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 .
Amendments
This Office Action is in response to the amendment filed on January 18, 2026.
Claims 1-2, 4-12, 14-15 and 17-20 have been amended.
No claims have been cancelled.
Claim 21 has been added.
The objections and rejections from the prior correspondence that are not restated herein are withdrawn.
Response to Arguments
Applicant's arguments filed on January 18, 2026 have been fully considered.
Applicant's arguments regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered but are not persuasive. Applicant argues that the examiner alleges that the claims recite mathematical concepts without identifying any mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Examiner respectfully disagrees. It should be noted that all independent claims 1, 11, 14, 17, and 19 recite the mathematical concept of “estimating the quality evaluation”, which involve mathematical calculations. Paragraph [0073] of the specification states that the model evaluation can be calculated by using the complexity of the estimation model. The provided examples for the estimation model complexity used to calculate the model evaluation are Rademacher complexity, VC dimension, or generalization error limit, which are all calculated numerical quantities resulting from mathematical calculations. Therefore, the claims 1, 11, 14, 17, and 19 recite mathematical concepts. Under step 2A Prong 2, the additional elements recited in the claims do not integrate the judicial exception into a practical application. For example, providing the learning apparatus to at least two manufacturers merely apply the exception of estimating the quality evaluation using generic computer components within the context of supply chain. Under step 2B, when considering the claims individually and as a whole, do not demonstrate an unconventional configuration or arrangements that would meet the criteria for “something more”. The recited units implemented using a generic processor perform a standard machine learning pipeline: receiving data, learning a model, evaluating a model, and sending the model evaluation. Thereby, the claims recite conventional arrangements of generic computer components applied to the particular business domain of supply chain.
Applicant's arguments regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered but are not persuasive. Applicant argues that MRZIGLOD relates to model-based quality predictions of a chemical compound as the outcome of a production process having more than one sub-process performed by the same manufacturer and not as steps of a supply chain. Applicant argues that examiner interprets "upstream process" as "encompassing several manufacturing steps performed before the final product is produced." However, such steps, when limited to operations by a single entity, do not reasonably constitute a "supply chain," which inherently involves the coordination and transfer of products, data, or materials among multiple, separate manufacturers.
Examiner respectfully disagrees. It should be noted that MRZIGLOD in view of DEVARAKONDA teaches a supply chain that comprises at least two manufacturers. MRZIGLOD teaches a quality prediction method for products in production process comprising one or more interrelated sub-processes, which MRZIGLOD denotes as a “supply chain” (see MRZIGLOD [0118]). Therefore, MRZIGLOD does not restrict the production process to operations performed by a single entity. Under broadest reasonable interpretation, “supply chain” can be interpreted as the interrelated sub-processes of a production process. The methods in MRZIGLOD pertain to receiving a description of a production process, as illustrated in MRZIGLOD [Fig. 6], from intermediate and/or final products to perform quality prediction. While MRZIGLOD refers to its production process as a “supply chain”, MRZIGLOD is not as explicit as DEVARAKONDA in teaching a supply chain that comprises at least two manufacturers. DEVARAKONDA teaches an automated supply chain intelligence system for providing recommendations to one or more upstream processes conducted by separate component factories in a production value stream (i.e., supply chain) to optimize the manufacturing process. For this reason, MRZIGLOD is not relied upon for teaching, but DEVARAKONDA teaches:
wherein the upstream process and the downstream process are part of the supply chain that comprises at least two manufacturers […] (DEVARAKONDA [0002] teaches: "Original equipment manufacturers (OEMs) deploy discrete and continuous manufacturing systems and processes to manufacture products that are technically complex and dependent on production value streams, a term that is used broadly in this disclosure to also encompass terms such as supply chains, value chains, and the like." DEVARAKONDA [0022] teaches: "One example embodiment takes the form of a system that includes an upstream machine-learning model corresponding to each of one or more upstream entities in a production value stream (i.e., supply chain that comprises at least two manufacturers) of a product.” DEVARAKONDA [0048] teaches: "The outputs and predictions from upstream modules are synchronized with the outputs and inputs in downstream modules (i.e., wherein the upstream process and the downstream process are part of the supply chain." DEVARAKONDA [0037] teaches: “[0037] The final-assembly process 108 could take place at a factory or other facility that is physically separate from each of the first-component factory 102, the second-component factory 104, and the third-component factory 106.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD and DEVARAKONDA before them, to include DEVARAKONDA’s processor and causal-analysis model, the action-and-alert process, and the implementation interface in MRZIGLOD’s production quality prediction process. One would have been motivated to make such a combination in order to “be able to accurately predict throughput of a production value stream, and to understand causes for variability and take corrective (i.e., corrective and/or preventative) action at appropriate times, among other goals.” And “prioritize corrective actions in order to meet financial, operational, and/or other objectives, including objectives related to meeting demand and service levels, customer satisfaction, maintaining of branding and/or market position, measuring the effectiveness of a production value stream, profitability and/or other financial targets, and/or reducing waste and liquidation of material to improve efficiency.” (DEVARAKONDA [0032]).
Applicant argues that independent claims 1, 11, 14, and 17, as amended, require that the "learning apparatus" or "learning program" be provided to "the at least two manufacturers”, and that none of the cited references discloses such features.
Examiner respectfully disagrees. It should be noted that the newly introduced reference of KANAGAVELU teaches at least two manufacturers to which the learning apparatus/program is provided, as shown in detail in the 103 rejections section. Specifically, KANAGAVELU teaches:
[…] at least two manufacturers to which the learning apparatus is provided. (KANAGAVELU [pg. 6, section D. Integrated Federated Learning with IIoT Platform for Smart Manufacturing] teaches: "Data analytics system - can also be deployed on on-premise (i.e., to which the learning apparatus/program is provided) or cloud servers. The streaming data are saved into a database. Using the historical data, companies could train machine learning models in different manners (i.e., Locally, Centralized and Federated). These models with different versions are packaged as docker containers and stored in model marketplace. Companies (i.e., at least two manufacturers) could deploy these models to provide predictions using real-time streaming data. The prediction and insights are helpful for operators to improve manufacturing process and efficiency.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD, DEVARAKONDA, and KANAGAVELU before them, to include KANAGAVELU’s federated learning framework for smart manufacturing in MRZIGLOD and DEVARAKONDA’s production quality prediction process. One would have been motivated to make such a combination in order to enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy (KANAGAVELU [pg. 1, Abstract]).
Similarly, KANAGAVELU discloses the “learning program” in the limitation “[…] at least two manufacturers to which the learning program is provided”, as required for independent claim 14.
Applicant argues that the cited references do not disclose "an improvement request message" that is received from an "evaluation apparatus" ... “in response to determining that the target process should be improved based on the model evaluation about each of the at least one upstream process of the supply chain” [claims 1, 11, 14].
Examiner respectfully disagrees. While the argument points to claims 1, 11, and 14 for the limitation, this limitation is recited in claims 2, 12, and 15. It should be noted that the combination of MRZIGLOD and DEVARAKONDA teaches this limitation. Specifically, MRZIGLOD teaches:
an improvement request receiving unit […] to receive an improvement request message […] (MRZIGLOD [0085] teaches: "For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis). It is preferred that these quantifications are displayed to an expert via a user interface. The expert is required to confirm the validity of the input by means of expert knowledge and/or the quantifications mentioned above. The expert shall decide if the piece of historical process time series shall be considered or rejected for training of the data-based model." MRZIGLOD [0089] teaches: "In a particular embodiment, the iteration step k) may be conducted via an optimizer, e.g. varying one or more of the production steps, process parameters thereof and/or derived quantities and assessing the resulting model outputs according to goodness of fit." Examiner's note: under BRI, an improvement request receiving unit […] to receive an improvement request message can be interpreted as the optimizer, which receives the proposition of the goodness of fit analysis together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty for the data-based model (i.e. quality-prediction model).)
