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 September 23, 2025, is as follows: Claims 1-8 and 10-20 are pending. Claim 9 is canceled. The applicant has amended Claims 1, 14, and 20, which have been considered below.
Applicant Argument Response:
Applicant’s arguments, see pages 7-12, filed date 11/19/25, with respect to Claims 1-8 and 10-20 have been fully considered and are not persuasive. The 35 U.S.C. § 101 rejection is sustained.
Applicant argues that the amended claims recite a specific technological solution because they require a camera system including a plurality of cameras positioned within the patient environment that captures video of patient movements and caregiver interactions, and require one or more artificial intelligence models to analyze that data and calculate post-operative risk scores. Applicant cites Specification paragraphs 28, 40, 49, and 91 as showing improved post-operative monitoring technology.
Examiner respectfully disagrees because the applicant’s evidence does not show that the claims recite an improvement to camera technology, AI technology, computer functionality, or interface technology itself under MPEP 2106.05(a). Under the broadest reasonable interpretation, claim 1 still recites receiving patient and video data, analyzing that data with one or more AI models to calculate a post-operative score, comparing the score to a threshold, generating an alert and recommendation, and presenting a GUI control. As explained in the new rejection, those elements perform their generic roles as data sources, analysis tools, and output components, and the claim does not recite any specific camera-processing technique, AI improvement, or computer-function improvement. Accordingly, the amendment does not overcome the current § 101 rejection because the claim still uses those components to carry out the post-operative risk evaluation workflow, rather than to improve the underlying technology itself.
Applicant argues that using “one or more artificial intelligence models” to analyze video and monitoring-device data is a specific technological application that cannot be performed mentally or manually, and therefore distinguishes the claims from a mental process and shows a technological improvement.
Examiner respectfully disagrees because, based on MPEP 2106.04(a)(2), a claim directed to a concept that can practically be performed in the human mind remains a mental process even if the claim requires a computer or AI to execute it. While it is factually true that a human brain is not an "artificial intelligence model," the underlying conceptual process recited in the claims (analyzing patient interactions to assess post-operative risk) is a mental task, meaning the AI is properly treated as an "additional element" rather than negating the abstract idea itself. Under MPEP 2106.05(f), because the claims recite the AI purely by its generic analytical function without detailing any specific technical improvements to the AI's architecture or training, this additional element fails to integrate the abstract idea into a practical application. Therefore, the actual rejection under 35 U.S.C. § 101 has not been overcome.
The applicant argues that when considered as a whole, the combination of a camera system capturing video data and artificial intelligence models analyzing that data to calculate a post-operative score constitutes a specific technological arrangement that solves a technical problem, thereby integrating the abstract idea into a practical application.
The Examiner maintains the rejection under 35 U.S.C. § 101 because gathering data of patient movements for AI evaluation is considered insignificant extra-solution activity under MPEP 2106.05(g) and does not meaningfully limit the abstract idea. The claim fails to recite a specific technological solution or improvement under MPEP 2106.05(a), as the specification describes the camera system and AI in generic terms (generic video cameras, machine learning algorithm) without disclosing any specialized hardware or novel architectural improvements. Therefore, the ordered combination merely links a data-gathering step to an abstract data-analysis step using generic tools, failing to integrate the abstract idea into a practical application. Refer to Spec, par. 0036, and 0040
The applicant argues that the combination of monitoring devices, cameras, and AI models to calculate a post-operative risk score is an unconventional approach that provides an inventive concept.
Examiner respectfully disagrees because, under MPEP 2106.05(f), achieving a more efficient or accurate result through the automation of an abstract idea does not constitute an inventive concept if the underlying implementation relies on generic tools. While paragraph [0029] of the specification details beneficial outcomes such as properly allocating resources and reducing costs, these are administrative and fundamental economic benefits rather than technological ones. An improvement in the accuracy of a mathematically calculated statistical prediction is viewed as an improvement to the abstract idea itself (e.g., the risk assessment) rather than a transformation of the technology used to perform it (see MPEP 2106.05(a)). Because the claims do not recite a technical implementation that goes beyond the "generic hardware structure" of the recited components (Spec., para. [0017]), the cameras and AI models continue to function in their ordinary capacity as tools for data acquisition and analysis. Consequently, the combination fails to provide an inventive concept because it does not amount to "significantly more" than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
The applicant argues that the ordered combination of data collection, intelligent processing, and actionable decision support provides an inventive concept because the cited prior art does not disclose this specific combination. Additionally, the applicant asserts that under the Updated Guidance, "close calls" regarding eligibility must be resolved in the applicant's favor based on a preponderance of the evidence.
Examiner respectfully disagrees because, under the governing framework of MPEP 2106.05, the standard for establishing an inventive concept is distinct from the standards for novelty and nonobviousness. As articulated in MPEP 2106.05(f), a claim that is novel or nonobvious under §§ 102 or 103 may still be ineligible under § 101 if it merely implements an abstract idea using generic tools. The applicant's argument that this combination is absent from the prior art addresses the application of the idea but does not establish a technological transformation.
