DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on May 19, 2026, for the application with serial number 18/158,643.
Claims 1, 13, and 19 are amended.
Claims 1-3, 8-13, 15, and 19 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims are subject matter because the claims recite a process that is rooted in computer technology. See Remarks p. 8. In response, the Examiner submits that the claims merely recite the concept of mitigating the effects of an austere event using a neural network (or neural networks). Contrary to the Applicant’s assertions, the steps of the claims could be implemented mentally or on paper by a human being, but a general purpose computer employing machine learning is recited for implementation. No apparent improvement to machine learning technology is recited in the claims. The use of a neural network implies the use of weighted nodes that are adjusted based on feedback to arrive at an optimal value. See, for example: Neural network (machine learning) – Wikipedia. Contrary to the Applicant’s assertions, a human being could predict the occurrence of an austere event based on historical data, and adjust a schedule accordingly.
The Applicant further submits that the claims provide a practical application of any abstract idea by improving the technical field of scheduling. See Remarks p. 10. In response, the Examiner submits that scheduling is not a technology or a technical field. Scheduling is a human activity. The present claims attempt to manage human behavior for adjusting a schedule. Again, the Examiner reiterates that the steps could be performed mentally or on paper by a human being, but a general purpose computer employing machine learning is recited for implementation. Automation of a manual task does not constitute an improvement to a technology or technical field. See MPEP §2106.05(a)[I]{ii}. Contrary to the Applicant’s assertions, the use of multiple models or an “agglomeration” is not an improvement to machine learning. Mathematical and machine learning models are conventionally combined, as evidenced by the cited prior art. Moreover, the difference between one model and multiple models is ambiguous, because a model can have several terms that each could be considered its own model.
The Applicant further submits that the claimed invention is subject matter eligible because the recited elements are not conventional. See Remarks p. 12. In response, the Examiner points out that lack of conventionality does not imply subject matter eligibility. The additional elements of the claims amount to generic computer hardware operating in a machine learning environment.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Wouhaybi reference, which is cited in the prior art rejection of the independent claims, below. The claims now stand rejected as being obvious over Johnson in view of Palladino and Wouhaybi. The Applicant’s arguments with respect to the newly amended language are moot in light of the newly cited reference, and the newly cited passages from Johnson.
The Applicant additionally contends that the motivation to combine Johnson and Palladino is impermissibly broad. According to the Applicant, the skilled artisan would not combine references regarding resource scheduling with microservices scheduling. See Remarks pp. 15-16. The Examiner respectfully disagrees. Both references deal with subject matter regarding scheduling of resources. The references are combinable, as set forth in the motivational statement, below. The Examiner additionally points to the breadth of the claims – adjusting biasing (weights) in a feedback loop to optimize and objective function (machine learning). Neural networks are known tools for making forecasts and predictions based on historical patterns, as taught by the cited references.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-3, 8-13, 15, and 19 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-3, 8-13, 15, and 19 are all directed to one of the four statutory categories of invention, the claims are directed to mitigating the effects of an austere event (as evidenced by exemplary independent claim 1; “implement the schedule change responsive to the mitigation command value in real- time, and thereby eliminate or mitigate the impact of the austere event”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “interpret a time sequence data;” “generate austere event data;” “detect a trend in the time sequence data;” “generate . . . a mitigation action command value;” “transmit the mitigation action command value;” and “implement a schedule change responsive to the mitigation command.” The steps are all steps for managing personal behavior related to the abstract idea of mitigating the effects of an austere event that, when considered alone and in combination, are part of the abstract idea of mitigating the effects of an austere event. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of mitigating the effects of an austere event. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes generating alerts regarding a predicted risk to a shortage in staffing or operational resources for an enterprise.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (an apparatus with circuits in independent claim 1; a method with circuits in independent claim 1; and a network with circuits in independent claim 19). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite machine learning elements, including an agglomeration of trained neural networks, but the abstract idea of mitigating the effects of an austere event is generally linked to a computer in a machine learning environment with neural network(s) for implementation. Therefore, the neural network(s) merely amount to a technological environment for implementing the abstract idea that does not provide a practical application or significantly more than an abstract idea. See MPEP §2106.05(h). The Examiner notes that the use of machine learning and a neural network implies the use of training steps and training data to iteratively “learn” from past events in a feedback loop. The claims require no more than a generic computer (an apparatus with circuits in independent claim 1; a method with circuits in independent claim 1; and a network with circuits in independent claim 19) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §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.