DEVARAKONDA further teaches: an improvement request message, which is sent by the evaluation apparatus in response to determining that the target process should be improved based on the model evaluation […] DEVARAKONDA [0026] teaches: "[0026] In an embodiment, the causal-analysis machine-learning model is configured to […] provide the identified one or more causal factor to the action-and-alert process." Additionally, DEVARAKONDA [0027-0028] teaches: "[0027] In an embodiment, the action-and-alert process is configured to: receive the identified one or more causal factor from the causal-analysis machine-learning model and predictions from the various (throughput and delay) models; generate, based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions (i.e., an improvement request message which is sent by the evaluation apparatus); and providing the one or both of one or more alerts and one or more recommended actions to the implementation interface. [0028] In an embodiment, the implementation interface is configured to: receive the one or both of one or more alerts and one or more recommended actions from the action-and-alert process; obtain and process response to the one or both of one or more alerts and one or more recommended actions; and provide data reflective of the response to one or more of one or more of the upstream entities, the final-assembly process, one or more of the upstream machine-learning models, and the final-assembly machine-learning model." DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” DEVARAKONDA [0045] teaches: “Moreover, the recommended actions 168 in some embodiments are prioritized based on quantified risk (e.g., based on minimizing a cost function, maximizing a reward function, and/or the like, where such function could take into account upstream materials, parts, components, process steps, and/or the like). The quantified risk can be based on operational measurements across the production value stream, relating to, e.g., upstream material deliveries, modules, process subsystems, operator efficiency, etc.” DEVARAKONDA [0046] teaches: “In some embodiments, although not pictured in FIG. 1, the implementation interface 160 also or instead generates commands for transmission to one, some, or all of the upstream nodes (e.g., the first-component factory 102, the second-component factory 104, and/or the third-component factory 106), in order to alter one or more operating parameters of the operations there (i.e., in response to determining that the target process should be improved based on the model evaluation […]).” Additionally, DEVARAKONDA [0134] teaches a processor.)
[…] that uses the at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
[…] the model evaluation about each of the at least one upstream process of the supply chain. (DEVARAKONDA [0039] teaches: “This historical data can include data items such as raw materials used, amount of material used, manufacturing steps, configuration of the manufacturing steps, actual duration of the manufacturing steps, quality metrics (i.e., model evaluation), energy expense, labor expense, production capacity of equipment, time taken to transport raw materials or parts and/or any one or more other data items deemed suitable by those of skill in the art for a given implementation. […] Each of them corresponds with a node that is upstream of the final-assembly process 108 in the production value stream (i.e., of the supply chain) that is depicted in FIG. 1. In at least one embodiment, each of the first-component historical data 140, the second-component historical data 142, and the third-component historical data 144 includes historical data pertaining to the corresponding operational metrics of the corresponding factory." DEVARAKONDA [0024] In an embodiment, each upstream machine-learning model is configured to: receive one or more operational metric corresponding to the respective upstream entity (or process step); generate, based on at least the received one or more operational metric corresponding to the respective upstream entity, upstream delay predictions corresponding to the respective upstream entity (i.e., about each of the at least one upstream process of the supply chain);”)
Applicant argues that the cited reference does not disclose “the process evaluation unit is configured to determine to improve an upstream process, among the at least one upstream process of the supply chain, which is given a model evaluation” [claims 5, 17, 19].
Examiner respectfully disagrees. While the argument points to claims 5, 17, and 19 for the limitation, this limitation is recited in claims 6, 18, and 20. It should be noted that the combination of MRZIGLOD and DEVARAKONDA teaches this limitation. Specifically, DEVARAKONDA further teaches:
wherein the process evaluation unit is uses the at least one processor to determine to improve an upstream process of the supply chain, among the at least one upstream process of the supply chain, which is given a model evaluation […] (DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process (i.e., a process evaluation unit) for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” Furthermore, DEVARAKONDA [0043] teaches: “The causal-analysis machine-learning model 124 provides identified causal factor 154 (i.e., which is given a model evaluation) to an action-and-alert process 126. Based at least in part on the identified causal factor 154, the action-and-alert process 126 generates alerts 156 and recommended actions 168, and transmits both to the implementation interface 160, which could include one or more user interfaces for human users (e.g., graphical user interfaces (GUIs), audiovisual interfaces, and/or the like) and/or one or more automated interfaces for carrying out automated processing of the alerts 156 and recommended actions 168.” DEVARAKONDA [0046] teaches: “In some embodiments, although not pictured in FIG. 1, the implementation interface 160 also or instead generates commands for transmission to one, some, or all of the upstream nodes (e.g., the first-component factory 102, the second-component factory 104, and/or the third-component factory 106), in order to alter one or more operating parameters of the operations there (i.e., to improve an upstream process of the supply chain, among the at least one upstream process of the supply chain).” DEVARAKONDA [0134] teaches the processor in which these methods are implemented (i.e., that uses at least one processor).)
Applicant argues that MRZIGLOD [0085] merely refers to a method of evaluating the goodness of fit of training data, lacking the disclosure of an improvement request message or an equivalent process that explicitly connects model evaluation to process improvement decisions concerning each upstream process. Applicant argues that even if the "optimizer" can reasonably be construed as the "improvement request receiving unit," there is no disclosure of "an improvement request message" being received from an "evaluation apparatus" "in response to determining that the target process should be improved based on the model evaluation about each of the at least one upstream processes of the supply chain.”
Examiner respectfully disagrees. It should be noted that DEVARAKONDA teaches an improvement request message sent from an evaluation apparatus in response to determining that the target process should be improved based on the model evaluation about each of the at least one upstream processes of the supply chain as shown above in pages 15-18 of this Office Action for claim 2 rejections.
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 1-21 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 and 19-21 are directed to a machine or an article of manufacture. Claims 11-18 are directed to a process.
With respect to claims 1, 11, and 14:
2A Prong 1: The claims recite an abstract idea. Specifically:
(Claims 1 and 14) generate, through learning, an estimation model […]
(Claim 11) generating […] through learning, an estimation model for […]
[…] estimating the quality evaluation of the downstream produced object from at least one production parameter, by using the at least one production parameter about the production of each of the produced objects of the target process and the quality evaluation of each downstream produced object produced by using of the each produced objects of the target process; (Mathematical concepts – Estimating a quality evaluation using production parameters involves mathematical calculations. Paragraph [0073] of the specification states that the model evaluation can be calculated by using the complexity of the estimation model. The provided examples for the estimation model complexity used to calculate the model evaluation are Rademacher complexity, VC dimension, or generalization error limit, which are all calculated numerical quantities resulting from mathematical calculations. – see MPEP § 2106.04(a)(2)(I))
calculate/calculating a model evaluation based on at least one of certainty or complexity of the estimation model; (Mathematical concepts – calculating a model evaluation involves mathematical calculations (see paragraph [0073]) – see MPEP § 2106.04(a)(2))
[…] evaluating at least one upstream process of the supply chain by using a model evaluation about each of the at least one upstream process which is upstream of the downstream process of the supply chain; (Mental process – A person can mentally evaluate at least one upstream process by using a model evaluation about each of at least one upstream process– see MPEP § 2106.04(a)(2)(III))
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
2A Prong 2: The additional elements recited in the claims do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claims 1) at least one processor; (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 1 and 14) a correspondence receiving unit that uses the at least one processor to receive […] (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 11) receiving, by a learning apparatus, […] (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
(Claim 14) A non-transitory computer readable medium having a learning program recorded thereon, wherein the learning program is executed by a computer having at least one processor to cause the computer to function as: […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] a correspondence between each produced object of a target process which is targeted and a quality evaluation of each downstream produced object produced in a downstream process of the supply chain by using each produced object of the target process; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
(Claims 1 and 14) a learning processing unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claims 1 and 14) a calculating unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 11) by the learning apparatus (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claims 1 and 14) a model evaluation sending unit that uses the at least one processor to send the model evaluation calculated by the calculating unit, to an evaluation apparatus for (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 11) sending, by the learning apparatus, the calculated model evaluation, to an evaluation apparatus for […] (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
wherein the upstream process and the downstream process are part of the supply chain that comprises at least two manufacturers to which the learning apparatus is provided. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claims 1) at least one processor; (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 1 and 14) a correspondence receiving unit that uses the at least one processor to receive […] (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 11) receiving, by a learning apparatus, […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claim 14) A non-transitory computer readable medium having a learning program recorded thereon, wherein the learning program is executed by a computer having at least one processor to cause the computer to function as: […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] a correspondence between each produced object of a target process which is targeted and a quality evaluation of each downstream produced object produced in a downstream process of the supply chain by using each produced object of the target process; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claims 1 and 14) a learning processing unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claims 1 and 14) a calculating unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 11) by the learning apparatus (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claims 1 and 14) a model evaluation sending unit that uses the at least one processor to send the model evaluation calculated by the calculating unit, to an evaluation apparatus for (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 11) sending, by the learning apparatus, the calculated model evaluation, to an evaluation apparatus for […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
wherein the upstream process and the downstream process are part of the supply chain that comprises at least two manufacturers to which the learning apparatus is provided. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 2, 12, and 15:
2A Prong 1: The claims recite an abstract idea. Specifically:
determining that the target process should be improved based on the model evaluation about each of the at least one upstream process of the supply chain. (Mental process – A person can mentally evaluate that a process should be improved based on an evaluation – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claims do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claim 2) an improvement request receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 12) wherein the learning apparatus […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 15) wherein the learning program causes the computer to further function as an improvement request receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] receive/receives an improvement request message which is sent by the evaluation apparatus in response to […] (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claim 2) an improvement request receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 12) wherein the learning apparatus […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 15) wherein the learning program causes the computer to further function as an improvement request receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] receive/receives an improvement request message which is sent by the evaluation apparatus in response to […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claims 3, 13 and 16:
2A Prong 1: The claims recite an abstract idea. Specifically:
selecting/selects a production parameter to be adjusted among the at least one production parameter in the target process, in response to a reception of the improvement request message. (Mental process – A person can mentally select a parameter to be adjusted in response to a request – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claims do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claim 3) a parameter selecting unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 13) wherein the learning apparatus […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 16) wherein the learning program causes the computer to further function as a parameter selecting unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claim 3) a parameter selecting unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 13) wherein the learning apparatus […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 16) wherein the learning program causes the computer to further function as a parameter selecting unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 4:
2A Prong 1: The claim recites an abstract idea. Specifically:
estimate a quality evaluation of the downstream produced object produced in the downstream process of the supply chain by using each produced object of the target process in a case of adjusting a production parameter selected by the parameter selecting unit. (Mathematical concepts and/or mental process – Estimating a quality evaluation can involve mathematical calculations (see paragraph [0073]) and/or a person can mentally estimate a quality evaluation using each produced object – see MPEP § 2106.04(a)(2))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
a quality evaluation estimating unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a quality evaluation estimating unit that uses the at least one processor to estimate (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claims 5, 17 and 19:
2A Prong 1: The claims recite an abstract idea. Specifically:
generating, for each of at least one upstream process of the supply chain, through learning, the estimation model for estimating a quality evaluation of a downstream produced object from at least one production parameter, by using a quality evaluation of each downstream produced object produced by using the at least one production parameter about the production of each upstream produced object according to the upstream process of the supply chain, and each upstream produced object of the upstream process of the supply chain (Mathematical concepts and/or mental process – Estimating a quality evaluation using production parameters involves mathematical calculations. Paragraph [0073] of the specification states that the model evaluation can be calculated by using the complexity of the estimation model. The provided examples for the estimation model complexity used to calculate the model evaluation are Rademacher complexity, VC dimension, or generalization error limit, which are all calculated numerical quantities resulting from mathematical calculations. – see MPEP § 2106.04(a)(2)(I))
evaluate/evaluating […] the at least one upstream process of the supply chain based on the model evaluation about each of the at least one upstream process of the supply chain. (Mental process – A person can mentally evaluate a process based on the model evaluation – see MPEP § 2106.04(a)(2)(III))
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claims 5) a model evaluation receiving unit that uses the at least one processor to (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 17) An evaluation method comprising: receiving, by an evaluation apparatus, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 19) A non-transitory computer readable medium having an evaluation program recorded thereon, wherein the evaluation program is executed by a computer to cause the computer to function as: a model evaluation receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] receive/receiving a model evaluation which is based on at least one of certainty or complexity of an estimation model, from a learning apparatus for (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
(Claim 17) by the evaluation apparatus, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claims 5 and 19) a process evaluation unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
wherein the supply chain includes at least two manufacturers. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claims 5) a model evaluation receiving unit that uses the at least one processor to (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 17) An evaluation method comprising: receiving, by an evaluation apparatus, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claim 19) A non-transitory computer readable medium having an evaluation program recorded thereon, wherein the evaluation program is executed by a computer to cause the computer to function as: a model evaluation receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
[…] receive/receiving a model evaluation which is based on at least one of certainty or complexity of an estimation model, from a learning apparatus for (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claim 17) by the evaluation apparatus, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claims 5 and 19) a process evaluation unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
wherein the supply chain includes at least two manufacturers. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 6, 18, and 20:
2A Prong 1: The claims recite an abstract idea. Specifically:
determine to improve an upstream process of the supply chain, among the at least one upstream process of the supply chain, which is given a model evaluation that is based on the estimation model whose certainty is greater than a standard value or whose complexity is less than a standard value. (Mental process – A person can determine to improve a process by mentally evaluating a model evaluation based on comparing whether an evaluation certainty is greater than a standard value or whose complexity is less than a standard value – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claims do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claims 6 and 20) wherein the process evaluation unit uses the at least one processor to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 18) wherein the evaluation apparatus is configured to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claims 6 and 20) wherein the process evaluation unit uses the at least one processor to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
(Claim 18) wherein the evaluation apparatus is configured to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 7:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
further comprising a message output unit that uses the at least one processor to output an improvement request message for the upstream process of the supply chain determined to be improved. (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising a message output unit that uses the at least one processor to output an improvement request message for the upstream process of the supply chain determined to be improved. (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 8:
2A Prong 1: The claim recites an abstract idea. Specifically:
generate a correspondence between each produced object of the at least one upstream process of the supply chain and the quality evaluation of each downstream produced object by using each association acquired by the association acquiring unit; (Mental process – A person can mentally generate a correspondence or with the physical aid of a pen and paper – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
an association acquiring unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
acquire, for each process of the at least one upstream process of the supply chain, an association between each produced object supplied from a process on the upstream side and each produced object of the process which is supplied to the downstream side; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
a correspondence generating unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
a correspondence sending unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
send the correspondence to the learning apparatus. (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
an association acquiring unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
acquire, for each process of the at least one upstream process of the supply chain, an association between each produced object supplied from a process on the upstream side and each produced object of the process which is supplied to the downstream side; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
a correspondence generating unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
a correspondence sending unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
send the correspondence to the learning apparatus. (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 9:
2A Prong 1: The claim recites an abstract idea. Specifically:
determining whether to start an evaluation of the at least one upstream process of the supply chain by the process evaluation unit, based on a quality evaluation of at least one downstream produced object. (Mental process – A person can mentally determine whether to start an evaluation based on a quality evaluation – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
further comprising a start determination unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising a start determination unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 10:
2A Prong 1: The claim recites an abstract idea. Specifically:
evaluate the at least one upstream process of the supply chain based on the model evaluation about each of the at least one upstream process of the supply chain. (Mental process – A person ca mentally evaluate a process based on an evaluation – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the learning apparatus includes at least one learning apparatus having each of at least one upstream process of the supply chain as a target process; and (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
an evaluation apparatus, wherein the evaluation apparatus includes: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
a model evaluation receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
receive, from the at least one learning apparatus, a model evaluation which is based on at least one of certainty or complexity of the estimation model; and (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
a process evaluation unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the learning apparatus includes at least one learning apparatus having each of at least one upstream process of the supply chain as a target process; and (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
an evaluation apparatus, wherein the evaluation apparatus includes: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
a model evaluation receiving unit that uses the at least one processor to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
receive, from the at least one learning apparatus, a model evaluation which is based on at least one of certainty or complexity of the estimation model; and (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
a process evaluation unit that uses the at least one processor to […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 21:
2A Prong 1: The claim(s) recite(s) an abstract idea. Specifically:
wherein each of the at least two manufacturers chooses the at least one production parameter without revealing it to any of the other at least two manufacturers. (Mental process – A person can mentally choose one parameter without revealing it to others – see MPEP § 2106.04(a)(2)(III))
Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
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.
Claims 1-4, 10-16, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over MRZIGLOD (US 20220068440 A1) in view of DEVARAKONDA (US 20220067622 A1) and KANAGAVELU ("Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning"), hereafter MRZIGLOD, DEVARAKONDA, and KANAGAVELU respectively.
