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 .
Response to Amendment
The Amendment filed on 12/22/2025 has been entered. Claim 7 is canceled. Claim 21 is added. Claims 1-6 and 8-21 remain pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-6, 8-10 and 21 are directed to a method, claims 11-15 are directed to a system and claims 16-20 are directed to a medium. Therefore, the claims are eligible under Step 1 for being directed to a process, a machine and a manufacture respectively.
Step 2A Prong 1:
Independent claims 1, 11 and 16 recite:
scheduling multiple pathways in a schedule, each pathway including a collection of related events - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and assigning related events to multiple pathways, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
training a neural network with a repository of historical rescheduling data to create a trained data set - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining data and modifying data to compensate for an artifact, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation;
assigning resources from a resource pool to each of the events of each of the pathways to create the schedule - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing or evaluating data and assigning resources to pathways, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
detecting a change in a resource assigned to an event of at least one of the multiple pathways in the schedule - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mental observations or evaluations, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
automatically adjusting at least one other event of the same at least one of the multiple pathways in the schedule in response to the detected change based on the trained data set to predict an optimal adjustment to the schedule with a prediction model - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing or evaluating data and adjusting the schedule, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
wherein adjusting the at least one other event automatically reschedules one or more other events in the same at least one of the multiple pathways - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing or evaluating data and adjusting the schedule, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claims 2, 12 and 17 recite:
wherein automatically adjusting the at least one other event in the schedule is further based on a cost function - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing or evaluating data and adjusting the schedule, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claims 3, 13 and 18 recite:
collecting first historical rescheduling data; transforming the collected first historical rescheduling data to create transformed second historical rescheduling data; and combining the first historical rescheduling data and the transformed second historical rescheduling data to create the repository of historical rescheduling data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of collecting, observing or evaluating data and adjusting the schedule, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claim 4 recites:
automatically adjusting the schedule of the at least one other events in the schedule by comparing all of the resources of the changed event to all of the resources of the other events existing on the schedule - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of observing or evaluating data and adjusting the schedule, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claim 5 recites:
creating a list of optimal resources that are a subset of all of the resources associated with the at least one other event based on the prediction model; and
comparing all of the resources of the changed event to the subset of all of the resources associated with the event to be changed - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining, evaluating and modifying data that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation;
Dependent claims 6, 14 and 19 recite:
wherein scheduling an event of a pathway in the schedule automatically schedules the other events in the collection of related events of the pathway in the schedule - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and assigning related events to multiple pathways, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Dependent claims 8, 14 and 19 recite:
computing a cost associated with assigning resources to the events; and
scheduling the events to minimize the cost - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining, evaluating and modifying data that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation.
Dependent claims 9, 15 and 20 recites:
wherein the detected change increases the cost for resources assigned to events; and further comprising: rescheduling the events to reduce the cost - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining, evaluating and modifying data that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation.
Dependent claim 10 recites:
wherein the rescheduling includes automatically shifting events forward in time, back in time, or both forward and back in time until the cost is reduced - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining, evaluating and modifying data that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation.
Dependent claim 21 recites:
wherein the neural network is trained in two stages, wherein a first stage comprises training the neural network with the repository of historical rescheduling data where rescheduling resolved resource conflicts, and wherein a second stage comprises training the neural network with modified training data, the modified training data created by transforming at least some of the historical rescheduling data such that rescheduling did not resolve resource conflicts - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining data and modifying data to compensate for an artifact, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper, or is a mathematical concept that is achievable through mathematical computation.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
Independent claims 1, 11 and 16:
A system for scheduling multiple pathways in a schedule, each pathway including a collection of related events, the system comprising: a memory including: an artificial intelligence module; a monitoring module; a scheduling module; and a processor - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
A computer-readable storage medium storing computer executable instructions, for scheduling multiple pathways in a schedule where each pathway including a collection of related events, which when executed by a processor of a computer cause the processor to - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Dependent claim 6:
presenting a graphical user interface with the schedule - the “presenting” step recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
Independent claims 1, 11 and 16:
A system for scheduling multiple pathways in a schedule, each pathway including a collection of related events, the system comprising: a memory including: an artificial intelligence module; a monitoring module; a scheduling module; and a processor - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
A computer-readable storage medium storing computer executable instructions, for scheduling multiple pathways in a schedule where each pathway including a collection of related events, which when executed by a processor of a computer cause the processor to - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Dependent claim 6:
presenting a graphical user interface with the schedule - which is a well-understood, routine, conventional activity similar to presenting offers and gathering statistics described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis or the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 9-13, 15-18 and 20-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Day et al. (hereinafter Day), US 20210193302 A1.
