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 is a non-final, first office action on the merits.
Claims 1-9 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 07/18/2023 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is 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 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
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 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).
Regarding Step 1
Claims 1-9 are directed to a optimization method. Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-9 are directed toward the judicial exception of an abstract idea.
Claim 1. A confidence-aware service pattern optimization method, wherein the service pattern optimization method comprises the following steps:
(1) inputting an original pattern Pa to be optimized, wherein the original pattern Pa consists of a plurality of participants and a workflow, a data flow, a resource flow and a value flow among the participants;
(2) initializing a candidate list PaList of the original pattern Pa;
(3) initializing a temperature T;
(4) initializing confidence C;
(5) initializing a maximum number of iterations IterMax;
(6) initializing a termination threshold Th;
(7) circularly searching a target pattern Pa* according to pattern optimization indexes, wherein the number of circulations is IterMax;
(8) reducing the temperature T; and
(9) if the pattern Pa* obtained at the end of the cycle in step (7) remains consistent for consecutive Th times, obtaining the Pa* as an optimized target pattern; otherwise, jumping to step (7).
The Applicant's Specification titled "A CONFIDENCE-AWARE SERVICE PATTERN OPTIMIZATION METHOD" emphasizes the need for data analysis, "In summary, the present disclosure relates to methods and systems directed to the field of service pattern calculation in software engineering to optimize a workflow or a process based on different math calculation and/or formula" (Spec. page 1).
As the bolded claim limitations above demonstrate, independent claim 1 recites the abstract idea of optimize a service pattern workflow or a process based on different math calculation and/or formula. which is “mathematical relationships, mathematical formulas or equations and/or mathematical calculations” expressly categorized under Mathematical Concepts. See MPEP §2106.04(a)(2)(II).
As the bolded claim limitations above demonstrate, independent claim 1 recites the abstract idea of optimize a service pattern workflow or a process based on different math calculation and/or formula. which is “commercial or legal interactions (including agreements in the form of contract; legal obligations; advertising, marketing or sales activities or behaviors; business relations” expressly categorized under Certain Methods Of Organizing Human Activity. See MPEP §2106.04(a)(2)(II).
Also, the claim limitations above demonstrate, independent claim 1 recites the abstract idea of optimize a service pattern workflow or a process by calculating pattern according to the evaluation indexes as to evaluate the service pattern as a whole which the user to quantitatively evaluate existing service pattern which covers mental processes concepts performed in the human mind (including an observation, evaluation, judgment, opinion), then it falls within the “mental process” grouping of abstract idea. Accordingly, the claims recite an abstract idea.
Dependent claims 2-9 further reiterate the same abstract ideas with further embellishments (bolded limitation), such as
claim 2 wherein in step (2), the candidate list PaList initializes four copies of the Pa.
claim 3 wherein in step (3), a calculating formula of the initialization temperature T is as follows: T=KX2; wherein K is set as a real number between 5 and 10, and X2 is a variance of an optimization index sequence formed by the original pattern Pa and a pattern generated after a random search of the original pattern Pa.
claims 4 wherein in step (4), the confidence C is any real number; when the confidence C is negative, it means that optimization potential of a current evolution direction is weak; when the confidence C is positive, it means that the optimization potential of the current evolution direction is strong; and an initial value of the confidence C is 0.
claims 5 wherein in step (5), a formula for initializing the maximum number of iterations IterMax is as follows: IterMax=(IActivity|/(|Event|+|Gateway|))! x(|Event|+|Gatewayl) wherein |Activity|,|Event| and |Gateway| are respectively the number of activities, number of events, the number of gateways contained in the optimized pattern, and ! represents the factorial.
claims 6 wherein in step (6), an initial value of the termination threshold Th is rounded up from log(IterMax).
claim 7 wherein in step (7), the pattern optimization indexes includes 6 individual indexes and 1 overall index, wherein the 6 individual indexes are pattern running time Ti, pattern running cost Co, pattern entropy En, data transfer efficiency DaEf, resource transfer efficiency ReEf, and value transfer efficiency VaEf; and
the overall index is pattern loss, and the formula is as follows:
Lo=(log(Ti)+log(Co))x3 En /(DaEf+ReEf+VaEf) wherein the pattern running time Ti, pattern running cost Co, pattern entropy En and pattern loss Lo are the smaller the better; and the data transfer efficiency DaEf, resource transfer efficiency ReEf and value transfer efficiency VaEf are the larger the better.