Regarding Claim 1:
MRZIGLOD teaches:
A learning apparatus comprising: a correspondence receiving unit […] to receive a correspondence between each produced object of a target process which is targeted and a quality evaluation of each downstream produced object produced in a downstream process of the supply chain by using each of the produced objects of the target process; (MRZIGLOD [0097] teaches: “Typically, the method mentioned above is run in a system for product-quality prediction (i.e., learning apparatus) comprising elements configured to conduct the method steps mentioned above. In one embodiment the quality-prediction model is stored in a model module.” MRZIGLOD [0028] teaches: "Historical process time series data are time series of process parameter values collected in previous batches or time periods as well as their respective values for quality attributes as measured." Additionally, MRZIGLOD [0049-0051] teaches: "[0049] The quality-prediction model is built by: [0050] a) Receiving a description of a production process as one or more interrelated sub-processes and their respective process parameters, [0051] b) Receiving a quality attribute of the product to be modeled and predicted, wherein said product may be final and/or intermediate product of the production process at stake (i.e., by using each of the produced objects of the target process)". MRZIGLOD [0117-0118] teaches that product quality prediction modeling results can provide process improvements for potential efficiency gains in the supply chain. MRZIGLOD [0097] teaches: “Typically, the method mentioned above is run in a system for product-quality prediction comprising elements configured to conduct the method steps mentioned above. In one embodiment the quality-prediction model is stored in a model module.” Examiner's note: Under broadest reasonable interpretation, to receive a correspondence between each produced object of a target process which is targeted can be interpreted as the quality-prediction model receiving a description of a production process as one or more interrelated sub-processes and their respective process parameters, where the description of the production process at stake maybe be for a final and/or intermediate product of the production process, and the quality evaluation of each downstream produced object produced in a downstream process can be interpreted as the quality attribute of the product to be modeled and predicted, which may be the final and or intermediate product of the production process at stake. The correspondence receiving unit can be interpreted as one of the elements described in MRZIGLOD [0097] configured to perform the steps mentioned above, which are stored in a model module containing the quality-prediction model. Furthermore, MRZIGLOD [0117-0118] describes that the quality prediction modeling results provide efficiency gains in the supply chain (i.e., of the supply chain).)
a learning processing unit […] to generate, through learning, an estimation model for estimating the quality evaluation of the downstream produced object from at least one production parameter, by using the at least one production parameter about the production of each of the produced objects of the target process and the quality evaluation of each downstream produced object produced by using each of the produced objects of the target process; (MRZIGLOD [0049-0052] teaches: "[0049] The quality-prediction model is built by (i.e., to generate an estimation model): [0050] a) Receiving a description of a production process as one or more interrelated sub-processes and their respective process parameters (i.e., from at least one production parameter about the production of each of the produced objects of the target process), [0051] b) Receiving a quality attribute (i.e., the quality evaluation of each downstream produced object produced) of the product to be modeled and predicted (i.e., through learning), wherein said product may be final and/or intermediate product of the production process (i.e., by using each of the produced objects of the target process) at stake, [0052] c) Receiving at least one sub-process which is suspected to influence the product quality attribute." Examiner's note: under BRI, for estimating the quality evaluation of the downstream produced object can be interpreted as "[...] to be modeled and predicted, wherein said product may be final". Furthermore, a learning processing unit can be interpreted as one of the elements described in MRZIGLOD [0097] configured to perform the steps mentioned above, which are stored in a model module containing the quality-prediction model.)
a calculating unit […] to calculate a model evaluation based on at least one of certainty or complexity of the estimation model; (MRZIGLOD [0085] teaches: "[…] For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model (i.e., to calculate a model evaluation) as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis) (i.e., based on at least one of certainty [...] of the estimation model). Furthermore, a calculating unit can be interpreted as one of the elements described in MRZIGLOD [0097] configured to perform the steps mentioned above, which are stored in a model module containing the quality-prediction model.)
[…] model evaluation calculated by the calculating unit […] (MRZIGLOD [0085] teaches: "[…] For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model (i.e., a model evaluation calculated by the calculating unit) as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis).” Examiner’s note: Under broadest reasonable interpretation, a calculating unit can be interpreted as one of the elements described in MRZIGLOD [0097] configured to perform the steps mentioned above, which are stored in a model module containing the quality-prediction model.)
However, MRZIGLOD is not relied upon for teaching:
at least one processor;
[…] that uses the at least one processor […]
a model evaluation sending unit that uses the at least one processor to send the model evaluation [...], to an evaluation apparatus for evaluating at least one upstream process of the supply chain by using a model evaluation about each of the at least one upstream process which is upstream of the downstream process of the supply chain;
wherein the upstream process and the downstream process are part of the supply chain that comprises at least two manufacturers to which the learning apparatus is provided.
However, DEVARAKONDA teaches: at least one processor; […] that uses the at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
a model evaluation sending unit that uses at least one processor to send the model evaluation […], to an evaluation apparatus for evaluating at least one upstream process of the supply chain by using a model evaluation about each of the at least one upstream process which is upstream of the downstream process of the supply chain; (DEVARAKONDA [0039] This historical data can include data items such as raw materials used, amount of material used, manufacturing steps, configuration of the manufacturing steps, actual duration of the manufacturing steps, quality metrics (i.e., model evaluation), energy expense, labor expense, production capacity of equipment, time taken to transport raw materials or parts and/or any one or more other data items deemed suitable by those of skill in the art for a given implementation. […] Each of them corresponds with a node that is upstream of the final-assembly process 108 in the production value stream (i.e., of the supply chain) that is depicted in FIG. 1. In at least one embodiment, each of the first-component historical data 140, the second-component historical data 142, and the third-component historical data 144 includes historical data pertaining to the corresponding operational metrics of the corresponding factory." DEVARAKONDA [0024] In an embodiment, each upstream machine-learning model is configured to: receive one or more operational metric corresponding to the respective upstream entity (or process step) (i.e., send the model evaluation […] to an evaluation apparatus); generate, based on at least the received one or more operational metric corresponding to the respective upstream entity, upstream delay predictions corresponding to the respective upstream entity (i.e., for evaluating at least one upstream process of the supply chain by using a model evaluation […] about each of the at least one upstream process which is upstream of the downstream process);” DEVARAKONDA [0120] teaches: "Additional data items include complexity 1416, quality 1420, reliability 1426, and commitment accuracy 1428. All of these data items are provided by way of example and not limitation. Any other data items mentioned herein could also or instead be used as features for training one or more machine-learning models. Various embodiments combine data across the production value stream, and include both historic KPIs, as well as forward-looking predictions and targets for key performance metrics." DEVARAKONDA [0041] teaches: "Moreover, each of the upstream machine-learning models provides (i.e., a model evaluation sending unit) predictions to both a final-assembly machine-learning model 122 and a causal-analysis machine-learning model 124." DEVARAKONDA [0133] FIG. 16 is a diagrammatic representation of a machine 1600 within which instructions 1612 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1600 to perform any one or more of the methodologies discussed herein may be executed.”)
wherein the upstream process and the downstream process are part of the supply chain that comprises at least two manufacturers […] (DEVARAKONDA [0002] teaches: "Original equipment manufacturers (OEMs) deploy discrete and continuous manufacturing systems and processes to manufacture products that are technically complex and dependent on production value streams, a term that is used broadly in this disclosure to also encompass terms such as supply chains, value chains, and the like." DEVARAKONDA [0022] teaches: "One example embodiment takes the form of a system that includes an upstream machine-learning model corresponding to each of one or more upstream entities in a production value stream (i.e., supply chain that comprises at least two manufacturers) of a product.” DEVARAKONDA [0048] teaches: "The outputs and predictions from upstream modules are synchronized with the outputs and inputs in downstream modules (i.e., wherein the upstream process and the downstream process are part of the supply chain." DEVARAKONDA [0037] teaches: “[0037] The final-assembly process 108 could take place at a factory or other facility that is physically separate from each of the first-component factory 102, the second-component factory 104, and the third-component factory 106.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD and DEVARAKONDA before them, to include DEVARAKONDA’s processor and causal-analysis model, the action-and-alert process, and the implementation interface in MRZIGLOD’s production quality prediction process. One would have been motivated to make such a combination in order to “be able to accurately predict throughput of a production value stream, and to understand causes for variability and take corrective (i.e., corrective and/or preventative) action at appropriate times, among other goals.” And “prioritize corrective actions in order to meet financial, operational, and/or other objectives, including objectives related to meeting demand and service levels, customer satisfaction, maintaining of branding and/or market position, measuring the effectiveness of a production value stream, profitability and/or other financial targets, and/or reducing waste and liquidation of material to improve efficiency.” (DEVARAKONDA [0032]).
