DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Amendments
Claims 1-23 are currently pending in this case and have been examined and
addressed below. This communication is a Final Rejection in response to the
Amendment to the Claims and Remarks filed on 01/29/2025.
Claims 1, 12, and 17 are amended claims.
Claims 2-11, 13-16, and 18-20 are previously presented.
Claims 21-23 are new claims.
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 1-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 12, and 17 are drawn to a system, an article of manufacture, and a method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claims 1 recites a system comprising generate a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; retrieve current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; compute values of one or more key performance indicator (KPI) metrics for the current statistics; generate an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulate a best case scenario by executing the workflow model on inputs including the department profile and best case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day; simulate a worst case scenario by executing the workflow model on inputs including the department profile and worst case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario; and output, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics.
Independent claim 12 recites an article of manufacture comprising generating a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile by operations including: retrieving hospital department data and patient data; generating a department profile for each resource from the retrieved hospital department data and patient data; retrieving current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; computing values of one or more key performance indicator (KPI) metrics for the current statistics; generating an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulating a best case scenario by executing the workflow model on inputs including the department profile and best case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day; simulating a worst case scenario by executing the workflow model on inputs including the department profile and worst case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario; and outputting, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics.
Independent claim 17 recites a method comprising generating a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; retrieving current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; computing values of one or more key performance indicator (KPI) metrics for the current statistics; generating an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having random variables representing at least patient arrival timeliness, patient no-show, and ED arrival; and outputting, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). The claims recite collecting data on hospital department resources to determine operational workflow performance in current, best-case, and worst-case scenarios. These steps organize patients and hospital staff by determining optimal scenarios to increase emergency department production. Because these limitation determine the optimal scenarios to implement following the analysis of hospital resource data, they constitute the management of personal behavior on part of healthcare providers and hospital administrative staff. Accordingly, the claims fall under “Certain Methods of Organizing Human Activity” grouping of abstract, and, thus, recite an abstract idea.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1, 12, and 17 recite at least one display device. Claim 1 and 12 recite at least one electronic processor. Claim 12 recites a non-transitory computer readable medium, at least one database, and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the
abstract idea by use of general-purpose computer which does not integrate the abstract
idea into a practical application.
Claim 17 recites simulating a best case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and best case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day and simulating a worst case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and worst case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario. The claim recites the Monte Carlo simulation and its use to carry out the steps of the abstract idea including compute values of the one or more KPI metrics for the simulated best case scenario and simulated worst case scenario is mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1, 12, and 17 recite at least one display device. Claim 1 and 12 recite at least one electronic processor. Claim 12 recites a non-transitory computer readable medium, at least one database, and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claim 17 recites simulating a best case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and best case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day and simulating a worst case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and worst case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario. The claim recites the Monte Carlo simulation and its use to carry out the steps of the abstract idea including compute values of the one or more KPI metrics for the simulated best case scenario and simulated worst case scenario is mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
For the reasons stated, these claims are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim(s) 2, 13, and 18 recite the best case values for the variables of the workflow model include values representing patient arrival timeliness that is better than the current statistics, patient no-shows that are lower than the current statistics, and ED arrivals that are lower than the current statistics, and the worst case values for the variables of the workflow model include values representing patient arrival timeliness that is worse than the current statistics, patient no-shows that are higher than the current statistics, and ED arrivals that are higher than the current statistics.
Dependent claim(s) 3 recites the best-case values for the variables of the workflow model include values representing all patients being on-time; and the worst-case values for the variables of the workflow model include values representing no patients being on-time.
Dependent claim(s) 4 recites the best-case values for the variables of the workflow model include values representing no patients being no-shows.
Dependent claim(s) 5 recites the best-case values for the variables of the workflow model include values representing no ED arrivals.
Dependent claim(s) 8, 14, and 20 recite retrieving a model workflow template; adjusting the model workflow template based on the current statistics for the hospital department.
Dependent claim(s) 9 recites to receive one or more user inputs indicative of a change one or more values of the workflow model to update at least one of the best case scenario and the worst case scenario; compare one or more updated values of the KPI metrics resulting from updating the update at least one of the best case scenario and the worst case scenario with previously- obtained value of KPI metrics; and update the workflow model when the updated values of the KPI metrics satisfy a predetermined update threshold.
Dependent claim(s) 11 recite wherein the hospital department is a medical imaging department and the active medical equipment inventory comprises an inventory of active medical imaging devices annotated at least by imaging modality.
Each of these steps of the preceding dependent claims 2-5, 8-9, 11, 13-14, 18, and 20 only serve to further limit or specify the features of independent claims 1, 12, or 17 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Dependent claim(s) 6 recites simulate at least one intermediate scenario by executing the workflow model on inputs including the department profile and intermediate values for the variables of the workflow model that are intermediate between the best case scenario and the worst case scenario, and compute values of the one or more KPI metrics for the simulated intermediate scenario; and further outputting, on at least one display device the values of the one or more KPI metrics computed for the simulated at least one intermediate scenario. The at least one display device is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Claim(s) 7, 16, and 19 merely describe(s) retrieving hospital department data and patient data from at least one database, the hospital department data including one or more of types of procedures to be performed, distribution of process time for each procedure, availability of resources, a maintenance schedule for the resources, downtime of the resources, a resource to staff ratio, a patient to nurse ratio, an order list of procedures, scheduled examinations, and unscheduled examinations, the patient data including one or more of a type of patient, records of no-shows or late arrivals to appointments, age, gender, need for sedation, presence of contrast allergies, presence of claustrophobia, and presence of foreign bodies; generating a department profile for each resource from the retrieved hospital department data and patient data; and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device. The at least one database and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device are additional elements, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim(s) 10 and 15 recite wherein the variables of the workflow model include random variables, and the at least one electronic processor is programmed to execute the workflow model on inputs including the random variables instantiated using Monte Carlo simulation. The claim recites the Monte Carlo simulation and its use to carry out the steps of the abstract idea including compute values of the one or more KPI metrics for the simulated best case scenario and simulated worst case scenario is mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dalal (WO 2020016451 A1) in view of Zhong (US 20180039742 A1) in view of Fitzgerald (A Queue-Based Monte Carlo Analysis to Support Decision Making for Implementation of an Emergency Department Fast Track) in view of Abo-Hamad (Simulation-based framework to improve patient experience in an emergency department).
