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 12/09/2025 is as follows:
Claims 1-30 are pending.
Claims 1,10-11, 20 and 30 were amended.
All pending claims have been considered below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/31/2025, 12/19/2025, and 02/12/2026 are in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
Response to Arguments
The Examiner has considered all arguments submitted on 12/09/2025 page 7 regarding claims 10, 20, and 30 in response to the non-final rejection under 35 U.S.C. 112(b) and found them persuasive. The Applicant addressed the lack of antecedent basis by replacing the term “medical” with bedside monitoring, as introduced in claims 1, 11, and 21. Accordingly, the rejections under 35 U.S.C. 112(b) have been withdrawn.
The Examiner has considered all arguments submitted on 12/09/2025 page 7-8 regarding claims 1-30 in response to the non-final rejection under 35 U.S.C. 101 see answers below.
Non-Statutory
The Examiner acknowledges that claim 11 has been amended to include the term "non-transitory," thereby qualifying it as manufacturing statutory category under 35 U.S.C. § 101. Accordingly, the rejection for non-statutory subject matter is withdrawn.
Subject Matter Analysis:
Applicant argues that the rejection should be withdrawn because the amended independent claims add features, are “not believed to be directed toward an abstract idea,” and, even if an abstract idea remains, any such idea is “integrated into a practical application.”
The examiner respectfully disagreed because , under the claim language’s broadest reasonable interpretation, the added limitations still recite monitoring data to detect alarm-defined incidents, using a generative AI model to generate a recommendation from those incidents, enabling adjustment of monitoring criteria, receiving a proposed change, and providing feedback, which the rejection reasonably characterizes as mental evaluation and human-interaction workflow, while the added “generative AI model,” bedside-monitoring context, and generic computing components are claimed only at a high functional level and do not recite a particular technological mechanism or improvement that would integrate the exception into a practical application or provide significantly more. Accordingly, the amendment changes the drafting but not the eligibility outcome, because the claim still centers on collecting information, evaluating it, recommending an action, and communicating guidance, implemented with generic technology in a medical-device environment.
The Examiner has considered all arguments submitted on 12/09/2025 page 8-10 regarding claims 1-30 in response to the non-final rejection under 35 U.S.C. 102 see answers below.
Applicant argues that amended Claim 1, "processing the one or more incidents utilizing a generative AI model to produce a recommendation based on the one or more incidents" is not anticipated by Shelton because Shelton does not disclose a generative AI model.
Applicant's argument is persuasive with respect to § 102. Under proper BRI, "processing the one or more incidents utilizing a generative AI model to produce a recommendation" requires applying a generative AI model a system that creates new content from learned data patterns specifically to detected incidents in order to output a recommendation derived from those incidents.
The Examiner agreed because ai generative is not describe in Shelton. Accordingly, the § 102 rejection of Claims 1, 11, and 21 is withdrawn.
However, Applicant's assertion that amended Claims 1, 11, and 21 are in condition for allowance is not persuasive, because the claims remain unpatentable by the submitted 35 U.S.C 103 below.
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.
Rejection Under 35 U.S.C. § 101 (Judicial Exception)
Claims 1-30 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis
Claims 1-30 directed to statutory subject matter, encompassing the following statutory categories:
Process (Claims 1-10): Under BRI (MPEP 2111), the language reciting "A computer-implemented method... comprising: interfacing... enabling adjustment... receiving a proposed change... and providing feedback" defines a series of acts or steps, aligning with the definition of a process in MPEP § 2106.03.
Machine (Claims 21-30): Under BRI (MPEP 2111), the language reciting "A computing system including a processor and memory configured to perform operations..." describes a concrete thing consisting of parts (processor and memory), aligning with the definition of a machine in MPEP § 2106.03.
Manufacture (Claims 11-20): Because recited a computer program product residing on a non-transitory computer readable medium qualifies as a statutory manufacture, as it represents a tangible article with stored instructions rather than a process.
The analysis continues for all claims, now proceeds to Step 2A (Prong One).
Step 2A, Prong One: Judicial Exception Analysis
The claims 1-30 are directed to a Certain Method of Organizing Human Activity and a Mental Process.
