DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 04/02/2026.
Status of Claims
Claims 2, 3, 9, 14, and 18 have been cancelled.
Claims 1, 4, 7, 8, 10, 13, 17, and 19 have been amended.
Claims 1, 4-8, 10-13, 15-17, and 19-20 are now pending.
Response to Arguments
The 35 U.S.C. 112(b) rejection of claim 4 is withdrawn in light of amendments.
Applicant's arguments filed on 04/02/2026 regarding the 35 U.S.C. 101 and 102 rejections of the claims have been fully considered. The Applicant argues the following.
(1) As per the 101 rejection, the Applicant argues that claim 1 as amended specifies that the systems are manufacturing machines and the process is a manufacturing process. The computer program controls the operation of the manufacturing machines, which integrates the alleged judicial exception into a practical application. Thus, when viewed as an ordered combination, the claim limitations amount to significantly more than the abstract idea.
The Examiner respectfully disagrees. The Examiner submits that although the claims now define the systems as manufacturing machines and the process as a manufacturing process, claims 1 and 13 are still directed to the abstract grouping of Mental Processes. The claims recite receiving value streams and event data, processing the data, generating metrics from the data, and publishing the results, which reflects mental processes. The fact that the data involves manufacturing machines and processes does not negate the fact that the steps are abstract. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer.
The Examiner also submits that the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more because the additional elements do not improve the functioning of a computer or improve upon another technology. The manufacturing machines are recognized as additional elements and controlled by a computer. However, the functioning of the manufacturing machine is not improved beyond its original capabilities or further controlled in response to implementing the claimed steps. Therefore, the 35 U.S.C. 101 rejection is maintained.
(2) As per the 102 rejection, the Applicant argues that Alfaras does not disclose all of the elements in amended claims 1 and 13.
The Examiner finds the Applicant’s arguments persuasive. Therefore, the 102 rejection has been withdrawn. However, upon further search and consideration, a new ground of 103 rejection is applied to claims 1 and 13. See details below.
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 .
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, 4-8, 10-13, 15-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention, “Web-Based Platforms for Applying Graph Theory and Critical Path Methodologies to Value Chain Management with Event Streaming Platforms”, is directed to an abstract idea, specifically Mental Processes, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1, 4-8, 10-13, 15-17, and 19-20 are directed to a statutory category, namely a process (claims 1, 4-8, and 10-12) and a machine (claims 13, 15-17, and 19-20).
Step 2A (1): Independent claims 1 and 13 are directed to an abstract idea of Mental Processes, based on the following claim limitations: “receiving,…, a value stream for a manufacturing process, wherein the value stream comprises a plurality of expected events in the manufacturing process involving a plurality of manufacturing machines; receiving,…, an event…, wherein the event was published ….and comprises an identifier for the manufacturing machine and a process identifier for the manufacturing process; processing,…, the event by comparing the event to the value stream; generating,…, a new event based on the processing; publishing,…, the new event …, wherein the new event is consumed…; generating,…, metrics for the manufacturing process; calculating,…, an optimal reordering point for raw materials based on the metrics; publishing,…, the metrics … to generate an alert in response to receiving certain metrics; and publishing,…, the metrics … to suggest an automation in response to receiving certain metrics.” These claims describes a process of receiving and analyzing manufacturing process data, generating and publishing events and metrics, and generating/suggesting a response, which can practically be performed mentally with pen and paper. Dependent claims 4-8, 10-12, 15-17, and 19-20 further describes the manufacturing process data, events, metrices, and responses. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Therefore, claims 1, 4-8, 10-13, 15-17, and 19-20 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1, 5, 7, 11-13, 15, and 20 recite additional elements of “a computer program; a plurality of manufacturing machines; event streaming platforms; an alerting platform; an automation platform (claims 1 and 13); by computer program (claims 5, 7, 11, 12, 15, and 20); training, by the computer program, a machine learning model using the effectiveness as a reinforcement learning weight (claims 12 and 20); and a system, comprising: an event streaming platform in communication with a plurality of manufacturing machines that are involved in a manufacturing process; an alerting platform; an automation platform; and a value chain platform comprising a computer processor an executing a computer program that is configured (claim 13)”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the abstract process of receiving and analyzing process data, generating and publishing events and metrics, and generating/suggesting a response. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)) Also, limitations that amount to merely indicating a field of use or technological environment (e.g. value chain mgt /manufacturing) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1, 4-8, 10-13, 15-17, and 19-20 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1, 5, 7, 11-13, 15, and 20 recite additional elements of “a computer program; a plurality of manufacturing machines; event streaming platforms; an alerting platform; an automation platform (claims 1 and 13); by computer program (claims 5, 7, 11, 12, 15, and 20); training, by the computer program, a machine learning model using the effectiveness as a reinforcement learning weight (claims 12 and 20); and a system, comprising: an event streaming platform in communication with a plurality of manufacturing machines that are involved in a manufacturing process; an alerting platform; an automation platform; and a value chain platform comprising a computer processor an executing a computer program that is configured (claim 13)”. These additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply a judicial exception. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1, 4-8, 10-13, 15-17, and 19-20 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 nonobviousness.
