DETAILED ACTION
Status
This communication is in response to the application filed on 4 June 2024. Claims 1-20 are pending and presented for examination.
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 .
Priority
Applicant’s claim for the benefit under 35 U.S.C. 119(e) to U.S. Provisional Application No. 63/583,157, filed on 15 September 2023, is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4 June 2024 was filed after the mailing date of the application on 4 June 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1-9), system (claims 10-15), and a computer-program product tangibly embodied in a non-transitory computer-readable medium, which is interpreted to be a claim to the medium itself (claims 16-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a computer-implemented method comprising: receiving an input dataset from one or more data sources for a subject of a set of subjects; detecting an outreach communication that has occurred or is scheduled to occur for the subject, wherein the outreach communication includes a recommendation that the subject seeks medical care at a particular medical facility; extracting a set of features from the input dataset; generating a derived feature from one or more features of the set of features; predicting a likelihood of the subject seeking care at the particular medical facility within a predefined time period by processing the extracted set of features and derived features using a machine-learning model; determining an upcoming resource demand at the particular medical facility based on the predicted likelihoods of the subjects seeking care at the particular medical facility; detecting that the predicted upcoming resource demand exceeds a threshold; and generating an output with a recommended action related to the particular medical facility in response to detecting that the predicted upcoming resource demand exceeds the threshold.
Independent claims 10 and 16 are analyzed in the same manner as claim 1 above since claim 10 is directed to a system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including the same or similar activities as at claim 1, and claim 16 is directed to a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform action including the same or similar activities as at claim 1.
The dependent claims (claims 2-9, 11-14, and 17-20) appear to be encompassed by the abstract idea of the independent claims since they merely indicate a feature being a prediction that critical medical care is sought (claims 2, 11, and 17), a feature being an estimated total of ER visits within a time period (claims 3, 12, and 18), a feature being whether the subject has a precondition, comorbidity, hospital admissions, or a statistic characterizing hospital admission length of stay (claim 4), the machine learning being Adaboost, an ensemble, self-learning, or a classifier sub-model (claims 5, 14, and 19), the threshold being specific to the facility based on resource allocation (claim 6), generating an updated resource schedule based on time slots and recommending implementing the updated schedule (claims 7, 15, and 20), the recommended action being to adjust subsequent communications to recommend the particular medical facility or another medical facility (claim 8), and/or the particular medical facility being a hospital department (claim 9).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of tracking medical facility outreach communications and responses in order to predict resource demand; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as … advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) based on the outreach communications (which are advertising and/or marketing activities – see Applicant ¶ 0043: “contact[ ] from the medical facility for a follow-up visit, for a test or treatment etc.”), tracking responses (including at the dependent claims – such as care being sought, visits, admissions, etc.), predicting resource demand (i.e., workload), and recommending demand response; and
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) based in part on the likelihood prediction and use of a machine learning model;
Applicant’s specification appears to literally indicate the traditional approach is to perform human analysis, where the application embodiments are that “a computer-implemented method is provided to determine resource usage prediction using machine learning models” – i.e., using computer implementation and mathematical models to perform or replace essentially the same human analysis. See Applicant ¶¶ 0003 and 0006.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the method being computer-implemented (at claim 1), using a system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions (at claim 1), and a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform action (at claim 16). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. Applicant ¶ 0116 indicates “The subject devices may include various types of computing systems such as portable handheld devices, general purpose computers such as personal computers and laptops, workstation computers, wearable devices, gaming systems, thin subjects, various messaging devices, sensors or other sensing devices, and the like” and ¶ 0118 indicates that “Server 1030 may include one or more general purpose computers, specialized server computers (including, by way of example, PC (personal computer) servers, UNIX® servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination”.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 and 10-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Almashor et al. (U.S. Patent Application Publication No. 2020/0320454, hereinafter Almashor) .
