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 .
Notice to Applicant
1. Claims 1, 19, and 20 have been amended. Claims 4, 5, 12, 14, and 18 have been canceled. Now, claims 1-3, 6-11, 13, 15-17, and 19-25 are pending.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on August 4, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-3, 6-11, 13, 15-17, and 19-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 19, and 20 are drawn to a process (method), a machine (device), and an apparatus (non-transitory computer-readable storage medium), each of which is within the four statutory categories. Claims 2, 3, 6-11, 13, 15-17, and 21-25 are further directed to an abstract idea on the grounds set out in detail below.
4. Claim 1 is directed to an abstract idea without significantly more. The claim, as a whole, falls under the grouping of mental processes and a method of organizing human activity of the Subject Matter Groupings of Abstract Ideas enumerated in Section I of the 2019 Revised Patent Eligibility Guidance.
Claim 1 recites the following elements:
Obtaining, from a first user, information about a subject….., the information comprising a diagnosis for the subject;
obtaining medical information about the subject …….., the medical information comprising one or more data types from a group comprising text result data, imaging data, and/or other 'omics data
identifying, based on the information about the subject, a treatment plan corresponding to the diagnosis and based on the medical information about the subject …………., the treatment plan comprising a set of tasks involving the subject, the set of tasks including a first subset of core tasks and a second subset of additional tasks;
generating…………a first set of parameters for the first subset of core tasks required by the treatment plan, wherein the first set of parameters indicates one or more timing windows for the set of tasks;
generating……a second set of parameters for the second subset of additional tasks based on at least a subset of the information about the subject, at least a subset of the first set of parameters for the first subset of core tasks, and additional information obtained……, wherein the second set of parameters comprises one or more timing parameters corresponding to performance times within the one or more timing windows and one or more ownership parameters;
identifying…..one or more additional tasks related to the set of tasks;
generating a resource allocation schedule for the subject using the set of tasks, the one or more additional tasks, first set of parameters, and the second set of parameters, the generating including:
assigning a first set of one or more tasks of the resource allocation schedule to a first entity; and
assigning a second set of one or more tasks of the resource allocation schedule to a second entity;
causing generation of respective scheduling data for the first entity and the second entity wherein the scheduling data corresponds to the performance times;
storing the resource allocation schedule ……. accessible to the first and the second entities …….. associated with the first and second entities;
providing the respective scheduling data to the first and second entities, wherein providing the respective scheduling data comprises causing display of appointment information;
receiving, from a second user or system, a health update about the subject that causes an adjustment of priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second entity from another resource allocation schedule;
in accordance with receiving the health update, generating an updated resource allocation schedule by updating one or more parameters of the resource allocation schedule according to the adjustment of priority, including generating updated respective scheduling data, wherein the updated respective scheduling data corresponds to different performances times than the respective scheduling data;
providing an indication of the updated resource allocation schedule to the first user;
providing the updated respective scheduling data to the first and second entities via respective notifications, wherein the respective notifications are provided with respective options for the first and second entities to override the updated respective scheduling data; and
replacing, ….., the stored resource allocation schedule with the updated resource allocation schedule, such that the updated resource allocation schedule is accessible via the first and second entities.
As drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including updated resource and schedule allocation, therefore, the process falls under a concept performed in the human mind (including an observation, evaluation, judgment, and opinion), which falls under a mental process. Accordingly, this Step 2A Prong 1 analysis concludes that claim 1 recites an abstract idea.
Additionally, as drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, obtaining medical information about a patient, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including updated resource and schedule allocation and sending a notification of the updated/replaced scheduling data, therefore, the process falls under managing personal behavior or relationships or interactions between people, which falls under organizing human activity. Accordingly, this Step 2A Prong 1 analysis concludes that claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. Beyond the limitations which recite the abstract idea, the claim includes the following additional element:
A user interface, one or more medical databases, a deterministic rules engine, an artificial intelligence (AI) engine, one or more databases, and a calendar application, inter-operable communications between respective devices, (see Specification paragraphs [0018], [0021], [0027], [0029], [0030]).
These elements, individually and in combination, are recited at high-levels of generality as generic computing components, used in ordinary capacities, such that it amounts to merely using a computer as a tool to perform the abstract idea. See MPEP § 2016.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and do not provide improvements to the functioning of computing systems or to another technology or technical field. This Step 2A Prong 2 analysis concludes that claim 1 is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, when considered individually and in combination, amount to merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea. Merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea cannot provide an inventive concept.
Accordingly, independent claim 1 does not qualify as patent-eligible subject matter.
The dependent claims (claims 2, 3, 6-11, 13, 15-17, and 21-25) have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, claims 2, 3, 6-11, 13, 15-17, and 21-25, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the dependent claim 2 includes the following additional elements: at least one of the deterministic rules engine and the AI engine; the dependent claim 8 includes the following additional elements: via the AI engine; and dependent claim 10 includes a first machine learning model of the AI engine and a second machine learning model of the AI engine; dependent claim 11 includes via the AI engine; dependent claim 13 includes via the AI engine; dependent claim 15 includes the AI engine, and dependent claim 21 includes one or more databases and the AI engine, claim 24 includes the AI engine, and claim 25 includes a display. These elements, individually and in combination, are recited at high-levels of generality as generic computing components, used in their ordinary capacities, such that they amount to merely using a computer as a tool to perform the abstract idea. See MPEP § 2016.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and do not provide improvements to the functioning of computing systems or to another technology or technical field. This Step 2A Prong 2 analysis concludes that dependent claims 2, 8, 10, 11, 13, 15, and 21 are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, when considered individually and in combination, amount to merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea. Merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea cannot provide an inventive concept.
The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the dependent claims do not introduce additional elements; and performing the further narrowed abstract ideas of the dependent claims on the additional elements of the independent claims, individually or in combination, does not impose any meaningful limits on practicing the abstract ideas and does not provide improvements to the functioning of computing systems or to another technology or technical field; therefore, the claims amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Similarly, the additional recited limitations of the dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
5. Claim 19 is directed to an abstract idea without significantly more. The claim, as a whole, falls under the grouping of mental processes and a method of organizing human activity of the Subject Matter Groupings of Abstract Ideas enumerated in Section I of the 2019 Revised Patent Eligibility Guidance.
