DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for priority. The certified copy has been filed in provisional Application No. 63377962, filed on September 30th, 2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on September 26th 2023 , April 26th 2024, October 30th 2024, and February 18th 2025 are being considered by the examiner.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception 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 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 1 states:
A computer-implemented method for mobile health care unit routing comprising:
loading hard, medium, and soft constraints into a mobile care unit dispatching tool, wherein at least some of the constraints include functions that yield value points, wherein the tool includes a memory storing an artificial intelligence enabled constraint solver and a processor configured to execute the constraint solver to apply the constraints to patient scheduling operations
identifying one or more mobile care units, and capabilities specific to each of the mobile care units within the tool, each of the mobile care units being a vehicle equipped with medical equipment and personnel, wherein the capabilities are retrieved from the memory and used to match patients to mobile care units based on constraint satisfaction
setting goals for optimizing routing of the mobile care units, at least one of which is maximizing value points, within the tool;
receiving a new patient into the constraint solver within the tool, wherein the constraint solver includes a trained model that predicts scheduling feasibility
applying the hard, medium, and soft constraints to the new patient;
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver; and
scheduling the new patient in a time slot within one of the queues of patients serviced by an assigned one of the mobile care units that maximizes the goals, while meeting the constraints.
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 hardware like “a computer”, “memory”, “processor”, and “vehicle”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “computer” language, “setting goals for optimizing routing of the mobile care units, at least one of which is maximizing value points, within the tool” in the context of this claim encompasses a mental process of a supervisor factoring in the rush hour traffic for their mobile unit driver before sending them to care for a patient. 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:
method for mobile health care unit routing comprising
identifying one or more mobile care units, and capabilities specific to each of the mobile care units within the tool;
setting goals for optimizing routing of the mobile care units, at least one of which is maximizing value points, within the tool;
applying the hard, medium, and soft constraints to the new patient;
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver; and
scheduling the new patient in a time slot within one of the queues of patients serviced by one of an assigned the mobile care units that maximizes the goals, while meeting the constraints.
as drafted, could also incorporate a healthcare professional following procedures for managing their equipment inventory levels while scheduling patients. Therefore, under the broadest reasonable interpretation, these steps include multiple abstract ideas that will be identified as a single abstract idea moving forward.
Independent claims 16 and 18 cover similar steps of applying constraints, identifying mobile units, setting goals for route optimization, receiving new patients into the care system, repeatedly scheduling patients for care based on optimized factors, and servicing patients based on a predicted frequency of care. These claim falls under the same category of an abstract idea and follows the same rationale as claim 1.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 6, reciting particular aspects of how “testing a feasibility of scheduling the new patient using a set of feasibility queues, wherein the scheduling operation is responsive to a successful feasibility test” or claim 15, “identifying one or more remote caregivers, and capabilities specific to each of the remote caregivers within the tool; setting goals for optimizing scheduling of the remote caregivers, at least one of which is maximizing value points, within the tool; iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the remote caregivers, using the constraint solver; and scheduling the new patient within a time slot serviced by one of the remote caregivers that maximizes the goals, while meeting the hard, medium, and soft constraints” may be a healthcare professional managing their equipment inventory levels while scheduling patients using pen or paper but for recitation of generic computer components).
Dependent claims 2, 6, 12, and 15 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. Independent claim 1's method recites additional elements such as “a computer”, “memory”, “processor” and “vehicle”. In addition to the generic components and additional elements listed above, independent claims 15's system also includes a “datastore”. The “a computer”, “memory”, “processor” and ”datastore” will be treated as generic computer components. The vehicle will be addressed later for any conventionality. In particular, the additional elements do not integrate the abstract idea into a practical application because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of “A computer”, and “a memory” and “a processor” and “iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver;” and “wherein the constraint solver includes a trained model that predicts scheduling feasibility” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [00144] “personal computers, tablet computers, smart phones, mobile devices, etc.”, where the specification is listing the system’s generic computers that may support this tool, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “loading hard, medium, and soft constraints into a mobile care unit dispatching tool, wherein at least some of the constraints include functions that yield value points, wherein the tool includes… storing an artificial intelligence enabled constraint solver… configured to execute the constraint solver to apply the constraints to patient scheduling operations” and “receiving a new patient into an artificial intelligence enabled constraint solver within the tool” amounts to mere data gathering, “wherein the capabilities are retrieved from the memory and used to match patients to mobile care units based on constraint satisfaction” amounts to selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Additionally, claim 15 “identifying one or more remote caregivers, and capabilities specific to each of the remote caregivers within the tool; setting goals for optimizing scheduling of the remote caregivers, at least one of which is maximizing value points, within the tool; iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the remote caregivers, using the constraint solver; and scheduling the new patient within a time slot serviced by one of the remote caregivers that maximizes the goals, while meeting the hard, medium, and soft constraints” are additional limitations which amount to invoking computers as a tool to perform the abstract idea, claim 2, “re-scheduling previously scheduled patients to accommodate the scheduled new patient” amounts to necessary data outputting, see MPEP 2106.05(g)), claim 6's recitation of “testing a feasibility of scheduling the new patient using a set of feasibility queues, wherein the scheduling operation is responsive to a successful feasibility test” and claim 12’s “receiving the new patient into the solver is responsive to performing a threshold evaluation on the new patient to determine eligibility for a health care service to be rendered in the new patient's home by a mobile care unit” and claim 19 “iteratively repeating the receiving, applying, iteratively solving, scheduling, and servicing operations to create successive daily queues of patients to be serviced by the mobile care units” and claim 20 “identifying a regularity within the successive daily queues of patients; and applying the regularity using the solver to create future daily queues of patients to be serviced by one the mobile care units.” amounts to insignificant 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.
