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 .
Claims 1-20 are pending. Claims 1-20 are rejected herein.
Amendment
In the amendment received 14 August 2025 the following occurred: claims 5 and 6 were amended.
Priority
This application claims priority to provisional application #63/218,519 and therefore has a priority date of 06 July 2021.
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 1-20 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: The Statutory Categories
Claims 1,10 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method for performing, using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital and a patient flow system configured to perform a demand analysis for a hospital to optimize a flow of patients within the hospital and a non-transitory computer-readable storage medium. All are within a statutory class for subject matter eligibility purposes.
Step 2A Prong One: The Abstract Idea
The limitations of (1 being representative): optimizing a flow of patients within the hospital […by…] receiving or accessing […] hospital capacity information about the hospital: receiving or accessing […] hospital data comprises information on patient admissions, patient discharges, and patient transfers for a previous period of time for each of a plurality of patient types; adapting […] parameters of a machine learning algorithm based on the hospital data to adapt the machine learning algorithm into an adapted machine learning algorithm; receiving or accessing […] clinical information about a plurality of patients currently admitted to the hospital; determining, […] based on output from the adapted […] algorithm and using the clinical information about the plurality of patient currently admitted in the hospital, and the hospital capacity information, a predicted patient flow for the hospital in real-time; detecting […] a deviation between the predicted patient flow and at least one actual data point from a current patient flow, wherein the deviation exceeds a threshold value; displaying […] the detected deviation for the hospital in real-time to at least one user, wherein the detected deviation is configured to assist the at least one user in modifying a capacity of the hospital; determining a plurality of suggested rearrangement of resources based on the detected deviation and predicted patient flow; performing a simulation of each suggested rearrangement of resources based on the detected deviation and predicted patient flow; performing a simulation of each suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement over a period of time, wherein the simulation is periodically adjusted based on the predicted patient flow; …as drafted is a process that, under the broadest reasonable interpretation, covers a certain method of organizing human activity (i.e., managing personal behavior including following rules or instructions) and/or mathematical concepts but for the recitation of generic computer components. That is, other than reciting a computer having a processor (claim 1,10,16), a non-transitory computer-readable storage medium (claim 16) the claimed invention amounts to managing personal behavior or interaction between people (i.e., a person following a series of rules or steps) and mathematical concepts (i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations, see MPEP 2106.04(a)(2)). For example, but for the various general-purpose computer elements, the claims encompass a person using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital. The Examiner notes that “certain methods of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic components and/or mathematical concepts then it falls within the “certain method of organizing human activity” and/or “mathematical concept” groupings of abstract idea. Accordingly the claim recites an abstract idea.
Step 2A Prong Two: The Practical Application
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a patient flow system (Claim 1 and 10; a generic computer per Spec. Para. 0065) that implements the abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., a generic general-purpose computer or components thereof) such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Independent claim 1 recites the limitation of and “modifying configuration of a hospital system based on selection of one of the simulated suggested arrangements…”. MPEP 2106.05(f) indicates that a consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The identified additional element from claim 1 is no more than mere recitation of the words “apply it” (or an equivalent) and/or are instructions to implement an abstract idea or other exception on a computer and therefore cannot provide a practical application or significantly more. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a general -purpose computer (and/or components thereof) to perform the noted steps amounts to no more than mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Independent claim 1 recites the limitation of and “modifying configuration of a hospital system based on selection of one of the simulated suggested arrangements…”. MPEP 2106.05(f) indicates that a consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The identified additional element from claim 1 is no more than mere recitation of the words “apply it” (or an equivalent) and/or are instructions to implement an abstract idea or other exception on a computer and therefore cannot provide a practical application or significantly more. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application or provide significantly more because they do not impose any meaningful limits on practicing the abstract idea.
