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 .
Information Disclosure Statement
The information disclosure statement filed on 11/08/2023 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the reference listed on the IDS, “20202729111” doesn’t exist. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Rejections - 35 USC § 102
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) 1-4, 6, 8-11, 13, 15-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al., “Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop floor” (hereinafter Li).
Regarding claims 1, 8, and 15, Li teaches a method for ontology adaptation, a computer system comprising one or mor processors, memories, and storage medium having in instructions to perform the method (pgs. 289-290, “All the algorithms were coded in Python 2.7, on a Windows 10 computer with 16GB of RAM and a 2.2-GHz CPU”), the method comprising:
constructing a process ontology for an industrial floor (pg. 285, fig. 4, object-process ontology);
generating a digital twin of the industrial floor (pg. 287, “Digital twin shop-floor”);
performing a simulation of the digital twin using the process ontology (pg. 286, “Step 1: An improved glow-worm swarm optimisation (IGSO) algorithm-based twin data feature analysis and simulation approach is proposed here to address the results of semantic retrieval to generate a deep learning training set, Set-1. First, based on the MT semantic model, the recommendation index is defined to adjust the recommendation range to achieve the preference feature of MTs. Subsequently, the multiobjective of MTs is simulated repeatedly to obtain the annotation of the optimisation results by determining by preference feature.”);
generating one or more new process ontologies based on inefficiencies identified during the simulation (pg. 286, “Step 2: A deep neural network is applied to analyse and retrain Set-1 to generate candidate personalised MR twin-data recommendation, Set2. First, the characterisation pre-processing and embedding layers are designed to reduce the computational complexity of the training process. Second, a double-hidden-layer structure of the backpropagation layer is designed to more accurately learn the mapping relation between the composite MR and its weight to obtain the relationship twin data.”); and
providing one or more recommendations to a user (pg. 286, figure 5, Data Recommendation Set Set-3; pg. 286, step 3, “it improves the optimization results to provide more accurate and effective guidance for production management and control”).
Regarding claims 2, 9, and 16, Li teaches the method of claim 1, system of claim 8, and computer program product of claim 15, wherein the simulation of the digital twin using the process ontology is simulated for a workflow identified by the user (pg. 288, fig. 7, sub-mt scheduling of ontology; pg. 286, “The twin data contain MT and MR data as well as the mapping-relationship data between them. The former can be directly obtained by the semantic modelling described from Sections 3.1 to 3.3, but the latter cannot be directly captured; therefore, we applied the deep neural network (DNN)-based approach proposed in Reference [28] to analyse the real-time simulation, real-time production, and historical production data to construct the mapping-relationship data… Step 1: An improved glow-worm swarm optimisation (IGSO) algorithm-based twin data feature analysis and simulation approach is proposed here to address the results of semantic retrieval to generate a deep learning training set, Set-1.”).
Regarding claims 3, 10, and 17, Li teaches the method of claim 1, system of claim 8, and computer program product of claim 15, wherein the simulation of the digital twin is performed utilizing one or more machine learning models pg. 286, “Step 2: A deep neural network is applied”).
Regarding claims 4, 11, and 18, Li teaches the method of claim 1, system of claim 8, and computer program product of claim 15, wherein the process ontology for the industrial floor is constructed utilizing one or more linguistic analysis techniques based on data received for the industrial floor (pg. 286, Fig. 5, Semantic ontology; pg. pg. 287, “For the construction process of CKO in DTS, the GRAONTO-based approach is sufficient for concept definition and relationship extraction of domain ontology.” CKO being the MTs-knowledge- ontology which includes the MTs-object ontology and MTs-manufacturing-process ontology, see fig. 4).
Regarding claims 6, 13, and 20, Li teaches the method of claim 1, system of claim 8, and computer program product of claim 15, wherein identifying the inefficiencies during the simulation further comprises: comparing the process ontology with the performance in the simulation of the digital twin and measuring one or more performance metrics (pg. 286, “Second, a double-hidden-layer structure of the backpropagation layer is designed to more accurately learn the mapping relation between the composite MR and its weight to obtain the relationship twin data.”).
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, 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) 5, 7, 12, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li as applied to claims 1, 8, and 15 above, and further in view of Guo et al., “A digital twin-based layout optimization method for discrete manufacturing workshop” (hereinafter Guo).
Regarding claims 5, 12, and 19, Li teaches the method of claim 1, system of claim 8, and computer program product of claim 15. Li does not explicitly teach one or more recommendations are provided to the user on an end user device using an intelligent real estate and facilities management solution. However, Guo teaches one or more recommendations are provided to the user on an end user device using an intelligent real estate and facilities management solution (pg. 1314, “the final results will be transmitted back to the physical layer to rearrange the entities in physical workshop, as shown in Fig. 5.” Fig. 5 showing the optimized manufacturing workshop layout.). One of ordinary skill in the art before the effective filing date would have been motivated to modify Li in the manner taught Guo so that staff at the workshop could see the optimized layouts and rearrange the factory floors, thereby realizing time reductions and cost savings (Guo, pg. 1316, optimization effects and cost savings in table 2).
Regarding claims 7 and 14, Li teaches the method of claim 1, and system of claim 8. Li does not explicitly teach the one or more recommendations provided to the user includes implementation details of the one or more new process ontologies. However Guo teaches the one or more recommendations provided to the user includes implementation details of the one or more new process ontologies (pg. 1314, fig. 5, optimized manufacturing floorplan layout; pg. 1316, fig. 8(b), process layout plan of door line station after optimization.). One of ordinary skill in the art before the effective filing date would have been motivated to modify Li in the manner taught Guo so that staff at the workshop could implement the optimized layouts and rearrange the factory floors, thereby realizing time reductions and cost savings (Guo, pg. 1316, optimization effects and cost savings in table 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fan et al., “A digital-twin visualized architecture for Flexible Manufacturing System” teaches a general architecture of digital-twin visualization for flexible manufacturing systems.
Thomsen et al. (US 2022/0277212) teaches AI extensions and intelligent validation for an industrial digital twin.
Stump et al. (US 2022/0334562) teaches a virtual design environment.
Johnson et al. (US 2021/0350294) teaches an operations optimization assignment control system with coupled subsystem models and digital twins.
Quiros Araya et al. (US 2020/0272911) teaches a cognitive automation engineering system.
Girardeau (US 2015/0169190) teaches multi-dimensional modeling of an industrial facility.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN W WATHEN whose telephone number is (571)270-5570. The examiner can normally be reached M-F 9-5:30pm.
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, James Trujillo can be reached at 571-272-3677. 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.
BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/ Primary Examiner, Art Unit 2151