DETAILED ACTION
Response to Amendment
This action is in response to the amendment after non-final filed 13 July 2026. Claims 8-27 are pending, wherein claims 14-27 are new.
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.
Applicant’s arguments have been fully considered with respect to the subject matter eligibility analysis under 35 USC 101 but are not persuasive. The rejection has been revised to address the claims as presently amended to consider the machine-learning and graphical interface limitations individually and in combination.
Unlike Core Wireless and Data Engine, the claimed interface does not provide a particular improvement to computer navigation or functionality. Rather, the graphical elements principally provide a particular manner of presenting and interacting with medical risk, timing, priority, and status information. Although these limitations provide specific and useful interface, they automate and facilitate the underlying medical-evaluation process rather than improve operation of the computer itself.
The present claims are directed to a generic data display and task management interface that happens to be applied to medical data. None of the claims recite any novel technical solution, an improvement over existing medical management systems, or any specific clinical problem being solved. The interface is essentially a conventional task management user interface with dynamic visual properties which is insufficient for eligibility under 35 USC 101.
Accordingly, Applicant’s arguments do not overcome the rejection under 35 USC 101 which is maintained and revised.
Claims 8-27 of the claimed invention are rejected under 35 USC 101 are directed to a judicial exception related to an abstract idea without significantly more.
Step 1
The claims recite a computer implemented interface (device, claims 8-10) and system (claims 11-13) configured to perform analysis and display result thus the claims are directed to the eligible statutory categories.
Step 2A, Prong 1
Claims 8 and 11 recite an abstract idea falling within the mental process grouping. In particular, the claims recite receiving patient information and evaluating that information to determine medical conditions or disorders, corresponding medical interventions, condition specific risk categories, recommended due dates, and associated medical action items. These limitations constitute medical evaluations, judgements, and recommendations of the type that may practically be performed by a medical professional reviewing patient information.
This is consistent with Electric Power Group, LLV v Alstrom S.A., 830 F.3d 1350, 1353-55 (Fed. Cir. 2016), which addressed collecting information, analyzing that information, and presenting the results where the analysis was recited in high level of generality. The court further explained that limiting the information collected and analyzed to particular content or sources does not necessarily change the abstract character of the information analysis. Here, claims 8 and 11 similarly identify particular patient information and evaluates that information to derive medical conditions, interventions, risk categories, recommended timing, and associated medical guidance. (MPEP 2106.04(1)(2)(III)).
Therefore, claims 8 and 11 are considered to recite the abstract mental process of evaluating patient information to determine patient specific medical risk, and displaying interventions, timing, and associated medical guidance. The Examiner does not characterize actual execution of the trained machine learning model or the computer implemented graphical interface operations as mental processes. Those limitations are considered additional under Step 2A, Prong 2.
Step 2A, Prong 2
The additional elements do not integrate the abstract idea into a practical application. Claims 8 and 11 use a trained machine learning model operating on a server to determine the condition specific risk categories based at least in part on physiological parameters received from patient operated medical devices. Although the claim specifies that the model is trained on labeled historic patient data and physician recommendations, the claim does not recite a particular model architecture, training technique, feature extraction process, transformation of data, or improvement to machine learning technology. Rather, the model is used as a technological tool to automate the evaluation process.
The Examiner has also considered the graphical interface limitations as an ordered combination. Each medical action item is displayed as a graphical element providing priority, status, risk category, recommended due date, and indication of the amount of time until the due date or how overdue the action is. At least one visual property, including color, is determined from the risk category and the amount of time until the recommended due date, and the graphical element is updated in response to a change in the action item status. Upon selection of an action item, the in-context detailed view is displayed from which scheduling may be initiated without navigating away from the interface. Although these limitations provide a particular interactive and dynamically updated presentation of the medical guidance, they principally determine how the underlying medical risk, timing, priority, and status information is communicated to and acted upon by a user rather than improving the operation of the computer itself.
This differs from Core Wireless Licensing S.A.R.L v. LG Electronics, Inc 880 F.3d 1356, 1362-63 (Fed. Cir. 2018), wherein the claimed application summary interface provided a particular improvement in computer navigation and direct access to application functionality. This also differs from Data Engine Technologies LLC v. Google LLC, 906 F.3d 999 (Fed. Circ. 2018), where selectable notebook tabs provided a particular computer specific mechanism for navigating complex three-dimensional spreadsheets. Here, the claimed graphical elements visually encode and provide access to medical guidance information but do not recite a comparable improvement to computer navigation, graphical processing, or other computer functionality. Hence, when considered as a whole, the additional elements use the machine learning model and the graphical interface as tools for automating, presenting, and facilitating action upon an underlying medical evaluation rather than integrating the abstract idea into a practical application.
The Examiner has also considered the limitation requiring, responsive to selection of an action item, display of an in-context detailed view from which scheduling may be initiated without navigating away from the user interface. Although this limitation reduces the need for the patient to leave the presently displayed interface when acting upon the medical guidance, it recites the result of maintaining the interaction within the user interface rather than a particular computer specific mechanism or interface architecture by which navigation is improved.
