DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-19 are pending and examined herein per Applicant’s 03/25/2024 filing with the USPTO.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/12/2024 and 12/12/2025 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. certain methods of organizing human activity and mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1-17 are to a method (process), claim 18 is to a medium (manufacture), and device (machine).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of certain methods of organizing human activity and a mental processes. Where certain methods of organizing human activity is defined as Where certain methods of organizing human activity include fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), see MPEP 2106.04(a). Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. A computer-implemented method for dispatching a plurality of agents to a plurality of tasks, the method comprising:
- identifying, by a processor, at least one agent-task pair from a plurality of possible agent-task pairs based on a primary cost function of a first assignment of agents to tasks to be used to generate a trimmed plurality of possible agent-task pairs;
- generating, by the processor, a second assignment of agents to tasks with the primary cost function that is at least as optimal as the primary cost function of the first assignment of agents to tasks by performing a first search on the trimmed plurality of possible agent-task pairs;
- generating, by the processor, a third assignment of agents to tasks with a secondary cost function that is at least as optimal as a secondary cost function of the second assignment of agents to tasks by performing a second search on the trimmed plurality of possible agent-task pairs, such that the primary cost function of the third assignment of agents to tasks is at least as optimal as the primary cost function of the second assignment of agents to tasks; and
- dispatching the plurality of agents to the plurality of tasks based on the third assignment of agents to tasks.
The specification does not express define bounds of what an agent is. It only provides examples, see “at least one of the plurality of agents is a drone” (Spec. [22]) and “agents may take the form of vehicles, quadrotors, mobile robots, drones, Unmanned Aerial Vehicle (UAVs), Unmanned Ground Vehicle (UGVs), Central Processing Unit (CPUs), electrical power units, machines”. (Spec. [40]). As described in the specification the agent could be non-human, but there is nothing prevention the agent per se from being a human. As such the term “agent” as broadly claimed and when read in light the specification could be anything – including a human. If the claim is read wherein the agent is a human the claim is found to be directed towards an abstract idea matching agents/humans with task based on cost – resource management to managing personal behavior. The claim is found to be directed to certain methods of organizing human activity.
Even if the agent is read to be one of the non-human type of agents listed in the specification the claims are still found to be directed towards an abstract idea. Specifically, mental processes – where the claims are to matching agents with task based on known information and the human minds ability to reason, but for the nominal recitation of the processor. Where given the know information about the agents and task, a person could create agent-task pairs using his ability to make observations. The person could also filter (trim) agent-task pairs based on cost using his ability to evaluate information. Finally the person, could further filter (trim) the pairs based on cost thereby optimizing cost with respect to the matched pairs. Th person using his ability to make a judgment call to dispatch the agents.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The dispatching elements are determined to be insignificant extra-solution activity – outputting the results of the abstract analysis.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. The claim uses generic components, see for example specification “processor unit 1230 may represent a single processor with one or more processor cores or an array of processors, each comprising one or more processor cores.” (Spec. [139]).
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
With respect to claim 2 which further defines secondary cost function or simply defines the information used in the analysis these elements do not add a practical application or significantly more to the abstract idea.
With respect to claims 3 and 14, which further define the primary cost function or simply defines the information used in the analysis these elements do not add a practical application or significantly more to the abstract idea.
With respect to claim 4, which further defines the trimming or filtering process “cost less optimal than or equal to the primary cost function of the first assignment” adds to the identified abstract idea.
With respect to claim 5, which further defines the generating of second assignment adds to the identified abstract idea.
With respect to claim 6, further defines the first replacement agent adds to the identified abstract idea – finding the optimal solution – agent and task pairing.
With respect to claim 7, adds a graph claims these elements are viewed as steps of outputting/insignificant extra solution activity that does not add a practical application or significantly more to the abstract idea.
With respect to claim 8, further defines the second optimization adds to the identified abstract idea.
With respect to claims 9 and 11, which further defines the generating of third assignment adds to the identified abstract idea.
With respect to claim 10, which further defines task associated with the sub-optimal secondary cost function or simply defines the information used in the analysis these elements do not add a practical application or significantly more to the abstract idea.
With respect to claim 12, which further defines the dispatch step which adds to outputting/insignificant extra solution activity that does not add a practical application or significantly more to the abstract idea.
With respect to claim 13, further defines the agent task paring adds to the identified abstract idea.
With respect to claim 15, further defines the agent which adds to the identified.
With respect to claim 16, further defines the task which adds to the identified.