Independent Claims
Claim 1: A computer-implemented method, executed on a computing device, comprising: interfacing with a bedside monitoring device to receive data signals, wherein the data signals have monitoring criteria;
enabling adjustment of one or more of the monitoring criteria;
detecting one or more incidents, including monitoring the data signals to detect one or more alarms based on the monitoring criteria;
processing the one or more incidents utilizing a generative AI model to produce a recommendation based on the one or more incidents;
enabling adjustment of one or more of the monitoring criteria;
receiving a proposed change from a user concerning the one or more monitoring criteria;
and providing feedback to the user concerning the proposed change.
Note: The bolded portions represent additional elements evaluated in Prong Two and Step 2B. The non-bolded portions represent the abstract idea. Applicant language was extracted from applicant PB number 20250054619.
Claim Classification & Mapping
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claims abstract idea recite a process of managing device configurations by facilitating an interaction wherein a user proposes a change to monitoring criteria and receives feedback regarding that proposal. This process aligns with the following abstract idea categories:
Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II)): The claims recite a workflow involving "enabling adjustment," receiving a "proposed change from a user," and "providing feedback to the user." This describes managing the behavior and interactions of users (e.g., healthcare providers) concerning the modification of device criteria. This falls under the sub-category of Managing Personal Behavior or Relationships or Interactions Between People (following rules or instructions for device management). The specification confirms the invention addresses the organization of human activity concerning alarm management, aiming to improve "Inefficient Alarm Prioritization and Response" and reduce the "complexity to healthcare provider workflows" (Spec., para. [0006], [0008]).
Mental Process (MPEP § 2106.04(a)(2)(III)): The claims recite "receiving a proposed change" and "providing feedback to the user concerning the proposed change." MPEP § 2106.04(a)(2)(III) defines mental processes as concepts performed in the human mind, including evaluations, judgments, and opinions. Under BRI (MPEP 2111), the act of providing feedback "concerning the proposed change" inherently requires an evaluation of the proposal and a judgment on the appropriate response. The specification supports this, indicating the feedback may include "justification" or "explanation" (Spec., para. [0010]), which are cognitive outputs. Even when performed on a computer, claims can recite a mental process if the underlying concept can be performed in the human mind and the computer is merely used as a tool (MPEP § 2106.04(a)(2)(III)(C)).
The limitations that start with detecting, processing and enabling under BRI recites a system that monitors data signals against monitoring criteria to detect alarm-defined incidents, uses a generative AI model to generate a recommendation from those incidents, and permits adjustment of the monitoring criteria that govern the alarm detection. These limitations characterize a mental process because, despite the use of a generative AI model, the core activity remains the conceptual collection, evaluation, and recommendation of actions based on incident data, rather than a technological improvement to the underlying computer or device.
The abstract nature of the claims is reinforced because the entire process is analogous to fundamental human organizational and cognitive activities (MPEP 2106.04):
A set of monitoring rules or thresholds is used to govern when alarms are recognized and when settings may be changed.
A clinician observes patient/device information, recognizes one or more alarm conditions as an incident, and considers whether a recommendation should be made from that incident data.
The clinician then communicates guidance, approval, denial, or suggested adjustment back to the user about whether the monitoring criteria should be changed.
Dependent Claims Analysis
The dependent claims 2-10, 12-20, and 22-30 also address an abstract idea (MPEP 2106.04(a)). These claims inherit the abstract idea of the independent claims.
(Claims 2, 12, 22 and 3, 13, 23): These claims recite under BRI (MPEP 2111) that the feedback includes "providing justification" (Cl. 2) or "providing explanation" (Cl. 3). This specifies the cognitive content of the feedback (formulating the reasoning). This is a Mental Process (MPEP § 2106.04(a)(2)(III)).
(Claims 4, 14, 24): These claims recite under BRI (MPEP 2111) "enabling the user to effectuate the proposed change." This is the subsequent administrative step of allowing the action within the workflow. This is a Certain Method of Organizing Human Activity (Managing personal behavior/workflow management) (MPEP § 2106.04(a)(2)(II)).