As per Claim 1, Dalal teaches an apparatus for generating benchmarking metrics of current operational workflow performance of a hospital department, the apparatus including at least one electronic processor programmed to: ([Para. 0010] At least one electronic processor)
generate a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; ([Para. 0045] The first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability (i.e. personnel profile). The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. RTLS 16 to be used to classify mobile medical equipment, typically only categories: in the hospital but not at the radiology lab or at the radiology lab (3) will apply. Examiner interprets the RTLS position data of the mobile medical equipment to be indicative of active medical equipment inventory.)
generate an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; ([Para. 0043] the workflow simulation may incorporate a prediction model for patient no-shows and cancellations. [Para. 0063] A scheduling learning engine 60 is configured to generate a workflow simulation model 62 which simulates the actual workflow. The model 62 captures all the tasks patients flow through including the process time (as a distribution) for each task, the resources necessary to perform the task like a CT room, portable ultrasound equipment, a nurse, a physician etc. The model 62 also captures the number of available resources and their schedules. By passing the patients appointment time and their procedure type to the model, the scheduling learning engine 60 can compute the KPIs like the patient wait/idle time, arrival to exit time, last patient exit time, staff/room/equipment utilization etc. This module can be developed using discrete event simulation or agent-based simulation techniques.)
on at least one display device ([Para. 0057] The display device 24)
Dalal does not explicitly teach, however Zhong teaches
retrieve current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show; ([Para. 0040] The system also takes into account hospital or clinic historical (i.e. past) patient visit log data. The past patient visit log data is shown and described in more detail in FIG. 4. The past patient visit log data should include at least past patient appointment date and time, and past patient visit outcome information wherein the past patient visit outcome information is at least sufficient to determine whether the past patient was a no show. Patients' past visit information 210 such as scheduled arrival time, real arrival time (i.e. patient arrival timeliness), or whether it is a cancellation or no-show (i.e. patient no-show), and the visit type, visit length, provider information, etc., are extracted to train the predictive model.)
compute values of one or more key performance indicator (KPI) metrics for the current statistics; ([Para. 0041] The patient arrival time is utilized by a predictive module 214 that takes into account the historic data for a general patient population to determine trends in vacancies, no-shows and late cancellations. The predictive module information is used by the appointment template module 206 and the template optimizer 220. The operational data of historical patient visits takes into account the different types of appointments offered and their usual length of visits. [Para. 0042] The scheduling optimizer 220 provides an optimal scheduling template 222 taking into account all relevant information and provides a system performance output from the output and feedback module 224. This information allows a user to review parameters taken into account for a given template such as the number of patient's a physician is expected to see each day or week, or the number of staff members that will be required to staff the schedule.[Para. 0043] FIGS. 7 and 8 show a bar chart illustrating the average patient length of stay by patient type by template 700 and the staff utilization by staff type by template 800, which are among the multiple objectives to be optimized, i.e., the shorter the patient length of stay and the higher the staff utilization (i.e. KPI) are preferred. Examiner interprets the number of patients physician is expected to see each day or week, the number of staff members that will be required to staff the schedule, average patient length of stay by patient type by template 700 and the staff utilization by staff type to be indicative of KPI metrics for the current statistics.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
Dalal/ Zhong does not explicitly teach, however Fitzgerald teaches
retrieve current statistics for the hospital department including at least one of emergency department (ED) arrival statistics; ([Pg.2 2.1. Setting and Data Sources] deidentified data from the hospital emergency department (ED)’s electronic health record (EHR) and management tool (Picis ED PulseCheck, Wakefield, MA), including major timestamps for each patient during their visit in addition to the patient’s recorded ESI level. Relevant timestamps included time of arrival, arrival time in the room, and time of departure.)
compute values of one or more key performance indicator (KPI) metrics for the current statistics;([Pg. 2 2.2 Model Design] we performed descriptive statistics on the patient records to determine average arrival rates by patient’s Emergency Severity Index (ESI), hour of day, and day of week using statistical functions in MATLAB. The distributions of current wait and service times were also computed for each ESI. [Pg. 3 2.2 Model Design] A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the current state scenarios of ESI-3 in which the median wait time is 21.46 (For the purposes of examiner, Examiner will be exclusively focusing on ESI-3 as the main reference for KPI metric analysis).)
simulate a worst-case scenario by executing the workflow model on inputs including the department profile and worst-case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst-case scenario; ([Pg. 2 1. Introduction] Queueing theory may be combined with Monte Carlo simulation or discrete event simulation to produce numerical results for complex models [Pg. 3 2.2 Model Design] The model determined the number of required providers (Pg. 3 Table 1: Provider-to-patient ratios) and the earliest time that the number of providers is available. A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 3 3.2 Monte Carlo Analysis] The model generated 300 sets of simulation data for each simulation scenario (see Table 4 for an abbreviated list of scenarios). [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the various median wait time based on the different ESI levels and scenarios. Examiner interprets that the worst-case scenario is the scenario in which there is less FT nursing staff utilized and the highest median wait time, therefore the worst scenario is indicated by FT is reassigned, 7 days, 12pm -8pm with a median wait time of 21.73.)
and output, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best-case scenario, the values of the one or more KPI metrics computed for the simulated worst-case scenario, and the values of the one or more KPI metrics computed for the current statistics. ([Pg. 5 Table 4: Median wait time by ESI and scenario] Table 4 displays the median wait times (KPI) by ESI and scenarios, in which the best-scenario for ESI -3 is FT nurse added, 7 days, 12pm-8pm with a wait time of 14.30, the worst scenario for ESI -3 is FT nurse reassigned, 7 days, 12pm-8pm with a median wait time of 21.73, and the current statistics with no FT nurse on schedule for ESI-3 is current state with a median wait time of 21.46.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, and incorporate Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, with the motivation of seeking ways to utilize existing resources more efficiently in an emergency department setting (Fitzgerald Abstract).
Dalal/ Zhong/ Fitzgerald do not explicitly teach, however Abo-Hamed teaches
simulate a best-case scenario by executing the workflow model on inputs including the department profile and best-case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best-case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day; ([Pg. 1 1. Introduction] Discrete-Event Simulation (DES) has proven to be an effective tool for process modeling and improvement. [Pg. 4 3.3.Key performance indicators selection] The ED manager has identified two main key performance areas: patient throughput and ED efficiency. The performance measures for patient throughput are the average waiting time and average length of stay (LOS), while for ED efficiency they are; ED productivity, resource utilization and layout efficiency (i.e. KPIs). Fig. 3 shows the breakdown of the key performance indicators (KPIs) according to the ED senior managers. [Pg. 9 6.1 Scenario design] The simulation scenarios tested were the impact of variation in medical staffing, increasing clinical assessment space and finally assessing the impact of incorporating a ‘zero-tolerance’ policy regarding exceeding the national 6-hour boarding time (i.e., length of stay). Therefore, distinct study scenario variables (Table 3) were added to the simulation model and run for a 3 month continuous blocks. [Pg. 9 6.2 Result analysis] Scenario 3 which comprised adding one physician (i.e. SHO doctor) from 9 pm to 7 am will reduce the queue length in the waiting room that keeps building up over the night time (especially weekends). Subsequently, the average waiting time of patients will shorten by 44% (98.68 minutes) and the percentage of treated patients (i.e. upper limit on a number of patients that could realistically be handled per day) will increase to 94% . [Pg. 10-11 6.2 Result analysis] Explore the potential benefits of all possible combinations of the three basic scenarios (Table 5). Scenario 7 (combination of no admission blockage, physical capacity of 18, and additional physician shift from 9pm -7am) dominates all other scenarios in terms of ED performance, with a % of patients treated at 95% (i.e. upper limit on a number of patients that could realistically be handled per day).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal, healthcare appointments based on patient availability probabilities as taught by Zhong, Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, and incorporate a simulation-based framework to improve patient experience in an emergency department taking into account patients percentage treated, with the motivation of providing timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process (Abo-Hamad Abstract).
