DETAILED ACTION
Claims 1-20 are pending and have been examined.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, system claims 1-7 are directed to a plurality of engines, method claims 8-17 are directed to a series of steps, and computer-readable medium claims 18-20 are directed to instructions being executable by a processing resource. Thus the claims are directed to a machine, process, and manufacture, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite batch recall assessment, including obtaining, analyzing, determining, comparing, and generating steps.
The limitations of obtaining, analyzing, determining, comparing, and generating, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite obtaining product data corresponding to each of a plurality of faulty products manufactured by an organization, wherein for each of the plurality of faulty products, the corresponding product data includes batch data of the faulty product and quality data describing a quality concern raised for the faulty product, wherein the quality data includes a quality parameter, from a plurality of quality parameters, indicating the quality concern raised for the faulty product; for each faulty product, of the plurality of faulty products, analyzing the batch data to identify a corresponding batch number indicating a product batch associated with the faulty product at the time of manufacturing of the faulty product; for each product batch, determining a corresponding count value for each of the plurality of quality parameters, wherein the corresponding count value is indicative of a number of faulty products, associated with the product batch, tagged with the respective quality parameter in the corresponding quality data; and comparing, for each quality parameter, the corresponding count value with a corresponding pre-determined threshold count value to determine a quality risk level associated with the product batch; and generating a batch risk notification for one or more product batches determined to have the quality risk level above a threshold risk level, the batch risk notification including recommendations for taking corrective actions, including recalling of the one or more product batches.
That is, other than reciting a plurality of engines in claims 1-7, and a processing resource in claims 18-20, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a plurality of engines, and a processing resource. The plurality of engines, and processing resource in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a plurality of engines, and a processing resource amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2-4 further describe the plurality of quality parameters, determining the corresponding count values for each product batch, and recite an additional clustering step. Claims 5-7 recite additional obtaining, determining, grouping, detecting, modifying, assigning, and comparing steps. Similarly, dependent claims 9-17, 19 and 20 recite additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a plurality of engines, and a processing resource.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0028 of the specification, “The engine(s) 102 may be implemented as a combination of hardware and programming, for example, programmable instructions to implement a variety of functionalities of the engine(s) 102. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the engine(s) 102 may be executable instructions. Such instructions may be stored on a non-transitory machine-readable storage medium which may be coupled either directly with the system 100 or indirectly (for example, through networked means). In an example, the engine(s) 102 may include a processing resource, for example, either a single processor or a combination of multiple processors, to execute such instructions. In the present examples, the non-transitory machine-readable storage medium may store instructions that, when executed by the processing resource, implement the engine(s) 102. In other examples, the engine(s) 102 may be implemented as electronic circuitry.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claim 1 includes a system defined by a plurality of engines, which is deemed software, with no accompanying hardware components (e.g., a physical system including inter alia, processor, server, etc.). Dependent claims 2-7 are rejected based upon the same rationale.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 4, 7, 8, 10, 12-14, 17, 18 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kaul et al (US 20240354929 A1).
