DETAILED ACTION
Status of the Application
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Final Action on the merits in response to the application filed on 05/22/2026.
Claims 1-17 have been cancelled.
Claims 18, 26, and 34 are amended.
Claims 18-37 remain pending in this application.
Foreign Priority
The Examiner/office acknowledges that the applicant claims foreign priority to the date 03/22/2021.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 102 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 103 rejections has been added.
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 18-25 are directed towards an apparatus, claims 26-30 are directed towards a method, and claims 34-37 are directed towards a computer-readable medium, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 18-37, the independent claims (claims 18, 26, and 34) are directed to managing resources and task, In independent claim 18, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, the computing apparatus:
in response to the resource allocation request, for each time interval of the time period:
determine a first number of tasks to be processed in a task queue within the time interval based on the task information;
determine a second number of tasks from the task queue that are unprocessed after a time interval that immediately precedes the time interval;
determine, based on the adjacency constraints for each adjacent node for the number of nodes, a resource allocation constraint for each resource, wherein the resource allocation constraint ensures that a range of nodes assigned to each resource does not overlap with a range of nodes assigned to any other resource; and
determine a subset of the number of nodes to be assigned to each resource in the time interval based on: (i) the first number of tasks, (ii) the second number of tasks, and (iii) the resource allocation constraint for each resource, wherein determining the subset of the number of nodes minimizes a cost function comprising a task waiting cost and a node operating cost over the time period.
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of apparatus, cloud computing environment, processor, device, interface, server, node (claim 26 cloud computing environment, processor, memory device, interface, server, node; claim 34, computer storage medium, cloud computing environment, processor, device, interface, server, node). The claims recite the steps are performed by the apparatus, cloud computing environment, processor, device, interface, server, node.
The limitations of
A computing apparatus for node allocation in a cloud computing environment, wherein a node relates to a processor core, the computing apparatus
comprising:
one or more processors;
at least one memory device coupled to the one or more processors; and
a data communications interface operably associated with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
receive, from a resource allocation demand server via the data communications interface, a resource allocation request which includes task information based on a task schedule, a time period including a plurality of time intervals, a number of resources available during each time interval, a number of nodes available during each time interval, and adjacency constraints for each adjacent node of the number of nodes;
provide, to the resource allocation demand server via the data communications interface, a resource allocation schedule which includes the subset of the number of nodes assigned to each resource for each time interval of the time period.
are mere data gathering and processing recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by apparatus, cloud computing environment, processor, device, interface, server, node. The apparatus, cloud computing environment, processor, device, interface, server, node are recited at a high level of generality. In limitation (a), apparatus, cloud computing environment, processor, device, interface, server, node are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The apparatus, cloud computing environment, processor, device, interface, server, node are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the apparatus, cloud computing environment, processor, device, interface, server, node. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and processing.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
A computing apparatus for node allocation in a cloud computing environment, wherein a node relates to a processor core, the computing apparatus
comprising:
one or more processors;
at least one memory device coupled to the one or more processors; and
a data communications interface operably associated with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
receive, from a resource allocation demand server via the data communications interface, a resource allocation request which includes task information based on a task schedule, a time period including a plurality of time intervals, a number of resources available during each time interval, a number of nodes available during each time interval, and adjacency constraints for each adjacent node of the number of nodes;
provide, to the resource allocation demand server via the data communications interface, a resource allocation schedule which includes the subset of the number of nodes assigned to each resource for each time interval of the time period.
