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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/373,459 received on 02/20/2026.
In accordance with Applicant’s amendment, claim 5 has been canceled. Claims 1-4, and 6-11 are amended, currently pending, and have been examined.
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119 or 35 U.S.C. 120.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 03/16/2026 has been reviewed and signed.
Response to Amendment
The amendment filed on 02/20/2026 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of amendment, the specification objection previously applied to the title in the specification is withdrawn.
Upon review of amendment, the §112(f) claim interpretation previously applied is withdrawn.
Upon review of amendment, the §112(b) rejections previously applied are withdrawn.
Upon review of amendment, the §112(a) rejections previously applied are withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the original claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 17): “The operations performed by the apparatus of Applicant's claim 1 could not practically be performed in the human mind or with a pen a paper. Rather, the human mind is not equipped to perform Applicant's claimed operations of analyzing production losses of a plurality of machines operating within a manufacturing area, selecting the correct improvement measure based on the analysis, and applying the selected improvement measure to at least one of the machines.”. In response, Examiner respectfully disagrees and notes that one of ordinary skill in the art would be able to reasonably perform the steps in the limitations, as currently recited. For example, one of ordinary skill in the art could reasonably determine production loses based on production capacity if for example, a manufacturing facility is shut down for a period of time (e.g., 2 hours). Said person would be able to determine the production loss of every production line in the facility, including every machine in the production lines with the help of pen and paper. One of ordinary skill in the art could also determine the root cause(s) of the shutdown via observation, identify various potential fixes via evaluation, and select one in specific to implement via judgement. Applicant is reminded that in most cases, “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.”); Alice, 573 U.S. at 223 (“Thus, if a patent’s recitation of a computer amounts to a mere instruction to implement an abstract idea on a computer, that addition cannot impart patent eligibility.”).
Applicant argues (Remarks at pg. 18): “analyzing operations of a plurality of machines as described in Applicant's claim 1 is not practically performed in the human mind. Furthermore, "[t]he mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." See Kim Memo at 2 (emphasis added).”. In response, Examiner reminds Applicant that as stated in the memorandum titled Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, the following is also communicated: “This memorandum is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance.”. Therefore, the analysis and considerations in the Office Action dated 01/28/2026 are consistent with the August 4, 2025 memorandum.
Applicant argues (Remarks at pgs. 22-23): “The additional elements of Applicant's claim 1 included above are significant at least because the claim includes a specific apparatus that is configured to analyze the operation of a plurality of machines in a predetermined manufacturing area and determine a production loss. Further, Applicant's apparatus is able to identify a production loss of each of the machines and combine the production losses of machines that share resources to generate loss occurrence factor information. Applicant's apparatus is further configured to select an improvement measure corresponding to the production loss information and the loss occurrence factor information, and automatically apply the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the manufacturing area. Accordingly, Applicant's claims 1, 10, and 11 include a specific technique and system for determining a production loss among a plurality of machines and automatically applying a selected improvement measure for improving the productivity of the machines in the manufacturing area. Therefore, similar to the claims in BASCOM, Applicant's claims 1, 10, and 11, at least as an ordered combination, include a non-conventional and non-generic arrangement of features comprising an inventive concept. As such, Applicant's claims are not directed to routine, conventional, or well-known activities. Consequently, even if Applicant's independent claims 1, 10, and 11 include an abstract idea, the claims include elements that singly and as an ordered combination amount to significantly more than the mere abstract idea, and therefore, Applicant's claims are patent-eligible for these reasons as well.”. In response, Examiner respectfully disagrees and notes that the additional elements recited in the claim limitations fail to add significantly more to the judicial exception for the following reasons (see §101 rejections section below for further details):
Regarding the computing additional elements, namely memory, processor, storage device, shared resource from the independent claims, and display unit from claims 4/9, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing) and does not amount to significantly more than the abstract idea itself. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea.
Regarding the limitations: and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, and identifying a production loss of each of the machines using the 4M data information and the production loss analysis model to generate production loss information, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea.
With respect to the limitation for and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area, this limitation fails to integrate the abstract idea into a practical application because it amounts to post-solution extra-solution activity (i.e., insignificant application), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g).
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the original claims are primarily raised in support of the amendment, which is believed to be fully addressed in the updated §103 rejections below.