Claims 1, 4-8, 10-13, 15-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alfaras et al. (US 2024/0281419 A1) in view of Schoening et al. (US 2017/0053239 A1).
As per claim 1 (Currently amended), Alfaras teaches a method, comprising (Alfaras e.g. Systems and methods for operating a data platform across various cloud and legacy environments to provide increased data visibility and quality management in accordance with embodiments of the disclosure are described herein [0006]. In some embodiments, a device includes a processor, a memory communicatively coupled to the processor, a data quality management logic. The logic is configured to select a target enterprise data warehouse (EDW) platform, monitor the EDW platform, produce peak demand hour data, analyze the EDW platform, generate a plurality of suggested actions, automatically select one of the suggested actions, and dynamically apply the selected suggested action [0006].):
Alfaras teaches receiving, by a computer program, a value stream for a process, wherein the value stream comprises a plurality of expected events in the process involving a plurality of systems; (Alfaras e.g. Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. Data quality management allows for custom script creation to validate pipelines and business rules (Abstract). Data pipelines play a crucial role in the management and processing of data within enterprise data warehouses (EDWs) [0003]. As data moves through these pipelines, metadata is generated to provide descriptive information about the data, including its source, structure, and quality [0003]. At its core, a data pipeline is designed to address the challenge of data integration by enabling organizations to ingest data from diverse sources, such as databases, applications, APIs, streaming platforms, and IoT devices, and deliver it to target systems, such as data warehouses, data lakes, or analytics platforms [0047]. Data pipelines typically involve several stages, including data extraction, transformation, loading (ETL), or ingestion (ELT), depending on the specific requirements of the use case and the technologies involved [0048]. Data pipelines play a role in enabling organizations to harness the value of their data assets by providing a reliable and scalable mechanism for data integration and processing [0050]. Metadata can refer to descriptive information that provides insights into the characteristics, structure, and context of the data. Metadata acts as a valuable resource for understanding and managing datasets effectively, as it encapsulates details such as the data's source, format, quality, creation date, and ownership [0056]. Data sources 210 in the context of data pipelines refer to the various origins from which data is collected, ingested, and processed within the pipeline. These sources encompass a wide range of data repositories, systems, applications, and devices that generate or store data relevant to an organization's operations [0108]. Loading data 220 can refer to the process of ingesting or importing data from various sources into the data pipeline for further processing, transformation, and analysis [0135].)
Alfaras teaches receiving, by the computer program, an event from an event streaming platform, wherein the event was published by the system and comprises a system identifier for the system and a process identifier for the process; (Alfaras e.g. Referring to FIG. 4, a schematic block diagram of a device 440 suitable for data pipeline quality management, in accordance with various embodiments of the disclosure is shown [0234]. Data source data 451 may include transactional data generated by operational systems, such as customer orders, sales transactions, financial transactions, or inventory records, providing a record of business activities and operations [0298]. Furthermore, in some embodiments, data source data 451 can include log data generated by IT systems, applications, or infrastructure components, capturing events, activities, and performance metrics for monitoring, troubleshooting, and analysis purposes. Log data may include server logs, application logs, network logs, security logs, or audit logs, providing insights into system behavior, user interactions, and operational metrics [0299]. Additionally, in more embodiments, data source data 451 may comprise external data obtained from third-party sources, such as market data providers, data brokers, government agencies, or public datasets, enriching internal data with external context and insights [0300]. Moreover, in certain embodiments, data source data 451 can include streaming data generated in real-time or near-real-time by sensors, IoT devices, social media feeds, or other streaming sources, capturing continuous streams of events, signals, or messages. Streaming data may include telemetry data, sensor readings, social media updates, or financial market feeds, enabling organizations to monitor, analyze, and respond to events and trends as they can occur [0301].)