are recited at a high level of generality. These elements amount to transmitting and processing data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of an apparatus, processor, interface, device, server, node, memory, model, computer storage medium to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 19-25, 27-33, 35 and 36 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 19, 27, 35 recite simulation model to compute quality of service. The dependent claims 19-25, 27-33, 35 and 36 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 19-25, 27-33, 35 and 36 recites apparatus, processor, interface, device, server, node, memory, model, computer storage medium which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 19-25, 27-33, 35 and 36 recites apparatus, processor, interface, device, server, node, memory, model, computer storage medium, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 19-25, 27-33, 35 and 36 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 18, 26, and 34. Therefore claims 19-25, 27-33, 35 and 36 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 18, 21, 23, 24, 26, 29, 31, 32, 34, 37 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20200411168, Thomas, et al. to hereinafter Thomas in view of United States Patent Publication US 20200382445, Calmon, et al
Referring to Claim 18, Thomas teaches the computing apparatus:
one or more processors; at least one memory device coupled to the one or more processors (
Thomas: Sec. 0003, memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory.); and
a data communications interface operably associated with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to (
Thomas: Sec. 0033, The computing device 102 can include or be operatively coupled to at least one memory 114 and at least one processor 112. The at least one memory 114 can further store executable instructions (e.g., the task management model development module 110, the model application module 118 and the task management module 122) that when executed by the at least one processor 112, facilitate performance of operations defined by the executable instruction. ):
receive, from a resource allocation demand server via the data communications interface, a resource allocation request which includes task information based on a task schedule, a time period including a plurality of time intervals (
Thomas: Sec. 0046, as new historical data sets are received over time, the task management model development module 110 can train and update the machine learning models (e.g., the one or more demand models 138 and/or the one or more TAT models 140 predication models) on the new historical data sets. The training and updating can be continuously and/or regularly performed in accordance with a defined schedule (e.g., every N minutes, hours, or days, every X sequential data sets, etc.), or performed in a cyclic manner such that each time a model training and updating procedure is completed, a new model training and updated procedure can be initiated. In some implementations, the new historical data sets can be combined with previously collected data sets that were used to train a previous version of the one or more demand models 138 and/or the one or more TAT models 140. ),
a number of resources available during each time interval, a number of nodes available during each time interval, and adjacency constraints for each adjacent node of the number of nodes (
Thomas: Sec. 0046, the model application module 118 can be configured to apply the one more demand models 138, TAT models 140 and/or staffing models 142 in accordance with a defined schedule (e.g., every N minute, hours, etc.) to generate new/updated predictive output data 120 regarding predicted tasks, predicted TATs for the currently pending and the predicted tasks for the next defined period of time (or time periods), and the estimated number of available staff to perform the tasks. For example, with respect to a hospital system wherein the tasks managed include EVS tasks, based on current state information for the hospital (e.g., provided by the dynamic system stat data 102) the model application module 118 can generate predictive output data 120 that includes information regarding predicted demand for EVS tasks (e.g., total number of tasks needed for the next N hours, total number of tasks by hospital unit, by task type, etc. for the next N hours and the like), and predicted TATs for the currently pending tasks and optionally the predicted tasks. The predictive output data 120 can also include information regarding the estimated number of staff available to perform the tasks (e.g., per shift, per timeframe, per unit, etc.). );
Thomas describes the managing of resources and task to include the time and number of resources and number of places to where task are to be executed.
in response to the resource allocation request, for each time interval of the time period:
determine a first number of tasks to be processed in a task queue within the time interval based on the task information (
Thomas: Sec. 0039, The task data 104 can include information regarding tasks of the dynamic system, including but not limited to: information identifying currently pending tasks, information regarding status of tasks being performed, information regarding timing of origination (e.g., when a request for the task was made), performance and completion of the tasks (e.g., that indicates task TAT), as well as various relevant attributes associated with the tasks.
Thomas: Sec. 0041, implementations in which the dynamic system includes a hospital, the task management model development module 110 can train and develop one or more demand models 138 to predict the distribution of EVS tasks within an upcoming timeframe (e.g., from a current point in time through the next hour, the next 12 hours, the next 24 hours, the next 48 hours, etc.) based on historical information that reflects the historical demand for the EVS tasks (e.g., amount and type of that tasks performed and/or requested for performance) under different operating conditions/contexts of the hospital. For instance, the different operating conditions/contexts of the hospital can be based on variances in bed occupancy levels, bed availability, bed status, number of patients waiting for beds, type/location of the beds, staff availability, supply availability, and the like. The predicted distribution of the EVS tasks can include the total number of predicted tasks, the types of the tasks, the location of the tasks, and other potential distinguishing attributes associated with the tasks that can have an impact on the task TATs and/or the resources that are needed (e.g., personnel, supplies, equipment, etc.) to fulfil the tasks in accordance with defined service level requirements for the tasks.);
Thomas describes the preforming of tasks includes requesting resources, which consist of tasks that need to be completed within a time period and location.
determine a second number of tasks from the task queue that are unprocessed after a time interval that immediately precedes the time interval (
Thomas: Sec. 0046, the model application module 118 can generate predictive output data 120 that includes information regarding predicted demand for EVS tasks (e.g., total number of tasks needed for the next N hours, total number of tasks by hospital unit, by task type, etc. for the next N hours and the like), and predicted TATs for the currently pending tasks and optionally the predicted tasks);
Thomas describes the managing of resources and task to include the time and number of resources and number of places to where the next task are to be executed within a time period.
determine, based on the adjacency constraints for each adjacent node for the number of nodes, a resource allocation constraint for each resource (
Thomas: Sec. 0054, The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).); and
Thomas describes the resource allocation constraints and various adjacency constraints that equivalent to the Applicant’s spec at 0034.
determine a subset of the number of nodes to be assigned to each resource in the time interval based on: (i) the first number of tasks, (ii) the second number of tasks, and (iii) the resource allocation constraint for each resource (
Thomas: Sec. 0054, The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).