Dependent Claims and Dependent Additional Elements
Claims 2-9,11-15 and 17-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes wherein the predicted patient flow comprises a predicted occupancy level or patient arrivals. Claim 3 merely describes wherein the deviation is a time series anomaly. Claim 4 merely describes wherein the step of detecting the time series anomaly involves a predictive confidence level approach. Claim 5 merely describes wherein the step of detecting the time series anomaly involves a statistical profiling approach or a clustering based unsupervised approach. Claim 6 merely describes modifying configuration of a hospital system based on selection of one of the simulated suggested arrangements comprises one or more of adjusting staffing in one or more location of the hospital system, and accelerating discharge of one or more patients in the hospital system. Claim 7 merely describes displaying on the user interface an indicator indicating that a aberrational event is similar to a previous hospital event. Claim 8 merely describes displaying a plurality of suggested actions to be taken in the hospital. Claim 9 merely describes adopting one suggested action of the plurality of suggested actions after receiving user input from the at least one user at the user interface and modifying the hospital capacity information based on the adopted at least one suggested action; and incorporating at least one change to the resources in the hospital. Claim 11 merely describes the user interface configured to display to the user an indicator indicating that the detected deviation is expected to be akin to a previous hospital event. Claim 12 merely describes the user interface is further configured to display a plurality of suggested actions to be taken in the hospital, configured to assist the at least one user in modifying the capacity of the hospital such that treatment can be provided to at least one patient currently admitted to the hospital or at least one patient expected to be admitted to the hospital. Claim 13 merely describes adopting at least one suggested action of the plurality of suggested actions after receiving user input from the at least one user at the user interface and modifying the hospital capacity information based on the adopted at least one suggested action. Claim 14 merely describes the predicted patient flow comprises a predicted occupancy level or a predicted level of patient arrivals at the hospital. Claim 15 merely describes the time series deviation is an anomaly.
Claim 17 merely describes the deviation is a time series anomaly. Claim 18 merely describes displaying, an indicator indicating that the deviation is expected to be akin to at least one previous hospital event that exhibited similar aberrational data. Claim 19 merely describes displaying a plurality of suggested actions to be taken int the hospital, and using this information to assist the at least one user in modifying the capacity of the hospital such that treatment will be provided to at least one patient currently admitted to the hospital or at least one patient expected to be admitted to the hospital. Claim 20 merely describes adopting the at least one suggested action of the plurality of suggested actions after receiving user input from the at least one user and modifying the hospital capacity information based on the adopted at least one suggested action; and incorporate at least one change to resources in the hospital.
The dependent claims recite a processor, a non-transitory computer readable storage medium, a user interface. The user interface generally links the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
The other additional elements of the dependent claims were considered generic computers or components thereof and were analyzed as were the generic computer(s) and computer component(s) of the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3,7,9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11705240 B2 (hereafter Perry) in view of US 10937543 B1 (hereafter Newton) in view of US 9311449 B2 Levin (Hospital Unit Demand Forecasting Tool).
Regarding Claim 1
Perry teaches:
[…]
receiving or accessing, by the patient flow system, hospital data about the hospital, [Perry teaches at Figure 2 Item 201 receiving a notification indicating a need for medical resource.]
[…]
detecting, by the processor of the patient flow system, a deviation between the predicted patient flow and at least one actual data point from a current patient flow, wherein the deviation exceeds a threshold value; [Perry teaches at column 2 teaches predicting the volume of patient that will present at an ED will be difficult since patients present with a wide spectrum of symptoms, ailments, and the like. Perry teaches at col. 3 line 7 that surge prediction will be estimated. Perry teaches at col. 4 line 18-line 20 that the stream of data can be analyzed to determine if a surge of patients into an emergency department will occur. Col. 4. line 18 teaches that the stream of data can be analyzed for an indication of demand signal, interpreted to be a deviation between the predicted patient flow and at least one actual data point. Perry teaches at col. 7 line 5-11 to determine if the identified need is unavailable, the system will compare the predicted surge level with the currently available, or future availability, of the identified at least one medical resource (e.g., staff, medical pressure monitor, etc.), location resource (e.g., bed, room, etc.). Perry teaches at col. 7 line 13-5 in an embodiment, the system will check the current capacity of a department and compare this capacity with a predicted surge level. The current capacity is the threshold value. The compared capacities are the deviation between the predicted patient flow and at least one actual data point from a current patient flow. Perry teaches at col. 5 line 20-25 teaches that the system will use a historical data to interpreted a symptom or a plurality of symptoms to determine patterns of predictive factors of medical care requirements. Perry teaches at col. 5 line 50-51 teaches the stream of data relating to a set of predictive factors of medical care requirements will be analyzed to predict a demand signal that will be subject to increased demand or unpredicted increased demand.]
