DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/24/2026 has been entered.
Status of the Claims
The status of the claims as of the response filed 9/24/2026 is as follows: Claims 3, 7, 13, and 17 remain cancelled. Claims 1 and 11 are currently amended. Claims 6, 8, 16, and 18 are as previously presented. Claims 2, 4-5, 9-10, 12, 14-15, and 19-20 are original. Claims 1-2, 4-6, 8-12, 14-16, and 18-20 are currently pending in the application and have been considered below.
Response to Amendments
Rejection Under 35 USC 112(a)
Claims 1 and 11 have been amended to remove the unsupported subject matter identified in para. 8 of the final rejection mailed 3/24/2026 such that the corresponding 35 USC 112(a) rejections are withdrawn.
Rejection Under 35 USC 112(b)
Claims 1 and 11 have been amended to resolve the indefiniteness identified in para. 9 of the final rejection mailed 3/24/2026 such that the corresponding 35 USC 112(b) rejections are withdrawn.
Response to Arguments
Rejection Under 35 USC 101
On pages 4-5 of the response filed 9/24/2026 Applicant argues that the newly-introduced step for selecting a monitoring device from the ranked remaining plurality of monitoring devices as a function of a condition parameter by minimizing a loss function computed as a function of the condition parameter and the ranked devices does not fall into the “certain methods of organizing human activity” grouping of abstract idea because it is “a computational operation executed by the computing device, which involves multiple streams of sophisticated large scale heterogeneous data transformation and correlation that is beyond the capacity of human organizational activity.” Applicant further asserts that “the loss function is not a single arithmetic comparison but a vector-based multivariable optimization in which the computing device compares one or more user preferences, each weighted by a respective coefficient representing its relative importance, and in which higher-order polynomial expressions may alternatively be employed.” Applicant’s arguments are fully considered, but are not persuasive. Examiner respectfully disagrees that this limitation, as presently drafted, is not abstract. A human actor managing their personal behavior would be capable of considering at least one “condition parameter” associated with a physiological status like a heart condition when making a judgment to select an appropriate monitoring device. Use of the mathematical concept of a loss function (which is itself an abstract concept) to perform the selection is also not outside the realm of what is practical for a human actor to achieve; para. [0024] of Applicant’s specification describes this function as “compar[ing] one or more user preferences to a mathematical expression representing a plurality of user conditions. Mathematical expression may include a linear combination of variables, weighted by coefficients representing relative importance of each goal parameter.” Examiner submits that a human actor managing their personal behavior when selecting or recommending an appropriate monitoring device for a patient would be more than capable of comparing user preferences to a linear combination of weighted variables. Even if the claim did require more complex mathematical operations that would not be achievable by a human actor managing their personal behavior (which it currently does not), minimizing a loss function clearly fits into the “mathematical concepts” grouping of abstract idea, as supported by the cited portion of the disclosure describing it as “a mathematical expression.” Use of a computer to perform the presently-recited selection step including a loss function minimization mathematical concept does not preclude such steps from being abstract and instead amounts to mere instructions to “apply” the abstract functions such that they are digitized and/or automated by using high-level computing components as a tool for computerized implementation (see MPEP 2106.05(f)).
On pages 6-7 Applicant argues that the machine learning models are not ‘black box’ type models and instead amount to “a specific, well-defined multi-stage machine-learning architecture” which “are technically integrated into a pipeline in which each stage constrains the next,” which “impose[s] meaningful limits on any alleged judicial exception by confining it to a particular machine-learning training and inference pipeline that matches specific sensor hardware to a physiological status.” Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that the machine learning aspects of the invention appear to merely invoke known machine-learning techniques for application to the otherwise-abstract process of selecting an appropriate monitoring device for a user by considering various device attributes and patient characteristics (e.g. including a disease or health condition determined by performing a diagnosis on patient data) and preferences such that the device selection operation is nominally automated/digitized. There is no evidence that the multi-stage architecture of the machine learning models solves any specific technical problem associated with current machine learning model architectures or training techniques, nor improves upon the underlying technology of a computer system or user monitoring device. Instead, known machine learning techniques (e.g. those listed as non-limiting examples in at least paras. [0013], [0016], [0018], & [0027]-[0037] of Applicant’s specification) are utilized in an otherwise-abstract sequential diagnostic and recommendation workflow to automate inefficiencies in current business practices of monitoring device recommendation and marketing (as outlined in para. [0002] of Applicant’s specification), which does not meaningfully limit the abstract idea or provide integration into a practical application.