Regarding independent claim 1, Day teaches a method for scheduling multiple pathways in a schedule, each pathway including a collection of related events ([0029] FIG. 1 illustrates a block diagram of an example, non-limiting system 100 for optimizing the sequencing and placement of patients in a dynamic medical system with shared and sub-specialized resources; [0033] The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include essentially any medical facility system with limited/fixed resources that provides medical treatment to patients in accordance with one or more defined workflows (or care pathways), wherein the timing of the workflows can be impacted by variable operating states/conditions of the dynamic medical system; [0034] defined tasks in patient care workflows or pathways are referred to herein as workflow events. The timing of patient care workflows can encompass the timing of initiation and completion of the entire workflow (e.g., from patient arrival to patient discharge), as well as the timing of initiation and completion (or duration) of individual workflow events (e.g., timing of initiation and completion of a procedure, timing of administration of medication, timing of movement of the patient to the recovery unit, etc.)), comprising:
training a neural network with a repository of historical rescheduling data to create a trained data set ([0104] With reference initially to FIG. 5A, illustrated is a high-level flow diagram an example, non-limiting process 501 for training and updating machine learning models configured to forecast future states of a dynamic medical system based on a current state of the dynamic medical system in accordance with one or more embodiments of the disclosed subject matter. In this regard, process 501 demonstrates the machine learning phase. In accordance with process 501, current state data 102 for a dynamic medical facility system (e.g., a perioperative system 200) can be collected and aggregated as historical state data 130 over time. At 502, the historical state data 130 and the perioperative system data 132 can be used to train and/or update the one or more timeline models 112 (e.g., by the case timeline forecasting component 110). At 504, the historical state data 130 and the perioperative system data 132 can also be used to train and/or update the one or more resource demand models 504 (e.g., by the resource demand forecasting component 114));
assigning resources from a resource pool to each of the events of each of the pathways to create the schedule ([0031] In one or more embodiments, the medical facility system management module 104 provides real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process. In some embodiments, the medical facility system management module 104 can also provide other optimal reactive solutions in real time regarding allocation and utilization of shared resources (e.g., staff, beds, medical supplies and equipment, etc.) between different specialty areas/departments of the dynamic medical facility system to further facilitate optimizing the efficiency and quality of the care delivery process while minimizing costs; Fig. 4, 404; [0101] The optimization component 118 can further regularly or continuously employ the forecasted data 406 as input one or more optimization heuristics to regularly or continuously determine reactive solutions data 412 for the dynamic medical facility system (e.g., including the patient sequence and timing solutions 140, the patient placement solutions 142 and/or the resource allocation solutions 144));
detecting a change in a resource assigned to an event of at least one of the multiple pathways in the schedule ([0034] In one or more embodiments, defined tasks in patient care workflows or pathways are referred to herein as workflow events. The timing of patient care workflows can encompass the timing of initiation and completion of the entire workflow (e.g., from patient arrival to patient discharge), as well as the timing of initiation and completion (or duration) of individual workflow events (e.g., timing of initiation and completion of a procedure, timing of administration of medication, timing of movement of the patient to the recovery unit, etc.). In this regard, depending on the type of medical facility system and the type of patient care workflows that are performed, a variety of variable operating states/conditions of the dynamic medical facility system can influence the timing of the workflows, such as changes in patient and staff scheduling (e.g., associated with cancelations, additions, staff members inability to arrive or work as scheduled, etc.), timing of arrival of patients, staff and other resources (e.g., ambulatory services, medical equipment/supplies, etc.), occurrence