DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/26/2025 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 6/26/2025.
Claims 1, 4-5, 8-10, and 19 have been amended and claims 6-7, 11-12, 14, and 17-18 have been cancelled.
Claims 1-5, 8-10, 13, 15-16, and 19 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Please see PTAB decision dated 4/30/2025. The Examiner asserts that merely amending in the words automatic production at a factory is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-5, 8-10, 13, 15-16, and 19, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims do not recite an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that transmitting an alert and recommended actions to a user interface so a human user can make a decision and implementing the human input merely adds the words apply it with the judicial exception (See MPEP 2106.05). The Applicant’s claims are merely using a general purpose computer to implement the abstract idea (See MPEP 2106.05), where there is no actual automation because it is still requiring input from human users to implement the decisions. The Examiner asserts merely automating tasks that are normally done by a human using a computer does not make the claims eligible. Applicant’s arguments are not persuasive.
The Examiner asserts the claimed machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. The Examiner asserts that the claimed machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). In addition, the machine learning models are so generically recited that they merely amount to software being run on a computer. Generically inputting the word automated into the claims does not make the claims eligible. The Examiner strongly recommends reviewing MPEP 2106.05 and the 2024 AI SME Update. Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible under 2B. The Examiner respectfully disagrees. The Examiner asserts the Applicant does not make any reference to the actual claim limitations, but rather merely makes the allegation. Please see PTAB decision dated 4/30/2025. Applicant’s arguments are not persuasive.
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 1-5, 8-10, 13, 15-16, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-5, 8-10, 13, 15-16, and 19 are directed toward a process, a product, and a system; which are statutory categories of invention. Additionally (Step 2A Prong One), independent claim 1 is directed toward a system for predicting capacity for industrial manufacturing of a product at a factory, the system comprising: one or more upstream prediction machine-learning models associated with the product manufactured at the factory and corresponding to each of one or more upstream entities in a production value stream of a product, each upstream prediction machine-learning model having been trained to generate upstream-delay-prediction data based on historical data associated with the product produced at the factory and corresponding to one or more operational metrics associated with the corresponding upstream entity; a final-assembly machine-learning model corresponding to a final-assembly process in the production at the factory of the product, the final-assembly machine-learning model having been trained to generate product-throughput-prediction data based on operational metrics associated with the final-assembly process at the factory and the upstream-delay-prediction data from each of the one or more upstream prediction machine-learning models; a causal-analysis machine-learning model for the production of the product at the factory, wherein the system is configured to train a neural network using training data to obtain the causal-analysis machine-learning model to identify one or more causal factors for one or both of the upstream-delay-prediction data and the product-throughput-prediction data, the training data comprising values of production delays for a production stream, wherein training the one or more upstream prediction machine-learning models, the final-assembly machine-learning model, and the causal-analysis machine-learning model comprises developing an algorithm over several epochs by varying the values of one or more variables affecting inputs to more closely map to a desired result, wherein a number of epochs of the training are set as a number of trials, a fixed time, or a computing budget, wherein each model includes a neural network comprising a series of neurons; an action-and-alert process for the production value stream of the product at the factory; and an implementation automated interface for the production value stream of the product at the factory, wherein: each of the one or more upstream prediction machine-learning models is configured to: receive one or more operational metrics associated with the corresponding upstream entity; generate, based on at least the received one or more operational metrics associated with the corresponding upstream entity, the upstream-delay-prediction data for the corresponding upstream entity; and provide the upstream-delay-prediction data to both the final-assembly machine-learning model and the causal-analysis machine- learning model; the final-assembly machine-learning model is configured to: receive one or more operational metrics associated with the final-assembly process; receive the upstream-delay-prediction data from each of the one or more upstream prediction machine-learning models; generate, based on at least the received one or more operational metrics associated with the final-assembly process and the upstream-delay-prediction data from each of the one or more upstream prediction machine-learning models, the product-throughput-prediction data for the product; and provide the product-throughput-prediction data to the causal-analysis machine-learning model; the causal-analysis machine-learning model is configured to: receive the upstream-delay-prediction data from each of the one or more upstream prediction machine-learning models; receive the product-throughput-prediction data from the final-assembly machine-learning model; identify, based on at least the received upstream- delay-prediction data from each of the one or more upstream machine- learning models and the product-throughput-prediction data from the final-assembly machine-learning model, the one or more causal factors for one or both of the upstream-delay-prediction data and the product-throughput- prediction data at the factory; and provide the identified one or more causal factors to the action-and-alert process; the action-and-alert process is configured to: receive the identified one or more causal factors from the causal-analysis machine-learning model; generate, based on at least the identified one or more causal factors, one or both of one or more alerts and one or more actions; and providing the one or both of one or more alerts and one or more actions to the automated implementation interface; the automated implementation interface is configured to: receive the one or both of one or more alerts and one or more actions from the action-and-alert process; generate, based on the one or both of one or more alerts and one or more actions with implementation commands for manufacturing the product at the factory, and transmit the implementation commands to the final-assembly process for automated processing of the one or more actions to alter one or more operating parameters of the final-assembly process at the factory (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are using trained machine learning models to make predictions on what will happen while manufacturing products to make recommendations so human users can make changes based on feedback and displayed recommendations to ensure the production system runs appropriately, where analyzing variables on a manufacturing system for making recommendations is a commercial interaction. The Applicant’s claimed limitations are merely production data to make recommendations, which is directed towards the abstract idea of Organizing Human Activity.
