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 .
Status of the Claims
The status of the claims as of the response filed 3/9/2026 is as follows: Claims 3, 7, 13, and 17 remain cancelled. Claims 1, 8, 11, and 18 are currently amended. Claims 6 and 16 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 adjust the indentation level of the “combining” and “ranking” limitations such that they are no longer recited as functions of the trained monitoring machine-learning model. Accordingly, the corresponding 35 USC 112(a) rejections are withdrawn.
Response to Arguments
Rejection Under 35 USC 101
On pages 4-6 of the response filed 3/9/2026 Applicant argues that the newly-introduced data transmission and receipt limitations are not mathematical concepts and cannot be performed mentally. Applicant further argues that the claims merely “involve” mathematical concepts (similar to Example 39) rather than “recite” mathematical concepts (like Example 47 does), asserting that “the claim does not set forth a mathematical relationship, formula, equation, or calculation using words or mathematical symbols,” instead reciting “a particular machine-implemented architecture and device-control operations, where any mathematical processing is embedded in the functioning of the machine-learning models rather than claimed as an abstract calculation.” Applicant’s arguments are fully considered, but are not persuasive. Examiner agrees that the newly-introduced limitations directed to transmitting and receiving data to and from a monitoring device are not abstract, but the recitation of additional elements beyond the abstract idea itself does not preclude a claim from reciting an additional element in the first place. These additional elements are instead evaluated in Steps 2A – Prong 2 and 2B. Examiner maintains that the claims recite mathematical concepts, rather than just involving or relying on the mathematical concepts. For example, the claims recite applying a monitoring training data set to an input layer of nodes, one or more intermediate layers of nodes, and an output layer of nodes; 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; and iteratively updating the monitoring machine-learning model as a function of the detected additional correlations. Such steps explicitly recite the mathematical architecture of the machine learning model and describe numerical adjustments and calculations made to the weights and correlations between the node representations. The claim further recites determining a device score for each monitoring device, combining the device scores, ranking each monitoring device score based on comparison to a threshold, and eliminating monitoring devices with device scores that do not reach the threshold. Such steps explicitly recite the calculation, comparison, and ranking of scores, which is a mathematical operation. Accordingly, Examiner maintains that the claims as presently drafted do recite mathematical concepts rather than simply “involving” or “relying” on them.
On pages 6-7 Applicant argues that claim 1 is integrated into a practical application because it “now expressly ties the model outputs to concrete device operation and sensor data acquisition” and “uses the model-driven selection to cause a physical sensor device to perform monitoring and to generate and return real physiological data” which “ties the claim to a particular machine and a specific technological proves involving device communications and sensor signal acquisition.” Applicant’s arguments are fully considered, but are not persuasive. Examiner first notes that these newly-introduced limitations are not supported by Applicant’s specification such that they are considered new matter, as explained in the 35 USC 112(a) rejections below. Regardless, these steps do not provide integration into a practical application. Sending a data collection instruction to a heartrate monitoring device and receiving heartrate data from the heartrate monitoring device does not qualify as use of a particular machine as outlined in MPEP 2106.05(b). There is no indication as to the specific architecture or specifications of the monitoring device beyond inclusion of a heartrate sensor, and the monitoring device is utilized for its conventional function (collecting heartrate data). Further, the steps of transmitting an instruction to the monitoring device to initiate heartbeat monitoring by the sensor and receiving generated heartbeat monitoring data from the monitoring device amount to insignificant extra-solution activity and mere instructions to “apply” the exception because these steps are nominally recited in a manner that is not integrated with any other steps of the main device analysis, selection, and outputting functions of the invention, and the received data is not further utilized in any way such that this step appears to be an effort to merely nominally “apply” the selected monitoring device for use with a patient (see MPEP 2106.05(f)-(g)).
On page 7 Applicant argues that “the claims’ ordered combination reflects a concrete technical pipeline” of a type “that the USPTO recognizes as a practical application because it uses machine-learning processing as part of a larger system that improves or enables device monitoring functionality, rather than claiming a mathematical concept as the end goal.” 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 and preferences such that the device selection operation is nominally automated/digitized. There is no evidence that the instant invention solves any 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 are utilized 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 provide integration into a practical application.
On pages 7-8 Applicant argues that the newly-introduced data transmission and receipt limitations are a “non-conventional and specific arrangement of steps” which “provides a technical improvement in the field of device-based physiological monitoring and recommendation systems.” Applicant’s arguments are fully considered, but are not persuasive. These data transmission steps amount to receiving or transmitting data over a network, which a is a well-understood, routine, and conventional activity as outlined in MPEP 2106.05(d)(II). Examiner further notes that it is well-understood, routine, and conventional to use a computing device to send an instruction to activate a monitoring device and receive sensed data back from the monitoring device subsequent to providing or selecting an appropriate monitoring device for a patient, as evidenced by at least Fig. 4 & [0128]-[0129] of Asthana et al. (US 20190371463 A1); [0170] & [0435]-[0455] of Reiner (US 20170068792 A1); [0009] & [0036] of Whitehurst (US 20170011182 A1); and [0041]-[0046] of Zhang et al. (US 20060253066 A1). Accordingly, these steps, even when considered in combination with the other additional elements of the instant claims, do not provide “significantly more” than the abstract idea or an inventive concept.