of medical complications, arrival of emergency patients, inefficient clinician performance (e.g., procedures taking longer than expected), occurrence of procedural errors, and a variety of other factors; the optimization component 118 determines changes to patient scheduling throughout the day with respect to patient sequence and timing 140 and/or patient placements 142, this information can be updated in the case scheduling systems and reflected in the current state data 102. The current state data 102 can also identify changes in case scheduling as they occur in real-time over the course of operation of the medical facility system as new cases are added, cases are canceled, rescheduled and/or the like. Accordingly, the reception component 106 can regularly or continuously receive updated information (e.g., in real-time) regarding what patient cases are scheduled for performance at the medical facility system within an upcoming timeframe (e.g., in the next hour, in the next 12 hours, the next 24 hours, the next 48 hours, the next week, etc.); [0046] In some implementations, as the optimization component 118 determines changes to patient scheduling throughout the day with respect to patient sequence and timing 140 and/or patient placements 142, this information can be updated in the case scheduling systems and reflected in the current state data 102); and
automatically adjusting at least one other event of the same at least one of the multiple pathways in the schedule in response to the detected change based on the trained data set to predict an optimal adjustment to the schedule with a prediction model ([0072] The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework to regularly and/or continuously forecast future state information for the medical facility system in real-time based on the current state data 102, the historical state data 130, and/or relevant information included the medical facility system data 132 that can influence and/or control the timing of defined workflows for the patient cases (e.g., including timing of initiation, completion and duration of defined workflow events), and resources needed; [0089] the sequence and timing optimizer 120 can adjust patient scheduling in real-time (e.g., by reordering the start times of the respective pending cases) based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, to converge on a new sequence for performing the pending cases that minimizes delays (e.g., over a defined timeframe), maximizes patient flow (e.g., over a defined timeframe), minimizes or eliminates blocking (e.g., over a defined timeframe), minimizes costs (e.g., over a defined timeframe), and/or minimizes surgeon idle time), wherein adjusting the at least one other event automatically reschedules one or more other events in the same at least one of the multiple pathways ([0089] the sequence and timing optimizer 120 can adjust patient scheduling in real-time (e.g., by reordering the start times of the respective pending cases) based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, to converge on a new sequence for performing the pending cases that minimizes delays (e.g., over a defined timeframe), maximizes patient flow (e.g., over a defined timeframe), minimizes or eliminates blocking (e.g., over a defined timeframe), minimizes costs (e.g., over a defined timeframe), and/or minimizes surgeon idle time).
Regarding dependent claim 2, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day further teaches wherein automatically adjusting the at least one other event in the schedule is further based on a cost function ([0064] With reference again to FIG. 1, the optimization criteria data 132 can comprise information regarding the optimization objectives of the dynamic medical care facility system. In various embodiments, the optimization objectives can include but are not limited to: minimizing delays, maximizing patient flow, minimizing blocking, minimizing costs, and minimizing surgeon idle time; [0070] In various implementations, the different optimization objectives are related and/or interconnected such that optimizing one can impact another in a positive or negative manner. In this regard, in some embodiments, the optimization criteria data 132 e can also include weights regarding the relative importance of the respective objectives that can be used by the optimization component 118 in formulating an optimization solution that balances and/or prioritizes the objectives according to their weights. The weights can be predefined and/or user adjusted as appropriate to meet the systems objectives in different contexts (e.g., the weights can vary for different time-frames, different days of the week, different procedures, different surgeons, etc.)).