[…]
Perry may not explicitly teach:
A method for performing, using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital, comprising: receiving or accessing, by the patient flow system, hospital capacity information about the hospital;
[…]
wherein the hospital data comprises information on patient admissions,
patient discharges,
and patient transfers for a previous period of time for each of a plurality of patient types;
adapting, by a processor of the patient flow system, parameters of a machine learning algorithm based on the hospital data to adapt the machine learning algorithm into an adapted machine learning algorithm;
receiving or accessing, by the patient flow system, clinical information about a plurality of patients currently admitted to the hospital;
determining, by the processor of the patient flow system based on output from the adapted machine learning algorithm and using the clinical information about the plurality of patients currently admitted in the hospital, and the hospital capacity information, a predicted patient flow for the hospital in real-time;
[…]
displaying, on a user interface, the detected deviation for the hospital in real-time to at least one user, wherein the detected deviation is configured to assist the at least one user in modifying a capacity of the hospital;
determining a plurality of suggested rearrangement of resources based on the detected deviation and predicted patient flow;
performing a simulation of each suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement over a period of time, wherein the simulation is periodically adjusted based on the predicted patient flow;
and modifying configuration of a hospital system based on selection of one of the simulated suggested arrangements.
Newton teaches:
[…]
adapting, by a processor of the patient flow system, parameters of a machine learning algorithm based on the hospital data to adapt the machine learning algorithm into an adapted machine learning algorithm; [Newton at col. 4 line 25-27 teaches that processors within the healthcare command center will collect and analyze data to provide situation awareness indicators and that the system will collect data pertaining to indicator of flow of patients through a healthcare venue, system, or systems. Newton teaches at col. 13 line 477-51 that using machine learning and/or other artificial intelligence methods, the processors will generate projects of demands on each of the units and will offer suggestions to a user in response to these projections. Newton teaches at col. 4 line 33-36 data collection and analysis provides predictive and cognitive analytics about future trends and events within a healthcare venue or systems. Newton teaches at col. 13 line 47-51 machine learning and/or other artificial intelligence methods, the processors will generate projections of demands on each of the units and will offer suggestions to a user in response to these projections.]
receiving or accessing, by the patient flow system, clinical information about a plurality of patients currently admitted to the hospital; [Newton at col. 4 line 25-27 teaches that processors within the healthcare command center will collect and analyze data to provide situational awareness indicators and that the system will collect data pertaining to indicator of flow of patients through the healthcare venue, system or systems.]
determining, by the processor of the patient flow system based on output from the adapted machine learning algorithm and using the clinical information about the plurality of patients currently admitted to the hospital; [Newton teaches at col. 13 line 47-51 using machine learning and/or other artificial intelligence methods to generate projections of demands on each of the unit. Newton teaches at col. 13 line 44-47 teaches the healthcare demand center processors analyze data to determine current demand on specialty service as well as the available capacities of each unit. Newton teaches at col. 12 line 11-13 use of information including historical data about the frequency of calls and the frequency of admission.]
[…]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the response to emergency department surge prediction of Perry to the systems and methods for predictive and automated and centralized real-time event detection and communication of Newton with the motivation of improving outcomes for patients, hospitals, caregivers and payors (Newton col. 1 line 42-43).