When evaluated as an ordered combination, the recited limitations gathering video data, processing it via AI, and displaying an alert consist of generic computer functions performing their generic roles of data acquisition, analysis, and presentation. The preponderance of intrinsic evidence establishes that the implementation is non-specialized; the specification acknowledges that the hardware employs a generic structure (Spec., para. [0017]) and employs standard surveillance tools and general machine learning algorithms (Spec., paras. [0036], [0040]). Because the record explicitly identifies these as generic tools, the determination of ineligibility is supported by the evidence and does not constitute a "close call." The ordered combination fails to provide "significantly more" than the abstract idea itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
Applicant’s arguments, see pages 12-15, filed date 11/19/25, with respect to Claims 1-3, 5-7, 9-11, and 13-20 have been fully considered, the 35 U.S.C. § 102 rejection is withdrawn, and 35 U.S.C. § 103 was submitted. Applicant' s arguments with respect to the newly-introduced subject matter of claims 1, 14, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Subject Matter Eligibility Rejection Under 35 U.S.C. § 101
Claims 1-8 and 10-20 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception, namely an abstract idea, without reciting additional 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
Step 1 asks whether the claims, as a whole, fall within one of the four statutory categories. Here, the claims do.
The claims are directed to statutory subject matter, encompassing the following statutory categories:
Process (Claims 14-19): The language reciting "receiving," "calculating," "issuing," "generating," "presenting," and, in claim 15, "automatically implementing" defines a series of acts or steps and therefore falls within the process category.
Machine (Claims 1-8 and 10-13): The language reciting "a system," "at least one processing device," and "a memory device storing instructions" describes a concrete thing made of parts working together and therefore falls within the machine category.
Manufacture (Claim 20): The language reciting "a non-transitory computer readable storage medium storing instructions" describes a tangible article of manufacture. Because claim 20 expressly recites "non-transitory," no separate Step 1 non-statutory media rejection is warranted.
Having confirmed that the claims are directed to statutory subject matter, the analysis proceeds to Step 2A, Prong One.
Step 2A, Prong One
Step 2A, Prong One asks whether the claims recite a judicial exception.
More specifically, claims 1-8 and 10-20, when read as a whole under the broadest reasonable interpretation (BRI), recite structural hardware elements such as processors, cameras, and medical monitors that are functionally configured to collect clinical data, use AI to calculate risk scores based on observed interactions, and output care recommendations. The dependent claims further expand upon these parent functions by detailing specific data inputs (e.g., electronic health records, time spent on the surgical table, quantity of caregivers) and specific organizational outputs (e.g., calculating pressure injury risks, assigning care statuses, and routing unassigned caregivers). Therefore, claims 1-8 and 10-20 have functional limitations that collectively recite two abstract ideas: a mental process of analyzing clinical data to evaluate post-operative patient risk, and a certain method of organizing human activity by managing interpersonal caregiver workflows and patient assignments.
Representative independent claim 1 recites the following, with additional elements shown in bold and the abstract idea left unbolded:
Claim 1.
A system for improving a prediction of post-operative patient risks within a patient environment, the system comprising:
at least one processing device;
and a memory device storing instructions which, when executed by the at least one processing device, cause the at least one processing device to:
receive data from one or more monitoring devices
and a camera system including a plurality of cameras positioned within the patient environment, wherein the camera system captures video data of patient movements
and caregiver interactions;
calculate a post-operative score based on the data received from the one or more monitoring devices and
the video data captured by the camera system, wherein the postoperative score is at least partially calculated by monitoring interactions between a patient and other persons
by utilizing one or more artificial intelligence models to analyze the data from the one or more monitoring devices including the video data captured by the camera system;
issue an alert when the post-operative score exceeds a threshold value;
generate at least one recommendation for improving the post-operative score;
and present a control on a graphical user interface for viewing the post-operative score or the at least one recommendation.
Independent Claims Abstract Classification Rationale
Under BRI, independent claims 1, 14, and 20 recite receiving patient-condition, movement, and interaction information from recited sources, evaluating that information to calculate a post-operative score, comparing the score to a threshold, and generating an alert or recommendation for caregiver use. More specifically, the claims recite "receive data," "calculate a post-operative score," calculate the score at least partly by "monitoring interactions between a patient and other persons," "issue an alert when the post-operative score exceeds a threshold value," and "generate at least one recommendation." Those limitations fall within the mental-process grouping because they recite observation, evaluation, comparison, and judgment performed on collected information. A human clinician could mirror that reasoning with observations, notes, and a threshold by assessing patient interactions and condition, judging whether risk is elevated, and deciding whether an alert or recommendation should follow.
