DETAILED ACTION
This office action is made final. Claims 1-20 are pending. 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
Applicant’s amendment date 02/05/2026, amended claims 1, 3, 5-8, 11-12, 14, 16-17, and 19.
Response to Amendment
The previously pending rejection to claims 1-20, under 35 USC 101 (Alice), will be maintained.
Response to Arguments
Applicant’s arguments received on date 02/05/2026 have been fully considered, but they are not persuasive. Moreover, any new grounds of rejection have been necessitated by Applicant's amendments to the claims. The art rejection has been updated to address these amendments.
Response to Arguments under 35 USC 112(a):
Applicant asserts that “the overall scope and context of the specification, when properly viewed in its entirety, provides clear and explicit support the subject matter of independent claims 1, 12, and 17, as well as all dependent claims.” Examiner respectfully disagrees.
As discussed below, under 35 USC 112(a), the disclosure does not provide explicit support for the claimed limitation(s):
The specification fails to describe the claimed features in claims 1, 12, and 17 recite, “extracts from the structured description, a list of the one or more components and the one or more processes, which are utilized for manufacturing equipment in accordance with the given design; performs a first machine learning process utilizing a first probabilistic model to predict cost attributes associated with the one or more components in the extracted list, based at least in part on first manufacturing-related data obtained from one or more potential suppliers of the one or more components in the extracted list; and performs a second machine learning process utilizing a second probabilistic model to predict the cost attributes associated with the one or more processes for manufacturing equipment in accordance with the given design, based in part on second manufacturing-related data obtained from one or more potential manufacturers.” Such full, clear, concise features are required.
Consequently, the written description requirement of 35 USC 112(a) has not been satisfied. Thus, it does not show that the inventor was in possession of the entire claimed subject matter as the filing date.
Response to Arguments under 35 USC 101:
Applicant asserts that “amended claim 1, and similarly claims 12 and 17 integrate any such abstract idea into a practical application by providing an improvement in a technical field.” Examiner respectfully disagrees.
As discussed above, under the second prong of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55.
Here, under the second prong of Step 2A, the only additional elements beyond the recited abstract idea of claim 1, and similarly claims 12 and 17, are the recitations of “a processing platform which implements an artificial intelligence system to intelligently predict costs for manufacturing equipment, the processing platform comprising at least one processor coupled to at least one memory, the processing platform, when executing program code is configured to: obtain, via a first graphical user interface connected to at least one of one or more original design manufacturers and one or more contract manufacturers, a structured description which specifies one or more components and one or more manufacturing processes associated with manufacturing equipment in accordance with a given design, wherein the given design is a new product design; executes a third machine learning process which utilizes a third-machine learning model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data;………..; and send, from the processing platform, the prediction results to one or more users,” and these additional elements, individually and in combination, are nothing more than computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Accordingly, contrary to Applicant’s assertions, the judicial exception is not integrated into a practical application under the second prong of Step 2A.
Response to Arguments under 35 USC 103:
Applicant's arguments with respect to the claim rejections have been considered, but are moot in view of the new ground(s) of rejection set forth below in this Office action. The art rejection has been updated to address these amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims claim 1, 12, and 17 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 12, and 17 recite, “executes a first machine learning process which utilizes based at least in part on first machine learning model to predict the costs associated with the one or more components based at least in part on first manufacturing related data obtained from one or more potential suppliers of the one or more components; executes a second machine learning process which utilizes a second machine learning model to predict the costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on second manufacturing-related data obtained from one or more potential manufacturers; and executes a third machine learning process which utilizes a third-machine learning model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data;.” As best understood, it appears that there is no support for the underlined recitation in the original disclosure of the present application for this limitation. Moreover, none of the drawings shows this particular feature. Therefore, this limitation of claims 1, 12, and 17 considered to be new matter. Appropriate correction is required.
Further, claims 2-11, 13-16, and 18-20 are rejected by their dependency on claims 1, 12, and 17.