Thomas: Sec. 0084, the resource monitoring component 808 can determine if the expected number of upcoming tasks for a particular hospital unit over an upcoming timeframe exceeds the exceeds the capacity of the expected number of available staff members in the upcoming timeframe. In some embodiments, the defined threshold or percentage can be defined by or otherwise based on one or more defined SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X). The notification component 812 can further generate alerts/notification regarding determined shortages in system resources (e.g., demand/TAT notification 128). For example, the notification component 812 can be configured to generate and send an electronic notification to a suitable entity regarding a determination that the available system resources are insufficient given the expected demand.); and
provide, to the resource allocation demand server via the data communications interface, a resource allocation schedule which includes the subset of the number of nodes assigned to each resource for each time interval of the time period (
Thomas: Sec. 0067, the staffing machine learning component 308 can evaluate the current state of the system, including current scheduling information, current staff assignments, current staff locations, current staff activities and the like, to predict the distribution of staff available to perform tasks in an upcoming time frame, in a particular unit, or the like. In accordance with this example, the staffing models 142 can include a model that estimates the number of staff members that will be available to perform tasks over the next hour, over the next 6 hours, over the next 24 hours, and the like.
Thomas: Sec. 0119, the data collection component 302 can regularly (e.g., every N minutes) collet sets of the dynamic system state data 102, wherein each set corresponds to state information that was generated over a defined time period. In this regard, with respect to a hospital system, the data collection component 302 can collect data every 30 minutes, every hour, etc., information regarding the tasks that were scheduled, the tasks that were performed, timing of performance, the attributes of the tasks, the operating condition of the hospital, and the like and. Each set can thus represent a historical snapshot in time of the hospital operations and can be used to learn correlations between different operating condition parameters on task TATs and demand for the tasks. ).
Thomas does not explicitly teach for node allocation in a cloud computing environment, wherein a node relates to a processor core, the computing apparatus; determine, based on the adjacency constraints for each adjacent node for the number of nodes, a resource allocation constraint for each resource, wherein the resource allocation constraint ensures that a range of nodes assigned to each resource does not overlap with a range of nodes assigned to any other resource; wherein determining the subset of the number of nodes minimizes a cost function comprising a task waiting cost and a node operating cost over the time period
However, Calmon teaches these limitations
for node allocation in a cloud computing environment, wherein a node relates to a processor core, the computing apparatus (
Calmon: Sec. 0020, potential benefits of Cloud platforms as IoT backend systems, however, the essentially centralized nature of the cloud environment does not fit well with the inherently decentralized nature of IoT.
Calmon: Sec. 0047, The first delay is with respect to accomplishing a processing load necessary for data acquisition, and the respective time for completing this first part can vary as a function of the number of processors dedicated to the VN.
Calmon: Sec. 0127, FIG. 6 is a flow chart illustrating an exemplary implementation of a market-based resource allocation process 600, according to at least one embodiment of the disclosure. As shown in FIG. 6, the exemplary market-based resource allocation process 600 initially obtains at least one application request at a given edge node in a multi-tier environment comprising one or more cloud resources and a plurality of edge nodes including the given edge node during step 610. As noted above, the edge nodes host a plurality of virtual nodes to process the at least one application request.
Calmon: Claim 1, obtaining at least one application request at a given edge node in a multi-tier environment comprising one or more cloud resources and a plurality of edge nodes including the given edge node, wherein the plurality of edge nodes host a plurality of virtual nodes to process the at least one application request;)
determine, based on the adjacency constraints for each adjacent node for the number of nodes, a resource allocation constraint for each resource, wherein the resource allocation constraint ensures that a range of nodes assigned to each resource does not overlap with a range of nodes assigned to any other resource (
Calmon: Sec. 0029, the explosive combinatorial nature of MINLP hinders the quick search for optimal solutions when it grows, in terms of the number of virtual nodes providing services and applications issuing requests.
Calmon: Sec. 0075, the collaboration process 148 is not able to find a neighbor master node to meet the request, then the request is forwarded to the centralized resource allocation manager 115 in the Cloud tier 310.
Calmon: Sec. 0081, hierarchical organization, one goal is to define which edge nodes 145 are master edge nodes according to two criteria: (i) edge nodes 145 that have a bigger amount of resources, and (ii) edge nodes 145 that reduce the distance between the other edge nodes and the cloud.