As per Claim 2, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claim 1,Dalal further teaches wherein: the best-case values for the variables of the workflow model, and the worst-case values for the variables of the workflow model. ([Para. 0053] The simulation operation includes updating and using the latest process distributions for workflow schedule simulations over a time period that allows statistically significant conclusions. In some examples, this process can be performed with manual time stamps by hospital staff, time stamps stored in the first database 12, or information provided by the RTLS 16. Since the hospital environment is constantly changing (e.g., a physician is getting quicker in performing a procedure), the timestamps allow to use the latest distributions that are statistically significant to use. In other examples, the timestamp data can be used in future scheduling operations (e.g., the hospital schedules more emergency patients in future weeks). Examiner interprets this to be indicative of ED arrivals that are higher than the current statistics. The simulation operation simulates the planned schedule, as well as “what-if’ scenarios using a set of latest recorded time stamps and an estimated patient arrival time. Examiner interprets that the “what-if” scenarios can include simulating no ED arrivals to determine the best-case values for the variables of the workflow model. In some examples, the simulation includes generating key performance indicators (KPIs) (e.g., patient wait time, last patient existing, and so forth) for each appointment in the planned schedule. In one illustrative embodiment, the simulation module 42 is implemented as FlexSim™ simulation software suitably configured with the foregoing information and linked to appropriate available data sources (e.g. the databases 12, 14, the RTFS 16, or so forth).)
Dalal does not explicitly teach, however Zhong teaches
Include patient arrival timeliness that is better than the current statistics, patient no-shows that are lower than the current statistics, and ED arrivals that are lower than the current statistics; include values representing patient arrival timeliness that is worse than the current statistics, patient no-shows that are higher than the current statistics ([Para. 0030] A predictive model is built using the historical data related to patient scheduling. Those data possesses information of past appointments (workday, time, appointment type, appointment length, provider, etc.) and the outcomes whether the patient is on-time, arrive in advance, late, no-show or cancellation, or the slot is unscheduled (appointment vacancy), etc. Examiner interprets that the inputting only patient data where the patient being on time or arrive in advance would be indicative of the best values for the variables of the workflow. [Para. 0040] The past patient visit log data should include at least past patient appointment date and time, and past patient visit outcome information wherein the past patient visit outcome information is at least sufficient to determine whether the past patient was a no show.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
As per Claim 3, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Zhong further teaches wherein: the best-case values for the variables of the workflow model include values representing all patients being on-time; and the worst-case values for the variables of the workflow model include values representing no patients being on-time. ([Para. 0030] A predictive model is built using the historical data related to patient scheduling. Those data possesses information of past appointments (workday, time, appointment type, appointment length, provider, etc.) and the outcomes whether the patient is on-time, arrive in advance, late, no-show or cancellation, or the slot is unscheduled (appointment vacancy), etc. Examiner interprets that the inputting only patient data where the patient being on time or arrive in advance would be indicative of the best values for the variables of the workflow and inputting only patient data where the patient is a no-show or cancellation would be indicative of worst-case values for the variable of the workflow model [Para. 0041] The patient arrival time is utilized by a predictive module 214 that takes into account the historic data for a general patient population to determine trends in vacancies, no-shows, and late cancellations.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
As per Claim 4, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claim 1, Zhong further teaches wherein: the best-case values for the variables of the workflow model include values representing no patients being no-shows. ([Para. 0030] A predictive model is built using the historical data related to patient scheduling. Those data possesses information of past appointments (workday, time, appointment type, appointment length, provider, etc.) and the outcomes whether the patient is on-time, arrive in advance, late, no-show or cancellation, or the slot is unscheduled (appointment vacancy), etc. Examiner interprets that the inputting only patient data where the patient being on time or arrive in advance would be indicative of the best values for the variables of the workflow.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
As per Claim 5, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claim 1, Dalal further teaches wherein: the best-case values for the variables of the workflow model include values representing no ED arrivals. ([Para. 0053] The simulation operation includes updating and using the latest process distributions for workflow schedule simulations over a time period that allows statistically significant conclusions. In some examples, this process can be performed with manual time stamps by hospital staff, time stamps stored in the first database 12, or information provided by the RTLS 16. Since the hospital environment is constantly changing (e.g., a physician is getting quicker in performing a procedure), the timestamps allow to use the latest distributions that are statistically significant to use. The simulation operation simulates the planned schedule, as well as “what-if’ scenarios using a set of latest recorded time stamps and an estimated patient arrival time. Examiner interprets that the “what-if” scenarios can include simulating no ED arrivals to determine the best-case values for the variables of the workflow model. In some examples, the simulation includes generating key performance indicators (KPIs) (e.g., patient wait time, last patient existing, and so forth) for each appointment in the planned schedule. In one illustrative embodiment, the simulation module 42 is implemented as FlexSim™ simulation software suitably configured with the foregoing information and linked to appropriate available data sources (e.g. the databases 12, 14, the RTFS 16, or so forth).)
As per Claim 6, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Fitzgerald further teaches wherein the at least one electronic processor is further programmed to: simulate at least one intermediate scenario by executing the workflow model on inputs including the department profile and intermediate values for the variables of the workflow model that are intermediate between the best case scenario and the worst case scenario, and compute values of the one or more KPI metrics for the simulated intermediate scenario; and further outputting, on at least one display device, the values of the one or more KPI metrics computed for the simulated at least one intermediate scenario. ([Pg. 2 1. Introduction] Queueing theory may be combined with Monte Carlo simulation or discrete event simulation to produce numerical results for complex models [Pg. 3 2.2 Model Design] The model determined the number of required providers (pg. 3 Table 1: Provider-to-patient ratios) and the earliest time that the number of providers is available. A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 3 3.2 Monte Carlo Analysis] The model generated 300 sets of simulation data for each simulation scenario (see Table 4 for an abbreviated list of scenarios). [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the various median wait time based on the different ESI levels and scenarios. Examiner interprets that the intermediate scenario is the scenario in which there is more FT nursing staff utilized and a median wait time between the best- and worst-case scenario, therefore the intermediate scenario is indicated by FT nurse added, weekdays, 12pm -8pm with a median wait time of 16.55.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, and incorporate Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, with the motivation of seeking ways to utilize existing resources more efficiently in an emergency department setting (Fitzgerald Abstract).