MRZIGLOD in view of DEVARAKONDA is not relied upon for teaching, but KANAGAVELU teaches: […] at least two manufacturers to which the learning apparatus is provided. (KANAGAVELU [pg. 6, section D. Integrated Federated Learning with IIoT Platform for Smart Manufacturing] teaches: "Data analytics system - can also be deployed on on-premise (i.e., to which the learning apparatus is provided) or cloud servers. The streaming data are saved into a database. Using the historical data, companies could train machine learning models in different manners (i.e., Locally, Centralized and Federated). These models with different versions are packaged as docker containers and stored in model marketplace. Companies (i.e., at least two manufacturers) could deploy these models to provide predictions using real-time streaming data. The prediction and insights are helpful for operators to improve manufacturing process and efficiency.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD, DEVARAKONDA, and KANAGAVELU before them, to include KANAGAVELU’s federated learning framework for smart manufacturing in MRZIGLOD and DEVARAKONDA’s production quality prediction process. One would have been motivated to make such a combination in order to enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy (KANAGAVELU [pg. 1, Abstract]).
Regarding Claim 2:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 1 as outlined above. MRZIGLOD further teaches:
an improvement request receiving unit […] to receive an improvement request message […] (MRZIGLOD [0085] teaches: "For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis). It is preferred that these quantifications are displayed to an expert via a user interface. The expert is required to confirm the validity of the input by means of expert knowledge and/or the quantifications mentioned above. The expert shall decide if the piece of historical process time series shall be considered or rejected for training of the data-based model." MRZIGLOD [0089] teaches: "In a particular embodiment, the iteration step k) may be conducted via an optimizer, e.g. varying one or more of the production steps, process parameters thereof and/or derived quantities and assessing the resulting model outputs according to goodness of fit." Examiner's note: under BRI, an improvement request receiving unit […] to receive an improvement request message can be interpreted as the optimizer, which receives the proposition of the goodness of fit analysis together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty for the data-based model (i.e. quality-prediction model).)
DEVARAKONDA further teaches: an improvement request message, which is sent by the evaluation apparatus in response to determining that the target process should be improved based on the model evaluation […] DEVARAKONDA [0026] teaches: "[0026] In an embodiment, the causal-analysis machine-learning model is configured to […] provide the identified one or more causal factor to the action-and-alert process." Additionally, DEVARAKONDA [0027-0028] teaches: "[0027] In an embodiment, the action-and-alert process is configured to: receive the identified one or more causal factor from the causal-analysis machine-learning model and predictions from the various (throughput and delay) models; generate, based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions (i.e., an improvement request message which is sent by the evaluation apparatus); and providing the one or both of one or more alerts and one or more recommended actions to the implementation interface. [0028] In an embodiment, the implementation interface is configured to: receive the one or both of one or more alerts and one or more recommended actions from the action-and-alert process; obtain and process response to the one or both of one or more alerts and one or more recommended actions; and provide data reflective of the response to one or more of one or more of the upstream entities, the final-assembly process, one or more of the upstream machine-learning models, and the final-assembly machine-learning model." DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” DEVARAKONDA [0045] teaches: “Moreover, the recommended actions 168 in some embodiments are prioritized based on quantified risk (e.g., based on minimizing a cost function, maximizing a reward function, and/or the like, where such function could take into account upstream materials, parts, components, process steps, and/or the like). The quantified risk can be based on operational measurements across the production value stream, relating to, e.g., upstream material deliveries, modules, process subsystems, operator efficiency, etc.” DEVARAKONDA [0046] teaches: “In some embodiments, although not pictured in FIG. 1, the implementation interface 160 also or instead generates commands for transmission to one, some, or all of the upstream nodes (e.g., the first-component factory 102, the second-component factory 104, and/or the third-component factory 106), in order to alter one or more operating parameters of the operations there (i.e., in response to determining that the target process should be improved based on the model evaluation […]).” Additionally, DEVARAKONDA [0134] teaches a processor.)
[…] that uses the at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
[…] the model evaluation about each of the at least one upstream process of the supply chain. (DEVARAKONDA [0039] teaches: “This historical data can include data items such as raw materials used, amount of material used, manufacturing steps, configuration of the manufacturing steps, actual duration of the manufacturing steps, quality metrics (i.e., model evaluation), energy expense, labor expense, production capacity of equipment, time taken to transport raw materials or parts and/or any one or more other data items deemed suitable by those of skill in the art for a given implementation. […] Each of them corresponds with a node that is upstream of the final-assembly process 108 in the production value stream (i.e., of the supply chain) that is depicted in FIG. 1. In at least one embodiment, each of the first-component historical data 140, the second-component historical data 142, and the third-component historical data 144 includes historical data pertaining to the corresponding operational metrics of the corresponding factory." DEVARAKONDA [0024] In an embodiment, each upstream machine-learning model is configured to: receive one or more operational metric corresponding to the respective upstream entity (or process step); generate, based on at least the received one or more operational metric corresponding to the respective upstream entity, upstream delay predictions corresponding to the respective upstream entity (i.e., about each of the at least one upstream process of the supply chain);”)
Regarding Claim 3:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 2 as outlined above. MRZIGLOD further teaches:
a parameter selecting unit […] to select a production parameter to be adjusted among the at least one production parameter in the target process, in response to a reception of the improvement request message. (MRZIGLOD [0093] To provide a list of process parameter or derived quantities thereof influencing the variation of the quality attributes of interest […].” MRZIGLOD [0094] teaches: "to define limits for a set of process variables or parameters derived from process variables (design space) (i.e., to select a production parameter to be adjusted among the at least one production parameter in the target process) to keep the product quality in a predefined spectrum […].” Examiner's note: MRZIGLOD teaches providing a list of process parameters influencing the variation of the quality attribute. Then, it defines limits for those process parameters or variables that influence the variation in order to keep the product quality in a predefined spectrum. Under broadest reasonable interpretation, in response to a reception of the improvement request message can be interpreted as defining the limits for process parameters or variables that influence variation in quality in order to keep the product quality in the predefined spectrum. Furthermore, a parameter selecting unit can be interpreted as one of the elements described in MRZIGLOD [0097] configured to perform the steps mentioned above, which are stored in a model module containing the quality-prediction model.)
DEVARAKONDA further teaches: […] that uses the at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
Regarding Claim 4:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 3 as outlined above. MRZIGLOD further teaches:
a quality evaluation estimating unit […] to estimate a quality evaluation of the downstream produced object produced in the downstream process by using each of the produced objects of the target process in a case of adjusting a production parameter selected by the parameter selecting unit. (MRZIGLOD [0095] teaches: "to output setpoints for process variables during different production steps and control the process with calculated set points (i.e., in a case of adjusting a production parameter selected) […].” MRZIGLOD [0096] teaches: "in particular cases to simulate quality results of possible process changes (i.e., to estimate a quality evaluation), e.g. by receiving time series data for a fictive batch in the prediction steps […]." MRZIGLOD [0010] teaches: "Prediction is usually run on a finished batch but may be conducted on a running batch provided real time data have been collected at the time of the prediction." MRZIGLOD [0124] teaches: "As the production process involved different steps and branches, a batch genealogy was built to connect the data points from different process steps, which were linked to a specific product batch (i.e., of the downstream produced object produced in the downstream process of the supply chain using each of the produced objects) at the end of the process (i.e., of the target process)." MRZIGLOD [0126] teaches: "Hence, the insights from the modeling results were used to identify the process steps and raw material properties of particular interest for further process optimization. According to some embodiments, outputs of the method of the invention were used to design experiments to check real impact of identified most influencing parameters." MRZIGLOD [0084] teaches: "As a matter of example, a piece of historical process time series may refer to a batch, wherein intermediate steps were apportioned, or several intermediates steps were joined for further processing." Additionally, MRZIGLOD [0093-0094] teach the process for selecting the production parameter for adjusting, as shown in the rejection for claim 3 above.)
