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 is a final office action in response to the amendment filed 12 November 2025. Claims 1-2 and 6 have been amended. Claims 7-12 are newly added. Claims 1-12 are pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1-2 and 6, and addition of new claims 7-12 has been entered.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(f) interpretation. The interpretation is respectfully withdrawn.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(b) rejections for lack of antecedent basis. The rejections are respectfully withdrawn.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 103 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant arguments regarding the 35 U.S.C. 103 rejection have been fully considered, but are moot in view of the new grounds of rejection necessitated by Applicant’s amendment to the claims because the arguments do not apply to the combination of references used in the current rejection detailed below.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the amended claims are not directed to an abstract idea but is patent eligible because they are directed to a specific technological solution for real-time manufacturing control systems. Applicant further asserts that the amended claim language transforms the claim form generic data collection to a specific manufacturing control system that interfaces with physical manufacturing equipment in real-time operations, and further limitations demonstrate integration with active manufacturing processes rather than abstract data manipulation. Applicant additionally asserts that the amended claims include significantly more than any alleged abstract concept due to specific technical limitations for interfacing with manufacturing equipment in real-time such that the system must handle real-time data streams from active manufacturing operations for coordinating software model changes with active physical manufacturing processes. Examiner respectfully disagrees.
Independent claims 1 and 6 include limitations for real-time data collection, analysis, and manufacturing process improvement recommendation output. To the extent that Applicant relies on “real-time” data “accumulation” as the practical application, 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. While the data collection, analysis, and output is related to manufacturing control systems, the claim language “... to investigate causes of defective products” is an intended use of the data collection, analysis, and output. The language of claim 6 “to automatically synchronizes process model updates with ongoing manufacturing operations by reflecting a latest on-site task process in a latest version model” is additionally construed as an intended result because synchronizing is not positively recited. Both claims 1 and 6 are void of limitations that actively control the manufacturing process using the data output in a manner that goes beyond reporting a recommendation.
New Claim 7 “triggers real-time alerts … and … generates corrective action recommendations.” Both are construed as data output that does not go beyond reporting for human decision making and do not integrate the underlying abstract idea into a practical application. Per MPEP 2106.05(a)(II) gathering and analyzing information using conventional techniques and displaying the result is an abstract concept. Merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea. The use of a graphical user interface, a known way for a user to interact with the computer, does not change the generic nature of the computer. Merely reciting a generic computer and generic computer functions cannot transform a patent-ineligible abstract idea into a patent eligible invention. New claim 8 includes the language “updates production line configuration” but does not include active claim language regarding how the update is accomplished. New Claim 9 includes the language “automatically adjusts manufacturing parameters through connected manufacturing equipment,” similarly does not include active claim language regarding how the adjustment is accomplished. As a result, the 35 U.S.C. 101 rejection is proper, maintained, and updated below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 and 7-12 are rejected under 35 U.S.C. 112(b) as failing to set forth the subject matter which the inventor or a joint inventor regards as the invention. Claim 1 includes the claim language: “a provision data generation unit which identifies the task and the on-site data related to the task from relevant data related to the process model corresponding to the model information, searches for 4M information associated with a finished product based on the association data to investigate causes of defective products, and externally provides the identified data.” It is unclear which data enumerated in the claim refers to “the identified data.” The claim identifies both “the task and the on-site” data. It is unclear whether “the identified data” refers to the task data, the on-site data, or both. The identified data of claim 6 is the “on-site” data. For examining purposes the claim language will be construed as referencing the “on-site” data as recited in claim 6.
Claims 2-5 and 7-12 depend from claim 1 and inherit the same deficiency.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a device and independent claim 6 recites a process for investigating defective products by comparing and analyzing on-site manufacturing data. The claims are directed to an abstract idea of collecting and analyzing data, and outputting the results of the collection and analysis, without significantly more.
Independent Claim 1 recites the following limitations:
an on-site data accumulation unit which accumulates on-site data;
an association data accumulation unit which accumulates a plurality of association data, which is data for managing relevance between tasks and which associates a task node corresponding to the task and the on-site data related to the task,
wherein the association data includes identification information of a task target and identification information of 4M information (HuMan, Machine, Method, Material) for each task target of each task;
a version management unit which manages a version identifier of a plurality of process models by associating it with model information of the plurality of process models, wherein the version management unit automatically synchronizes process model updates with ongoing manufacturing operations by reflecting a latest on-site task process in a latest version model;
a version identifier update unit which, each time any process model among the plurality of process models is updated, updates the version identifier managed by the version management unit by associating it with the model information of the updated process model;
an association data registration unit which registers association data based on a process model corresponding to a specific version identifier among the plurality of process models, wherein the association data registration unit operates in real-time manufacturing environments to create structured data mappings between task nodes and manufacturing equipment identifiers from data generation devices connected via a network, and wherein the association data registration unit receives on-site data from the data generation devices and structures the on-site data based on a prescribed data structure defined by definition information created from the process model;
a model information search unit which searches the version management unit with date/time or version identifier corresponding to an updated process model as a search key, and searches for model information that coincides with the search key among the plurality of process models; and
a provision data generation unit which identifies the task and the on-site data related to the task from relevant data related to the process model corresponding to the model information, searches for 4M information associated with a finished product based on the association data to investigate causes of defective products, and externally provides the identified data.