Regarding dependent claim 3, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day further teaches further comprising:
collecting first historical rescheduling data ([0101] The optimization component 118 can further regularly or continuously employ the forecasted data 406 as input one or more optimization heuristics to regularly or continuously determine reactive solutions data 412 for the dynamic medical facility system (e.g., including the patient sequence and timing solutions 140, the patient placement solutions 142 and/or the resource allocation solutions 144). For example, at 408, the optimization component can receive the current stat data and the forecasted data 406. At 410, the optimization component 118 can employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data 406, and relevant parameters included in the current state data 102, and the perioperative system data (e.g., system rules/constraints and defined optimization criteria));
transforming the collected first historical rescheduling data to create transformed second historical rescheduling data ([0102] The determined reactive solutions data 412 can further be automatically applied and/or applied with manual discretion in real-time to control or guide the operations of the dynamic medical facility system. As a result, the state of the dynamic medical facility system can change (e.g., based on implementing the recommended patient sequence and timing solutions, the patient placement solutions 142 and/or the resource allocation solutions 144), which will further be reflected in the newly generated current state data 102); and
combining the first historical rescheduling data and the transformed second historical rescheduling data to create the repository of historical rescheduling data ([0102] The forecasting component 108 can further account for the system response when again evaluating the current state data 102 to determine updated forecasted data, and the optimization component 406 can again account for the updated forecasted data when determining new reactive solutions data. This predictive/reactive process can be iteratively performed over a course of operation of the dynamic medical facility system (e.g., every 5 minutes, every 10 minutes, etc.) to provide real-time solutions that account for the current state of the dynamic medical facility system; [0054] The historical state data 130 can provide historical state information for the dynamic medical facility system (and/or one or more similar medical facility systems) collected over time providing detailed information regarding performance of historical patient cases and their workflows under varying operating conditions and contexts of the dynamic medical facility system).
Regarding dependent claim 4, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day further teaches further comprising:
automatically adjusting the schedule of the at least one other events in the schedule by comparing all of the resources of the changed event to all of the resources of the other events existing on the schedule ([0087] the optimization component 118 can model the entire perioperative system as a large-scale combinatorial optimization problem with fixed constraints that control or influence patient sequencing and timing, placement and/or resource allocation (e.g., as defined be medical facility system data 132) and one or more optimization objectives, and adjustable variables corresponding to patient/case sequencing and timing, patient placement (e.g., with respect to a physical location/area), and resource allocation (e.g., assignment of staff and other resources). The optimization component 118 can further iteratively adjust these variables based on the current state data 102, the timeline forecasts 136 and the resource demand forecasts 138 to converge on optimal solutions regarding patient sequencing and timing, patient placement and/or resource allocation that “best” achieves and/or balances (e.g., in accordance with defined weights) the one or more optimization objectives).
Regarding dependent claim 5, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day further teaches further comprising:
creating a list of optimal resources that are a subset of all of the resources associated with the at least one other event based on the prediction model ([0070] the different optimization objectives are related and/or interconnected such that optimizing one can impact another in a positive or negative manner. In this regard, in some embodiments, the optimization criteria data 132 e can also include weights regarding the relative importance of the respective objectives that can be used by the optimization component 118 in formulating an optimization solution that balances and/or prioritizes the objectives according to their weights. The weights can be predefined and/or user adjusted as appropriate to meet the systems objectives in different contexts (e.g., the weights can vary for different time-frames, different days of the week, different procedures, different surgeons, etc.)); and
comparing all of the resources of the changed event to the subset of all of the resources associated with the event to be changed ([0095] In many implementations, optimal solutions regarding patient sequence and timing, patient placement and resource allocation are highly interrelated. In this regard, in addition to evaluating these solutions in isolation, the optimization component 118 can further employ combinatorial optimization techniques to iteratively evaluate different combinations of patient sequence and timing solution options determined by the sequence and timing optimizer 120, patient placement solution options determined by the patient placement optimizer, and resource allocation solution options determined by the resource allocation optimizer 124, to converge on an optimal combination of these solutions that best achieves and/or balances (e.g., in accordance with a defined weighting schemed) the one or more optimization objectives).