The independent claims also recite "generate at least one recommendation" for caregiver use, which, in the context of the claimed post-operative risk workflow, also includes the abstract idea grouping of certain methods of organizing human activity, specifically managing personal behavior or relationships. That is because the recommendation is not merely a private internal assessment; it is generated for caregiver response within the clinical environment. The specification confirms that purpose by explaining that caregivers may properly allocate resources within a clinical care environment based on patient risk and that patient handling may be adjusted based on need and availability (Spec., para. [0029, 0091]).
Dependent Claims Analysis
The dependent claims 2-8 and 10-19 are also directed to an abstract idea.
Claims 2 and 10: These claims add particular devices or device environments, such as networked medical devices, smartphones, or tablets. Under BRI, they identify additional sources or tools for collecting or monitoring information used in the same risk-evaluation process. They inherit the abstract idea of the independent claims, while those recited devices remain additional elements for Prong Two and Step 2B.
Claims 3, 4, 7, 8, 11, 12, and 19: These claims narrow the kinds of information analyzed or the kind of score produced, such as a pressure injury risk score, infection risk score, time on a surgical table, quantity of caregivers entering a surgical environment, patient movement, EHR data, or adverse event data. Under BRI, they remain directed to evaluating information to assess risk, which is a mental process.
Claims 6, 13, 17, and 18: These claims narrow the kind of recommendation or follow-on decision, such as care-status assignment, caregiver reassignment, patient relocation, or workload-based reassignment. Under BRI, they remain directed to the underlying evaluative process and additionally recite managing personal behavior or relationships.
Claims 5 and 15: These claims add approval-driven automatic implementation of the recommendation. Under BRI, they still depend on the same abstract evaluation and recommendation workflow, and the approval-based follow-through reflects workflow management layered on top of the underlying abstract idea.
Claims 3 and 16: These claims specify that the recommendation includes turning a patient to reduce pressure-injury risk. Under BRI, these claims still recite a recommendation generated from the abstract analysis rather than affirmatively reciting a treatment step as the focus of the claim.
Claims 1-8 and 10-20 recite the abstract idea of evaluating patient and clinical-environment information to assess post-operative risk and decide a responsive action, with certain dependent claims further reciting workflow management for caregivers and patient assignment. The analysis therefore proceeds to Step 2A, Prong Two.
Step 2A, Prong Two: Integration Into a Practical Application
Step 2A, Prong Two asks whether the additional elements, alone and in combination with the judicial exception, apply that exception in a meaningful way so that the claim is more than the abstract idea itself. Here, the additional elements do not do so. They collect information, execute the analysis, and present the result in a healthcare environment, but they do not recite a specific technical mechanism that improves camera operation, model operation, computer operation, or any other technology.
Evaluation of Independent Claims 1, 14, and 20 Additional Elements
The additional elements to be evaluated are: "at least one processing device," "memory device storing instructions," "one or more monitoring devices," "camera system including a plurality of cameras positioned within the patient environment," "one or more artificial intelligence models," and "graphical user interface" control.
Processing device and memory
These elements do not integrate the abstract idea into a practical application because the claims do not use them in a manner that improves computer functionality. MPEP 2106.05(a) asks whether the claim recites an improvement in the functioning of a computer or another technology, and MPEP 2106.05(f) explains that merely using a computer as a tool to perform an abstract idea does not impose a meaningful limit. That is what the claims do here. The processor and memory are recited only as the components that execute instructions to receive data, calculate a score, issue an alert, generate a recommendation, and present the result. The claim does not recite any particular processor architecture, memory arrangement, or specialized computing technique that improves the operation of the computer itself.
Monitoring devices and camera system
These elements do not integrate the abstract idea into a practical application because the claims do not recite an improvement in sensing, camera operation, or machine functionality. MPEP 2106.05(b) explains that merely applying an exception with a particular machine does not integrate the exception into a practical application unless the machine imposes a meaningful limit beyond being a generic tool, and MPEP 2106.05(g) explains that data gathering is generally insignificant extra-solution activity. Here, the monitoring devices and camera system are recited as sources of information used in the risk analysis. The claim identifies what data is collected and where it is collected, but it does not recite a new sensing technique, a new camera-control method, a new image-processing pipeline, or a new arrangement that improves how the devices themselves operate.
Artificial intelligence models
This element does not integrate the abstract idea into a practical application because the claims do not recite an improvement in AI technology or another technical field. MPEP 2106.05(a) requires a claim to recite an actual improvement in computer functionality or other technology, not merely the use of advanced computing terminology. The AI model does not by itself show practical application; the claim must recite how the AI technology itself is improved or how it is applied in a manner that imposes a meaningful technological limit. Here, the claims invoke AI at a functional level as the tool that analyzes the collected data to generate the post-operative score. They do not recite how the model is trained, what features it extracts, how it processes video differently from known approaches, or how the model architecture itself is improved.