Independent claim 6 recites the following limitations:
accumulating the on-site data in an on-site data accumulation database;
accumulating a plurality of association data, which is data for managing relevance between tasks and which associates a task node corresponding to the task and the on-site data related to the task,
wherein the association data includes identification information of a task target and identification information of 4M information (HuMan, Machine, Method, Material) for each task target of each task, in an association data accumulation database;
using a version management database and managing a version identifier of a plurality of process models by associating it with model information of the plurality of process models to automatically synchronizes process model updates with ongoing manufacturing operations by reflecting a latest on-site task process in a latest version model;
each time any process model among the plurality of process models is updated, updating the version identifier managed by the version management database by associating it with the model information of an updated process model;
registering association data based on model information of a process model corresponding to a specific version identifier among the plurality of process models, such that the registering association data operates in real-time manufacturing environments to create structured data mappings between task nodes and manufacturing equipment identifiers from data generation devices connected via a network, and
wherein the association data registration step includes receiving on-site data from the data generation devices and structuring the on-site data based on a prescribed data structure defined by definition information created from the process model;
a model searching the version management database with date/time or version identifier corresponding to the updated process model as a search key, and searching for model information that coincides with the search key among the plurality of process models; and
identifying the task and on-site data related to the task from relevant data related to the process model corresponding to the model information, searching for 4M information associated with a finished product based on the association data to investigate causes of defective products, and externally providing the identified on-site data.
Under Step 1, independent claims 1 and 6 recite at least one step or act including accumulating on-site data. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong 1, the limitations recited in claims 1 and 6 for accumulating data, accumulating a plurality of association data, managing a version identifier of process models, synchronizing process model updates, updating the version identifier, registering association data, receiving on-site data, searching for model information that coincides with a search key, identifying the task and on-site data related to the task, searching for 4M information, and externally providing the identified data as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (comparing information and making observations, evaluations, judgments, and opinions based on the data analysis). A project manager could mentally or with pen and paper determine the root cause of a product defect by analyzing process and task data, and make a determination of how to adjust the manufacturing plan to mitigate defects in a product. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea. See MPEP 2106.04(a)(2)(III).
Accumulating task, association, and on-site data, updating version identifiers in a database, registering association data , and displaying current production data are data gathering and transmission steps that are construed as insignificant extra-solution activity because these steps merely provide input for the data processing steps. Transmitting information for display is insignificant extra-solution activity because the display of a result or output of the data analysis step does not impose meaningful limits on the claim. See MPEP 2106.05(g).
Under Step 2A Rong Two, the judicial exception of claims 1 and 6 is not integrated into a practical application. In particular, the claims only recite a processor, database, interface, and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0017] states: “The data collection display system 100 includes a CPU (Central Processing Unit) which performs the overall control of the information system 1, a storage device (Read Only Memory: ROM) which stores various control programs for performing the control of the information system 1, a primary storage device (Random Access Memory: RAM) which temporarily stores information processed by the CPU, and an HDD (Hard Disk Drive), and the following functions are realized by the CPU executing the respective control programs stored in the ROM.” The use of a graphical user interface, a known way for a user to interact with the computer, does not change the generic nature of the computer. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Generating a recommendation for adjusting a manufacturing plan or process based on data analysis is an improvement to a manufacturing process, not an improvement to the functioning of a computer or to any other technology or technical field. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Independent claims 1 and 6 include limitations for real-time data collection, analysis, and manufacturing process improvement recommendation output. To the extent that Applicant relies on “real-time” data “accumulation” as the practical application, 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.