Claim 19 recites the following elements:
obtaining, from a first user, information about a subject…..the information comprising a diagnosis for the subject;
obtaining medical information about the subject …….., the medical information comprising one or more data types from a group comprising text result data, imaging data, and/or other 'omics data;
identifying, based on the information about the subject, a treatment plan corresponding to the diagnosis and based on the medical information about the subject………, the treatment plan comprising a set of tasks involving the subject, the set of tasks including a first subset of core tasks and a second subset of additional tasks;
generating….a first set of parameters for the first subset of core tasks required by the treatment plan, wherein the first set of parameters indicates one or more timing windows for the set of tasks;
generating….a second set of parameters for the second subset of additional tasks based on at least a subset of the information about the subject, at least a subset of the first set of parameters for the first subset of core tasks, and additional information obtained….., wherein the second set of parameters comprises one or more timing parameters corresponding to performance times within the one or more timing windows and one or more ownership parameters;
identifying…..one or more additional tasks related to the set of tasks;
generating a resource allocation schedule for the subject using the set of tasks, the one or more additional tasks, first set of parameters, and the second set of parameters, the generating including:
assigning a first set of one or more tasks of the resource allocation schedule to a first entity; and
assigning a second set of one or more tasks of the resource allocation schedule to a second entity;
causing generation of respective scheduling data for the first entity and the second entity, wherein the scheduling data corresponds to the performance times;
storing the resource allocation schedule …….accessible to the first and second entities ………associated with the first and second entities;
providing the respective scheduling data to the first and second entities, wherein providing the respective scheduling data comprises causing display of appointment information;
receiving, from a second user or system, a health update about the subject that causes an adjustment of priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second entity from another resource allocation schedule;
in accordance with receiving the health update, generating an updated resource allocation schedule by updating one or more parameters of the resource allocation schedule according to the adjustment of priority, including generating updated respective scheduling data, wherein the updated respective scheduling data corresponds to different performances times than the respective scheduling data;
providing an indication of the updated resource allocation schedule to the first user;
providing the updated respective scheduling data to the first and second entities via respective notifications, wherein the respective notifications are provided with respective options for the first and second entities to override the updated respective scheduling data; and
replacing, ….., the stored resource allocation schedule with the updated resource allocation schedule, such that the updated resource allocation schedule is accessible via the first and second entities..
As drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including providing updated task and allocation scheduling data, therefore, the process falls under a concept performed in the human mind (including an observation, evaluation, judgment, and opinion), which falls under a mental process. Accordingly, this Step 2A Prong 1 analysis concludes that claim 19 recites an abstract idea.
Additionally, as drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, obtaining medical information about a patient, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including updated resource and schedule allocation and sending a notification of the updated/replaced scheduling data, therefore, the process falls under managing personal behavior or relationships or interactions between people, which falls under organizing human activity. Accordingly, this Step 2A Prong 1 analysis concludes that claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. Beyond the limitations which recite the abstract idea, the claim includes the following additional element:
one or more processors, memory, one or more programs stored in the memory, a user interface, one or more medical databases, via a deterministic rules engine, via an artificial intelligence (AI) engine, one or more databases.
These elements, individually and in combination, are recited at high-levels of generality as generic computing components, used in ordinary capacities, such that it amounts to merely using a computer as a tool to perform the abstract idea. See MPEP § 2016.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and do not provide improvements to the functioning of computing systems or to another technology or technical field. This Step 2A Prong 2 analysis concludes that claim 19 is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, when considered individually and in combination, amount to merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea. Merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea cannot provide an inventive concept.
Accordingly, independent claim 19 does not qualify as patent-eligible subject matter.
6. Claim 20 is directed to an abstract idea without significantly more. The claim, as a whole, falls under the grouping of mental processes and a method of organizing human activity of the Subject Matter Groupings of Abstract Ideas enumerated in Section I of the 2019 Revised Patent Eligibility Guidance.
Claim 20 recites the following elements:
obtaining, from a first user, information about a subject….., the information comprising a diagnosis for the subject;
obtaining medical information about the subject ………, the medical information comprising one or more data types from a group comprising text result data, imaging data, and/or other 'omics data
identifying, based on the information about the subject, a treatment plan corresponding to the diagnosis and based on the medical information about the subject ……, the treatment plan comprising a set of tasks involving the subject, the set of tasks including a first subset of core tasks and a second subset of additional tasks;
generating, via a deterministic rules engine, a first set of parameters for the first subset of core tasks required by the treatment plan, wherein the first set of parameters indicates one or more timing windows for the set of tasks;
generating, via an artificial intelligence (AI) engine, a second set of parameters for the second subset of additional tasks based on at least a subset of the information about the subject, at least a subset of the first set of parameters for the first subset of core tasks, and additional information obtained from one or more databases, wherein second set of parameters comprises one or more timing parameters corresponding to performance times within the one or more timing windows and one or more ownership parameters;
identifying, via the AI engine, one or more additional tasks related to the set of tasks;
generating a resource allocation schedule for the subject using the set of tasks, the one or more additional tasks, first set of parameters, and the second set of parameters, the generating including:
assigning a first set of one or more tasks of the resource allocation schedule to a first entity; and
assigning a second set of one or more tasks of the resource allocation schedule to a second entity;
causing generation of respective scheduling data for the first entity and the second entity wherein the scheduling data corresponds to the performance times;
storing the resource allocation schedule ….. accessible to the first and the second entities ……associated with the first and second entities;
providing the respective scheduling data to the first and second entities, wherein providing the respective scheduling data comprises causing display of appointment information;
receiving, from a second user or system, a health update about the subject that causes an adjustment of priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second entity from another resource allocation schedule;
in accordance with receiving the health update, generating an updated resource allocation schedule by updating one or more parameters of the resource allocation schedule according to the adjustment of priority, including generating updated respective scheduling data, wherein the updated respective scheduling data corresponds to different performances times than the respective scheduling data;
providing an indication of the updated resource allocation schedule to the first user;
providing the updated respective scheduling data to the first and second entities via respective notifications, wherein the respective notifications are provided with respective options for the first and second entities to override the updated respective scheduling data; and
replacing the stored resource allocation schedule with the updated resource allocation schedule, such that the updated resource allocation schedule is accessible via the first and second entities..
As drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including providing updated task and allocation scheduling data, therefore, the process falls under a concept performed in the human mind (including an observation, evaluation, judgment, and opinion), which falls under a mental process. Accordingly, this Step 2A Prong 1 analysis concludes that claim 20 recites an abstract idea.
Additionally, as drafted, these elements represent a process that, under its broadest reasonable interpretation, encompasses obtaining patient information, obtaining medical information about a patient, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including updated resource and schedule allocation and sending a notification of the updated/replaced scheduling data, therefore, the process falls under managing personal behavior or relationships or interactions between people, which falls under organizing human activity. Accordingly, this Step 2A Prong 1 analysis concludes that claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. Beyond the limitations which recite the abstract idea, the claim includes the following additional elements:
A non-transitory computer-readable storage medium storing one or more programs, a computing device having one or more processors and memory, a user interface, one or more medical databases, via a deterministic rules engine, via an artificial intelligence (AI) engine, one or more databases, via inter-operable communications between respective devices.
These elements, individually and in combination, are recited at high-levels of generality as generic computing components, used in their ordinary capacities, such that they amount to merely using a computer as a tool to perform the abstract idea. See MPEP § 2016.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and do not provide improvements to the functioning of computing systems or to another technology or technical field. This Step 2A Prong 2 analysis concludes that claim 20 is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, when considered individually and in combination, amount to merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea. Merely using a computer, in its ordinary capacity, as a tool to perform an abstract idea cannot provide an inventive concept.
Accordingly, independent claim 20 does not qualify as patent-eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claims 1-3, 6-11, 13, 15-17, and 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2020/0411170, Brown, et al., hereinafter Brown in view of United States Patent Application Publication Number 2017/0124526, Sanderford, et al., hereinafter Sanderford.
8. Regarding claim 1, Brown discloses a method of task management, comprising:
Obtaining, from a first user, information about a subject via a user interface, the information comprising a diagnosis for the subject, (para. 44, the static/semi-static system data can include information stored one or more databases regarding defined system protocols, regulations, operating requirements, employee administrative information, patient electronic health records (EHRs), patient imaging studies, patient laboratory studies, clinical orders/notes, patient preference information and para. 73, the care plan information can be automatically generated and provided by an artificial intelligence (AI) system configured to evaluate a patient's condition, diagnosis, needs and medical history and generate a care plan accordingly);
obtaining medical information about the subject from one or more medical databases, the medical information comprising one or more data types from a group comprising text result data, imaging data, and/or other 'omics data, (para. 44, the static/semi-static system data can include information stored one or more databases regarding defined system protocols, regulations, operating requirements, employee administrative information, patient electronic health records (EHRs), patient imaging studies, patient laboratory studies, clinical orders/notes, patient preference information, employee performance records);
identifying, based on the information about the subject, a treatment plan corresponding to the diagnosis and based on the medical information about the subject from the one or more medical databases, the treatment plan comprising a set of tasks involving the subject, the set of tasks including a first subset of core tasks and a second subset of additional tasks, (para. 44, the static/semi-static system data can include information stored one or more databases regarding defined system protocols, regulations, operating requirements, employee administrative information, patient electronic health records (EHRs), patient imaging studies, patient laboratory studies, clinical orders/notes, patient preference information, employee performance records, para. 73, the care plan information can be automatically generated and provided by an artificial intelligence (AI) system configured to evaluate a patient's condition, diagnosis, needs and medical history and generate a care plan accordingly, and para. 84, depending on the type of operating entity and timeframe evaluated, the healthcare tasks can include tasks scheduled for performance as specific points in time (e.g., patient appointments scheduled for specific dates and times), healthcare tasks scheduled and/or requested for performance over a relatively recent timeframe or window of time (e.g., the next hour, the next 24 hours, between 2:00 pm and 5:00 pm, etc.), healthcare tasks that need to be performed as soon as possible (e.g., urgent/critical tasks));
generating, via a deterministic rules engine, a first set of parameters for the first subset of core tasks required by the treatment plan, wherein the first set of parameters indicates one or more timing windows for the set of tasks, (para. 56, the optimization criteria can include (but is not limited to), facilitating optimal patient flow, minimizing delay between performance of tasks, meeting fixed constraints (e.g., regarding timing and order, location, quality/standard of care, etc.), meeting patient preferences/needs, maximizing utilization of resources, para. 65, The rules/requirements associated with a task can also include information identifying or indicating a relative priority level of a task (e.g., in accordance with a defined priority level coding/ranking scheme), as well as information identifying or indicating any performance dependency constraints with other tasks, and para. 131, the task optimization analysis component 702 can employ various machine learning and/or statistical task optimization models/algorithms to facilitate determinizing how to schedule tasks and assign resources to the tasks based on the various parameters/variables described above (e.g., associated with the tasks, the patients associated with the tasks, the healthcare workers that perform the tasks, the non-human resources needed for the tasks, and in some implementations, the forecasted task demand and resource availability).);;
identifying, via the AI engine, one or more additional tasks related to the set of tasks, (para. 7, the availability analysis component can employ machine learning and artificial intelligence to facilitate determining the availability information (e.g., using one or more models developed/trained based on historical activity information for the healthcare workers and historical performance of the healthcare tasks under various operating conditions/contexts of the healthcare system), and para. 105, the task assessment module 106 can include task assessment machine learning component 318 to facilitate determining one or more of these task parameters in real-time using various suitable machine learning and/or artificial intelligence (AI)-based schemes, and para. 75, the task reporting systems can include an AI system that determines new tasks to be performed based on information regarding results/outputs of previously completed tasks, monitored changes in patient conditions/status, monitored changes in healthcare worker task performance);
generating a resource allocation schedule for the subject using the set of tasks, the one or more additional tasks, first set of parameters, and the second set of parameters, the generating including: (para. 33, a system is provided that can facilitate optimizing scheduling of different healthcare tasks and assigning resources to the different healthcare tasks in real-time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment, and para. 37, (e.g., the task assessment module 110, the resources assessment module 114, and the task scheduling and resource assignment optimization module 118) that when executed by the at least one processor 122, facilitate performance of operations defined by the executable instructions. In some embodiments, the memory 124 can also store one or more of the various data sources and/or data structures of system 100 (e.g., the healthcare information systems/sources 102, the dynamic operating data 104, the static/semi-static system data 106, the indexed task data 112, the resource availability data 116, and the task scheduling and resource assignment information 126) and para. 146, The task optimization analysis component 702 can also determine a second task scheduling and resource assignment information 126 using a second optimization model configured to determine an optimal task scheduling and resource assignment scheme using a second optimization model configured to determine an alternative scheme that focuses more heavily on meeting patient preferences and para. 153, the system can determine a first subset of available healthcare workers of to perform the currently pending healthcare tasks based on monitoring activity data for the healthcare workers (e.g., using worker activity monitoring component 504). At 1006, the system can determine a second subset of qualified healthcare workers included in the first subset of available healthcare workers based on defined worker capability information and defined capability requirements of the currently pending healthcare tasks.)