Calmon: Sec. 0086, edge node 145 having as its origin a node of any other layer (cloud tier 110 or sensor tier 170). The requests coming from either the cloud tier 110 or sensor tier 170 are generated through applications of the users. Requests can also be passed from one edge node 145 to another edge node 145 in the edge tier 140, in case the original edge node 145 is not able to allocate the resources needed to meet the request.
Calmon: Sec. 0087, For this reason, edge nodes 145 that have connections between themselves perform horizontal collaboration, which means that a master edge node can send a request to another master node if the master edge node has not been able to allocate the request to any of its slave nodes, thereby allowing a larger portion of the Edge Tier to be used to allocate requests, promoting collaboration and better edge tier resource sharing.
Calmon: Sec. 0124, When a request arrives on an edge node 145, the edge node 145 may not be able to serve the request, either because the edge node 145 does not have sufficient spare resources, or because the data types of the edge node 145 do not match the requested data type. In this case, the edge node 145 will forward the request to one of its neighbor nodes 145.)
wherein determining the subset of the number of nodes minimizes a cost function comprising a task waiting cost and a node operating cost over the time period (
Calmon: Sec. 0026, As mentioned above, the resource allocation activity aims to ensure the proper meeting of application requirements by the infrastructure, while minimizing its operational cost. Additional goals include: load balancing, whereby resources should be allocated in a manner so that utilization is balanced across all resources of a particular type; and fault tolerance, whereby resources are allocated in a manner such that the impact of a failure of individual components does not compromise the overall system performance, among others.
Calmon: Sec. 0081, hierarchical organization, one goal is to define which edge nodes 145 are master edge nodes according to two criteria: (i) edge nodes 145 that have a bigger amount of resources, and (ii) edge nodes 145 that reduce the distance between the other edge nodes and the cloud.
Calmon: Sec. 0117, Cu(νe ij ) is the cost of updating the VN data. When allocating a request to a virtual node, the edge node 145 checks if the request data is updated. If the data is not updated, Cu(νe ij )=0, if the data is updated, then this cost corresponds to the time required to update the data. Thus, Cu(νe ij ) is defined, for example, as in Equation (10):
Cu(vei)=u(δrk)ceiτ(δrk).(10)
Calmon: Sec. 0121, Cq(νe ij ) is the average queue time that a request waits to be answered by a virtual node. The goal of using this cost is to prevent requests from being forwarded to virtual nodes that have large queues of requests. This queue cost is calculated, for example, as in Equation (12):
Cq(veij)=1m∑k=0mTf(vij,rk)-T0(vij,rk).(12));
Thomas and Calmon are both directed to the analysis of resource allocation (See Thomas at 0025, 0029, 0060; Calmon at 0003, 0018, 0026). Thomas discloses that additional elements, such as the task management module can be considered (See Thomas at 0032). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Thomas, which teaches detecting and repairing task information problems in view of Calmon, to efficiently apply analysis of resource allocation to improving the processing of collected data to include cost information. (See Calmon at 0005, 0026, 0096).
Referring to Claim 21, Thomas teaches the computing apparatus of claim 18 wherein the resource allocation request further includes a maximum number of available nodes, and determining the number of nodes to be assigned to each resource for each time interval is further based on the maximum number of available nodes (
Thomas: Sec. 0054, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).).
Thomas describes the maximum number of task and staffing.
Referring to Claim 23, Thomas teaches the computing apparatus of claim 18 wherein the resource allocation request further includes a quality of service constraint, and determining the number of nodes to be assigned to each resource for each time interval is further based on the quality of service constraint (
Thomas: Sec. 0028, the system can determine an optimal order/sequence for performing currently pending EVS tasks to minimize the TATs (and consequently reduce wait times) while meeting any defined operating constraints.
Thomas: Sec. 0044, The task management model development module 110 can also train and develop of one or more machine learning models to determine the expected number of staff available for performing the currently pending, and optionally the forecasted tasks, based on learned correlation between various factors in the historical dynamic system state data 102 that influence staff availability. For example, the task management development module 110 can train and develop one or more staffing models 142 to estimate the number of available staff (e.g., EVS workers) by shift or timeframe, by unit, or the like.
Thomas: Sec. 0048, the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.
Thomas describes determining the number of staff available in queue, which is being interpreting as quality of service constraints.
Thomas: Sec. 0028, The system can also employ combinatorial mathematical optimization to determine the number of resources to allocate to the respective units over the upcoming timeframe to minimize service level agreement (SLA) violations and maintain smooth patient flow based on the predicted task TATs and the expected demand.