Newton may not explicitly teach:
A method for performing, using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital, comprising: receiving or accessing, by the patient flow system, hospital capacity information about the hospital;
[…]
wherein the hospital data comprises information on patient admissions,
patient discharges,
and patient transfers for a previous period of time for each of a plurality of patient types;
[…]
displaying, on a user interface, the detected deviation for the hospital in real-time to at least one user, wherein the detected deviation is configured to assist the at least one user in modifying a capacity of the hospital;
determining a plurality of suggested rearrangement of resources based on the detected deviation and predicted patient flow;
performing a simulation of each suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement over a period of time, wherein the simulation is periodically adjusted based on the predicted patient flow;
and modifying configuration of a hospital system based on the selection of one of the simulated suggested arrangements.
Levin teaches:
A method for performing, using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital, comprising: receiving or accessing, by the patient flow system, hospital capacity information about the hospital; [Levin teaches at col. 5 line 54-58 a method of forecasting a demand for a particular hospital unit includes executing an algorithm designed to use capacity, total inflow and total outflow to determine the demand for a particular unit. Levin teaches at col. 6 line 8-9 teaches that the present invention includes a near real-time PICU resource demand forecasting model that forecasts demand for nurse staffing and bed resources up to 72 hour into the future to ultimately (1) improve daily decision making by reducing uncertainty in PICU system state projections, (2) improve coordination across the children’s hospital system and (3) facilitate proactive interventions to eliminate bottlenecks, avoid underutilization, and improve access. Levin teaches at claim 10 the method of claim 1 further comprising predicting the stochastic arrivals using a feedback mechanisms whereby the probability of stochastic arrival being admitted to the particular hospital unit is a function of difference between forecasted demand and available capacity. Levin teaches at col. 7 line 19-2 flow equations are used to mathematically describe the relationship between each component model and how they will work in concert to forecast nursing care and bed demand. Levin teaches at col. 6, line 57-60 teaches that conversely, nurses will be pulled from the schedule when demand is expected to be low. Collectively, this teaches using a patient flow system, a demand analysis for a hospital to optimize a flow of patients within the hospital, comprising: receiving or accessing, by the patient flow system, hospital capacity information about the hospital.]
[…]
wherein the hospital data comprises information on patient admissions, [Levin teaches temporal variables will be included as predictor variables to account for patterns in admissions and discharges in future inpatient units. Levin teaches at Figure 4B stochastic arrivals decomposed by admissions source, interpreted to be the hospital data comprises information on patient admissions.]
patient discharges, [Levin teaches temporal variables will be included as predictor variables to account for patterns in admissions and discharges in future inpatient units.]
and patient transfers for a previous period of time for each of a plurality of patient types; [Levin teaches that transfer type admissions will be predicted because facilities may lack available capacity.]
[…]
displaying, on a user interface, the detected deviation for the hospital in real-time to at least one user, wherein the detected deviation is configured to assist the at least one user in modifying a capacity of the hospital; [Levin teaches at col. 13 line mean absolute deviation (MAD) the mean absolute deviation (MAD) to assess accuracy of PICU-forecast at each future time point. Levin teaches at col. 15 line 24-28 teaches preliminary discrete time logistic regression models were displayed to the expert panel in an iterative process to further refine order selection and grouping.]
determining a plurality of suggested rearrangement of resources based on the detected deviation and predicted patient flow; [Levin teaches at col. 5 line 54-58 a method of forecasting a demand for a particular hospital unit includes executing an algorithm designed to use capacity, total inflow and total outflow to determine the demand for a particular unit. Levin teaches at col. 6 line 8-9 teaches that the present invention includes a near real-time PICU resource demand forecasting model that forecasts demand for nurse staffing and bed resources up to 72 hour into the future to ultimately (1) improve daily decision making by reducing uncertainty in PICU system state projections, (2) improve coordination across the children’s hospital system and (3) facilitate proactive interventions to eliminate bottlenecks, avoid underutilization, and improve access. Levin teaches at claim 10 the method of claim 1 further comprising predicting the stochastic arrivals using a feedback mechanisms whereby the probability of stochastic arrival being admitted to the particular hospital unit is a function of difference between forecasted demand and available capacity. Levin teaches at col. 7 line 19-2 flow equations are used to mathematically describe the relationship between each component model and how they will work in concert to forecast nursing care and bed demand. Levin teaches at col. 6, line 57-60 teaches that conversely, nurses will be pulled from the schedule when demand is expected to be low. Collectively, this teaches determining a plurality of suggested rearrangement of resources based on the detected deviation and predicted patient flow.]