Regarding dependent claim 9, Day teaches all the limitations as set forth in the rejection of claim 5 that is incorporated. Day further teaches wherein the detected change increases the cost for resources assigned to events; and further comprising: rescheduling the events to reduce the cost ([0031] [0039] [0040] [0089] [0089] For example, the sequence and timing optimizer 120 can determine how to order or sequence initiating performance of pending patient cases based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, that best achieves and/or balances the one or more optimization objectives. In this regard, the sequence and timing optimizer 120 can adjust patient scheduling in real-time (e.g., by reordering the start times of the respective pending cases) based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, to converge on a new sequence for performing the pending cases that minimizes delays (e.g., over a defined timeframe), maximizes patient flow (e.g., over a defined timeframe), minimizes or eliminates blocking (e.g., over a defined timeframe), minimizes costs (e.g., over a defined timeframe), and/or minimizes surgeon idle time. If more than one optimization objective is integrated into the optimization problem, the sequence and timing optimizer 120 can employ a defined weighting scheme for balancing the respective objectives; [0094] The resource allocation optimizer 124 can similarly determine how to allocate system resources based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, that best achieves and/or balances the one or more optimization objectives).
Regarding dependent claim 10, Day teaches all the limitations as set forth in the rejection of claim 9 that is incorporated. Day further teaches wherein the rescheduling includes automatically shifting events forward in time, back in time, or both forward and back in time until the cost is reduced ([0089] the sequence and timing optimizer 120 can determine how to order or sequence initiating performance of pending patient cases based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, that best achieves and/or balances the one or more optimization objectives. In this regard, the sequence and timing optimizer 120 can adjust patient scheduling in real-time (e.g., by reordering the start times of the respective pending cases) based on relevant parameters included in the current state data 102, the timeline forecasts 136, the resource demand forecasts 138, and relevant constraints included in the medical facility system data 132, to converge on a new sequence for performing the pending cases that minimizes delays (e.g., over a defined timeframe), maximizes patient flow (e.g., over a defined timeframe), minimizes or eliminates blocking (e.g., over a defined timeframe), minimizes costs (e.g., over a defined timeframe), and/or minimizes surgeon idle time).
Regarding independent claim 11, it is a system claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Day further teaches a system for scheduling multiple pathways in a schedule, each pathway including a collection of related events, the system (Fig. 1, 100; [0029]-[0030]; Fig. 12, 1200; [0129]) comprising:
a memory including ([0030]; Fig. 12, 1206; [0131]):
an artificial intelligence module (Fig. 1, 104; [0030]);
a monitoring module (Fig. 1, 108; [0072]);
a scheduling module (Fig. 1, 118, 126; [0086]; [0096]); and
a processor configured to (Fig. 12, 1204; [0129]).
Regarding dependent claim 12, it is a system claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 13, it is a system claim that corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 15, claim 15 contains substantially similar limitations to those found in claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Regarding independent claim 16, it is a medium claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Day further teaches a computer-readable storage medium storing computer executable instructions, for scheduling multiple pathways in a schedule where each pathway including a collection of related events, which when executed by a processor of a computer cause the processor to perform the method of claim 1 ([0029]; [0121]).
Regarding dependent claim 17, it is a medium claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 18, it is a medium claim that corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 20, claim 20 contains substantially similar limitations to those found in claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Regarding dependent claim 21, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day further teaches wherein the neural network is trained in two stages, wherein a first stage comprises training the neural network with the repository of historical rescheduling data where rescheduling resolved resource conflicts (Fig. 6; [0109] In various embodiments, the compliance monitoring component 602 can monitor and evaluate the timeline forecasts 136 and the resource demand forecasts 138 in view of the possible reactive solutions (e.g., the optimal patient sequence and timing solutions 140, the patient placement solutions 142 and/or the resource demand solutions 144) to determine whether the forecasted case workflow timing and/or the forecasted demand needs violate any defined rules/policies of the dynamic medical facility system (e.g., as defined in the medical facility system data 132); [0110] Similarly, the compliance monitoring component 602 can evaluated the forecasted demand in view of current and scheduled resource assignments (e.g., staff assignments) to determine whether available system resources at the different procedural areas at the different times comply with a resource constraint for the perioperative system based on forecasted demand … the compliance monitoring component 602 can identify imbalances between the forecasted demand for the resources and available system resources at the different procedural areas at the different times. The alert component 604 can similarly generate alerts regarding