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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-2, 4-6, 8-12, 14-16, and 18-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 each recite “transmit, to the monitoring device, a monitoring instruction to initiate monitoring of the user’s heartbeat using the sensor configured to monitor a heartbeat of the user” and “receive, from the monitoring device, heartbeat monitoring data generated by the sensor during initiated monitoring.” Applicant’s original specification does not provide sufficient written support for transmitting an instruction to a monitoring device to initiate monitoring of a user’s heartbeat, nor for receiving generated heartbeat monitoring data from the monitoring device during imitated monitoring. At most, para. [0014] describes example functions of monitoring devices that are evaluated and selected by the invention, including detecting, analyzing, and transmitting information concerning a body signal such as a vital sign. However, this appears to be a description of what monitoring devices actually are, and there is no indication that a selected monitoring device is actually in communication with the computer architecture of the invention in a manner that would allow the system to positively transmit an initiation/activation instruction to a selected monitoring device and actually receive monitored body signals generated by the initiated monitoring. The invention only appears to only handle “monitoring devices” as a data object that can be evaluated and selected according to various monitoring device attributes matched with user characteristics and preferences with the ultimate output of the invention being the display of a selected/recommended device type to a user (as in Figs. 1 & 5, [0026], [0046]). Because the original disclosure does not describe the system of the invention being in communication with a selected monitoring device in any way, the limitations “transmit, to the monitoring device, a monitoring instruction to initiate monitoring of the user’s heartbeat using the sensor configured to monitor a heartbeat of the user” and “receive, from the monitoring device, heartbeat monitoring data generated by the sensor during initiated monitoring” constitute new matter and are rejected under 35 U.S.C. 112(a). Dependent claims 2, 4-6, 8-10, 12, 14-16, and 18-20 are also rejected on this basis because they inherit the new matter limitations of independent claims 1 or 11.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-6, 8-12, 14-16, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 each recite “transmit, to the monitoring device, a monitoring instruction to initiate monitoring of the user’s heartbeat using the sensor configured to monitor a heartbeat of the user” and “receive, from the monitoring device, heartbeat monitoring data generated by the sensor during initiated monitoring.” In each claim, these limitations directly follow a sequence of steps for determining device scores for a plurality of monitoring devices, ranking each device score, eliminating a plurality of monitoring devices with scores that do not reach a threshold, and ranking a remaining plurality of devices. It is therefore unclear which specific monitoring device is being referenced in the transmitting and receiving steps, because there are previous recitations of a plurality of monitoring devices. For purposes of examination, Examiner will interpret “the monitoring device” in these limitations as a final monitoring device selected via the scoring, ranking, eliminating, and reranking process, though Examiner notes that there is no final step recited of positively selecting a final device from the ranked candidates. Dependent claims 2, 4-6, 8-10, 12, 14-16, and 18-20 are also rejected on this basis because they inherit the indefinite subject matter due to their dependence on claims 1 or 11.
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. Specifically, exemplary claim 1 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; and
transmit, to the monitoring device, a monitoring instruction to initiate monitoring of the user’s heartbeat using the sensor configured to monitor a heartbeat of the user;
receive, from the monitoring device, heartbeat monitoring data generated by the sensor during initiated monitoring; 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 decision tree or other similar classifier) 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 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 present the selected device, 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; transmit, to the monitoring device, a monitoring instruction to initiate monitoring of the user’s heartbeat using the sensor configured to monitor a heartbeat of the user; receive, from the monitoring device, heartbeat monitoring data generated by the sensor during initiated monitoring; 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 additional elements of transmitting an instruction to the monitoring device to initiate heartbeat monitoring by the sensor and receiving generated heartbeat monitoring data from the monitoring device amount to insignificant extra-solution activity and mere instructions to “apply” the exception because these steps are nominally recited in a manner that is not integrated with any other steps of the main device analysis, selection, and outputting functions of the invention, and the received data is not further utilized in any way such that this step appears to be an effort to merely nominally “apply” the selected monitoring device for use with a patient (see MPEP 2106.05(f)-(g)). The use of a GUI to obtain input information and present output information 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, 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 transmitting a monitoring instruction to a monitoring device and receiving heartbeat monitoring data from the monitoring device, as well as 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. transmitting an instruction, receiving heartbeat monitoring data, 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). Examiner further notes that it is well-understood, routine, and conventional to use a computing device to send an instruction to activate a monitoring device and receive sensed data back from the monitoring device subsequent to providing or selecting an appropriate monitoring device for a patient, as evidenced by at least Fig. 4 & [0128]-[0129] of Asthana et al. (US 20190371463 A1); [0170] & [0435]-[0455] of Reiner (US 20170068792 A1); [0009] & [0036] of Whitehurst (US 20170011182 A1); and [0041]-[0046] of Zhang et al. (US 20060253066 A1).
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, high-level machine learning models, and nominal communication with a monitoring device 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 Booth et al. (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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684