Claim 1: Almashor discloses a computer-implemented method comprising:
receiving an input dataset from one or more data sources for a subject of a set of subjects (see Almashor at least at, e.g., ¶¶ 0005, “receiving facility data comprising historical facility data and current facility data. The method also includes receiving demand data. The method also includes determining projected resource demand based on the facility data and demand data based on the facility data and the demand data using cognitive computing technique”, 0065, “the system may aid a nurse in guiding a patient from room to room within a hospital based on a sequence of tests the patient needs”, 0070, “Current resource demand can include, for example, information that is known about how many patients are waiting to be seen, what their conditions are, and what resources are expected to be needed in treating the patient”, 0071, “demand data may include or be derived from data received from one or more vehicle(s) 440. For example, ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle. For example, the system may receive data from a vehicle 440 that indicates that the patient is having an asthma attack and the vehicle will arrive at the hospital in 15 minutes, and from this received data the system may determine that there will be additional demand in 15 minutes for various resources (e.g., staff, nurses, doctors, rooms, medical equipment, prescription medicine, etc.) associated with treating this patient for this condition”; citation hereafter by number only) ;
detecting an outreach communication that has occurred or is scheduled to occur for the subject, wherein the outreach communication includes a recommendation that the subject seeks medical care at a particular medical facility (0061, “A procedural plan can also include a list of steps to be followed by a user of the system, such as for example, taking a patient to various locations in a facility to carry out various tasks (e.g., taking vitals, obtaining x-rays, drawing blood, etc.)”, 0070, “Current resource demand can include, for example, information that is known about how many patients are waiting to be seen, what their conditions are, and what resources are expected to be needed in treating the patient”, 0071, “demand data may include or be derived from data received from one or more vehicle(s) 440. For example, ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle. For example, the system may receive data from a vehicle 440 that indicates that the patient is having an asthma attack and the vehicle will arrive at the hospital in 15 minutes, and from this received data the system may determine that there will be additional demand in 15 minutes for various resources (e.g., staff, nurses, doctors, rooms, medical equipment, prescription medicine, etc.) associated with treating this patient for this condition”);
extracting a set of features from the input dataset (0066, “if the system detects that John Doe has entered a hospital, the resource plan visualization server 410 may predict the resources John Doe will need based on the previous visits made by John Doe”, 0070, “Current resource demand can include, for example, information that is known about how many patients are waiting to be seen, what their conditions are, and what resources are expected to be needed in treating the patient”, 0071, “ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle”);
generating a derived feature from one or more features of the set of features (0050, “a report aggregates and averages rounding times of hundreds or thousands of nurses throughout a facility”, 0071, “demand data may include or be derived from data received from one or more vehicle(s) 440. For example, ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle. For example, the system may receive data from a vehicle 440 that indicates that the patient is having an asthma attack and the vehicle will arrive at the hospital in 15 minutes, and from this received data the system may determine that there will be additional demand in 15 minutes for various resources (e.g., staff, nurses, doctors, rooms, medical equipment, prescription medicine, etc.) associated with treating this patient for this condition”);
predicting a likelihood of the subject seeking care at the particular medical facility within a predefined time period by processing the extracted set of features and derived features using a machine-learning model (0066, 0070, 0071, as above);
determining an upcoming resource demand at the particular medical facility based on the predicted likelihoods of the subjects seeking care at the particular medical facility (0005, “receiving facility data comprising historical facility data and current facility data. The method also includes receiving demand data. The method also includes determining projected resource demand based on the facility data and demand data based on the facility data and the demand data using cognitive computing technique”);
detecting that the predicted upcoming resource demand exceeds a threshold (0062, “In some embodiments, rooms or portions of a facility may be associated with different colors, icons or images that denote different information. For example, in a zoomed out view of a hospital, each ward may be displayed as a box having a circular dot for every nurse in the ward and a rectangle for every bed. In some embodiments, colors can be used to convey additional information. For example, a red dot or a red rectangle may represent a deficiency of nurses or beds, respectively. Similarly, the boxes associated with wards may have different colors based on whether the ward meets a desired threshold (e.g., a ratio of nurses to beds, a maximum rounding time, etc.), allowing a user to quickly make a visual assessment and comparison of wards to one another. The desired thresholds and/or shapes and colors associated with virtual content may be configurable by a user”; and
generating an output with a recommended action related to the particular medical facility in response to detecting that the predicted upcoming resource demand exceeds the threshold (0060, “Generation of the resource plan can include predicting future demand, generating recommendations for modifications to available resources (e.g., a recommendation to buy more beds, schedule more staff to work, etc.)”, 0062, “rooms or portions of a facility may be associated with different colors, icons or images that denote different information. For example, in a zoomed out view of a hospital, each ward may be displayed as a box having a circular dot for every nurse in the ward and a rectangle for every bed. In some embodiments, colors can be used to convey additional information. For example, a red dot or a red rectangle may represent a deficiency of nurses or beds, respectively. Similarly, the boxes associated with wards may have different colors based on whether the ward meets a desired threshold (e.g., a ratio of nurses to beds, a maximum rounding time, etc.), allowing a user to quickly make a visual assessment and comparison of wards to one another. The desired thresholds and/or shapes and colors associated with virtual content may be configurable by a user”).