Claim Objections
Claim 9 is objected to because of the following informalities: Claim 9 depends from claim 5, which has been cancelled. Appropriate correction is required. For the purpose of compact prosecution, Examiner is interpreting claim 9 as being dependent from claim 1.
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 1-4, and 6-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-4, and 6-9 is/are directed to an apparatus (i.e., Manufacture), claim(s) 10 is/are directed to a system (i.e., Machine), and claim(s) 11 is/are directed to a method (i.e., Process). Therefore, the claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
As drafted, the limitations recited by claims 1-4, and 6-11 fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1 recites a production information processing apparatus comprising a processor and a storage device with the following limitations:
wherein the storage device stores: a shared resource that is a resource shared among a plurality of machines belonging to a predetermined manufacturing area; 4M (Machine, Man, Material, and Method) data information that is time- series data of operating states per unit time of the machines and the resource related to the machines; and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, (But for the additional elements – underlined – recited in this claim limitation, the step for “defines a criterion for determining a production loss” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and wherein the processor is configured to execute operations including: identifying a production loss of each of the machines using the 4M data information and the production loss analysis model to generate production loss information; (But for the additional elements – underlined – recited in this claim limitation, the steps for “identifying a production loss”, and “to generate production loss information” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
combining the production loss information of one of the machines and 4M data information of another of the machines that is different from the machine and shares the resource, using the production loss information and the shared resource, to classify an occurrence factor of the production loss and generate loss occurrence factor information; (But for the additional elements – underlined – recited in this claim limitation, the steps for “combining the production loss”, “classify an occurrence factor”, and “generate loss occurrence factor information” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
selecting an improvement measure corresponding to the production loss information and the loss occurrence factor information from among a plurality of possible improvement measures stored by the storage device in association with the production loss and the occurrence factor of the loss; (But for the additional elements – underlined – recited in this claim limitation, the steps for “selecting an improvement measure corresponding to the production loss information and the loss occurrence factor information from among a plurality of possible improvement measures” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area. (This limitation is an additional element to be analyzed in Step 2A2 and Step 2B).
Independent claims 10 and 11 recite a system and a method with limitations that are substantially similar to the limitations recited by independent claim 1, therefore, the same analysis applies. The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claim(s) is/are:
From claims 1/10/11: a storage device, a processor, a shared resource, the production loss analysis model, and automatically applying the selected improvement measure to at least one machine of the plurality of machines.
Dependent claims 2-9 further narrow the abstract idea and introduce the following additional elements for consideration under said steps:
From claims 4/9: a display.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely memory, processor, storage device, shared resource from the independent claims, and display unit from claims 4/9, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
With respect to the limitations and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, and identifying a production loss of each of the machines using the 4M data information and the production loss analysis model to generate production loss information, these limitations fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitation for and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area, this limitation fails to integrate the abstract idea into a practical application because it amounts to post-solution extra-solution activity (i.e., insignificant application).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely memory, processor, storage device, shared resource from the independent claims, and display unit from claims 4/9, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing) and does not amount to significantly more than the abstract idea itself. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the limitations: and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, and identifying a production loss of each of the machines using the 4M data information and the production loss analysis model to generate production loss information, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitation for and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area, this limitation fails to integrate the abstract idea into a practical application because it amounts to post-solution extra-solution activity (i.e., insignificant application), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-3, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Willison et al. (US 20210304036 A1, hereinafter “Willison”), in view of Sage et al. (US 20180107737 A1, hereinafter “Sage”), in further view of Isles et al. (US 20080291486 A1, hereinafter “Isles”).