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 non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 12, and 17 recite an abstract idea. claims 1, 12, and 17 include “obtain, one or more original design manufacturers and one or more contract manufacturers, a structured description which specifies at least one of one or more components and one or more processes associated with manufacturing equipment in accordance with a given design, wherein the given design is a new product design; and perform, a process to predict cost attributes associated with the one or more components and the one or more manufacturing processes specified by the structured description, and generate prediction results which comprise the predicted cost attributes, the predicted attributes comprising predicted costs associated with the one or more components predicted costs associated with the one or more manufacturing processes specified in the structured description, and predicted labor costs associated with the one or more manufacturing processes; utilizes the structured description to determine the one or more components and the one or more manufacturing processes, for manufacturing equipment in accordance with the given design; executes a process which utilizes based model to predict the costs associated with the one or more components based at least in part on first manufacturing related data obtained from one or more potential suppliers of the one or more components; executes a second process which utilizes a second model to predict the costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on second manufacturing-related data obtained from one or more potential manufacturers; executes a third process which utilizes a third model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data; generates the prediction results based at least in part on the predicted costs associated with the one or more components, the one or more manufacturing processes, and the predicted labor costs; and sends the prediction results to one or more users”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and mathematical relationships because the elements describe a process for computing a predicted attribute associated with manufacturing the equipment. As a result, claims 1, 12, and 17 recite an abstract idea under Step 2A Prong One.
Claims 2-11, 13-16, and 18-20 further describe the process for computing a predicted attribute associated with manufacturing the equipment. As a result, claims 2-11, 13-16, and 18-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 12, and 17.
With respect to Step 2A Prong Two of the framework, claims 1, 12, and 17 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 12, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 12, and 17 include an artificial intelligence system, a first machine learning, a second machine learning, a third machine learning, a processing platform, processor, memory, program code, a non-transitory processor-readable storage medium, a graphical user interface, software programs, and processing device. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 12, and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-7, 10-11, 13-16, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 12, and 17. As a result, claims 2-7, 10-11, 13-16, and 18-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 12, and 17.
Claims 8-9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8-9 include interfaces. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 8-9 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 12, and 17 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 12, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 12, and 17 include an artificial intelligence system, a first machine learning, a second machine learning, a processing platform, processor, memory, program code, a non-transitory processor-readable storage medium, a graphical user interface, software programs, and processing device. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 12, and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-7, 10-11, 13-16, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 12, and 17. As a result, claims 2-7, 10-11, 13-16, and 18-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 12, and 17.
Claims 8-9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8-9 include interfaces. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 8-9 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 6, 8-12, 15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maury et al. (US Pub No. 2022/0019204) in view of Coffman et al. (US Pub No. 2018/0120813).
Regarding claims 1, 12, and 17, Maury discloses an apparatus comprising:
a processing platform which implements an artificial intelligence system to intelligently predict costs for manufacturing equipment, the processing platform comprising at least one processor coupled to at least one memory, the processing platform, when executing program code (see Maury, Figs. 9-11 paras [0027], [0011], & [0182]), is configured to:
obtain, via a first graphical user interface connected to at least one of one or more original design manufacturers and one or more contract manufacturers, a structured description which specifies one or more components and one or more manufacturing processes associated with manufacturing equipment in accordance with a given design, wherein the given design is a new product design (see Maury, paras [0028] & [0070], wherein interfaces, along with quality metrics for each stage of the assembly process….. Dashboards (user interfaces) are used to capture operating system tolerances across the manufacturing and assembly work process; paras [0176]-[0177], wherein supply contracts with the supplier; para [0197], wherein a database of assembly components. This database includes information identifying the physical structure of each assembly component, and identifying the components and assemblies that are combined to form the assembly component and information identifying the manner in which the identified components and assemblies are combined; para [0001], wherein the manufacturing and assembly of commercially and non-commercially available products, including drive motors and new electric vehicles is a complex process, involving design, fabrication, tooling, automation, sub-assembly production, final assembly and various forms of testing, evaluation and quality control measures; and para [0043], wherein the inherent attributes of this data model will also propagate bug fixes, enhancements and major upgrades as the model adapts to new product platforms);