The remaining dependent claims 3-5, 7-11, 13, 14, and 17 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.
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, other than the abstract idea per se, 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 vehicle. Anthony et al. (US20030137426) demonstrates in paragraph [0016] that “a conventional automobile” that a vehicle 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.
To elaborate:
“loading hard, medium, and soft constraints into a mobile care unit dispatching tool, wherein at least some of the constraints include functions that yield value points, wherein the tool includes a memory storing an artificial intelligence enabled constraint solver and a processor configured to execute the constraint solver to apply the constraints to patient scheduling operations,” is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver;” , is equivalently, Arranging a hierarchy of groups and sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii)
“wherein the capabilities are retrieved from the memory and used to match patients to mobile care units based on constraint satisfaction” , is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
“receiving a new patient into an artificial intelligence enabled constraint solver within the tool”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“loading hard, medium, and soft constraints into a mobile care unit dispatching tool, wherein at least some of the constraints include functions that yield value points”, is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
Dependent claims recite additional elements that amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. To elaborate:
claim 2’s “re-scheduling previously scheduled patients to accommodate the scheduled new patient” is equivalently, arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
claim 6's “testing a feasibility of scheduling the new patient using a set of feasibility queues, wherein the scheduling operation is responsive to a successful feasibility test” Determining an estimated outcome and setting a price, OIP Techs., MPEP 2106.05(d)(II)(v)
claim 12’s “receiving the new patient into the solver is responsive to performing a threshold evaluation on the new patient to determine eligibility for a health care service to be rendered in the new patient's home by a mobile care unit” is equivalently, arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
claim 15 “identifying one or more remote caregivers, and capabilities specific to each of the remote caregivers within the tool”, is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
claim 15 “setting goals for optimizing scheduling of the remote caregivers, at least one of which is maximizing value points, within the tool” , is equivalently, Arranging a hierarchy of groups and sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii)
claim 15 “iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the remote caregivers, using the constraint solver;” , is equivalently, arranging a hierarchy of groups and sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii)
claim 15 “scheduling the new patient within a time slot serviced by one of the remote caregivers that maximizes the goals, while meeting the hard, medium, and soft constraints”, is equivalently, electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii);
claim 19 “iteratively repeating the receiving, applying, iteratively solving, scheduling, and servicing operations to create successive daily queues of patients to be serviced by the mobile care units” , is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi).
claim 20 “identifying a regularity within the successive daily queues of patients; and applying the regularity using the solver to create future daily queues of patients to be serviced by one the mobile care units.”, is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Day et al. (US20210193302) in view of Watson et al. (US 20190311799).
Regarding claim 1, Day teaches.
A computer-implemented method for mobile health care unit routing comprising: ([0003] “methods, apparatus and/or computer program products are described that provide for optimizing the sequencing and placement of patients in a dynamic medical system”)
loading hard, medium, and soft constraints ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria”) into a mobile care unit dispatching tool, ([0033] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include… ambulatory services system”) wherein at least some of the constraints include functions that yield value points; ([Figure 4’s] “forecasted data” above, where forecasted data are value points; see optionally [0004] “an optimization component that employs a heuristic-based optimization mechanism to determine optimal reactive solutions regarding patient sequencing,” where heuristic mechanisms comprise functions that provide data points) wherein the tool includes a memory ([0004] “a system is provided that comprises a memory that stores computer executable components”) storing an artificial intelligence enabled constraint solver ([0084] “In some embodiments, the case timeline forecasting component 110 and/or the resource demand forecasting component 114 can employ one or more artificial intelligence techniques”; see also [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 (e.g., including the timeline forecasts 136 and/or the resource demand forecasts 138), and defined system constraints (e.g., system architecture constraints, defined workflow constraints, defined staffing constraints, and defined system rule/policy based constraints, as respectively provided by the medical facility system data 132) that control or influence patient sequencing and timing, placement and/or resource allocation.” Where the use of artificial intelligence comprises a constraint solver) and a processor ([0004] “a system is provided that comprises … a processor that executes the computer executable components stored in the memory”) configured to execute the constraint solver to apply the constraints to patient scheduling operations ([0027] “a management system is provided that consumes real-time data feeds from IT platforms associated with various modality specific and phase specific components of the perioperative system and performs a complex optimization routine across the various the components as the state and context changes over the course of the day (e.g., as patients arrive early or late, add-ons/cancelations occur, schedules change, staff move between areas, etc.) to optimally place and sequence arriving and transitioning patients in real time.”; see also [0086] above)