Alfaras teaches processing, by the computer program, the event by comparing the event to the value stream; (Alfaras e.g. Data accuracy monitoring often may involve conducting regular audits or reviews of data to identify and address inaccuracies, inconsistencies, or errors [0060]. This may include manual inspection of data samples, comparison of data against external sources or benchmarks, or validation of data against predefined business rules or standards [0060]. Referring to FIG. 8, a flowchart depicting a process 800 for generating metadata exceptions in accordance with various embodiments of the disclosure is shown [0364]. In many embodiments, the process 800 can configure source and target systems for metadata comparison (block 810). When evaluating metadata in a data pipeline, comparing source and target systems can involve examining the characteristics, attributes, and properties of the data sources and destinations within the pipeline [0364]. The source system typically refers to the original data repositories or sources from which data is extracted or ingested into the pipeline [0365]. The target system, on the other hand, may represent the destination or final storage location where data is loaded or transformed within the pipeline [0365]. Comparing metadata between the source and target systems can involves assessing factors such as schema definitions, data types, field mappings, data transformations, data quality rules, and other metadata attributes to ensure consistency, accuracy, and alignment between the data at different stages of the pipeline [0365].)
Alfaras teaches generating, by the computer program, a new event based on the processing; (Alfaras e.g. In further embodiments, the process 800 can generate metadata exceptions (block 860). In a variety of embodiments, this can be done in response to a comparison that is done between the target and source system data. The comparison can be done locally or remotely through a cloud-based service provider or other device. Any generated results may be stored within a database for further processing [0369]. The metadata exception type can vary and include, but is not limited to, having one or more column missing, detecting a primary key mismatch, finding a column data type mismatch, or a column size mismatch, etc. Each of these types of exceptions can be monitored as desired [0366]. For example, the comparison and exception generation can be done based on a selected type of exceptions, or may be done automatically or in response to one or more events (Fig. 8 and [0370]).)
Alfaras teaches publishing, by the computer program, the new event to the event streaming platform, wherein the new event is consumed by the plurality of systems; (Alfaras e.g. However, if the metadata exception generation is indicated as a success, various embodiments of the process 900 can mark the metadata monitoring status as linked (block 960). Linking a metadata monitoring status in a data pipeline can refer to establishing a connection or association between the metadata monitoring status and the relevant components, processes, or entities within the pipeline (Fig. 9 and [0374]). This linkage enables organizations to track and monitor the status, health, and performance of the data pipeline in real-time or near real-time. By linking metadata monitoring status to specific pipeline components or processes, organizations can gain insights into the operational status, data quality, and reliability of each stage of the pipeline, including data ingestion, transformation, loading, and delivery. Additionally, linking metadata monitoring status can facilitate reporting, auditing, and compliance efforts by providing a comprehensive view of the pipeline's performance and adherence to predefined metrics, thresholds, or service-level agreements (SLAs) [0374]. Referring to FIG. 11, a flowchart depicting a process 1100 for executing a generated alter script in accordance with various embodiments of the disclosure is shown [0383]. In many embodiments, the process 1100 can trigger a sync process to propagate changes from a source system to a target system (block 1110). This propagation can be automatic in nature, or may be set at a predefined time or in response to a specific event. The propagation may also be transmitted from the source system to the target system, but may also be retrieved by the target system from the source system [0384].)
Alfaras teaches generating, by the computer program, metrics for the process; publishing, by the computer program, the metrics to an alerting platform, wherein the alerting platform is configured to generate an alert in response to receiving certain metrics; and (Alfaras e.g. In some embodiments, data quality management logic is further configured to produce a plurality of metrics based on the monitoring of the EDW platform [0013]. In some embodiments, the metrics are based on the delay time and the produced peak demand hour data [0014]. In some embodiments, the analysis of the EDW platform is at least based on the produced plurality of metrics [0015]. One of the characteristics of data warehouses is their ability to structure and organize data in a way that facilitates analysis and decision-making. They typically employ a dimensional modeling approach, which may involve organizing data into dimensions and facts [0042]. Dimensions represent the descriptive attributes of data, such as time, geography, product, or customer, while facts are the measurable metrics or events being analyzed, such as sales revenue, inventory levels, or website traffic [0042]. In some embodiments, telemetry data 328 can encompass real-time measurements crucial for monitoring and optimizing data performance, content, and/or validity [0218]. Telemetry data 328 related to data pipeline quality management typically encompasses a wide range of metrics, statistics, and insights gathered from the operation and performance of the data pipeline itself [0218]. In more embodiments, the process 1300 can configure key processes with different thresholds and grouping for alerting a user (block 1330) [0397]. In additional embodiments, the process 1300 can send alerts to take further action (block 1340). These alerts can be sent through an application, web portal, email, text, etc. They can be configured to alert a user to review and subsequently take further action [0398].)