performing a simulation of each suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement over a period of time, wherein the simulation is periodically adjusted based on the predicted patient flow; [Levin teaches at col. 7 line 19-21 that flow equations are used to mathematically describe the relationship between each component model and how they will work in concern to forecast nursing care and bed demand. Levin teaches at col. 7 line 4-5 that the method also projects expected demand for nursing and bed resources up to 72 hours in the future. Levin teaches at col. 6, line 57-60 teaches that conversely, nurses will be pulled from the schedule when demand is expected to be low. Levin teaches at Figure 8 visualizing a graph with forecast horizon in hours on the x axis and cumulative probability of discharge on the y axis. This is interpreted as visualizing a corresponding impact of adopting the suggested (taught below) rearrangement over a period of time. Levin teaches at col. 7 line 5-10 that PICU-forecast uses patient information that is available electronically in real-time to continually updated and forecast as time progresses. Levin teaches at col. 7 line 8-10 that the PICU-forecast includes sub-models describing the PICU current state, expected inflow (i.e., arrivals), and expected outflow (i.e., departures). Collectively, Levin teaches performing a simulation of each suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement to determine and visualize a corresponding impact of adopting the suggested rearrangement over a period of time, wherein the simulation is periodically adjusted based on the predicted patient flow.]
and modifying configuration of a hospital system based on the selection of one of the simulated suggested arrangements. [Levin teaches at col. 7 line 19-21 that flow equations are used to mathematically describe the relationship between each component model and how they will work in concern to forecast nursing care and bed demand. Levin teaches at col. 7 line 4-5 that the method also projects expected demand for nursing and bed resources up to 72 hours in the future. Levin teaches at col. 6, line 57-60 teaches that conversely, nurses will be pulled from the schedule when demand is expected to be low. This teaches and modifying configuration of a hospital system based on the selection of one of the simulated suggested rearrangements.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the response to emergency department surge prediction of Perry to the systems and methods for predictive and automated and centralized real-time event detection and communication of Newton to the hospital unit demand forecasting tool of Levin with the motivation of meeting the steady increases in demand for hospital services (Levin, col. 1, line 31-33).
Regarding Claim 10 and 16
Due to their similarity to Claim 1, Claim(s) 10 and 16 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Regarding Claim 2
Perry/Newton/Levin teach the method of claim 1. Perry/Newton/Levin further teach:
wherein the predicted patient flow comprises a predicted occupancy level or a predicted level of patient arrivals at the hospital. [Perry teaches at column 2 teaches predicting the volume of patient that will present at an ED will be difficult since patients present with a wide spectrum of symptoms, ailments, and the like.]
Regarding Claim 3
Perry/Newton/Levin teach the method of claim 1. Perry/Newton/Levin further teach:
wherein the deviation is a time series anomaly. [Levin teaches at Figure 4A a time series analysis shows anomaly deviations in terms of weekly PICU admissions.]
Regarding Claim 15 and 17
Due their similarity to Claim 3, Claim(s) 15 and 17 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 7
Perry/Newton/Levin teach the method of claim 1. Perry/Newton/Levin further teach:
further comprising: displaying, on the user interface to the at least one user, an indicator indicating that the deviation is expected to be akin to at least one previous hospital event that exhibited similar aberrational data. [Newton teaches at col. 14 line 10-12 the change in indicators causes the healthcare command system to generate alternative transportation plans for prospective patient. Newton teaches at col .14 line 18-21 that a healthcare command center will calculate wellness indices for all patient in a healthcare venue and will record and track changes in indices over time. Newton teaches at col. 14 line 34 that index data will be displayed in real time.]
