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 Claims
In the response dated March 13th, 2026, Applicant amended claims 11 and 17. Claims 11, 13-17, and 19-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 18th, 2025 has been entered.
Priority
Acknowledgment is made of applicant’s claim for priority. The certified copy has been filed in parent Application No. 63410688, filed on September 28th, 2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on April 2nd 2024 is being considered by the examiner.
Response to Arguments
In response to the argument put forward in the amendment, Examiner will address them in the order they were presented.
Regarding pages 9-10, Applicant’s arguments have been considered and are unpersuasive. The rejection under 35. USC. 112(b) has been withdrawn.
Regarding pages 10-13, Applicant’s arguments regarding subject matter eligibility under 35 U.S.C. 101 have been considered but are moot in view of the amended claim language.
Regarding page 14-16, Applicant’s arguments regarding the amended claim language have been considered but are moot in view of the amendments.
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 11, 13-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1
The claims recite subject matter within a statutory category as a process, machine, and/or article of manufacture. However, it will be shown in the following steps, that claims 11, 13-17, and 19-20 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 11 recites:
A computer implemented method, comprising:
receiving a patient list having a plurality of patients to be visited;
receiving one or more symptoms associated with each patient on the patient list;
receiving patient geolocation based on a known address or GPS information of a device associated with a respective patient;
determining a time-to-arrival at each patient's geolocation;
determining a severity score for each patient on the patient list based on the one or more symptoms and diagnosis of each patient;
wherein the severity score is calculated by assigning a numerical value to each of the one or more symptoms associated with each patient;
determining a travel score for each patient based on the patient location information and using the time-to-arrival;
determining a readiness score for each patient based on whether the patient's labs are ready and whether prescriptions have been distributed to the patient;
calculating a patient priority score for each patient on the patient list by combining the severity score, the travel score, and the readiness score;
creating an ordered patient list having the plurality of patients ordered based on the priority score of each patient;
generating a visitation route, including driving directions, for a care team member to visit two or more of the plurality of patients at their respective home addresses using the ordered patient list to determine an order of visitation of the two or more of the plurality of patients;
dynamically updating the priority score and the visitation route in real-time based on changes in at least one of traffic conditions, patient symptom updates, or lab availability
outputting the visitation route to a device associated with the care team member
and wherein the method uses machine learning algorithms that generate predictive models based on historical travel patterns and patient outcome data to optimize sequencing and placement of patients by analyzing historical data and real-time feeds to minimize the total travel time across multiple care teams and to automatically redistribute patients between care team members when real-time conditions change.
The broadest reasonable interpretation of these steps includes mental processes and/or organizing human activity because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms like “a computer”, “geolocation” or “GPS information”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “computer” language, “determining a travel score for each patient based on the patient location information and using the time-to-arrival;” in the context of this claim encompasses a mental process of the user judging the time it will take to reach a destination based on estimated traffic, environmental conditions, and more. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps of:
receiving a patient list having a plurality of patients to be visited;
receiving one or more symptoms associated with each patient on the patient list;
determining a severity score for each patient on the patient list based on the one or more symptoms and diagnosis of each patient;
wherein the severity score is calculated by assigning a numerical value to each of the one or more symptoms associated with each patient;
determining a travel score for each patient based on the patient location information and using the time-to-arrival;
determining a readiness score for each patient based on whether the patient's labs are ready and whether prescriptions have been distributed to the patient;
calculating a patient priority score for each patient on the patient list by combining the severity score, the travel score, and the readiness score;
creating an ordered patient list having the plurality of patients ordered based on the priority score of each patient;
generating a visitation route, including driving directions, for a care team member to visit two or more of the plurality of patients at their respective home addresses using the ordered patient list to determine an order of visitation of the two or more of the plurality of patients;
in the context of this claim encompasses a mental process of a healthcare provider planning a round of patient check-ins in a hospital setting. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. These steps, as drafted, under the broadest reasonable interpretation, include mental processes.
Independent claim 17 is directed to a system and non-transitory computer readable medium that cover the same limitations and are directed to the same abstract idea. Moving forward, claim 17 will follow the same analysis as claim 11.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 14, reciting particular aspects of how “determining the patient priority score includes providing a higher priority to patients with more severe symptoms” may be performed in the mind but for recitation of generic computer components).
