general knowledge in the fields of health monitoring and data processing, and Applicant has provided no evidence that any element or combination is unconventional.
For the foregoing reasons, the claim remains directed to the abstract idea of monitoring health, predicting decline, and generating wellness plans, implemented using conventional computer components performing their routine functions. The rejection under 35 U.S.C. 101 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub. No.: US 20210166137 A1 to Neumann; A system for generating a longevity element and an instruction set for a longevity element plan, the system including at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least an element of user-reported data, determine, using the at least an element of user-reported data and a first machine-learning process, a longevity element, calculate, using a longevity element and at least a second element of data, a compensatory supplement, and generate, using the at least an element of user-reported data and at least a longevity element, an instruction set for a longevity element plan.
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/E.B.W/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683