assigning a first set of one or more tasks of the resource allocation schedule to a first entity, (para. 33, a system is provided that can facilitate optimizing scheduling of different healthcare tasks and assigning resources to the different healthcare tasks in real-time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment, and para. 37, (e.g., the task assessment module 110, the resources assessment module 114, and the task scheduling and resource assignment optimization module 118) that when executed by the at least one processor 122, facilitate performance of operations defined by the executable instructions. In some embodiments, the memory 124 can also store one or more of the various data sources and/or data structures of system 100 (e.g., the healthcare information systems/sources 102, the dynamic operating data 104, the static/semi-static system data 106, the indexed task data 112, the resource availability data 116, and the task scheduling and resource assignment information 126) and para. 146, The task optimization analysis component 702 can also determine a second task scheduling and resource assignment information 126 using a second optimization model configured to determine an optimal task scheduling and resource assignment scheme using a second optimization model configured to determine an alternative scheme that focuses more heavily on meeting patient preferences and para. 153, the system can determine a first subset of available healthcare workers of to perform the currently pending healthcare tasks based on monitoring activity data for the healthcare workers (e.g., using worker activity monitoring component 504). At 1006, the system can determine a second subset of qualified healthcare workers included in the first subset of available healthcare workers based on defined worker capability information and defined capability requirements of the currently pending healthcare tasks.); and
assigning a second set of one or more tasks of the resource allocation schedule to a second entity, (para. 33, a system is provided that can facilitate optimizing scheduling of different healthcare tasks and assigning resources to the different healthcare tasks in real-time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment, and para. 37, (e.g., the task assessment module 110, the resources assessment module 114, and the task scheduling and resource assignment optimization module 118) that when executed by the at least one processor 122, facilitate performance of operations defined by the executable instructions. In some embodiments, the memory 124 can also store one or more of the various data sources and/or data structures of system 100 (e.g., the healthcare information systems/sources 102, the dynamic operating data 104, the static/semi-static system data 106, the indexed task data 112, the resource availability data 116, and the task scheduling and resource assignment information 126) and para. 146, The task optimization analysis component 702 can also determine a second task scheduling and resource assignment information 126 using a second optimization model configured to determine an optimal task scheduling and resource assignment scheme using a second optimization model configured to determine an alternative scheme that focuses more heavily on meeting patient preferences and para. 153, the system can determine a first subset of available healthcare workers of to perform the currently pending healthcare tasks based on monitoring activity data for the healthcare workers (e.g., using worker activity monitoring component 504). At 1006, the system can determine a second subset of qualified healthcare workers included in the first subset of available healthcare workers based on defined worker capability information and defined capability requirements of the currently pending healthcare tasks.);
causing generation of respective scheduling data for the first and second entity wherein the scheduling data corresponds to the performance times, (Fig. 3, para. 18, example task assessment module that facilitates determining information regarding currently pending and forecasted healthcare tasks for performance by one or more operating entities of an integrated healthcare system),
storing the resource allocation schedule in a database accessible to the first and the second entities via inter-operable communications between respective devices associated with the first and second entities, (para. 38, the healthcare delivery optimization server device 108 can be or correspond to a distributed computing system including a network of interconnected devices (e.g., back-end servers, front-end servers, dedicated machines, virtual machines, client devices, etc.), machine, databases, datastores and the like and para. 51, the resource assessment module 114 can be configured to extract and evaluate the dynamic operating data 104 regarding the activity of the various healthcare workers in real-time in view of any scheduling constraints for the healthcare workers to determine resource availability data 116 regarding availability of the respective healthcare workers to perform currently pending tasks and/or upcoming tasks (e.g., known, scheduled, and/or forecasted));
providing the respective scheduling data to the first and second entities, wherein providing the respective scheduling data comprises causing display of appointment information, (para. 47, the task assessment module 110 can evaluate information for a patient identifying or indicating scheduled procedures, appointments and checkups, identifying clinical orders, defining a prescribed care plan, defining a medication regimen, defining a feeding regimen, tracking patient status, identifying patient transfer needs (including current and destination location of the patient), providing real-time feedback requesting or indicating immediate or future medical care (e.g., provided by the patient, gathered indirectly via a patient monitoring system)).
in accordance with receiving the health update, generating an updated resource allocation schedule by updating one or more parameters of the resource allocation schedule according to the adjustment of priority, including generating updated respective scheduling data, wherein the updated respective scheduling data corresponds to different performances times than the respective scheduling data, (para. 43, the dynamic operating data 104 can include information provided by patient scheduling systems, patient monitoring systems, patient tracking systems, operational data logging systems, workflow tracking systems, resource tracking/monitoring systems, and the like. In one or more implementations, the dynamic operating data 104 can include various types of information for each individual operating entity that identifies or indicates the various healthcare tasks that are needed for performance by/at the operating entity at a current point in time/or over a defined, upcoming timeframe (e.g., the current workday, the next 24 hours, the next week, the next month, etc.) to account for known and optionally forecasted patient needs at the current point in time and over. The dynamic operating data 104 can also include information regarding the state, status/condition, availability, movement, location, and other dynamic parameters associated with the healthcare resources that are needed to perform the healthcare tasks and/or the patient associated with the healthcare task and para. 75, the AI system can evaluate newly received laboratory data for a patient to determine a new task that needs to be performed in response to the specific values reflected in the laboratory data.);
providing an indication of the updated resource allocation schedule to the first user, (para. 149, the task assignment component 714 can generate and send a task assignment message to a device associated with the healthcare worker comprising information that recommends the healthcare worker perform the supplemental healthcare task during the timeslot).