On pages 7-8 Applicant argues that the newly-introduced ‘selecting’ step is “inseparably tied to the physical detection characteristics of specific sensor modalities rather than to any generic computation, and this arrangement is not the ‘apply it on a computer’ combination contemplated by MPEP 2106.05(f).” Applicant’s arguments are fully considered, but are not persuasive. As indicated above, the ‘selecting’ step is part of the abstract idea itself because it reflects judgments and mathematical operations that could be performed by a human actor managing their personal behavior to identify and recommend an appropriate device for a given patient. Because these functions are part of the abstract idea itself, they cannot provide “significantly more” than the abstract idea and thus do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”)
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 1-2, 4-6, 8-12, 14-16, and 18-20.
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 1-2, 4-6, 8-12, 14-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-2, 4-6, and 8-10 are directed to a system (i.e. a machine) and claims 11-12, 14-16, and 18-20 are directed to a method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1 and 11 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity (e.g. managing personal behavior, interactions, or relationships between people) and mathematical concepts. Specifically, claim 1 (as representative) recites:
A system for presenting a monitoring device identification, the system comprising: a computing device, the computing device configured to:
obtain, from a graphical user interface, a user profile;
identify a user condition as a function of a user profile using a condition machine learning model comprising a classifier and further comprising:
receiving user condition training data correlating a plurality of user profiles to a plurality of conditions;
training, iteratively, the condition machine learning model using the condition training data, wherein training the condition machine learning model includes retraining the condition machine learning model with feedback from previous iterations of the condition machine learning model; and
identifying the user condition using the trained condition machine learning model, wherein the user condition comprises a heart condition;
generate a training data classifier as a function of unfiltered training data using a classification algorithm;
filter elements of the unfiltered training data using the training data classifier to generate a plurality of filtered monitoring training data sets each containing a plurality of data entries classifying condition elements to categories of monitoring devices and/or detection methods;
select at least one filtered monitoring training data set of the plurality of monitoring training data sets as a function of the heart condition using the training data classifier;
determine, as a function of the user condition comprising the heart condition, a monitoring device of a plurality of monitoring devices relating to the user condition comprising the heart condition, wherein the monitoring device comprises a sensor configured to monitor a user’s heartbeat, wherein determining the monitoring device utilizes a monitoring machine-learning model trained using the selected at least one monitoring training data set which further comprises:
applying the selected at least one monitoring training data set to:
an input layer of nodes comprising a condition element of the user condition generated by the trained condition machine learning model;
one or more intermediate layers of nodes; and
an output layer of nodes comprising a detection method;
adjusting one or more connections and one or more weights of applied weighted values between nodes in adjacent layers of the monitoring machine-learning model;
detecting additional correlations between the output layer of nodes and the input layer of nodes;
iteratively updating the monitoring machine-learning model as a function of the detected additional correlations;
training, iteratively, the update monitoring machine-learning model using the selected at least one monitoring training data set, wherein the monitoring machine-learning model includes retraining the monitoring machine-learning model with feedback from previous iterations of the monitoring machine learning model; and
determining the monitoring device as a function of the trained monitoring machine-learning model and the user condition, wherein the monitoring machine-learning model is configured as a function of the selected at least one monitoring training data set to:
determine a device score for each monitoring device of the plurality of monitoring devices based on a plurality of user conditions, wherein the device score represents an ability of the monitoring device to provide data concerning the user condition of the plurality of user conditions;
combine the devices scores for each monitoring device concerning the user condition and a secondary user condition;
rank each monitoring device score based on the combined device score compared to a threshold relative to the user condition as a function of the condition machine learning model and the secondary user condition, wherein ranking each monitoring device further comprises:
eliminating a plurality of monitoring devices with device scores that does not reach the threshold; and
ranking a remaining plurality of monitoring devices;
select the monitoring device from the ranked remaining plurality of monitoring devices as a function of a condition parameter, wherein the condition parameter is associated with a physiological status corresponding to the heart condition, and wherein selecting the monitoring device comprises minimizing a loss function computed as a function of the condition parameter and the ranked remaining plurality of devices; and
present the monitoring device at the graphical user interface.