Regarding claims 1/10/11: Willison teaches an apparatus comprising a processor and a storage device ([0106] The modeling system 110 includes a processor 112, a data storage 114, and a communication component 116. The modeling system 110 can be implemented with more than one computer server distributed over a wide geographic area and connected via the network 102. The processor 112, the data storage 114 and the communication component 116 may be combined into a fewer number of components or may be separated into further components.; [0108] For example, the communication component 116 can receive cell data generated by the manufacturing assembly line 120 and store the cell data in the data storage 114 or the external data storage 108.), a system comprising a processing unit and a storage unit ([0106] The modeling system 110 includes a processor 112, a data storage 114.; [0108] For example, the communication component 116 can receive cell data generated by the manufacturing assembly line 120 and store the cell data in the data storage 114 or the external data storage 108.) and a method ([0004] The various embodiments described herein generally relate to systems and methods for modeling a manufacturing assembly line.), with the following limitations:
4M (Machine, Man, Material, and Method) data information that is time- series data of operating states per unit time of the machines and the resource related to the machines; ([0126] the data may include subcomponent assembly information, environmental factors, supplier information, material composition data, facility information, ERP and transaction information, shipping data, component packaging data, and the like.; [0123] The device data may also include data related to the device configuration for one or more devices 124, defining various parameters of settings for the processing completed by those devices 124. [0124] For example, the line production data may define the number of defective products produced by the line, a particular cell 122, or a particular device 124, in a given period of time.; [0128] a human operator may inspect one or more portions of the manufacturing assembly line 120, and electronically input the data into the computer device 104, the manufacturing assembly line 120, or the modeling system 110.);
and a production loss analysis model that defines a criterion for determining a production loss from a combination of the operating states per unit time in the 4M data information, ([0005] train the predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.; [0006] the line production data may be representative of a defect level of the active manufacturing assembly line, the one or more production associations may include one or more associations between the cell data of each cell and the defect level of the active manufacturing assembly line, and the predictive model may be trained to predict the defect level of the manufacturing assembly line.; [0032] determine an efficiency score for each cell configuration by applying the extracted feature data to a predictive model generated for predicting a production level of the manufacturing assembly line; determine at least one target cell configuration from the plurality of cell configurations based on the efficiency score for each cell configuration; and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.; [0040] determining the efficiency score for each cell configuration may involve: determining a defect level of the manufacturing assembly line when that cell configuration is applied to a corresponding cell; and determining the efficiency score for that cell configuration based on the defect level.);
and wherein the processor is configured to execute operations including: identifying a production loss of each of the machines using the 4M data information and the production loss analysis model to generate production loss information; ([0006] the line production data may be representative of a defect level of the active manufacturing assembly line, the one or more production associations may include one or more associations between the cell data of each cell and the defect level of the active manufacturing assembly line, and the predictive model may be trained to predict the defect level of the manufacturing assembly line.; [0124] The line production data may also include data related to the defect level or quality level of the manufacturing assembly line 120 (or a portion thereof). For example, the line production data may define the number of defective products produced by the line, a particular cell 122, or a particular device 124, in a given period of time.);
using the production loss information and the shared resource, to classify an occurrence factor of the production loss and generate loss occurrence factor information; ([0004] The disclosed systems and methods may relate to training models for predicting one or more properties of the manufacturing assembly line, such as a production level.; [0009] identifying the one or more statistically significant production associations may involve: determining a probability value of each production association in the plurality of production associations being unassociated; and identifying a production association as the one of the one or more statistically significant associations if the probability value of that production association is less than or equal to a predetermined significance level.; [0105] Reference is first made to FIG. 1, which illustrates an example block diagram 100 of a modeling system 110 in communication with a manufacturing assembly line 120, an external data storage 108, and a computing device 104 via a network 102. Although only one manufacturing assembly line 120 and one computing device 104 are shown in FIG. 1, the modeling system 110 can be in communication with a greater number of manufacturing assembly lines 120 and/or computing devices 104. The modeling system 110 can communicate with the manufacturing assembly line(s) 120 and computing device(s) 104 over a wide geographic area via the network 102.)
Willison doesn’t teach:
wherein the storage device stores: a shared resource that is a resource shared among a plurality of machines belonging to a predetermined manufacturing area;
combining the production loss information of one of the machines and 4M data information of another of the machines that is different from the machine and shares the resource,
selecting an improvement measure corresponding to the production loss information and the loss occurrence factor information from among a plurality of possible improvement measures stored by the storage device in association with the production loss and the occurrence factor of the loss;
and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area.