Under Step 2B, the claims 1 and 6 do not include that are sufficient to amount to significantly more than the judicial exception. The Specification does not provide additional details about the computer system that would distinguish it from any generic processing devices that communicate with one another in a network environment. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2-5 and 7-12 include the abstract ideas of independent claim 1. The limitations of the dependent claims merely narrow the recited mental process of collecting and analyzing data, and displaying the results of the collection and analysis by describing how the data is further manipulated, displayed, and analyzed to determine what kind of task process was used to manufacture the defective product and how each of the tasks configuring that task process was conducted, and thereby investigate the cause of defect. (See at least Applicant’s Specification at paragraph [0099]). New Claim 7 “triggers real-time alerts … and … generates corrective action recommendations.” Both are construed as data output that does not go beyond reporting for human decision making and do not integrate the underlying abstract idea into a practical application. Per MPEP 2106.05(a)(II) gathering and analyzing information using conventional techniques and displaying the result is an abstract concept. Merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea. The use of a graphical user interface, a known way for a user to interact with the computer, does not change the generic nature of the computer. Merely reciting a generic computer and generic computer functions cannot transform a patent-ineligible abstract idea into a patent eligible invention. New claim 8 includes the language “updates production line configuration” but does not include active claim language regarding how the update is accomplished. New Claim 9 includes the language “automatically adjusts manufacturing parameters through connected manufacturing equipment,” similarly does not include active claim language regarding how the adjustment is accomplished.
The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claims 1 and 6 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101. Therefore claims 1-12 are ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 3, 5-6, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over De et al. (US 2009/0150325) in view of Miyamoto et al. (US 2019/0271969).
Regarding Amended Claim 1, De et al. discloses a data collection display system, comprising: a processor coupled to a memory storing instructions for the processor to function as: an on-site data accumulation unit which accumulates on-site data; (the present invention relates to a system and method for root cause analysis of the failure of a manufactured product. De et al. [para. 0001]. … manufacturing data source 104 includes structured and unstructured data related to manufacturing processes and information on materials. … manufacturing data processing module 204 processes manufacturing information extracted from manufacturing data sources 104. De et al. [para. 0045-0048; Fig. 1-3]. … the present invention or any of its components, may be embodied in the form of a computer system…the computer includes a memory. De et al. [para. 0136-0137]);
a association data accumulation unit which accumulates a plurality of association data, which is data for managing relevance between tasks and which associates a task node corresponding to the task and the on-site data related to the task, (FIG. 2 includes historical warranty claim forms 102, manufacturing data sources 104, knowledge model generating module 106, and knowledge model 108. Knowledge model generating module 106 includes a field data processing module 202, a manufacturing data-processing module 204, and a knowledge model structure 206. … Knowledge model structure 206 defines the classes and sub-classes related to field failure as well as manufacturing processes and materials. … Knowledge model 108 is therefore based on a structured causal relationship. In various embodiments of the present invention, any knowledge modeling tool known in the art may be used. De et al. [para. 0045-0056]. … Mapping module 406 extracts various data points in the corrective action reports, based on various classes and sub-classes in the knowledge model structure. De et al. [para. 0065, 0080-0081 (modeling), 0122 (ontology); Fig. 5]);
a version management unit which manages a version identifier of a plurality of process models by associating it with model information of the plurality of process models; (MES data is processed to extract information related to various manufacturing processes and activities related to various sub-parts of a manufactured product. MES data is sorted for parts, processes, activities and variations related to specifications or control limits. The sorted MES data is stored in a manufacturing data warehouse. A defective process is identified, based on the extent of the deviation from a normal specification. Further, the type of defect in the defective process is also identified. De et al. [para. 0118]);
De et al. fails to explicitly disclose wherein the version management unit automatically synchronizes process model updates with ongoing manufacturing operations by reflecting a latest on-site task process in a latest version model; a version identifier update unit which, each time any process model among the plurality of process models is updated, updates the version identifier managed by the version management unit by associating it with the model information of the updated process model; a association data registration unit which registers association data based on a process model corresponding to a specific version identifier among the plurality of process models, wherein the association data registration unit operates in real-time manufacturing environments to create structured data mappings between task nodes and manufacturing equipment identifiers from data generation devices connected via a network, and wherein the association data registration unit receives on-site data from the data generation devices and structures the on-site data based on a prescribed data structure defined by definition information created from the process model; a model information search unit which searches the version management unit with date/time or version identifier corresponding to an updated process model as a search key, and searches for model information that coincides with the search key among the plurality of process models. Miyamoto et al. discloses these limitations. (… the information collection and display system 1 includes an association data model creation unit 10, an association data registration unit 11, an association data search unit 12, an accumulation data acquisition unit 13, an analysis data accumulation unit 14, an association data accumulation unit 15, a data provision API unit 16, a comparison data definition unit 17, and a temporary accumulation unit 18. … the association data registration unit 11 determines whether the site data 