Alfaras teaches publishing, by the computer program, the metrics to an automation platform, wherein the automation platform is configured to suggest an automation in response to receiving certain metrics. (Alfaras e.g. The logic is configured to select a target enterprise data warehouse (EDW) platform, monitor the EDW platform, produce peak demand hour data, analyze the EDW platform, generate a plurality of suggested actions, automatically select one of the suggested actions, and dynamically apply the selected suggested action [0006]. Referring to FIG. 15, a flowchart depicting a process 1500 for configuring data warehouses in accordance with various embodiments of the disclosure is shown [0408]. In many embodiments, the process 1500 can select a target enterprise data warehouse (EDW) platform (block 1510) [0408]. In a number of embodiments, the process 1500 can monitor query processing on the data warehouse and produce metrics on queries (block 1520). Monitoring queries allows organizations to assess the performance and efficiency of their data pipelines [0409]. In further embodiments, the process 1500 can determine if the metrics are being analyzed manually or automatically (block 1535). In certain embodiments, the automatic process may be provided as a service that can be selected by a user, administrator, or a logic if enabled [0411]. However, if it is determined that an automatic analysis is desired, the numerous embodiments of the process 1500 can execute an automated analysis (block 1540) [0413]. The automated analysis can include setting one or more thresholds, analyzing various data, and/or providing one or more suggested actions in response to the analysis. In some embodiments, the automated analysis is generated by one or more machine-learning processes [0413].)
Alfaras does not explicitly teach, however, Schoening teaches (1) that the process is a manufacturing process, (2) the plurality of systems comprise a plurality of manufacturing machines, and (3) calculating, by the computer program, an optimal reordering point for raw materials based on the metrics (Schoening e.g. A manufacturing process and inventory management or tracking system uses a radio frequency identification (RFID) detection system which may be, for example, a phased array antenna based RFID detection system, to track and manage material storage and flow of material in a manufacturing process or plant [0017]. The management or tracking system operates to track and to provide the location of various inventory within an inventory region of the plant and may operate in conjunction with the various machines that implement manufacturing stages or steps of the manufacturing process to assure that the correct materials (e.g., inventory, machine parts, etc.) and processing procedures are used at or on the various production machines of the process to produce a particular product as defined by a job number or job order [0017]. The job numbers may also be associated with or define manufacturing steps that need to be taken in the plant to produce the product associated with the job number. The process management system may then implement or manage a particular production run used for a job number by tracking the RFID tags for the various materials to be used in the production run for the job number during the production run to assure that the correct materials are used in the production run and to assure that the correct processing steps or procedures are used at each of the various stages of the production run [0018]. A method of controlling a manufacturing process includes storing manufacturing item information in a computer readable memory for each of a set of manufacturing items, the manufacturing item information for a particular manufacturing item including a radio frequency tag identifier and manufacturing item identification information defining the identity of the manufacturing item and storing manufacturing process information defining manufacturing information associated with a manufacturing job [0031]. The manufacturing process tracking system may operate with manufacturing items to be used in a manufacturing job that are removable pieces of equipment used on the manufacturing equipment when implementing the manufacturing job, such as a dye, a press, or a machine tool. The manufacturing items may also or instead be raw materials or intermediate materials used in the manufacturing job to produce the product [0036]. FIG. 1 illustrates an example inventory and process management or tracking system 10 including a command system 12 connected to an RFID detection and tracking system. The command system 12 is also connected to one or more process or manufacturing controllers 16, each controlling process activities in one of a set of manufacturing stages 18a to 18d associated with a manufacturing process 19 [0047]. The command system 12 may send an enable signal to enable the operation of the production equipment only when the correct materials are in place for the job order being implemented or run on the equipment. In addition, the command system 12 may determine or calculate how much paper material is used or is removed from the roll (e.g., by tracking the amount of paper used in the corrugator machine) for the job order and thus may calculate the remaining length or amount of paper on the roll and store this length or amount in the electronic record for the RFID tag on the paper roll when the roll is removed from the corrugator machine. The command system 12 may perform this record keeping automatically in order to prevent inventory stockouts and in order to reorder new stock if needed [0065].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Alfaras’s Data Management Platform that can monitor key processes and provide data visibility with Schoening’s Inventory and Process Management System that track and manages a manufacturing process involving manufacturing machines and raw materials in order to increase the efficiencies of the plant and to increase the quality of the plant production by reducing or eliminating waste, manufacturing errors and shipping errors in the production facility (Schoening e.g. [0017]).