As per Claim 7, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Dalal further teaches wherein the at least one electronic processor is programmed to generate the department profile by operations including: ([Para. 0010] At least one electronic processor)
retrieving hospital department data and patient data from at least one database, the hospital department data including one or more of types of procedures to be performed, distribution of process time for each procedure, availability of resources, a maintenance schedule for the resources, downtime of the resources, a resource to staff ratio, a patient to nurse ratio, an order list of procedures, scheduled examinations, and unscheduled examinations, the patient data including one or more of a type of patient, records of no-shows or late arrivals to appointments, age, gender, need for sedation, presence of contrast allergies, presence of claustrophobia, and presence of foreign bodies; ([Para. 0045] the first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability. In some example, the first database 12 can be an electronic medical record (EMR) database. Examiner interprets that the EMR would include patient information about type of patient, age, gender. The second database 14 is configured to store “present” information such as real-time patient and staff locations (e.g., via GPS data), along with real-time environmental information (e.g., weather data, traffic data, and so forth). [Para. 0003] early, late or no-show outpatients; delayed arrival of inpatients due to longer-than- anticipated transportation time from another hospital department; unpredictable number and timing of emergency patients; reduced staff availability due to staff illnesses, etc.; patient-to- patient variations in the actual time to perform a procedure (e.g., complications that extend a procedure); availability of equipment or rooms (e.g., limited number of available rooms & equipment or break-down of equipment), among others. Examiner interprets that due to the claim language reciting “one or more of” prior to the statement of hospital department and patient data, therefore under broadest reasonable interpretation, any one or more of would be indicative of any one or more of the hospital department and patient data would be sufficient to read on the claims.)
generating a department profile for each resource from the retrieved hospital department data and patient data; ([Para. 0052] the real-time patient location information and the real-time staff location information can be retrieved from the second database 14. The simulation operation includes updating and using the latest process distributions for workflow schedule simulations over a time period that allows statistically significant conclusions.)
and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device. ([Para. 0057] display device 24 to display the workflow schedule computed at 102 and the one or more workflow schedule adjustment options developed at 106. The workflow schedule 46 and the adjustment options 48 can be displayed via the GUI 28 as diagrammatically indicated in FIGURE 1. [Para. 0050] At the beginning of the day the current workflow schedule may be set to a planned schedule 50, which is updated throughout the day by way of acceptance of proposed adjustment options 48 generated by the optimization module 44 of the analytics module 42.)
As per Claim 8, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Dalal further teaches wherein the at least one electronic processor is programmed to generate the workflow model by operations including: retrieving a model workflow template; adjusting the model workflow template based on the current statistics for the hospital department. ([Para. 0009] simulating a workflow schedule of medical examinations or medical therapy sessions using data including workflow timestamps and a planned schedule; determining one or more workflow schedule adjustment options for adjusting the workflow schedule to comply with the constraint data; and controlling a display device of the workstation to display the workflow schedule and the one or more workflow schedule adjustment options.)
As per Claim 9, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Dalal further teaches wherein the at least one electronic processor is programmed to:
receive one or more user inputs indicative of a change one or more values of the workflow model to update at least one of the best-case scenario and the worst-case scenario; ([Para. 0052] simulate a workflow schedule using data including at least one of workflow timestamps, staff schedules, real-time patient location information, real-time staff location information, real-time staff location weather information, real-time staff location traffic information, and a planned schedule. For example, the workflow timestamps and the staff schedules can be retrieved from the first database 12, and the real-time patient location information and the real-time staff location information can be retrieved from the second database 14. The simulation operation includes updating and using the latest process distributions for workflow schedule simulations over a time period that allows statistically significant conclusions. In some examples, this process can be performed with manual time stamps by hospital staff, time stamps stored in the first database 12, or information provided by the RTLS 16. Since the hospital environment is constantly changing (e.g., a physician is getting quicker in performing a procedure), the timestamps allow to use the latest distributions that are statistically significant to use. In other examples, the timestamp data can be used in future scheduling operations (e.g., the hospital schedules more emergency patients in future weeks). Examiner interprets that any changes made to the user inputs would inherently change the values of the workflow model, thereby the best-case and worst-case scenarios would obviously be changed as a result.)
compare one or more updated values of the KPI metrics resulting from updating the update at least one of the best-case scenario and the worst-case scenario with previously obtained value of KPI metrics; ([Para. 0052] The simulation operation simulates the planned schedule, as well as “what-if’ scenarios using a set of latest recorded time stamps and an estimated patient arrival time. In some examples, the simulation includes generating key performance indicators (KPIs) (e.g., patient wait time, last patient existing, and so forth) for each appointment in the planned schedule. [Para. 0055] The KPIs are computed for each simulated workflow schedule and the options are ranked by the scores. [Para. 0057] The one or more workflow schedule adjustment options developed at 106, preferably as a ranked list (ranked by their KPI scores) and optionally listed with those scores. Examiner interprets the best-case and worst-case scenarios would obviously change in view of updated values of the KPI metrics.)
and update the workflow model when the updated values of the KPI metrics satisfy a predetermined update threshold. ([Para. 0055] Each candidate adjustment is analyzed by invoking the simulation module 42 to simulate the workflow schedule with that adjustment, and the KPIs are computed for the resulting simulated workflow schedule to assign a score for that workflow schedule - and for the corresponding candidate adjustment. The KPIs are computed for each simulated workflow schedule and the options are ranked by the scores. [Para. 0057] The display device 24 to display the workflow schedule computed at 102 and the one or more workflow schedule adjustment options developed at 106, preferably as a ranked list (ranked by their KPI scores) and optionally listed with those scores. The system may automatically choose one or more adjustments scoring highest in terms of KPIs, or may propose the highest scoring adjustment(s) to laboratory personnel via the user interface for user selection. The Specification defines the a predetermined change threshold is satisfied (e.g., if a desired change has occurred) (Para. 0039). Examiner interprets that the system automatically choosing on or more adjustments scoring the highest in terms of KPIs meets the predetermined change threshold.)