identifying one or more mobile care units, ([Figure 4] “Facility system management module 104” identifies mobile care units; see also ([0033] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include… ambulatory services system”) and capabilities specific to each of the mobile care units within the tool, ([0033] “the dynamic medical facility system can include but is not limited to: a hospital, a specialized hospital unit, a surgery center, a specialized care provider facility ( e.g., a clinic or office), an outpatient facility, an ambulatory services system, a nursing home facility, an imaging/diagnostic facility, a traveling/in-home patient care system, a rehabilitation provider system, and the like” see also [0058] “The staff data 132c can also include information regarding their different qualifications, capabilities, authorizations for performing certain workflow events, skill levels, proficiency levels, performance levels, and the like.” where the staff data is associated with the facility system management module [comprising the mobile care unit])
setting goals for optimizing routing of the mobile care units, at least one of which is maximizing value points, within the tool; ([0031] “the medical facility system management module 104 provides real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process.” Where the facility system management module [comprising the mobile care units] provides real-time decision support regarding patient’s arrival and moving [i.e., optimizing routing] by maximizing value points)
receiving a new patient ([Figure 4] “receive current state data” where current state data includes a new patient) into the constraint solver within the tool, wherein the constraint solver includes a trained model that predicts scheduling feasibility ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework”; see also [0027] “a management system is provided that consumes real-time data feeds from IT platforms associated with various modality specific and phase specific components of the perioperative system and performs a complex optimization routine across the various the components as the state and context changes over the course of the day (e.g., as patients arrive early or late, add-ons/cancelations occur, schedules change, staff move between areas, etc.) to optimally place and sequence arriving and transitioning patients in real time.” comprises a constraint solver)
applying the hard, medium, and soft constraints to the new patient ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria”) see also “receive current state data” and “constraints” where applying constraints to new patients is continuous);
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver; and ([0046] “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.”)
scheduling the new patient in a time slot within one of the queues of patients serviced by an assigned one of the mobile care units that maximizes the goals, while satisfying the constraints, ([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” where optimization component’s sequencing and timing [i.e., timeslot] schedules servicing for the medical facility system [comprising mobile care unit]; see also [0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)”)) wherein a scheduling output ([0034] “depending on the type of medical facility system and the type of patient care workflows that are performed, a variety of variable operating states/conditions of the dynamic medical facility system can influence the timing of the workflows, such as changes in patient and staff scheduling (e.g., associated with cancelations, additions, staff members inability to arrive or work as scheduled, etc.), timing of arrival of patients, staff and other resources (e.g., ambulatory services, medical equipment/supplies, etc.), occurrence of medical complications, arrival of emergency patients, inefficient clinician performance (e.g., procedures taking longer than expected), occurrence of procedural errors, and a variety of other factors”) is stored in the memory and sent to the assigned mobile care unit via a network interface for execution. ([0098] “With these embodiments, one or more features and functionalities of the system 100 can be deployed as a web-application, a cloud-application, a thin client application, a thick client application, a native client application, a hybrid client application, or the like, wherein one or more of the front-end components (e.g., reporting component 126) are provided at client device (not shown) and one or more of the back-end components (e.g., the data collection component 112, care outcomes forecasting component 114, etc.) are provided in the cloud, on a virtualized server, a virtualized data store, a remote server, a remote data store, a local data center, etc. (not shown), and accessed via a network (e.g., the Internet). In this regard, the current state data systems/sources 102, the medical facility system management module 104, one or more components of the medical facility management module, the historical state data 132 and/or the medical facility system data 132 can be physically separated yet communicatively coupled via one or more networks.”; see also ([0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)”)
Regarding claim 1, Day does not explicitly teach, as taught by Watson:
each of the mobile care units being a vehicle equipped with medical equipment and personnel, wherein the capabilities are retrieved from the memory and used to match patients to mobile care units based on constraint satisfaction ([0005] “in the methods, assigning the transport vehicle by the hard asset deployment module, and optionally assigning the one or more crew members by the crew assignment module, may comprise use of the vehicle profile dataset and a crew profile dataset to calculate weight, balance and fuel requirements.” Where the profile datasets comprise information to support patient matching based on constraint satisfaction)
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 integrating Day’s decision modeling into network vehicles that transport patients. This would have increased the accuracy of decisions for treating patients, thereby making a safer standard of care. Watson is adaptable to Day as both inventions utilize computing systems to triage patients for effectively managing resources. Day would have found Watson’s teaching while searching for improvements to the existing communication methods as Watson indicates in paragraph [0003] “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.”
Regarding claim 2, Day teaches all of the limitations of claim 1. Day also teaches:
re-scheduling previously scheduled patients to accommodate the scheduled new patient. ([0046] “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.”)
