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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “First, Applicant submits that claim 1 does not fall into the category of abstract ideas. The Office considers mental processes as involving concepts performed in the human mind. The subject matter of claim 1 cannot be performed in the human mind even if with the aid of pen and paper, such as at least for example, "instructing the task to be executed by the first user according to the schedule.",” Examiner respectfully disagrees. As can be seen below with respect to Step 2A, Prong 1, the present claims recite several abstract limitations related to “Certain Methods of Organizing Human Activity.” Examiner notes that the cited limitation above, as drafted, under considerations of the broadest reasonable interpretation, does not recite any particular additional elements for consideration under Step 2A, Prong 2 or Step 2B. Therefore, the limitation related to “instructing,” as drafted, is part of the abstract limitations for consideration under Step 2A, Prong 1.
Regarding Applicant’s assertion of “Applicant submits that the claimed method does not recite a mental process, but rather an analytical method for instructing the execution of tasks according to a generated schedule and assigned to an identified user based on analyzing the predicted values of the generated analytical report. Based on such instructions task associated with a given location (i.e., first geographic location) is assigned to a relevant user and scheduled for execution based on scheduling factors.,” Examiner respectfully disagrees. Examiner respectfully asserts that instructing tasks or generating schedules, as drafted, under consideration of the broadest reasonable interpretation, is part of the abstract limitations for consideration under Step 2A, Prong 1. These limitations are related to “Certain Methods of Organizing Human Activity.” These limitations do not provide any improvements to any technology or technical field.
Regarding Applicant’s assertion of “Therefore, the claimed method improves the generation of schedules and the assignment of tasks and improves the functioning of the data processing application. Applicant respectfully submits that the claimed subject matter does not recite an abstract idea that is not integrated into a practical application.,” Examiner respectfully disagrees. Examiner respectfully asserts that an improvement related to generating schedules and assigning tasks would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “Indeed, as described in the application as filed: a system can be implemented to provide real-time monitoring of scheduling performance and feedback on action item execution. This allows for continuous optimization of the scheduling process based on user feedback and evolving business needs. The feedback loop enables iterative improvements to the system, resulting in more accurate insights and enhanced scheduling performance over time. In some instances, by leveraging an existing predictive analytics service as part of an analytics solution, key aspects of the current scheduling feature can be improved. Specifically, the recipient selection process can be optimized, and the timing of dashboard delivery can be improved. By building upon smart algorithms already embedded in an analytics cloud solution, greater intelligence (including insight, data analysis, definition of content generation, task patterns, etc.) can be embedded into these scheduling functionalities, as in the below listed key aspects. See Application at paragraph [0037].,” Examiner respectfully disagrees. Examiner preliminarily notes that the present claims do not recite, under considerations of the broadest reasonable interpretation, real-time monitoring, feedback on action item execution, a feedback loop enabling iterative improvements on the system, or even the cloud. Applicant is reminded that it is impermissible to import limitations from the specification into the claims. (See MPEP 2111.01(II)) In order for Applicant to be given the argued interpretation, the claims must positively recite the argued subject matter. Therefore, Examiner has not found Applicant’s assertion related to the improvements cited in the specification as persuasive.
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Further, while Brown's assignment is based on how well the worker can fulfill the time constrained based on worker's availability and current location (id.), Brown has not been shown to disclose "assigning a task to be executed by a first user," the first user being identified "based on analyzing the predicted values of the analytical report" that are for measurement data for a data object. Rather, Brown evaluates performance data for the workers themselves, which is not a predicted measurement data for a data object, which is to be used to identify a user to which a task is assigned.,” Examiner respectfully disagrees. As can be seen in at least the cited portions of Brown below, the reference is related to assigning the user to the task based on the forecasted upcoming available time that is a healthcare worker is not performing or anticipated to perform a healthcare task. Therefore, Examiner respectfully disagrees with Applicant’s assertion.
Regarding Applicant’s assertion of “Applicants respectfully traverse these rejections and the assertions and holdings therein, because Brown and Shah, whether alone or in combination, have not been shown to teach or suggest each and every element of the amended claims as required by law.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Ali reference to cure the deficiencies of the prior art combination of the record.
Accordingly, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103.