Graphical user interface control
This element does not integrate the abstract idea into a practical application because the claims do not recite an improvement in user-interface technology. MPEP 2106.05(a) asks whether the interface itself is technologically improved, and MPEP 2106.05(g) explains that outputting, displaying, or presenting the results of an abstract process generally amounts to insignificant post-solution activity unless the claim recites a specific improvement in the interface technology itself. That is not present here. The claims do not recite any specific interface mechanics, data navigation technique, display rendering improvement, or user interface architecture that changes how the GUI itself operates. Instead, the GUI is where the results of the risk analysis are viewed.
When viewed as a whole, the combination of these additional elements still does not integrate the abstract idea into a practical application. MPEP 2106.05 instructs that the claim must be evaluated as an ordered combination, but the ordered combination here remains a computerized implementation of the same abstract evaluation-and-response workflow. Together, the elements collect clinical data, analyze that data to produce a risk score, compare the score to a threshold, and communicate the result to caregivers in a clinical environment.
Dependent Claims Analysis
Limitations already identified in Prong One as part of the abstract idea are not re-evaluated as additional elements.
The new additional elements recited by the dependent claims do not integrate the abstract idea into a practical application.
Claim 2: Adds one or more medical devices connected to a network, including a patient bed, a spot monitor, a contact-free continuous monitoring device, or an infusion pump. These are additional data-source components, but the claim does not recite any improvement in how those devices operate, communicate, sense, or control treatment. Under MPEP 2106.05(b) and 2106.05(g), using these devices as sources of information in the claimed analysis is a machine environment and data-gathering arrangement, not a practical application based on improved device technology.
Claims 5 and 15: Add automatically implementing the recommendation after receiving an approval input. This is a new additional element because it adds follow-on execution after the recommendation is generated. But the claims do not recite any specific technological control mechanism, device-control protocol, or machine-operation improvement by which implementation occurs. Under MPEP 2106.05(a) and 2106.05(g), this is follow-on execution and input handling, not a recited improvement in technology.
Claim 10: Adds communications devices including a smartphone or tablet to monitor interactions. These are additional device limitations, but the claim does not recite any improvement in smartphone, tablet, or communications technology. Under MPEP 2106.05(b), these devices are used as tools in the claimed environment, not as technologically improved components.
As an ordered combination, the new additional elements recited by dependent claims 2, 5, 10, and 15 still do not integrate the abstract idea into a practical application. They add more data-source devices, approval-based follow-on execution, and portable communications devices, but they do not recite a specific technological mechanism that improves the operation of those components or otherwise imposes a meaningful limit on the abstract idea. The claims therefore remain directed to using additional tools in the same risk-assessment workflow, not to a technological improvement.
Because the additional elements do not integrate the recited abstract idea into a practical application, the analysis proceeds to Step 2B.
Step 2B: Inventive Concept Analysis
Step 2B asks whether the additional elements, individually and as an ordered combination, amount to significantly more than the judicial exception itself. The key question is whether the claim adds more than generic tools performing their ordinary functions at a high level of generality. However, here the additional elements perform their ordinary roles of gathering data, executing instructions, analyzing information, and presenting results, and the claims do not recite a non-ordinary technical arrangement or other inventive concept that transforms the abstract idea into patent-eligible subject matter.
Evaluation of Independent Claims 1, 14, and 20 Additional Elements
The additional elements are: "at least one processing device," "memory device storing instructions," "one or more monitoring devices," "camera system including a plurality of cameras positioned within the patient environment," "one or more artificial intelligence models," and "graphical user interface" control.
Processing device and memory
These elements do not add significant value because the claims treat them as a platform for abstract data evaluation rather than a specific technological computing arrangement. MPEP 2106.05(f) states that merely using a computer to perform an abstract idea does not establish inventiveness, and MPEP 2106.05(a) considers whether the claim improves computer functionality. The claims mention "at least one processing device" and "a memory device storing instructions" but lack details on processor architecture, memory configuration, or technical improvements.
Monitoring devices and camera system
The claims' elements (monitoring devices, camera system) are deemed non-significantly limiting because they are described as abstract risk-analysis sources, not as specific sensing arrangements that alter device operation. Data collection alone is not inventive (MPEP 2106.05(b), 2106.05(g)). The claims and specification lack details on sensing protocols, synchronization, camera-control logic, image processing, or hardware configurations. The system merely receives data from various sources (e.g., medical devices, camera systems) for analysis, indicating these elements are data acquisition tools, not technical improvements in sensing or camera technology. Refer to Spec., par. [0038 and 0048]
Artificial intelligence model
The claims lack technical specificity for the AI limitation. They recite AI as an analytical tool (MPEP 2106.05(f)), not an improvement to the technology itself (MPEP 2106.05(a)). The claims only mention "one or more artificial intelligence models" without detailing the architecture, training, feature extraction, or inference, making it a functional instruction for data analysis. The specification supports this, describing AI as the analytic tool (e.g., "machine learning algorithm") used to generate outputs. Refer to Spec. 0040
Graphical user interface control
The GUI element is not a technical improvement; it merely presents the results of an abstract process (MPEP 2106.05(g)) and receives follow-on user input. The claims lack any technically specific interface improvements like architecture, rendering, navigation, or user-interface mechanisms (MPEP 2106.05(a)). The specification confirms the GUI's role is presentation and input via elements such as the post-operative score, recommendation indication, and approval/request icons (Spec., paras. 0018).