perform, via the artificial intelligence system of the processing platform, a machine learning process to predict cost attributes associated with the one or more components and the one or more manufacturing processes specified by the structured description, and generate prediction results which comprise the predicted cost attributes, the predicted attributes comprising predicted costs associated with the one or more components predicted costs associated with the one or more manufacturing processes specified in the structured description, wherein in performing the machine learning process, the artificial intelligence system of the processing platform (see Maury, paras [0027], [0011], [0069]-[0070], [0101], & [0136], wherein referring to Fig. 7 shows a machine learning techniques automating data annotation intelligence to train and improve data object decision intelligence through the relational and ontological data structure to indicate sources of manufacturing or quality problems; para [0043], wherein predictive failure analysis where manufacturing and assembly machinery have operating ranges, data collected from these components is analyzed in real-time and compared to historical data captured on the edge, based on measurements and course of action…….The inherent attributes of this data model will also propagate bug fixes, enhancements and major upgrades as the model adapts to new product platforms, configurations and manufacturing techniques available through the neural network and data ontology; paras [0176]-[0177], wherein supply contracts with the supplier; para [0197], wherein a database of assembly components. This database includes information identifying the physical structure of each assembly component, and identifying the components and assemblies that are combined to form the assembly component and information identifying the manner in which the identified components and assemblies are combined; paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; para [0177], wherein each supplied component from each supplier can also be identified in the database with related information such as component cost; and para [0196], wherein the database of components includes details of each component in sufficient detail for production of any selected product and includes one or more of identification of at least one supplier of each identified component along with available information related to pricing and delivery terms available from such identified supplier, identification of the physical characteristics of each said component including materials from which said component is fabricated, details of form, fit and finish, any specifications provided by the manufacturer of said component with the sale of such component, and when available, full specifications for 3D fabrication such as a database of 3D parameters, the identification of suppliers of and the specifications of 3D equipment suitable for the component, a cross reference to a supplier database for components);
utilizes the structured description to determine the one or more components and the one or more manufacturing processes, for manufacturing equipment in accordance with the given design (see Maury, para [0197], wherein a database of assembly components. This database includes information identifying the physical structure of each assembly component, and identifying the components and assemblies that are combined to form the assembly component and information identifying the manner in which the identified components and assemblies are combined; and paras [0168] & [0182], wherein a database of available products for selection. This database is conceptually part of the product module. The product module contains extensive information about the product, including all information necessary for specification and assembly of the product. This includes a list of all components that need to be purchased for construction of the product, identification of the available suppliers of each component, a full list of available suppliers for components information related to the prices each supplier charges for each component (including information on multiple suppliers of individual components when such component is available from more than one supplier);
executes a first machine learning process which utilizes based at least in part on first machine learning model to predict the costs associated with the one or more components based at least in part on first manufacturing related data obtained from one or more potential suppliers of the one or more components (see Maury, paras [0069]-[0070], [0101], & [0136], wherein referring to Fig. 7 shows a machine learning techniques automating data annotation intelligence to train and improve data object decision intelligence through the relational and ontological data structure to indicate sources of manufacturing or quality problems; paras [0176]-[0177], wherein supply contracts with the supplier; para [0197], wherein a database of assembly components. This database includes information identifying the physical structure of each assembly component, and identifying the components and assemblies that are combined to form the assembly component and information identifying the manner in which the identified components and assemblies are combined; paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; and para [0177], wherein each supplied component from each supplier can also be identified in the database with related information such as component cost);
executes a second machine learning process which utilizes a second machine learning model to predict the costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on second manufacturing-related data obtained from one or more potential manufacturers (see Maury, paras [0069]-[0070], [0101], & [0136], wherein referring to Fig. 7 shows a machine learning techniques automating data annotation intelligence to train and improve data object decision intelligence through the relational and ontological data structure to indicate sources of manufacturing or quality problems; paras [0196]-[0197], wherein identification of the physical characteristics of each said component including materials from which said component is fabricated, details of form, fit and finish, any specifications provided by the manufacturer of said component with the sale of such component, and when available, full specifications for 3D fabrication such as a database of 3D parameters, the identification of suppliers of and the specifications of 3D equipment suitable for the component, a cross reference to a supplier database for components……a database of assembly components. This database includes information identifying the physical structure of each assembly component, and identifying the components and assemblies that are combined to form the assembly component and information identifying the manner in which the identified components and assemblies are combined; paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; and paras [0176]-[0177], wherein….supply contracts with the supplier each supplied component from each supplier can also be identified in the database with related information such as component cost); and