Brown does not explicitly disclose the following:
generating, via an artificial intelligence (AI) engine, a second set of parameters for the second subset of additional tasks based on at least a subset of the information about the subject, at least a subset of the first set of parameters for the first subset of core tasks, and additional information obtained from one or more databases, wherein the second set of parameters comprises one or more timing parameters corresponding to performance times within the one or more timing windows and one or more ownership parameters;
receiving, from a second user or system, a health update about the subject that causes an adjustment of priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second entity from another resource allocation schedule,;
providing the updated respective scheduling data to the first and second entities via respective notifications, wherein the respective notifications are provided with respective options for the first and second entities to override the updated respective scheduling data; and
replacing, in the database, the stored resource allocation schedule with the updated resource allocation schedule, such that the updated resource allocation schedule is accessible via the first and second entities.
However, Sanderford teaches the following:
generating, via an artificial intelligence (AI) engine, a second set of parameters for the second subset of additional tasks based on at least a subset of the information about the subject, at least a subset of the first set of parameters for the first subset of core tasks, and additional information obtained from one or more databases, wherein the second set of parameters comprises one or more timing parameters corresponding to performance times within the one or more timing windows and one or more ownership parameters, (para. 149, the step of assigning based on the computed comparison S4300 can be based on a threshold 1470 according to an example. In one scenario the threshold 1470 can be one of a timing criteria or timing threshold 1472, a spatial distance 1474, or the prediction value 1140. The timing threshold 1472 can be a set time, a time duration, or a relative time based on any timer. The spatial distance 1474 threshold can be a set distance or a relative distance based on one of the sensing system 1300, the appointment location 1135, and the patient location 2220. The prediction value 1140 threshold can be a fixed value based on any prediction value associated with any appointment entry component, para. 281, Updating the initial duration estimate by replacing the estimate with the time duration measure on the timer; (ii) Summing the durations yielding improved forecast or subsequently scheduled appointments; (iii) notifying at least one of the remaining patients scheduled on that same day of the improved forecast of scheduler. This process results in a more accurate estimation of the highly variable duration appointment time by resolving timing variability on the day of the scheduled appointment, and para. 283, use of machine learning or artificial intelligence to improve the PAD estimate and restacking method, creating virtual tours of the doctor's office that are visible through the patient device, incorporating a messaging platform that can expand to allow for remote diagnosis via photo/video HIPAA compliant communication between patients and practitioners, as well as a rating/score for providers (whether publicly visible or not) so that certain providers can be ranked higher or receive specific endorsements from the scheduling system based on their performance);
receiving, from a second user or system, a health update about the subject that causes an adjustment of priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second entity from another resource allocation schedule, (para. 165, An unscheduled patient can be added into the ordered list 1110 for emergency reasons or other reasons, causing the following appointments to restack accordingly);
providing the updated respective scheduling data to the first and second entities via respective notifications, wherein the respective notifications are provided with respective options for the first and second entities to override the updated respective scheduling data, (para. 78, patient device updates the scheduling system 1000 with a patient location 2220 using the one or more sensor systems 1300. The patient device 1414 receives communication from the processing circuitry 1400. In one example the communication is a reminder of the appointment timing or a notification that the appointment timing is revised with options to accept or to reject the revised appointment timing. When the revised appointment timing is rejected the next closest appointment timing can be offered or the patient can be offered to request a new appointment timing); and
replacing, in the database, the stored resource allocation schedule with the updated resource allocation schedule, such that the updated resource allocation schedule is accessible via the first and second entities, (para. 78, para. 78, patient device updates the scheduling system 1000 with a patient location 2220 using the one or more sensor systems 1300. The patient device 1414 receives communication from the processing circuitry 1400. In one example the communication is a reminder of the appointment timing or a notification that the appointment timing is revised with options to accept or to reject the revised appointment timing. When the revised appointment timing is rejected the next closest appointment timing can be offered or the patient can be offered to request a new appointment timing, and para. 165, An unscheduled patient can be added into the ordered list 1110 for emergency reasons or other reasons, causing the following appointments to restack accordingly).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Brown with the teaching of Sanderford. As suggested by Sanderford, one would have been motivated to include these features to communicate any revised timing to the patient, thereby minimizing a total waiting time, (Sanderford - Abstract), to modify the system of Brown with the teaching of Sanderford.
9. Regarding claim 2, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the set of tasks are identified using at least one of the deterministic rules engine and the AI engine, (para. 75, the task reporting systems can include an AI system that determines new tasks to be performed based on information regarding results/outputs of previously completed tasks, monitored changes in patient conditions/status, monitored changes in healthcare worker task performance).
10. Regarding claim 3, Brown discloses the method of claim 1 as described above. Brown further discloses further comprising, prior to identifying the set of tasks:
identifying a set of treatment options for the subject based on the information about the subject, wherein each treatment option in the set of treatment options has a corresponding set of tasks, (para. 59, the task scheduling and resource assignment optimization module 118 can evaluate information for an entire operating environment (e.g., of a single entity or an integrated healthcare system) regarding what needs to be done and when, who is available to do it, and who is the best person/persons to do it, based on a variety of complex and dynamic variables, to determine how to schedule performance of the tasks with respect to time and location and how to assign resources (e.g., workers) to the tasks to ensure the tasks are performed in the most efficient and effective (e.g., in terms of achieve quality of care, in terms of minimizing costs, in terms of maximizing revenue, and the like) manner across all possible options, using the right resources at the right time for the right patient in the right place. From the perspective of the operating entity, or entities in embodiments in which the disclosed techniques are applied to an integrated healthcare system, the optimal scheduling and resource assignment scheme for all (or a grouped subset) of known tasks to be performed (e.g., within a defined, upcoming timeframe) can be selected to balance one or more of the following goals: minimize delays between performance of the healthcare tasks, ensure all healthcare tasks are delivered in accordance with defined quality and regulatory requirements, maximize utilization of available resources, minimize losses, maximize revenue, and meet patient preferences with respect to when, where and who performs healthcare tasks. In embodiments in which the disclosed techniques are applied to an integrated healthcare system involving a plurality of different operating entities, the optimal task scheduling and resource assignment scheme can further balance the goals across the entire system, further considering the many additional constraints associated with coordinating and synchronizing performance of healthcare tasks with interdependencies with respect to order of performance, timing of performance and resources used.); and
receiving a selection of a first option of the set of treatment options, wherein the set of tasks correspond to the first option, (para. 136, These components include filtering component 704, task-worker matching component 706, cost analysis component 708, task compensation evaluation component 710 and supplemental task selection component 712).