The combination of the supplementary elements remains insufficient. When considered collectively, the processor and memory constitute the computing platform, while the monitoring devices and cameras serve as sources of input data. The AI models perform data analysis, and the graphical user interface (GUI) displays the results. This sequential configuration continues to implement an abstract risk assessment and recommendation workflow, rather than incorporating a technically specific arrangement that extends beyond the mere abstract concept executed with the recited tools. The specification encapsulates the system within these same terms, indicating that the monitoring system may receive input data from one or more clinical care environment systems and compute one or more post-operative scores based on this data analysis. Refer to spec, [0017, 0028-0029].
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.
Claim(s) 1-3, 5-7, 10-11, and 13- 20 are rejected under 35 U.S.C. 103 as being unpatentable over US11504071 prior art Terry, and further in view of Derenne - US 2012.0075464A1.
Terry teaches, Claim 1.
A system for improving a prediction of post-operative patient risks within a patient environment, the system comprising: (Terry, Col. 1, ll.10-35, Col. 1, ll.40-67, Col.18, ll. 54- 67, Col. 3, ll. 48-55)
at least one processing device; (Terry, fig. 2- fig. 3, fig. 6, col.1,ll.45-67)
Terry discloses an "analytics engine" and computing devices that perform patient-risk.
and a memory device storing instructions which, when executed by the at least one processing device, cause the at least one processing device to: (Terry, fig. 2- fig. 3, fig. 6)
receive data from one or more monitoring devices (Terry, fig. 2- fig. 3, fig. 6)
and a camera system including a plurality of cameras positioned ; (Terry, ...a photo… with a camera...WOUNDVUE..camera...Col. 17, ll. 50-55; …fundus imaging system…col. 56, ll. 5-15; …nurse enters the room as indicated… the PSA receives…locating…caregiver is in the room…Col. 30. ll.39-50; Col. 29, ll. 50-60…receives patient movement data…;
calculate a post-operative score based on the data received from the one or more monitoring devices and (Terry, fig. 2- fig. 3, fig. 6, Col. 1, ll.10-35, Col. 1, ll.40-67, Col.18, ll. 54- 67, Col.22, ll. 31 -55, Col. 16, ll 25-40)
Terry's system monitors caregivers ("other persons") using tags to track their presence in patient rooms, thus monitoring "interactions." This location data is sent to an "analytics engine 20," which processes it to "perform risk assessments," demonstrating how interaction data is used in "calculation."
by utilizing one or more artificial intelligence models to analyze the data from the one or more monitoring devices including ; (Col. 46, ll.45-61)
Terry describes calculating a risk score using AI models applied to monitoring device data.
issue an alert when the post-operative score exceeds a threshold value; (Terry, fig. 2, Col. 20, ll.10-67)
Terry’s system uses sensor and caregiver-location data to calculate a risk score and, if it gets too high, alerts staff and recommends or automatically starts fixes like turning the patient or activating a pressure-relief mattress.
generate at least one recommendation for improving the post-operative score; (Terry, fig. 2, Col. 20, ll.10-67)
Terry expressly states that the “analytics engine 20 initiates one or more alerts to one or more caregivers,” and explains those alerts may include messages or automatic interventions (e.g., activate alternating-pressure mattress or initiate rounding), which directly maps to the claim limitation to “generate at least one recommendation for improving the post-operative score.”
and present a control on a graphical user interface for viewing the post-operative score or the at least one recommendation. (Terry, fig. 2, Col. 20, ll.10-67, Col. 2, ll. 1 -25)
Terry discloses risk scores and recommendations shown on multiple graphical displays and sent to caregiver mobile devices, which supports the claim that a GUI control is presented for viewing the post-operative score or recommendation.
35 U.S.C 103 Obviousness Rationale
Claim 1 recites and a camera system including a plurality of cameras positioned .
The above limitation requires multiple cameras located in a patient care area that record continuous visual images of patient motion and staff engagement . Terry teaches tracking caregiver location and patient movement, as shown by patient movement data as monitored by load cells at Column 29 lines 50 through 60 and receives information from the locating system that the caregiver is in the room at Column 30 lines 39 through 50. This reads on patient movements and caregiver interactions because the system tracks physical motion and staff presence in the patient area.
However, Terry does not teach a camera system including a plurality of cameras positioned within the patient environment, wherein the camera system captures video data of patient movements and caregiver interactions. Derenne teaches that missing feature, as shown by three video cameras 22 positioned within a single room 28 at paragraph 0048 and processed to detect when a clinician enters a room at paragraph 0068 and predict behavior that leads to someone getting out of bed at paragraph 0121, which reads on a camera system including a plurality of cameras positioned within the patient environment, wherein the camera system captures video data of patient movements and caregiver interactions because multiple cameras record video within the room to monitor both the patient and the staff.