generates the prediction results based at least in part on the predicted costs associated with the one or more components, the one or more manufacturing processes (see Maury, paras [0192]-[0193], wherein generate a list of all of the components needed for each selected product and the machine module will identify all machines needed for the two products……including the identification of the components needed to make the product, the available sources of supply of each component, the quantities of components needed to meet the desired production volumes and the prices sought by each supplier; paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; para [0177], wherein each supplied component from each supplier can also be identified in the database with related information such as component cost…..the supplier database can include extensive information related to the components supplied by each supplier….identifying each supplied component is desirable, but also desirable is the identification of alternative components that are available to permit efficient changes in the source of supply from one supplier to another supplier of the same component during the course of operating the manufacturing facility. The specification of each available component from each supplier can be contained in this database and made available for management of the workflow. Desirable information about each component might include the dimensions (including tolerances), weight, material composition and any assembly features such as alignment markings or orientation indicators. Commercial information related to each component can also be provided such as the identification of the purchase and supply contracts with the supplier, the cost of each component (along with details of any volume purchase agreements) and volumes of each component actually supplied by the supplier. The supplier database can also show the identification of the product into which each purchased component will be assembled; and para [0196], wherein the database of components includes details of each component in sufficient detail for production of any selected product and includes one or more of identification of at least one supplier of each identified component along with available information related to pricing and delivery terms available from such identified supplier, identification of the physical characteristics of each said component including materials from which said component is fabricated, details of form, fit and finish, any specifications provided by the manufacturer of said component with the sale of such component, and when available, full specifications for 3D fabrication such as a database of 3D parameters, the identification of suppliers of and the specifications of 3D equipment suitable for the component, a cross reference to a supplier database for components).
Maury et al. fails to explicitly disclose predicted labor costs associated with the one or more manufacturing processes; executes a third machine learning process which utilizes a third-machine learning model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data; generates the prediction results based the predicted labor costs; and sends the prediction results to one or more users.
Analogous art Coffman et al. discloses predicted labor costs associated with the one or more manufacturing processes (see Coffman, abstract, wherein a set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object; para [0082], wherein a predicted cost associated with the manufacture of a physical object can be calculated by engine 509 as a function of axioms indicating a number of CNC operations used to manufacture an object, setup time, and cycle time multiplied by machine rate (including fixed costs, operating costs and/or labor costs) and further multiplied by a quantity of objects requested to be manufactured);
Analogous art Coffman et al. discloses executes a third machine learning process which utilizes a third-machine learning model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data (see Coffman, abstract, wherein a set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object; paras [0082]-[0084], wherein a predicted cost associated with the manufacture of a physical object can be calculated by engine 509 as a function of axioms indicating a number of CNC operations used to manufacture an object, setup time, and cycle time multiplied by machine rate (including fixed costs, operating costs and/or labor costs) and further multiplied by a quantity of objects requested to be manufactured…..knowledge aggregator/reasoning engine 509 can predict whether is feasible or not to manufacture a physical object using a specific fabrication material; and para [0079], wherein the predicted values are probabilities and are therefore restricted to (0, 1) through the logistic distribution function because logistic regression predicts the probability of particular outcomes. Some of the axioms generated by logistic regression classifiers include what type of blanks can be used in the manufacture process of a physical object, and other suitable axioms expressed as true or false associated with the manufacture of a physical object);
Analogous art Coffman et al. discloses generates the prediction results based at least in part on the predicted costs associated with the one or more components, the one or more manufacturing processes, and the predicted labor costs (see Coffman, paras [0027] & [0063]-[0067], wherein predictions regarding manufacturing process, including estimated times, optimal costs, comparisons of fabrication materials, and other suitable information. The classifications and/or predictions are reliable; that is, assessments for the manufacture of similar products result in similar or equivalent outcomes. The subject technology operates in near real-time and thus, optimizes manufacturing process by decreasing overhead associated with human-based estimations….paras [0082]-[0084], wherein a predicted cost associated with the manufacture of a physical object can be calculated by engine 509 as a function of axioms indicating a number of CNC operations used to manufacture an object, setup time, and cycle time multiplied by machine rate (including fixed costs, operating costs and/or labor costs) and further multiplied by a quantity of objects requested to be manufactured…..knowledge aggregator/reasoning engine 509 can predict whether is feasible or not to manufacture a physical object using a specific fabrication material)and
Analogous art Coffman et al. discloses sends the prediction results to one or more users (see Coffman, para [0033], wherein server 109 processes the prediction request(s) and sends prediction report 107 to compute device 105 via network 103 in near real-time).