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-9 are directed to a method, claims 10-15 are directed to a system, and claims 16-20 are directed to a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 10, and 16 recite assigning a task to be executed by a first user, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including following rules or instructions. Claim 1 recites limitations, similarly recited in claims 10 and 16, including “obtaining time series data including measurement data for data objects over a timeline and in relation to geographic locations; determining patterns in the time series data associated with one or more of the data objects; executing a forecasting algorithm to generate an analytical report including predicted values for measurement data for a data object of the one or more data objects over a specified future time period and associated with a first geographic location of the geographic locations; based on evaluating the generated analytical report, assigning a task to be executed by a first user, the task being associated with the first geographic location, wherein automatically assigning the task comprises identifying the first user based on analyzing the predicted values of the analytical report; receiving input defining factors for scheduling tasks, the factors including historical performance, data trends, and objective for the data object; based on evaluation of the input, generating a schedule for the task; and in response to generating the schedule, instructing the task to be executed by the first user according to the schedule.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of the preamble, covers an abstract idea but for the recitation of generic computer components. That is, other than reciting the preamble language, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2-4, 6-9, 12-13, 15, and 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 5, 11, 14, 17, and 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 10, and 16 do not integrate the judicial exception into a practical application. Independent claim 1 is directed to a “computer-implemented method,” which is recited in the preamble of the claim. Independent claim 10 is directed to a system comprising “one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising.” Independent claim 16 is directed to “a non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising,” which is recited in the preamble of the claim. Independent claim 1 recites the additional element, similarly recited in claims 10 and 16, of “automatically assigning a task to be executed by a first user” and “automatically generating a schedule for the task.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-4, 6-9, 12-13, 15, and 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 5, 11, 14, 17, and 20 recite performing, under broadest reasonable interpretation of the claimed invention, abstract limitations “automatically.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 10, and 16 do not comprise anything significantly more than the judicial exception. Independent claim 1 is directed to a “computer-implemented method,” which is recited in the preamble of the claim. Independent claim 10 is directed to a system comprising “one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising.” Independent claim 16 is directed to “a non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising,” which is recited in the preamble of the claim. Independent claim 1 recites the additional element, similarly recited in claims 10 and 16, of “automatically assigning a task to be executed by a first user” and “automatically generating a schedule for the task.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, is not anything significantly more than the judicial exception.
Dependent claims 2-4, 6-9, 12-13, 15, and 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 5, 11, 14, 17, and 20 recite performing, under broadest reasonable interpretation of the claimed invention, abstract limitations “automatically.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brown (US 20200411170 A1) in view of Ali et al. (US 20250165849 A1).
Regarding claim 1, Brown teaches a computer-implemented method comprising (Figs. 7, 9):
obtaining time series data including measurement data for data objects over a timeline and in relation to geographic locations ([0074] teaches the system includes systems that provide dynamic operating data for an operating entity or group of entities including a task reporting system, one or more task scheduling systems, one or more task forecasting systems, one or more patient monitoring systems, one or more worker monitoring systems, and more, wherein [0073] teaches the patient information includes a patient care plan that includes a list or timeline of prescribed clinical treatments, which can be provided to the AI system to generate care planning accordingly, wherein [0011] teaches the system monitors activity of healthcare workers of a healthcare system over a defined time period including monitoring performance of healthcare tasks scheduled for performance over the defined period of time, wherein the system can determine, based on the monitoring, a timeslot within the defined time period in which a healthcare worker of the healthcare workers is not performing, anticipated to, or scheduled to perform a task, and a task optimization analysis component that determines a task for performance by a healthcare worker during the timeslot, wherein [0014] teaches determining supplemental health care tasks based on location of a healthcare worker and a duration of the timeslot, as well as in [0042] teaches the dynamic operating data includes the various healthcare tasks that are needed for performance over a defined, upcoming timeframe, such as the next 24 hours or next week, to account for known and optionally forecasted patient needs over the current time, wherein the information includes location and other dynamic parameters related to healthcare tasks, as well as in [0048] teaches determining information associated with each task including a time of origination of the task, a time or timeframe for completing the task, an expected duration of the task, a location of the task, and the like; see also: [0049, 0052-0053, 0075, 0148-0149]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.);
determining patterns in the time series data associated with one or more of the data objects ([0120] teaches the resource availability analysis component can be configured to evaluate the information received by the resource monitoring component in order to determine availability information regarding known times or timeframes over a defined upcoming timeframe, wherein the system can determine if a worker is currently performing a task or not based on learned correlations/patterns in the worker sensory feedback data and the worker location data that reflect whether a task, or a specific task, is being performed or not, wherein [0138] teaches evaluating information regarding pending tasks for performance over a defined, upcoming timeframe based on evaluating the availability information of healthcare workers in order to identify a subset of available workers that are available to perform, as well as in [0107] teaches the task assessment machine learning component can employ various types of machine learning techniques for learning explicitly or implicitly how to segment care plan information into discrete tasks, wherein the machine learning component can provide different patterns of independence; see also: [0139-0141]);