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 11's method recites additional elements such as a computer, geolocation and GPS information. In addition to the generic components and additional elements listed above, independent claim 17' s product also includes a non-transitory computer readable medium. The non-transitory computer readable medium will be treated as a generic computer component. The remining elements will be considered additional elements and further analyzed for any conventionality. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amount to mere instructions to apply an exception (such as recitation of “A computer implemented method,” and “wherein the method uses machine learning algorithms” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0056] “can include any suitable computer hardware”, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (recitation of “receiving patient geolocation based on a known address or GPS information of a device associated with a respective patient” or “determining a time-to-arrival at each patient's geolocation”, or “outputting the visitation route to a device associated with the care team member” or “dynamically updating the priority score and the visitation route in real-time based on changes in at least one of traffic conditions, patient symptom updates, or lab availability’ or “generate predictive models based on historical travel patterns and patient outcome data to optimize sequencing and placement of patients by analyzing historical data and real-time feeds to minimize the total travel time across multiple care team and to automatically redistribute patients between care team members when real-time conditions change.” amounts to insignificant application, see MPEP 2106.05(g))
The dependent claims 13-16, 19-20 do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a GPS. Niles (Pat. 5420593) demonstrates in (para. 26) “additional satellite carrier signals will be found somewhat faster than in a conventional GPS receiver.” that a GPS was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of geolocation. Tekinay (US20010027110) demonstrates in paragraph [0004], “Some conventional geolocation systems identify the geolocation of a wireless terminal by determining the time-of-arrival of the line-of-sight component of a signal transmitted by the wireless terminal” that geolocation was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of machine learning algorithms. Chickering et al. (US20020184139) demonstrates in paragraph [0070], “invention is started by obtaining a probabilistic model 300, such as by learning or creating one using conventional machine learning techniques” that machine learning algorithms were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
“receiving patient geolocation based on a known address or GPS information of a device associated with a respective patient” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“determining a time-to-arrival at each patient's qeolocation”, is equivalently, Determining an estimated outcome, OIP Techs., MPEP 2106.05(d)(II)(v)
“outputting the visitation route to a device associated with the care team member” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“dynamically updating the priority score and the visitation route in real-time based on changes in at least one of traffic conditions, patient symptom updates, or lab availability” is equivalently, arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
“generate predictive models based on historical travel patterns and patient outcome data to optimize sequencing and placement of patients by analyzing historical data and real-time feeds to minimize the total travel time across multiple care team” is equivalently, arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
“to automatically redistribute patients between care team members when real-time conditions change.” is equivalently, Determining an estimated outcome, OIP Techs., MPEP 2106.05(d)(II)(v)
Dependent claims recite additional subject matter which further limit the abstract idea taught in the independent claims and, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable by Day et. al. (US20210193302) in view of Watson et al. (US20190311799) and Sipula (WO2020053759).
Regarding claim 11, Day teaches:
A computer implemented method, comprising: receiving a patient list having a plurality of patients to be visited; ([0042] “The current state data 102 can also include information current operating conditions of the medical facility system, such as current bed status and availability, current patient and staff locations, current staff assignments and tasks being performed, and the like.” where current state data contains a patient list)
receiving one or more symptoms associated with each patient on the patient list; ([0071]“In various embodiments, the medical facility system management module 104 facilitates determining how to optimally sequence, place and allocate resources for adynamic medical facility system (e.g., a perioperative system) to achieve one or more of the optimization objectives based on the multitude of inputs included in the current state data 102, the historical state data 130 and the medical facility system data 132. In this regard, the reception component 106 can receive the current state data 102 from one or more current state data sources/system 101 associated with the medical care facility system in real-time over the course of operation of the dynamic medical facility. For example, the one or more current state data sources/systems 101 can include (but are not limited to): case scheduling systems, staff scheduling systems, case status tracking systems, patient location tracking systems, patient vital signs monitoring systems, resource status and location monitoring systems, bed status tracking systems, staff location tracking and activity monitoring systems, data entry systems and the like.” Where vital signs monitoring system information [i.e., symptoms] is associated with the patient list)
determining a severity score for each patient on the patient list based on the one or more symptoms and diagnoses of each patient; ([Day 0057] “In other implementations, the case workflow data 132b can define different workflows for different types of surgical procedures, different patients ( e.g., grouped by age or another demographic factor, level of acuity, severity, comorbidity, medical history, etc.).” where workflows of severity and surgical procedure are completed on behalf of diagnoses and symptoms)