11. Regarding claim 6, Brown discloses the method of claim 1 as described above. Brown further discloses further comprising:
after generating the updated resource allocation schedule, receiving information about performance of a task of the set of tasks, (para. 75, the AI system can evaluate newly received laboratory data for a patient to determine a new task that needs to be performed in response to the specific values reflected in the laboratory data and para. 136, These components include filtering component 704, task-worker matching component 706, cost analysis component 708, task compensation evaluation component 710 and supplemental task selection component 712); and
further updating the resource allocation schedule based on the received information, wherein updating the resource allocation schedule includes updating a status of the task and one or more other tasks of the set of tasks, (para. 49, task assessment module 110 can further regularly and/or continuously update the indexed task data 112 in real-time over the course of operation the integrated healthcare system to reflect changes to the integrated healthcare system).
12. Regarding claim 7, Brown discloses the method of claims 1 and 6 as described above. Brown further discloses wherein updating the resource allocation schedule comprises adjusting timing parameters for at least a subset of the set of tasks, (para. 53, timing of imitation of tasks and status of progression through the tasks (e.g., to facilitate determining expected time of completion of the tasks), and the like. In some implementations, the resource assessment module 110 can also forecast estimated timing of completion of task that are in-progress (e.g., using one or more machine learning techniques)).
13. Regarding claim 8, Brown discloses the method of claim 1 as described above. Brown further discloses further comprising:
after generating the resource allocation schedule, receiving additional information about the subject, wherein the additional information comprises at least one of a test result for the subject, a health update for the subject, and availability of the subject, (para. 53, The resource assessment module 114 can also receive and track information regarding location of the respective healthcare workers, movement of the healthcare workers, mobility state in association with traveling from one location to another (e.g., walking, driving, riding as a passenger, etc.). In some embodiments, the resource assessment module 114 can employ one or more machine learning techniques to learn and forecast information regarding availability of certain healthcare workers based on analysis of historical dynamic operating data 104 associated with the specific healthcare workers or similar healthcare workers (e.g., with similar job titles, skill levels, location etc.) under various operating conditions/contexts of the healthcare environment and para. 80, The one or more patient monitoring systems 232 can include various systems configured to track and monitor dynamic patient data 234 information regarding the location and physiological state of patients. For example, the patient monitoring systems can 232 track location and movement data regarding the real-time location and movement of the patients. The patient monitoring systems 232 can also include systems that monitor and receive real-time physiological data for patients regarding their current physiological state from one or more biofeedback devices and/or audio/visual monitoring devices);
identifying, via the AI engine, one or more candidate tasks for the resource allocation schedule based on the additional information about the subject, (para. 59, the task scheduling and resource assignment optimization module 118 can evaluate information for an entire operating environment (e.g., of a single entity or an integrated healthcare system) regarding what needs to be done and when, who is available to do it, and who is the best person/persons to do it, based on a variety of complex and dynamic variables, to determine how to schedule performance of the tasks with respect to time and location and how to assign resources (e.g., workers) to the tasks to ensure the tasks are performed in the most efficient and effective (e.g., in terms of achieve quality of care, in terms of minimizing costs, in terms of maximizing revenue, and the like) manner across all possible options, using the right resources at the right time for the right patient in the right place. From the perspective of the operating entity, or entities in embodiments in which the disclosed techniques are applied to an integrated healthcare system, the optimal scheduling and resource assignment scheme for all (or a grouped subset) of known tasks to be performed (e.g., within a defined, upcoming timeframe) can be selected to balance one or more of the following goals: minimize delays between performance of the healthcare tasks, ensure all healthcare tasks are delivered in accordance with defined quality and regulatory requirements, maximize utilization of available resources, minimize losses, maximize revenue, and meet patient preferences with respect to when, where and who performs healthcare tasks. In embodiments in which the disclosed techniques are applied to an integrated healthcare system involving a plurality of different operating entities, the optimal task scheduling and resource assignment scheme can further balance the goals across the entire system, further considering the many additional constraints associated with coordinating and synchronizing performance of healthcare tasks with interdependencies with respect to order of performance, timing of performance and resources used.); and
providing information about the one or more candidate tasks to the first user, (para. 6, comprise a task information extraction component that receives information identifying currently pending healthcare tasks for performance by healthcare workers of a healthcare system.).
14. Regarding claim 9, Brown discloses the method of claims 1 and 8 as described above. Brown further discloses further comprising, in response to a selection of the one or more candidate tasks by the first user, generating a revised resource allocation schedule that incorporates the one or more candidate tasks, (para. 47, The task assessment component involves the extraction, evaluation and indexing of information from the healthcare information systems/sources 102 regarding healthcare tasks performed, being performed, scheduled for performance and/or anticipated (forecasted) for performance by respective operating entities included in the integrated healthcare system).
15. Regarding claim 10, Brown discloses the method of claims 1 and 8 as described above. Brown further discloses wherein the second set of parameters are generated by a first machine learning model of the AI engine, and wherein the one or more candidate tasks are identified by a second machine learning model of the AI engine, (para. 106, these machine learning and/or AI schemes can involve the development, training (e.g., by the task assessment machine learning component 318), and/or application (e.g., by the task identification component 304, the task grouping component 308, the task ordering component 310, etc.) of various machine learning models based on analysis of historical data provided by the one or more healthcare information systems/sources regarding the historical operations of the healthcare system under various dynamic operating conditions/contexts).
16. Regarding claim 11, Brown discloses the method of claim 1 as described above. Brown further discloses further comprising, in accordance with generating the resource allocation schedule, generating, via the AI engine, one or more documents corresponding to the resource allocation schedule based on the information about the subject, (para. 13, the idle time can include a time associated with a healthcare task that be used to multitask, such as time between seeing patients, time while waiting for laboratory results, and the like, in which the healthcare worker can perform a telemedicine task or review documents using mobile device).