Regarding claim 3, Day teaches all of the limitations of claim 2. Day also teaches
wherein the re-scheduling includes moving the previously scheduled patients between mobile care units. ([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” where patient placements is moving between mobile care units)
Regarding claim 4, Day teaches all of the limitations of claim 2. Day also teaches:
wherein the re-scheduling repeats iteratively to optimize a set of patient schedules. ([0046] “the reception component 106 can regularly or continuously receive updated information (e.g., in real-time) regarding what patient cases are scheduled for performance at the medical facility system within an upcoming timeframe” where the reception component updates state data [i.e. patient scheduling] for the optimization component)
Regarding claim 5, Day teaches all of the limitations of claim 1. Day also teaches:
wherein a function that yields value points includes a variable from an output of another of the functions that yield value points. ([Figure 1] “reception component” and “optimization component” where each component [comprising a function] processes data [comprising yields value points] for the other component [comprising from an output of another function]; see optionally [Figure 4] Heuristic based optimization mechanism, which comprises a networked set of functions to optimize the outputs of other functions )
Regarding claim 6, Day teaches all of the limitations of claim 1. Day also teaches:
testing a feasibility of scheduling the new patient using a set of feasibility queues, wherein the scheduling operation is responsive to a successful feasibility test. ([0031] “the medical facility system management module 104 provides real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process.” Where the facility system management module [comprising the mobile care units] provides real-time decision support [comprising a feasibility of scheduling] regarding patient’s arrival and moving [i.e., optimizing routing] by maximizing value points; see also [Figure 4] “Forecasting component” (108) comprises a set of feasibility queues in the management module)
Regarding claim 7, Day teaches all of the limitations of claim 1. Day also teaches:
wherein the value points can be positive or negative. ([0070] “the different optimization objectives are related and/or interconnected such that optimizing one can impact another in a positive or negative manner.”)
Regarding claim 8, Day all of the limitations of claim 1. Day also teaches:
wherein at least some of the hard constraints include Boolean expressions (([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework” where artificial intelligence models use Boolean expressions to determine the weight associated with each artificial neuron while processing information) that prevent scheduling solutions that fail any of the hard constraints. ([0073] “In one or more embodiments, the future state information can include timeline forecasts 136 regarding the future timing of initiation and/or completion of workflows and discrete workflow events of the active and pending patient cases.” is future timing of scheduling solutions that fail any hard constraints)
Regarding claim 9, Day teaches all of the limitations of claim 1. Day also teaches:
wherein at least some of the constraints include thresholds, wherein an output above the threshold yields a different function or value than an output below the threshold. ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework”” where the thresholds of different weighted artificial neurons yield different values; see also [0063] “The sixth rule states that patients arriving super early ( e.g., which can be relative to a defined threshold) of their scheduled start time will be assigned to beds not traditionally staffed” where meeting the threshold yields a different function)
Regarding claim 10, Day teaches all of the limitations of claim 1. Day also teaches:
wherein the goals further include sequentially meeting all the hard constraints, a majority of the medium constraints, and some of the soft constraints. (see “patient sequence and timing (140)” above, where the goals are sequentially met; 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.” where the constraints are priority levels to meet)
Regarding claim 11, Day teaches all of the limitations of claim 1. Day also teaches:
wherein the hard, medium, and soft constraints (see “priority rankings” above) include patient constraints, mobile care unit constraints, and general constraints, the computer-implemented method further comprising: applying the patient constraints, the mobile care unit constraints, and the general constraints ([0042] “current state data 102 can include information regarding current and pending patient cases of the medical facility system, including case status information with tracking of progression of workflow events, current patient status, and the like. 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 comprises patient, mobile car unit, and general constraints.) to one or more of the new patient, the mobile care units, and a market where the computer-implemented method is being performed. ([0033] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include essentially any medical facility system with limited/fixed resources that provides medical treatment to patients in accordance with one or more defined workflows (or care pathways), wherein the timing of the workflows can be impacted by variable operating states/ conditions of the dynamic medical system.” Where the management module uses state data to apply said constraints)
Regarding claim 12, Day teaches all of the limitations of claim 1. Day also teaches:
wherein receiving the new patient into the solver is responsive to performing a threshold evaluation on the new patient to determine eligibility for a health care service to be rendered in the new patient's home by a mobile care unit. ([0093] “The patient placement optimizer 122 can determine where to place patients at different procedural areas throughout their workflows based on relevant parameters included in the current state data… the patient placement optimizer 122 can determine which patients to place in specific preoperative, interoperative and postoperative areas/beds at different times during their preoperative process that accounts for the current and future workflow timing of all cases, considers all patients different medical needs at different times…, considers placement prioritization constraints in view of bed availability …, considers staffing constraints in view of current and future staffing assignments/availability …, and the like” where the patient optimizer performs a threshold evaluation based on relevant parameters [comprising eligibility for a healthcare service to be rendered])
Regarding claim 13, Day teaches all of the limitations of claim 1. Day also teaches:
wherein the hard, medium, and soft constraints have differing weighting factors. ([0073] “forecasting component 108 can include case timeline forecasting component 110 to forecast the timeline information (e.g., the timeline forecasts) using one or more machine learning/AI techniques based on relevant factors included in the current state data 102” where the artificial neurons in the framework use a weighted factor to process information; see optionally [0087] “the timeline forecasts 136 and the resource demand forecasts 138 to converge on optimal solutions regarding patient sequencing and timing, patient placement and/or resource allocation that “best” achieves and/or balances (e.g., in accordance with defined weights) the one or more optimization objectives” where the)
Regarding claim 14, Day teaches all of the limitations of claim 1. Day also teaches:
wherein the weighting factors are each a fixed value or a function. ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework”, where the artificial neurons in a machine learning model use weighted values to create a working neural network; see optionally [0072] “the optimization criteria data 132e can also include weights regarding the relative importance of the respective objectives that can be used by the optimization component 118 in formulating an optimization solution that balances and/or prioritizes the objectives according to their weights. The weights can be predefined and/or user adjusted as appropriate to meet the systems objectives in different contexts (e.g., the weights can vary for different time-frames, different days of the week, different procedures, different surgeons, etc.).”)