Claim(s) 1-3, 8-13, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20090089092 A1 to Johnson et al. (hereinafter ‘JOHNSON’) in view of US 20210075700 A1 to Palladino et al. (hereinafter ‘PALLADINO’) and US 20220222583 A1 to Wouhaybi (hereinafter ‘WOUHAYBI’).
Claim 1 (Currently Amended)
JOHNSON discloses an apparatus (see ¶[0023]; a system with a processor) comprising: a time sequence interpretation circuit structured to interpret a time sequence data (see abstract and ¶[0022]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources) generated via an artificial intelligence (AI) model (see ¶[0072]; the decision support rules can be example-based, evidential reasoning based, fuzzy logic-based, case-based, and/or other artificial intelligence-based, for example).
JOHNSON does not specifically disclose, but WOUHAYBI discloses, wherein the Al model comprises a plurality of agglomerate network circuits and a plurality of connector circuits, each connector circuit structured to bias at least one of an input to a corresponding agglomerate network circuit or an output of the corresponding agglomerate network circuit (see abstract and ¶[0002], [00039] & [0162]; clustered federated learning. The model handler circuitry 200 determines that only cluster(s) associated with the context data is/are to be updated, control proceeds to block 1316. At block 1316, the model handler circuitry 200 updates weights for the cluster(s) associated with the context data based on the label(s). For example, the model trainer circuitry 230 can update weights of the neurons 704A of the first cluster 714 of the third ML model 700 based on the labeled data using any AI/ML training/retraining technique. Nodes are connected to each other in a neural network).
JOHNSON further discloses an austere event detection circuit structured to generate austere event data by automatically predicting (see abstract; calculating a predicted duration to deliver the healthcare to each patient), based at least in part on the time sequence data (see ¶[0073]; automatically tracking or receiving input of additions and deletions in workload, and re-calculate the availability in scheduling of resources 110 accordingly to meet the change in workload), an occurrence of an austere event (see ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.) by identifying one or more properties within the time sequence data and correlating the identified properties to known patterns of historic schedule data (see abstract and ¶[0022] & [0027]-[0028]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources. Activity data can be stored in order to create historical data (such as that visualized in histograms 305) such that the disclosed system 100 evolves or learns over time, as well as can improves accuracy of forecast durations and can increase a probability of achieving a schedule of tasks within a predetermined time and usage threshold);
using a first neural network trained to detect a trend in the time sequence data indicative of the austere event (see again ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.).
a mitigation circuit (see ¶[0013] and Fig. 6; mitigate schedule risk) structured to generate, based at least in part on the time sequence data and the austere event data (see ¶[0051] and [0064]; forecast probability or confidence or risk of probability of delay of finishing the activity. Detect a nurse calling in to indicate absence or tardiness),
a mitigation action command value structured to trigger a real-time schedule change in advance of the occurrence of the austere event (see ¶[0054]-[0055]; output an alarm representative to an alert to a problem).
JOHNSON does not specifically disclose, but PALLADINO discloses, using a second neural network trained with data associating past austere events with schedule changes that successfully eliminated and/or otherwise mitigated the impact of the past austere events on one or more business operations (see ¶[0051]; the system 100 can calculate a ripple effect of these above-described example variations and interdependencies and can change the schedule of resources 110 to minimize delays and minimize schedule risk, where the changes can include adjusting the forecast start times, adjusting the forecast duration, adjusting the forecast completion time, suggesting added resources 105, or adjusting the forecast locations of the resources 110 to minimize the risk in the revised schedule of resources 110 in delivery of healthcare service to the patients 115. See also ¶[0044]; measure actual attributes and procedure times and use neural networks to refine or adjust the calculation of forecast durations),
JOHNSON further discloses wherein the schedule change is structured to effect a change of a property of the time sequence data prior to the occurrence of the austere event to eliminate or mitigate an impact of the austere event on one or more entities associated with the time sequence data (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources); and
a mitigation action provisioning circuit structured to transmit the mitigation action command value (see ¶[0088] and Fig. 10; output an alert to a user interface) to one or more systems external to the apparatus (see ¶[0110]-[0112]; program modules executed by machines in networked environments. Information is transferred over a network. Use logical connectors to one or more remote computers), said external systems being configured to implement the schedule change responsive to the mitigation command value in real- time, and thereby eliminate or mitigate the impact of the austere event (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources. See also ¶[0052] and [0067]; an embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.).
wherein the apparatus is structured to feed the mitigation action command value to the AI model to train the AI model to adapt to the trend (see Fig. 2; refine or adjust a prediction made using historical data).
