DETAILED ACTION
This non final office action is in response to applicant’s filing of application number 18/761,163 on 06/12/2026. Claims 21-40 are pending and under examination.
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 .
Information Disclosure Statement
The Information Disclosure Statements filed on 4/21/2026 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner’s note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Under Step 1: Claims 1-8 are directed to a method. Claims 9-16 are directed to a computer program product comprising a non-transitory computer-readable storage medium. Claim 17-20 are directed to a system. Each of the claims falls under one of the four statutory classes of invention.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). Thus, the claims fall under one of the four statutory classes of invention under step One.
Under Step 2A, prong 1:
The claims recite the limitations in the abstract idea highlighted in non-bold and the additional elements in bold.
Claim 1 recites:
identifying, by one or more processors, candidate autonomous robots located within a distance of a pickup location and having capabilities sufficient to fulfill a batch;
computing, for each candidate autonomous robot, a resource-usage score that reflects predicted resources to be consumed in fulfilling the batch;
selecting a subset of the candidate autonomous robots whose resource-usage scores are below a resource-usage threshold;
transmitting a batch-availability message to devices associated with the subset;
waiting a time interval for an acceptance message from any one of the autonomous robots of the subset;
when no acceptance message is received during the time interval, relaxing the resource-usage threshold and repeating the selecting, transmitting, and waiting steps until an acceptance message is received; and
in response to receiving the acceptance message, assigning the batch to the accepting autonomous robot.
Claim 2 further recites a weighted combination of predicted electrical-energy consumption, travel distance to the pickup location, and estimated traversal time.
Claim 3further recites wherein computing the resource-usage score comprises executing a machine learning model trained on historical fulfillment data generated by a fleet of autonomous robots.
Claim 4 further recites after assigning the batch, updating a usage-history log for the accepting autonomous robot to include actual resources consumed in fulfilling the batch, the usage- history log being employed to retrain the machine learning model.
Claim 5 further recites wherein relaxing the resource-usage threshold comprises incrementally increasing the threshold by a fixed percentage after each iteration until the threshold reaches a predefined maximum associated with a fleet-level utilization policy.
Claim 6 further recites wherein the batch-availability message is transmitted over a low-latency wireless mesh network and includes a unique batch identifier together with a validity timeout.
Claim 7 further recites wherein the time interval is dynamically adjusted based on a current density of unassigned batches awaiting fulfillment within a warehouse.
Claim 8 further recites wherein identifying the candidate autonomous robots comprises excluding agents whose battery-charge level is below a minimum charge threshold.
Claim 9 recites:
identifying, by one or more processors, candidate autonomous robots located within a distance of a pickup location and having capabilities sufficient to fulfill a batch;
computing, for each candidate autonomous robot, a resource-usage score that reflects predicted resources to be consumed in fulfilling the batch;
selecting a subset of the candidate autonomous robots whose resource-usage scores are below a resource-usage threshold;
transmitting a batch-availability message to devices associated with the subset;
waiting a time interval for an acceptance message from any one of the autonomous robots of the subset;
when no acceptance message is received during the time interval, relaxing the resource-usage threshold and repeating the selecting, transmitting, and waiting steps until an acceptance message is received; and in response to receiving the acceptance message, assigning the batch to the accepting autonomous robot.
Claim 10 further recites a weighted combination of predicted electrical-energy consumption, travel distance to the pickup location, and estimated traversal time.
Claim 11 further recites executing a machine learning model trained on historical fulfillment data generated by a fleet of autonomous robots.
Claim 12 further recites:
after assigning the batch, updating a usage-history log for the accepting autonomous robot to include actual resources consumed in fulfilling the batch, the usage- history log being employed to retrain the machine learning model.
Claim 13 further recites incrementally increasing the threshold by a fixed percentage after each iteration until the threshold reaches a predefined maximum associated with a fleet-level utilization policy.
Claim 14 further recites wherein the batch-availability message is transmitted over a low-latency wireless mesh network and includes a unique batch identifier together with a validity timeout.
Claim 15 further recites wherein the time interval is dynamically adjusted based on a current density of unassigned batches awaiting fulfillment within a warehouse.
Claim 16 further recites excluding agents whose battery-charge level is below a minimum charge threshold.
Claim 17 recites:
identifying, by one or more processors, candidate autonomous robots located within a distance of a pickup location and having capabilities sufficient to fulfill a batch;
computing, for each candidate autonomous robot, a resource-usage score that reflects predicted resources to be consumed in fulfilling the batch;
selecting a subset of the candidate autonomous robots whose resource-usage scores are below a resource-usage threshold;
transmitting a batch-availability message to devices associated with the subset;
waiting a time interval for an acceptance message from any one of the autonomous robots of the subset;
when no acceptance message is received during the time interval, relaxing the resource-usage threshold and repeating the selecting, transmitting, and waiting steps until an acceptance message is received; and in response to receiving the acceptance message, assigning the batch to the accepting autonomous robot.