DEVARAKONDA further teaches: […] that uses the at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
Regarding Claim 10:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 1 as outlined above. MRZIGLOD further teaches:
wherein the learning apparatus includes at least one learning apparatus having each of at least one upstream process of the supply chain as a target process; (MRZIGLOD [0097] teaches: “Typically, the method mentioned above is run in a system for product-quality prediction (i.e., learning apparatus includes at least one learning apparatus) comprising elements configured to conduct the method steps mentioned above. In one embodiment the quality-prediction model is stored in a model module.” MRZIGLOD [0049-0051] teaches: "[0049] The quality-prediction model is built by: [0050] a) Receiving a description of a production process as one or more interrelated sub-processes and their respective process parameters, [0051] b) Receiving a quality attribute of the product to be modeled and predicted, wherein said product may be final and/or intermediate product of the production process at stake.” MRZIGLOD [0118] teaches: “Besides potential efficiency gains in the supply chain, the results of the quality prediction modeling were also used to improve process understanding by quantifying the impact different production factors had on process variation.” Examiner’s note: MRZIGLOD [Fig. 6] illustrates one example a production process starting from raw materials, reactions as intermediate process, and finally the final product. Under broadest reasonable interpretation, having each of at least one upstream process can be interpreted as each step preceding the final product in the production process. The target process can be interpreted as the production process at stake.)
[…] a model evaluation which is based on at least one of certainty or complexity of the estimation model; (MRZIGLOD [0085] teaches: "[…] For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model (i.e., a model evaluation) as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis) (i.e., based on at least one of certainty [...] of the estimation model).)
MRZIGLOD is not relied upon for teaching, but DEVARAKONDA teaches: an evaluation apparatus, wherein the evaluation apparatus includes: (Under broadest reasonable interpretation, the evaluation apparatus can be interpreted as the combined processing of the causal-analysis model, the action-and-alert process, and the implementation interface (DEVARAKONDA [0026-0028]).)
a model evaluation receiving unit that uses the at least one processor to receive, from the at least one learning apparatus, a model evaluation […] (DEVARAKONDA [0027] teaches: “In an embodiment, the action-and-alert process (i.e., a model evaluation receiving unit) is configured to: receive the identified one or more causal factor from the causal-analysis machine-learning model and predictions from the various (throughput and delay) models (i.e., to receive a model evaluation from the at least one learning apparatus);” DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., that uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.”)
a process evaluation unit that uses the at least one processor to evaluate the at least one upstream process of the supply chain based on the model evaluation about each of the at least one upstream process of the supply chain. (DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process (i.e., a process evaluation unit) for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” Furthermore, DEVARAKONDA [0043] teaches the causal-analysis machine learning model receiving prediction from each upstream machine learning models (i.e., based on the model evaluation about each of the at least one upstream process) to identify causal factors, which are then received by the action-and-alert process for further processing. The action-and-alert uses the identified causal factors to generate one or more recommended actions (i.e., to evaluate the at least one upstream process) for the production value stream (i.e., supply chain). DEVARAKONDA [0134] teaches the processor in which these methods are implemented (i.e., that uses at least one processor).)
Regarding Claim 11:
The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale.
Regarding Claim 12:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Claim 13:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 12 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 14:
The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. MRZIGLOD further teaches:
A non-transitory computer readable medium having a learning program recorded thereon, wherein the learning program is executed by a computer […] to cause the computer to function as: (MRZIGLOD [claim 16] “A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device, cause the device to:”)
MRZIGLOD is not relied upon for teaching, but DEVARAKONDA teaches: […] computer having at least one processor […] (DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD and DEVARAKONDA before them, to include DEVARAKONDA’s processor and causal-analysis model, the action-and-alert process, and the implementation interface in MRZIGLOD’s production quality prediction process. One would have been motivated to make such a combination in order to “be able to accurately predict throughput of a production value stream, and to understand causes for variability and take corrective (i.e., corrective and/or preventative) action at appropriate times, among other goals.” And “prioritize corrective actions in order to meet financial, operational, and/or other objectives, including objectives related to meeting demand and service levels, customer satisfaction, maintaining of branding and/or market position, measuring the effectiveness of a production value stream, profitability and/or other financial targets, and/or reducing waste and liquidation of material to improve efficiency.” (DEVARAKONDA [0032]).
Regarding Claim 15:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 14 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 2 and 12 and is rejected for similar reasons as claims 2 and 12 using similar teachings and rationale.
Regarding Claim 16:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 3 and 13 and is rejected for similar reasons as claims 3 and 13 using similar teachings and rationale.
Regarding Claim 21:
MRZIGLOD in view of DEVARAKONDA and KANAGAVELU teaches the elements of claim 1 as outlined above. KANAGAVELU further teaches:
wherein each of the at least two manufacturers chooses the at least one production parameter without revealing it to any of the other at least two manufacturers. (KANAGAVELU [pg. 1, Abstract] "The MPCenabled FL framework has been integrated in an IoT platform for smart manufacturing. It enables a set of companies (i.e., each of the at least two manufacturers) to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy, model accuracy vis-a‘-vis traditional machine learning methods and execution efficiency in terms of communication cost and execution time." KANAGAVELU [pg. 2, section I. Introduction' "Secure MPC is a class of cryptography techniques that allow a number of parties to compute a joint function over their sensitive data sets. Only the output of the joint function is disclosed without revealing participants’ private inputs (i.e., without revealing it to any of the other at least two manufacturers)." KANAGAVELU [pg. 7, section C. Model Prediction Accuracy] "[...] federated learning achieves comparable prediction accuracy to centralized learning, which verify the effectiveness of federated learning for smart manufacturing, without compromising data privacy." KANAGAVELU [pg. 7, section C. Model Prediction Accuracy] teaches: “Four motor data sets (refer to Section IV-A) belonging to independent companies were used. We select any three company data sets as training data and the remaining one as testing data, in a round-robin manner. Additive and Shamir secret sharing MPC obtained the same experimental results for SimpleNN and ComplexNN models respectively. As shown in Table II, federated learning achieves comparable prediction accuracy to centralized learning, which verify the effectiveness of federated learning for smart manufacturing, without compromising data privacy.” Examiner’s note: KANAGAVELU [pg. 6, section A. Use case: Fault Detection in Electrical Machines] teaches that the federated learning framework disclosed can be applied to the manufacturing process of motors to enhance the reliability of the process as well as to reduce costs of operation and maintenance. In the experiment, each motor was subjected to thermal aging processes to determine the health of the motor based on the resulting different fault types. Additionally, paragraph [0062] of the specification states: “the production parameter is not limited to the control parameter only, but may be any parameter which relates to four factors of production; "material", "Machine", "Method", and "Man" which are called as "4M". Therefore, under broadest reasonable interpretation, at least two manufacturers chooses at least one production parameter can be interpreted as the selected method of subjecting the motors to thermal aging processing as part of determining fault types for enhancing the reliability of the manufacturing process of the motors by different independent companies using their respective data sets on-premises.)
Claims 5-7 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over MRZIGLOD in view of DEVARAKONDA.
Regarding Claim 5:
MRZIGLOD teaches:
[…] a model evaluation which is based on at least one of certainty or complexity of an estimation model, from a learning apparatus for generating, for each of at least one upstream process of a supply chain, through learning, the estimation model […] (MRZIGLOD [0085] teaches: "In a particular embodiment of the method of the invention a goodness of fit for the training of the data-based model is conducted for each piece of historical process time series. […] For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis) (i.e., a model evaluation which is based on at least one of certainty [...] of an estimation model).” MRZIGLOD [0049-0052] teaches: "[0049] The quality-prediction model is built by (i.e., from a learning apparatus for generating […] the estimation model): [0050] a) Receiving a description of a production process as one or more interrelated sub-processes and their respective process parameters, [0051] b) Receiving a quality attribute of the product to be modeled and predicted, wherein said product may be final and/or intermediate product of the production process at stake (i.e., for each of at least one upstream process […]), [0052] c) Receiving at least one sub-process which is suspected to influence the product quality attribute." MRZIGLOD [0059] teaches: "i) Building quality-prediction model propositions in: [0060] a. Training (i.e., generating […] through learning) of one or more data-based prediction models using the historical time series data and/or the values of the derived quantities of g), preferably using the subset of process parameters and/or derived quantities of step h)." Furthermore, MRZIGLOD [0117-0118] describes that the quality prediction modeling results provide efficiency gains in the supply chain (i.e., of the supply chain), which is the process described in MRZIGLOD [0049].)