Maury discloses a system for improving quality control services and optimizing modeling. Coffman discloses directed to inferring predictions related to the manufacture process of the physical object. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maury, regarding the Intelligent Data Object Model for Distributed Product Manufacturing, Assembly and Facility Infrastructure, to have included predicted labor costs associated with the one or more manufacturing processes; executes a third machine learning process which utilizes a third-machine learning model to predict the labor costs associated with the one or more manufacturing processes for manufacturing equipment in accordance with the given design, based at least in part on a knowledge base of labor cost data; generates the prediction results based the predicted labor costs; and sends the prediction results to one or more users because both inventions teach predicting manufacture processes. Further, 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.
Regarding claims 4, 15, and 20, Maury discloses the apparatus of claim 1, wherein the first manufacturing-related data comprises current and historical cost data corresponding to component costs of a plurality of suppliers and the second manufacturing-related data comprises manufacturing processes and cost data such associated with the manufacturing processes (see Maury, paras [0177]-[0178], wherein with this information, it can be readily determined whether and how each component is incorporated into any number of final products. Each supplied component from each supplier can also be identified in the database with related information such as component cost, its original supplier (particularly where there has been a change in suppliers during the history of production of a product), the availability of similar components from other suppliers, and, as mentioned above, the intermediate and final assemblies into which the component is assembled; and paras [0178] & [0195], wherein the information that is contemplated includes at least one supplier of each identified manufacturing machine, an indication of the price of each identified manufacturing machine and the approximate dimensions of each identified manufacturing machine).
Regarding claim 6, Maury discloses the apparatus of claim 1, wherein the predicted cost attributes associated with the one or more components comprises cost attributes of suppliers that are grouped based on two or more geographic regions (see Maury, paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; para [0177], wherein each supplied component from each supplier can also be identified in the database with related information such as component cost…..; and para [0043], wherein manufacturing and assembly machinery have operating ranges, data collected from these components is analyzed in real-time and compared to historical data captured on the edge, adjustments to cooling or other environmental constraints can auto adjust based on measurements and course of action…Optimize the operating range of the equipment and deliver higher productivity and efficiency back to the manufacturing and assembly line. The inherent attributes of this data model will also propagate bug fixes, enhancements and major upgrades as the model adapts to new product platforms, configurations and manufacturing techniques available through the neural network and data ontology; and para [0005], wherein AI, ML, and other related infrastructure to enable a decentralized object-oriented franchisee-based manufacturing and assembly facility, e.g., electric vehicle production, across multiple locations and geographies).
Regarding claim 8, Maury discloses the apparatus of claim 1, wherein the processing platform, when executing program code, is further configured to provide an interface for obtaining the second manufacturing-related data from the one or more potential manufacturers (see Maury, paras [0028] & [0070], wherein interfaces, along with quality metrics for each stage of the assembly process….. Dashboards (user interfaces) are used to capture operating system tolerances across the manufacturing and assembly work process; and para [0168], wherein the data included within the system includes a full list of available suppliers for components as well as manufacturing machines that are expected to be needed for manufacture of any of the available products).