Regarding claim 15, Day teaches all of the limitations of claim 1. Day also teaches:
further comprising: identifying one or more remote caregivers, ([0036] “These different areas of the perioperative system are generally associated with different types of staff ( e.g., clinicians, technicians, etc.)… ”) and capabilities specific to each of the remote caregivers within the tool; ([0051] “the current staff information can identify the staff (e.g., nurses, physicians, technicians, etc.)… the operating conditions data 102b can also include information regarding the current staff fatigue level, stress level and the like (e.g., captured via one or more medical monitoring/biofeedback devices).” where the operating conditions of the staff are specific capabilities)
setting goals for optimizing scheduling of the remote caregivers, at least one of which is maximizing value points, within the tool; ([0051] “information regarding current surgeon activity can be used by the optimization component 118 to facilitate determining how to manage operations of the medical facility system in real-time” where the optimization component [comprising the tool] manages operations [comprising setting goals for optimizing scheduling] of the surgeon [comprising the caregiver])
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the remote caregivers, using the constraint solver; ([0046] “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 identifying changes in case scheduling [i.e., solving scheduling options] as new cases are added [i.e., adding new patients] are serviced by the caregivers)
and scheduling the new patient within a time slot serviced by one of the remote caregivers that maximizes the goals, while meeting the hard, medium, and soft constraints. ([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” where optimization component’s sequencing and timing [i.e., timeslot] schedules servicing for the medical facility system [comprising mobile care unit])
Regarding claim 16, Day teaches:
A mobile health care unit routing tool comprising: ([0004] “a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory.”
a datastore ([0135] “computer 1202”) comprising: hard constraints; medium constraints; soft constraints, ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria”) wherein at least some of the constraints include functions that yield value points; ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria” where the heuristic based optimization priorities incoming information by categorizing the information based on preceding criteria)
resources including one or more mobile care units, ([0033] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include… ambulatory services system”)
and capabilities specific to each of the mobile care units, ([0033] “the dynamic medical facility system can include but is not limited to: a hospital, a specialized hospital unit, a surgery center, a specialized care provider facility ( e.g., a clinic or office), an outpatient facility, an ambulatory services system, a nursing home facility, an imaging/diagnostic facility, a traveling/in-home patient care system, a rehabilitation provider system, and the like” see also [0058] “The staff data 132c can also include information regarding their different qualifications, capabilities, authorizations for performing certain workflow events, skill levels, proficiency levels, performance levels, and the like.” where the staff data is associated with the facility system management module [comprising the mobile care unit]))
and goals for optimizing routing of the mobile care units, at least one of which is maximizing value points; ([0031] “the medical facility system management module 104 provides real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process.” Where the facility system management module [comprising the mobile care units] provides real-time decision support regarding patient’s arrival and moving [i.e., optimizing routing] by maximizing value points)
and a processor ([0004] “a system is provided that comprises … a processor that executes the computer executable components stored in the memory”) to execute an artificial intelligence enabled constraint solver that includes a trained model that predicts scheduling feasibility to: ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework”; see also [0027] “a management system is provided that consumes real-time data feeds from IT platforms associated with various modality specific and phase specific components of the perioperative system and performs a complex optimization routine across the various the components as the state and context changes over the course of the day (e.g., as patients arrive early or late, add-ons/cancelations occur, schedules change, staff move between areas, etc.) to optimally place and sequence arriving and transitioning patients in real time.” comprises a constraint solver)
apply the hard, medium, and soft constraints to a new patient received within the tool; ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria”; see also [0101] “The optimization component 118 can further regularly or continuously employ the forecasted data 406 as input one or more optimization heuristics to regularly or continuously determine reactive solutions data 412 for the dynamic medical facility system (e.g., including the patient sequence and timing solutions 140, the patient placement solutions 142 and/or the resource allocation solutions 144).”; see also “receive current state data” and “constraints” where applying constraints to new patients is continuous)
iteratively solve scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver; ([0046] “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 scheduling )
and schedule the new patient in a time slot within one of the queues of patients serviced by an assigned one of the mobile care units that maximizes the goals, while satisfying the constraints, ([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” where optimization component’s sequencing and timing [i.e., timeslot] schedules servicing for the medical facility system [comprising mobile care unit]; see optionally [0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)”)) wherein a scheduling output ([0034] “depending on the type of medical facility system and the type of patient care workflows that are performed, a variety of variable operating states/conditions of the dynamic medical facility system can influence the timing of the workflows, such as changes in patient and staff scheduling (e.g., associated with cancelations, additions, staff members inability to arrive or work as scheduled, etc.), timing of arrival of patients, staff and other resources (e.g., ambulatory services, medical equipment/supplies, etc.), occurrence of medical complications, arrival of emergency patients, inefficient clinician performance (e.g., procedures taking longer than expected), occurrence of procedural errors, and a variety of other factors”) is stored in the datastore and