100 acquired according to the definition information 300 (400) is task information, information of a worker, information of a machine, information of a work procedure, or information of a material (part), on the basis of identification information assigned to the site data 100. Hereinafter, initial letters M of the worker (Man), the machine (Machine), the work procedure (Method), and the material (Material) may be taken and these may be referred to as 4M information (or 4M nodes) or task association information. Miyamoto et al. [para. 0046-0056; Fig. 2]. … data of the connection relation of the respective nodes connected on the basis of the connection information 314 is matched with the association data 200 described in FIG. 3 and is stored in the association data accumulation unit 15. Miyamoto et al. [para. 0077-0081; Fig. 3, 6-7]. … when a property value of the task 1 node is changed and a new task 1 node is used, the task node can be updated by matching the application end date and time of the previous task 1 node with an application start date and time of the new task 1 node and revising the task 1 node with the new task 1 node. Miyamoto et al. [para. 0109-0113]). … The association data registration unit 11 acquires generation date and time information (for example, 112, 122, 132, 142, 152, and 162) included in the site data 100 for the generated task node and registers the generation date and time information as start information of the generated task node in the association data accumulation unit 15 (step S110). Then, the association data registration unit 11 updates a property value of the task node, for example, data in the extension information 510 (step S111). … when it is determined in step S106 that the identification information acquired from the site data 100 is the 4M information different from the task information (step S106: No), the association data registration unit 11 searches for the 4M information corresponding to the identification information from the association data accumulation unit 15 (step S118). When it is determined that the 4M information corresponding to the identification information is accumulated in the association data accumulation unit 15 (step S119: Yes), the 4M information is completely registered as existing information at the time of constructing the manufacturing process. Therefore, the association data registration unit 11 updates the property value (step S120) and proceeds to step S112. Miyamoto et al. [para. 0118-0127; Fig. 15-18]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. to include the data processing steps of Miyamoto et al. to associate the plurality of pieces of information included in the site data… so that site data at each site can be easily analyzed (Miyamoto et al. [para. 0012]), in a manner that would have yielded predictable results at the relevant time.
and a provision data generation unit which identifies the task and the on-site data related to the task from relevant data related to the process model corresponding to the model information (… an ontology is developed by correlating the field data and the manufacturing data, based on the knowledge model structure. The structured and unstructured data extracted from the field failure and manufacturing information is organized in accordance with the knowledge model structure, to generate the knowledge model. De et al. [para. 0122-0125]. … an interactive graphical user interface is used to iteratively select the most relevant instance of the cause of the failure of the manufactured product, based on the user's judgment at each stage. … final inference with regard to the root cause of the failure of the manufactured product is generated. De et al. [para. 0130-0136; Fig. 11]).
De et al. fails to explicitly disclose a provision data generation unit that searches for 4M information associated with a finished product based on the association data to investigate causes of defective products, and externally provides the identified data. Miyamoto et al. discloses this limitation. (In the information collection and display system 1, when a problem occurs in the finished product, it is possible to search the 4M information associated with the finished product by the association data 200 and it is possible to find a cause for a problem in a predetermined manufacturing process (task). In the embodiment, the case where identification information is assigned to each of the site data 100 collected or generated by the data generation device 5 in each manufacturing process is described as an example. In the information collection and display system 1, association between tasks can be managed by accumulating the association data 200 defining the association of machines, workers, and the like for the tasks, over a plurality of manufacturing processes (tasks). … The site data 100 (actual data) shown by each identification information of the association data 200 is accumulated in the externally managed transaction data accumulation unit 4. Miyamoto et al. [para. 0060-0062, 0067-0073]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De t al. to include unit that searches for 4M information associated with a finished product based on the association data to investigate causes of defective products, and externally provides the identified data as disclosed by Miyamoto et al. to associate the plurality of pieces of information included in the site data… so that site data at each site can be easily analyzed (Miyamoto et al. [para. 0012]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 3, De et al. and Miyamoto et al. combined disclose the data collection display system, wherein: the version management unit manages a version identifier of the process model, and an update date/time of the process model corresponding to the version identifier; and the model information search unit displays a screen including an input column for inputting, as a search condition of the updated process model, at least a search date/time or the version identifier corresponding to the updated process model. Miyamoto et al. discloses this limitation. (… the site data 100 (see FIG. 7) such as identification information, a generation date and time, and an actual measurement value is accumulated in the transaction data accumulation unit 4. Miyamoto et al. [para. 0044]…. The association data model creation unit 10 reads master data corresponding to model data from the master data accumulation unit 3, on the basis of the model data input from the user interface 7, and creates definition information 300. … displays the searched 4M information (second information) on the user interface 7 by hatching, color coding, or the like to provide the 4M information to the user. Miyamoto et al. [para. 0050-0054]. … the association data registration unit 11 updates a property value of the task node, for example, data in the extension information 510 (step S111). … when it is determined in step S106 that the identification information acquired from the site data 100 is the 4M information different from the task information (step S106: No), the