17. Regarding claim 13, Brown discloses the method of claim 1 as described above. Brown further discloses further comprising assigning, via the AI engine, respective entities to the second subset of additional tasks, wherein the one or more ownership parameters identify the respective assigned entities, and wherein the respective assigned entities include the first entity and the second entity, (para. 9, to determine the task assignment scheme based on priority information associated with respective tasks of the currently pending tasks that identifies priority levels of the respective tasks. The task optimization analysis component can also evaluate costs associated with different task assignment schemes that assign the one or more healthcare workers to the currently pending tasks in different manners and selects the task assignment scheme based on the task assignment scheme minimizing the costs and para. 44, The static/semi-static system data 106 can include information associated with the respective operating entities, employees of the operating entities, and patients that does not change over time and/or may change at a relatively infrequent rate compared to the dynamic operating data).
18. Regarding claim 15, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the at least the subset of the information about the subject, the at least the subset of the first set of parameters for the first subset of core tasks, and the additional information are provided to the AI engine as a vector of inputs, (para. 107, the task assessment machine learning component 318 can employ various types of machine learning techniques for learning explicitly or implicitly how segment care plan information into discrete tasks, how to group tasks, and/or how to order tasks via an automatic classification system and process. Inferring or learning can employ a probabilistic or statistical-based analysis to infer an action that is to be executed. For example, in some implementations, a support vector machine (SVM) classifier can be employed).
19. Regarding claim 16, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the additional information comprises medical information and calendar information, (para. 42, the dynamic operating data 104 can include information regarding operating conditions of a healthcare operating entity (e.g., a hospital, a surgery centers, a nursing home, etc.) that constantly or regularly change over relatively short timeframes during a course of operation of a healthcare operating entity (e.g., by the second, by the minute, by the hour, by the day, etc.). For example, the dynamic operating data 104 can include information provided by patient scheduling systems, patient monitoring systems, patient tracking systems, operational data logging systems, workflow tracking systems, resource tracking/monitoring systems, and the like. In one or more implementations, the dynamic operating data 104 can include various types of information for each individual operating entity that identifies or indicates the various healthcare tasks that are needed for performance by/at the operating entity at a current point in time/or over a defined, upcoming timeframe (e.g., the current workday, the next 24 hours, the next week, the next month, etc.) to account for known and optionally forecasted patient needs at the current point in time and over).
20. Regarding claim 17, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the additional information comprises information about potential entities able to perform one or more tasks of the set of tasks and future calendar information for the potential entities, (para. 42, the dynamic operating data 104 can include information regarding operating conditions of a healthcare operating entity (e.g., a hospital, a surgery centers, a nursing home, etc.) that constantly or regularly change over relatively short timeframes during a course of operation of a healthcare operating entity (e.g., by the second, by the minute, by the hour, by the day, etc.). For example, the dynamic operating data 104 can include information provided by patient scheduling systems, patient monitoring systems, patient tracking systems, operational data logging systems, workflow tracking systems, resource tracking/monitoring systems, and the like. In one or more implementations, the dynamic operating data 104 can include various types of information for each individual operating entity that identifies or indicates the various healthcare tasks that are needed for performance by/at the operating entity at a current point in time/or over a defined, upcoming timeframe (e.g., the current workday, the next 24 hours, the next week, the next month, etc.) to account for known and optionally forecasted patient needs at the current point in time and over).
21. Regarding claim 19, this claim is rejected for the same reasons as set forth above with regard to claim 1. Brown further discloses one or more processors; memory; and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions, (para. 37, The at least one memory 124 can store executable instructions (e.g., the task assessment module 110, the resources assessment module 114, and the task scheduling and resource assignment optimization module 118) that when executed by the at least one processor 122, facilitate performance of operations defined by the executable instructions. In some embodiments, the memory 124 can also store one or more of the various data sources and/or data structures of system 100 (e.g., the healthcare information systems/sources 102, the dynamic operating data 104, the static/semi-static system data 106, the indexed task data 112, the resource availability data 116, and the task scheduling and resource assignment information 126). In other embodiments, one or more of the various data sources and/or data structures of system 100 can be stored in other memory (e.g., at a remote device or system), that is accessible to the healthcare delivery optimization server device 108 (e.g., via one or more networks).).
22. Regarding claim 20, this claim is rejected for the same reasons as set forth above with regard to claim 1. Brown further discloses a non-transitory computer-readable storage medium storing one or more programs configured for execution by a computing device having one or more processors and memory, the one or more programs comprising instructions, (para. 159, The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing).
23. Regarding claim 21, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the at least a subset of the information about the subject, the at least a subset of the first set of parameters for the first subset of core tasks, and the additional information obtained from one or more databases are provided to the AI engine as a vector of inputs, (para. 80, the dynamic patient data 234 can include real-time data regarding monitored physiological parameters of the patient, movement of the patient, appearance of the patient, and the like, that can be used to determine if clinical care is necessitated and if so, what healthcare tasks are involved (e.g., administering medication, performing a medical procedure, helping the patient out of bed, etc.), and para. 108, A learning classifier is a function that maps an input attribute vector, k=(k1, k2, k3, k4, kn), to a confidence that the input belongs to a learning class—that is, f(k)=confidence(class)).
24. Regarding claim 22, Brown discloses the method of claim 1 as described above. Brown further discloses wherein the first set of parameters further indicates a set of two or more potential owners for a particular task of the set of tasks, and wherein the one or more ownership parameters of the second set of parameters indicate a particular owner from the set of two or more potential owners, (para. 6, The computer executable components further comprise a task optimization analysis component that determines a task assignment scheme that assigns one or more of the healthcare workers to the currently pending tasks in a manner that minimizes a total amount of delay between timing of origination of the currently pending tasks and timing of initiation of performance of the currently pending tasks based on the availability information, and para. 8, wherein the task optimization analysis component restricts assignment of the one or more healthcare workers to the currently pending tasks based on the subsets).