sent to the assigned mobile care unit via a network interface for execution. ([0098] “With these embodiments, one or more features and functionalities of the system 100 can be deployed as a web-application, a cloud-application, a thin client application, a thick client application, a native client application, a hybrid client application, or the like, wherein one or more of the front-end components (e.g., reporting component 126) are provided at client device (not shown) and one or more of the back-end components (e.g., the data collection component 112, care outcomes forecasting component 114, etc.) are provided in the cloud, on a virtualized server, a virtualized data store, a remote server, a remote data store, a local data center, etc. (not shown), and accessed via a network (e.g., the Internet). In this regard, the current state data systems/sources 102, the medical facility system management module 104, one or more components of the medical facility management module, the historical state data 132 and/or the medical facility system data 132 can be physically separated yet communicatively coupled via one or more networks.” Which comprises the scheduling of patients as information passed along via the network)
Regarding claim 16, Day does not explicitly teach, as taught by Watson:
each of the mobile care units being a vehicle equipped with medical equipment and personnel, wherein the capabilities are used to match patients to mobile care units based on constraint satisfaction ([0005] “in the methods, assigning the transport vehicle by the hard asset deployment module, and optionally assigning the one or more crew members by the crew assignment module, may comprise use of the vehicle profile dataset and a crew profile dataset to calculate weight, balance and fuel requirements.” Where the profile datasets comprise information to support patient matching based on constraint satisfaction)
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 integrating Day’s decision modeling into network vehicles that transport patients. This would have increased the accuracy of decisions for treating patients, thereby making a safer standard of care. Watson is adaptable to Day as both inventions utilize computing systems to triage patients for effectively managing resources. Day would have found Watson’s teaching while searching for improvements to the existing communication methods as Watson indicates in paragraph [0003] “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.”
Regarding claim 17, Day teaches all of the limitations of claim 16. Day also teaches:
wherein a function that yields value points includes a variable from an output of another of the functions that yield value points. ([Figure 1] “reception component” and “optimization component” where each component [comprising a function] processes data [comprising yields value points] for the other component [comprising from an output of another function])
Regarding claim 18, Day teaches:
A computer-implemented method for mobile health care unit routing comprising:
loading hard, medium, and soft constraints into a mobile care unit dispatching tool, ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria”) wherein the tool includes a memory ([0004] “a system is provided that comprises a memory that stores computer executable components”) storing an artificial intelligence enabled constraint solver ([0084] “In some embodiments, the case timeline forecasting component 110 and/or the resource demand forecasting component 114 can employ one or more artificial intelligence techniques”; see also [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 (e.g., including the timeline forecasts 136 and/or the resource demand forecasts 138), and defined system constraints (e.g., system architecture constraints, defined workflow constraints, defined staffing constraints, and defined system rule/policy based constraints, as respectively provided by the medical facility system data 132) that control or influence patient sequencing and timing, placement and/or resource allocation.” Where the use of artificial intelligence comprises a constraint solver) and a processor ([0004] “a system is provided that comprises … a processor that executes the computer executable components stored in the memory”) configured to execute the constraint solver to apply the constraints to patient scheduling operations ([0027] “a management system is provided that consumes real-time data feeds from IT platforms associated with various modality specific and phase specific components of the perioperative system and performs a complex optimization routine across the various the components as the state and context changes over the course of the day (e.g., as patients arrive early or late, add-ons/cancelations occur, schedules change, staff move between areas, etc.) to optimally place and sequence arriving and transitioning patients in real time.”; see also [0086] above)
wherein at least some of the constraints include functions that yield value points; ([Figure 4’s] “forecasted data” above, where forecasted data are value points; see optionally [0004] “an optimization component that employs a heuristic-based optimization mechanism to determine optimal reactive solutions regarding patient sequencing,” where heuristic mechanisms comprise functions that provide data points)
identifying one or more mobile care units, ([0033] “The dynamic medical facility system controlled and/or managed by the medical facility system management module 104 can include… ambulatory services system”) and capabilities specific to each of the mobile care units within the tool, ([0033] “the dynamic medical facility system can include but is not limited to: a hospital, a specialized hospital unit, a surgery center, a specialized care provider facility ( e.g., a clinic or office), an outpatient facility, an ambulatory services system, a nursing home facility, an imaging/diagnostic facility, a traveling/in-home patient care system, a rehabilitation provider system, and the like” see also [0058] “The staff data 132c can also include information regarding their different qualifications, capabilities, authorizations for performing certain workflow events, skill levels, proficiency levels, performance levels, and the like.” where the staff data is associated with the facility system management module [comprising the mobile care unit])
setting goals for optimizing routing of the mobile care units, at least one of which is maximizing value points, within the tool; ([0031] “the medical facility system management module 104 provides real-time decision support regarding how to optimally place and sequence arriving and transitioning patients as they arrive and move through a dynamic medical facility system to facilitate optimizing the efficiency and quality of the medical care delivery process.” Where the facility system management module [comprising the mobile care units] provides real-time decision support regarding patient’s arrival and moving [i.e., optimizing routing] by maximizing value points)