(Claims 5-8, 10, 15-18, 20, 25-28, 30): These claims recite under BRI (MPEP 2111) specifics about the subject matter involved in the abstract process, such as the criteria being "thresholds" (Cl. 5); the data signals concerning "details of the bedside monitoring device and/or uses" (Cl. 6); or the hardware including "sub-medical devices" (Cl. 10). These limitations merely define the data or environment upon which the abstract method operates. They inherit the abstract idea of the independent claim; merely narrowing the abstract idea by specifying the type of data or environment involved does not negate the identification of the judicial exception (MPEP 2106.04(a)).
(Claims 9, 19, 29): These claims recite under BRI (MPEP 2111) that the machine-defined criteria is defined via "massive data sets processed by ML." This describes the method used to establish the criteria through data analysis and evaluation. This is a Mental Process (MPEP § 2106.04(a)(2)(III)).
Because claims 1–30 recite abstract ideas (a Certain Method of Organizing Human Activity and a Mental Process), the analysis proceeds to Prong Two to evaluate whether the additional elements integrate the judicial exception into a practical application.
Step 2A, Prong Two: Integration into a Practical Application
The claims fail to integrate the abstract idea into a practical application because the additional elements merely provide a generic technological environment and link the abstract idea to a specific field of use.
Evaluation of Additional Elements
(Computing Environment - Computing device, processor, memory, computer readable medium, computing system, instructions): The recitation of these elements (Claims 1, 11, 21) fails to integrate the abstract idea because it:
(MPEP § 2106.05(f)): Amounts to mere instructions to apply the idea on a computer. Under BRI (MPEP 2111), these elements are recited generically as the platform to execute the abstract process of managing configuration changes. The claims merely state the method is "computer-implemented" (Claim 1) or performed by a "computing system" (Claim 21).
(MPEP § 2106.05(a)): Does not improve the functioning of the computer itself. The claims do not recite improvements to computer hardware; they utilize standard hardware to implement the abstract workflow.
(MPEP § 2106.05(h)): Merely links the idea to a particular technological environment. It applies the abstract management process within a generic computer system.
(Bedside Monitoring Device and Interfacing): The recitation of "interfacing with a bedside monitoring device" (Claims 1, 11, 21) fails to integrate the abstract idea because it:
(MPEP § 2106.05(g)): Involves only insignificant extra-solution activity (generic data gathering). Under BRI (MPEP 2111), "interfacing... to receive data signals" (which have the criteria) is merely the prerequisite data gathering and context setting necessary to perform the abstract management process.
(MPEP § 2106.05(h)): Merely links the idea to a particular technological environment or field of use. It confines the abstract idea (managing proposed changes and providing feedback) to the field of bedside monitoring devices. This linkage is insufficient for integration.
(MPEP § 2106.05(a)): Does not improve the functioning of the computer or other technology. The claims do not recite a specific technical improvement to how the devices communicate (e.g., a novel networking protocol); rather, they claim the abstract process of reviewing proposed changes to the settings of those devices.
(generative AI model): The recitation of a generative AI model does not integrate the abstract idea into a practical application because it is claimed only as a result-oriented level as a tool that processes incidents to produce a recommendation, without reciting a particular model architecture, a specific technical manner of operation, or any improvement to the computer, bedside monitor, or alarm-detection technology. As USPTO guidance explains for AI limitations, when the claim merely invokes AI or a neural network to achieve a desired outcome, rather than claiming a particular technological solution, the limitation is treated as no more than instructions to apply the exception on a computer or as a general link to a technological environment, not a meaningful limit on the exception. Refer to MPEP 2106.05
Combination Analysis: When viewed as a whole, the combination of these elements does not integrate the abstract idea. The claims describe using a generic computer system to interface with a monitoring device to facilitate the abstract workflow of reviewing proposed setting changes and providing feedback. This automation of an organizational and cognitive process does not transform the abstract idea into an eligible application.
Dependent Claims Analysis
The dependent claims add only minor limitations that fail to provide the necessary integration.
(Claims 2-5, 12-15, 22-25): These claims further describe the abstract idea does not have additional elements.
(Claims 9, 19, 29): These claims add that criteria are defined via "ML." Invoking ML generically to define criteria amounts to mere instructions to apply the idea using a computer tool (f) and fails to improve computer functionality (a), as no specific improvement to ML techniques or computer functionality is claimed.