[…] for estimating a quality evaluation of a downstream produced object from at least one production parameter, by using a quality evaluation of each downstream produced object produced by using the at least one production parameter about the production of each upstream produced object according to the upstream process of the supply chain, and each upstream produced object of the upstream process of the supply chain; (MRZIGLOD [0026] teaches: "In a preferred embodiment several quality-prediction models are provided, each one calculating one quality attribute." Additionally, MRZIGLOD [0062] teaches: "calculating the influence of each process parameters and/or derived quantities on the values of the quality attribute and performing a goodness of fit analysis;")
However, MRZIGLOD is not relied upon for teaching:
An evaluation apparatus comprising: a model evaluation receiving unit that uses the at least one processor to receive a model evaluation […]
a process evaluation unit that uses the at least one processor to evaluate the at least one upstream process of the supply chain based on the model evaluation about each of the at least one upstream process of the supply chain;
wherein the supply chain includes at least two manufacturers.
However, DEVARAKONDA teaches: An evaluation apparatus comprising: a model evaluation receiving unit that uses the at least one processor to receive a model evaluation […] (DEVARAKONDA [0027] teaches: “In an embodiment, the action-and-alert process (i.e., a model evaluation receiving unit) is configured to: receive the identified one or more causal factor from the causal-analysis machine-learning model and predictions from the various (throughput and delay) models (i.e., to receive a model evaluation);” DEVARAKONDA [0134] teaches: “The machine 1600 may include processors 1602 (i.e., that uses the at least one processor to), memory 1604, and I/O components 1606, which may be configured to communicate with each other via a bus 1644.” A person of ordinary skill in the art would recognize that MRZIGLOD’s methods can be implemented using DEVARAKONDA’s processor. Moreover, under broadest reasonable interpretation, the evaluation apparatus can be interpreted as the combined processing of the causal-analysis model, the action-and-alert process, and the implementation interface (DEVARAKONDA [0026-0028]).)
a process evaluation unit that uses the at least one processor to evaluate the at least one upstream process of the supply chain based on the model evaluation about each of the at least one upstream process of the supply chain; (DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process (i.e., a process evaluation unit) for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” Furthermore, DEVARAKONDA [0043] teaches the causal-analysis machine learning model receiving prediction from each upstream machine learning models (i.e., based on the model evaluation about each of the at least one upstream process) to identify causal factors, which are then received by the action-and-alert process for further processing. The action-and-alert uses the identified causal factors to generate one or more recommended actions (i.e., to evaluate the at least one upstream process) for the production value stream (i.e., supply chain). DEVARAKONDA [0134] teaches the processor in which these methods are implemented (i.e., that uses at least one processor).)
wherein the supply chain includes at least two manufacturers. (DEVARAKONDA [0002] teaches: "Original equipment manufacturers (OEMs) deploy discrete and continuous manufacturing systems and processes to manufacture products that are technically complex and dependent on production value streams, a term that is used broadly in this disclosure to also encompass terms such as supply chains, value chains, and the like." DEVARAKONDA [0022] teaches: "One example embodiment takes the form of a system that includes an upstream machine-learning model corresponding to each of one or more upstream entities in a production value stream (i.e., supply chain that includes at least two manufacturers) of a product.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD and DEVARAKONDA before them, to include DEVARAKONDA’s processor and causal-analysis model, the action-and-alert process, and the implementation interface in MRZIGLOD’s production quality prediction process. One would have been motivated to make such a combination in order to “be able to accurately predict throughput of a production value stream, and to understand causes for variability and take corrective (i.e., corrective and/or preventative) action at appropriate times, among other goals.” And “prioritize corrective actions in order to meet financial, operational, and/or other objectives, including objectives related to meeting demand and service levels, customer satisfaction, maintaining of branding and/or market position, measuring the effectiveness of a production value stream, profitability and/or other financial targets, and/or reducing waste and liquidation of material to improve efficiency.” (DEVARAKONDA [0032]).
Regarding Claim 6:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 5 as outlined above. DEVARAKONDA further teaches:
wherein the process evaluation unit is uses the at least one processor to determine to improve an upstream process of the supply chain, among the at least one upstream process of the supply chain, which is given a model evaluation […] (DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process (i.e., a process evaluation unit) for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” Furthermore, DEVARAKONDA [0043] teaches: “The causal-analysis machine-learning model 124 provides identified causal factor 154 (i.e., which is given a model evaluation) to an action-and-alert process 126. Based at least in part on the identified causal factor 154, the action-and-alert process 126 generates alerts 156 and recommended actions 168, and transmits both to the implementation interface 160, which could include one or more user interfaces for human users (e.g., graphical user interfaces (GUIs), audiovisual interfaces, and/or the like) and/or one or more automated interfaces for carrying out automated processing of the alerts 156 and recommended actions 168.” DEVARAKONDA [0046] teaches: “In some embodiments, although not pictured in FIG. 1, the implementation interface 160 also or instead generates commands for transmission to one, some, or all of the upstream nodes (e.g., the first-component factory 102, the second-component factory 104, and/or the third-component factory 106), in order to alter one or more operating parameters of the operations there (i.e., to improve an upstream process of the supply chain, among the at least one upstream process of the supply chain).” DEVARAKONDA [0134] teaches the processor in which these methods are implemented (i.e., that uses at least one processor).)
MRZIGLOD further teaches: […] a model evaluation that is based on the estimation model whose certainty is greater than a standard value or whose complexity is less than a standard value. (MRZIGLOD [0085] teaches: "In a particular embodiment of the method of the invention a goodness of fit for the training of the data-based model is conducted (i.e., to calculate a model evaluation) for each piece of historical process time series. […] For each piece of time series, a model proposition leading to the best goodness of fit is calculated together with a quantification of the uncertainty of the model as well as a quantification of how much each input is contributing to the output uncertainty (sensitivity analysis) (i.e., based on at least one of certainty [...] of the estimation model). It is preferred that these quantifications are displayed to an expert via a user interface. The expert is required to confirm the validity of the input by means of expert knowledge and/or the quantifications mentioned above. The expert shall decide if the piece of historical process time series shall be considered or rejected for training of the data-based model. In other words, it is preferred that historical process time series (input) is controlled for goodness of fit for training the data-based model. It is most preferred that such control is conducted in a semi-automatic way, that is that expert knowledge is considered in validating the input.” Additionally, MRZIGLOD [0108] teaches: “In a subsequent step, this additionally gained process understanding was used to propose a variety of preventive measures that were implemented and allowed to bring the process back into its normal operating range.” Examiner’s note: Under broadest reasonable interpretation, a model evaluation […] whose certainty is greater than a standard value can be interpreted as the quantification of uncertainty of the model based on each piece of time series, which is validated by expert knowledge received from a database (see MRZIGLOD [0055]) to decide whether or not each piece of time series data shall be considered for training the data-based prediction model. Additionally, MRZIGLOD [0108] teaches that the gained process understanding, such as identifying the most influencing process factors affecting the quality of a product, were used to propose preventive methods to bring the process back into its normal operating range (i.e., standard value).)
Regarding Claim 7:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 6 as outlined above. DEVARAKONDA further teaches:
a message output unit that uses the at least one processor to output an improvement request message for the upstream process of the supply chain determined to be improved. (DEVARAKONDA [0026] teaches: "[0026] In an embodiment, the causal-analysis machine-learning model is configured to […] provide the identified one or more causal factor to the action-and-alert process." Additionally, DEVARAKONDA [0027-0028] teaches: "[0027] In an embodiment, the action-and-alert process is configured to: receive the identified one or more causal factor from the causal-analysis machine-learning model and predictions from the various (throughput and delay) models; generate, based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions (i.e., an improvement request message for the upstream process of the supply chain); and providing the one or both of one or more alerts and one or more recommended actions to the implementation interface. [0028] In an embodiment, the implementation interface is configured to: receive the one or both of one or more alerts and one or more recommended actions from the action-and-alert process; obtain and process response to the one or both of one or more alerts and one or more recommended actions; and provide data reflective of the response to one or more of one or more of the upstream entities, the final-assembly process, one or more of the upstream machine-learning models, and the final-assembly machine-learning model." DEVARAKONDA [0029] teaches: “The method also includes providing, by the causal-analysis machine-learning model, the identified one or more causal factors to an action-and-alert process for the production value stream (i.e., of the supply chain). The method further includes generating, by the action-and-alert process, and based on at least the identified one or more causal factors, one or both of one or more alerts and one or more recommended actions.” DEVARAKONDA [0045] teaches: “Moreover, the recommended actions 168 in some embodiments are prioritized based on quantified risk (e.g., based on minimizing a cost function, maximizing a reward function, and/or the like, where such function could take into account upstream materials, parts, components, process steps, and/or the like). The quantified risk can be based on operational measurements across the production value stream, relating to, e.g., upstream material deliveries, modules, process subsystems, operator efficiency, etc.” DEVARAKONDA [0046] teaches: “In some embodiments, although not pictured in FIG. 1, the implementation interface 160 also or instead generates commands for transmission to one, some, or all of the upstream nodes (e.g., the first-component factory 102, the second-component factory 104, and/or the third-component factory 106), in order to alter one or more operating parameters of the operations there (i.e., to be determined to be improved).” Additionally, DEVARAKONDA [0134] teaches a processor.)