Claim 18 further recites wherein the resource-usage score comprises a weighted combination of predicted electrical-energy consumption, travel distance to the pickup location, and estimated traversal time.
Claim 19 further recites executing a machine-learning model trained on historical fulfillment data generated by a fleet of autonomous robots.
Claim 20 further recites incrementally increasing the threshold by a fixed percentage after each iteration until the threshold reaches a predefined maximum associated with a fleet-level utilization policy.
Regarding claims 1, 9 and 17, applicant is to be noted that the steps or functions of “identifying", “selecting”, “waiting” and “assigning”, involve mental processes and/or generic computer functions. The step or function of “computing” is similar to a mathematical function. The step or function of “transmitting” is an insignificant post solution activity.
Claims 1, 9, and 17 are found to include at least one judicial exception, that the resent claims recite steps that can be performed using the human mind, pen, and paper. According to the 2019 Revised Guidance, concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) fall into the category of mental processes. See 2019 Revised Guidance, 84 Fed. Reg. at 52.
Under Step 2A, prong 2:
The claims as a whole integrate the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55. In particular, the claims recite the following bolded limitations understood to be additional limitations:
Claims 1, 9 and 17 recite a processor and a machine-learning model. In particular, the claimed "processor0" is recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component. The claimed limitations pertaining to machine learning amount merely to the very definition of (the training aspect of) supervised machine learning. As such, the independent claims do not reflect any improvement in machine learning (or in another technology/functioning of a computer), and the machine learning limitations are merely generic computer elements.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claimed "processor" is also seen as generic computer components for identifying, computing, selecting, transmitting, waiting and assigning data as the processor performs generic functions without an inventive concept as such do not amount to significantly more than the abstract idea. See Paragraphs 0019-0023 in the specification.
The claimed processor and machine learning model as claimed elements are interpreted as being recited at a high level of generality and even if the claims recited in the affirmative. The type of data being manipulated does not impose meaningful limitations or render the idea less abstract. Looking at the elements as a combination does not add anything more than the elements analyzed individually.
Therefore, the claims do not amount to significantly more than the abstract idea itself. Applicant is reminded that a statutory claim would recite an automated machine implemented method or system with specific structures for performing the claimed invention so as to provide an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment.
Each claim as a whole, does not amount to significantly more than the abstract idea itself. This is because each claim does not effect an improvement to another technology or technical field; the claim does not amount to an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment.
Under Step 2B, since the only steps outside the judicial exception are generic computer hardware and software and generic data gathering and processing steps, each of which is an insignificant extra-solution activity, the claim(s) does not include significantly more than the judicial exception.
As a result of the above analysis, claim 1, as well as claims 9 and 17, do not appear to be patent eligible under 101. Accordingly, claims 1, 9 and 17 are directed to an abstract idea.
The dependent claim(s) when analyzed and each taken as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations) fail(s) to establish that the claim(s) is/are not directed to an abstract idea.
The above recited limitations provide meaningful limitations that transforms the abstract idea into patent eligible. The claims as a whole effect an improvement to another technology or technical field. These limitations in combination provide meaningful limitations beyond generally linking the use of the abstract idea to a practical application.
NOTE: Currently there are no outstanding prior art rejections under 35 USC§ 102 or 35 USC§ 103.
The claims would be allowable if overcome the 35 USC§ 101 rejection
Hongbo (WO-2019223703-A1) teaches an order processing method,
comprising: receiving at least one order to be processed, and putting the at least one order to be processed in an order pool, classifying some or all of orders to be processed in the order pool into at least one batch task. For any batch task in the at least one batch task, assigning the batch task to a corresponding target work station, selecting a target inventory container for a hit order item for orders to be processed in the batch task, and selecting a target robot for moving the target inventory container for the batch task. Controlling the target robot to move the target inventory container for the hit order item to the target work station corresponding to the batch task.
Wise et al (US Publication No. 10562707-B1) teaches a method using one or more robots to assist a human in order fulfillment includes, receiving, by a server, an order comprising an order item, receiving, by the server, a selection by the human of the order robot, sending the order, by the server, to one or more of the order robot and a human-operated device available to the human, sending, by the server, to the order robot, a direction to assist the human to pick the order item, determining, by the server, that the order is nearly complete, determining, by the server, that an order robot replacement suitable for fulfilling a next order has not yet been sent, sending a dispatch instruction, by the server, to the order robot replacement instructing its dispatch; determining, by the server, that the order is complete.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. As per attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROMAIN JEANTY whose telephone number is (571) 272-6732. The examiner can normally be reached M-F 9:00AM to 5:30PM.
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/RJ/
/Romain Jeanty/Primary Examiner, Art Unit 3624
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