wherein the severity score is calculated by assigning a numerical value to each of the one or more symptoms associated with each patient; (see [Day 0057] above and [Day 0027] “The overall optimization process employs a number of embedded sub-modules that consume real-time feeds and historical data and invokes machine learning algorithms which provide outputs that serve as inputs to a time-based optimization heuristic” where the time-based optimization heuristic for workflow procedures uses a numerical value associated to a patient’s comorbidities undergoing treatment)
determining a travel score for each patient based on the patient location information and using time to arrival; ([Day 0057] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include essentially any medical facility system with limited/fixed resources that provides medical treatment to patients in accordance with one or more defined workflows (or care pathways), wherein the timing of the workflows can be impacted by variable operating states/ conditions of the dynamic medical system. … These various types of medical facility systems often follow defined workflows for patient treatment that define tasks to be performed and rules regarding when, where and by whom the tasks are to be performed. For example, … the tasks can include … clinical tasks (e.g., performing a clinical exam/assessment, performing a medical procedure, administering medication, and other such clinical tasks involving providing medical care to and/or assessing the patient, etc.), procedural tasks involving moving or transitioning a patient to different areas of the facility, and the like.” Where the variable states/conditions are a travel score; see also [0074] “For example, the timeline forecasts 136 can include information for currently active cases… and the duration of time between initiation and completion of downstream workflow events (e.g., following surgery, patient John. B is expected to be moved to bed in the PACU of type XYZ within 15 minutes or by 1:30 pm, following placement in the PACU, patient John. B is expected to remain in the PACU for 45 minutes and then be discharged at 2:15, and so one)” where the duration of time between initiation and completion of downstream workflow events comprises a time-to-arrival)
calculating a patient priority score for each patient on the patient list by combining the severity score, the travel score, and the readiness score; (see [Day Figure 1]; also “medical facility system management module” and “workflow data” above where the variable states/conditions [i.e., travel score] and workflow data [i.e., severity and readiness] are used in the same management module [i.e., combined to create a priority score])
creating an ordered patient list having the plurality of patients ordered based on the priority score of each patient; ([Day 0046] “In some implementations, the patient case data 102a can also identify and/or describe the workflow to be followed for the case, a priority level of the case, the physician/clinician(s) assigned to the case, and any defined scheduling information for the case ( e.g., regarding a scheduled time/date of the case). In other implementations, this information can be looked up in the medical facility system data 132 based on information identifying the patient and/or the case number.” Where the workflow to be followed [i.e., an ordered patient list] is based on a priority level [i.e., a priority score] See also “sequence … incoming and transitioning patients” above)
generating a visitation route, … for a care team member to visit two or more of the plurality of patients at their respective home addresses using the ordered patient list to determine an order of visitation of the two or more of the plurality of patients; ([Day 0086] "In this regard, the optimization component 118 can perform a complex optimization routine continuously as patients arrive, schedules change, staff move between areas and add-ons/cancelations occur to determine how to sequence and place patients in real-time using a complex heuristic-based algorithm with embedded rules and machine learning components (e.g., corresponding to the timeline forecasts 136 and the resource demand forecasts 138) to optimize the sequencing and placement of patients in an environment of shared and sub-specialized resources." Where sequencing and placement of patients comprises determining an order of visitation and route for a plurality of patients; see also [0058] “The staff data can include… home address/location” and [0033] “the dynamic medical facility system can include… a traveling/in-home patient care system”)
dynamically updating the priority score and the visitation route in real-time based on changes in at least one of traffic conditions, patient symptom updates, or lab availability ([0046] “the optimization component 118 determines changes to patient scheduling throughout the day with respect to patient sequence and timing 140 and/or patient placements 142, this information can be updated in the case scheduling systems and reflected in the current state data 102. The current state data 102 can also identify changes in case scheduling as they occur in real-time over the course of operation of the medical facility system as new cases are added, cases are canceled, rescheduled and/or the like.” Where the chances in the current state data comprise continuous updates of the visitation routes; see also [0059] “the system can assign different priority ranks to different types of cases/procedures, such that higher priority ranking cases are prioritized for performance before lower prioritized cases. The system policy data 132d can also include rule/policies regarding patient placement with respect to physical locations (e.g., floors, units, rooms, pods, bays, beds, etc.), and staff. For example, different areas of the perioperative system can provide different types of resources (e.g., different bed types) and/or serve different patient care needs. In this regard, the system policy data 132d can define hierarchical placement preferences for placing patients/cases to specific areas/beds and/or clinicians based on their priority level, level of acuity, procedure type, or another factor,” Where the system comprises a dynamic update of prioritization based on the patient acuity [i.e., the patient symptom updates])