Regarding claim 9, Maury discloses the apparatus of claim 1, wherein the processing platform, when executing program code, is further configured to provide an interface for obtaining at least a portion of the first manufacturing-related data from one or more potential suppliers of the one or more components (see Maury, paras [0028] & [0070], wherein interfaces, along with quality metrics for each stage of the assembly process….. Dashboards (user interfaces) are used to capture operating system tolerances across the manufacturing and assembly work process; and para [0168], wherein the data included within the system includes a full list of available suppliers for components as well as manufacturing machines that are expected to be needed for manufacture of any of the available products).
Regarding claim 10, Maury discloses the apparatus of claim 1, wherein the one or more components in the structured description comprise one or more of commodities and raw materials (see Maury, para [0091], wherein Supply Chain-design, planning, execution, monitoring and control of all raw material, parts).
Regarding claim 11, Maury discloses the apparatus of claim 1, wherein the one or more manufacturing processes in the structured description comprise at least one of assembly-based processes and engineering-based processes (see Maury, para [0006], wherein structural representation of a manufacturing facility and assembly line; and para [0025], wherein the use of advanced engineering and design methods in conjunction with virtual manufacturing techniques to simulate workflows and quality control).
Claims 2-3, 13-14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Maury et al. (US Pub No. 2022/0019204), in view of Coffman et al. (US Pub No. 2018/0120813), and further in view of Reddy et al. (US Pat No. 10,366,362).
Regarding claims 2, 13, and 18, Maury discloses the apparatus of claim 1, wherein the artificial intelligence system of the processing platform
performs an analysis on the prediction results (see Maury, para [0043], wherein manufacturing and assembly machinery have operating ranges, data collected from these components is analyzed in real-time and compared to historical data captured on the edge, adjustments to cooling or other environmental constraints can auto adjust based on measurements and course of action…).
Maury et al. and Coffman et al. combined fail to explicitly disclose generates one or more recommendations based on the analysis.
Analogous art Reddy et al. discloses performs an analysis on the prediction results (see Reddy, abstract, wherein forecasting and strategy outcomes optimization solution based upon metric analysis of characteristics, and/or other controlled attributes (collectively feature metrics) for products and/or services. A model that represents the relationship between periodic outcome variable (Y) and causal variables (X1 ... Xn) is formulated using one or more suitable algorithmic techniques or models (e.g., linear regression, time series regression, Bayesian or a combination)); and
Analogous art Reddy et al. discloses generates one or more recommendations based on the analysis (see Reddy, column 3, lines 16-20, wherein a decision maker(s) is presented with an interpretative assessment of the integration of causal factors to outcomes and recommendations presented by the model and optimization engine for decision making pertaining to the product and/or service).
Maury discloses a system for improving quality control services and optimizing modeling. Reddy discloses directed to processing of models for manufacture processes through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maury, regarding the Intelligent Data Object Model for Distributed Product Manufacturing, Assembly and Facility Infrastructure, to have included performs an analysis on the prediction results; and generates one or more recommendations based on the analysis because both inventions teach improving decision making. Further, 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.
Regarding claims 3, 14, and 19, Maury discloses the apparatus of claim 2, wherein the one or more recommendations, as set forth above with claim 2.
negotiating with the one or more potential manufacturers entities to manufacture the equipment in accordance with the given design (see Maury, para [0117], wherein commercial information related to each component can also be provided such as the identification of the purchase and supply contracts with the supplier, the cost of each component (along with details of any volume purchase agreements) and volumes of each component actually supplied by the supplier. The supplier database can also show the identification of the product into which each purchased component will be assembled. With this information, the database can show the full product manufacturing details from contracts for purchase of each component to delivery of the final product; and para [0043], wherein …Optimize the operating range of the equipment and deliver higher productivity and efficiency back to the manufacturing and assembly line. The inherent attributes of this data model will also propagate bug fixes, enhancements and major upgrades as the model adapts to new product platforms, configurations and manufacturing techniques available through the neural network and data ontology).
Maury et al. fails to explicitly disclose a computation-based strategy.