A person of ordinary skill in the art, namely a healthcare technology engineer with experience designing patient monitoring systems, would have combined before filling data Terry with Derenne to augment clinical decision making and early detection of patient deterioration as taught by Terry at Column 27 lines 50 through 67, by integrating the multiple video cameras of Derenne into the analytics platform of Terry, because using video cameras allows the system to visually predict behavior that leads to a fall as taught by Derenne at paragraph 0124 instead of relying solely on bed sensors. Doing so would have predictably improved the risk assessment accuracy of the monitoring system by adding visual behavioral data to the existing physiological data.
Claim 1 recites calculate a post-operative score based on the data received from the one or more monitoring devices and .
Under the broadest reasonable interpretation, this limitation requires determining a patient risk value using machine learning algorithms that process both sensor data and recorded visual images together. Terry teaches utilizing artificial intelligence models to analyze monitoring device data to calculate a score, as shown by analytics platform 20 that implements artificial intelligence AI to process data at Column 31 lines 24 through 25 and algorithms of analytics engine establish a risk profile for each patient at Column 31 lines 1 through 3. This reads on calculate a post-operative score by utilizing one or more artificial intelligence models to analyze the data from the one or more monitoring devices because the system uses AI algorithms to process incoming medical device data and output a patient risk value. However, Terry does not teach including the video data captured by the camera system in the artificial intelligence analysis to calculate the score. Derenne teaches that missing feature, as shown by computer device processes the image signals and determines what condition a patient is in at the Abstract and analysis of Video camera images that are used for preventing patient falls, reducing the chances and or spread of infection at paragraph 0003, which reads on including the video data captured by the camera system because visual records of the patient are computationally processed to evaluate the risk of falls or infections.
A person of ordinary skill in the art, namely a healthcare technology engineer with experience designing patient monitoring systems, would have combined before filling data Terry with Derenne to augment clinical decision making and early detection of patient deterioration as taught by Terry at Column 27 lines 58 through 60, by modifying the AI analytics platform of Terry to also receive and process the video image signals of Derenne, because doing so allows the AI to factor in visual patient actions, such as patient movement of sheets, patient clearance of one or more objects patient leaning forward as taught by Derenne at paragraph 0011, which are critical indicators of risk not captured by physiological vital signs alone. Doing so would have predictably resulted in a more accurate and robust artificial intelligence risk scoring system by synthesizing both physiological sensor data and visual behavioral data into a single comprehensive patient assessment.
Accordingly, the combination of Terry and Derenne renders calculate a post-operative score based on the data received from the one or more monitoring devices and the video data captured by the camera system, wherein the postoperative score is at least partially calculated by monitoring interactions between a patient and other persons by utilizing one or more artificial intelligence models to analyze the data from the one or more monitoring devices including the video data captured by the camera system obvious.
Claim 2. Terry, in combination with Derenne, teaches:
The system of claim 1, further comprising one or more medical devices connected to a network, and wherein the one or more medical devices include at least one of a patient bed, a spot monitor, a contact-free continuous monitoring device, and an infusion pump. (Terry, Col. 48, ll. 40-49, Fig. 2, Col. 22, ll.15-30, Col.24, ll. 55-67, Col.15, ll. 15-30)
Terry describes healthcare equipment linked via a communication infrastructure, specifically comprising items from the defined list. Terry's system includes equipment 12 connected via a communications network , and this equipment includes a patient bed and a monitor.
Claim 3. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the post-operative score includes a pressure injury risk score, and the pressure injury risk score is calculated by analyzing the data, and the at least one recommendation includes turning a patient to reduce the pressure injury risk score.(Terry, abstract, Col. 20, ll 15-30)
Terry describes a system where the risk metric assesses pressure injury likelihood by processing data, and suggestions include repositioning the patient.
Claim 5. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the instructions, when executed by the at least one processing device, further cause the at least one processing device to:
automatically implement the at least one recommendation when an input approving the at least one recommendation is received. (Terry, Col.1, ll. 40-67, Col. 20, 5 -30)
Terry describes the patient’s pressure injury score that triggers the automatic activation of a pressure injury prevention protocol.
Claim 6. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the at least one recommendation includes assigning a patient care status to the patient based on a level of care required by the patient. (Terry, Figure 2, figure 8, Col.29, ll.35-55, col. 30, 1-45)
Terry describe a system's suggestions for patient care are determined by categorizing a patient's status according to the specific amount of care they need. For example, analytics engine receives patient movement data to indicates the probability of bed exit, then notify to one or more clinicians to be attended resulting in a change of status.