and outputting the visitation route to a device associated with the care team member ([Day 0027] "ln this regard, the management system can output information that provides a specific sequence and placement of patients to minimize delay, maximize efficiency and satisfy a variety of rules and constraints." Where output information [i.e., a visitation route] occurs to a device; see also [0097] “[0097] For example, in the embodiment shown, the reporting component 126 can include a display component 128 configured to facilitate generating a graphical visualization and/or graphical user interface (GUI) for displaying at one or more user devices (not shown) that includes the timeline forecasts 136, the resource demand forecasts 138, the patient sequence and timing solutions 140, the patient placement solutions 142, and/or the resource allocation solutions 144. The one or more user devices can include for example, display monitors and/or computing devices associated with entities involved in the care delivery process at the dynamic medical facility system and/or involved in the patients care (e.g., staff of the perioperative system, the patients themselves, friends/family of the patients, etc.).” where the system comprises a user specific device to display patient visits)
and wherein the method uses machine learning algorithms that generate predictive models based on historical travel patterns and patient outcome data to optimize sequencing and placement of patients by analyzing historical data and real-time feeds to minimize the total travel time across multiple care teams ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework to regularly and/or continuously forecast future state information for the medical facility system in real-time based on the current state data 102, the historical state data 130, and/or relevant information included the medical facility system data 132 that can influence and/or control the timing of defined workflows for the patient cases (e.g., including timing of initiation, completion and duration of defined workflow events), and resources needed.” And [0054] “the historical state data 130 can provide… patient outcomes” where workflow events comprise travel patterns)
and to automatically redistribute patients between care team members when real-time conditions change ([0086] “The optimization component 118 can further employ a complex heuristic-based optimization mechanism to determine optimal reactive solutions regarding patient sequencing, patient placement and/or resource allocation that achieves and/or balances (e.g., in accordance with defined weights) the one or more optimization objectives (e.g., as provided by the optimization criteria data 132e) based on relevant parameters included in the current state data 102, the future state information” where patient placement and resource allocation based on current and future state data comprises automatically redistributing patients when real conditions change.)
Regarding claim 11, Day does not explicitly teach, as taught by Watson:
receiving patient geolocation based on a known address or GPS information of a device associated with a respective patient; ([0102] “the mission planning/tracking application 154 may access the GPS tracking application 162 (see FIG. 2A), which receives location information from the vehicle 109 transporting the patient 102 and/or one of the crew member(s) traveling with the patient 102” where the vehicle associated with the patient contains GPS information)
determining a time-to-arrival at each patient's geolocation; ([Watson 0005] “In the methods, during patient transport the MPTM may update one or more dashboards, including the dispatcher dashboard, with … the estimated time of arrival”)
generating a visitation route, including driving directions, ([Watson 0008] “a transportation plan, wherein the transportation plan comprises a sending location, a receiving location, a mode of transportation, and a level of crew specialization” where the transportation plan is the driving directions for a visitation route)
Regarding claim 11, Day-Watson does not explicitly teach, as taught by Sipula:
determining a readiness score for each patient based on patient whether the patient's labs are ready and whether prescriptions have been distributed to the patient; ([page 11] “The patient’s medical information may include pharmacy prescription; medical laboratory test results;… Further… one or more of the following may be determined and displayed: a patient diagnosis; the level of therapy administered to the patient, such as first line or second line therapy; and whether the medicine prescribed is provided at optimal or sub-optimal dose,” see also [Sipula page 20] “Further, where the patient’s medical information is in the form of a pharmacy prescription and medical laboratory test results, new patient-specific clinical output statements that describe the patient’s clinical condition and health risk status will be generated by the system,” where the lab test results and prescription results depict the readiness of a patient’s treatment)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Day with the teachings of Watson, with a reasonable expectation of success, by explicitly including a GPS monitoring system for tracking the time-based movements of people in a hospital system. This would have sped up the provider’s care of patients with more data to optimize workflows. Watson is adaptable to Day as both inventions forecast and optimize medical facility resources to treat patients. Day would have found Watson’s teaching while searching for solutions regarding “transferring a patient from a first location (e.g., a first hospital) to a second location (e.g., a second hospital) requires a number of different telephone calls because all of the connections between the relevant parties are made via the telephone. Further, each of the relevant parties has no knowledge of what any of the other parties are doing and has no specific knowledge with regard to the condition of the patient.” in the healthcare industry [Watson 0003].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Day-Watson with the teachings of Sipula, with a reasonable expectation of success, by explicitly adding a pharmacology and laboratory tracking system to the hospital workflows. This would have improved the safety of patients by ensuring treatment protocols are followed properly. Sipula is adaptable to Day and Watson as these inventions use computer systems to track healthcare setting workflows. Day would have found Sipula’s teaching while addressing “the global shortage of a health work-force that seems to continue to further diminish with each passing year” [page 1] necessitates a need for optimized healthcare settings.