As per Claim 10, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Dalal further teaches wherein the variables of the workflow model include random variables, and the at least one electronic processor is programmed to execute the workflow model on inputs including the random variables instantiated using Monte Carlo simulation. ([Para. 0066] the scheduling learning engine 60 iteratively simulates an action of randomly assigning an order that is to be scheduled to an appointment time. Placing an order in an empty slot in adherence to patient preference, positively rewards the system and any violations, for example placing an Outpatient order which was reserved for Inpatient will result in a negative reward. Any such rules can be coded into the reward system. The slot duration estimates to perform a certain imaging procedure like a“ Liver Biopsy” can be a random draw from the probability distribution of the curated historical data which the workflow model 62 can provide. Several patient schedules can be generated using Monte Carlo sampling techniques. [Para. 0039] a schedule learning engine performs Monte Carlo simulation of possible schedules)
As per Claim 11, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad teach the apparatus of claims 1, Dalal further teaches wherein the hospital department is a medical imaging department and the active medical equipment inventory comprises an inventory of active medical imaging devices annotated at least by imaging modality. ([Para. 0045] The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. RTLS 16 to be used to classify mobile medical equipment, typically only categories: in the hospital but not at the radiology lab or at the radiology lab (3) will apply. Examiner interprets the RTLS position data of the mobile medical equipment to be indicative of active medical equipment inventory. [Para. 0042] The imaging study orders which are scheduled by the schedule learning engine are suitably input as a list of orders. Fields may be provided to indicate study priority, medical imaging procedure (from which can be derived the imaging modality and hence the imaging rooms that can perform the procedure), and patient class (e.g., in-patient or out-patient). Examiner interprets the imaging study orders to be indicative of annotated at least by modality.)
As per Claim 12, Dalal teaches a non-transitory computer readable medium storing instructions executable by at least one electronic processor to perform a method for generating benchmarking metrics of current operational workflow performance of a hospital department, the method including: ([Para. 0009] a non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor.)
generating a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile by operations including: ([Para. 0045] The first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability (i.e. personnel profile). The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. RTLS 16 to be used to classify mobile medical equipment, typically only categories: in the hospital but not at the radiology lab or at the radiology lab (3) will apply. Examiner interprets the RTLS position data of the mobile medical equipment to be indicative of active medical equipment inventory.)
retrieving hospital department data and patient data from at least one database; ([Para. 0045] the first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability. In some example, the first database 12 can be an electronic medical record (EMR) database. Examiner interprets that the EMR would include patient information about type of patient, age, gender. The second database 14 is configured to store “present” information such as real-time patient and staff locations (e.g., via GPS data), along with real-time environmental information (e.g., weather data, traffic data, and so forth). The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. [Para. 0003] early, late or no-show outpatients; delayed arrival of inpatients due to longer-than- anticipated transportation time from another hospital department; unpredictable number and timing of emergency patients; reduced staff availability due to staff illnesses, etc.; patient-to- patient variations in the actual time to perform a procedure (e.g., complications that extend a procedure); availability of equipment or rooms (e.g., limited number of available rooms & equipment or break-down of equipment), among others. Examiner interprets that due to the claim language reciting “one or more of” prior to the statement of hospital department and patient data, therefore under broadest reasonable interpretation, any one or more of would be indicative of any one or more of the hospital department and patient data would be sufficient to read on the claims.)
generating a department profile for each resource from the retrieved hospital
department data and patient data; ([Para. 0045] The first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability (i.e. personnel profile). The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. RTLS 16 to be used to classify mobile medical equipment, typically only categories: in the hospital but not at the radiology lab or at the radiology lab (3) will apply. Examiner interprets the RTLS position data of the mobile medical equipment to be indicative of active medical equipment inventory.)
and providing a user interface via which the generated profiles are displayed on the at least one display device and via which a user can modify the generated profiles using via at least one user input device; ([Para. 0057] display device 24 to display the workflow schedule computed at 102 and the one or more workflow schedule adjustment options developed at 106. The workflow schedule 46 and the adjustment options 48 can be displayed via the GUI 28 as diagrammatically indicated in FIGURE 1. [Para. 0050] At the beginning of the day the current workflow schedule may be set to a planned schedule 50, which is updated throughout the day by way of acceptance of proposed adjustment options 48 generated by the optimization module 44 of the analytics module 42.)
generating an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; ([Para. 0043] the workflow simulation may incorporate a prediction model for patient no-shows and cancellations. [Para. 0063] A scheduling learning engine 60 is configured to generate a workflow simulation model 62 which simulates the actual workflow. The model 62 captures all the tasks patients flow through including the process time (as a distribution) for each task, the resources necessary to perform the task like a CT room, portable ultrasound equipment, a nurse, a physician etc. The model 62 also captures the number of available resources and their schedules. By passing the patients appointment time and their procedure type to the model, the scheduling learning engine 60 can compute the KPIs like the patient wait/idle time, arrival to exit time, last patient exit time, staff/room/equipment utilization etc. This module can be developed using discrete event simulation or agent-based simulation techniques.)
on at least one display device ([Para. 0057] The display device 24)
Dalal does not explicitly teach, however Zhong teaches
retrieving current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show; ([Para. 0040] The system also takes into account hospital or clinic historical (i.e. past) patient visit log data. The past patient visit log data is shown and described in more detail in FIG. 4. The past patient visit log data should include at least past patient appointment date and time, and past patient visit outcome information wherein the past patient visit outcome information is at least sufficient to determine whether the past patient was a no show. Patients' past visit information 210 such as scheduled arrival time, real arrival time (i.e. patient arrival timeliness), or whether it is a cancellation or no-show (i.e. patient no-show), and the visit type, visit length, provider information, etc., are extracted to train the predictive model.)
computing values of one or more key performance indicator (KPI) metrics for the current statistics; ([Para. 0041] The patient arrival time is utilized by a predictive module 214 that takes into account the historic data for a general patient population to determine trends in vacancies, no-shows and late cancellations. The predictive module information is used by the appointment template module 206 and the template optimizer 220. The operational data of historical patient visits takes into account the different types of appointments offered and their usual length of visits. [Para. 0042] The scheduling optimizer 220 provides an optimal scheduling template 222 taking into account all relevant information and provides a system performance output from the output and feedback module 224. This information allows a user to review parameters taken into account for a given template such as the number of patient's a physician is expected to see each day or week, or the number of staff members that will be required to staff the schedule.[Para. 0043] FIGS. 7 and 8 show a bar chart illustrating the average patient length of stay by patient type by template 700 and the staff utilization by staff type by template 800, which are among the multiple objectives to be optimized, i.e., the shorter the patient length of stay and the higher the staff utilization (i.e. KPI) are preferred. Examiner interprets the number of patients physician is expected to see each day or week, the number of staff members that will be required to staff the schedule, average patient length of stay by patient type by template 700 and the staff utilization by staff type to be indicative of KPI metrics for the current statistics.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
Dalal/ Zhong do not teach, however Fitzgerald teaches
retrieve current statistics for the hospital department including at least one of emergency department (ED) arrival statistics; ([Pg.2 2.1. Setting and Data Sources] deidentified data from the hospital (emergency department) ED’s electronic health record (EHR) and management tool (Picis ED PulseCheck, Wakefield, MA), including major timestamps for each patient during their visit in addition to the patient’s recorded ESI level. Relevant timestamps included time of arrival, arrival time in the room, and time of departure.)