Conclusion: Because the claims are directed to an abstract idea without integrating it into a practical application, the analysis proceeds to Step 2B.
Step 2B: Inventive Concept Analysis
The claims lack an inventive concept because the additional elements do not amount to significantly more than the judicial exception itself.
Evaluation of Additional Elements
(Computing Environment):
(MPEP § 2106.05(d) - WURC): Under BRI (MPEP § 2111), these elements are WURC. The use of generic processors, memory, and computer readable media for data processing and executing instructions is conventional. The specification (MPEP § 2111) confirms these are generic components: "Examples of computing device 12 may include, but are not limited to: a personal computer, a server computer, a series of server computers..." (Spec., para. [0057]).
(MPEP § 2106.05(f) - Mere Instructions): The specification states the process "may be implemented as a server-side... client-side... or hybrid" across ordinary platforms (Spec., paras. [0056]–[0061]), so the claims only tell a computer to do the idea. Under § 2106.05(f), reciting "computer-implemented" execution without a concrete technical solution does not add significantly more.
(MPEP § 2106.05(a)): The disclosure lists "a personal computer, a server computer, a series of server computers" and general OS like "Windows™, Android™, iOS™, Linux™" (Spec., paras. [0057], [0061]), showing no improvement to how the computer itself works. Under § 2106.05(a), using highlight hardware/software to carry out the workflow does not improve the computer.
(MPEP § 2106.05(h)): The workflow is placed in "web browser... mobile device user interface" contexts and high-level networks "wireless access point... 802.11x" (Spec., paras. [0060-0064]), which is merely a generic computing environment. Under § 2106.05(h), confining the abstract management to that field of use does not integrate the exception into a practical application.
(Bedside Monitoring Device and Interfacing):
(MPEP § 2106.05(d) - WURC): Bedside monitoring devices are conventional medical equipment. The specification (MPEP § 2111) lists conventional examples, such as ECGs and pulse oximeters (Spec., para. [0080]). Interfacing with such devices to receive data signals is a routine activity. The specification describes standard connection methods (e.g., wired/wireless networks) (Spec., para. [0063], [0069]-[0070]).
(MPEP § 2106.05(g) - Insignificant Activity): The claims recite "interfacing ... to receive data signals"—a prerequisite intake step that simply collects data before the core evaluation/feedback occurs (Spec., para. [0009]). Under § 2106.05(g), the specification shows ordinary coupling via "wireless access point" or "wired connection" to obtain those signals, i.e., routine data gathering (Spec., paras. [0069]–[0070]).
(MPEP § 2106.05(h) - Linking to Environment): Anchoring the workflow to "bedside monitoring device" usage merely places the abstract review/feedback process in a medical-device context (Spec., para. [0009]). This field-of-use tie—standard hospital networks and devices like ECG/pulse oximeter—is classic § 2106.05(h) linkage, not integration (Spec., paras. [0064], [0080]).
(MPEP § 2106.05(a)): Nothing in the claims or specification changes how the computers or devices operate; they use ordinary processors/memory and existing network paths (Spec., paras. [0057], [0063]). Because no particular technical mechanism (e.g., new protocol, data structure, or device control logic) is claimed, § 2106.05(a) is not met—there is use of technology, not an improvement.
(generative AI model): The generative AI model, as claimed, is used in a generic, result-oriented manner to produce recommendations from incident data, without specifying any concrete model architecture, training method, or technical advancement to the underlying computer or monitoring system. The generative AI is treated broadly as a generic content-generation tool, not as an inventive technical solution, and thus does not amount to significantly more than the abstract idea itself. Refer to MPEP 2106.05
Combination Analysis: When considered as an ordered combination, the additional elements merely recite the implementation of the abstract idea (managing proposed changes and providing feedback) using high-level tools (generic computer, monitoring devices) in their intended environment. This combination does not amount to significantly more than the abstract idea itself (MPEP § 2106.05).
Dependent Claims Analysis
(Claims 2-5, 12-15, 22-25): These claims further describe the abstract idea does not have additional elements.