Thomas: Sec. 0036, The task management model development module 110 can also employ the historical sets of the dynamic system state data 102 to develop and train one or more machine learning models (e.g., one or more staffing models 142) to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0054, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload)).
Thomas describes the managing of resources and task to include the time and number of resources and number of places to where task are to be executed.
Referring to Claim 24, Thomas teaches the computing apparatus of claim 18 wherein the resource allocation constraint includes a change in the number of nodes available to the resource from the time interval and the time interval that immediately precedes the time interval, and determining the number of nodes to be assigned to each resource for each time interval is further based on the number of nodes available to the resource for each time interval (
Thomas: Sec. 0046, the model application module 118 can generate predictive output data 120 that includes information regarding predicted demand for EVS tasks (e.g., total number of tasks needed for the next N hours, total number of tasks by hospital unit, by task type, etc. for the next N hours and the like), and predicted TATs for the currently pending tasks and optionally the predicted tasks… The combined (new and previously used for training) data sets can further be used to train each updated instance of the demand models 138 and/or TAT models 140. In other embodiments, the task management model development module 110 can cycle
Thomas: Sec. 0050, the model application module 118 can be configured to apply the one more demand models 138, TAT models 140 and/or staffing models 142 in accordance with a defined schedule (e.g., every N minute, hours, etc.) to generate new/updated predictive output data 120 regarding predicted tasks, predicted TATs for the currently pending and the predicted tasks for the next defined period of time (or time periods), and the estimated number of available staff to perform the tasks…The model application module 118 can further regenerate this predictive output data 120 every M minutes (e.g., every 30 minutes, every 60 minutes or another in accordance with another suitable time schedule) to account for changes in the state of the dynamic system over time. In this regard, each time the model application reapplies the one or more demand models, the one or more TAT models 140 and/or the one or more staffing models 142, the model application module 118 can receive updated system state data that reflect the current state of the system at the point in time.);
Thomas describes the managing and updating of resources and task to include the time and number of resources and number of places to where the next task are to be executed within a time period.
Thomas: Sec. 0028, The system can also employ combinatorial mathematical optimization to determine the number of resources to allocate to the respective units over the upcoming timeframe to minimize service level agreement (SLA) violations and maintain smooth patient flow based on the predicted task TATs and the expected demand.
Thomas: Sec. 0036, The task management model development module 110 can also employ the historical sets of the dynamic system state data 102 to develop and train one or more machine learning models (e.g., one or more staffing models 142) to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0054, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload)).
Thomas describes the managing of resources and task to include the time and number of resources and number of places to where task are to be executed.
Claims 26, 29, 31, 32 recite limitations that stand rejected via the art citations and rationale applied to claims 18, 21, 23, 24.
Claims 34 and 37 recite limitations that stand rejected via the art citations and rationale applied to claims 18 and 21. Regarding, a non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform operations (
Thomas: Sec. 0033, The computing device 102 can include or be operatively coupled to at least one memory 114 and at least one processor 112. The at least one memory 114 can further store executable instructions (e.g., the task management model development module 110, the model application module 118 and the task management module 122) that when executed by the at least one processor 112, facilitate performance of operations defined by the executable instruction.
Thomas: Sec. 0129, One or more embodiments can be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out one or more aspects of the present embodiments.
Thomas: Sec. 0146, computer-executable instructions of a computer program product that runs on a computer and/or computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types.)
Claims 19, 20, 27, 28, 30, 33, 35, 36 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20200411168, Thomas, et al. to hereinafter Thomas in view of United States Patent Publication US 20200382445, Calmon, et al., to hereinafter Calmon in view of United States Patent Publication US 20090300183, Feng, et al
Referring to Claim 19, Thomas teaches the computing apparatus of claim 18 wherein the plurality of program instructions further cause the computing apparatus to:
compute, via a simulation model, a quality of service measure for each time interval based on the number of nodes to be assigned to each resource for each time interval (
Thomas: Sec. 0028, the system can determine an optimal order/sequence for performing currently pending EVS tasks to minimize the TATs (and consequently reduce wait times) while meeting any defined operating constraints.
Thomas: Sec. 0036, to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0044, The task management model development module 110 can also train and develop of one or more machine learning models to determine the expected number of staff available for performing the currently pending, and optionally the forecasted tasks, based on learned correlation between various factors in the historical dynamic system state data 102 that influence staff availability. For example, the task management development module 110 can train and develop one or more staffing models 142 to estimate the number of available staff (e.g., EVS workers) by shift or timeframe, by unit, or the like.