As per claim 4 (Currently amended), Alfaras in view of Schoening teach the method of claim 1, Alfaras teaches the value stream is received in formatted JavaScript Object Notation (JSON), YAML Ain’t Markup Language (YAML), or Extensible Markup Language (XML) documents (Alfaras e.g. In the realm of data pipeline data sources, API, XML, and JSON are commonly considered as supplemental data 215, providing additional datasets that complement primary data sources and enrich analytical capabilities (Fig. 2 and [0128]). XML (eXtensible Markup Language) and JSON (JavaScript Object Notation) are both formats commonly used for representing structured data in a human-readable format. XML and JSON data may be sourced from various sources, such as web services, data feeds, or file imports. These formats are widely used for exchanging data between different systems and platforms, making them valuable supplemental data 215 for data pipelines [0129]. XML and JSON data can contain diverse types of information, including product catalogs, customer profiles, transaction records, and more [0129].)
As per claim 5 (Original), Alfaras in view of Schoening teach the method of claim 1, Alfaras teaches further comprising: receiving, by computer program, policy information for an organization, wherein the computer program further processes the policy information with the events. (Alfaras e.g. Another important aspect of data quality management is data governance, which may involve defining policies, procedures, and standards for managing data quality throughout the organization [0053]. Governance is another aspect of data quality management, involving the establishment of policies, processes, and standards to ensure the proper management, usage, and protection of data assets [0067]. Data governance 150 in terms of data pipelines can refer to the establishment of policies, processes, and controls to ensure the quality, integrity, security, and compliance of data as it flows through the pipeline [0102]. In some embodiments, a data validation logic 431 can refer to the set of rules, algorithms, and criteria used to assess the quality, integrity, and consistency of data as it moves through the pipeline. This logic can encompass various components and configurations tailored to specific data quality requirements and business objectives [0236]. In a number of embodiments, a metadata management logic 432 can refer to the set of processes, rules, and mechanisms used to govern, control, and leverage metadata throughout the data pipeline lifecycle. Metadata management logic 432 encompasses various components and configurations aimed at ensuring the consistency, accuracy, and usefulness of metadata for data management, analysis, and decision-making purposes [0242].)
As per claim 6 (Original), Alfaras in view of Schoening teach the method of claim 1, Alfaras teaches wherein the event comprises an inventory event, a job event, an action event, and/or a system status event. (Alfaras e.g. One of the characteristics of data warehouses is their ability to structure and organize data in a way that facilitates analysis and decision-making. They typically employ a dimensional modeling approach, which may involve organizing data into dimensions and facts [0042]. Dimensions represent the descriptive attributes of data, such as time, geography, product, or customer, while facts are the measurable metrics or events being analyzed, such as sales revenue, inventory levels, or website traffic [0042]. Data source data 451 may include transactional data generated by operational systems, such as customer orders, sales transactions, financial transactions, or inventory records, providing a record of business activities and operations [0298]. Furthermore, in some embodiments, data source data 451 can include log data generated by IT systems, applications, or infrastructure components, capturing events, activities, and performance metrics for monitoring, troubleshooting, and analysis purposes. Log data may include server logs, application logs, network logs, security logs, or audit logs, providing insights into system behavior, user interactions, and operational metrics [0299].)