Regarding claim 13, Day teaches all of the limitations of claim 11. Day also teaches:
wherein determining the patient priority score includes providing a lower priority for farther patients ([0039] " Although many perioperative systems schedule patient procedures to be performed for the day before the day begins, a plethora of factors can change over the course of the day that require the care delivery team to reschedule patients, reassign patients to different rooms, reprioritize patients, reallocate resources to the patients, and the like. For example, with respect to a perioperative system that begins a day with a set schedule of patient cases to be completed (including scheduled timing of the cases, clinicians assigned to the cases, etc.), over the course of the day patients may arrive early, late or not at all, emergent patients may be added, patients may experience complications that can result in the patient requiring additional procedures and/or unexpected care needs, staff availability may change, medical supplies and/or equipment may not be available as expected, procedures may take longer than expected, and a variety of other factors may occur." Where different rooms [i.e., farther patients] is a factor in patient cases to be completed [i.e., patient priority]; see also [0059]" For instance, the system can assign different priority ranks to different types of cases/procedures, such that higher priority ranking cases are prioritized for performance before lower prioritized cases.")
Regarding claim 14, Day teaches all of the limitations of claim 13. Day also teaches:
wherein determining the patient priority score includes providing a higher priority to patients with more severe symptoms (see "complications” above, where complications [i.e., severity of symptoms] affects the patient cases to be completed [i.e., patient priority]; see also "higher priority ranking cases are prioritized" above, where ranking is due to complications)
Regarding claim 15, Day teaches all of the limitations of claim 14. Day also teaches:
wherein generating the visitation route includes generating the most time efficient driving directions for the care team member based on care team member location information. ([0033] The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include… traveling/in-home patient care system” and [0086] "ln this regard, the optimization component 118 can perform a complex optimization routine continuously as patients arrive, schedules change, staff move between areas and add-ons/cancelations occur to determine how to sequence and place patients in real-time using a complex heuristic-based algorithm with embedded rules and machine learning components (e.g., corresponding to the timeline forecasts 136 and the resource demand forecasts 138) to optimize the sequencing and placement of patients in an environment of shared and subspecialized resources." Where performing a complex optimization routine [i.e., generating a time efficient route] is based in part on staff movements [i.e., team member locations]).
Regarding claim 16, Day teaches all of the limitations of claim 15. Day does not explicitly teach, as taught by Watson:
further comprising updating the visitation route based on new information after every visit to reroute the care team member if there is a change in a patient's priority. ([0106] "The mission planning/tracking application 154 (see FIG. 2A) may be configured to update the patient's condition, update the patient's orders, and/or modify the transport plan based on information provided to the mission planning/tracking application 154”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Day with the teachings of Watson, with a reasonable expectation of success, by explicitly including a GPS monitoring system for tracking and updating the time-based movements of people in a hospital system. This would have sped up the provider’s care of patients with more data to optimize workflows. Watson is adaptable to Day as both inventions forecast and optimize medical facility resources to treat patients. Day would have found Watson’s teaching while searching for solutions regarding “transferring a patient from a first location (e.g., a first hospital) to a second location (e.g., a second hospital) requires a number of different telephone calls because all of the connections between the relevant parties are made via the telephone. Further, each of the relevant parties has no knowledge of what any of the other parties are doing and has no specific knowledge with regard to the condition of the patient.” in the healthcare industry [Watson 0003].