Each of the italicized steps, when considered as a whole, describe mathematical concepts and/or instructions that a user could follow to manage their personal behavior and/or interactions with others during a medical monitoring device selection process. For example, a user (e.g. a clinician or other person with medical knowledge or training) could obtain information about a patient and determine a condition (e.g. a heart condition) of the patient based on the patient information by training/fitting, using, and updating a classifier model (e.g. a linear regression equation, decision tree, or other simple classifier as disclosed via examples in at least [0016], [0030], & [0032] of Applicant’s specification) in a diagnostic procedure. The clinician could observe an unfiltered set of training data and use a classification algorithm to filter and select a specific subset of training data related to a particular type of monitoring device or method for use in fitting a monitoring device selection model. The clinician could then select an appropriate monitoring device associated with the heart condition by using their medical expertise and mathematical calculations to create, evaluate, and adjust a layered node structure connected by weighted values based on the filtered training dataset that outputs an appropriate device correlated with a patient’s condition(s), e.g. a heartrate monitor when the patient is diagnosed with an arrhythmia and cardiovascular disease. Such device determinations could be made by assigning a device score to each device, performing mathematical calculations to combine scores in the case of multiple user conditions, ranking each device based on the scores, comparing the scores to a threshold and eliminating unsuitable devices not meeting the threshold device score, and ranking the remaining devices. Finally, the user could use their clinical expertise to select an appropriate device from the ranked list of remaining devices by considering the patient’s physiological status and choosing a device that optimizes/minimizes a mathematical loss function evaluating various dimensions such as user preferences, costs, physiological considerations, etc., and finally present the selected device as an output, e.g. by discussing their conclusion with a patient or providing a written report detailing their recommendations. In accordance with the Updated SME Examples published in July 2024, when a claim can be considered to recite an abstract idea in two groupings, the Examiner should identify such groupings and may treat the claims in the same manner as claims reciting a single judicial exception. Thus, the steps recited in claim 1 recite an abstract idea in the form of mathematical concepts and certain methods of organizing human activity, and are considered together as a single abstract idea for further analysis. Claim 11 recites substantially similar limitations as exemplary claim 1 and is also found to recite an abstract idea under the same analysis.
Dependent claims 2, 4-6, 8-10, 12, 14-16, and 18-20 inherit the limitations that recite an abstract idea from their dependence on claims 1 or 11, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2, 4-6, 8-10, 12, 14-16, and 18-20 recite further limitations that, under their broadest reasonable interpretations, further limit the abstract idea recited in the independent claims.
Specifically, claims 2 and 12 recite that the user profile further comprises a biological extraction, which a clinician would be able to obtain and analyze when diagnosing the patient’s condition (e.g. by considering past sensor readings or diagnostic test results in a patient’s chart).
Claims 4 and 14 recite that the detection method includes a method to indicate a condition state, which a clinician would be able to glean from a reference table correlating a condition to a detection method to indicate a condition state.
Claims 5 and 15 recite that determining the monitoring device further comprises measuring situational information of a first condition element in conjunction with a second condition element, which a clinician could consider when selecting a monitoring device by incorporating knowledge of the different types of parameters measured by each device into their selection process.
Claims 6 and 16 recite that the monitored situational information is the location of the first condition element in relation to the second condition element, which a clinician could achieve by further considering the location/placement required for each monitoring device to monitor their respective parameters when selecting the monitoring device.
Claims 8 and 18 recite generating the monitoring model by determining a plurality of candidate monitoring devices and selecting the monitoring device from the plurality of candidate devices, which a clinician could achieve by using the reference dataset to determine several candidates and then select a most appropriate device using their medical expertise.
Claims 9 and 19 recite presenting a plurality of candidate monitoring devices, obtaining a user preference, ranking the plurality of candidate monitoring devices as a function of the user preference, and selecting the monitoring device as a function of the user preference. A clinician could perform these steps as a certain method of organizing human activity by communicating a list of device candidates to the patient, receiving a preference (e.g. color, cost, size, or other parameter of the device) from the patient, ranking the candidate devices based on the preference parameter, and ultimately selecting one of the candidate devices that best matches the patient’s preference.
Claims 10 and 20 recite generating a parameter estimation using the ranked plurality of candidate devices and the user condition, computing a difference between the ranked plurality of candidate devices and the user condition as a function of the parameter estimate, and selecting the monitoring device for the user as a function of computing the difference. A clinician could achieve these steps by coming up with scores (i.e. parameter estimates) for the candidate devices, comparing differences between the scores for each candidate, and selecting the final monitoring device based on the score comparisons.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1 and 11 do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 11 include the additional elements of a computing device configured to perform the various steps; obtaining a user profile specifically from a graphical user interface; the user condition identification model being a trained machine learning classifier model; determining the monitoring device specifically via a monitoring machine-learning model trained using the selected monitoring training data set; and presenting the monitoring device at the graphical user interface.