Independent claims 8 and 19 are directed toward a non-transitory computer-readable storage medium for predicting capacity for industrial manufacturing of a product at a factory, the computer- readable storage medium including instructions that when executed by a computer, cause the computer to: train a neural network using training data to obtain acausal-analysis machine-learning model, the training data comprising values of production delays for a production stream, the training developing an algorithm over several epochs by varying the values of one or more variables affecting inputs to more closely map to a desired result, wherein a number of epochs of the training are set as a number of trials, a fixed time, or a computing budget, wherein each model includes a neural network comprising a series of neurons; receive, by the causal-analysis machine-learning model for a production value stream of a product, upstream-delay-prediction data from each of a plurality of upstream prediction machine-learning models associated with the product manufactured at the factory, each upstream prediction machine-learning model corresponding to a respective upstream entity in a plurality of upstream entities in the production value stream each upstream prediction machine-learning model having been trained to generate the upstream-delay-prediction data based on historical data associated with the product produced at the factory and corresponding to one or more operational metrics associated with the corresponding upstream entity; receive, by the causal-analysis machine-learning model, product-throughput-prediction data from a final-assembly machine-learning model for the production value stream, the final-assembly machine-learning model having been trained to generate the product-throughput-prediction data based on operational metrics associated with a final-assembly process at the factory and the upstream-delay- prediction data from each of the one or more upstream prediction machine-learning models; identify, by the causal-analysis machine-learning model, and based on at least the received upstream-delay-prediction data and the product-throughput-prediction data, one or more causal factors associated with the manufacturing of the product at the factory for one or both of the upstream-delay- prediction data and the product-throughput-prediction data, the causal-analysis machine-learning model having been trained to identify the one or more causal factors based on the upstream-delay-prediction data and the product-throughput-prediction data; provide, 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; generate, 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 actions; provide, by the action-and-alert process, the one or both of one or more alerts and one or more actions to an automated implementation interface for the production value stream, generate, by the automated implementation interface based on the one or both of one or more alerts and one or more actions, implementation commands for manufacturing the product at the factory; and transmit the implementation commands from the automated implementation interface to the final-assembly process for automated processing of the one or more actions to alter one or more operating parameters of the final-assembly process at the factory (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are using trained machine learning models to make predictions on what will happen while manufacturing products to make recommendations so human users can make changes based on feedback and displayed recommendations to ensure the production system runs appropriately, where analyzing variables on a manufacturing system for making recommendations is a commercial interaction. The Applicant’s claimed limitations are merely production data to make recommendations, which is directed towards the abstract idea of Organizing Human Activity.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the above “a system; the system comprising: one or more upstream prediction machine-learning models; each upstream prediction machine-learning model having been trained; a final-assembly machine-learning model; the final-assembly machine-learning model having been trained; a causal-analysis machine-learning model; wherein the system is configured to train a neural network using training data to obtain the causal-analysis machine-learning model; includes a neural network comprising a series of neurons; an implementation automated interface; receive one or more operational metrics; providing the one or both of one or more alerts and one or more actions to the automated implementation interface; the automated implementation interface is configured to: receive the one or both of one or more alerts and one or more actions from the action-and-alert process; transmit the implementation commands to the final-assembly process for automated processing of the one or more actions to alter one or more operating parameters of the final-assembly process at the factory” (claim 1) and “a non-transitory computer-readable storage medium; the computer- readable storage medium including instructions that when executed by a computer, cause the computer to: train a neural network using training data to obtain acausal-analysis machine-learning model; includes a neural network comprising a series of neurons; receive, by the causal-analysis machine-learning model for a production value stream of a product; each upstream prediction machine-learning model having been trained; the final-assembly machine-learning model having been trained; the causal-analysis machine-learning model having been trained; provide, by the action-and-alert process, the one or both of one or more alerts and one or more actions to an automated implementation interface for the production value stream; and transmit the implementation commands from the automated implementation interface to the final-assembly process for automated processing of the one or more actions” (claims 8 and 19) steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception.
Also, the claimed “a system, upstream machine learning model, final assembly machine learning model, product, causal analysis machine learning model, automated implementation interface, a user interface, an automated interface, upstream entity, implementation interface, and non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to; factory; neural network comprising a series of neurons” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
The Examiner asserts the claimed machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05).