Sage teaches:
wherein the storage device stores: a shared resource that is a resource shared among a plurality of machines belonging to a predetermined manufacturing area; ([0034] Other records 132 include other information concerning the downtime of the machine. For example, the other records 132 may be information concerning the product being made by a machine over time, the shift using the machine, the crew or operator at the machine, the equipment schedule (whether the machine is in production or not) and the calendar date. The other records 132 may be organized as any type of data structure such as a record or file. Information in the records 132 may alternatively be already included in the downtime records 130 so that the other records 132 are not needed. The downtime records 130 and other data records 132 are stored in each memory 127.; [0031] it may look at the efficiency of a particular operator at each node, sum the efficiencies, and divide by the number of nodes to form an answer.; [0033] each of the nodes 122 may store records for a specific machine.);
combining the production loss information of one of the machines and 4M data information of another of the machines that is different from the machine and shares the resource, ([0031] The aggregation circuit 126 obtains answers from each of the nodes 122 after it forwards a query to the nodes 122. For example, a query might ask the efficiency of a particular operator or the efficiency of a product line. The aggregation circuit 126 takes the individual answers it receives from the individual nodes 122 and forms an answer. For example, it may look at the efficiency of a particular operator at each node, sum the efficiencies, and divide by the number of nodes to form an answer.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Willison with Sage’s features listed above. One would’ve been motivated to do so in order to populate and/or time slice the record 130 into different time windows (Sage; [0035]). By incorporating the teachings of Sage, one would’ve been able to combine the production loss information from multiple machines with shared resource information.
Sage doesn’t teach:
selecting an improvement measure corresponding to the production loss information and the loss occurrence factor information from among a plurality of possible improvement measures stored by the storage device in association with the production loss and the occurrence factor of the loss;
and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area.
Isles teaches:
selecting an improvement measure corresponding to the production loss information and the loss occurrence factor information from among a plurality of possible improvement measures stored by the storage device in association with the production loss and the occurrence factor of the loss; ([0006] in today's mail processing lifecycle, an individual composing a document with a document composition or printstream processing tool that is destined for processing in a mail processing facility is not able in advance (real-time) to account for factors or occurrences within the mail processing facility that impacts its ability to render the document (e.g., machine downtime, inventory restrictions).; [Fig. 3] Step 310: Are any of the standardization or corrective actions acceptable?, Step 312: Initiate action through operator intervention and/or through an automated process so that the action affects its corresponding phase);
and automatically applying the selected improvement measure to at least one machine of the plurality of machines to improve productivity of the predetermined manufacturing area. ([Figs. 3-4]; [Fig. 3] Step 312: Initiate action through operator intervention and/or through an automated process so that the action affects its corresponding phase; [0069] The receiving party determines that the presented corrective action yields an advantage and/or benefit to the mail processing effort (event 310). If an action is not acceptable (event 309), no action is taken (event 302). However, if the determination at 310 is that a recommended action is acceptable (event 311), the recommended action is initiated (event 312). Depending on the configuration requirements, the action may be performed in coordination with the requesting party or in some instances, automatically.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Isles’ features listed above. One would’ve been motivated to do so in order to yield an advantage and/or benefit to the mail processing effort (Isles; [0069]). By incorporating the teachings of Isles, one would’ve been able to select an improvement measure and automatically implement said improvement measure.
Regarding claim 2: The combination of Willison, Sage and Isles teaches the apparatus according to claim 1. Willison doesn’t explicitly teach:
wherein the resource shared by the machines is associated with each combination of two or more machines as the shared resource.
Sage teaches:
wherein the resource shared by the machines is associated with each combination of two or more machines as the shared resource. ([0034] Other records 132 include other information concerning the downtime of the machine. For example, the other records 132 may be information concerning the product being made by a machine over time, the shift using the machine, the crew or operator at the machine, the equipment schedule (whether the machine is in production or not) and the calendar date. The other records 132 may be organized as any type of data structure such as a record or file. Information in the records 132 may alternatively be already included in the downtime records 130 so that the other records 132 are not needed. The downtime records 130 and other data records 132 are stored in each memory 127.; [0031] it may look at the efficiency of a particular operator at each node, sum the efficiencies, and divide by the number of nodes to form an answer.; [0033] each of the nodes 122 may store records for a specific machine.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Sage’s additional features listed above. One would’ve been motivated to do so in order to populate and/or time slice the record 130 into different time windows (Sage; [0035]). By incorporating the teachings of Sage, one would’ve been able to associate a resource with more than one machine in the system.