The combination of JOHNSON and PALLADINO does not explicitly disclose, but WOUHAYBI discloses, wherein feeding the mitigation action command value to the Al model adjusts one or more biases of at least one of the plurality of connector circuits to form a feedback loop structured to reach equilibrium and optimization of biases in the plurality of agglomerate network circuits (see ¶[0002] and [0056]; apply weighting values to the data during the processing of the data. Such weighting values are determined during a training process. Federated learning enables devices to train neural networks locally using data observed by the devices and sends the new weights to a central location for integration into other machine learning models. Use trial and error to reach optimal model performance. Use any other type of optimization).
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]). PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. It would have been obvious for one of ordinary skill in the art at the time of invention to include the neural network trained with successful remedial actions as taught by PALLADINO in the system executing the method of JOHNSON with the motivation to mitigate risk to delivered services.
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]) that uses weighted neural networks (see ¶[0044], [0072], and [0080]; use artificial neural networks. An embodiment of the step 930 of identifying resources 110 of the critical path can include calculating a weighted parametric mathematical algorithm including: exceeding a threshold or having a highest number of independencies relative to other resources 110, or exceeding a threshold or having a highest risk of unavailability relative to other resources 110, etc). PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. WOUHAYBI discloses clustered federated learning using multiple neural network that are trained with feedback to reach an optimum. It would have been obvious to include the federated learning as taught by WOUHAYBI in the system executing the method of JOHNSON and PALLADINO with the motivation to determine a schedule with minimal risk using a neural network.
Claim 2 (Original)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the apparatus further comprises an external event interpretation circuit structured to interpret external event data (see ¶[0108]; manage changes to a schedule to accommodate changes that are internally or externally induced).
Claim 3 (Original)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 2.
JOHNSON further discloses wherein the external event data corresponds to at least one of: weather; a supply chain; equipment status; an employee health event; an employee life event; or a geo-political event (see ¶[0003]; can focus more fully on the value added core processes that achieve the stated mission and less on activity responding to variations such as delays, accelerations, backups, underutilized assets, unplanned overtime by staff and stock outs of material, equipment, people and space that is impacted during the course of delivering healthcare. See also ¶[0100]; costs associated with investment in additional or replacement resources 110 (e.g., capitalized equipment, consumable stock, physical plant, staffing levels, training of staff, recruiting staff, etc.), attraction or drop-off in admission of patients 115 to receive delivery of healthcare relative to threshold, or an ability to meet one or more financial targets relative to threshold. See also ¶[0045] and [0048]; medical equipment and equipment availability. See also ¶[0073]; a staff person calls in sick).
Claim 8 (Previously Presented)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the trend is one of: an overuse of a piece of equipment (see ¶[0003] and [0046]-[0048]; stock outs of equipment. Occupation or use of resources, such as equipment. Tracked event data includes an unavailability of equipment); a number of vacations (see ¶[0082]; the surgeon desires not to be available due to vacation); timing of vacations (see again ¶[0082]; the surgeon desires not to be available due to vacation); or a shrinking resource pool (see ¶[0051], [0073], and [0107]; track deletions in workload. Examples include a staff person calls in sick).
Claim 9 (Original)
The combination of JOHNSON and PALLADINO discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the austere event is at least one of: an equipment malfunction (see ¶[0048]; malfunctions in personnel or equipment); a shortage in receiving equipment (see again ¶[0048]; an unavailability of equipment); a delay in receiving equipment (see again ¶[0048]; unavailability of equipment, including delay in procedure); a shortage in materials (see ¶[0003]; stock outs of material); a shortage of products for sale; a natural disaster; an inclement weather event; or a shortage in personnel (see ¶[0026]; staffing shortfalls).
Claim 10 (Currently Amended)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the mitigation circuit is further structured to predict the occurrence of the austere event by analyzing the time sequence data (see ¶[0054]; output an alarm representative on an alert to a problem. Illustrate risk to the schedule of resources).
Claim 11 (Original)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the mitigation action command value corresponds to generation of an alert (see ¶[0054]; output an alarm representative on an alert to a problem. Illustrate risk to the schedule of resources).
Claim 12 (Original)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the apparatus as set forth in claim 1.
JOHNSON further discloses wherein the mitigation action command value corresponds to adjusting a bias of a connector circuit in an agglomerate network (see ¶[0080]; calculating a weighted parametric mathematical algorithm. See also ¶[0044]; measure attributes using an artificial neural network).