Claim 2: Almashor discloses the computer-implemented method of claim 1, wherein a feature of the set of features indicates a prediction that critical medical care is sought by the subject (0071, “demand data may include or be derived from data received from one or more vehicle(s) 440. For example, ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle. For example, the system may receive data from a vehicle 440 that indicates that the patient is having an asthma attack and the vehicle will arrive at the hospital in 15 minutes, and from this received data the system may determine that there will be additional demand in 15 minutes for various resources (e.g., staff, nurses, doctors, rooms, medical equipment, prescription medicine, etc.) associated with treating this patient for this condition”).
Claim 3: Almashor discloses the computer-implemented method of claim 1, wherein a feature of the set of features indicates an estimated total number of emergency-room visits that the subject has had within a defined time period or across a life of the subject (0079, “Facility data can include historical and current data such as for example, historical facility data (e.g., historical records regarding resources, supplies, demand, etc.), historical data of other facilities, historical and current data from facility sensors 430, vehicles 440 and external data servers 450, and other such data. For example, in the context of a medical facility, data received or accessed by the cognitive system 606 can include historical surgery data, physiological historical data for patients, electronic medical records, social media data of patients or other local users, surgeon or other medical staff data, and sensor data” – the historical data and electronic medical records including ER visits, 0048 as indicating data from, and a projection for, three month and one month time periods, “facility sensors 430 detect a large influx of patients into an emergency room of the facility or a number of ambulances (i.e., vehicles 440) provide data indicating an influx of new patients”).
Claim 4: Almashor discloses the computer-implemented method of claim 1, wherein a feature of the set of features indicates whether the subject has one or more preconditions, one or more comorbidities, a count or statistic as to a number of times that the subject has been admitted into a hospital or a statistic characterizing a length of stay of one or more hospital admissions (0067, “vehicles 440 (e.g., ambulances, delivery trucks, etc.), may include sensors and may intermittently provide data to resource plan visualization server 410 that can be used to forecast demand, such as the identification of an incoming patient, the patient's condition, and the location and/or expected time of arrival of the vehicle”, 0071, “ambulances may transmit information about one or more patients in transit, the condition of the patient(s), the location of the vehicle, the destination of the vehicle and the estimated time of arrival of the vehicle. For example, the system may receive data from a vehicle 440 that indicates that the patient is having an asthma attack and the vehicle will arrive at the hospital in 15 minutes”).
Claim 5: Almashor discloses the computer-implemented method of claim 1, wherein the machine-learning model includes an Adaboost model, an ensemble model, self-learning model or one or more classifier sub-models (Almashor at 0055, unsupervised training – i.e., self-learning).