As per claim 1, Kaul et al disclose a system (i.e., production system 110 provides the captured images to the analytics system 120 for analysis, ¶ 0020) comprising:
a data acquisition engine to: obtain product data corresponding to each of a plurality of faulty products manufactured by an organization (i.e., The analytics system 120 may use one or more machine-learning models to determine the properties of the product (e.g., detect and classify deviations from product specifications), ¶ 0020), wherein for each of the plurality of faulty products, the corresponding product data includes batch data of the faulty product and quality data describing a quality concern raised for the faulty product, wherein the quality data includes a quality parameter, from a plurality of quality parameters, indicating the quality concern raised for the faulty product (i.e., These GUIs allow users to manage and analyze batches of products, with the ability to track, search, and classify product defects through captured images. Users can also select specific images, which could be used for further analysis, reporting, or machine learning applications as model training or quality assurance checks, ¶ 0067);
a faulty batch identification engine to: for each faulty product, of the plurality of faulty products, analyze the batch data to identify a corresponding batch number indicating a product batch associated with the faulty product at the time of manufacturing of the faulty product (i.e., Referring to FIG. 3, GUI 300 includes a navigation bar with tabs for different functionalities at the top. The functionalities include “Find Batches,” currently selected. On the left side of the GUI 300, there is a panel titled “Find Batches” with fields to input or select criteria such as “File Name,” “Line Name,” “building Name,” and “Product Name.” There are also options for specifying the start and end date and a button labeled “search for batches.”, ¶ 0068);
a batch risk assessment engine to: for each product batch, determine a corresponding count value for each of the plurality of quality parameters, wherein the corresponding count value is indicative of a number of faulty products, associated with the product batch, tagged with the respective quality parameter in the corresponding quality data (i.e., Below the filters, there's a table with columns for “Image Name,” “Site,” “Building,” “Line,” “Product,” “Batch,” “Camera,” “Machine,” “Timestamp,” “Label Source,” “Defects,” and “Unique Image Type.” The table lists several entries, each corresponding to an image captured during the manufacturing process, along with associated data such as the site, specific building, line number, and product details. Each row also indicates the timestamp of when the image was captured, the source of the labeling, the type of defect detected, and whether the image is unique, ¶ 0068); and
compare, for each quality parameter, the corresponding count value with a corresponding pre-determined threshold count value to determine a quality risk level associated with the product batch (i.e., “Total Inspected” indicates the number of items inspected in each batch. “Total Accepted” indicates the number of items that passed inspection. “Total Rejected” indicates the number of items that failed inspection. The “Comments” column includes notes related to the batch, with the last entry stating “Ignore this batch.” Rows 5 to 7 also include “Stage 1,” “Stage 2,” and “Stage 3” in the “RULE” column, followed by “Row” indications, which references specific steps or checkpoints in the batch process. The table shows varying numbers of inspected, accepted, and rejected items, which could be used to track performance, yield, and quality over time, ¶ 0071); and
a batch recall recommendation engine to: generate a batch risk notification for one or more product batches determined to have the quality risk level above a threshold risk level, the batch risk notification including recommendations for taking corrective actions, including recalling of the one or more product batches (i.e., Correcting these errors may require re-inspection, disposal of good products, or recall of defective products released into the market, ¶ 0004, wherein The analytics system 120 initiates 660 a corrective action based on the grouped defects. The corrective action may include creating a report to record the defect and a batch the defect was found in, issuing a notification to alert a user (e.g., a human inspector), isolating or quarantining the affected batch to prevent it from being distributed or used further, and conducting an investigation, ¶ 0079).
As per claim 3, Kaul et al disclose the faulty batch identification engine is to: cluster the plurality of faulty products into a plurality of product clusters based on the batch number of each of the plurality of faulty products, wherein each product cluster includes faulty products, from the plurality of faulty products, manufactured as part of a same product batch indicated by a single batch number, and wherein each product cluster corresponds to a single product batch corresponding to the single batch number (i.e., Below the filters, there's a table with columns for “Image Name,” “Site,” “Building,” “Line,” “Product,” “Batch,” “Camera,” “Machine,” “Timestamp,” “Label Source,” “Defects,” and “Unique Image Type.” The table lists several entries, each corresponding to an image captured during the manufacturing process, along with associated data such as the site, specific building, line number, and product details. Each row also indicates the timestamp of when the image was captured, the source of the labeling, the type of defect detected, ¶ 0069).
