DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to amendments filed on 8/4/2026. Claims 1-20 are currently pending and have been considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-20 are determined to be directed to an abstract idea.
The claims 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without providing a practical application integration and without providing significantly more.
As per Step 1 of the subject matter eligibility analysis, Claims 1-20 are directed to a method, computing device (i.e., apparatus), and medium (i.e., product) which are within the four statutory categories of invention.
As per Step 2A-Prong 1 of the subject matter eligibility analysis, Claims 1, 19 and 20 recite the abstract idea of robot scheduling by acquiring a global average number of tasks to be executed in a specified scenario and a local average number of tasks to be executed in regions, wherein the specified scenario is divided into a plurality of regions in advance, and the specified scenario is a physical scenario in which the robot is scheduled to execute tasks to be executed in the plurality of regions; predicting a global task execution loss of the specified scenario according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions; determining a robot cross-region scheduling policy according to the global task execution loss; and performing cross-region scheduling on a target robot in the specified scenario by using the robot cross-region scheduling policy; which include mental processes (observing and evaluating task data for judgment for robot scheduling), and certain methods of organizing human activity based on fundamental economic practice (managing and facilitating robot scheduling based on task data and scheduling policy), and managing personal behavior and interactions between people (following rules and instructions to perform robot scheduling based on task data and scheduling policy). Claims 2-18 are directed to the abstract idea of claim 1 with further details on the parameters/attributes of the abstract idea which includes mental processes and certain methods of organizing human activity for similar reasons as provided above for claim 1. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
As per Step 2A-Prong 2 of the subject matter eligibility analysis, while the claims 1-20 recite additional limitations which are hardware or software elements, such as computing device, comprising: a memory; and a processor; wherein the memory is configured to store therein computer-executable instructions, non-transitory computer-readable storage medium having stored therein computer instructions that, when executed by a processor, cause the processor to…; these limitations are not enough to qualify as a practical application being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
As per Step 2B of the subject matter eligibility analysis, while the claims 1-20 recite additional limitations which are hardware or software elements, such as computing device, comprising: a memory; and a processor; wherein the memory is configured to store therein computer-executable instructions, non-transitory computer-readable storage medium having stored therein computer instructions that, when executed by a processor, cause the processor to…; these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do provide significantly more to an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claims 1-3, 8-10, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over by Sarkar et al (US 20200004588 A1) in view of Gombolay et al (US 20220226994 A1).
As per Claim 1, Sarkar teaches a robot scheduling method (Abstract) comprising:
acquiring a global average number of tasks to be executed in a specified scenario and a local average number of tasks to be executed in regions, and the specified scenario is a physical scenario in which the robot is scheduled to execute tasks to be executed (para. 0019, “The goal is to schedule optimally a maximum number of the set of non-preemptive or atomic tasks within a deadline corresponding to each of the set of non-preemptive or atomic tasks.”, para. 0020, “Still further, since the available resources (especially robots) are fixed in number and a number of task in-flows may be very large during a particular time of the day and during a certain period of the year, and a large number of tasks are bound to miss their deadline during these high demand periods. For example, considering a scenario of the smart warehouse environment, movement of goods or objects in and out of the smart warehouse environment poses a major challenge in terms of scheduling of tasks (relating to the movement to goods). All the tasks imported in a wave of tasks are required to be finished as soon as possible using the limited number of resources (robots) and then the next wave is imported.”; para. 0048, “According to an embodiment of the present disclosure, the scheduling technique comprises two phases. In the first phase, the scheduling technique (implemented via the one or more hardware processors 104) attempts an assignment the set of schedulable tasks, that is, the scheduling technique assigns a maximum number of tasks (amongst the plurality of tasks) to a robot, wherein one or more high performance loss tasks are scheduled with a high priority. In the second phase, the scheduling technique attempts to assign the set of non-schedulable tasks accumulated in the first phase such that corresponding accumulated performance loss may also be minimized. The implementation of the scheduling technique to partition and schedule the set of non-preemptive tasks or the sporadic tasks having the deadline on the multi-processor system may now be considered in detail.”);