Claim 13 (Currently Amended)
JOHNSON discloses a method comprising: interpreting, via a time sequence interpretation circuit (see ¶[0023]; a system with a processor), time sequence data (see abstract and ¶[0022]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources) generated via an artificial intelligence (AI) model (see ¶[0072]; the decision support rules can be example-based, evidential reasoning based, fuzzy logic-based, case-based, and/or other artificial intelligence-based, for example).
JOHNSON does not specifically disclose, but WOUHAYBI discloses, wherein the Al model comprises a plurality of agglomerate network circuits and a plurality of connector circuits, each connector circuit structured to bias at least one of an input to a corresponding agglomerate network circuit or an output of the corresponding agglomerate network circuit (see abstract and ¶[0002], [00039] & [0162]; clustered federated learning. The model handler circuitry 200 determines that only cluster(s) associated with the context data is/are to be updated, control proceeds to block 1316. At block 1316, the model handler circuitry 200 updates weights for the cluster(s) associated with the context data based on the label(s). For example, the model trainer circuitry 230 can update weights of the neurons 704A of the first cluster 714 of the third ML model 700 based on the labeled data using any AI/ML training/retraining technique. Nodes are connected to each other in a neural network).
JOHNSON further discloses, generating, via an austere event detection circuit, austere event data, by automatically predicting (see abstract; calculating a predicted duration to deliver the healthcare to each patient), based at least in part on the time sequence data (see ¶[0073]; automatically tracking or receiving input of additions and deletions in workload, and re-calculate the availability in scheduling of resources 110 accordingly to meet the change in workload), an occurrence of an austere event (see ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.), using a first neural network trained to detect a trend in the time sequence data indicative of the austere event (see ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.) by identifying one or more properties within the time sequence data and correlating the identified properties to known patterns of historic schedule data (see abstract and ¶[0022] & [0027]-[0028]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources. Activity data can be stored in order to create historical data (such as that visualized in histograms 305) such that the disclosed system 100 evolves or learns over time, as well as can improves accuracy of forecast durations and can increase a probability of achieving a schedule of tasks within a predetermined time and usage threshold);
generating, via a mitigation circuit and based at least in part on the time sequence data and the austere event data (see ¶[0051] and [0064]; forecast probability or confidence or risk of probability of delay of finishing the activity. Detect a nurse calling in to indicate absence or tardiness), a mitigation action command value structured to trigger a real-time schedule change in advance of the occurrence of the austere event (see ¶[0054]-[0055]; output an alarm representative to an alert to a problem),
JOHNSON does not specifically disclose, but PALLADINO discloses, using a second neural network trained with data associating past austere events with schedule changes that successfully eliminated and/or otherwise mitigated the impact of the past austere events on one or more business operations (see ¶[0051]; the system 100 can calculate a ripple effect of these above-described example variations and interdependencies and can change the schedule of resources 110 to minimize delays and minimize schedule risk, where the changes can include adjusting the forecast start times, adjusting the forecast duration, adjusting the forecast completion time, suggesting added resources 105, or adjusting the forecast locations of the resources 110 to minimize the risk in the revised schedule of resources 110 in delivery of healthcare service to the patients 115. See also ¶[0044]; measure actual attributes and procedure times and use neural networks to refine or adjust the calculation of forecast durations),
wherein the schedule change is structured to effect a change of a property of the time sequence data prior to the occurrence of the austere event to eliminate or mitigate an impact of the austere event on one or more entities associated with the time sequence data (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources); and
transmitting, via a mitigation action provisioning circuit, the mitigation action command value (see ¶[0088] and Fig. 10; output an alert to a user interface) to one or more systems external to the apparatus (see ¶[0110]-[0112]; program modules executed by machines in networked environments. Information is transferred over a network. Use logical connectors to one or more remote computers), said external systems being configured to implement the schedule change responsive to the mitigation command value in real- time, and thereby eliminate or mitigate the impact of the austere event (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources. See also ¶[0052] and [0067]; an embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.). and
feeding the mitigation action command value to the AI model to train the AI model to adapt to the trend (see Fig. 2; refine or adjust a prediction made using historical data).
The combination of JOHNSON and PALLADINO does not explicitly disclose, but WOUHAYBI discloses, wherein feeding the mitigation action command value to the Al model adjusts one or more biases of at least one of the plurality of connector circuits to form a feedback loop structured to reach equilibrium and optimization of biases in the plurality of agglomerate network circuits (see ¶[0002] and [0056]; apply weighting values to the data during the processing of the data. Such weighting values are determined during a training process. Federated learning enables devices to train neural networks locally using data observed by the devices and sends the new weights to a central location for integration into other machine learning models. Use trial and error to reach optimal model performance. Use any other type of optimization).