Regarding Claim 15 and 17
Due their similarity to Claim 7, Claim(s) 11 and 18 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Regarding Claim 8
Perry/Newton/Levin teach the patient flow system of claim 11. Perry/Newton/Levin further teach:
comprising: displaying, on the user interface to the at least one user, a plurality of suggested actions to be taken in the hospital as a displayed plurality of suggested actions, [Levin at Figure 7 teaches displaying a list of the intervening actions taken on a graph of discharge probability.]
wherein the displayed plurality of suggested actions is configured to assist the at least one user in modifying the capacity of the hospital such that treatment can be provided to at least one patient currently admitted to the hospital or at least one patient expected to be admitted to the hospital. [Levin at Figure 7 teaches displaying a list of the intervening actions taken on a graph of discharge probability. This teaches displaying a plurality of suggested actions configured to assist the at least one user in modifying the capacity of the hospital such that treatment can be provided to at least one patient currently admitted to the hospital.]
Regarding Claim 12 and 19
Due to their similarity to Claim 8, Claim(s) 12 and 19 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Regarding Claim 9
Perry/Newton/Levin teach the method of claim 8. Perry/Newton/Levin further teach:
further comprising: adopting at least one suggested action of the plurality of suggested actions after receiving user input from the at least one user at the user interface; [Perry teaches at Figure 3 Item 350 input devices for receiving user input. Perry teaches at Figure 2 Item 201 receiving a notification indicating a need for medical resource. Perry teaches at Figure 2 Item 204 identifying at least one action to supply the identified and at Item 205 performing the at least one action.]
modifying the hospital capacity information based on the adopted at least one suggested action; [Perry teaches col. 7 line 12-13 the system will check the current capacity of a department and compare this capacity with a predicted surge level. Perry teaches at col. 7 line 14-15 the system will check scheduling of patient care and/or staff. Perry teaches at col. 7 line 29-31 if a demand signal indicates that more staff is necessary then the system will alert the healthcare facility. Perry teaches at col. 7 line 33-26 the system will, in reaction to the demand signal, identify at least one action that could be performed to supply the identified need and will subsequently perform one or more of the identified actions at 205.]
and incorporating at least one change to resources in the hospital. [Perry teaches col. 7 line 12-13 the system will check the current capacity of a department and compare this capacity with a predicted surge level. Perry teaches at col. 7 line 14-15 the system will check scheduling of patient care and/or staff. Perry teaches at col. 7 line 29-31 if a demand signal indicates that more staff is necessary then the system will alert the healthcare facility. Perry teaches at col. 7 line 33-26 the system will, in reaction to the alert, identify at least one action that could be performed to supply the identified need and will subsequently perform one or more of the identified actions at 205.]
Regarding Claim 20
Due to its similarity to Claim 9, Claim(s) 20 is similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9.
Regarding Claim 13
Perry/Newton/Levin teach the patient flow system of claim 12. Perry/Newton/Levin further teach:
wherein the processor is further configured (i) adopt at least one suggested action of the plurality of suggested actions after receiving user input from the at least one user at the user interface; [Levin at Figure 7 teaches displaying a list of the intervening actions taken on a graph of discharge probability. The displayed actions are interpreted as adopted actions.]
and (ii) modify the hospital capacity information based on the adopted at least one suggested action. [Levin teaches at col. 5 line 54-58 a method of forecasting a demand for a particular hospital unit includes executing an algorithm designed to use capacity, total inflow and total outflow to determine the demand for a particular unit. Levin teaches at col. 6 line 8-9 teaches that the present invention includes a near real-time PICU resource demand forecasting model that forecasts demand for nurse staffing and bed resources up to 72 hour into the future to ultimately (1) improve daily decision making by reducing uncertainty in PICU system state projections, (2) improve coordination across the children’s hospital system and (3) facilitate proactive interventions to eliminate bottlenecks, avoid underutilization, and improve access. Levin teaches at col 5. line 54-58 logging a total number of beds in the particular hospital unit and available nursing slots to determine a capacity for the particular hospital unit. Thus the alteration of nurse staffing by the system ultimately modifies the hospital capacity information based on the adopted at least one suggested action.]