association data registration unit 11 searches for the 4M information corresponding to the identification information from the association data accumulation unit 15 (step S118). When it is determined that the 4M information corresponding to the identification information is accumulated in the association data accumulation unit 15 (step S119: Yes), the 4M information is completely registered as existing information at the time of constructing the manufacturing process. Therefore, the association data registration unit 11 updates the property value (step S120) and proceeds to step S112. Miyamoto et al. [para. 0118-0127; Fig. 15-18]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. to include the data processing steps of Miyamoto et al. to associate the plurality of pieces of information included in the site data… so that site data at each site can be easily analyzed (Miyamoto et al. [para. 0012]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 5, De et al. and Miyamoto et al. combined disclose the data collection display system, wherein: the version management unit manages on-site data management information of the on-site data accumulation unit as model information of the process model; and the model information search unit: acquires a latest version process model based on the latest version model; changes the on-site data management information to on-site data management information of the latest version process model among the model information of the acquired process model as a result of the search conducted based on the search date/time or the version identifier of the process model; and acquires on-site data based on the on-site data management information of the latest version process model. Miyamoto et al. discloses this limitation. (… the information collection and display system 1 includes an association data model creation unit 10, an association data registration unit 11, an association data search unit 12, an accumulation data acquisition unit 13, an analysis data accumulation unit 14, an association data accumulation unit 15, a data provision API unit 16, a comparison data definition unit 17, and a temporary accumulation unit 18. … the association data registration unit 11 determines whether the site data 100 acquired according to the definition information 300 (400) is task information, information of a worker, information of a machine, information of a work procedure, or information of a material (part), on the basis of identification information assigned to the site data 100. Hereinafter, initial letters M of the worker (Man), the machine (Machine), the work procedure (Method), and the material (Material) may be taken and these may be referred to as 4M information (or 4M nodes) or task association information. Miyamoto et al. [para. 0046-0056, 60-73; Fig. 2]. … data of the connection relation of the respective nodes connected on the basis of the connection information 314 is matched with the association data 200 described in FIG. 3 and is stored in the association data accumulation unit 15. Miyamoto et al. [para. 0077-0081; Fig. 3, 6-7]. … when a property value of the task 1 node is changed and a new task 1 node is used, the task node can be updated by matching the application end date and time of the previous task 1 node with an application start date and time of the new task 1 node and revising the task 1 node with the new task 1 node. Miyamoto et al. [para. 0109-0113]). … The association data registration unit 11 acquires generation date and time information (for example, 112, 122, 132, 142, 152, and 162) included in the site data 100 for the generated task node and registers the generation date and time information as start information of the generated task node in the association data accumulation unit 15 (step S110). Then, the association data registration unit 11 updates a property value of the task node, for example, data in the extension information 510 (step S111). … when it is determined in step S106 that the identification information acquired from the site data 100 is the 4M information different from the task information (step S106: No), the association data registration unit 11 searches for the 4M information corresponding to the identification information from the association data accumulation unit 15 (step S118). When it is determined that the 4M information corresponding to the identification information is accumulated in the association data accumulation unit 15 (step S119: Yes), the 4M information is completely registered as existing information at the time of constructing the manufacturing process. Therefore, the association data registration unit 11 updates the property value (step S120) and proceeds to step S112. Miyamoto et al. [para. 0118-0127; Fig. 15-18]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. to include the data processing steps of Miyamoto et al. to associate the plurality of pieces of information included in the site data… so that site data at each site can be easily analyzed (Miyamoto et al. [para. 0012]), in a manner that would have yielded predictable results at the relevant time.
Regarding Amended Claim 6, claim 6 recites substantially similar limitations to those of claim 1 and is therefore rejected based upon the same prior art reference combination, reasoning, and rationale. Claim 6 is directed to a process, which is disclosed by De. Et al. [para. 0010]: “An object of the present invention is to provide a system and method for root cause analysis of a manufactured product.”
Regarding New Claim 11, De et al. and Miyamoto et al. combined disclose the data collection display system, wherein the version management unit maintains process model genealogy tracking that records parent-child relationships between process model versions and their associated manufacturing outcomes, and the model information search unit enables comparative analysis of manufacturing performance across different process model lineages. (This data is aggregated, based on the product, the manufacturing processes, the possible defects in the manufacturing processes, and the possible sources of the defects in the manufacturing processes. De et al. [para. 0062]. … Mapping module 406 extracts various data points in the corrective action reports, based on various classes and sub-classes in the knowledge model structure. De et al. [para. 0065]. … One or more instances included in knowledge model 108 have multiple parents or children. De et al. [para. 0079]. …the learning of the knowledge model and its conversion to the Bayesian network is carried out at periodic intervals with updated data records and information. Du et al. [para. 0080-0092]. the knowledge model is a network of entities that are related to the field failure and manufacturing attributes. Each entity in the knowledge model represents an instance of a parameter that is related to one of the field failure and manufacturing attributes. The entities are connected in a parent-and-child relationship. Each instance of the knowledge model may be associated with a number of states. De et al. [para. 0124]).