Regarding claim 17, Day teaches:
A non-transitory computer readable medium, comprising computer executable instructions configured to cause a computer to perform a method, the method, comprising: ([0120] “computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.”)
receiving a patient list having a plurality of patients to be visited; ([0042] “The current state data 102 can also include information current operating conditions of the medical facility system, such as current bed status and availability, current patient and staff locations, current staff assignments and tasks being performed, and the like.” where current state data contains a patient list)
receiving one or more symptoms associated with each patient on the patient list; ([0071]“In various embodiments, the medical facility system management module 104 facilitates determining how to optimally sequence, place and allocate resources for adynamic medical facility system (e.g., a perioperative system) to achieve one or more of the optimization objectives based on the multitude of inputs included in the current state data 102, the historical state data 130 and the medical facility system data 132. In this regard, the reception component 106 can receive the current state data 102 from one or more current state data sources/system 101 associated with the medical care facility system in real-time over the course of operation of the dynamic medical facility. For example, the one or more current state data sources/systems 101 can include (but are not limited to): case scheduling systems, staff scheduling systems, case status tracking systems, patient location tracking systems, patient vital signs monitoring systems, resource status and location monitoring systems, bed status tracking systems, staff location tracking and activity monitoring systems, data entry systems and the like.” Where vital signs monitoring system information [i.e., symptoms] is associated with the patient list)
determining a severity score for each patient on the patient list based on the one or more symptoms of each patient; ([Day 0057] “In other implementations, the case workflow data 132b can define different workflows for different types of surgical procedures, different patients ( e.g., grouped by age or another demographic factor, level of acuity, severity, comorbidity, medical history, etc.).” where workflows of severity and surgical procedure are completed on behalf of diagnoses and symptoms)
wherein the severity score is calculated by assigning a numerical value to each of the one or more symptoms associated with each patient (see [Day 0057] above and [Day 0027] “The overall optimization process employs a number of embedded sub-modules that consume real-time feeds and historical data and invokes machine learning algorithms which provide outputs that serve as inputs to a time-based optimization heuristic” where the time-based optimization heuristic for workflow procedures uses a numerical value associated to a patient’s comorbidities or symptoms undergoing treatment)
determining a travel score for each patient based on the patient location information and using time-to-arrival; ([Day 0057] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include essentially any medical facility system with limited/fixed resources that provides medical treatment to patients in accordance with one or more defined workflows (or care pathways), wherein the timing of the workflows can be impacted by variable operating states/ conditions of the dynamic medical system. … These various types of medical facility systems often follow defined workflows for patient treatment that define tasks to be performed and rules regarding when, where and by whom the tasks are to be performed. For example, … the tasks can include … clinical tasks (e.g., performing a clinical exam/assessment, performing a medical procedure, administering medication, and other such clinical tasks involving providing medical care to and/or assessing the patient, etc.), procedural tasks involving moving or transitioning a patient to different areas of the facility, and the like.” Where the variable states/conditions are a travel score; see also [0074] “For example, the timeline forecasts 136 can include information for currently active cases… and the duration of time between initiation and completion of downstream workflow events (e.g., following surgery, patient John. B is expected to be moved to bed in the PACU of type XYZ within 15 minutes or by 1:30 pm, following placement in the PACU, patient John. B is expected to remain in the PACU for 45 minutes and then be discharged at 2:15, and so one)” where the duration of time between initiation and completion of downstream workflow events comprises a time-to-arrival)
calculating a patient priority score for each patient on the patient list by combining the severity score, the travel score, and the readiness score; (see [Day Figure 1] along with “medical facility system management module” and “workflow data” above where the variable states/conditions [i.e., travel score] and workflow data [i.e., severity and readiness] are used in the same management module [i.e., combined to create a priority score])
creating an ordered patient list having the plurality of patients ordered based on the priority score of each patient; ([Day 0046] “In some implementations, the patient case data 102a can also identify and/or describe the workflow to be followed for the case, a priority level of the case, the physician/clinician(s) assigned to the case, and any defined scheduling information for the case ( e.g., regarding a scheduled time/date of the case). In other implementations, this information can be looked up in the medical facility system data 132 based on information identifying the patient and/or the case number.” Where the workflow to be followed [i.e., an ordered patient list] is based on a priority level [i.e., a priority score] See also “sequence … incoming and transitioning patients” above)