Regarding claim 3: The combination of Willison, Sage and Isles teaches the apparatus according to claim 1. Willison further teaches:
wherein the storage device stores a loss occurrence factor analysis model that defines a criterion for determining an occurrence factor of the production loss from a combination of the production loss information of the machine and the operating state per unit time in the 4M data information of the other machine that is different from the machine and shares the resource, and the processor classifies the occurrence factor of the production loss using the loss occurrence factor analysis model. ([0108] the communication component 116 can receive cell data generated by the manufacturing assembly line 120 and store the cell data in the data storage 114; the data storage 114 can be used to store an operating system and programs. For instance, the operating system provides various basic operational processes for the processor 112. The programs may include various user programs so that a user can interact with the processor 112 to perform various functions such as, but not limited to, viewing and/or manipulating the stored data, models, as well as retrieving and/or transmitting data.; [0162] The trained model can then be used to predict one or more properties of a manufacturing assembly line 120. The inputs and outputs of the trained model can depend on the data used to train the model. For example, continuing with the example of the manufacturing assembly line 120A, the model may be trained to predict the production level of the manufacturing assembly line 120A based on the input states and positions of the cells 122A, using training data from the manufacturing assembly line 120A, which can include cell input states, cell positions, and associated production levels. As another example, the model may be used to predict the quality level of the parts produced by the manufacturing assembly line 120A based on the input states and positions of the cells 122A.; [0169] The trained models can be used by the modeling system 110 in various ways, depending on the type of model. For example, a model trained to predict production levels may be used to identify bottlenecks or other inefficiencies in a manufacturing assembly line 120. Various types of root cause analysis or other types of troubleshooting may be used to identify and correct the inefficiencies in the manufacturing assembly line 120 identified by the model. In another example, a model trained to predict production levels may be used to identify underperforming sections of a manufacturing assembly line 120 and that may require maintenance. Various types of predictive maintenance can be performed on these sections to maximize production. As another example, a model trained to predict product quality levels may be used to identify problematic portions of the manufacturing assembly line causing defects in the produced parts.).
Claims 4, 6, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Willison et al. (US 20210304036 A1, hereinafter “Willison”), in view of Sage et al. (US 20180107737 A1, hereinafter “Sage”), in further view of Isles et al. (US 20080291486 A1, hereinafter “Isles”) as applied to claim 1 above, in further view of Brandon et al. (US 20150106912 A1, hereinafter “Brandon”).
Regarding claim 4: The combination of Willison, Sage and Isles teaches the apparatus according to claim 1. Willison doesn’t explicitly teach:
further comprising a display in communication with the processor, wherein the processor is further configured to: present the loss occurrence factor information on the display, wherein the loss occurrence factor information presented on the display includes the production loss of a target machine and an occurrence factor of the loss in time series in parallel with a state of another machine sharing a resource with the machine and the shared resource.
Brandon teaches:
further comprising a display in communication with the processor, wherein the processor is further configured to: present the loss occurrence factor information on the display, wherein the loss occurrence factor information presented on the display includes the production loss of a target machine and an occurrence factor of the loss in time series in parallel with a state of another machine sharing a resource with the machine and the shared resource. ([Fig. 2] Remote Expert 210, Machines 206; [0046] FIG. 3 shows an example of an overview screen 300 for a remote monitoring platform, wherein the system may automatically calculate an OEE score based on data collected from each machine. Each machine summary 302 shows an OEE score calculated from Availability, Performance, and Quality scores, and a chart 304 that displays data representing recent machine performance charted over time. The machine summary 302 may display additional data, such as the current running speed of the machine in cycles, the parts produced by the machine in the past 24 hours, and the downtime since the last run. Selecting one of the machine summaries 302 may present the user with more data for the selected machine.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Brandon’s features listed above. One would’ve been motivated to do so in order to provide to a user a detailed downtime report and analysis for machines (Brandon; [0047]). By incorporating the teachings of Brandon, one would’ve been able to display downtime information for multiple machines sharing a resource.