Claims 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over De et al. (US 2009/0150325) in view of Miyamoto et al. (US 2019/0271969), and in further view of Nakayama et al. (US 2006/0026595).
Regarding Amended Claim 2, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the processor further functions as a model data accumulation unit including: a model under edit that manages a process model in which its update has been started and which is being updated among the plurality of process models; a latest version model that manages a latest version process model of the model under edit in which its update has been completed; and a model history as the version management unit. Nakayama et al. discloses this limitation. (… when the output data in any of the processes is updated, the state registration unit may correspondingly update for registration the input data corresponding to the updated output data in the process that uses the updated output data as input data. Nakayama et al. [para. 0012-0016, 0024-0032]. … The process information database 2 stores information related to respective processes that constitute a workflow. … The workflow shown in FIG. 2 is defined based on various data (refer to the tables shown in FIGS. 3 to 13) stored in the aforementioned process information database 2 and allows a plurality of processes to have order relations with each other along a task performance procedure. … The process information table stores a process ID, name of each process, a person in charge of each process, a division and e-mail address of the person in charge (FIG. 3). … The process state management table stores a process ID, processing state of each process, refix date when the processing state has been updated (FIG. 4). … When an update data entry of the design specification document occurs, a file name unique in the system is given to the data and registered in the output information management table together with its original file name, registered date, and the like (S1401). FIG. 15 shows the output information management table indicating a state where the updated design specification document (version 1.2) has been registered. Nakayama et al. [para. 0083-0090, 0100-0105, 0138; Fig. 18-26]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing steps of Nakayama et al. to contribute to automatization of the management task and increase in operational efficiency in the task involving high-level parallel operations (Nakayama et al. [para. 0011]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 4, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein: the model information search unit displays, as a part of the screen, a process model display column that visually shows the process model corresponding to the version identifier or the search date/time input in the input column, accepts a selection of certain task nodes of the process model shown in the process model display column, and displays a trace origin search condition column in which a search condition of a trace origin for tracking the on-site data concerning a part of the process model for which the selection has been accepted can be input. Nakayama et al. discloses this limitation. (The workflow shown in FIG. 2 is defined based on various data (refer to the tables shown in FIGS. 3 to 13) stored in the aforementioned process information database 2 and allows a plurality of processes to have order relations with each other along a task performance procedure. The workflow can include a flow going from a plurality of processes to one process in a concentrated manner or flow going from one process to a plurality of processes in a divergent manner. Nakayama et al. [para. 0087]. … creation processing of the HTML GUI window that displays operation items in each process performed by the HTML GUI creation unit 12 will be described with reference to the flowchart of FIG. 19. … the flow proceeds to S1702 to search previous "Completed" histories with respect to the detailed specification creation process (S1702) (refer to FIG. 16). Nakayama et al. [para. 0115-0120; Fig. 18-26]. … as shown in FIG. 18, a hyperlink is inserted into a version number of the input information displayed on the HTML GUI, and the user follows the link to a version number setting window where the user can specify a particular version number. As a matter of course, a button or the like for allowing the version number setting window as described above to be displayed may be provided on the HTML GUI. … the output data obtained as a processing result in each process within the workflow may be divided into a plurality of sections. More specifically, section information of output data is defined based on the information of the section management table (refer to FIG. 24). Nakayama et al. [para. 0138-0140]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing and data search steps of Nakayama et al. to contribute to automatization of the management task and increase in operational efficiency in the task involving high-level parallel operations (Nakayama et al. [para. 0011]), in a manner that would have yielded predictable results at the relevant time.
Claims 7-9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over De et al. (US 2009/0150325) in view of Miyamoto et al. (US 2019/0271969), and in further view of Shah et al. (US 2024/0160191).
Regarding New claim 7, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the association data registration unit automatically triggers real-time alerts when manufacturing equipment performance deviates from predetermined thresholds defined in the process model, and the provision data generation unit generates corrective action recommendations by analyzing historical patterns of similar deviations stored in the association data. Shah et al. discloses this limitation. (The data historian 110 can further comprise one or more components for visualizing or analyzing the industrial data, or for generating reports or alerts based on the industrial data. … The analytics component 204 can be configured to apply analytics to the operational and status data collected from the automation systems. The analytics component 204 can reference information contained in an industrial asset model herein as part of this analysis. Analytics component 204 can apply one or more of a variety of analytics applications or algorithms to operational and status data. … The industrial asset model 418 can be utilized to predict how the industrial device will perform under different conditions, and to identify potential issues before they occur, thus enabling for proactive maintenance and improving overall asset reliability. Shah et al. [para. 0004-0006, 0047-0055]. … The remediation component 228 can generate one or more of a variety of recommendations applicable to an industrial device herein (e.g., in response to a determination that a quality indicator determined by the quality component 224) does not satisfy a defined data quality threshold). For example, the remediation component 228 can generate a recommendation, to apply to the industrial device or industrial asset model, in order to satisfy such a defined data quality threshold. The remediation component 228 can utilize a remediation model that has been generated (e.g., by the machine learning component 242) based on past remedies applicable to various industrial devices (e.g., previous or past industrial devices) in order to generate the aforementioned recommendation. Shah et al. [para. 0064-0071]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing and data search steps of Shah et al. to facilitate improved operations control, such as with improved predicative maintenance (Shah et al. [para. 0079]), in a manner that would have yielded predictable results at the relevant time.