identified resource imbalances. In addition, the optimization component 118 can further employ the heuristic-based optimization mechanism to determine the optimal reactive solutions based on the identified imbalances), and wherein a second stage comprises training the neural network with modified training data, the modified training data created by transforming at least some of the historical rescheduling data such that rescheduling did not resolve resource conflicts ([0111] For example, FIGS. 7A and 7B provide a chart that identifies forecasted resource demands and identified resource supply/demand imbalances for a perioperative system over an upcoming timeframe in accordance with one or more embodiments of the disclosed subject matter. In the embodiment shown in FIGS. 7A and 7B, the chart encompasses the current workday (i.e., “TODAY”), as shown in FIG. 7A, and the following workday (i.e., “TOMORROW”), as shown in FIG. 7B. The chart provides information for each hour of the workday regarding forecasted resource demands for nurses in different Pods in the general surgery department as divided in to preoperative Pods (e.g., Pods L, K and E), and postoperative Pods (e.g., B, A and C). The chart also provides similar informaiton for the combined preoperative and postoperative Pods associated with the IR department (e.g., Pods H, G and D). For the respective hours of the day, the chart identifies the total expected demand of nurses needed (e.g., as “Total Demand”) for each group of Pods, either the first group of Pods corresponding to the preoperative surgery Pods (e.g., Pods L, K and E), the second group of Pods corresponding to the postoperative surgery Pods (e.g., Pods B, A and C), or the third group of Pods corresponding to the pre and post IR Pods (e.g., Pods H, G, and D). For the respective hours of the day, the chart also identifies the total amount of nurses planned/scheduled for assignment to the respective Pod groups. The dashed boxes are drawn around identified imbalances, where the total forecasted demand exceeds the total planned demand. Accordingly, a constraint is triggered (as marked with “Y” to indicate a constraint is need) for processing by the optimization component 118 to determine how to remedy the imbalance. In addition, the compliance monitoring component 602 can also identify situation in which the Net amount of planned/scheduled resources exceeds the forecasted demand. The optimization component 118 can also use this informaiton to determine where to pull resources from to redistribute as appropriate to account for the identified imbalances).
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.
Claims 6, 8, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Day as applied in claims 1, 11 and 16, in view of Johnson et al. (hereinafter Johnson), US 20160063192 A1.
Regarding dependent claim 6, Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Day does not explicitly disclose further comprising:
presenting a graphical user interface with the schedule, wherein scheduling an event of a pathway in the schedule automatically schedules the other events in the collection of related events of the pathway in the schedule.
However, in the same field of endeavor, Johnson teaches presenting a graphical user interface with the schedule, wherein scheduling an event of a pathway in the schedule automatically schedules the other events in the collection of related events of the pathway in the schedule ([0156] Referring to FIG. 14, an ability to play the day plan and/or schedule forward, an ability to replay the day as it was scheduled and/or emulate it as it actually occurred, and an ability to view a summary indicator as to the overall process state are explained. The assets of the process and their tasks 1402 are explicitly mapped as to their interdependencies, as has been discussed above. The relationships between tasks are not equal relative to their impact upon the schedule; some tasks are able to be the limiting factor in completing a schedule on time while others are not as they do not have interdependencies. Task 1404 is on the “critical path” 1406 while task 1408 is not. The clustered set of activities associated with a subsection of the day, such as an operation scheduled for a given room, are managed intensely. One operating room 1422 may impact another 1424 operating room's schedule in that task 1413 can not begin until task 1411 completes at 1410 since the same surgeon, for example, is involved. The risk of a late start is calculated from the methods and systems already discussed above and is visualized at 1412 for the attention of process stakeholders. The visualization may also be summary in nature, such as a red/yellow/green indicator or a “happy face”. The summary indicators, 1426, 1427, 1428 enable the rapid scan of hundreds of activities such that the tasks involving intervention to keep to the schedule can be addressed while the rest of the system remains on schedule. The setpoints 1432 that transition from one process summary state to another can be deterministic or probabilistic 1430. Deterministic settings are rule based such that if a schedule is now late or if a downstream task such as 1413 to 1411 is to be impacted for a given duration, a happy face may turn from a smile 1426 to a frown 1430 to a sad face 1428 depending upon the setpoints).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of a graphical user interface producing schedules of a day and ability to adjust schedules as suggested in Johnson into Day’s system because both of these systems are addressing optimizing state transition set points for schedule risk management. This modification would have been motivated by the desire for scheduling clinical activities and procedures that incorporate variation, staff and equipment preferences, interdependencies and information flow into the clinical delivery of healthcare that can “look ahead” and enable “what-if” capability for prospective decision support (Johnson, [0009]).