This also differs from Core Wireless and Data Engine, wherein the claimed interfaces recited particular computer specific mechanisms that changes how users navigated and accessed computerized information and functionality. Here, the “without navigating away” limitation facilitates the user’s access to and use of the medical guidance within the interface but does not recite a particular technological manner of accomplishing that result or otherwise improve operation of the computer itself. Accordingly, the limitation, considered alone and in combination with the remaining graphical interface limitations, does not integrate the recited medical evaluation process into a practical application. Consistent with MPEP 2106.05(f), merely reciting the idea or outcome of a solution, without limiting the claim to a particular manner of accomplishing that result, does not integrate the judicial exception into a practical application.
Step 2B
The additional elements, individually and as a combination, do not amount to significantly more than the judicial exception. Consistent with Berkheimer v HP Inc., 881 F.3d 1360, 1368-69 (Fed. Cir. 2018), whether an additional element is well-understood, routine, and conventional is a factual determination (MPEP 2106.05(d)(I)(2) and 2106.07(a)(III)).
Here, the specification of the present invention provides factual support for the conventional nature of the generic computing environment. The specification states that “well known features may not have been described in detail” (para 42); describes the display device as “any device capable of generating a graphical image,” including LCD, TFT< DLP, and OLED displays (para 61); identifies the processing device as including “one or more general purpose processing devices” (para 121); and states that the disclosed apparatus may comprise a “general purpose computer” and that “[v]arious general purpose systems may be used” (para 131-132). These disclosures support a finding that the generic processor, server, memory, patient device, and display components, wherein performing their ordinary computer functions, are well-understood, routine, and conventional (MPEP 2106.05(d)).
The Examiner does not rely in a finding that the particular machine learning model implementation or claimed graphical interface arrangement is itself well-understood, routine, and conventional. Rather, considered as an ordered combination, the machine learning model automates the underlying medical risk evaluation and the graphical interface communicates the resulting risk, timing, priority, and status information and facilitates scheduling. The additional elements therefore use ordinary computer functionality and facilitate the abstract medical evaluation process without providing an inventive concept distinct from that process.
Claims 9 and 12 do not cure the fundamental 101 deficiency of the base claim as these claims are directed to the collection, analysis, and display of information on the user interface as a tool.
Claims 10 and 13 do not cure the fundamental 101 deficiency of the base claim as these claims are directed to well-understood, conventional, and routine measurement devices in the manner outlined according to the Berkheimer analysis.
Claims 14 and 21 specify the input data to the machine learning model (physiological parameters, diagnostic tests, personal history, family history) but does not recite any novel technical solution or improvement. Specifying input data does not confer eligibility on a generic interface that merely receives and displays that data.
Claims 15 and 22 specify a particular visual representation (ring-shaped progress bar) with color-coding based on time thresholds. Specifying a particular UI visual design (progress bar, color scheme) does not confer eligibility without demonstrating technical improvement or solving a specific technical problem.
Claims 16 and 23 specify that upon scheduling an appointment, the interface updates the action item’s status, color, and time-to-due-date calculation. Specifying routine state updates and property changes does not confer eligibility.
Claims 17 and 24 specify that the interface receives completed intervention results, updates status/color, transmits results to the server for ML analysis, receives outcome determination, and displays color-coded results. The claim recites a generic data flow with external ML analysis and conventional UI updates. Integrating with an external ML system does not confer eligibility on the interface itself.
Claims 18 and 25 specify that results file receipt includes electronic signature capture and direct import from external facilities. Specifying conventional file import and signature capture does not confer eligibility.
Claims 19 and 26 specify that action items can be organized hierarchically with parent containers holding child items. Specifying hierarchical data structures does not confer eligibility.
Claims 20 and 27 specify that the interface integrates with a calendar system to display, reschedule, and cancel appointments. Specifying calendar integration does not confer eligibility.
Claim Rejections - 35 USC § 103
Applicant’s arguments filed with respect to the 35 USC 103 rejection based on Aoun et al. (US Publication no. 2022/0101989) in view of McCabe et al. (US Publication no. 2022/0230715) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Allowable Subject Matter
Claims 8-27 would be allowable should the requirements of the 35 USC 101 rejection be properly traversed.
The following is an examiner’s statement of reasons for allowance: the prior art fails to teach the combination of elements recited in the claims, notably, among other things displaying action items with the features in a graphical element of a recommended due date, a risk category and enabling scheduling for each action item from in a manner that does not require navigating away from the user interface.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DeVries et al. (US Publication no. 2023/0170065) teaches receiving data characterizing health care information from a patient, determining a health outcome evaluation for the patient based on the received healthcare information, determining a risk prediction for the patient based on health outcome evaluation, determining treatment recommendations based on the determined risk prediction, and providing a treatment recommendation. However, the effective filing date fails to qualify the reference as prior art.
Cox et al. (US Application no. 2017/0286622) teaches that patient risk may be based on machine learning techniques. The data for the machine learning is acquired from patient populations. The reference fails to display the action items along with the risk category and recommended due data of action items. Nor permits scheduling of the action item.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN T GEDEON whose telephone number is (571)272-3447. The examiner can normally be reached M-F 8:00 am to 5:30 PM ET.
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/BRIAN T GEDEON/Primary Examiner, Art Unit 3796 24 September 2026