As per claim 7 (Currently amended), Alfaras in view of Schoening teach the method of claim 1, further comprising: periodically pinging, by the computer program, the manufacturing machines for a manufacturing system status
Alfaras teaches periodically pinging, by the computer program the systems for the system status (Alfaras e.g. Linking a metadata monitoring status in a data pipeline can refer to establishing a connection or association between the metadata monitoring status and the relevant components, processes, or entities within the pipeline (Fig. 9 and [0374]). This linkage enables organizations to track and monitor the status, health, and performance of the data pipeline in real-time or near real-time. By linking metadata monitoring status to specific pipeline components or processes, organizations can gain insights into the operational status, data quality, and reliability of each stage of the pipeline, including data ingestion, transformation, loading, and delivery [0374].) Alfaras does not explicitly teach, however, Schoening teaches pinging the manufacturing machines for a manufacturing system status (Schoening e.g. A manufacturing process and inventory management or tracking system uses a radio frequency identification (RFID) detection system which may be, for example, a phased array antenna based RFID detection system, to track and manage material storage and flow of material in a manufacturing process or plant [0017]. The management or tracking system operates to track and to provide the location of various inventory within an inventory region of the plant and may operate in conjunction with the various machines that implement manufacturing stages or steps of the manufacturing process to assure that the correct materials (e.g., inventory, machine parts, etc.) and processing procedures are used at or on the various production machines of the process to produce a particular product as defined by a job number or job order [0017]. When a job order or production run designed to produce a particular product is started or implemented within the plant, the command system 12 begins to track or manage that job order using the RFID tags within the plant to assure that the correct raw materials are used in the production run for the job order, to assure that intermediate materials created during the manufacturing steps for the production run for the job order are provided to the correct or to the appropriate machines during the production run when creating the final product, that the machines used to implement the production run for the job order have the correct raw materials (e.g., paper, ink, glue, etc.) provided thereto, equipment (printing plates, etc.) installed thereon, and programming (e.g., printing procedures, banding procedures, etc.) installed or loaded therein, etc. an for assuring that the correct final products are produced and shipped to the customer as called for by the job order or job number [0063]. FIG. 3 depicts various materials, goods, or equipment in the corrugated plant 50 that may be tagged with the RFID tags 22. These materials, goods or equipment include, as examples only, raw materials such as a roll of paper material 70; intermediate products such as individual corrugated sheets 72 or a stack of corrugated sheets 74; equipment such as a trolley or cart or pallet 76 carrying a stack of corrugated sheets; process supplies such as a paint supply 78, a printing plate or a dye or any other machine tool 80, or an ink supply cartridge 82; and finished goods such as individual corrugated boxes 84 or a pallet of banded corrugated boxes 86 [0071].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Alfaras’s Data Management Platform that can monitor key processes and provide data visibility with Schoening’s Inventory and Process Management System that track and manages a manufacturing process involving manufacturing machines and raw materials in order to increase the efficiencies of the plant and to increase the quality of the plant production by reducing or eliminating waste, manufacturing errors and shipping errors in the production facility (Schoening e.g. [0017]).
As per claim 8 (Currently amended), Alfaras in view of Schoening teach the method of claim 1, Alfaras teaches wherein the processing calculates service metrics, state metrics, and capacity metrics for the plurality of systems (Alfaras e.g. Furthermore, in some embodiments, data source data 451 can include log data generated by IT systems, applications, or infrastructure components, capturing events, activities, and performance metrics for monitoring, troubleshooting, and analysis purposes. Log data may include server logs, application logs, network logs, security logs, or audit logs, providing insights into system behavior, user interactions, and operational metrics [0299]. Furthermore, in some embodiments, end user data 456 may encompass derived or calculated metrics that have been derived from raw or source data through data transformation or calculation processes. This can include calculated fields, ratios, or indicators that provide additional context or insights into business performance, efficiency, or effectiveness [0329]. Metadata monitoring tracks changes, updates, and usage patterns of metadata, providing visibility into metadata evolution and usage trends, enabling organizations to assess the health and effectiveness of their metadata management practices [0246]. Additionally, linking metadata monitoring status can facilitate reporting, auditing, and compliance efforts by providing a comprehensive view of the pipeline's performance and adherence to predefined metrics, thresholds, or service-level agreements (SLAs) [0374].). Schoening teaches calculating metrics for manufacturing machines (Schoening e.g. The management system employs a detection and tracking system that uses RFID tags attached to various different materials in the plant, such as raw materials, intermediate products or finished goods, to detect and track the location of these materials at any time and or at any location in the plant including in an inventory region of the plant (including a spare parts inventory) and a manufacturing region of the plant [0018]. The manufacturing process tracking system may operate with manufacturing items to be used in a manufacturing job that are removable pieces of equipment used on the manufacturing equipment when implementing the manufacturing job, such as a dye, a press, or a machine tool. The manufacturing items may also or instead be raw materials or intermediate materials used in the manufacturing job to produce the product [0036]. FIG. 1 illustrates an example inventory and process management or tracking system 10 including a command system 12 connected to an RFID detection and tracking system. The command system 12 is also connected to one or more process or manufacturing controllers 16, each controlling process activities in one of a set of manufacturing stages 18a to 18d associated with a manufacturing process 19 [0047]. The command system 12 may send an enable signal to enable the operation of the production equipment only when the correct materials are in place for the job order being implemented or run on the equipment. In addition, the command system 12 may determine or calculate how much paper material is used or is removed from the roll (e.g., by tracking the amount of paper used in the corrugator machine) for the job order and thus may calculate the remaining length or amount of paper on the roll and store this length or amount in the electronic record for the RFID tag on the paper roll when the roll is removed from the corrugator machine. The command system 12 may perform this record keeping automatically in order to prevent inventory stockouts and in order to reorder new stock if needed [0065].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Alfaras’s Data Management Platform that can monitor key processes and provide data visibility with Schoening’s Inventory and Process Management System that track and manages a manufacturing process involving manufacturing machines and raw materials in order to increase the efficiencies of the plant and to increase the quality of the plant production by reducing or eliminating waste, manufacturing errors and shipping errors in the production facility (Schoening e.g. [0017]).