Regarding claim 6: The combination of Willison, Sage and Isles teaches the apparatus according to claim 1. Willison doesn’t explicitly teach:
wherein one or more improvement measures are defined for one type of the production loss. ([0072] Some status may require local action to maintain the machine, such as clearing a clog, replacing a worn or broken part, or performing other necessary maintenance.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Brandon’s additional features listed above. One would’ve been motivated to do so in order to determine one or more remedial actions (Brandon; [0072]). By incorporating the teachings of Brandon, one would’ve been able to define improvement measures based on the type of production loss event.
Regarding claim 7: The combination of Willison, Sage, Isles, and Brandon teaches the apparatus according to claim 6. Willison doesn’t explicitly teach:
wherein one or more improvement measures are defined for one type of the production loss, and types of the improvement measures are also defined.
Brandon teaches:
wherein one or more improvement measures are defined for one type of the production loss, and types of the improvement measures are also defined. ([0072] Some status may require local action to maintain the machine, such as clearing a clog, replacing a worn or broken part, or performing other necessary maintenance.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Brandon’s additional features listed above. One would’ve been motivated to do so in order to determine one or more remedial actions (Brandon; [0072]). By incorporating the teachings of Brandon, one would’ve been able to define improvement measures based on the type of production loss event.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Willison et al. (US 20210304036 A1, hereinafter “Willison”), in view of Sage et al. (US 20180107737 A1, hereinafter “Sage”), in further view of Isles et al. (US 20080291486 A1, hereinafter “Isles”), in further view of Brandon et al. (US 20150106912 A1, hereinafter “Brandon”) as applied to claim 6 above, in further view of Abelow (US 20120069131 A1, hereinafter “Abelow”).
Regarding claim 8: The combination of Willison, Sage, Isles, and Brandon teaches the apparatus according to claim 6. Willison doesn’t explicitly teach:
wherein one or more improvement measures are defined for one type of the production loss, and priorities of the improvement measures are defined.
Brandon teaches:
wherein one or more improvement measures are defined for one type of the production loss, ([0072] Some status may require local action to maintain the machine, such as clearing a clog, replacing a worn or broken part, or performing other necessary maintenance.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Brandon’s additional features listed above. One would’ve been motivated to do so in order to determine one or more remedial actions (Brandon; [0072]). By incorporating the teachings of Brandon, one would’ve been able to define improvement measures based on the type of production loss event.
Brandon doesn’t teach:
and priorities of the improvement measures are defined.
Abelow teaches:
and priorities of the improvement measures are defined. ([1351] An action list to achieve like the top 25%--What should I do? (In priority order).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Abelow’s features listed above. One would’ve been motivated to do so in order to generate ranked data 4875 and ranked reports 4877 (Abelow; [1351]). By incorporating the teachings of Abelow, one would’ve been able to prioritize the improvement measures.
Regarding claim 9: The combination of Willison, Sage, and Isles teaches the apparatus according to claim 1. Willison doesn’t explicitly teach:
further comprising a display in communication with the processor, wherein the processor is further configured to: present the output improvement measures on the display, wherein the processor is configured to rearrange the output improvement measures in order of occurrence frequency and present, on the display, the rearranged improvement measures in order of priority of improvement measures, and wherein, if combinations of target machines are the same for the same improvement measure, the processor is configured to eliminate duplication and add up occurrence frequencies.
Abelow teaches:
further comprising a display in communication with the processor, wherein the processor is further configured to: present the output improvement measures on the display, wherein the processor is configured to rearrange the output improvement measures in order of occurrence frequency ([1362] These findings may be reported 4894 4895 with recommendations such as by some examples illustrated in part in Stage 2 4896 which lists the top five actions 4897 in ranked order with the most frequent first);
and present, on the display, the rearranged improvement measures in order of priority of improvement measures, and wherein, if combinations of target machines are the same for the same improvement measure, the processor is configured to eliminate duplication and add up occurrence frequencies. ([1351] An action list to achieve like the top 25%--What should I do? (In priority order).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Willison with Abelow’s additional features listed above. One would’ve been motivated to do so in order to generate ranked data 4875 and ranked reports 4877 (Abelow; [1351]). By incorporating the teachings of Abelow, one would’ve been able to arrange the improvement measures by frequency and prioritize the improvement measures.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sklar (US 20090099887 A1), which discloses a method of undertaking and implementing a project using at least one concept, method or tool which integrates Lean Six Sigma and Sustainability Concepts.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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