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 .
DETAILED ACTION
This Office Action is sent in response to Application’s Communication received on 04/15/2022 for application number 17/721753. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawing, Abstract, Oath/Declaration, and Claims.
Claims (1-6, 17) and 15 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/15/2022 was filed prior to current Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of (1-6, 17) and 15 in the reply filed on 07/07/2025 is acknowledged.
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 therefore, subject to the conditions and requirements of this title.
Claims (1-6, 17) and 15 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.
Step 1: Claims (1-6, 17) and 15 are drawn to a method each of which is within the four statutory categories (e.g., a process, a machine).
Step 2A - Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1.
generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority weight;
transmitting the first message to the counterpart computing device;
receiving a second message from the counterpart computing device; and
calculating the local solution for the computing device based on the local observation, the priority weight, and the second message.
The limitations recite “generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority weight” which recites a mathematical concept”. For example, the claimed “generating” under its broadest reasonable interpretation when read in light of the specification encompasses using mathematical formulas, as describes in paragraphs [0052-0069], to bring solutions with priority weight, wherein the priority weight are based on mathematical formulas.
The limitations recite “calculating the local solution for the computing device based on the local observation, the priority weight, and the second message” which recites a mathematical concept”. For example, the claimed “generating” under its broadest reasonable interpretation when read in light of the specification encompasses using mathematical formulas, as describes in paragraphs [0052-0069] to calculate a local solution for the computing device based on the local observation. As described in paragraphs [0052-0069], the calculation is based on mathematical formulas.
Step 2A Prong 2:
Claim 1 recites additional elements such as “transmitting the first message to the counterpart computing device” and “receiving a second message from the counterpart computing device” which are recited at a high level, the elements are merely reciting the words that pertain to a generic computer (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The “applying” is an additional element amount to merely the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. The limitation does not integrate the judicial exception into a practical application.
Dependent claims (2-6) and 17 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims (2-6) and 17 are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
The “transmitting” and “receiving” steps are considered insignificant extra solution activity. The limitations are mere data gathering and output using processing circuitry that is recited at a high level of generality and amount to processing input data using processing circuitry that recited at high level of generality using a generic computer. Even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.
Dependent claims (2-6) and 17 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims (2-6) and 17 are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
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 1-6, 15 and 17 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Parker, JR. et al. US Patent Application Publication US 20220417105 A1 (hereinafter Parker) in view of Abeliuk et al. US Patent Application Publication US 20210257049 A1 (hereinafter Abeliuk).
Regarding claim 1, Parker teaches A method, performed by a computing device, for computing a local solution of a multi- objective optimization problem, the method comprising (FIG. 10, [0174], [0179] wherein Parker describes overall multiobjective optimization process that allocates nodal territories)
Parker teaches generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority… (Claim 15 text, [0006-0024], [0081], [0097] wherein Parker incorporates an artificial intelligence that include a communications device or system for determining an updated network topology subset allocation based on the updated operational deployment route, and recalculates another operational deployment route based on the updated network topology subset allocation and new external information. The artificial intelligence processor continues to recalculate updated operational deployment routes based on updated resource nodal locations and additional external information using the newly calculated operational deployment route. The artificial intelligence processor continuously develops optimal deployment routes based on new input and updated information from the field, which is continuously communicated to the field by the communication device or system).
Parker does not teach using weight.
However in analogous art of distributed multi-objective optimization, Abeliuk teaches using weight ([0038], [0062], [0078], [0115], [0131] wherein Abeliuk describes the weights/parameters of the networks that are adjusted using optimization methods based on gradient ascent/descent in which an objective function that is maximized/minimized).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Parker with Abeliuk by incorporating the method of using weights of Abeliuk into the method generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority of Parker for the purpose of incorporating multiple ways to extend the proposed method to multubjective optimization (Abeliuk: [02334]).