Regarding dependent claim 8, the combination of Day and Johnson teaches all the limitations as set forth in the rejection of claim 6 that is incorporated. Johnson further teaches further comprising:
computing a cost associated with assigning resources to the events; and scheduling the events to minimize the cost ([0176] The risk 1431 of task(s) completing changes through a day 1418. As shown in the example of FIG. 14, a total daily schedule risk 1424 and/or a specific task or sequence of tasks 1410, 1412 may be of interest to an administrator and/or other personnel involved in the process. The state definition optimizer 714 solves state changes 1432 for multiple view of time such as a task sequence or start 1410 and/or a day, month, hour, and/or moment of schedule duration 1416, for example; [0177] In an example, schedule risk states are adjusted to help achieve departmental level objective(s) such as completing a clinical case load with staff overtime at less than two hours at a service level exceeding eighty percent (80%) likelihood at an end of a shift and a ninety-five percent (95%) likelihood at an end of a maximum overtime period. To achieve both service level and cost objectives, cases during progression of the case should not be allowed to propagate delays through interdependent people, tasks, and/or resources. Workflow(s) and awareness to eliminate dynamic critical path delays that can propagate, when targeted at eliminating preventable delays early enough in the process, can increase the likelihood of meeting the global objectives (of service level and cost for the department)).
Regarding dependent claim 14, claim 14 contains substantially similar limitations to those found in claims 6 and 8. Therefore, it is rejected for the same reason as claims 6 and 8 above.
Regarding dependent claim 19, claim 19 contains substantially similar limitations to those found in claims 6 and 8. Therefore, it is rejected for the same reason as claims 6 and 8 above.
Response to Arguments
Applicant's arguments filed 12/22/2025 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response.
(1) Regarding to 35 U.S.C 101 rejection, Applicant alleges that under the Step 2A, Prong 1 analysis of the subject matter eligibility analysis under 35 U.S.C. § 101, the Patent Office must find that the claims as a whole are directed to an abstract idea. It is not enough to find that a claim involves an abstract idea. Rather, the claim as a whole must directed to an abstract idea. As set forth in MPEP § 2106.04(II)(A)(1).
Examiner respectfully disagrees. According to MPEP § 2106.04(II)(A)(1), in Step 2A, Prong One, this part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed in the rejection above, the claim is directed to an abstract idea that encompasses mental processes including evaluations or observations that are practically capable of being performed in the human mind with the assistance of pen and paper, and mathematical concepts that are achievable through mathematical computation. The claim places no limits on how the steps are performed. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Applicant further alleges the present claims are similar to Examples 38/39 and dissimilar from Example 41 and Applicant asserts that the claims do not recite a mathematical concept. Accordingly, the claims as a whole are not directed to an abstract idea.
Examiner respectfully disagrees. With respect to Subject Matter Eligibility Examples 38 and 39, these examples were found to not recite any of the judicial exceptions enumerated in the 2019 PEG (Mathematical Concepts, Mental Processes, Certain Methods Of Organizing Human Activity). Similar to claim 2 of Example 47, instant claim 1 includes limitations that recite abstract ideas, such as Mental Processes and Mathematical Concepts. As discussed above, claim 1 recites limitations encompassing mental processes including evaluation and observation that are practically capable of being performed in the human mind with the assistance of pen and paper, and mathematical concepts that are achievable through mathematical computation. As discussed above, the additional elements of claim 1 do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea. As further discussed above, nothing in the claim provides significantly more than that abstract idea. As such, the claim is ineligible.