executing a forecasting algorithm to generate an analytical report including predicted values for measurement data for a data object of the one or more data objects over a specified future time period and associated with a first geographic location of the geographic locations ([0074] teaches the system includes systems that provide dynamic operating data for an operating entity or group of entities including a task reporting system, one or more task scheduling systems, one or more task forecasting systems, one or more patient monitoring systems, one or more worker monitoring systems, and more, wherein [0073] teaches the patient information includes a patient care plan that includes a list or timeline of prescribed clinical treatments, which can be provided to the AI system to generate care planning accordingly, wherein [0139] teaches the task worker matching component can match healthcare workers with a task based on various criteria including by location and timeframe, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, as well as in [0034] teaches evaluating the information using various machine learning models to determine how to schedule performance of the tasks with respect to time and location and how to assign resources in the most efficient manner, wherein [0053] teaches the resource assessment module can receive the dynamic operating data that tracks performance of healthcare tasks, wherein the module can forecast estimated timing of completion of tasks that are in progress and can further evaluate scheduling information to determine information regarding amount of time certain workers have available between scheduled tasks, taking into account locations of the scheduled task and expected time the scheduled task takes to complete, wherein the machine learning techniques can involve forecasting upcoming demand and expected time in which the healthcare worker will have to perform the healthcare task given the forecasted demand, as well as in [0077] teaches the task forecasting systems can include systems configured to generate forecasted data for a system regarding forecasted tasks expected to arise over a defined, upcoming timeframe, wherein the forecasted task data can identify general demand information regarding expected demand for all tasks and expected tasks of a specific type over a specific time frame and expected duration and location of the task; see also: [0111, 0122, 0125]);
based on evaluating the generated analytical report, automatically assigning a task to be executed by a first user, the task being associated with the first geographic location ([0074] teaches the system includes systems that provide dynamic operating data for an operating entity or group of entities including a task reporting system, one or more task scheduling systems, one or more task forecasting systems, one or more patient monitoring systems, one or more worker monitoring systems, and more, wherein [0073] teaches the patient information includes a patient care plan that includes a list or timeline of prescribed clinical treatments, which can be provided to the AI system to generate care planning accordingly, wherein [0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0139, 0144-0145]),
wherein automatically assigning the task comprises identifying the first user based on analyzing the predicted values of the analytical report ([0074] teaches the system includes systems that provide dynamic operating data for an operating entity or group of entities including a task reporting system, one or more task scheduling systems, one or more task forecasting systems, one or more patient monitoring systems, one or more worker monitoring systems, and more, wherein [0073] teaches the patient information includes a patient care plan that includes a list or timeline of prescribed clinical treatments, which can be provided to the AI system to generate care planning accordingly, wherein [0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0139, 0144-0145]);
receiving input defining factors for scheduling tasks ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria; see also: [0139, 0144-0145, 0148-0149]),
the factors including historical performance ([0014] teaches the task optimization models are configured to infer optimal tasks for performance by the healthcare workers based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system, wherein [0130] teaches the task optimization analysis component to facilitate assigning the healthcare workers to tasks, wherein the workers can be identified based on a performance rating that reflects the historical performance level of the healthcare worker’s performance of the corresponding task, as well as in [0069] teaches the task capability information can include information that is historically tracked and scored performance metrics for respective tasks, wherein the performance metrics can include general performance rating provided by the system that reflects the performance quality, efficiency, and proficiency of the worker in association with performance of each task, wherein the performance metrics include information regarding historical error rate; see also: [0072, 0132, 0137]),
data trends ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0120] teaches the resource availability analysis component can be configured to evaluate the information received by the resource monitoring component in order to determine availability information regarding known times or timeframes over a defined upcoming timeframe, wherein the system can determine if a worker is currently performing a task or not based on learned correlations/patterns in the worker sensory feedback data and the worker location data that reflect whether a task, or a specific task, is being performed or not, wherein [0138] teaches evaluating information regarding pending tasks for performance over a defined, upcoming timeframe based on evaluating the availability information of healthcare workers in order to identify a subset of available workers that are available to perform, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria; see also: [0139, 0144-0145, 0148-0149]), and
objective for the data object ([0014] teaches the task optimization models are configured to infer optimal tasks for performance by the healthcare workers based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system, wherein [0130] teaches the task optimization analysis component to facilitate assigning the healthcare workers to tasks, wherein the workers can be identified based on a performance rating that reflects the historical performance level of the healthcare worker’s performance of the corresponding task, as well as in [0069] teaches the task capability information can include information that is historically tracked and scored performance metrics for respective tasks, wherein the performance metrics can include general performance rating provided by the system that reflects the performance quality, efficiency, and proficiency of the worker in association with performance of each task, wherein the performance metrics include information regarding historical error rate, wherein [0137] teaches the task optimization component can employ a filtering component to facilitate restricting the pool of workers to assign to pending tasks based on one or more criteria, such as worker requirements and worker availability, wherein the assignment can be restricted based on the workers performance rating, wherein the defined worker capability information includes comprises representative information can be evaluated to determine an optimal task assignment scheme; see also: [0072, 0138]);
based on evaluation of the input, automatically generating a schedule for the task ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0129] teaches determining how to schedule tasks and assign workers based on healthcare worker information regarding capabilities, qualifications, and more, wherein [0010-0011] teach an availability analysis component that determines, based on the monitoring, a timeslot that forecasts an upcoming need of respective healthcare workers, wherein the timeslot is used to determine a supplemental task for performance by the healthcare worker, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0069, 0120, 0139, 0144-0145]); and
in response to generating the schedule, instructing the task to be executed by the first user according to the schedule ([0011] teaches monitoring performance of healthcare tasks scheduled for performance and determining a timeslot within the defined period in which a healthcare worker is not scheduled to perform a task and determining a supplemental healthcare task for performance by the healthcare worker during the timeslot, wherein [0012] teaches a task assignment component that generates and sends a task assignment message to a device associated with the healthcare worker comprising information that recommends that the healthcare worker perform the supplemental task during the timeslot, wherein the assignment message includes monetary compensation information, as well as in [0148-0149] teach generating task scheduling and resource assignment information that can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment schemes, wherein the task assignment component 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; see also: [0048, 0157]).