Regarding New Claim 8, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the version management unit maintains a synchronized manufacturing execution system interface that automatically updates production line configurations when process model versions change, and the provision data generation unit provides real-time manufacturing performance dashboards that display current production status against target specifications defined in an active process model version. Shah et al. discloses these limitations. (Industrial automation systems often include one or more human-machine interfaces (HMIs) 114 that allow plant personnel to view telemetry and status data associated with the automation systems, and to control some aspects of system operation. Shah et al. [para. 0004-0006, 0047-0055]…. industrial asset models herein (e.g., the industrial asset model 418) can be generated by the machine learning component 242, based on historical and/or current industrial data. Shah et al. [para. 0085]. … Industrial data writeback can occur, for instance, when a change made to an industrial asset model 418 is propagated to the industrial devices or systems that it represents. Such writeback capability can maintain synchronization between an industrial device herein and an industrial asset model that includes a representation of that industrial device. To propagate such a change to an industrial device, a change component 220 can (e.g., using an industrial asset model 418) first determine a change applicable to an industrial device represented in an industrial asset model (e.g., industrial asset model 418). …With this updated industrial asset model, the device interface component 206 can then apply the aforementioned change to the industrial device. Shah et al. [para. 0086, 0090-0097]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing and data search steps of Shah et al. to facilitate improved operations control, such as with improved predicative maintenance (Shah et al. [para. 0079]), in a manner that would have yielded predictable results at the relevant time.
Regarding New Claim 9, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the model information search unit includes a predictive analytics engine that forecasts potential manufacturing defects based on current on-site data trends and historical association data patterns, and automatically adjusts manufacturing parameters through connected manufacturing equipment to prevent predicted defects. (Industrial automation systems often include one or more human-machine interfaces (HMIs) 114 that allow plant personnel to view telemetry and status data associated with the automation systems, and to control some aspects of system operation. Shah et al. [para. 0004-0006, 0047-0055]. … The analytics component 204 can be configured to apply analytics to the operational and status data collected from the automation systems. Shah et al. [para. 0052-0055]. … various embodiments described herein can employ artificial-intelligence or machine learning systems and techniques to facilitate learning user behavior, context-based scenarios, preferences, etc. in order to facilitate taking automated action with high degrees of confidence. … machine learning component 242 can comprise an artificial intelligence and/or machine learning model that can be trained (e.g., via supervised and/or unsupervised techniques) to perform the above or below-described functions using historical training data comprising various context conditions that correspond to various augmented network optimization operations. Shah et al. [para. 0071]. … the machine learning component 242 can utilize past or historical industrial data 806 applicable to one or more of a variety of industrial devices in order to generate a remediation model, which can be utilized in order to generate a recommendation applicable to a corresponding industrial device (e.g., to correct a problem or defect with the respective industrial device). Shah et al. [para. 0083-0090]. …Example analytics applications 1002 can include, but are not limited to, predictive maintenance applications or algorithms, energy management or prediction applications or algorithms, batch reporting applications or algorithms, applications or algorithms capable of predicting asset failures, asset performance management application or algorithms, or other suitable applications or algorithms. Shah et al. [para. 0090-0097]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing and data search steps of Shah et al. to facilitate improved operations control, such as with improved predicative maintenance (Shah et al. [para. 0079]), in a manner that would have yielded predictable results at the relevant time.