(Claims 9, 19, 29): The specification states the "machine-defined monitoring criteria may be defined via massive data sets processed by ML" (Spec., paras. [0011], [0013]), invoking ML is a high-level tool call that, under § 2106.05(f), amounts to mere instructions rather than a specific technical solution. The disclosure describes ML and computing platforms in generic terms (e.g., ordinary processors/memory and generic ML definitions, Spec., paras. [0272]), so § 2106.05(a) is not satisfied and the ML step functions as general preparatory analysis—insignificant under § 2106.05(g). Confined to bedside monitoring, the ML limitation is a field-of-use linkage under § 2106.05(h) and does not integrate the judicial exception into a practical application.
Combination Analysis: The dependent claims add only conventional limitations or routine organizational activities that, even when combined with the independent claims, do not provide an inventive concept amounting to significantly more.
Conclusion
The claims are directed to an abstract idea and the additional elements, individually and in combination, do not provide an inventive concept. Therefore, Claims 1-30 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-30 are rejected under 103 as being obvious with Shelton - US20240221878A1 in view of US Lima-20240395392.
Claim 1. Shelton teaches limitations.
A computer-implemented method, executed on a computing device, comprising: (Shelton, par. 0007) Shelton describes a computer-implemented process performed by a computing device within a surgical system, matching the preamble under BRI.
interfacing with a bedside monitoring device to receive data signals, wherein the data signals have monitoring criteria; (Shelton, par. 0031, 0100, 0009) Shelton’s surgical hub interfaces with medical devices to obtain measurements used to determine allowable operation ranges, aligning with interfacing to receive data governed by criteria.
detecting one or more incidents, including monitoring the data signals to detect one or more alarms based on the monitoring criteria; Shelton, "A device may determine whether the adjustment input configuration is within or outside of the allowable operation range... The device may send an alert (e.g., an alert message) to an HCP, notifying that an adjustment input configuration has been received and is outside of the determined allowable operation range," par. [0010]; "device alerts such as alerts for sensor failure, observations exceeding a defined range, observations exceeding an observable range," par. [0113].
Shelton’s surgical hub continuously monitors data signals against established criteria and triggers alarms when those signals exceed defined thresholds, thereby identifying incidents. The system evaluates adjustment input configurations and sensor observations, issuing alerts to healthcare professionals whenever operational ranges are surpassed.
processing the one or more incidents utilizing (Shelton, "A device may use historical data and predict one or more devices that are most effective for one or more surgical steps. Based on the historical data, the device may provide and/or integrate a product recommendation to a purchasing and hospital inventory management system," par. [0259]; "the computing device may send ML data to an HCP. The ML data may be or may include information and/or analysis that caused the adjustment input configuration. For example, the ML data may be or may include at least one of frequency information of other HCPs using the adjusted input configuration for the current surgical step or a success rate of the surgical operation using the adjusted input configuration," par. [0223]; "Based on a determination that the adjustment input configuration is outside of the determined allowable operation range, the device may block the adjustment input configuration to control the surgical device. The device may send an alert (e.g., an alert message) to an HCP," par. [0010].)
Shelton discloses detecting incidents such as alarm conditions when monitored signals exceed allowable ranges processing these events, and generating actionable recommendations for healthcare professionals based on those incidents. The system leverages historical and incident-derived data to predict optimal device usage and provides guidance directly tied to the detected operational events.
enabling adjustment of one or more of the monitoring criteria;( Shelton, "A computing device may send a request message to the HCP. The request message may indicate whether the allowable operation range needs to be revised, e.g., based on the adjustment input configuration," par. [0222]; "the surgical hub 50954 may configure a revised allowable operation range 50958 that extends from the allowable operation range 50956 and account for the adjusted input configuration 50952 from the surgeon," par. [0238]; "the HCP may provide an updated operation range. The device may use the updated operation range and configure the data to train the ML model and configure to provide an updated allowable operation range," par. [0243], 0024, fig. 13, 0111)
Shelton describe allowable operation range against which data signals are compared to detect alarm conditions. The system enables healthcare professionals to revise and update this range based on input configurations, and implements these adjustments.
receiving a proposed change from a user concerning the one or more monitoring criteria; (Shelton, par. 0010, fig. 13) Shelton receives an adjustment input configuration from personnel, which is a user-originated proposal to change operational parameters.
and providing feedback to the user concerning the proposed change. (Shelton, par. 0010) Shelton evaluates the input against the allowable range and sends an alert to the user when it is out of range, providing feedback on the proposal.