Analogous art Reddy et al. discloses a computation-based strategy for negotiating with the one or more potential manufacturing entities to manufacture the equipment in accordance with the given design (see Reddy, column 5, lines 7-19 and abstract, wherein forecasting and strategy outcomes optimization solution based upon metric analysis of characteristics, and/or other controlled attributes (collectively feature metrics) for products and/or services. A model that represents the relationship between periodic outcome variable (Y) and causal variables (X1 ... Xn) is formulated using one or more suitable algorithmic techniques or models (e.g., linear regression, time series regression, Bayesian or a combination)).
One of ordinary skill in the art would have recognized that applying the known technique of Reddy would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claims 5, 7, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Maury et al. (US Pub No. 2022/0019204), in view of Coffman et al. (US Pub No. 2018/0120813), and further in view of Chen et al. (US Pub No. 2012/0303411).
Regarding claim 5, Maury discloses the apparatus of claim 1, wherein the predicted cost attributes associated with the one or more manufacturing processes for manufacturing the equipment of the given design (see Maury, paras [0010]-[0011], wherein referring to Figs. 2-3 show a neural network to monitor, analyze and train predictive data models and algorithms through the manufacturing and assembly system architecture; and para [0177], wherein each supplied component from each supplier can also be identified in the database with related information such as component cost…..the supplier database can include extensive information related to the components supplied by each supplier….identifying each supplied component is desirable, but also desirable is the identification of alternative components that are available to permit efficient changes in the source of supply from one supplier to another supplier of the same component during the course of operating the manufacturing facility. The specification of each available component from each supplier can be contained in this database and made available for management of the workflow……Commercial information related to each component can also be provided such as the identification of the purchase and supply contracts with the supplier, the cost of each component (along with details of any volume purchase agreements) and volumes of each component actually supplied by the supplier. The supplier database can also show the identification of the product into which each purchased component will be assembled).
Maury et al. and Coffman et al. combined fail to explicitly disclose comprises a time series cost for manufacturing comprising two or more manufacturing dates.
Analogous art Chen et al. discloses a time series cost for manufacturing comprising two or more manufacturing dates (see Chen, para [0040], wherein Figs. 6-8 illustrate an example application of the method for the model of the price dynamics, showing respectively, the mapping from the time series price data to the price modes, the estimation of the Markov model for price dynamics from the price modes and mode durations, and the forecast price predictions; and para [0062], wherein referring to Fig. 6 shows a date or time-stamp or a time-period index column 56, e.g., Week, e.g.,)……. is stored as product attributes table 75, shown here configured for a product set, e.g., from a processed-food category identified by UPC column 54, with fields (columns) describing the manufacturer in column 64, packaging including size in column 66).
Maury discloses a system for improving quality control services and optimizing modeling. Chen discloses directed to processing of models for manufacture processes through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maury, regarding the Intelligent Data Object Model for Distributed Product Manufacturing, Assembly and Facility Infrastructure, to have included a time series cost for manufacturing comprising two or more manufacturing dates because both inventions teach generating operational efficiency. Further, 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.
Regarding claims 7 & 16, Maury discloses the apparatus of claim 1, wherein the second machine learning model, as set forth above with claim 1.
Maury et al. and Coffman et al. combined fail to explicitly disclose a seasonal autoregressive integrated moving average based prediction model.
Analogous art Chen et al. discloses a seasonal autoregressive integrated moving average based prediction model (see Chen, para [0064], wherein the seasonal variations may reflect the increased consumption or demand for the product during a particular period of the year such as a season or a holiday event; and claim 8, wherein predicting the overall market demand, by extrapolating historic demand for designated product set in retail categories using one of: autoregressive, moving average or exponential smoothing models for stable and complete product categories).
One of ordinary skill in the art would have recognized that applying the known technique of Reddy would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 5.
Regarding claims 12-16 rejected based upon the same rationale as the rejection of claims 1-4 and 7, respectively, since they are the method claims corresponding to the apparatus claims.
Regarding claims 17-20 rejected based upon the same rationale as the rejection of claims 1-4, respectively, since they are the non-transitory processor-readable storage medium claims corresponding to the apparatus claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 3/12/2026