However, Brown does not explicitly teach obtaining time series data; determining patterns in the time series data.
From the same or similar field of endeavor, Ali teaches obtaining time series data ([0079] teaches the proxy assignment module employs predictive analytics to anticipate when key personnel may not be available, wherein this module could use a time series forecasting module to predict availability based on historical data regarding leave, workload patterns, and past delegation records, wherein the system can proactively suggest alternative assignees who have the required skillset and permissions, as well as in [0085] teaches the system continuously monitors workflow efficiency, task completion rates, and user engagement, wherein the data feeds into the AI models, which apply unsupervised learning techniques to detect patterns and anomalies, thereby offering insights for further system optimization, wherein the continuous monitoring phase is based on data from a time series database that is optimized for storing and retrieving time-stamped data; see also: [0053, 0058, 0062]);
determining patterns in the time series data ([0079] teaches the proxy assignment module employs predictive analytics to anticipate when key personnel may not be available, wherein this module could use a time series forecasting module to predict availability based on historical data regarding leave, workload patterns, and past delegation records, wherein the system can proactively suggest alternative assignees who have the required skillset and permissions, as well as in [0085] teaches the system continuously monitors workflow efficiency, task completion rates, and user engagement, wherein the data feeds into the AI models, which apply unsupervised learning techniques to detect patterns and anomalies, thereby offering insights for further system optimization, wherein the continuous monitoring phase is based on data from a time series database that is optimized for storing and retrieving time-stamped data; see also: [0053, 0058, 0062]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Brown to incorporate the teachings of Ali to include obtaining time series data; determining patterns in the time series data. One would have been motivated to do so in order to anticipate when key personnel may not be available to handle tasks through the use of time series forecasting, thus ensuring that there is no delay in incident response due to personnel unavailability (Ali, [0079]). By incorporating the teachings of Ali, one would have been able to embed greater intelligence including task patterns into scheduling functionality in order to improve scheduling systems (Ali, [0042]).
Regarding claims 10 and 16, the combination of Brown and Ali teaches limitations already addressed by the rejection of claim 1 above. Regarding claim 10, Brown teaches a system comprising (Fig. 1): one or more processors (Fig. 1 and [0037] teach the system includes a memory storing executable instructions that when executed by at least one processor can facilitate performance of operations defined by the executable instructions, wherein the processor and memory implement the instructions stored by the memory; see also: [0036], Fig. 12); and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising (Fig. 1 and [0037] teach the system includes a memory storing executable instructions that when executed by at least one processor can facilitate performance of operations defined by the executable instructions, wherein the processor and memory implement the instructions stored by the memory; see also: [0036], Fig. 12). Regarding claim 16, Brown teaches a non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising ([0158] teaches the computer program product can include a computer readable storage media having computer readable program instructions thereon for causing a processor to carry out one or more aspects of the invention, as well as in [0159-0160] teach a computer readable storage medium being a tangible device to retain and store instructions for use by an instruction execution device ). Accordingly, claims 10 and 16 are rejected as being unpatentable over Brown in view of Ali.
Regarding claims 2, 11, and 17, the combination of Brown and Ali teaches all the limitations of claims 1, 10, and 16 above.