Thomas: Sec. 0048, the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.
Thomas describes determining the number of staff available in queue, which is being interpreting as quality of service measure.
Thomas in view of Calmon does not explicitly teach via a simulation model.
However, Feng teaches via a simulation model (
Feng: Sec. 0104, however, advantages. It will be seen in the simulation section that there are almost no accumulated flows at nodes.).
Thomas, Calmon, and Feng are all directed to the analysis of resource allocation (See Thomas at 0025, 0029, 0060; Calmon at 0003, 0018, 0026; Feng at 0010, 0030, 0031). Thomas discloses that additional elements, such as the task management module can be considered (See Thomas at 0032). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Thomas in view of Calmon, which teaches detecting and repairing task information problems in view of Feng, to efficiently apply analysis of resource allocation to enhancing the capability to simulate data. (See Feng at 0085, 0113).
Referring to Claim 20, Thomas teaches the computing apparatus of claim 19 wherein the quality of service measure comprises a waiting time for the first number of tasks and the second number of tasks to be processed or a queue size for each node (
Thomas: Sec. 0028, the system can determine an optimal order/sequence for performing currently pending EVS tasks to minimize the TATs (and consequently reduce wait times) while meeting any defined operating constraints.
Thomas: Sec. 0036, to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0041, implementations in which the dynamic system includes a hospital, the task management model development module 110 can train and develop one or more demand models 138 to predict the distribution of EVS tasks within an upcoming timeframe (e.g., from a current point in time through the next hour, the next 12 hours, the next 24 hours, the next 48 hours, etc.) based on historical information that reflects the historical demand for the EVS tasks (e.g., amount and type of that tasks performed and/or requested for performance) under different operating conditions/contexts of the hospital. For instance, the different operating conditions/contexts of the hospital can be based on variances in bed occupancy levels, bed availability, bed status, number of patients waiting for beds, type/location of the beds, staff availability, supply availability, and the like. The predicted distribution of the EVS tasks can include the total number of predicted tasks, the types of the tasks, the location of the tasks, and other potential distinguishing attributes associated with the tasks that can have an impact on the task TATs and/or the resources that are needed (e.g., personnel, supplies, equipment, etc.) to fulfil the tasks in accordance with defined service level requirements for the tasks.
Thomas: Sec. 0048, the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.).
Thomas describes determining the waiting time for EVS task and the number of staff available in queue.
Claims 27, 28, 35, 36 recite limitations that stand rejected via the art citations and rationale applied to claims 19 and 20.
Claims 22, 25, 30, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20200411168, Thomas, et al. to hereinafter Thomas in view of United States Patent Publication US 20200382445, Calmon, et al., to hereinafter Calmon in view of United States Patent Publication US 20180260253, Nanda, et al
Referring to Claim 22, Thomas teaches the computing apparatus of claim 18 wherein the resource allocation request further includes a social distance constraint (See Nanda), and determining the number of nodes to be assigned to each resource for each time interval is further based on the social distance constraint (See Nanda) (
Thomas: Sec. 0028, The system can also employ combinatorial mathematical optimization to determine the number of resources to allocate to the respective units over the upcoming timeframe to minimize service level agreement (SLA) violations and maintain smooth patient flow based on the predicted task TATs and the expected demand.
Thomas: Sec. 0036, The task management model development module 110 can also employ the historical sets of the dynamic system state data 102 to develop and train one or more machine learning models (e.g., one or more staffing models 142) to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0054, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload)).
Thomas describes the managing of resources and task to include the time and number of resources and number of places to where task are to be executed.
Thomas does not explicitly teach a social distance constraint.
However, Nanda teaches a social distance constraint (
Nanda: Sec. 0094, Maximum cost rule, in which shipping from store A to hub B will incur cost of transportation, fuel, etc., to help control the costs associated with shipping the items.
Nanda: Sec. 0095, Maximum distance rule, in which the items should be fulfilled from stores and warehouses within a predefined maximum distance from a customer location.).
Thomas, Calmon, and Nanda are all directed to the analysis of resource allocation (See Thomas at 0025, 0029, 0060; Calmon at 0003, 0018, 0026; Nanda at 0015-0017, 0031). Thomas discloses that additional elements, such as the task management module can be considered (See Thomas at 0032). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Thomas in view of Calmon, which teaches detecting and repairing task information problems in view of Nanda, to efficiently apply analysis of resource allocation to improving the organizing of task and resources . (See Nanda at 0028, 0032).