Obviousness Rationale:
processing the one or more incidents utilizing a generative AI model to produce a recommendation based on the one or more incidents.
Under the broadest reasonable interpretation, this limitation requires processing incident data with a generative AI model to output a recommendation responsive to those incidents. Shelton teaches processing incident-related surgical data to generate recommendation outputs. However, Shelton does not teach that the model is specifically a generative AI model.
Lima teaches that missing feature, as shown by “AI based recommendation system applies
generative models to provide guidance [par. 0034], The AI based recommendation system
can be configured to receive three-dimensional (3D) image data that is associated with a patient, where the 3D image data includes volumetric data; and detect one or more anatomical treatment sites [par. 0005] also see figure 5-10, par. 0037 ” which reads on a generative AI model producing recommendation output based on sensor data about an injury/anomaly treatment site.
A person of ordinary skill in the art would have combined, before the filing date, Shelton with Lima to improve Shelton’s recommendation engine by using a known generative AI system form Lima for surgical recommendation generation, because Shelton already uses ML-driven surgical data analysis to produce recommendation outputs, and Lima shows that generative AI more specializes genus, was known therefore a POSITA could use the generative AI from Lima to generate surgical recommendations and highly accurate predictions for a wide range of language [Lima, par. 0003]. Doing so would have predictably resulted in Shelton’s incident-based surgical system using a generative AI model to generate recommendation output from incident data, which is no more than the predictable use of a known AI recommendation technique in the same surgical decision-support field.
Claim 2. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein providing feedback to the user concerning the proposed change includes: providing justification to the user concerning the proposed change. (Shelton, par. 0010) Shelton’s alert states that the input is outside the allowable range, supplying the reason that justifies the feedback.
Claim 3. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein providing feedback to the user concerning the proposed change includes: providing explanation to the user concerning the proposed change. (Shelton, par. 0010) The alert explains the status of the proposal by indicating it is outside the determined allowable range.
Claim 4. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1, further comprising: enabling the user to effectuate the proposed change of the one or more monitoring criteria. (Shelton, par. 0024, fig. 13) Shelton shows applying an adjustment input to change allowable ranges, while only blocking inputs outside the range implies valid inputs are effectuated.
Claim 5. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein the one or more of monitoring criteria includes one or more thresholds. (Shelton, par. 0009) Shelton defines upper and lower bounds of an allowable range, which operate as thresholds.
Claim 6. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein the data signals concern one or more details of the bedside monitoring device and/or uses of the bedside monitoring device. (Shelton, par. 0099) Shelton includes device details (e.g., model, settings) and operational information, showing data about both device attributes and use.
Claim 7. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein the monitoring criteria includes user-defined monitoring criteria. (Shelton, par. 0024, fig.13) Shelton allows a health-care professional’s input to adjust ranges, yielding user-defined criteria.
Claim 8. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein the monitoring criteria includes machine-defined monitoring criteria. (Shelton, par. 0007) Shelton determines allowable ranges using a machine-learning model, establishing machine-defined criteria.
Claim 9. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 8 wherein the machine-defined monitoring criteria is defined via massive data sets processed by ML. (Shelton, par. 0007, 0009, fig. 13, 0141, 0124) Shelton leverages large data availability and ML/AI techniques to derive models from many procedures, satisfying criteria defined via massive datasets.
Claim 10. Shelton in combination with Lima teaches limitations.
The computer-implemented method of claim 1 wherein the bedside monitoring device includes one or more sub-medical devices. (Shelton, par. 0087) Shelton describes modular instruments with discrete components that function as sub-medical devices within the overall apparatus.
Note: claims 11-30 are rejected with the same paragraphs and explanation above for being very similar than claims 1-10.
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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA DAMIAN RUIZ whose telephone number is (571)272-0409. The examiner can normally be reached 0800-1800.
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/KAREN A HRANEK/Primary Examiner, Art Unit 3684