The use of a computing device, a graphical user interface, and specifically machine learning models in these claims merely serve to automate the mathematical concepts and/or certain methods of human activity such that they amount to the words “apply it” with a computer. For example, obtaining a user profile, identifying a user condition as a function of the profile, selecting/filtering training data, fitting a model to the selected training data, and determining the monitoring device as a function of the fitted model and the user condition via various scoring and ranking methods are steps that could otherwise be achieved by mathematical operations and/or a clinician managing their personal behavior (as explained above), and the use of the computing device, GUI, and two high-level “black box” type machine learning models merely digitize/automate these functions so that they are achieved in an electronic environment (see MPEP 2106.05(f)). The use of a GUI to obtain input information and present output information also amounts to insignificant extra-solution activity in the form of necessary data gathering and the nominal display of data because this element is merely nominally recited as a means to electronically input and output data for and from the main abstract analysis steps. Accordingly, each independent claim as a whole is directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2, 4-6, 8-10, 12, 14-16, and 18-20 is also not integrated into a practical application under a similar analysis as above. Claims 2, 4-6, 8-10, 12, 14-16, and 18-20 do not recite any new additional elements and are performed with the same computing elements as the independent claims such that they also amount to instructions to “apply” the judicial exception with a computer as explained above.
Accordingly, the additional elements of claims 1-2, 4-6, 8-12, 14-16, and 18-20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-2, 4-6, 8-12, 14-16, and 18-20 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device, a GUI, and high level machine learning models used for performing the obtaining, identifying, receiving, training, generating, filtering, selecting, determining, applying, adjusting, detecting, updating, combining, ranking, selecting, presenting, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes the following portions of Applicant’s specification:
- [0008], [0047], [0050]-[0054], disclosing many types of known computing devices that can implement the invention;
- [0011], [0055]-[0057], contemplating many types of user input elements and providing an expansive definition of a graphical user interface;
- [0013], [0016], [0022], [0030], disclosing many types of known machine learning methods that could be used to achieve the user condition determination, training data filtering, and device selection functions of claims 1 and 11.
From these disclosures, one of ordinary skill in the art would understand that many types of generic computing devices with GUI input elements and high-level machine learning models could be used to implement the invention. Further, the combination of these additional elements is not expanded upon in the specification as a unique arrangement and as such relies on the knowledge of one of ordinary skill in the art to understand the combination of components within a computer (e.g. a processor implementing software code in combination with a GUI) as a well-known and generic combination for automating an abstract idea that could otherwise be performed via mathematical concepts and/or certain methods of organizing human activity and thus do not provide an inventive concept. Examiner further notes that the training/configuration and use of computerized machine learning models for medical diagnosis and/or health resource/treatment recommendation/scoring are well-understood, routine, and conventional, as evidenced by at least Asthana et al. (US 20190371463 A1) [0071]-[0080] & [0100]; Allen et al. (US 20180075194 A1) Fig. 1 & [0024]; Asthana et al. (Reference X on the PTO-892 mailed 2/22/2023) first para. of section III on Pg 15; Kononenko (Reference U on the PTO-892 mailed 6/29/2023) Pg 90 second para. & section 3.2 on Pgs 94-96; and Davenport et al. (Reference V on the PTO-892 mailed 6/29/2023) abstract & section “Diagnosis and treatment applications” on Pgs 95-96.
Regarding the functional additional elements, as noted above, the steps of obtaining and presenting data with a GUI amounts to insignificant extra-solution activity. These activities are also nothing more than those recognized as well-understood, routine, and conventional computer functions performed using generic computer components; for example, receiving or transmitting data over a network (i.e. obtaining a user profile from a GUI for subsequent analysis at a computing device, and sending a monitoring device selection from the computing device to the GUI for presentation) is recognized as a well-understood, routine, and conventional function previously known to the industry, as outlined in MPEP 2106.05(d)(II).
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computing device, GUI, and high-level machine learning models in combination is to digitize and/or automate a diagnosis and medical device selection operation that could otherwise be achieved via mathematical operations and certain methods of organizing human activity. Thus, when considered as a whole and in combination, claims 1-2, 4-6, 8-12, 14-16, and 18-20 are not patent eligible.
Subject Matter Free from Prior Art
The prior art of record fails to expressly teach or suggest, either alone or in combination, each and every feature of the independent claims, as explained in more detail in paras. 33-40 of the non-final rejection mailed 10/31/2023. Upon completion of an updated prior art search, Examiner submits that Koay (Reference U on the accompanying PTO-892) is relevant to the field of Applicant’s invention, but does not explicitly teach or suggest each and every limitation of the amended claims. Accordingly, the prior art, either alone or in combination, does not disclose or render obvious all the features of the independent claims and they are found to be free of prior art, as are the claims depending therefrom.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
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/KAREN A HRANEK/Primary Examiner, Art Unit 3684