predicting a global task execution loss of the specified scenario according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions (para. 0006, “a method for scheduling a set of non-preemptive tasks in a multi-robot environment is provided, the method comprising: defining, by one or more hardware processors, a plurality of tasks, wherein each the plurality of tasks is a non-preemptive task characterized by an execution time, a specified time interval for completing the non-preemptive task and a performance loss function per time unit; partitioning, by a scheduling technique, the plurality of tasks into a set of schedulable tasks and a set of non-schedulable tasks, wherein the set of schedulable tasks comprise a subset of tasks to be executed within a predefined time interval and having a maximum performance loss value, and wherein the set of non-schedulable tasks comprise a subset of tasks failing to execute within the predefined time interval and with a low performance loss value; and scheduling, in a pre-defined order, the plurality of tasks partitioned by performing a plurality of steps wherein the plurality of steps comprise: (i) sorting, by the scheduling technique, the plurality of tasks partitioned in non-increasing order of one or more performance loss values corresponding to the plurality of tasks partitioned; (ii) scheduling the set of schedulable tasks in the multi-robot environment by assigning at least one of the set of schedulable tasks to each robot amongst a plurality of robots operating in the multi-robot environment by implementing a compaction mechanism, wherein the scheduling comprises: (a) determining, by the scheduling technique, a possibility of execution of each of the set of schedulable tasks in the multi-robot environment; (b) determining, by the scheduling technique, a possibility of scheduling a set of pre-assigned tasks amongst the plurality of robots, wherein the set of pre-assigned tasks comprise one or more tasks other than the set of schedulable tasks and the set of non-schedulable tasks; and (c) scheduling, by the compaction mechanism, the set of schedulable tasks in the multi-robot environment upon determining the possibility of execution of each of the set of schedulable tasks and the possibility of scheduling the set of pre-assigned tasks; and (iii) scheduling, by a list-scheduling technique, the set of non-schedulable tasks amongst the plurality of robots; categorizing, by the scheduling technique, one or more schedulable tasks amongst the set of schedulable tasks as a non-schedulable task upon determining either one of: (i) a least possibility of the execution of each of the schedulable task amongst the set of schedulable tasks; or (ii) a least possibility of scheduling the set of pre-assigned tasks; performing, based upon the one or more performance loss values corresponding to the plurality of tasks, a load balancing of the plurality of tasks partitioned, to optimize the scheduling of the set of non-preemptive tasks in the multi-robot environment; performing iteratively, the step of identification of the one or more non-schedulable tasks by implementing the scheduling technique, until each the set of non-schedulable tasks is scheduled in the multi-robot environment.”; para. 0019, “The problem of scheduling a set of non-preemptive or atomic tasks to a plurality of homogeneous and autonomous robots in a smart environment, especially a smart warehouse environment, is closely similar to a non-preemptive scheduling or a sporadic process scheduling on a homogeneous multi-processors system. The goal is to schedule optimally a maximum number of the set of non-preemptive or atomic tasks within a deadline corresponding to each of the set of non-preemptive or atomic tasks. Further, since each of the set of non-preemptive or atomic tasks has a deadline and a performance loss attributed to it that may be incurred on a multi-robot system if any of the set of non-preemptive tasks is not completed within its corresponding deadline, the goal of a task scheduling system is to reduce the number of deadline misses and minimization of the performance loss.”);
determining a robot cross-region scheduling policy according to the global task execution loss (See para. 0006, 0019, 0020, 0048), and
performing cross-region scheduling on a target robot in the specified scenario by using the robot cross-region scheduling policy (See para. 0006, 0019, 0020, 0048).
While Sarkar teaches task location distance being embedded in execution time calculations (para. 0081, “execution time of each of the plurality of tasks is equivalent to from and back distance from a task location and a packaging dock, in the WPS. A set of coordinates corresponding to the task location and the packaging dock were obtained from a standard capacity-constraint vehicle routing problem”), it does not expressly specify; however, Gombolay teaches wherein the specified scenario is divided into a plurality of regions in advance (Fig. 3, para. 0011, 0015-0016, regarding graph listing location nodes (i.e. regions))
It would have been obvious, before the earliest effective filing date of the invention, for an ordinary skill to modify Sarkar with the aforementioned teachings from Gombolay, in the field of scheduling robots, with the motivation to define a problem with more information to enable an improved and more accurate scheduling solution.