compute values of one or more key performance indicator (KPI) metrics for the current statistics;([Pg. 2 2.2 Model Design] we performed descriptive statistics on the patient records to determine average arrival rates by patient’s Emergency Severity Index (ESI), hour of day, and day of week using statistical functions in MATLAB. The distributions of current wait and service times were also computed for each ESI. [Pg. 3 2.2 Model Design] A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the current state scenarios of ESI-3 in which the median wait time is 21.46 (For the purposes of examiner, Examiner will be exclusively focusing on ESI-3 as the main reference for KPI metric analysis).)
simulating a worst-case scenario by executing the workflow model on inputs including the department profile and worst-case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst-case scenario; ([Pg. 2 1. Introduction] Queueing theory may be combined with Monte Carlo simulation or discrete event simulation to produce numerical results for complex models [Pg. 3 2.2 Model Design] The model determined the number of required providers (Pg. 3 Table 1: Provider-to-patient ratios) and the earliest time that the number of providers is available. A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 3 3.2 Monte Carlo Analysis] The model generated 300 sets of simulation data for each simulation scenario (see Table 4 for an abbreviated list of scenarios). [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the various median wait time based on the different ESI levels and scenarios. Examiner interprets that the worst-case scenario is the scenario in which there is less FT nursing staff utilized and the highest median wait time, therefore the worst scenario is indicated by FT is reassigned, 7 days, 12pm -8pm with a median wait time of 21.73.)
and outputting, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best-case scenario, the values of the one or more KPI metrics computed for the simulated worst-case scenario, and the values of the one or more KPI metrics computed for the current statistics. ([Pg. 5 Table 4: Median wait time by ESI and scenario] Table 4 displays the median wait times (KPI) by ESI and scenarios, in which the best-scenario for ESI -3 is FT nurse added, 7 days, 12pm-8pm with a wait time of 14.30, the worst scenario for ESI -3 is FT nurse reassigned, 7 days, 12pm-8pm with a median wait time of 21.73, and the current statistics with no FT nurse on schedule for ESI-3 is current state with a median wait time of 21.46.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, and incorporate Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, with the motivation of seeking ways to utilize existing resources more efficiently in an emergency department setting (Fitzgerald Abstract).
Dalal/ Zhong/ Fitzgerald do not explicitly teach, however Abo-Hamed teaches
simulating a best-case scenario by executing the workflow model on inputs including the department profile and best-case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best-case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day; ([Pg. 1 1. Introduction] Discrete-Event Simulation (DES) has proven to be an effective tool for process modeling and improvement. [Pg. 4 3.3.Key performance indicators selection] The ED manager has identified two main key performance areas: patient throughput and ED efficiency. The performance measures for patient throughput are the average waiting time and average length of stay (LOS), while for ED efficiency they are; ED productivity, resource utilization and layout efficiency (i.e. KPIs). Fig. 3 shows the breakdown of the key performance indicators (KPIs) according to the ED senior managers. [Pg. 9 6.1 Scenario design] The simulation scenarios tested were the impact of variation in medical staffing, increasing clinical assessment space and finally assessing the impact of incorporating a ‘zero-tolerance’ policy regarding exceeding the national 6-hour boarding time (i.e., length of stay). Therefore, distinct study scenario variables (Table 3) were added to the simulation model and run for a 3 month continuous blocks. [Pg. 9 6.2 Result analysis] Scenario 3 which comprised adding one physician (i.e. SHO doctor) from 9 pm to 7 am will reduce the queue length in the waiting room that keeps building up over the night time (especially weekends). Subsequently, the average waiting time of patients will shorten by 44% (98.68 minutes) and the percentage of treated patients (i.e. upper limit on a number of patients that could realistically be handled per day) will increase to 94% . [Pg. 10-11 6.2 Result analysis] Explore the potential benefits of all possible combinations of the three basic scenarios (Table 5). Scenario 7 (combination of no admission blockage, physical capacity of 18, and additional physician shift from 9pm -7am) dominates all other scenarios in terms of ED performance, with a % of patients treated at 95% (i.e. upper limit on a number of patients that could realistically be handled per day).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal, healthcare appointments based on patient availability probabilities as taught by Zhong, Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, and incorporate a simulation-based framework to improve patient experience in an emergency department taking into account patients percentage treated, with the motivation of providing timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process (Abo-Hamad Abstract).
As per Claim 13, Claim(s) 13 is/are analogous to Claim(s) 2, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
As per Claim 14, Claim(s) 14 is/are analogous to Claim(s) 8, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
As per Claim 15, Claim(s) 15 is/are analogous to Claim(s) 10, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 10.
As per Claim 16, Claim(s) 16 is/are analogous to Claim(s) 7, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
As per Claim 17, Dalal teaches a method for generating benchmarking metrics of current operational workflow performance of a hospital department, the method including:
generating a department profile identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; ([Para. 0045] The first database 12 is configured to store “past” information such as workflow schedule process time stamps, staffing schedules and clinical resource availability (i.e. personnel profile). The RTLS device 16 generates position data of the staff and patients (and optionally also mobile medical equipment that may be assigned to the laboratory on an occasional basis), and stores this data in the second database 14. RTLS 16 to be used to classify mobile medical equipment, typically only categories: in the hospital but not at the radiology lab or at the radiology lab (3) will apply. Examiner interprets the RTLS position data of the mobile medical equipment to be indicative of active medical equipment inventory.)
generating an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having random variables representing at least patient arrival timeliness, patient no-show, and ED arrival; ([Para. 0043] the workflow simulation may incorporate a prediction model for patient no-shows and cancellations. [Para. 0063] A scheduling learning engine 60 is configured to generate a workflow simulation model 62 which simulates the actual workflow. The model 62 captures all the tasks patients flow through including the process time (as a distribution) for each task, the resources necessary to perform the task like a CT room, portable ultrasound equipment, a nurse, a physician etc. The model 62 also captures the number of available resources and their schedules. By passing the patients appointment time and their procedure type to the model, the scheduling learning engine 60 can compute the KPIs like the patient wait/idle time, arrival to exit time, last patient exit time, staff/room/equipment utilization etc. This module can be developed using discrete event simulation or agent-based simulation techniques.)