Regarding Claim 14
Perry/Newton/Levin teach the patient flow system of claim 10. Perry/Newton/Levin further teach:
the predicted patient flow comprises a predicted occupancy level or a predicted level of patient arrivals at the hospital. [Perry at column 2 teaches predicting the volume of patient that will present at an ED will be difficult since patients present with a wide spectrum of symptoms, ailments, and the like.]
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11705240 B2 (hereafter Perry) in view of US 10937543 B1 (hereafter Newton) further in view of US 9311449 B2 (hereafter Levin) further in view of Cerqueira (Early Anomaly Detection in Time Series).
Regarding Claim 4
Perry/Newton/Levin teach the method of claim 3. Perry/Newton/Levin may not explicitly teach:
wherein the step of detecting the time series anomaly involves a clustering based unsupervised approach.
Cerqueira teaches:
wherein the step of detecting the time series anomaly involves a clustering based unsupervised approach. [Cerqueira teaches at pg. 15 Isolation Forest, a state of the art unsupervised model based approach to anomaly detection. Cerqueira teaches that Isolation Forest is a profiling technique includes creating paths resulting from partitioning the data (i.e. teaches “clustering” of data).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the response to emergency department surge prediction of Perry to the systems and methods for predictive and automated and centralized real-time event detection and communication of Newton to the hospital unit demand forecasting tool of Levin to the early anomaly detection in time series of Cerqueira with the motivation of better predicting AHE and TE events (Cerqueira pg. 2).
Regarding Claim 5
Perry/Newton/Levin teach the method of claim 3. Perry/Newton/Levin may not explicitly teach:
wherein the step of detecting the time series anomaly involves a statistical profiling approach or clustering based unsupervised approach.
Cerqueira teaches:
wherein the step of detecting the time series anomaly involves a statistical profiling approach or clustering based unsupervised approach. [Cerqueira teaches at the Abstract detection of anomalous events in time series data, an application that is essential in many domains. Cerqueira teaches using the observation window of each subsequence and of each physiological signal, carried out using statistical functions includes skewness, kurtosis, slope, median, minimum, maximum, variance, mean, standard deviation, and inter-quartile range. Cerqueira’s teachings are such that the encompass the use of statistical profiling to ultimately detect the time series anomaly.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the response to emergency department surge prediction of Perry to the systems and methods for predictive and automated and centralized real-time event detection and communication of Newton to the hospital unit demand forecasting tool of Levin to the early anomaly detection in time series of Cerqueira with the motivation of better predicting AHE and TE events (Cerqueira pg. 2).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11705240 B2 (hereafter Perry) in view of US 10937543 B1 (hereafter Newton) further in view of US 9311449 B2 (hereafter Levin) in view of US 2016/0371441 A1 (hereafter Day).
Regarding Claim 6
Perry/Newton/Levin teach the method of claim 3. Perry/Newton/Levin may not explicitly teach:
wherein modifying configuration of a hospital system based on selection of the simulated suggested arrangements comprises one or more of adjusting staffing in one or more locations of the hospital system,
and accelerating discharge of one or more patients in the hospital system.
Day teaches:
wherein modifying configuration of a hospital system based on selection of the simulated suggested arrangements comprises one or more of adjusting staffing in one or more locations of the hospital system, [Day teaches at para. [0061] certain aspects include functions such as admitting, bed management, scheduling, staffing, environmental systems, transport dispatchers and other related functions, for example. Day teaches at para. [0063] after being parsed, transformed, and integrated into derived entities 306, calculations are performed by multiple purpose analytics engine 308 and simulation based hospital operations prediction system 310 to identify states that indicate how patient flow is currently occurring or will occur forecasted in the future. Day teaches at para. [0138] the example control system additionally includes triggering the one or more mitigating actions with the respect to the plurality of clinical protocols; and monitoring effects of the mitigating actions and adjust resource assignments based on the effects of the mitigating actions. Day teaches at para. [0067] the resources of activities will be in one or more departments of a hospital, located in a single connected building, a campus or multiple facilities such as affiliated clinicians and a patient’s monitored home location. Collectively, this teaches wherein modifying configuration of a hospital system based on selection of the simulated suggested arrangements comprises one or more of adjusting staffing in one or more locations of the hospital system.]