Claim 6: Almashor discloses the computer-implemented method of claim 1, wherein the threshold is specifically identified for the particular medical facility based on current resource allocations and/or scheduled resource allocations (0060, “Generation of the resource plan can include predicting future demand, generating recommendations for modifications to available resources (e.g., a recommendation to buy more beds, schedule more staff to work, etc.)”, 0062, “rooms or portions of a facility may be associated with different colors, icons or images that denote different information. For example, in a zoomed out view of a hospital, each ward may be displayed as a box having a circular dot for every nurse in the ward and a rectangle for every bed. In some embodiments, colors can be used to convey additional information. For example, a red dot or a red rectangle may represent a deficiency of nurses or beds, respectively. Similarly, the boxes associated with wards may have different colors based on whether the ward meets a desired threshold (e.g., a ratio of nurses to beds, a maximum rounding time, etc.), allowing a user to quickly make a visual assessment and comparison of wards to one another. The desired thresholds and/or shapes and colors associated with virtual content may be configurable by a user”).
Claim 7: Almashor discloses the computer-implemented method of claim 1, further comprising, in response to detecting that the predicted upcoming resource demand exceeds the threshold: generating a proposed updated schedule that assigns resources to time slots associated with the particular medical facility, wherein the proposed updated schedule proposes adding a new resource to one or more time slots associated with the particular medical facility or proposes extending at least one time slot currently assigned to a given resource; wherein the recommended action is to authorize and implement the proposed updated schedule (0060, “generating recommendations for modifications to available resources (e.g., a recommendation to buy more beds, schedule more staff to work, etc.), and generating instructions for providing a visualization of the resource plan and/or procedural plan”).
Claims 10-20 are rejected on the same basis as claims 1-3, 5, and 7 above since Almashor discloses a system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions the same as, or similar to, the activities at claims 1-3, 5, and 7 (for claims 10-15), and a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform action including activities that are the same as, or similar to, the activities at claims 1-3, 5, and 7 (for claims 16-20) (See Almashor at 0004).
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Almashor in view of Day et al. (U.S. Patent Application Publication No. 2021/0193302 hereinafter Day) .
Claim 8: Almashor discloses the computer-implemented method of claim 1, but does not appear to explicitly disclose wherein the recommended action is to adjust a subsequent outreach communication from recommending that another subject seek medical care at the particular medical facility to recommending that other subject seek medical care at a different medical facility. However, Almashor indicates that “The requirements data 604 received or accessed by the cognitive system 606 can include restrictions or constraints to be applied by the cognitive system 606 when generating a procedural plan. For example, in the context of a procedural plan for performing a surgery, the restrictions can include one or more of: the number of patients should not exceed the number of available beds in the ward, surgeons and other staff required for a procedure should be available at the time of a scheduled procedure, equipment for pre-operation, operation and post operations should be available for the procedure, clinically patients should be treated in a specified amount of time based on the procedure and/or condition, hospital targets should be met or optimized, and surgeon preference should be considered by the system. Based on the facility data and the requirements data, the cognitive system 606 can utilize a demand prediction module 608 to predict demand of the available resources and a procedural plan generation module 610 to generate a procedural plan (e.g., plan for surgery) and any associated recommendations” (see Almashor at 0079) and a medical facility may be a department or ward per the light of Applicant’s specification (see at least Applicant ¶¶ 0025, 0038, 0041); therefore, it would appear fully within the reasonable interpretation of Almashor to include transferring, moving, or recommending a patient be placed in another facility or department, such as moving a patient from an intensive care unit (ICU) to a regular room based on demand and availability. However, Almashor does not appear to specifically discuss such transfers or moves. Day, though, teaches “resource demand forecasts 138 regarding forecasted demand for resources (e.g., staff, beds, equipment, supplies, etc.) at the medical facility system with respect to time (e.g., when the resources will be needed), amount (e.g., number of resources that will be needed), type (e.g., type of staff with respect to role/qualifications, type of medical supplies/equipment, etc.), and location (e.g., where the resources will be needed). With these embodiments, the forecasting component 108 can include resource demand forecasting component 114 to forecast the future resource demands using one or more machine learning/AI techniques based on the current state data 102, the historical state data 130, and/or relevant information included the medical facility system data 132 that can influence and/or control the amount, type, timing and location of resources needed by the dynamic medical facility system” (Day at 0076), including “real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process” (Day at 0031), where “The patient may then be moved to the holding room prior to entering the interoperative phase areas (e.g., the operating room and/or the IR room) or moved directly from the floor, ER or ICU to the interoperative phase areas. From the operating room, the patient can be transferred to the PACU or ICU if necessary. Following recovery in the PACU, the patient will then be moved back to the floor for post-op recovery” (Day at 0037) (PACU being “post-anesthesia care unit” per Day at 0036), using “a perioperative system, maximining patient flow can include maximizing the number of patients moving through the system and receiving the surgery over a defined time frame, such as the entire workday, the peak hours (e.g., 8:00 am to 4:30 pm), or another defined time frame” (Day at 0066). Therefore, the Examiner understands and finds that to recommend another facility, such as a different ward, is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to optimally place and sequence arriving and transitioning patients.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the recommendations of Almashor with the transferring of Day in order to recommend another facility, such as a different ward, so as to optimally place and sequence arriving and transitioning patients.