As per Claim 2, Sarkar in view of Gombolay teaches a method as recited above for Claim 1. Sarkar further teaches wherein predicting the global task execution loss of the specified scenario according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions comprises: predicting object idle information about the regions, respectively, according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions; and calculating the global task execution loss of the specified scenario according to the object idle information about the regions (para. 0067-0068, regarding assigning task to idle robots; also see para. 0006, 0019, 0020, 0048).
As per Claim 3, Sarkar in view of Gombolay teaches a method as recited above for Claim 1. Sarkar further teaches wherein predicting the global task execution loss of the specified scenario according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions comprises: predicting object idle information about the regions, respectively, according to the global average number of tasks to be executed and the local average number of tasks to be executed in the regions; and calculating the global task execution loss of the specified scenario according to the object idle information about the regions (para. 0067-0068, regarding assigning task to idle robots; also see para. 0006, 0019, 0020, 0048).
As per Claim 8, Sarkar in view of Gombolay teaches a method as recited above for Claim 1. Sarkar further teaches wherein before performing the cross-region scheduling on the target robot in the specified scenario by using the robot cross-region scheduling policy, the method further comprises: determining a target robot to be scheduled according to the robot cross-region scheduling policy and a preset constraint (para. 0067-0068, regarding assigning task to idle robots; also see para. 0006, 0019, 0020, 0048).
As per Claim 9, Sarkar in view of Gombolay teaches a method as recited above for Claim 1. Sarkar further teaches wherein acquiring the global average number of tasks to be executed in the specified scenario and the local average number of tasks to be executed in the regions comprises: counting a total number of tasks to be executed in the specified scenario, a total number of robots, a total number of tasks to be executed in regions and current distribution information about robots in the regions; calculating the global average number of tasks to be executed in the specified scenario, according to the total number of tasks to be executed in the specified scenario and the total number of robots; and calculating the local average number of tasks to be executed in the regions, respectively, according to the total number of tasks to be executed and the current distribution information about the robots in the regions (para. 0020, “Still further, since the available resources (especially robots) are fixed in number and a number of task in-flows may be very large during a particular time of the day and during a certain period of the year, and a large number of tasks are bound to miss their deadline during these high demand periods. For example, considering a scenario of the smart warehouse environment, movement of goods or objects in and out of the smart warehouse environment poses a major challenge in terms of scheduling of tasks (relating to the movement to goods). All the tasks imported in a wave of tasks are required to be finished as soon as possible using the limited number of resources (robots) and then the next wave is imported.”; para. 0083, “d.sub.i=e.sub.i, wherein a deadline d.sub.i of task is set identical to corresponding execution time e.sub.i. In this case, only m (that is, total available number of robots) tasks meet deadline, while a large number of tasks are bound to miss deadlines. Although a rare scenario, but considered to highlight the efficiency of the proposed disclosure”; para. 0095, “In an embodiment, for the datasets with 100 tasks, the number of robots were fixed to 10, while the number of available robots were varied for the datasets with 1000 tasks starting between 10 and 140 robots, thereby determining how robust the task scheduler is with varying number of resources, and how does the performance improves with the increasing number of resources”; also see para. 0094 and 0096).
As per Claim 10, Sarkar in view of Gombolay teaches a method as recited above for Claim 9. Sarkar further teaches wherein counting the total number of tasks to be executed in the specified scenario and the total number of tasks to be executed in the regions comprises: acquiring attribute information about a task to be executed in the specified scenario; determining a quantity coefficient of the task to be executed according to the attribute information about the task to be executed; and counting the total number of tasks to be executed in the specified scenario and the total number of tasks to be executed in the regions according to the quantity coefficient of the task to be executed (para. 0020, “Still further, since the available resources (especially robots) are fixed in number and a number of task in-flows may be very large during a particular time of the day and during a certain period of the year, and a large number of tasks are bound to miss their deadline during these high demand periods. For example, considering a scenario of the smart warehouse environment, movement of goods or objects in and out of the smart warehouse environment poses a major challenge in terms of scheduling of tasks (relating to the movement to goods). All the tasks imported in a wave of tasks are required to be finished as soon as possible using the limited number of resources (robots) and then the next wave is imported.”; para. 0083, “d.sub.i=e.sub.i, wherein a deadline d.sub.i of task is set identical to corresponding execution time e.sub.i. In this case, only m (that is, total available number of robots) tasks meet deadline, while a large number of tasks are bound to miss deadlines. Although a rare scenario, but considered to highlight the efficiency of the proposed disclosure”; para. 0095, “In an embodiment, for the datasets with 100 tasks, the number of robots were fixed to 10, while the number of available robots were varied for the datasets with 1000 tasks starting between 10 and 140 robots, thereby determining how robust the task scheduler is with varying number of resources, and how does the performance improves with the increasing number of resources”; also see para. 0094 and 0096).