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]). PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. It would have been obvious for one of ordinary skill in the art at the time of invention to include the neural network trained with successful remedial actions as taught by PALLADINO in the system executing the method of JOHNSON with the motivation to mitigate risk to delivered services.
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]) that uses weighted neural networks (see ¶[0044], [0072], and [0080]; use artificial neural networks. An embodiment of the step 930 of identifying resources 110 of the critical path can include calculating a weighted parametric mathematical algorithm including: exceeding a threshold or having a highest number of independencies relative to other resources 110, or exceeding a threshold or having a highest risk of unavailability relative to other resources 110, etc).. PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. WOUHAYBI discloses clustered federated learning using multiple neural network that are trained with feedback to reach an optimum. It would have been obvious to include the federated learning as taught by WOUHAYBI in the system executing the method of JOHNSON and PALLADINO with the motivation to determine a schedule with minimal risk using a neural network.
Claim 15 (Previously Presented)
The combination of JOHNSON, PALLADINO, and WOUHAYBI discloses the method as set forth in claim 13.
JOHNSON further discloses wherein the external event data corresponds to at least one of: weather; a supply chain; equipment status; an employee health event; an employee life event; or a geo-political event (see ¶[0003]; can focus more fully on the value added core processes that achieve the stated mission and less on activity responding to variations such as delays, accelerations, backups, underutilized assets, unplanned overtime by staff and stock outs of material, equipment, people and space that is impacted during the course of delivering healthcare. See also ¶[0100]; costs associated with investment in additional or replacement resources 110 (e.g., capitalized equipment, consumable stock, physical plant, staffing levels, training of staff, recruiting staff, etc.), attraction or drop-off in admission of patients 115 to receive delivery of healthcare relative to threshold, or an ability to meet one or more financial targets relative to threshold. See also ¶[0045] and [0048]; medical equipment and equipment availability. See also ¶[0073]; a staff person calls in sick).
Claim 19 (Currently Amended)
JOHNSON discloses an agglomerate network (see ¶[0110]; program modules executed by machines in networked environment), comprising: a time sequencer circuit structured to output a time sequence data (see abstract and ¶[0022]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources) generated via an artificial intelligence (AI) model (see ¶[0072]; the decision support rules can be example-based, evidential reasoning based, fuzzy logic-based, case-based, and/or other artificial intelligence-based, for example).
JOHNSON does not specifically disclose, but WOUHAYBI discloses, wherein the Al model comprises a plurality of agglomerate network circuits and a plurality of connector circuits, each connector circuit structured to bias at least one of an input to a corresponding agglomerate network circuit or an output of the corresponding agglomerate network circuit (see abstract and ¶[0002], [00039] & [0162]; clustered federated learning. The model handler circuitry 200 determines that only cluster(s) associated with the context data is/are to be updated, control proceeds to block 1316. At block 1316, the model handler circuitry 200 updates weights for the cluster(s) associated with the context data based on the label(s). For example, the model trainer circuitry 230 can update weights of the neurons 704A of the first cluster 714 of the third ML model 700 based on the labeled data using any AI/ML training/retraining technique. Nodes are connected to each other in a neural network).
JOHNSON further discloses a connector circuit (see ¶[0023]; a controller comprising at least one processor) structured to adjust at least one of an input to the time sequencer circuit or the time sequence data outputted by the time sequencer circuit (see ¶[0033] and [0039]; receive an input and updates to values or probabilities or risk based on historical data. Generate a probability density function); and
an austere event circuit structured to: interpret the time sequence data (see ¶[0108]; manage changes to a schedule to accommodate changes that are internally or externally induced).