As per claim 10 (Currently amended), Alfaras in view of Schoening teach the method of claim 1, Alfaras teach wherein the automation comprises reducing time at a bottleneck step in response to the certain metric identifying a bottleneck in the process. (Alfaras e.g. Quality assurance in data pipelines 120 may involve performance monitoring and optimization to ensure that data processing meets predefined service level agreements (SLAs) and performance targets [0093]. This may include monitoring data throughput, latency, and resource utilization, as well as identifying bottlenecks or areas for optimization in data processing workflows [0093]. By continuously monitoring and optimizing performance, organizations can enhance the efficiency and reliability of data pipelines, enabling timely and accurate delivery of data to downstream systems and applications [0093]. Additionally, performance metrics and benchmarks can be defined to evaluate the throughput, latency, and resource utilization of the pipeline under different workload conditions [0355]. In a number of embodiments, the process 1500 can monitor query processing on the data warehouse and produce metrics on queries (block 1520). Monitoring queries allows organizations to assess the performance and efficiency of their data pipelines. By tracking query execution times, resource utilization, and throughput metrics, organizations can identify bottlenecks, optimize query performance, and ensure that the pipeline meets service-level agreements (SLAs) for data processing and delivery [0409].) Alfaras does not explicitly teach, however, Schoening teaches a manufacturing process (Schoening e.g. A manufacturing process and inventory management or tracking system uses a radio frequency identification (RFID) detection system which may be, for example, a phased array antenna based RFID detection system, to track and manage material storage and flow of material in a manufacturing process or plant [0017].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Alfaras’s Data Management Platform that can monitor key processes and provide data visibility with Schoening’s Inventory and Process Management System that track and manages a manufacturing process involving manufacturing machines and raw materials in order to increase the efficiencies of the plant and to increase the quality of the plant production by reducing or eliminating waste, manufacturing errors and shipping errors in the production facility (Schoening e.g. [0017]).
As per claim 11 (Original), Alfaras in view of Schoening teach the method of claim 1, Alfaras teaches further comprising: generating, by the computer program, an insight based on the metrics using historical data by identifying an effectiveness of a decision on the historical data, wherein the insight is based on the effectiveness. (Alfaras e.g. Data warehouses are designed to support analytical processing and reporting tasks. They store historical data over time, enabling users to analyze trends, patterns, and historical performance. This historical perspective is useful for strategic decision-making, as it provides context and insight into past events and trends [0043]. Historical data sets provide insights into data trends, patterns, and changes over different time periods, enabling organizations to identify data anomalies, trends, or outliers that may indicate data quality issues or anomalies [0221]. Foundation data 454 may also include historical data, which captures past transactions, events, or trends over time, enabling organizations to analyze historical performance, identify patterns, and make informed decisions based on historical insights [0317].)
As per claim 12 (Original), Alfaras in view of Schoening teach the method of claim 11, Alfaras teaches further comprising: training, by the computer program, a machine learning model using the effectiveness as a reinforcement learning weight (Alfaras e.g. Data lakes provide a centralized repository for storing diverse data types and formats, enabling data scientists, analysts, and other stakeholders to perform ad-hoc queries, data discovery, and machine learning experiments [0148]. Finally, in numerous additional embodiments, data may be processed into a format usable by a machine-learning model 326 (e.g., feature vectors), and or other pre-processing techniques. The machine-learning (“ML”) model 326 may be any type of ML model, such as supervised models, reinforcement models, and/or unsupervised models [0231]. Furthermore, in some embodiments, end user data 456 may encompass derived or calculated metrics that have been derived from raw or source data through data transformation or calculation processes. This can include calculated fields, ratios, or indicators that provide additional context or insights into business performance, efficiency, or effectiveness [0329]. End user data 456 may also include predictive or prescriptive analytics models that leverage advanced statistical techniques or machine learning algorithms to forecast future trends, identify anomalies, or recommend optimal courses of action [0329]. Reporting data 458 may also incorporate predictive analytics models or forecasting techniques that anticipate future trends or outcomes based on historical data patterns [0342].).