generating a visitation route… for a care team member to visit two or more of the plurality of patients at their respective home addresses using the ordered patient list to determine an order of visitation of the two or more of the plurality of patients; ([Day 0086] "In this regard, the optimization component 118 can perform a complex optimization routine continuously as patients arrive, schedules change, staff move between areas and add-ons/cancelations occur to determine how to sequence and place patients in real-time using a complex heuristic-based algorithm with embedded rules and machine learning components (e.g., corresponding to the timeline forecasts 136 and the resource demand forecasts 138) to optimize the sequencing and placement of patients in an environment of shared and sub-specialized resources." Where sequencing and placement of patients comprises determining an order of visitation and route for a plurality of patients; see also [0058] “The staff data can include… home address/location” and [0033] “the dynamic medical facility system can include… a traveling/in-home patient care system”)
outputting the visitation route to a device associated with the care team member ([Day 0027] "ln this regard, the management system can output information that provides a specific sequence and placement of patients to minimize delay, maximize efficiency and satisfy a variety of rules and constraints." Where output information [i.e., a visitation route] occurs to a device; see also [0097] “[0097] For example, in the embodiment shown, the reporting component 126 can include a display component 128 configured to facilitate generating a graphical visualization and/or graphical user interface (GUI) for displaying at one or more user devices (not shown) that includes the timeline forecasts 136, the resource demand forecasts 138, the patient sequence and timing solutions 140, the patient placement solutions 142, and/or the resource allocation solutions 144. The one or more user devices can include for example, display monitors and/or computing devices associated with entities involved in the care delivery process at the dynamic medical facility system and/or involved in the patients care (e.g., staff of the perioperative system, the patients themselves, friends/family of the patients, etc.).” where the system comprises a user specific device to display patient visits)
dynamically updating the priority score and the visitation route in real-time based on changes in at least one of traffic conditions, patient symptom updates, or lab availability ([0046] “the optimization component 118 determines changes to patient scheduling throughout the day with respect to patient sequence and timing 140 and/or patient placements 142, this information can be updated in the case scheduling systems and reflected in the current state data 102. The current state data 102 can also identify changes in case scheduling as they occur in real-time over the course of operation of the medical facility system as new cases are added, cases are canceled, rescheduled and/or the like.” Where the chances in the current state data comprise continuous updates of the visitation routes; see also [0059] “the system can assign different priority ranks to different types of cases/procedures, such that higher priority ranking cases are prioritized for performance before lower prioritized cases. The system policy data 132d can also include rule/policies regarding patient placement with respect to physical locations (e.g., floors, units, rooms, pods, bays, beds, etc.), and staff. For example, different areas of the perioperative system can provide different types of resources (e.g., different bed types) and/or serve different patient care needs. In this regard, the system policy data 132d can define hierarchical placement preferences for placing patients/cases to specific areas/beds and/or clinicians based on their priority level, level of acuity, procedure type, or another factor,” Where the system comprises a dynamic update of prioritization based on the patient acuity [i.e., the patient symptom updates])
and wherein the method uses machine learning algorithms that generate predictive models based on historical travel patterns and patient outcome data to optimize sequencing and placement of patients by analyzing historical data and real-time feeds to minimize the total travel time across multiple care teams ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework to regularly and/or continuously forecast future state information for the medical facility system in real-time based on the current state data 102, the historical state data 130, and/or relevant information included the medical facility system data 132 that can influence and/or control the timing of defined workflows for the patient cases (e.g., including timing of initiation, completion and duration of defined workflow events), and resources needed.” And [0054] “the historical state data 130 can provide… patient outcomes” where workflow events comprise travel patterns) and to automatically redistribute patients between care team members when real-time conditions change. ([0086] “The optimization component 118 can further employ a complex heuristic-based optimization mechanism to determine optimal reactive solutions regarding patient sequencing, patient placement and/or resource allocation that achieves and/or balances (e.g., in accordance with defined weights) the one or more optimization objectives (e.g., as provided by the optimization criteria data 132e) based on relevant parameters included in the current state data 102, the future state information” where patient placement and resource allocation based on current and future state data comprises automatically redistributing patients when real conditions change.)