claims 8 wherein in step (7), when the target pattern Pa* is circularly searched, steps of each search are as follows: (7-1) setting a search step size St, wherein if C is greater than or equal to 0, St is 1, and if C is less than 0, St is the smaller one in 1-C and |Activityl/2; (7-2) performing activity execution sequence exchange for St times for the optimization pattern Pa to be optimized to obtain Paf;
(7-3) performing activity execution platform exchange for St times for the optimization pattern Pa to be optimized to obtain Pad;
(7-4) performing the transformation occurred in step (7-2) and step (7-3) for the optimization pattern Pa to be optimized to obtain Pah simultaneously;
(7-5) letting the pattern candidate list PaList=[Pa, Pah];
(7-6) comparing individual indexes of pattern optimization in a pattern candidate list of the last iteration and a pattern candidate list of this round one by one; if the pattern index of this round is better, increasing the confidence C by 1; and
if the pattern index of this round is worse, decreasing the confidence C by 1;(7-7) if the value of confidence C is unchanged or larger after step (7-6), adopting the pattern candidate list generated in this round;
otherwise, adopting the pattern candidate list generated in the last round of iteration as PaList; and
(7-8) comparing the optimization indexes of four patterns in PaList, if an optimal pattern of the optimization indexes is one of Pad and Pah, adopting this pattern as the pattern Pa to be optimized to enter a next iteration; and
if the optimal pattern of the optimization indexes is Pa, adopting, according to a probability of an optimal pattern of Paf, Pad and Pah as the pattern Pa to be optimized to enter the next iteration;
wherein DIFF = - | an optimization index value of Pa - an average optimization index value of Pahl, otherwise remaining Pa as the pattern to be optimized.
claim 9 wherein in step (8), the method of reducing the temperature T is as follows: T=T/log(1+IterT+reg(C)) wherein IterT is the number of times the temperature T has decreased, reg(C)=(ec_1)/(ec+1).
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claim 1.
Part I. 2A-PRONG TWO (ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION) and PART III. DETERMINE WHETHER ANY ELEMENT, OR COMBINATION, AMOUNT TO “SIGNIFICANTLY MORE” THAN THE ABSTRACT IDEA ITSELF
Claim 1 does not include any additional element because there is no additional elements, there is nothing to analyze under prong 2 and step 2B.
Claims 2-9 are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to independent claim 1.
Allowable Subject Matter
Regarding the 35 USC 102/103 rejection, No art rejections has been put forth in the rejection.
Closest prior art to the invention include Wang CN 112733999: A service mode constructing method based on self-correcting mechanism particle swarm optimization algorithm, Pozarycki et al. US 2006/0026466: Support methodology for diagnostic patterns, and Ambursa, Faruku Umar, et al. "A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids." The Journal of Supercomputing 73.5 (2017): 2018-2051.
None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in independent claims, inputting an original pattern Pa to be optimized, wherein the original pattern Pa consists of a plurality of participants and a workflow, a data flow, a resource flow and a value flow among the participants;
(2) initializing a candidate list PaList of the original pattern Pa;
(3) initializing a temperature T;
(4) initializing confidence C;
(5) initializing a maximum number of iterations IterMax;
(6) initializing a termination threshold Th;
(7) circularly searching a target pattern Pa* according to pattern optimization indexes, wherein the number of circulations is IterMax;
(8) reducing the temperature T; and
(9) if the pattern Pa* obtained at the end of the cycle in step (7) remains consistent for consecutive Th times, obtaining the Pa* as an optimized target pattern; otherwise, jumping to step (7)”. The reason for not applying a rejection under 35 USC 102/103 of claims 1-9 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ambursa, Faruku Umar, et al. "A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids." The Journal of Supercomputing 73.5 (2017): 2018-2051.
Yu, Yang, Hui Ma, and Mengjie Zhang. "F-MOGP: A novel many-objective evolutionary approach to QoS-aware data intensive web service composition." 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015.
Rao WO 2022/016108: Intelligent workflow engine for manufacturing process.
Warner CA 2950420: System and method for fault analysis and prioritization.
Wang CN 112733999: A service mode constructing method based on self-correcting mechanism particle swarm optimization algorithm.
Pozarycki et al. US 2006/0026466: Support methodology for diagnostic patterns.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAMZEH OBAID whose telephone number is (313)446-4941. The examiner can normally be reached M-F 8 am-5 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
Prosecution Insights