receiving a new patient ([Figure 4] “receive current state data” where current state data includes a new patient) into an artificial intelligence enabled constraint solver within the tool, wherein the constraint solver includes a trained model that predicts scheduling feasibility; ([0072] “The forecasting component 108 can further employ a machine learning/artificial intelligence (AI) framework”; see also [0027] “a management system is provided that consumes real-time data feeds from IT platforms associated with various modality specific and phase specific components of the perioperative system and performs a complex optimization routine across the various the components as the state and context changes over the course of the day (e.g., as patients arrive early or late, add-ons/cancelations occur, schedules change, staff move between areas, etc.) to optimally place and sequence arriving and transitioning patients in real time.” comprises a constraint solver)
applying the hard, medium, and soft constraints to the new patient; ([Figure 4] “(410) Employ a heuristic based optimization mechanism to determine optimal reactive solutions to account for the forecasted data, the current state data, system rules/ constraints, and defined optimization criteria” see also “receive current state data” and “constraints” where applying constraints to new patients is continuous)
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one the mobile care units, using the constraint solver; ([0046] “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 identifying changes in case scheduling is solving scheduling options)
scheduling the new patient in a time slot within one of the queues of patients serviced by an assigned one of the mobile care units that maximizes the goals, while satisfying the constraints, ([0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)” see “new cases are added, cases are canceled” above, where the system is integrated with the scheduling software; see also “the sequence and timing optimizer 120 can adjust patient scheduling in real-time”; see also [0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)”)) wherein a scheduling output ([0034] “depending on the type of medical facility system and the type of patient care workflows that are performed, a variety of variable operating states/conditions of the dynamic medical facility system can influence the timing of the workflows, such as changes in patient and staff scheduling (e.g., associated with cancelations, additions, staff members inability to arrive or work as scheduled, etc.), timing of arrival of patients, staff and other resources (e.g., ambulatory services, medical equipment/supplies, etc.), occurrence of medical complications, arrival of emergency patients, inefficient clinician performance (e.g., procedures taking longer than expected), occurrence of procedural errors, and a variety of other factors”) is stored in the memory and sent to the assigned mobile care unit via a network interface for execution; ([0098] “With these embodiments, one or more features and functionalities of the system 100 can be deployed as a web-application, a cloud-application, a thin client application, a thick client application, a native client application, a hybrid client application, or the like, wherein one or more of the front-end components (e.g., reporting component 126) are provided at client device (not shown) and one or more of the back-end components (e.g., the data collection component 112, care outcomes forecasting component 114, etc.) are provided in the cloud, on a virtualized server, a virtualized data store, a remote server, a remote data store, a local data center, etc. (not shown), and accessed via a network (e.g., the Internet). In this regard, the current state data systems/sources 102, the medical facility system management module 104, one or more components of the medical facility management module, the historical state data 132 and/or the medical facility system data 132 can be physically separated yet communicatively coupled via one or more networks.” Which comprises the scheduling of patients as information passed along via the network see also [0094] “the resource allocation optimizer can further determine how to assign staff to patients/cases and physical areas of the perioperative system (e.g., units, rooms, floors, pods, bays, beds, etc.)”)
servicing the patients according to the queues of patients, the queues of patients created by the solver; ([0116] “patients receive medical treatment in accordance with a defined perioperative workflow”)
identifying a regularity within the queues of patients; ([0081] “can employ one or more timeline models 112 to forecast and/or facilitate forecasting the case timeline information ( e.g., the timeline forecasts 136) based relevant parameters included in the current state data 102, the historical state data 130 and the medical facility systems data” where case timeline information based on historical state data comprises a regularity within the queues of patients)
and applying the regularity using the solver to create future queues of patients to be serviced by one the mobile care units. ([0085] “models trained to predict case workflow timeline information based on learned correlations between timing of case workflows (including overall timing and timing of discrete workflow events) and various relevant parameters (and/or the parameter values) included in the historical state data 130 and the medical facility system data 132.” are applying the regularity using the solver, where workflows are future queues of patients to be serviced)
Regarding claim 18, Day does not explicitly teach, as taught by Watson:
each of the mobile care units being a vehicle equipped with medical equipment and personnel, wherein the capabilities are retrieved from the memory and used to match patients to mobile care units based on constraint satisfaction ([0005] “in the methods, assigning the transport vehicle by the hard asset deployment module, and optionally assigning the one or more crew members by the crew assignment module, may comprise use of the vehicle profile dataset and a crew profile dataset to calculate weight, balance and fuel requirements.” Where the profile datasets comprise information to support patient matching based on constraint satisfaction)
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 integrating Day’s decision modeling into network vehicles that transport patients. This would have increased the accuracy of decisions for treating patients, thereby making a safer standard of care. Watson is adaptable to Day as both inventions utilize computing systems to triage patients for effectively managing resources. Day would have found Watson’s teaching while searching for improvements to the existing communication methods as Watson indicates in paragraph [0003] “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.”