As per claims 19-20, claims 19-20 recite substantially similar limitations as claim 1; therefore, claims 19-20 are rejected with the same reasoning, rationale and motivation as recited above for claim 1.
As per claims 19-20, Sarkar further teaches a computing device, comprising: a memory; and a processor; wherein the memory is configured to store therein computer-executable instructions, and the processor is configured to…; and a non-transitory computer-readable storage medium having stored therein computer instructions that, when executed by a processor, cause the processor to (para. 0007-0008).
Response to Arguments
Applicant’s arguments have been fully considered and would not overcome all of the rejections.
Arguments under 35 USC 101:
Applicants argued that the claims do not recite an abstract idea. Examiner respectfully disagrees.
Applicant’s claims are directed to the abstract idea of mental processes (observing and evaluating task data for judgment for robot scheduling), and certain methods of organizing human activity based on fundamental economic practice (managing and facilitating robot scheduling based on task data and scheduling policy), and managing personal behavior and interactions between people (following rules and instructions to perform robot scheduling based on task data and scheduling policy).
Applicants argued that the claims are directed to practical application or significantly more of an abstract idea. Examiner respectfully disagrees.
Additional elements -generic computing technologies- of the claims are not enough to qualify as “practical application” or “significantly more” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do provide practical application or significantly more to an abstract idea (MPEP 2106.05 (f) & (h)). Further, note that the claims are directed to a scheduling problem with no limitation defining any improvement to robotics technology.
Arguments under 35 USC 103:
Arguments focus on the interpretation of Global vs Local. The rejection interprets these terms under broadest reasonable interpretation as whole set vs subset of tasks. Based on this interpretation Sarkar in view of Gombolay teaches the argued limitations as recited in the rejection section above.
Conclusion
Claims 4-7 and 11-18 are not rejected with prior art, wherein the novelty is in the combination of all limitations. Closest prior art to the invention of these claims includes: Sarkar et al (US 20200004588 A1) and Gombolay et al (US 20220226994 A1) with relevant sections recited in the rejection section above.
Additional relevant art not relied upon includes:
Jacquemart et al (US 12493852 B2), regarding “A method for managing a plurality of autonomous mobile robots, referred to as picking robots, for order-picking of products stored in a warehouse having a plurality of product storage spaces, a plurality of areas for stock-picking by a plurality of operators, and a plurality of areas for the collection of products picked by the picking robots. Also provided are a management system and an order-picking method.”
Jeon (US 12373787 B2), regarding “Provided is a method of operating a server. The method includes generating a sequence for a plurality of orders based on shipments included in each of the plurality of orders, acquiring a plurality of groups including one or more consecutive orders among the plurality of sorted orders according to the generated sequence, allocating the plurality of groups to a plurality of robots such that the groups and robots are matched one-to-one, and allocating tasks to the plurality of robots based on orders included in each group.”
Hance et al (US 10723555 B2), regarding “A method includes maintaining an inventory database for each warehouse of a plurality of warehouses, where a plurality of robots are deployed at each warehouse, where the inventory database for each warehouse is updated based on messages sent by the plurality of robots at the warehouse during performance of tasks by the plurality of robots at the warehouse. The method further includes receiving an order and determining, for each warehouse, a projected availability time for an item that satisfies the order to be available for pickup at the warehouse based on the inventory database for the warehouse. The method additionally includes selecting a warehouse from the plurality of warehouses based on the projected availability time determined for each warehouse. The method further includes causing at least one robot at the selected warehouse to prepare for pickup the item that satisfies the order at the selected warehouse.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