Regarding claim 17, Day does not explicitly teach, as taught by Watson:
receiving patient geolocation based on a known address or GPS information of a device associated with a respective patient; ([0102] “the mission planning/tracking application 154 may access the GPS tracking application 162 (see FIG. 2A), which receives location information from the vehicle 109 transporting the patient 102 and/or one of the crew member(s) traveling with the patient 102” where the vehicle associated with the patient contains GPS information)
determining a time-to-arrival at each patient's geolocation; ([0005] “In the methods, during patient transport the MPTM may update one or more dashboards, including the dispatcher dashboard, with … the estimated time of arrival”)
generating a visitation route, including driving directions, ([Watson 0008] “a transportation plan, wherein the transportation plan comprises a sending location, a receiving location, a mode of transportation, and a level of crew specialization” where the transportation plan is the driving directions for a visitation route)
Regarding claim 17, Day-Watson as a combination does not teach, as taught by Sipula:
determining a readiness score for each patient based on patient whether the patient's labs are ready and whether prescriptions have been distributed to the patient; ([Sipula page 11] “ The patient’s medical information may include pharmacy prescription; medical laboratory test results;… Further… one or more of the following may be determined and displayed: a patient diagnosis; the level of therapy administered to the patient, such as first line or second line therapy; and whether the medicine prescribed is provided at optimal or sub-optimal dose,” see also [Sipula page 20] “Further, where the patient’s medical information is in the form of a pharmacy prescription and medical laboratory test results, new patient-specific clinical output statements that describe the patient’s clinical condition and health risk status will be generated by the system,” where the lab test results and prescription results depict the readiness of a patient’s treatment)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Day with the teachings of Watson, with a reasonable expectation of success, by explicitly including a GPS monitoring system for tracking the time-based movements of people in a hospital system. This would have sped up the provider’s care of patients with more data to optimize workflows. Watson is adaptable to Day as both inventions forecast and optimize medical facility resources to treat patients. Day would have found Watson’s teaching while searching for solutions regarding “transferring a patient from a first location (e.g., a first hospital) to a second location (e.g., a second hospital) requires a number of different telephone calls because all of the connections between the relevant parties are made via the telephone. Further, each of the relevant parties has no knowledge of what any of the other parties are doing and has no specific knowledge with regard to the condition of the patient.” in the healthcare industry [Watson 0003].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Day-Watson with the teachings of Sipula, with a reasonable expectation of success, by explicitly adding a pharmacology and laboratory tracking system to the hospital workflows. This would have improved the safety of patients by ensuring treatment protocols are followed properly. Sipula is adaptable to Day and Watson as these inventions use computer systems to track healthcare setting workflows. Day would have found Sipula’s teaching while addressing how “the global shortage of a health work-force that seems to continue to further diminish with each passing year” [page 1] necessitates a need for optimized healthcare settings.
Regarding claim 19, Day teaches all of the limitations of claim 17. Day also teaches:
wherein determining the patient priority score includes providing a lower priority for farther patients. ([0039] " Although many perioperative systems schedule patient procedures to be performed for the day before the day begins, a plethora of factors can change over the course of the day that require the care delivery team to reschedule patients, reassign patients to different rooms, reprioritize patients, reallocate resources to the patients, and the like. For example, with respect to a perioperative system that begins a day with a set schedule of patient cases to be completed (including scheduled timing of the cases, clinicians assigned to the cases, etc.), over the course of the day patients may arrive early, late or not at all, emergent patients may be added, patients may experience complications that can result in the patient requiring additional procedures and/or unexpected care needs, staff availability may change, medical supplies and/or equipment may not be available as expected, procedures may take longer than expected, and a variety of other factors may occur." Where different rooms [i.e., farther patients] is a factor in patient cases to be completed [i.e., patient priority]; see also [0059]" For instance, the system can assign different priority ranks to different types of cases/procedures, such that higher priority ranking cases are prioritized for performance before lower prioritized cases.")
Regarding claim 20, Day teaches all of the limitations of claim 17. Day also teaches:
wherein determining the patient priority score includes providing a higher priority to patients with more severe symptoms. (see "complications” above, where complications [i.e., severity of symptoms] affects the patient cases to be completed [i.e., patient priority]; see also "higher priority ranking cases are prioritized" above, where ranking is due to complications)
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found on PTO-892 of the prior office action and below.
Chiu et al. (US20150324539) discloses a patient emergency response system that uses location approximation to optimize a route for a healthcare professional.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/R.A.S/Examiner, Art Unit 3792
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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