Brown further teaches wherein automatically assigning the task to be executed by the first user comprises: generating a definition for the task to be performed in relation to the data object ([0119] teaches the supply tracking component can further extract and monitor dynamic operating data regarding the availability and location of supplies, tool, equipment, instruments, and the like, wherein the dynamic operating conditions can track the current locations of mobile supplies, instruments, and equipment, the status of various supplies, instruments, and equipment, the available quantities of these resources, and the like, wherein [0033] teaches the system facilitates 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 dynamic operating conditions associated with the healthcare environment, wherein the system can match the necessary system wide resources to the monitored real-time demand to achieve optimal throughput, wherein the components for each discrete service are catalogued whether it be human labor, supplies, equipment, or technology, wherein the system can dynamically flex resources across multiple service lines according to skill and attribution, wherein [0065] teaches rules and requirements associated with defined tasks include required medical supplies, instruments, equipment, etc., wherein [0081] teaches the dynamic operating conditions data includes information regarding supplies/equipment used in association with the performance of tasks including supply/equipment availability, supply/equipment location, and the like, and wherein [0128] teaches the task optimization system can determine how to schedule tasks and assign resources including workers, medical supplies, instruments, and equipment to currently pending and forecasted tasks based on task variables provided with the indexed task data and resource availability data, wherein the task variables and attributes include the task time variables including identifying a fixed timeframe for completing a task, as well as an expected duration of the task, as well as the location of the task and start and end locations of the task; see also: [0072, 0080]),
wherein the definition for the task is associated with defining a quantity associated with the data object to be allocated to the first geographic location for the specified time period ([0119] teaches the supply tracking component can further extract and monitor dynamic operating data regarding the availability and location of supplies, tool, equipment, instruments, and the like, wherein the dynamic operating conditions can track the current locations of mobile supplies, instruments, and equipment, the status of various supplies, instruments, and equipment, the available quantities of these resources, and the like, wherein [0033] teaches the system facilitates 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 dynamic operating conditions associated with the healthcare environment, wherein the system can match the necessary system wide resources to the monitored real-time demand to achieve optimal throughput, wherein the components for each discrete service are catalogued whether it be human labor, supplies, equipment, or technology, wherein the system can dynamically flex resources across multiple service lines according to skill and attribution, wherein [0065] teaches rules and requirements associated with defined tasks include required medical supplies, instruments, equipment, etc., wherein [0081] teaches the dynamic operating conditions data includes information regarding supplies/equipment used in association with the performance of tasks including supply/equipment availability, supply/equipment location, and the like, and wherein [0128] teaches the task optimization system can determine how to schedule tasks and assign resources including workers, medical supplies, instruments, and equipment to currently pending and forecasted tasks based on task variables provided with the indexed task data and resource availability data, wherein the task variables and attributes include the task time variables including identifying a fixed timeframe for completing a task, as well as an expected duration of the task, as well as the location of the task and start and end locations of the task; see also: [0072, 0080]).
Regarding claims 3, 12, and 18, the combination of Brown and Ali teaches all the limitations of claims 1, 10, and 16 above.
Brown further teaches wherein identifying the first user comprises identifying the first user based on evaluating performance data of a plurality of users ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0139, 0144-0145]), the plurality of users being identified as associated with at least one of the geographic locations related to the measurement data for the data objects ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0139, 0144-0145]).
Regarding claims 4, 13, and 19, the combination of Brown and Ali teaches all the limitations of claims 1, 10, and 16 above.
Brown further teaches wherein the schedule for the task is to be executed by two or more users including the first user ([0012] teaches a task assignment component that generates and sends a task assignment message to a device associated with the healthcare worker comprising information that recommends that the healthcare worker perform the supplemental task during the timeslot, wherein [0048] teaches identifying and associating dependency information with the respective tasks regarding dependencies between two or more tasks, such as dependencies that control performance of tasks by a healthcare team including two or more healthcare workers, as well as in [0148-0149] teach generating task scheduling and resource assignment information that can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment schemes, wherein the task assignment component 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; see also: [0011, 0092, 0157]),
wherein the two or more users are associated with measurement data for instances of data objects from the time series data ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0048] teaches identifying and associating dependency information with the respective tasks regarding dependencies between two or more tasks, such as dependencies that control performance of tasks by a healthcare team including two or more healthcare workers, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0092, 0139, 0144-0145]),
wherein the instances of the data objects are associated with the first geographic location ([0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score, wherein [0141] teaches the task-worker matching component can thus generate and associate a score with each potential task-worker combination that provides an indication of how well the combination facilitates achieving or meeting one or more optimization goals of the healthcare system, wherein the task optimization models can look at all the scores for all the task-worker combinations as a whole in view of additional parameters in terms of timing, locations, and resources assigned in order to determine a final task scheduling and resource assignment scheme that satisfies the one or more optimization criteria, as well as in [0148-0149] teach the task scheduling and resource assignment information can be regularly updated in real-time regarding the determined optimal scheduling/resource assignment scheme, wherein the task assignment component can further provide the task scheduling and resource assignment information to the healthcare workers directly to facilitate performing the healthcare tasks in accordance with the prescribed optimal scheduling and resource assignment scheme, wherein the task assignment component can generate and send a task assignment message to a device associated with the healthcare worker; see also: [0139, 0144-0145]).