Claim 7. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the interactions include at least one of measuring a time the patient spends on a surgical table during a surgery, detecting a quantity of caregivers that entered a surgical environment during the surgery, and monitoring one or more patient movements during or after the surgery. (Terry, Col.29, ll. 30-35, Col. 30, ll 5-40)
Terry describes that patient movement is monitored using sensors like load cells, pressure sensors, or force-sensitive resistors integrated into the bed or chair.
Claim 10. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the interactions are automatically monitored using one or more communications devices that include a smartphone or a tablet computer. (Terry, fig. 1-3, fig. 5A, Fig. 4A, Col. 29 ll.35-60, )
Claim 11. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the post-operative score is calculated by analyzing electronic health records pertaining to the patient, wherein the electronic health records include at least one of the patient demographics, a description of a surgery site, a site progression description of the surgery site during and after the surgery, the patient’s medical history, the patient’s diet, and observations made before, during, or after the surgery. (Terry, fig. 1-2, fig. 3, fig. 6, fig. 8, fig. 9, fig. 10, Col. 1 ll. 45-67, Col. 2 ll. 10-20)
The Terry scores are calculated by an analytics engine in a healthcare facility to assess a patient's risk for developing sepsis, falling, or developing a pressure injury, based on data collected from various patient monitoring equipment and Initial assessment that include patient history.
Claim 13. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the recommendation includes assigning the patient to a new location or assigning a new caregiver to the patient. (Terry, Col. 24, ll.35-50, Col. 29, ll. 1-10)
Terry describes a system where the suggested intervention involves moving the patient to a different room.
Claim 19. Terry, in combination with Derenne, teaches:
The method of claim 14, wherein the post-operative score is calculated based on one or more of a time spent on a surgical table during the surgery, a quantity of caregivers that entered a surgical environment, and patient movement.. (Terry, fig. 1-2, fig. 3, fig. 6, fig. 8, fig. 9, fig. 10, Col. 1 ll. 45-67, Col. 2 ll. 10-20)
The Terry scores are calculated by an analytics engine in a healthcare facility to assess a patient's risk for developing sepsis, falling, or developing a pressure injury, based on data collected from various patient monitoring equipment (Include patient movement) and Initial assessment that include patient history.
Note: Claims 14-18 and 20 are rejected with the same analysis above because they are very similar to Claims 1-3, 5-7, and 10-11.
Claim(s) 4, 8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over US11504071 prior art Terry in combination with Derenne - US 2012.0075464A1 in further view of US20140167917A2-Wallace prior art.
Claim 4. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the post-operative score includes an infection risk score calculated based on at least a quantity of caregivers that entered a surgical environment. (Terry, Col.20, ll. 29-53)
Terry teaches calculating an infection (sepsis) risk score (Abstract) and tracking caregivers entering a patient's room, but fails to disclose monitoring the quantity of caregivers entering a specific surgical environment (e.g., Operating Room) or using this specific metric as input for the infection score calculation.
Wallace teaches the missing element, calculating infection risk based on the quantity of staff in the surgical environment. Wallace analyzes Acquired Infections (Risk Drivers) (Wallace, FIG. 2,) and explicitly identifies Staff (caregivers) Occupancy Level (quantity) as a driver (Wallace, FIG. 2). Wallace integrates this analysis with data collected from the OR (Operating Room/surgical environment) (Wallace, FIG. 1).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Terry with Wallace because both references share the purpose of assessing infection risks in healthcare settings using location and interaction data. Terry seeks to assess the risk of sepsis, and Wallace provides a system and method for disease mapping and infection control (Wallace, Abstract). A POSITA would recognize that Wallace's teaching that Staff Occupancy Level (Wallace, FIG. 2) in the OR (Wallace, FIG. 1) is an infection risk driver provides a known technique that could be applied to Terry's system, utilizing Terry's existing locating system 62 (Terry, 0045) to monitor the quantity of caregivers in the surgical environment to enhance the infection score calculation.
A person of ordinary skill in the art would have been motivated to integrate the calculation based on the quantity of caregivers in the surgical environment from Wallace into the system of Terry to achieve the benefit of improved infection control by incorporating known risk drivers. As Wallace teaches that analyzing these drivers facilitates Infection Control Policy (Wallace, FIG. 2, par. 0039) leading to Reduced Morality & Morbidity (Wallace, FIG. 2, 0058).
Furthermore, the proposed combination is obvious under the flexible approach mandated by KSR because it represents the Use of known technique to improve similar devices (methods, or products) in the same way. The technique of using staff occupancy level (quantity) in the OR (from Wallace) to calculate infection risk is known (as evidenced by Wallace, FIG. 1, FIG. 2). Applying this known technique to the analogous risk assessment system of Terry predictably improves its infection prediction (sepsis risk score) in the same manner to achieve a more comprehensive infection risk model.
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. Terry already discloses the infrastructure (RTLS) necessary to track the location and quantity of caregivers, and Wallace provides the analytical approach linking staff quantity in the OR to infection risk (Wallace, FIG. 1, FIG. 2).