With respect to claim 17, further defines the generic computer components which does not add a practical application or significantly more to the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-10, 12-15, and 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li (US 2023/0306334 A1)
Claims 1, 18, and 19
Li teaches a computer-implemented method for dispatching a plurality of agents to a plurality of tasks, the method comprising (Li abstract “method and system for task assignment in autonomous mobile devices” and [22] “a dispatcher 113 whose primary function is task assignment and decides which autonomous mobile device should perform specific tasks and at a particular time”. It should be noted [14] “autonomous mobile devices or sometimes referred herein as agents”):
- identifying, by a processor, at least one agent-task pair from a plurality of possible agent-task pairs based on a primary cost function of a first assignment of agents to tasks to be used to generate a trimmed plurality of possible agent-task pairs (Li [40] “a probable combinations of two or more tasks assigned to the agent. The search space for the probable combinations of two or more tasks is based on a current combination of the two or more tasks. The current combination includes grouping of the two or more tasks assigned to the agent and updating the search space for the probable combinations of two or more tasks assigned to the agent based on a value proposed by the agent corresponding to each of the two or more tasks . . . cost estimation of the bid proposed by the agent. The present system tracks the assignments in the current combination for an outbid against the current bid value”);
- generating, by the processor, a second assignment of agents to tasks with the primary cost function that is at least as optimal as the primary cost function of the first assignment of agents to tasks by performing a first search on the trimmed plurality of possible agent-task pairs (Li [37] “agent determines an optimized value to propose based on the information related to the neighboring one or more agents proposing a value for the same task. Also, the new value herein may be a first value or a second value or a third value or so on and so forth depending on the number of times the proposed value is evaluated/re-evaluated. Further, the optimization is based on a heuristic cost estimation including one or more factors.” and [5] “the task assignment planner, the evaluated value in response to the received information to generate a new value to be associated with the task, wherein the new value is based on a heuristic cost estimation of the agent corresponding to the plurality of tasks assigned to the agent and generate, by the task assignment planner, a task assignment plan for the agent based on the new value to be associated with the task”);
- generating, by the processor, a third assignment of agents to tasks with a secondary cost function that is at least as optimal as a secondary cost function of the second assignment of agents to tasks by performing a second search on the trimmed plurality of possible agent-task pairs, such that the primary cost function of the third assignment of agents to tasks is at least as optimal as the primary cost function of the second assignment of agents to tasks (Li [37] “agent determines an optimized value to propose based on the information related to the neighboring one or more agents proposing a value for the same task. Also, the new value herein may be a first value or a second value or a third value or so on and so forth depending on the number of times the proposed value is evaluated/re-evaluated. Further, the optimization is based on a heuristic cost estimation including one or more factors.” And [5] the task assignment planner, the evaluated value in response to the received information to generate a new value to be associated with the task, wherein the new value is based on a heuristic cost estimation of the agent corresponding to the plurality of tasks assigned to the agent and generate, by the task assignment planner, a task assignment plan for the agent based on the new value to be associated with the task); and
- dispatching the plurality of agents to the plurality of tasks based on the third assignment of agents to tasks (Li [14] “the robots start to execute the task. In one embodiment, controlling the plan execution includes managing the entire lifecycle of plan execution including determining several plan execution values.”).
Li also teaches the claimed limitation of claim 18, which are substantially similar to those rejected above therefore this claim is rejected for the same reasoning given above. Li further teaches the additionally claimed limitations of a non-transitory computer-readable medium comprising computer program code stored thereon for dispatching a plurality of agents to a plurality of tasks, wherein the code, when executed by one or more processors, causes the one or more processors to perform a method (Li [34] “the method 400 can be implemented in any suitable hardware, software, firmware, or combination thereof.” and [51] “non-transitory computer-readable medium and executable by one or more computing devices”):
Li also teaches the claimed limitation of claim 19, which are substantially similar to those rejected above therefore this claim is rejected for the same reasoning given above. Li further teaches the additionally claimed limitations of a computing device comprising one or more processors operable to perform a method for dispatching a plurality of agents to a plurality of tasks, wherein the method comprises (Li [21] “cloud platform includes one or more processors” and [22] “cloud platform 110 includes a dispatcher 113 whose primary function is task assignment and decides which autonomous mobile device should perform specific tasks”):
Claim 2
Li teaches all the limitations of the method of claim 1, wherein the secondary cost function represents variation of a cost among agents assigned to each task (Li [32] and [37]).
Claim 3
Li teaches all the limitations of the method of claim 1, wherein the primary cost function of the first assignment is based on a cost of a least-optimal agent-task pair in the first assignment (Li [15] and [19] see “not necessarily the optimal solution” and “suboptimal” as the equivalent of the claimed least-optimal).
Claim 4
Li teaches all the limitations of the method of claim 1, wherein the trimmed plurality of possible agent-task pairs is generated by removing agent-task pairs from the plurality of possible agent-task pairs associated with a cost less optimal than or equal to the primary cost function of the first assignment (Li [35] see lower than base).