However, Brown does not explicitly teach measurement data for instances of data objects from the time series data.
From the same or similar field of endeavor, Ali further teaches measurement data for instances of data objects from the time series data ([0079] teaches the proxy assignment module employs predictive analytics to anticipate when key personnel may not be available, wherein this module could use a time series forecasting module to predict availability based on historical data regarding leave, workload patterns, and past delegation records, wherein the system can proactively suggest alternative assignees who have the required skillset and permissions, as well as in [0085] teaches the system continuously monitors workflow efficiency, task completion rates, and user engagement, wherein the data feeds into the AI models, which apply unsupervised learning techniques to detect patterns and anomalies, thereby offering insights for further system optimization, wherein the continuous monitoring phase is based on data from a time series database that is optimized for storing and retrieving time-stamped data; see also: [0053, 0058, 0062]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Brown and Ali to incorporate the further teachings of Ali to include measurement data for instances of data objects from the time series data. One would have been motivated to do so in order to anticipate when key personnel may not be available to handle tasks through the use of time series forecasting, thus ensuring that there is no delay in incident response due to personnel unavailability (Ali, [0079]). By incorporating the teachings of Ali, one would have been able to embed greater intelligence including task patterns into scheduling functionality in order to improve scheduling systems (Ali, [0042]).
Regarding claims 5, 14, and 20, the combination of Brown and Ali teaches all the limitations of claims 4, 13, and 19 above.
Brown further teaches wherein generating the schedule for the assigned task comprises: performing data analytics over historical data associated with the first geographic location and a plurality of users ([0014] teaches the task optimization models are configured to infer optimal tasks for performance by the healthcare workers based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system, wherein [0130] teaches the task optimization analysis component to facilitate assigning the healthcare workers to tasks, wherein the workers can be identified based on a performance rating that reflects the historical performance level of the healthcare worker’s performance of the corresponding task, as well as in [0069] teaches the task capability information can include information that is historically tracked and scored performance metrics for respective tasks, wherein the performance metrics can include general performance rating provided by the system that reflects the performance quality, efficiency, and proficiency of the worker in association with performance of each task, wherein the performance metrics include information regarding historical error rate; see also: [0072, 0132, 0137]);
obtaining assignment factors including performance objectives associated with the data objects ([0014] teaches the task optimization models are configured to infer optimal tasks for performance by the healthcare workers based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system, wherein [0130] teaches the task optimization analysis component to facilitate assigning the healthcare workers to tasks, wherein the workers can be identified based on a performance rating that reflects the historical performance level of the healthcare worker’s performance of the corresponding task, as well as in [0069] teaches the task capability information can include information that is historically tracked and scored performance metrics for respective tasks, wherein the performance metrics can include general performance rating provided by the system that reflects the performance quality, efficiency, and proficiency of the worker in association with performance of each task, wherein the performance metrics include information regarding historical error rate, wherein [0137] teaches the task optimization component can employ a filtering component to facilitate restricting the pool of workers to assign to pending tasks based on one or more criteria, such as worker requirements and worker availability, wherein the assignment can be restricted based on the workers performance rating, wherein the defined worker capability information includes comprises representative information can be evaluated to determine an optimal task assignment scheme; see also: [0072, 0138]); and
automatically identifying the schedule based on evaluating the data analytics over the historical data according to the assignment factors ([0014] teaches the task optimization models are configured to infer optimal tasks for performance by the healthcare workers based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system, wherein [0130] teaches the task optimization analysis component to facilitate assigning the healthcare workers to tasks, wherein the workers can be identified based on a performance rating that reflects the historical performance level of the healthcare worker’s performance of the corresponding task, as well as in [0069] teaches the task capability information can include information that is historically tracked and scored performance metrics for respective tasks, wherein the performance metrics can include general performance rating provided by the system that reflects the performance quality, efficiency, and proficiency of the worker in association with performance of each task, wherein the performance metrics include information regarding historical error rate, wherein [0137] teaches the task optimization component can employ a filtering component to facilitate restricting the pool of workers to assign to pending tasks based on one or more criteria, such as worker requirements and worker availability, wherein the assignment can be restricted based on the workers performance rating, wherein the defined worker capability information includes comprises representative information can be evaluated to determine an optimal task assignment scheme, wherein [0138] teaches restricting the assignment based on the workers performance rating, wherein [0075] teaches the task reporting systems can include an automated system configured to automatically determine and generate new tasks to be performed based on new data points included in the dynamic operating data, wherein [0140] teaches the task worker matching component can score task-worker assignment options based on various criteria, wherein the scoring can reflect the degree of correspondence between worker availability and time/location constraints associated with the task, which provides a measure of how well the worker can fulfill the time constraints based on the worker’s availability and current location, wherein the scoring can score the combination based on how close the expected task duration matches the expected worker time availability duration, wherein the lesser the difference the better the score; see also: [0072]).