Referring to Claim 25, Thomas teaches the computing apparatus of claim 18 wherein determining the number of nodes to be assigned to each resource for each time interval is further based on a determining a flow rate (See Nanda) of tasks being processed based on an arrival time, and the resource allocation schedule is based on the flow rate (See Nanda) of tasks being processed for each time interval (
Thomas: Sec. 0085, The resource evaluation component 806 can also include system flow monitoring component 810 to facilitate determining whether the expected demand and/or predicted task TATs indicate a violation or potential violation to a defined SLA requirement included in the SLA information 802. For example, the system flow monitoring component 810 can determine if a forecasted TAT for specific task or group of tasks (e.g., tasks associated with medical unit B, tasks of a specific type etc.) exceeds a maximum allotted TAT for the task or groups of tasks. The notification component 812 can further be configured to generate and send a notification (e.g., a demand/TAT notification 128) to an appropriate entity regarding the violation or potential violation to the SLA requirement.).
Thomas: Sec. 0028, The system can also employ combinatorial mathematical optimization to determine the number of resources to allocate to the respective units over the upcoming timeframe to minimize service level agreement (SLA) violations and maintain smooth patient flow based on the predicted task TATs and the expected demand.
Thomas: Sec. 0036, The task management model development module 110 can also employ the historical sets of the dynamic system state data 102 to develop and train one or more machine learning models (e.g., one or more staffing models 142) to predict or estimate the number of staff that are or will be available to perform the tasks (e.g., the currently pending tasks and optionally the forecasted tasks).
Thomas: Sec. 0054, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload)).
Thomas describes the managing and flow of resources and task to include the time and number of resources and number of places to where task are to be executed.
Thomas in view of Calmon does not explicitly teach flow rate.
However, Nanda teaches flow rate (
Nanda: Sec. 0006, Resource allocation problems encountered in stream processing systems have been considered heretofore without satisfactory resolution. Multiple data streams flow into the stream processing system to be processed and eventually to lead to valuable output. Examples of such processing include matching, aggregation, summarization, etc. Each stream requires certain amount of resource from the nodes to be processed. The nodes need to decide how much flow to admit into the system. The overall objective is to maximize a system utility function, which is a concave function of the amount of processed flow rates.
Nanda: Sec. 0033, incoming flows are inelastic, [25] first addressed the joint routing and scheduling problem, where they showed that a queue-length-based scheduling policy guarantees stability of the buffers as long as the arrival rates lie within the capacity region of the network. In the context of wireline networks, the idea of a distributed flow control based on a system-wide optimization problem was developed in [17], and followed by many others, see [22] for a survey. More recently, the approach has been adapted to address the problem of serving elastic traffic over wireless newtorks [19, 7], where rate control algorithms are introduced that adapt the flow rates as a function of the entry queue length. In [19], a dual congestion controller is used assuming flow rate can be adjusted instantaneously in response to congestion feedback in the network).
Thomas, Calmon, and Nanda are all directed to the analysis of resource allocation (See Thomas at 0025, 0029, 0060; Calmon at 0003, 0018, 0026; Nanda at 0015-0017, 0031). Thomas discloses that additional elements, such as the task management module can be considered (See Thomas at 0032). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Thomas in view of Calmon, which teaches detecting and repairing task information problems in view of Nanda, to efficiently apply analysis of resource allocation to improving the organizing of task and resources . (See Nanda at 0028, 0032).
Claims 30 and 33 recite limitations that stand rejected via the art citations and rationale applied to claims 22 and 25.
Page 9 of 13
Response to Arguments
Applicant’s arguments filed 05/22/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/22/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 8-11 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
A computing apparatus for node allocation in a cloud computing environment, wherein a node relates to a processor core, the computing apparatus
comprising:
one or more processors;
at least one memory device coupled to the one or more processors; and
a data communications interface operably associated with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
receive, from a resource allocation demand server via the data communications interface, a resource allocation request which includes task information based on a task schedule, a time period including a plurality of time intervals, a number of resources available during each time interval, a number of nodes available during each time interval, and adjacency constraints for each adjacent node of the number of nodes;
in response to the resource allocation request, for each time interval of the time period:
determine a first number of tasks to be processed in a task queue within the time interval based on the task information;
determine a second number of tasks from the task queue that are unprocessed after a time interval that immediately precedes the time interval;
determine, based on the adjacency constraints for each adjacent node for the number of nodes, a resource allocation constraint for each resource, wherein the resource allocation constraint ensures that a range of nodes assigned to each resource does not overlap with a range of nodes assigned to any other resource; and
determine a subset of the number of nodes to be assigned to each resource in the time interval based on: (i) the first number of tasks, (ii) the second number of tasks, and (iii) the resource allocation constraint for each resource, wherein determining the subset of the number of nodes minimizes a cost function comprising a task waiting cost and a node operating cost over the time period; and
provide, to the resource allocation demand server via the data communications interface, a resource allocation schedule which includes the subset of the number of nodes assigned to each resource for each time interval of the time period.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing resources and task, which constitutes methods related to commercial interaction such as business relations which are still considered an abstract idea under the 2019 PEG. The processors are comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 9 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
“(i) receiving, from a resource allocation demand server, a defined resource allocation request including adjacency constraints for each adjacent node; (ii) determining, based on the adjacency constraints, a resource allocation constraint for each resource that ensures non- overlapping node ranges; (iii) determining a subset of nodes by minimizing a cost function comprising a task waiting cost and a node operating cost over the time period; and (iv) transmitting the resulting resource allocation schedule to the demand server.”