Regarding Claim 17:
The claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Claim 18:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 17 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding Claim 19:
The claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale. MRZIGLOD is not relied upon for teaching, but DEVARAKONDA also teaches:
A non-transitory computer readable medium having an evaluation program recorded thereon, wherein the evaluation program is executed by a computer including at least one processor to cause the computer to function as: (DEVARAKONDA [0030] teaches “Another embodiment takes the form of a system that includes a communication interface, a hardware processor, and data storage that contains instructions executable by the hardware processor for carrying out the functions listed in the preceding paragraph.” Examiner’s note: Under broadest reasonable interpretation, the evaluation program can be interpreted as the combined processing of the causal-analysis model, the action-and-alert process, and the implementation interface, (DEVARAKONDA [0026-0028]).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD and DEVARAKONDA before them, to include DEVARAKONDA’s hardware processor and causal-analysis model, the action-and-alert process, and the implementation interface in MRZIGLOD’s production quality prediction process. One would have been motivated to make such a combination in order to “be able to accurately predict throughput of a production value stream, and to understand causes for variability and take corrective (i.e., corrective and/or preventative) action at appropriate times, among other goals.” And “prioritize corrective actions in order to meet financial, operational, and/or other objectives, including objectives related to meeting demand and service levels, customer satisfaction, maintaining of branding and/or market position, measuring the effectiveness of a production value stream, profitability and/or other financial targets, and/or reducing waste and liquidation of material to improve efficiency.” (DEVARAKONDA [0032]).
Regarding Claim 20:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 19 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 6 and 18 and is rejected for similar reasons as claims 6 and 18 using similar teachings and rationale.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over MRZIGLOD in view of DEVARAKONDA and KANAGAVELU as applied to claim 5 above, and further in view of TAMAKI (US 20060047454 A1), hereafter TAMAKI.
Regarding Claim 8:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 5 as outlined above. DEVARAKONDA further teaches:
an association acquiring unit that uses the at least one processor to acquire, for each process of the at least one upstream process, an association between each produced object supplied from a process on the upstream side and each produced object of the process which is supplied to the downstream side; (DEVARAKONDA [0048] teaches: "The present systems and methods provide an interconnected system of intelligence across a production value stream. The outputs and predictions from upstream modules are synchronized with the outputs and inputs in downstream modules. […] The present systems and methods synchronize inputs and outputs across the entire landscape of the production value stream." Additionally, DEVARAKONDA [0134] teaches a processor.)
a correspondence sending unit that uses the at least one processor to send the correspondence to the learning apparatus. (DEVARAKONDA [0047] teaches: "Furthermore, although not pictured in FIG. 1, in some embodiments, the implementation interface 160 generates and transmits implementation feedback to one, some, or all of the upstream machine-learning models (e.g., the first-component machine-learning model 116, the second-component machine-learning model 118, and/or the third-component machine-learning model 120), to refine and improve their respective performance and learn adaptively. In various embodiments, the implementation commands 162 and the implementation feedback 164 reflect information such as one or more of the alerts 156 that were acted upon (or not acted upon) and/or one or more of the recommended actions 168 that were taken (or not taken) by one or more human users and/or one or more automated processes. In at least one embodiment, the implementation feedback 164 is associated with tracking and storing user and/or system actions in real time and measuring associated costs and/or rewards across a production value stream". Additionally, DEVARAKONDA [0022] teaches: "One example embodiment takes the form of a system that includes an upstream machine-learning model corresponding to each of one or more upstream entities in a production value stream of a product; an end-production machine-learning model that learns and represents and end-to-end production process in a value stream for complex manufactured products;" Examiner's note: under broadest reasonable interpretation, the "model evaluation sending unit" can be interpreted as the implementation interface 160, which generates and transmits implementation feedback to upstream machine-learning models (i.e., upstream process) to refine and improve their respective performance (i.e., evaluate at least one upstream process), the feedback or recommendation to improve being based on an upstream machine-learning model corresponding to each upstream entity in the production value stream.)
However, MRZIGLOD in view of DEVARAKONDA is not relied upon for teaching, but TAMAKI teaches: a correspondence generating unit that uses the at least one processor to generate a correspondence between each produced object of the at least one upstream process and the quality evaluation of each downstream produced object by using each association acquired by the association acquiring unit; and (TAMAKI [0024] teaches: "[...] based on the statistical mutual correlation magnitude and the sequence information thus obtained, determining the connecting structure information between the manufacturing processes, and automatically determining the manufacturing process providing the cause of product quality variation from the variation causing process candidates." TAMAKI [0108] teaches: "The causation analysis apparatus of quality variation 140 includes a correlation analysis module of quality variation 141 and a causation analysis module of quality variation 142 not only to extract the candidates for the cause of product quality variation but also to trace the fundamental cause." TAMAKI [0109] teaches: "The causation analysis apparatus of quality variation 140 is supplied with the product quality history data from the quality history data collection apparatus 116, the manufacturing history data from the manufacturing history data collection apparatuses 112, 113, 114, the individual identification information from the manufacturing line control apparatus 120 and the manufacturing sequence information from the manufacturing sequence information management apparatus 130, and outputs a process constituting the fundamental cause of product quality variation." TAMAKI [0154] teaches: "[0154] Next, the causation analysis module of quality variation 142, to determine the causation direction of the undirected graph showing the inter-process variation propagation, converts the undirected graph of variation propagation into a directed graph indicating the causation direction based on the manufacturing sequence information. In sequence to covert the undirected graph into a directed graph of the quality variation causal network model, the causation analysis module of quality variation 142 acquires the manufacturing sequence information and automatically determines the direction of the graph by translating the time priority (which process is executed before which process) between the process.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD, DEVARAKONDA, and TAMAKI before them, to include TAMAKI's manufacturing sequence information into MRZIGLOD and DEVARAKONDA’s production quality prediction process. One would have been motivated to make such a combination in order to identify the product that has caused a change in the quality of a produced object (i.e., to maintain high product quality production). (TAMAKI [0019]))
Regarding Claim 9:
MRZIGLOD in view of DEVARAKONDA teaches the elements of claim 5 as outlined above. However, MRZIGLOD in view of DEVARAKONDA is not relied upon for teaching, but TAMAKI teaches:
a start determination unit that uses the at least one processor to determine whether to start an evaluation of the at least one upstream process by the process evaluation unit, based on a quality evaluation of at least one downstream produced object. (TAMAKI [0134] teaches: "[...] the correlation analysis module of quality variation (141 in FIG. 1) of the causation analysis apparatus of quality variation (140 in FIG. 1) selects the candidates for the process causing the quality variation from the manufacturing history." TAMAKI [0137] teaches: "Based on the relative magnitude of the correlation magnitudes described above, the correlation analysis module of quality variation 141 extracts the edge 422 from process B432 to the product quality 411 and the edge 424 from process D434 to the product quality 411 out of the four edges 421, 422, 423, 424 of the quality variation correlation network model as candidates for the causation connection to the quality variation from the process causing the quality variation". Furthermore, Fig. 10 shows a flowchart in which if there is no correlation, there is no need to identify a quality variation in the production process.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of MRZIGLOD, DEVARAKONDA, and TAMAKI before them, to include TAMAKI's manufacturing sequence information into MRZIGLOD and DEVARAKONDA’s production quality prediction process. One would have been motivated to make such a combination in order to identify the product that has caused a change in the quality of a produced object (i.e., to maintain high product quality production). (TAMAKI [0019]))
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alvaro S Laham Bauzo whose telephone number is (571)272-5650. The examiner can normally be reached Mon-Fri 7:30 AM - 11:00 AM | 1:00 PM - 5:30 PM ET.
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/A.S.L./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146