Regarding claims 6 and 15, the combination of Brown and Ali teaches all the limitations of claims 1 and 10 above.
Brown further teaches wherein the data objects are defined for products ([0056] teaches determining how to schedule performance of the healthcare tasks to optimize utilization of available resources in view of patient preference and need, wherein the task scheduling and resource optimization is based on maximizing revenue, wherein [0072] teaches the finance data includes financial information pertaining to costs associated healthcare task and services, wherein the finance data includes costs associated with different procedures and utilization of different resources, wherein the procedure information include supplies/equipment, costs, personnel costs, and more, wherein [0128] teaches the task optimization system can determine how to schedule tasks and assign resources including workers, medical supplies, instruments, and equipment to currently pending and forecasted tasks based on task variables provided with the indexed task data and resource availability data, wherein the task variables and attributes include the task time variables including identifying a fixed timeframe for completing a task, as well as an expected duration of the task, as well as the location of the task and start and end locations of the task; see also: [0058, 0131, 0134]),
and wherein the measurement data includes revenue data for the products at time points over the timeline ([0056] teaches determining how to schedule performance of the healthcare tasks to optimize utilization of available resources in view of patient preference and need, wherein the task scheduling and resource optimization is based on maximizing revenue, wherein [0072] teaches the finance data includes financial information pertaining to costs associated healthcare task and services, wherein the finance data includes costs associated with different procedures and utilization of different resources, wherein the procedure information include supplies/equipment, costs, personnel costs, and more, wherein [0128] teaches the task optimization system can determine how to schedule tasks and assign resources including workers, medical supplies, instruments, and equipment to currently pending and forecasted tasks based on task variables provided with the indexed task data and resource availability data, wherein the task variables and attributes include the task time variables including identifying a fixed timeframe for completing a task, as well as an expected duration of the task, as well as the location of the task and start and end locations of the task; see also: [0058, 0131, 0134]).
Regarding claim 8, the combination of Brown and Ali teaches all the limitations of claim 1 above.
Brown further teaches wherein the timeline is defined according to a scale including discrete time ranges ([0051] teaches the resource assessment module can provide information regarding the availability of the respective workers at a current point in time and over a defined, upcoming timeframe, such as a workday or week, wherein the resource availability data identifies or indicates durations of time until the respective workers will become available to perform a healthcare task, the amount of time the respective workers have available to perform certain tasks, and locations of the respective workers, wherein the resource availability data can include information that identifies expected timeslots over a defined upcoming timeframe, such as the next 24 hours, in which one or more workers have available time to perform, including durations of the timeslots and locations associated with the time slots, as well as in [0111] teaches identifying one or more timeslots in which a healthcare worker is not performing or scheduled to perform a healthcare task, wherein this can include available timeslots identified in the health care worker’s schedule, as well as forecasted available timeslots, wherein the data can include information that identifies where the workers will be over the defined upcoming timeframe, wherein [0073] teaches the patient information includes a patient care plan that includes a list or timeline of prescribed clinical treatments, which can be provided to the AI system to generate care planning accordingly; see also: [0113]).
Regarding claim 9, the combination of Brown and Ali teaches all the limitations of claim 1 above.