and arguments at pg. 9 seems to describe a “particular way” managing resources and task are part of the abstract idea. “ The Applicant is basically relying on the system elements as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner.
Next, the regarding the following arguments:
Pg. 10 “The robust optimization model may improve node allocation efficiency while
simultaneously considering adjacency of grouped nodes." Id. at [0027].
The specification further discloses that "an adjacency constraint can be utilized by the optimization process to ensure that the node range (e.g., desk range) assigned to each resource (e.g., ground handler) does not overlap." Id. at [0048]. The specification also discloses that "the system ensures that the desk range assigned to each ground handler does not overlap." Id. at [0063]. Moreover, the specification discloses "an objective function of the optimization instruction set 150 (e.g., results of the optimization process), can include a linear program to minimize the waiting cost and the node (e.g., check-in desk) operating costs over the optimization window T." Id. at [0037].”
Examiner finds Applicant has failed to adequately point out what technological process in the claimed invention has improved. Using an optimization model to adjust and managing resources and task, is considered an improvement to an existing business process and not an improvement to the functioning of a computer, or any other technology or technological field.
Then, regarding the following arguments
Pg. 10 “The claims further recite a specific, non-abstract optimization process: (a) enforcing non-overlapping node ranges across resources based on adjacency constraints, and (b) minimizing a defined cost function combining task waiting cost and node operating cost. These are not generic steps of "managing resources" but rather a particular computational solution to the technical problem of efficiently allocating adjacent physical resources (processor cores) while minimizing cost - a problem rooted in computer technology that requires specific algorithmic computation. The claimed process produces a specific, tangible output (a resource allocation schedule) that is transmitted to a demand server for implementation, constituting a practical application of the optimization.”
that claims do not show or break down how this is actually executed, at this point it’s just an aspirational statement, the technical application is NOT particular, and furthermore the claim invention does not “improves the functioning of a computer or improves another technology or technical field.” or “an improvement to another technology or technical field (e.g., The ordered combination of receiving a resource allocation request with adjacency constraints for physically adjacent nodes, determining non-overlapping resource allocation constraints, minimizing a dual-cost objective function comprising task waiting cost and node operating cost, and transmitting the resulting allocation schedule to a demand server in a cloud computing environment provides an inventive concept.).” of pg.11. As, the claims are clear steps for managing resources and task and not the improvement of the hardware or even software. Furthermore, merely adding novel or non-routine components to the claimed idea does not necessarily turn an abstract idea into something concrete, per Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335 (Fed. Cir. 2013). The limitations steps of receiving… determining…and providing… are each represented by the abstract idea of managing resources and task. Furthermore, the steps of the receiving by a server are representative of data storage, record keeping, data updates; each of which are well-known-routine, and conventional activities identified by the courts as not providing significantly more to an abstract idea (see MPEP § 2106.05(d)(ii), Ultramercial (2013), Alice (2014)). Regardless, there are rejections against all claims under 35 USC 103 presenting that the claims are not novel/obvious/inventive concept with respect to the prior art identified. Thus, this argument is not persuasive.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing resources and task will fall under. The 2019 PEG which states:
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).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ringrose et al., U.S. Pub. 20060041458, (discussing the operation history utilization of terminal electric device, a server electric device, and an application server).
Ringrose et al., W.O. Pub. 2006010134, (discussing the allocation of resources and simulating constraints).
Xu et al., Holistic Resource Allocation For Multicore Real-Time Systems, https://par.nsf.gov/servlets/purl/10108226, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2019 (discussing the monitoring of home appliances regarding power consumption).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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