Regarding New Claim 12, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the association data registration unit implements machine learning algorithms that continuously optimize data structure mappings based on manufacturing outcome feedback, and the provision data generation unit generates automated process improvement recommendations by identifying correlation patterns between process model changes and quality improvements. (Industrial automation systems often include one or more human-machine interfaces (HMIs) 114 that allow plant personnel to view telemetry and status data associated with the automation systems, and to control some aspects of system operation. Shah et al. [para. 0004-0006, 0047-0055]. … various embodiments described herein can employ artificial-intelligence or machine learning systems and techniques to facilitate learning user behavior, context-based scenarios, preferences, etc. in order to facilitate taking automated action with high degrees of confidence. … machine learning component 242 can comprise an artificial intelligence and/or machine learning model that can be trained (e.g., via supervised and/or unsupervised techniques) to perform the above or below-described functions using historical training data comprising various context conditions that correspond to various augmented network optimization operations. Shah et al. [para. 0071]. … the machine learning component 242 can utilize past or historical industrial data 806 applicable to one or more of a variety of industrial devices in order to generate a remediation model, which can be utilized in order to generate a recommendation applicable to a corresponding industrial device (e.g., to correct a problem or defect with the respective industrial device). Shah et al. [para. 0083-0090].… new version of an industrial asset model 418 can result from a change to a PLC program or a different software or hardware change to a device represented in the industrial asset model 418. By storing multiple versions of an industrial asset model 418, comparisons (e.g., in performance, size, or based on other suitable metrics) between the versions of the industrial asset model can be facilitated. … the model comparison component 232 can determine a difference between the first version of the industrial asset model and the second version of the industrial asset model. In some embodiments, such a difference can comprise a difference in respective outputs between the first version of the industrial asset model and the second version of the industrial asset model. Shah et al. [para. 0090-0100]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data processing steps of De et al. and Miyamoto et al. combined to include the data processing and data search steps of Shah et al. to facilitate improved operations control, such as with improved predicative maintenance. (Shah et al. [para. 0079]), in a manner that would have yielded predictable results at the relevant time.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over De et al. (US 2009/0150325) in view of Miyamoto et al. (US 2019/0271969), and in further view of Hyatt et al. (US 2021/0398267).
Regarding New Claim 10, De et al. and Miyamoto et al. combined fail to explicitly disclose the data collection display system, wherein the association data includes manufacturing batch identifiers and supplier quality metrics for each task target, and the provision data generation unit performs automated supplier performance analysis by correlating defect patterns with specific supplier batches across multiple process model versions. Hyatt et al. discloses these limitations. (… multiple automated visual inspection appliances (VIA) for a production plant and a centralized data collection and analytics server (DCAS) that gathers and analyzes data from the VIAs. The DCAS can then provide reports, dashboards and alerts to determine production trends in the manufacturing plant and thus improve the quality and productivity of the plant. … analysis is selected from the group consisting of: root cause analysis of detected defects; predictive maintenance analysis—based on detecting trends in defect or deviations that are not defects; intensity of the defects—analysis of trends to increasing occurrences of defects per period of time; … supplier analysis comparing product raw material suppliers vs defects; and relationship analysis between different production stages of the same item. Hyatt et al. [para. 0006, 0010-0015]. … DB 154 is a database (e.g., as known in the art) and stores data transmitted by VIAs 110A, B, C and n and also results and interim results of analysis by engine 152. DB 154 also stores configuration data defined in DCAS 150 for system 100 including VIA profiles. A VIA profile includes information about each VIA in system 100 including but not limited to: unique identifier, name, physical mounting details, position in plant, plant geolocation, items inspected, reference images of items inspected, profiles of items inspected, inspection results and so forth. Hyatt et al. [para. 0035-0043]. … The following data is collected by each VIA 110 per item 20 as a result of the inspection process. This data is herein referred to as “per-item collected data”, and one or more of per-item collected data is referred to as “collected data”: … Record of decision by VIA whether item has a defect; [0061] Image of the defects; Number of defects; Records of deviations from good item samples which are not significant enough to be reported as defects but can imply to issues in the production line; Item unique ID; Plant Work/Job order/Batch ID. Hyatt et al. [para. 0058-0077]). It would have been obvious to one of ordinary skill in the art of manufacturing information systems data management before the effective filing date of the claimed invention to modify the data collection and processing steps of De et al. and Miyamoto et al. combined to include the data collection, processing and data search steps of Hyatt et al. to provide the capability to share data from different parts of the plants or across multiple plants (Hyatt et al. [para. 0004]), in a manner that would have yielded predictable results at the relevant time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
King et al. (US 2022/0214668) – user interface component that generates a manufacturability report regarding a product design in relation to a manufacturing process. The manufacturability report can indicate whether a product feature included in the product design is permissible based on a plurality of manufacturing considerations associated with the manufacturing process.
Madsen et al. (US 11,209,345) – model analyzes failure of the detected voids with respect to time and generates deterministic output data indicating failure over a deterministic timeframe. A prognostic analyzer processes the deterministic output data from the at least one deterministic model and generates a failure prediction for the as manufactured part.
Dor et al. (US 2002/0072162) - creating a defect knowledge library containing case study information of wafer defects on semiconductor wafers. The method comprises creating a database entry that contains a case study of a specific defect including defect information that comprises one or more defect images and storing the database entry for subsequent access. The database entries are stored on a server and are accessible by a plurality of clients.
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 LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao WU can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623