Brown further teaches comprising: periodically collecting new measurement data for the data objects ([0086] teaches the task assessment module can include task information extraction component to extract the task information from the healthcare information system and sources regarding various healthcare tasks to be performed over a defined, upcoming timeframe, wherein the task information extraction component can regularly or continuously extract and receive the task information over a course of operation of the system to account for new tasks that arise over the course of operation, wherein [0049] teaches generating indexed task data that comprises one or more data structures that organize and index task information regarding all the discrete tasks, their associated attributes, and interdependencies, wherein the task assessment module can further regularly and continuously update the indexed task data in real-time over the course of operation to reflect the changes to the integrated system, wherein the module can regularly and continuously update the indexed task data to reflect progress to the performance and completion of tasks, to reflect new tasks, to reflect changes in operating conditions that effect the tasks, and the like, wherein the task data can correspond to a dynamic monitor of what healthcare tasks should be done to take care of all current and forecasted patient needs from a current point in time to a defined point in the future, while also providing relevant information that can influence how healthcare tasks are performed; see also: [0011, 0124]); and
performing continuous monitoring of activities associated with one or more data objects, wherein each activity is associated with at least a portion of the new measurement data ([0011] teaches monitoring activity of healthcare workers over a defined period of time in association with operation of the healthcare system, wherein [0086] teaches the task assessment module can include task information extraction component to extract the task information from the healthcare information system and sources regarding various healthcare tasks to be performed over a defined, upcoming timeframe, wherein the task information extraction component can regularly or continuously extract and receive the task information over a course of operation of the system to account for new tasks that arise over the course of operation, as well as in [0104] teaches the task status monitoring component can further regularly and/or continuously update the indexed task data in real time over the course of an operation to reflect changes in the status of the tasks, wherein the received and monitored tracked task performance data identifies when currently pending tasks are initiated and when they are completed, wherein [0049] teaches generating indexed task data that comprises one or more data structures that organize and index task information regarding all the discrete tasks, their associated attributes, and interdependencies, wherein the task assessment module can further regularly and continuously update the indexed task data in real-time over the course of operation to reflect the changes to the integrated system, wherein the module can regularly and continuously update the indexed task data to reflect progress to the performance and completion of tasks, to reflect new tasks, to reflect changes in operating conditions that effect the tasks, and the like, wherein the task data can correspond to a dynamic monitor of what healthcare tasks should be done to take care of all current and forecasted patient needs from a current point in time to a defined point in the future, while also providing relevant information that can influence how healthcare tasks are performed; see also: [0124]).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Brown (US 20200411170 A1) in view of Ali et al. (US 20250165849 A1) in view of Shah (US 20160147955 A1).
Regarding claim 7, the combination of Brown and Ali teaches all the limitations of claim 1 above.
However, Brown does not explicitly teach wherein the time series data is associated with a plurality of time points defined over the timeline, wherein each time point is associated with one or more of the geographic locations.
From the same or similar field of endeavor, Shah teaches wherein the time series data is associated with a plurality of time points defined over the timeline ([0007] teaches scheduling, tracking, and executing performance of a set of tasks by defining a time series and associating a current time occurrence on the time series and associating a future time occurrence on the time series, which is a succeeding time occurrence on the time series in relation to the current time occurrence, wherein [0043] teaches the time series module associates a current time occurrence and future time occurrence on the time series, wherein the future time occurrence is in relation to a successive time slot on the time series, wherein [0066] teaches the system can process scheduling tasks and accordingly tie different tasks of the work lists on a time series and identifies timelines associated with different tasks, wherein the system can examine the status of the tasks on the time series and generates an automated output indicative of tasks status, wherein [0051] teaches defining an interaction zone rule that includes defining a time series, wherein the interaction zone rule is related to a future time occurrence that refers to a point on the time series when an event occurs, wherein the interaction zone rule is based on location, wherein [0059] teaches monitoring the location and detecting spatial presence upon occurrence of a triggering event, wherein the event is placed upon the time series, wherein the time series is dynamically updated based on the performance status of the task, and wherein Fig. 4 and [0066] teach the system can tie different tasks of the work lists on a time series and identifies timelines associated with the different tasks; see also: [0069, 0084]),
wherein each time point is associated with one or more of the geographic locations ([0007] teaches scheduling, tracking, and executing performance of a set of tasks by defining a time series and associating a current time occurrence on the time series and associating a future time occurrence on the time series, which is a succeeding time occurrence on the time series in relation to the current time occurrence, wherein [0043] teaches the time series module associates a current time occurrence and future time occurrence on the time series, wherein the future time occurrence is in relation to a successive time slot on the time series, wherein [0066] teaches the system can process scheduling tasks and accordingly tie different tasks of the work lists on a time series and identifies timelines associated with different tasks, wherein the system can examine the status of the tasks on the time series and generates an automated output indicative of tasks status, wherein [0051] teaches defining an interaction zone rule that includes defining a time series, wherein the interaction zone rule is related to a future time occurrence that refers to a point on the time series when an event occurs, wherein the interaction zone rule is based on location, wherein [0059] teaches monitoring the location and detecting spatial presence upon occurrence of a triggering event, wherein the event is placed upon the time series, wherein the time series is dynamically updated based on the performance status of the task; see also: [0066, 0069, 0084]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Brown and Ali to incorporate the teachings of Shah to include wherein the time series data is associated with a plurality of time points defined over the timeline, wherein each time point is associated with one or more of the geographic locations. One would have been motivated to do so in order to allow tracking of detailed statistics on work lists in order to use techniques that improve and optimize clinical operations by performing “big data” analytics (Shah, [0106]). By incorporating the teachings of Shah, one would have been able to generate easy to do worklists that make it easy for clinical personnel to get their jobs done faster and correctly thereby increasing their productivity (Shah, [0109]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jain et al. (US 20210241139 A1) discloses identifying time series data points showing a pattern or trend of attributes or capability levels over time
Park et al. (US 20220237540 A1) discloses identifying patterns in time series model data
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.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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