on at least one display device ([Para. 0057] The display device 24)
Dalal does not explicitly teach, however Zhong teaches
retrieving current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show; ([Para. 0040] The system also takes into account hospital or clinic historical (i.e. past) patient visit log data. The past patient visit log data is shown and described in more detail in FIG. 4. The past patient visit log data should include at least past patient appointment date and time, and past patient visit outcome information wherein the past patient visit outcome information is at least sufficient to determine whether the past patient was a no show. Patients' past visit information 210 such as scheduled arrival time, real arrival time (i.e. patient arrival timeliness), or whether it is a cancellation or no-show (i.e. patient no-show), and the visit type, visit length, provider information, etc., are extracted to train the predictive model.)
compute values of one or more key performance indicator (KPI) metrics for the current statistics; ([Para. 0041] The patient arrival time is utilized by a predictive module 214 that takes into account the historic data for a general patient population to determine trends in vacancies, no-shows and late cancellations. The predictive module information is used by the appointment template module 206 and the template optimizer 220. The operational data of historical patient visits takes into account the different types of appointments offered and their usual length of visits. [Para. 0042] The scheduling optimizer 220 provides an optimal scheduling template 222 taking into account all relevant information and provides a system performance output from the output and feedback module 224. This information allows a user to review parameters taken into account for a given template such as the number of patient's a physician is expected to see each day or week, or the number of staff members that will be required to staff the schedule.[Para. 0043] FIGS. 7 and 8 show a bar chart illustrating the average patient length of stay by patient type by template 700 and the staff utilization by staff type by template 800, which are among the multiple objectives to be optimized, i.e., the shorter the patient length of stay and the higher the staff utilization (i.e. KPI) are preferred. Examiner interprets the number of patients physician is expected to see each day or week, the number of staff members that will be required to staff the schedule, average patient length of stay by patient type by template 700 and the staff utilization by staff type to be indicative of KPI metrics for the current statistics.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, with the motivation of creating and adjusting a computerized patient scheduling system (Zhong Para. 0001).
Dalal/ Zhong do not explicitly teach, however Fitzgerald teaches
retrieve current statistics for the hospital department including at least one of emergency department (ED) arrival statistics; ([Pg.2 2.1. Setting and Data Sources] deidentified data from the hospital ED’s electronic health record (EHR) and management tool (Picis ED PulseCheck, Wakefield, MA), including major timestamps for each patient during their visit in addition to the patient’s recorded ESI level. Relevant timestamps included time of arrival, arrival time in the room, and time of departure.)
compute values of one or more key performance indicator (KPI) metrics for the current statistics;([Pg. 2 2.2 Model Design] we performed descriptive statistics on the patient records to determine average arrival rates by patient’s Emergency Severity Index (ESI), hour of day, and day of week using statistical functions in MATLAB. The distributions of current wait and service times were also computed for each ESI. [Pg. 3 2.2 Model Design] A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the current state scenarios of ESI-3 in which the median wait time is 21.46 (For the purposes of examiner, Examiner will be exclusively focusing on ESI-3 as the main reference for KPI metric analysis).)
simulating a worst-case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and worst-case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst-case scenario; ([Pg. 2 1. Introduction] Queueing theory may be combined with Monte Carlo simulation or discrete event simulation to produce numerical results for complex models [Pg. 3 2.2 Model Design] The model determined the number of required providers (Table 1: Provider-to-patient ratios) and the earliest time that the number of providers is available. A variety of simulation scenarios were evaluated, exploring variable nurse scheduling, fast track hours of operation, and fast track days of operation. The “current state” simulation used the current nurse schedule without a fast track in operation. The primary outcome metrics for this study were patient wait time and nursing resource demand. Examiner interprets the patient wait time and nursing resource demand to be indicative of computed values of one or more key performance indicator (KPI) metrics for the current statistics. [Pg. 3 3.2 Monte Carlo Analysis] The model generated 300 sets of simulation data for each simulation scenario (see Table 4 for an abbreviated list of scenarios). [Pg. 4 Table 4: Median wait time by ESI and scenario] The table display the various median wait time based on the different ESI levels and scenarios. Examiner interprets that the worst-case scenario is the scenario in which there is less FT nursing staff utilized and the highest median wait time, therefore the worst scenario is indicated by FT is reassigned, 7 days, 12pm -8pm with a median wait time of 21.73.)
and outputting, as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best-case scenario, the values of the one or more KPI metrics computed for the simulated worst-case scenario, and the values of the one or more KPI metrics computed for the current statistics. ([Pg. 5 Table 4: Median wait time by ESI and scenario] Table 4 displays the median wait times (KPI) by ESI and scenarios, in which the best-scenario for ESI -3 is FT nurse added, 7 days, 12pm-8pm with a wait time of 14.30, the worst scenario for ESI -3 is FT nurse reassigned, 7 days, 12pm-8pm with a median wait time of 21.73, and the current statistics with no FT nurse on schedule for ESI-3 is current state with a median wait time of 21.46.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal and incorporate scheduling patient healthcare appointments based on patient availability probabilities as taught by Zhong, and incorporate Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, with the motivation of seeking ways to utilize existing resources more efficiently in an emergency department setting (Fitzgerald Abstract).
Dalal/ Zhong/ Fitzgerald do not explicitly teach, however Abo-Hamad teaches
simulating a best-case scenario by executing the workflow model using Monte Carlo simulation on inputs including the department profile and best-case values for the random variables of the workflow model and compute values of the one or more KPI metrics for the simulated best-case scenario, wherein the best case scenario comprises an upper limit on a number of patients that could realistically be handled per day; ([Pg. 1 1. Introduction] Discrete-Event Simulation (DES) has proven to be an effective tool for process modeling and improvement. [Pg. 4 3.3.Key performance indicators selection] The ED manager has identified two main key performance areas: patient throughput and ED efficiency. The performance measures for patient throughput are the average waiting time and average length of stay (LOS), while for ED efficiency they are; ED productivity, resource utilization and layout efficiency (i.e. KPIs). Fig. 3 shows the breakdown of the key performance indicators (KPIs) according to the ED senior managers. [Pg. 9 6.1 Scenario design] The simulation scenarios tested were the impact of variation in medical staffing, increasing clinical assessment space and finally assessing the impact of incorporating a ‘zero-tolerance’ policy regarding exceeding the national 6-hour boarding time (i.e., length of stay). Therefore, distinct study scenario variables (Table 3) were added to the simulation model and run for a 3 month continuous blocks. [Pg. 9 6.2 Result analysis] Scenario 3 which comprised adding one physician (i.e. SHO doctor) from 9 pm to 7 am will reduce the queue length in the waiting room that keeps building up over the night time (especially weekends). Subsequently, the average waiting time of patients will shorten by 44% (98.68 minutes) and the percentage of treated patients (i.e. upper limit on a number of patients that could realistically be handled per day) will increase to 94% . [Pg. 10-11 6.2 Result analysis] Explore the potential benefits of all possible combinations of the three basic scenarios (Table 5). Scenario 7 (combination of no admission blockage, physical capacity of 18, and additional physician shift from 9pm -7am) dominates all other scenarios in terms of ED performance, with a % of patients treated at 95% (i.e. upper limit on a number of patients that could realistically be handled per day).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal, healthcare appointments based on patient availability probabilities as taught by Zhong, Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, and incorporate a simulation-based framework to improve patient experience in an emergency department taking into account patients percentage treated, with the motivation of providing timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process (Abo-Hamad Abstract).