In addition, dependent claims 2-5, 9-10, 13, 15-16 further narrow the abstract idea and dependent claims 9-10 additionally recite “receiving one or more operational metrics” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
The claimed “a system, upstream machine learning model, final assembly machine learning model, product, causal analysis machine learning model, automated implementation interface, a user interface, an automated interface, upstream entity, implementation interface, and non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to; factory; neural network comprising a series of neurons” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, Method; System; and Product claims 1-5, 8-10, 13, 15-16, and 19 recite a system, upstream machine learning model, final assembly machine learning model, product, causal analysis machine learning model, automated implementation interface, a user interface, an automated interface, upstream entity, implementation interface, and non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to; factory; neural network comprising a series of neurons; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0133-0136 and Figures 16-17. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment.
Also, the above “a system; the system comprising: one or more upstream prediction machine-learning models; each upstream prediction machine-learning model having been trained; a final-assembly machine-learning model; the final-assembly machine-learning model having been trained; a causal-analysis machine-learning model; wherein the system is configured to train a neural network using training data to obtain the causal-analysis machine-learning model; includes a neural network comprising a series of neurons; an implementation automated interface; receive one or more operational metrics; providing the one or both of one or more alerts and one or more actions to the automated implementation interface; the automated implementation interface is configured to: receive the one or both of one or more alerts and one or more actions from the action-and-alert process; transmit the implementation commands to the final-assembly process for automated processing of the one or more actions to alter one or more operating parameters of the final-assembly process at the factory” (claim 1) and “a non-transitory computer-readable storage medium; the computer- readable storage medium including instructions that when executed by a computer, cause the computer to: train a neural network using training data to obtain acausal-analysis machine-learning model; includes a neural network comprising a series of neurons; receive, by the causal-analysis machine-learning model for a production value stream of a product; each upstream prediction machine-learning model having been trained; the final-assembly machine-learning model having been trained; the causal-analysis machine-learning model having been trained; provide, by the action-and-alert process, the one or both of one or more alerts and one or more actions to an automated implementation interface for the production value stream; and transmit the implementation commands from the automated implementation interface to the final-assembly process for automated processing of the one or more actions” (claims 8 and 19) steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-5, 9-10, 13, 15-16 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 9-10 additionally recite “receiving one or more operational metrics” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-5, 8-10, 13, 15-16, and 19 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1, 8 and 19 disclose a system for automating production intelligence across value streams using interconnected machine learning models, that actually recites how the machine learning models interact with each other.
Regarding a possible 103 rejection: The closest prior art of record is:
Cantor et al. (US 2013/0325763 A1) – which discloses predicting likelihood of on time product delivery through different machine learning analysis.
Klose (US 2015/0242263 A1) – which discloses a data flow alert system for monitoring throughput of tasks to determine if product delivery is on time.
Szeto et al. (US 2017/0124487 A1) – which discloses machine learning models for deployment analysis.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1-5, 8-10, 13, 15-16, and 19, such as the specific details as to how the different machine learning models interact with one another. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a non-transitory computer-readable storage medium for predicting capacity for industrial manufacturing of a product at a factory, the computer- readable storage medium including instructions that when executed by a computer, cause the computer to: train a neural network using training data to obtain acausal-analysis machine-learning model, the training data comprising values of production delays for a production stream, the training developing an algorithm over several epochs by varying the values of one or more variables affecting inputs to more closely map to a desired result, wherein a number of epochs of the training are set as a number of trials, a fixed time, or a computing budget, wherein each model includes a neural network comprising a series of neurons; receive, by the causal-analysis machine-learning model for a production value stream of a product, upstream-delay-prediction data from each of a plurality of upstream prediction machine-learning models associated with the product manufactured at the factory, each upstream prediction machine-learning model corresponding to a respective upstream entity in a plurality of upstream entities in the production value stream each upstream prediction machine-learning model having been trained to generate the upstream-delay-prediction data based on historical data associated with the product produced at the factory and corresponding to one or more operational metrics associated with the corresponding upstream entity; receive, by the causal-analysis machine-learning model, product-throughput-prediction data from a final-assembly machine-learning model for the production value stream, the final-assembly machine-learning model having been trained to generate the product-throughput-prediction data based on operational metrics associated with a final-assembly process at the factory and the upstream-delay- prediction data from each of the one or more upstream prediction machine-learning models; identify, by the causal-analysis machine-learning model, and based on at least the received upstream-delay-prediction data and the product-throughput-prediction data, one or more causal factors associated with the manufacturing of the product at the factory for one or both of the upstream-delay- prediction data and the product-throughput-prediction data, the causal-analysis machine-learning model having been trained to identify the one or more causal factors based on the upstream-delay-prediction data and the product-throughput-prediction data; provide, 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; generate, 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 actions; provide, by the action-and-alert process, the one or both of one or more alerts and one or more actions to an automated implementation interface for the production value stream, generate, by the automated implementation interface based on the one or both of one or more alerts and one or more actions, implementation commands for manufacturing the product at the factory; and transmit the implementation commands from the automated implementation interface to the final-assembly process for automated processing of the one or more actions to alter one or more operating parameters of the final-assembly process at the factory (as required by the independent claims)”, thus rendering claims 1-5, 8-10, 13, 15-16, and 19 as allowable over the prior art.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625