Regarding claim 19, Day teaches all of the limitations of claim 18. Day also teaches:
further comprising: iteratively repeating the receiving, applying, iteratively solving, scheduling, and servicing operations to create successive daily queues of patients to be serviced by the mobile care units. ([0071] “the reception component 106 can collect, extract or otherwise receive the current state data 102 continuously and/or regularly over the course of operation of the dynamic medical facility as the current state data 102 is generated by and/or entered into the one or more current state data systems/sources 101.” where continuous state data updates [i.e., receiving the data] causes the system to repeat applying, solving, scheduling, and servicing operations; see also [0046] “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).” Where patient case priorities comprise successive queues of daily cases to be serviced by the mobile care units)
Regarding claim 20, Day teaches all of the limitations of claim 19. Day also teaches:
further comprising: identifying a regularity within the successive daily queues of patients; ([0081] “can employ one or more timeline models 112 to forecast and/or facilitate forecasting the case timeline information ( e.g., the timeline forecasts 136) based relevant parameters included in the current state data 102, the historical state data 130 and the medical facility systems data” where regularity has successive daily queues of patients)
applying the regularity using the solver to create future daily queues of patients to be serviced by one the mobile care units. (([0085] “models trained to predict case workflow timeline information based on learned correlations between timing of case workflows (including overall timing and timing of discrete workflow events) and various relevant parameters (and/or the parameter values) included in the historical state data 130 and the medical facility system data 132.” where the workflows are future daily queues of patients)
Response to Arguments
Regarding page 10, Applicant’s arguments have been fully considered but are not persuasive. Applicant argues that the claims as amended do not amount to a mental process or abstract idea, in part, because they require special computing to infrastructure and cannot be practically performed by a human mind. The Examiner respectfully disagrees. Per MPEP 2106.04(a)(2)(III) a claimed invention may encompass an abstract idea if it represents concepts that can be practically performed in the human mind (with or without the aid of pencil and paper or a computer) such as observations, evaluations, judgments, and opinions. Under the broadest reasonable interpretation, the identified features of the claim encompass mental process because it represents an emergency service provider managing a region’s equipment inventory levels while determining the best way to schedule patients based on the severity of their sickness. These steps are one of more of observations, evaluations, judgments, and opinions. Furthermore, while prong 1a is only directed to an abstract idea and not a practical application of the abstract idea, these functions can also be performed by generalized computers (see Spec. Para. [00143-144] which does not limit the computers to functionally performed the described steps using any specialized equipment). Because the identified features of the claim can be performed in the human mind, the claims are directed to an abstract idea.
Regarding page 10-11, Applicant’s arguments have been fully considered but are not persuasive. Applicant argues that the claims recite a technical improvement in the field of healthcare logistics and mobile unit coordination. The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014). Here, the Applicant’s argued problem is not a technological problem caused by the (the technological environment to which the claims are confined). The problem of scheduling a mobile care units in different geographic locations with different availabilities throughout the day was not a problem cause by the computer, is it a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a management problem. Because no technological problem is present, the claims do not provide a practical application.
Regarding page 11-12, Applicant’s arguments have been fully considered but are not persuasive. Applicant describes the claims are unconventional because the prior art does not disclose the constraint taxonomy or value-point framework of the present application. The Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis because these limitations perform steps that do not practically apply the abstract idea. To elaborate, the combination of constraint stratification, value solvers, AI solvers, real-time scheduling and dispatching, and structured data storage and retrieval, under broadest reasonable interpretation, are understood to be conventional activities of such as storing and retrieving information in memory (see MPEP 2106.05(d)(II)(iv)); electronic recordkeeping, (see MPEP 2106.05(d)(II)(iii)); and receiving or transmitting data over a network (see MPEP 2106.05(d)(II)(i)). The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions [see Spec. Para. 143-144]) is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.). The additional elements that were found to represent extra-solution activity were analyzed and determined to represent well-understood, routine, conventional activities in the field. As such, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea and the claims are not subject matter eligible.
Regarding page 12-14, Applicant’s arguments have been fully considered but are not persuasive. Applicant argues that the prior art does not teach stratified constraints, embedded value-generating functions, and goal-based optimization. MPEP 2111 states that claims must be given their broadest reasonable interpretation in light of the specification. The Examiner maintains that the reference cited teach the claims. To elaborate, hard, medium, and soft constraints can incorporate a wide array of categorization, which is included in heuristic functions used to prioritize and sort data, as is the case in Day. Additionally, Day’s previous recited material elaborates on the teachings of value functions and further describes heuristic functions also incorporate heuristic-based optimization which comprises associating functions that lead to value points. Similarly, these heuristic based optimization mechanisms encompass goal-based optimization, as previously applied. The claims, as amended, are addressed with the same rationale and elaborated upon ion the previous 103 rejection, written above.
Additional Considerations
Balwani et al. (US20160253464) discloses an appointment healthcare system that facilitate appointment checks at various locations including health service centers, and improve accuracy and efficiency of appointment scheduling.
Thomas et al. (US20210098090) discloses a system that employs machine learning models to identify complex patients and predict patient outcomes.
Myers et al. (US20210217529) discloses a system assesses clinical risk of a patent using various systems like an automobile computing system.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.A.S/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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