Claim 5
Li teaches all the limitations of the method of claim 1, wherein generating the second assignment is performed by:
- identifying, from the trimmed plurality of possible agent-task pairs, a first replacement agent-task pair associated with a more optimal cost compared to the primary cost function of the first assignment (Li [31]);
- generating a graph representation of the plurality of possible agent-task pairs (Li [31-32]);
- performing a depth-first search of the graph to find an augmenting path connecting said first replacement agent-task pair (Li fig. 3, [19], and [21] see traversing trees or graphs); and
- if said augmenting path is found, generating the second assignment based on the augmenting path (Li [37]);
- otherwise, using the first assignment as the second assignment (Li [37]).
Claim 6
Li teaches all the limitations of the method of claim 5, wherein the first replacement agent-task pair is an agent-task pair associated with a most optimal cost for the same task (Li [32]).
Claim 7
Li teaches all the limitations of the method of claim 1, wherein the plurality of possible agent-task pairs is represented by a graph, wherein the graph comprises a first plurality of vertices each corresponding to one of the plurality of agents, a second plurality of vertices each corresponding to one of the plurality of tasks, and a plurality of edges each connecting a vertex from the first plurality of vertices to a vertex in the second plurality of vertices, wherein each edge represents a possible agent-task pair, and wherein a weight of each edge represents a cost of said agent- task pair (Li [16], [18], and [21]).
Claim 8
Li teaches all the limitations of the method of claim 1, wherein the second assignment optimizes the primary cost function (Li [21] and [31]).
Claim 9
Li teaches all the limitations of the method of claim 1, wherein generating the third assignment is performed by:
- identifying from the second assignment a task associated with a sub-optimal secondary cost function (Li [28]);
- identifying one or more agents assigned to said task causing said task to be associated with the sub-optimal secondary cost function (Li [19] and [28]);
- identifying, from the trimmed plurality of possible agent-task pairs, one or more replacement agents that when replacing the identified one or more agents, the secondary cost function becomes more optimal (Li [19] and [28]);
- generating a graph representation of the plurality of possible agent-task pairs (Li fig. 3);
- performing a depth-first search of the graph to find an augmenting path connecting one of the one or more replacement agents to said task (Li [21] see traversing trees or graphs); and
- if said augmenting path is found, generating the third assignment based on the augmenting path (Li [37]);
- otherwise, using the second assignment as the third assignment (Li [37]).
Claim 10
Li teaches all the limitations of the method of claim 9, wherein said task associated with the sub-optimal secondary cost function corresponds to a task associated with a least optimal secondary cost function of the second assignment (Li [31] ).
Claim 12
Li teaches all the limitations of the method of claim 1, wherein each agent is dispatched to no more than one task at a time (Li [31]).
Claim 13
Li teaches all the limitations of the method of claim 1, wherein each task requires to be processed by a specific number of agents and wherein at least one task is dispatched with no more than the required specific number of agents for said task (Li [14]).
Claim 14
Li teaches all the limitations of the method of claim 1, wherein the primary cost function represents a temporal duration of processing of a task by an agent (Li [42]).
Claim 15
Li teaches all the limitations of the method of claim 1, wherein at least one of the plurality of agents is a drone (Li [14], see autonomous mobile devices).
Claim 17
Li teaches all the limitations of the method of claim 1, wherein the processor comprises a plurality of processors of the plurality of agents, and wherein the method is implemented in a distributed manner (Li [16] and [21]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2023/0306334 A1) as applied above and in further view of Tuysuzoglu et al (US 2022/0019952 A1)
Claim 11
Li teaches all the limitations of the method of claim 1, however Li does not expressly teach wherein the third assignment is Pareto optimal.
Tuysuzoglu teaches, in an analogous art, the claimed limitation of wherein the third assignment is Pareto optimal (Tuysuzoglu [50]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Li the third assignment is Pareto optimal as taught by Tuysuzoglu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2023/0306334 A1) as applied above and in further view of Wankewycz (US 2022/0041299 A1)
Claim 16
Li teaches all the limitations of the method of claim 1, however Li does not expressly teach wherein at least one of the plurality of tasks is to neutralize a drone.
Wankewycz teaches, in an analogous art, the claimed limitation of wherein at least one of the plurality of tasks is to neutralize a drone (Wankewycz [662], where mission is the equivalent of the claimed tasks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Li the at least one of the plurality of tasks is to neutralize a drone as taught by Wankewycz since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang et al (US 2025/0128837 A1) teaches a drone and a method of neutralizing an illegal drone performed by the drone. The method includes, by interoperating with a ground management device installed in a protection zone, detecting an illegal drone flying in the protection zone without permission, tracking the illegal drone having mobility using a detection sensor mounted on the drone at the same time as the illegal drone is detected, and by performing a direct collision on a center frame of the tracked illegal drone, capturing the illegal drone on which the direct collision is performed.
Wang et al (CN 114545975 A) teaches multi-unmanned aerial vehicle system task allocation method combined with multi-target evolutionary algorithm and contract network algorithm, firstly, setting the constraint condition and the target function, constructing multi-target optimization model of multi-unmanned aerial vehicle cooperative task allocation.
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/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623