generate austere event data by automatically predicting (see abstract; calculating a predicted duration to deliver the healthcare to each patient), based at least in part on the time sequence data (see ¶[0073]; automatically tracking or receiving input of additions and deletions in workload, and re-calculate the availability in scheduling of resources 110 accordingly to meet the change in workload), an occurrence of an austere event (see ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.), using a first neural network trained to detect a trend in the time sequence data indicative of the austere event (see again ¶[0052] and [0067]; An embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.) by identifying one or more properties within the time sequence data and correlating the identified properties to known patterns of historic schedule data (see abstract and ¶[0022] & [0027]-[0028]; a scheduling plan that includes predictions. Generate a recommendation regarding tasks of resources. Activity data can be stored in order to create historical data (such as that visualized in histograms 305) such that the disclosed system 100 evolves or learns over time, as well as can improves accuracy of forecast durations and can increase a probability of achieving a schedule of tasks within a predetermined time and usage threshold);
generate, based at least in part on the time sequence data and the austere event data (see ¶[0051] and [0064]; forecast probability or confidence or risk of probability of delay of finishing the activity. Detect a nurse calling in to indicate absence or tardiness), a mitigation action command value structured to trigger an adjustment to the connector circuit in advance of the occurrence of the austere event to the connector circuit (see ¶[0054]-[0055]; output an alarm representative to an alert to a problem).
JOHNSON does not specifically disclose, but PALLADINO discloses, using a second neural network trained with data associating past austere events with schedule changes that successfully eliminated and/or otherwise mitigated the impact of the past austere events on one or more business operations (see abstract and ¶[0085]; employ hidden Markov models or convolution neural networks with a training history of successful conditions/states the anomalies were detected in and successful remedial actions),
JOHNSON further discloses wherein the adjustment is structured to effect a change of at least one of the input to the time sequencer circuit or the time sequence data outputted by the time sequencer circuit prior to the occurrence of the austere event to eliminate or mitigate an impact of the austere event on one or more entities associated with the time sequence data (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources); and
transmit the mitigation action command value (see ¶[0088] and Fig. 10; output an alert to a user interface) to one or more external systems (see ¶[0110]-[0112]; program modules executed by machines in networked environments. Information is transferred over a network. Use logical connectors to one or more remote computers), said external systems being configured to implement the schedule change responsive to the mitigation command value in real-time, and thereby eliminate or mitigate the impact of the austere event (see again ¶[0013] and Fig. 6; mitigate schedule risk. See also ¶[0088] and [0107]; output an alert illustrative of a change in risk. Anticipatory alerts can be provided to resolve or revise the schedule of resources. See also ¶[0052] and [0067]; an embodiment the system 100 can manage interdependencies in such a way that the appropriate factors can be given action (e.g., re-scheduling other resources 105 of same function) if those factors left unmanaged or along current trend increase a likelihood of delay in the schedule start or duration of procedure or diminish objectives of the institution (e.g., patient satisfaction, capacity, low infection rates, costs, revenue, resource utilization, rate of return (ROI), etc. An embodiment of the "what will-be" view 715 can include predicted or trend information (e.g., risk or confidence, interdependencies, availability/readiness of resources 110 or patients 115, variation from forecast duration, etc.) a future time period.). and
feed the mitigation action command value to the AI model to train the AI model to adapt to the trend (see Fig. 2; refine or adjust a prediction made using historical data).
The combination of JOHNSON and PALLADINO does not explicitly disclose, but WOUHAYBI discloses, wherein feeding the mitigation action command value to the Al model adjusts one or more biases of at least one of the plurality of connector circuits to form a feedback loop structured to reach equilibrium and optimization of biases in the plurality of agglomerate network circuits (see ¶[0002] and [0056]; apply weighting values to the data during the processing of the data. Such weighting values are determined during a training process. Federated learning enables devices to train neural networks locally using data observed by the devices and sends the new weights to a central location for integration into other machine learning models. Use trial and error to reach optimal model performance. Use any other type of optimization).
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]). PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. It would have been obvious for one of ordinary skill in the art at the time of invention to include the neural network trained with successful remedial actions as taught by PALLADINO in the system executing the method of JOHNSON with the motivation to mitigate risk to delivered services.
JOHNSON discloses delivery of services to a patient that includes mitigating schedule risk in delivered services (see ¶[0003] and [0013]) that uses weighted neural networks (see ¶[0044], [0072], and [0080]; use artificial neural networks. An embodiment of the step 930 of identifying resources 110 of the critical path can include calculating a weighted parametric mathematical algorithm including: exceeding a threshold or having a highest number of independencies relative to other resources 110, or exceeding a threshold or having a highest risk of unavailability relative to other resources 110, etc).. PALLADINO discloses automatic repair of degrading services by using a neural network model trained with successful remedial actions. WOUHAYBI discloses clustered federated learning using multiple neural network that are trained with feedback to reach an optimum. It would have been obvious to include the federated learning as taught by WOUHAYBI in the system executing the method of JOHNSON and PALLADINO with the motivation to determine a schedule with minimal risk using a neural network.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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