The rationale for combining in this manner is that to recommend another facility, such as a different ward, is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to optimally place and sequence arriving and transitioning patients as explained above.
Claim 9: Almashor discloses the computer-implemented method of claim 1, wherein the particular medical facility is a first department in a hospital, and the recommended action is to reassign at least one resource from a second department in the hospital to the first department for a specified time period. However, Almashor indicates wards as departments (Almashor at 0062), where a medical facility may be a department or ward per the light of Applicant’s specification (see at least Applicant ¶¶ 0025, 0038, 0041); therefore, it would appear fully within the reasonable interpretation of Almashor to include transferring, moving, or recommending a patient be placed in another facility or department, such as moving a patient from an intensive care unit (ICU) to a regular room based on demand and availability. However, Almashor does not appear to specifically discuss such transfers or moves. Day, though, teaches “resource demand forecasts 138 regarding forecasted demand for resources (e.g., staff, beds, equipment, supplies, etc.) at the medical facility system with respect to time (e.g., when the resources will be needed), amount (e.g., number of resources that will be needed), type (e.g., type of staff with respect to role/qualifications, type of medical supplies/equipment, etc.), and location (e.g., where the resources will be needed). With these embodiments, the forecasting component 108 can include resource demand forecasting component 114 to forecast the future resource demands using one or more machine learning/AI techniques based on the current state data 102, the historical state data 130, and/or relevant information included the medical facility system data 132 that can influence and/or control the amount, type, timing and location of resources needed by the dynamic medical facility system” (Day at 0076), including “real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process” (Day at 0031), where “The patient may then be moved to the holding room prior to entering the interoperative phase areas (e.g., the operating room and/or the IR room) or moved directly from the floor, ER or ICU to the interoperative phase areas. From the operating room, the patient can be transferred to the PACU or ICU if necessary. Following recovery in the PACU, the patient will then be moved back to the floor for post-op recovery” (Day at 0037) (PACU being “post-anesthesia care unit” per Day at 0036), using “a perioperative system, maximining patient flow can include maximizing the number of patients moving through the system and receiving the surgery over a defined time frame, such as the entire workday, the peak hours (e.g., 8:00 am to 4:30 pm), or another defined time frame” (Day at 0066). Therefore, the Examiner understands and finds that to recommend another facility, such as a different ward/department, is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to optimally place and sequence arriving and transitioning patients.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the recommendations of Almashor with the transferring of Day in order to recommend another facility, such as a different ward/department, so as to optimally place and sequence arriving and transitioning patients.
The rationale for combining in this manner is that to recommend another facility, such as a different ward/department, is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to optimally place and sequence arriving and transitioning patients as explained above
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Monahan et al., The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper. Interact J Med Res. 2023 Jul 10;12:e42016. doi: 10.2196/42016. PMID: 37428536; PMCID: PMC10366955, indicating the importance of modeling emergency department demands to avoid crowding and provide adequate resources.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT D GARTLAND whose telephone number is (571)270-5501. The examiner can normally be reached M-F 8:30 AM - 5 PM.
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685