As per claim 13 (Currently amended), Alfaras teaches a system, comprising: an event streaming platform in communication with a plurality of systems; an alerting platform; an automation platform; and a value chain platform comprising a computer processor an executing a computer program that is configured to (Alfaras e.g. Systems and methods for operating a data platform across various cloud and legacy environments to provide increased data visibility and quality management in accordance with embodiments of the disclosure are described herein [0006]. Aspects of the present disclosure may be embodied as an apparatus, system, method, or computer program product [0077]. In some embodiments, a device includes a processor, a memory communicatively coupled to the processor, a data quality management logic. The logic is configured to select a target enterprise data warehouse (EDW) platform, monitor the EDW platform, produce peak demand hour data, analyze the EDW platform, generate a plurality of suggested actions, automatically select one of the suggested actions, and dynamically apply the selected suggested action [0006]. Referring to FIG. 2, a schematic block diagram of an enterprise data warehouse 200, in accordance with various embodiments of the disclosure is shown [0108]. Furthermore, in some embodiments, data sources 210 may include external data feeds or streams from third-party sources, such as social media platforms, web services, or IoT devices, which provide real-time or streaming data for analysis [0109]. Referring to FIG. 4, a schematic block diagram of a device 440 suitable for data pipeline quality management, in accordance with various embodiments of the disclosure is shown [0234].) Alfaras does not explicitly teach, however, Schoening teaches a plurality of manufacturing machines that are involved in a manufacturing process (Schoening e.g. The process management system operates in conjunction with the various machines that implement manufacturing stages or steps of the manufacturing process to assure that the correct materials and processing procedures are used at or on the various production machines of the process to produce a particular product as defined by a job number or job order (Abstract).) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Alfaras’s Data Management Platform that can monitor key processes and provide data visibility with Schoening’s Inventory and Process Management System that track and manages a manufacturing process involving manufacturing machines and raw materials in order to increase the efficiencies of the plant and to increase the quality of the plant production by reducing or eliminating waste, manufacturing errors and shipping errors in the production facility (Schoening e.g. [0017]).
Alfaras in view of Schoening teach receive a value stream for the manufacturing process, wherein the value stream comprises a plurality of expected events in the manufacturing process involving a plurality of manufacturing machines; to receive an event from the event streaming platform, wherein the event was published by one of the plurality of manufacturing machines and comprises a system identifier for the manufacturing machine and a process identifier for the manufacturing process; to process the event by comparing the event to the value stream; to generate a new event based on the processing; to publish the new event to the event streaming platform, wherein the new event is consumed by the plurality of manufacturing machines; to generate metrics for the manufacturing process; to calculate an optimal reordering point for raw materials based on the metrics; to publish the metrics to an alerting platform, wherein the alerting platform is configured to generate an alert in response to receiving certain metrics; and to publish the metrics to an automation platform, wherein the automation platform is configured to suggest an automation in response to receiving certain metrics. (See claim 1 response.)
As per claim 15 (Original), Alfaras in view of Schoening teach the system of claim 13, wherein the computer program is further configured to receive policy information for an organization and to further process the policy information with the events. (See claim 5 response.)
As per claim 16 (Original), Alfaras in view of Schoening teach the system of claim 13, wherein the event comprises an inventory event, a job event, an action event, and/or a system status event. (See claim 6 response.)
As per claim 17 (Currently amended), Alfaras in view of Schoening teach the system of claim 13, wherein the processing calculates service metrics, state metrics, and capacity metrics for the plurality of manufacturing machines. (See claim 8 response.)
As per claim 19 (Currently amended), Alfaras in view of Schoening teach the system of claim 13, wherein the automation comprises reducing time at a bottleneck step in response to the certain metric identifying a bottleneck in the manufacturing process. (See claim 10 response.)
As per claim 20 (Original), Alfaras in view of Schoening teach the system of claim 13, wherein the computer program is further configured to generate an insight based on the metrics using historical data by identifying an effectiveness of a decision on the historical data, wherein the insight is based on the effectiveness and to train a machine learning model using the effectiveness as a reinforcement learning weight. (See claims 11 and 12 responses.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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