As per Claim 18, Claim(s) 18 is/are analogous to Claim(s) 2, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
As per Claim 19, Claim(s) 19 is/are analogous to Claim(s) 7, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
As per Claim 20, Claim(s) 20 is/are analogous to Claim(s) 8, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
As per Claim 21, Dalal/ Zhong/ Fitzgerald/ Abo-Hamad disclose the apparatus of claim 1, Abo-Hamad further teaches wherein the worst case scenario comprises a lower limit on the number of patients that could realistically be handled per day. ([Pg. 1 1. Introduction] Discrete-Event Simulation (DES) has proven to be an effective tool for process modeling and improvement. [Pg. 4 3.3.Key performance indicators selection] The ED manager has identified two main key performance areas: patient throughput and ED efficiency. The performance measures for patient throughput are the average waiting time and average length of stay (LOS), while for ED efficiency they are; ED productivity, resource utilization and layout efficiency (i.e. KPIs). Fig. 3 shows the breakdown of the key performance indicators (KPIs) according to the ED senior managers. [Pg. 9 6.1 Scenario design] The simulation scenarios tested were the impact of variation in medical staffing, increasing clinical assessment space and finally assessing the impact of incorporating a ‘zero-tolerance’ policy regarding exceeding the national 6-hour boarding time (i.e., length of stay). Therefore, distinct study scenario variables (Table 3) were added to the simulation model and run for a 3 month continuous blocks. [Pg. 10 6.2. Result analysis] Based off Table 4, the worst case scenario was the base scenario (i.e. admission blockage, physical capacity of 12) and scenario 1 (i.e. no admission blockage, physical capacity of 12) which both results in a percentage of 83% in terms of patient treated (i.e. a lower limit on the number of patients that could realistically be handled per day). Based off Table 6, which comprised a combination of the previous scenarios, the worst case scenario was the base scenario of (i.e. admission blockage, physical capacity of 12) resulting in 83% of patients treatment.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of optimizing patient schedules based on patient workflow and resource availability as taught by Dalal, healthcare appointments based on patient availability probabilities as taught by Zhong, Monte-Carlo analysis to support decision making in an emergency room department as taught by Fitzgerald, and incorporate a simulation-based framework to improve patient experience in an emergency department taking into account patients percentage treated, with the motivation of providing timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process (Abo-Hamad Abstract).
As per Claim 22, Claim(s) 22 is/are analogous to Claim(s) 21, thus Claim(s) 22 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 21.
As per Claim 23, Claim(s) 23 is/are analogous to Claim(s) 21, thus Claim(s) 23 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 22.
Response to Arguments
Applicant's arguments, see pgs. 11-17 “Rejections under 35 U.S.C. 101” filed 01/29/2025 have been fully considered but they are not persuasive.
Applicant argues that claim 1 integrates any potential abstract idea into at least the practical application of outputting on at least one display device as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics. The ability to display and continuously update benchmarks indicative of efficiency in a healthcare system – and thus can be used in real world scenarios. Applicant argues that the present teachings enable meaningful comparisons in similar institutions and address the problem of known systems that do not provide a meaningful comparison. Examiner respectfully disagrees. The limitation of outputting as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics is part of the abstract idea. The at least one display device is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons. An improvement to the abstract ideas of display and continuously update benchmarks indicative of efficiency in a healthcare system, as well as enable meaningful comparisons in similar institutions and address the problem of known systems that do not provide a meaningful comparison, does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to displaying and updating data. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Applicant argues that the steps in claim 1 are directed to a particular ordered process that may be performed by a computer processor (effectively rendering it a special purpose computer) that includes specific timing of the various process steps integrates the abstract idea into a practical application. Examiner respectfully disagrees. As noted above in the analysis of Step 2A, Prong 2 and Step 2B, the use of a computer processor does not amount to a particular machine because this element well-known and well-understood computer components in the art. The use of these computer components are not considered a particular machine because these computer components are being utilized within their intended use and no modification
was made to any of the components to provide any technical improvement. An improvement to the abstract idea of a particular ordered process that includes specific timing of the various process steps, does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to performing a series of steps/ instructions to output data. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
However, Applicant’s arguments that the claims do not recite a mental process because a number of steps cannot be practically performed in the mind is persuasive. However, Applicant’s arguments is still directed towards the abstract idea of organizing human activity. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to obtain and extract hospital management data. The steps of
generating an executable workflow model for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulate a best case scenario by executing the workflow model on inputs including the department profile and best case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario, wherein the best case scenario comprises an upper limit on the number of patients that could realistically be handled per day, and the worst case scenario comprises a lower limit on the number of patients that could realistically be handled per day; simulate a worst case scenario by executing the workflow model on inputs including the department profile and worst case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario; and output as benchmarks indicating work flow efficiency, the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics accordingly are best categorized as human tasks. Examiner notes that the at least one display device are not treated as part of the abstract idea (See Step 2A, Prong 2 and Step 2B analysis in the above rejection). The use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG 4 (January 7,2019) at pg. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1). Because the claim elements fall under a series of rules or instructions that a person or person would follow to obtain and extract medical data, the claimed invention is directed to an abstract idea. The claims recite collecting data on hospital department resources to determine operational workflow performance in current, best-case, and worst-case scenarios. These steps organize patients and hospital staff by determining optimal scenarios to increase emergency department production. Because these limitation determine the optimal scenarios to implement following the analysis of hospital resource data, they constitute the management of personal behavior on part of healthcare providers and hospital administrative staff. Accordingly, the claims fall under “Certain Methods of Organizing Human Activity” grouping of abstract, and, thus, recite an abstract idea.
Applicant's arguments, see pgs. 17-18 “Rejections under 35 U.S.C. 103”, filed 01/29/2025 have been fully considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Abo-Hamad, as per the rejection above.
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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/P.K.E./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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