Applicant further alleges the claims do indeed incorporate the alleged abstract idea into a practical application. The claims also comprise a practical application because the invention applies and uses the alleged abstract idea in a particular technological environment. The claims recite a combination of elements encompassing a very specific method for a system to detect changes in a resource assigned to an event, and automatically adjust at least one other event in the schedule in response to the detected change (based on the trained data set to predict an optimal adjustment to the schedule with a prediction model). The claims recite a solution necessarily rooted in specific components of a computerized analysis system. The elements of the independent claims, for example, when viewed as a whole integrate the alleged abstract idea into a practical application by sufficiently limiting the use of the alleged abstract idea to the very specific automated rescheduling. The claims are not just a drafting effort designed to monopolize all rescheduling; instead, the claims provide a very limited and specific method for rescheduling using the claimed system in a very specific way.
Examiner respectfully disagrees. One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a). The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. While the disclosure states that “there are so many linked processes and resources with any given procedure that there would be an unreasonable number of permutations of possible rescheduling changes for staff to manually reschedule the day. Moreover, attempts to manually reschedule with so many permutations would be reckless and irresponsible, especially in a clinical setting” [0005]), there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea rather than to any technology. See MPEP 2106.05(a). Any purported improvements are provided by the judicial exception alone, i.e. mathematical calculations/relationships, thus the claim as a whole does not integrate the judicial exception into a practical application nor provide significantly more than the judicial exception. Thus, the claims are patent ineligible and are rejected under 35 U.S.C. 101 as detailed in the rejections set forth above.
(2) Regarding to 35 U.S.C 102 rejection, the pending claims have been considered but they are moot in view of the new ground(s) of rejections presented above.
(3) Regarding to 35 U.S.C 103 rejection, Applicant argues Day in view of Johnson fails to suggest or render obvious scheduling multiple pathways in a schedule, each pathway including a collection of related events, comprising: training a neural network with a repository of historical rescheduling data to create a trained data set; assigning resources from a resource pool to each of the events of each of the pathways to create the schedule; detecting a change in a resource assigned to an event of at least one of the multiple pathways in the schedule; and automatically adjusting at least one other event of the same at least one of the multiple pathways in the schedule in response to the detected change based on the trained data set to predict an optimal adjustment to the schedule with a prediction model, wherein adjusting the at least one other event automatically reschedules one or more other events in the same at least one of the multiple pathways. Johnson further teaches wherein moving the event of the pathway in the schedule automatically reschedules the other events in the collection of related events of the pathway in the schedule ([0157] An ability to play the day forward is an improvement to the art of service-oriented processes. Prior to the start of a shift, for example, the day's schedule is advanced through time 1414 by adjusting at 1420 the virtual time 1418, such as by a slider bar or dial, and watching the schedule unfold along with the relative risks of delays or early completions of the assets of the process). Paragraph [0157] of Johnson teaches manually manipulating an entire day's schedule by adjusting a virtual time, such as by a slider bar or dial. Manually manipulating an entire day's schedule does not disclose or suggest automatically adjusting one or more events in a pathway based on an adjustment to one event in the pathway.
Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091,231 USPQ 375 (Fed. Cir. 1986). Day teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Johnson teaches all the limitations of claims 6 and 7 “presenting a graphical user interface with the schedule, wherein scheduling an event of a pathway in the schedule automatically schedules the other events in the collection of related events of the pathway in the schedule wherein moving the event of the pathway in the schedule automatically reschedules the other events in the collection of related events of the pathway in the schedule”. Johnson’s Fig, 14, [0156]-[0157] depicts the graphical user interface for user to schedule and adjust other schedules when detecting changes of related events. Thus, the combination of Day in view of Johnson is considered to teach claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Brown et al. (US 20200411170 A1) discloses coordinating and optimizing healthcare resource utilization and delivery of healthcare services across an integrated healthcare system using a machine learning framework.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
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/AMY P HOANG/ Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143 /JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143