25. Regarding claim 23, Brown discloses the method of claim 1 as described above. Brown further discloses wherein providing the respective scheduling data to the first and second entities comprises causing a set of calendar entries to be generated from the first and second entities, (Fig. 3, para. 18, example task assessment module that facilitates determining information regarding currently pending and forecasted healthcare tasks for performance by one or more operating entities of an integrated healthcare system and para. 149, the task assignment component 714 can generate and send a task assignment message to a device associated with the healthcare worker comprising information that recommends the healthcare worker perform the supplemental healthcare task during the timeslot. The task assignment component 714 can also provide the task scheduling and resource assignment information 126 to individual patients to provide a real-time schedule of activities for each patient with anticipated date/time of event and coordinates the sequencing of various activities and services to be rendered (and updated in real-time)). Examiner interprets ‘a real-time schedule of activities for each patient with anticipated date/time of event’ to encompass ‘a set of calendar entries’.
26. Regarding claim 24, Brown discloses the method of claim 1 as described above. Brown further discloses wherein generating the updated resource allocation schedule comprises identifying, via the Al engine, one or more additional candidate tasks for the resource allocation schedule based on the health update about the subject, (para. 33, a system is provided that can facilitate optimizing scheduling of different healthcare tasks and assigning resources to the different healthcare tasks in real-time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment, and para. 37, (e.g., the task assessment module 110, the resources assessment module 114, and the task scheduling and resource assignment optimization module 118) that when executed by the at least one processor 122, facilitate performance of operations defined by the executable instructions. In some embodiments, the memory 124 can also store one or more of the various data sources and/or data structures of system 100 (e.g., the healthcare information systems/sources 102, the dynamic operating data 104, the static/semi-static system data 106, the indexed task data 112, the resource availability data 116, and the task scheduling and resource assignment information 126) and para. 146, The task optimization analysis component 702 can also determine a second task scheduling and resource assignment information 126 using a second optimization model configured to determine an optimal task scheduling and resource assignment scheme using a second optimization model configured to determine an alternative scheme that focuses more heavily on meeting patient preferences and para. 153, the system can determine a first subset of available healthcare workers of to perform the currently pending healthcare tasks based on monitoring activity data for the healthcare workers (e.g., using worker activity monitoring component 504). At 1006, the system can determine a second subset of qualified healthcare workers included in the first subset of available healthcare workers based on defined worker capability information and defined capability requirements of the currently pending healthcare tasks).
27. Regarding claim 25, Brown discloses the method of claim 1 as described above. Brown further discloses wherein providing the updated respective scheduling data to the first and second entities and to the first user comprises causing display of respective updated appointment information for the first and second entities, (para. 149, the task assignment component 714 can generate and send a task assignment message to a device associated with the healthcare worker comprising information that recommends the healthcare worker perform the supplemental healthcare task during the timeslot. The task assignment component 714 can also provide the task scheduling and resource assignment information 126 to individual patients to provide a real-time schedule of activities for each patient with anticipated date/time of event and coordinates the sequencing of various activities and services to be rendered (and updated in real-time)).
Response to Arguments
28. Applicant's arguments filed April 29, 2025 have been fully considered but they are not persuasive.
A. Applicant argues that amended claim 1 is similar to Example 42 of the 2019 PEG, and submits that is inherent to claim 1 that data is transformed from at least one of the respective formats of the medical information in order to perform the "identifying, based on the information about the subject, a treatment plan corresponding to the diagnosis and based on the medical information about the subject from the one or more medical databases."
In response, Examiner respectfully disagrees. With regard to Example 42, this example is not analogous to the claims in the present application. The present claims do not follow the same fact pattern as Example 42. In the claimed invention of Example 42, the claim recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message in real time to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. The present application does not convert the data and transmit an update message to all of the users. In the limitation that Applicant points to in their remarks, “the "treatment plan," is not in the same format as the "subject diagnosis" or the "medical information about the subject from one or more medical databases, the medical information comprising one or more data types from a group comprising text result data, imaging data, and/or other 'omics data."”, this limitation of the present application under its broadest reasonable interpretation, encompasses obtaining medical information from one or more databases comprising different data types, it does NOT convert the updated information of different data types into a standardized format and transmit a message to all of the users automatically in real time when the information is updated. Therefore, the present claims are not analogous to those in example 42.
B. Applicant argues that the claims recite a specific way of identifying certain tasks with a deterministic rules engine, and using the output from that to generate additional tasks with an AI engine,
In response, Examiner respectfully disagrees. These limitations amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With regard to McRO, the features of the claims in the present application are not similar to McRO’s claimed features, because the features in the present application do not demonstrate an improvement to a technology as shown with the features in McRO. The present claims do not follow the same fact pattern as the claims in McRO. McRO claimed automated lip-synchronization of 3-D characters. Rather, the present claims describe actions that facilitate identifying certain tasks and generating additional tasks.
Lastly, in order for an alleged application of an abstract idea to be considered
eligible, it must amount to significantly more than the abstract idea (i.e., pass step 2B of
the Mayo test). As shown in the rejection above, the application of the abstract idea
recited merely applies the idea in a generic computer environment (A user interface, one or more medical databases, a deterministic rules engine, an artificial intelligence (AI) engine, one or more databases, and a calendar application, inter-operable communications between respective devices) using generic computer functions (obtaining patient information, identifying a set of tasks, generating parameters for the task, identifying additional tasks, generating a resource allocation schedule, and providing information about the tasks to the entity, including updated resource and schedule allocation). Accordingly, it does not amount to significantly more, the claims do not recite additional limitations that integrate the exception into a Practical Application, and the application of the abstract idea is therefore not eligible.
Accordingly, it does not amount to significantly more, and the application of the abstract idea is therefore not eligible.
C. Applicant argues that Brown does not teach adjusting "priority of one or more tasks of the set of tasks with respect to other tasks assigned to the first entity or second
entity from another resource allocation schedule," based on "receiving ... a health update about the subject" and "generating, via a deterministic rules engine, a first set of parameters for [a]first subset of core tasks required by the treatment plan" including "one or more timing windows," and "generating, via an artificial intelligence (AI) engine, a second set of parameters for [a] second subset of additional tasks," based on "at least a subset of the first set of parameters," including "timing parameters corresponding to performance times within the one or more timing windows".
In response, Examiner respectfully disagrees. Applicant’s arguments with respect to claims 1-3, 6-11, 13, 15-17, and 19-25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Method and system for optimizing employee scheduling in a patient care environment (US 20040039628 A1) teaches managing a health clinic, and in particular to managing/scheduling employees to work in the clinic
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amber Misiaszek whose telephone number is 571-270-1362. The examiner can normally be reached M-F 8:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682
Prosecution Insights