Parker teaches transmitting the first message to the counterpart computing device; receiving a second message from the counterpart computing device; and calculating the local solution for the computing device based on the local observation, the priority weight, and the second message (Claim 15 text, [0006-0024], [0081], [0097], [0164] wherein Parker deploys staff locations, home locations and nodal priority, the system will capture deployed staff knowledge and aggregate nodal activity efficiency as additional criteria in its routing process. This will enable the system to get smarter with each successive route, as it can consider deployed staff efficiency in addition to availability and travel distance. Additionally, as the system gets smarter over time, knowledge of how efficient certain staff are at completing specific tasks will also inform the routing. The ability to send the most proficient resources to a given node enables the collective system to be as efficient as possible at completing required tasks, wherein Parker incorporates an artificial intelligence that include a communications device or system for determining an updated network topology subset allocation based on the updated operational deployment route, and recalculates another operational deployment route based on the updated network topology subset allocation and new external information. The artificial intelligence processor continues to recalculate updated operational deployment routes based on updated resource nodal locations and additional external information using the newly calculated operational deployment route. The artificial intelligence processor continuously develops optimal deployment routes based on new input and updated information from the field, which is continuously communicated to the field by the communication device or system).
Regarding claim 2, Parker as modified by Abeliuk teaches wherein the generating the first message comprises: obtaining the first message by inputting the local observation and the priority weight to a messenger neural network (Claim 15 text, [0006-0024], [0081], [0097] wherein Parker incorporates an artificial intelligence that include a communications device or system for determining an updated network topology subset allocation based on the updated operational deployment route, and recalculates another operational deployment route based on the updated network topology subset allocation and new external information. The artificial intelligence processor continues to recalculate updated operational deployment routes based on updated resource nodal locations and additional external information using the newly calculated operational deployment route. The artificial intelligence processor continuously develops optimal deployment routes based on new input and updated information from the field, which is continuously communicated to the field by the communication device or system).
Regarding claim 3, Parker as modified by Abeliuk teaches wherein the messenger neural network is pre-trained to convert the local observation and the priority weight into the first message having a quantized value ([0006], [0033], [0039] wherein Abeliuk Introduces the ability to quantify the importance/weighting of any node thereby informing its nodal priority)
Regarding claim 4, Parker as modified by Abeliuk teaches wherein the calculating the local solution comprises: obtaining the local solution by inputting the local observation, the priority weight, and the second message to an optimizer neural network (Claim 15 text, [0006-0024], [0081], [0097] wherein Parker incorporates an artificial intelligence that include a communications device or system for determining an updated network topology subset allocation based on the updated operational deployment route, and recalculates another operational deployment route based on the updated network topology subset allocation and new external information. The artificial intelligence processor continues to recalculate updated operational deployment routes based on updated resource nodal locations and additional external information using the newly calculated operational deployment route. The artificial intelligence processor continuously develops optimal deployment routes based on new input and updated information from the field, which is continuously communicated to the field by the communication device or system).
Regarding claim 5, Parker as modified by Abeliuk teaches wherein the optimizer neural network is pre-trained to calculate the local solution which maximizes a global objective of the computing device and the counterpart computing device under a constraint for a local objective of the computing device, the priority weight, and the global objective ([0025], [0029] wherein Parker provides a centralized driven neural network for node routing enabled by artificial intelligence and enables the establishment of a neural network of knowledge regarding routing that systematically gets smarter as time progresses thereby enabling event-by-event optimization. Because the system continues to “learn” from the collective inputs of all supervisors and deployed staff, over time, if offers the foundation for a neural network of intelligent routing and management)
Regarding claim 6, Parker as modified by Abeliuk teaches acquiring at least one of the local observations and the priority weight from one or more terminals ([0039-0040] wherein Abeliuk teaches receiving from a user via an input interface or in a communication via a network interface. The constraints can also be received from a software process or computing system via a messaging protocol, a network connection, or other communication mechanism).
Regarding claim 15, the claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 17, Parker as modified by Abeliuk teaches A computer program stored in a computer-readable medium for executing the steps respectively included in the method according to claim 1 ([0080]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASSAN MRABI whose telephone number is (571)272-8875. The examiner can normally be reached on Monday-Friday, 7:30am-5pm. Alt, Friday, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASSAN MRABI/Examiner, Art Unit 2144