and accelerating discharge of one or more patients in the hospital system. [Day teaches as an example, command center engine 302 will use cross-system data, forecasts and probabilistic alerts to automatically and optimally control patient discharge and environmental services activity to better serve patients and improve hospital operational performance, such as reducing waiting delays for procedures and accelerating discharges.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the response to emergency department surge prediction of Perry to the systems and methods for predictive and automated and centralized real-time event detection and communication of Newton to the hospital unit demand forecasting tool of Levin to the system-wide probabilistic altering and activation of Day with the motivation of providing system-wide probabilistic alerting and activation that automatically controls the resources and conditionally allocates capacity across departments to achieve a minimal wait state through an interval of time (Day at para. [0001]).
Response to Arguments
Applicant argues that the claims as a whole are not directed to an abstract idea, and further asserts that the claims comprise a practical application and amount to significantly more than the abstract idea, and thus are directed to statutory subject matter under 35 U.S.C. 101
Each of these assertions has been responded to below in due course where Applicant provided specific argumentative details.
Applicant argues that the claims as a whole are not directed to the abstract idea of certain methods of organizing human activity or mathematical concepts.
The Examiner disagrees. The Examiner furthermore respectfully acknowledges that the mere mention of generalized machine learning is not necessarily reciting mathematical concepts and is taking this opportunity to qualify. The Examiner would like to clarify that it was adapting the parameters of the machine learning algorithm that was determined to be mathematical concepts.
In short, it depends on the actual claim limitation. If, in addition to the fact that the claim is describing the general process of “adapting…parameters of a machine learning algorithm based on the hospital data to adapt the machine learning algorithm into an adapted machine learning algorithm;…” the Specification goes into great details about the process of adapting parameters for machine learning, involving specific named mathematical techniques then it becomes harder to argue against the categorization of the abstract idea as being a mathematical process. This happens to be the case with the instant application.
Further, an independent claim limitation literally recites “adapting, by a processor of the patient flow system, parameters of a machine learning algorithm based on the hospital data;…” and then recites various levels of mathematical processes related to adapting parameters of a machine learning algorithm in the Specification.
For example, the Specification at para. [0006] teaches the present disclosure is directed at inventive methods and systems for predicting a patient flow for a hospital in real-time using modeling techniques that automatically adapt over time and take into consideration patient type-specific data in calibrating model parameters. The Specification at para. [0049] indicates that the length of stay model 130 will be based on an inverted empirical cumulative distribution function (CDF) model. The Specification at para. [0054] which recites that the predicted arrival counts will be computed using the specific equation recited therein. The Specification at para. [0059] teaches that the trajectory of the patients through the hospital, and optionally between wards or units of a hospital, is achieved with machine learning based algorithms such as kernel based methods such a K-nearest neighbor (KNN) classifiers and support vector machine (SVM) classifiers among others. Next sentence, at Specification para. [0059] indicates that patient similarity on a multitude of parameters forms an important part of the clustering algorithms. The Specification teaches at para. [0072] that the clustering involves an unsupervised machine learning task while the predictive confidence level approach involves supervised learning. Lastly, the Specification at para. [0084] teaches at step 706 of the method, a processor of the patient flow system adapts parameters of a machine learning algorithm based on the hospital data. Thus, taken collectively, the broadest reasonable interpretation of the limitation describing adjusting the parameters of a machine learning algorithm based on hospital data encompasses a mathematical concept.
Applicant argues that the claims as a whole are not directed to the abstract idea of certain methods of organizing activity or mathematical concepts
The Examiner respectfully disagrees. In the response above the Examiner has demonstrated that Applicant is reciting at least one limitation directed to a mathematical process. Furthermore, the remaining claim limitations are overwhelmingly directed to a certain method of organizing human activity because they are literally directed to modeling patient flow and adjusting various implementations, such as those impacting hospital capacity. The independent claim ends