Claim 8. Terry, in combination with Derenne, teaches:
The system of claim 7, wherein the post-operative score is calculated based on .(Terry, Col.29, ll. 30-35, Col. 30, ll 5-40)
Terry teaches calculating the score based on patient movements, but fails to disclose the use of surgical table duration or surgical environment traffic count during surgery as inputs for the score calculation.
Wallace teaches these missing elements by describing a system for location and movement monitoring of Entities (Wallace, para. [0030]), which include patients and staff (caregivers) (Wallace, para. [0025]). This monitoring captures the necessary data, as the system can determine the allow-time of an Entity within a Geographical Area and monitor the operational flow within a Geographical Area (Wallace, para. [0030]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Terry with Wallace because both references share the purpose of comprehensive risk assessment in healthcare by integrating diverse data sources. Terry seeks to assess medical risks of a patient and Wallace provides a system that integrates Hospital Information Systems including OR, ADT, etc. (Wallace, FIG. 1). A POSITA would combine these by incorporating the surgical duration data (OR/ADT) and staff quantity data (Occupancy Level) identified by Wallace (Wallace, FIG. 1, FIG. 2) into Terry's multi-factor risk engine alongside the existing patient movement data (Terry, 0103).
A person of ordinary skill in the art would have been motivated to integrate the time on the surgical table and the quantity of caregivers in the surgical environment from Wallace into the system of Terry to achieve the benefit of improved risk prediction by accounting for known intraoperative factors. As Wallace teaches that analyzing these comprehensive data sources facilitates Quality Performance Improvement (Wallace, FIG. 1, par. 0057).
Furthermore, the proposed combination is obvious under the flexible approach mandated by KSR because it represents Combining prior art elements according to known methods to yield predictable results. The integration of Wallace's surgical environment risk drivers (time/quantity) into Terry's risk assessment system involves utilizing known methods, specifically the integration of data from standard hospital information systems as evidenced by Wallace (FIG. 1). Each element (patient movement, surgical duration, staff quantity) performs its established function as a risk contributor. The resulting combination yields only the predictable result of a comprehensive post-operative risk score and demonstrates no unexpected synergy.
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. The data sources (OR, ADT, Location tracking) are standard hospital systems identified by Wallace (FIG. 1), and Terry's system is explicitly designed to integrate multiple data sources for risk analysis (Terry, Abstract).
Claim 12. Terry, in combination with Derenne, teaches:
The system of claim 1, wherein the post-operative score is calculated by analyzing adverse event data including . (Terry, Col.9, ll. 24-40, fig. 2, Col.55, ll 39-50)
Terry describes utilizing information regarding the incident's patient history. However, Terry fails to disclose that the analysis of this adverse event data includes a location where an adverse patient event occurred and one or more caregivers assigned to the patient when the adverse patient event occurred.
Wallace discloses a system that continuously monitors and records the historical location and interactions between individually tracked "Entities," which are defined to include both staff (caregivers) and patients. By analyzing this stored historical data for the specific time an adverse event occurred, the system can determine precisely which caregivers were in proximity to or in contact with the patient.(Wallace, paras. 0025, 0029, 0030-0031, 0034).
It would have been obvious to one of ordinary skill in the art to combine the teachings of Terry with Wallace because both references share the common goal of leveraging data analysis to improve patient safety and risk assessment in a healthcare setting. Terry provides a framework for calculating risk scores based on historical data like "history of falls", while Wallace provides a method for capturing and analyzing highly detailed contextual data surrounding patient and caregiver interactions to facilitate a review of safety policies (Wallace, para. [0023]). A skilled artisan, seeking to make Terry's fall risk score more predictive and actionable, would have been motivated to enhance the generic "history of falls" data point with the specific contextual details of location and personnel taught by Wallace.
A person of ordinary skill in the art would have been motivated to integrate the contextual event analysis from Wallace into the risk calculation of Terry to achieve the benefit of a more precise and actionable risk assessment. Wallace teaches that analyzing such detailed historical data enables a facility to understand... infectious disease risk exposure and to effectively prioritize infection control practices related thereto (Wallace, para. [0035]). Applying this same principle to fall events would predictably allow a facility to identify high-risk locations or circumstances, leading to more targeted and effective fall prevention strategies.
Furthermore, the proposed combination is obvious because it represents combining prior art elements according to known methods to yield predictable results. The integration of location and caregiver data associated with a past adverse event into the Terry risk algorithm involves utilizing known data analysis methods. Terry’s system already possesses an analytics engine for calculating risk scores and a locating system capable of tracking patients and caregivers. Wallace teaches the specific technique of analyzing this type of location and interaction data historically. Each element performs its established function: Terry's engine analyzes data to produce a score, and Wallace's method provides more granular data for that analysis. The resulting combination yields only the predictable result of a more accurate risk score and demonstrates no unexpected synergy. A POSITA would have had a reasonable expectation of success in this integration, as it requires only a standard modification to a software algorithm to incorporate additional, available data fields into its calculation.
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.
2. 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684