As per claim 4, Kaul et al disclose to for each quality parameter, from the plurality of quality parameters, identify, from the product cluster corresponding to the product batch, one or more faulty products tagged with the quality parameter (i.e., Below the filters, there's a table with columns for “Image Name,” “Site,” “Building,” “Line,” “Product,” “Batch,” “Camera,” “Machine,” “Timestamp,” “Label Source,” “Defects,” and “Unique Image Type.” The table lists several entries, each corresponding to an image captured during the manufacturing process, along with associated data such as the site, specific building, line number, and product details. Each row also indicates the timestamp of when the image was captured, the source of the labeling, the type of defect detected, ¶ 0069);
for each identified faulty product, analyze the quality data to obtain a quality parameter description provided against the quality parameter (i.e., The “Label Source” column references specific codes, perhaps linked to a classification system or a database of defects. The “Defects” column categorizes the type of defect identified in the images. Finally, there's a mention of “Unique Image Type,” which refers to whether an image shows a unique defect or is a duplicate of another image in terms of the defect shown, ¶ 0070);
analyze, for each identified faulty product, the quality parameter description, to ascertain if the quality parameter for the identified faulty product satisfies a predefined condition (i.e., the post-processed images are enriched with heatmaps that vary in intensity based on the model's confidence in the defect classification. These heatmaps offer a straightforward, intuitive way for users to gauge the model's certainty about the presence and severity of defects, ¶ 0040);
upon ascertaining that the quality parameter for the identified faulty product satisfies the predefined condition, increase the corresponding count value of the quality parameter by a numerical value of one; and upon ascertaining that the quality parameter for the identified faulty product does not satisfy the predefined condition, disregard the identified faulty product by not updating the corresponding count value (i.e., “Total Inspected” indicates the number of items inspected in each batch. “Total Accepted” indicates the number of items that passed inspection. “Total Rejected” indicates the number of items that failed inspection. The “Comments” column includes notes related to the batch, with the last entry stating “Ignore this batch.” Rows 5 to 7 also include “Stage 1,” “Stage 2,” and “Stage 3” in the “RULE” column, followed by “Row” indications, which references specific steps or checkpoints in the batch process, ¶ 0071).
As per claim 7, Kaul et al disclose for each product batch, assign a corresponding status to the respective quality parameter based on a difference between the corresponding count value and the corresponding pre-determined threshold count value, wherein the corresponding status indicates if the corresponding pre-determined threshold count value is breached for the respective quality parameter; obtain a mapping table indicating a corresponding quality risk level for each different possible status combination of the plurality of quality parameters, wherein the mapping table is generated based on a comparison of corresponding pre-determined threshold count values of different quality parameters of the plurality of quality parameters; and for each product batch, compare the corresponding statuses assigned to each of the plurality of quality parameters with the mapping table to determine the quality risk level associated with the product batch (i.e., The GUI 400 shows a record table of status of a batch. There are several columns in the table, including “S.No,” “Row,” “rule,” “batch ID,” “start time,” “end time,” totally inspected,” “totally accepted,” “totally rejected,” and “comments.” “S. No” stands for serial number, marking the sequence of records. “Row” contains numerical values that could possibly indicate a sorting or grouping mechanism. “RULE” describes the operation applied, with entries like “split_batch,” “split_batch_single_record,” and mentions of different stages. “Batch ID” indicates unique identifiers for batches being processed. The table shows varying numbers of inspected, accepted, and rejected items, which could be used to track performance, yield, and quality over time, ¶ 0071).
As per claim 12, Kaul et al disclose for the respective quality parameter being a deviation associated with a faulty product, the quality data includes a risk rating value, a severity value, and a disposition value, and wherein the predefined condition includes at least one of: the risk rating value being high; the severity value being high; and the disposition value being return (i.e., a GUI 400 in a table format. The GUI 400 shows a record table of status of a batch. There are several columns in the table, including “S.No,” “Row,” “rule,” “batch ID,” “start time,” “end time,” totally inspected,” “totally accepted,” “totally rejected,” and “comments.” “S. No” stands for serial number, marking the sequence of records. “Row” contains numerical values that could possibly indicate a sorting or grouping mechanism. The table shows varying numbers of inspected, accepted, and rejected items, which could be used to track performance, yield, and quality over time, ¶ 0071).
As per claim 13, Kaul et al disclose for the respective quality parameter being a corrective and preventive action (CAPA) taken for a faulty product, the quality data includes a severity value, a category value, a risk priority number (RPN) category, and a threshold breach value, and wherein the predefined condition includes at least one of: the severity value being high; the category value being product; the RPN category being red; and the threshold breach value being true (i.e., The analytics system 120 initiates 660 a corrective action based on the grouped defects. The corrective action may include creating a report to record the defect and a batch the defect was found in, issuing a notification to alert a user (e.g., a human inspector), isolating or quarantining the affected batch to prevent it from being distributed or used further, and conducting an investigation, ¶ 0079. On the left side of the GUI 300, there is a panel titled “Find Batches” with fields to input or select criteria such as “File Name,” “Line Name,” “building Name,” and “Product Name.” There are also options for specifying the start and end date and a button labeled “search for batches.” The main panel of the GUI 300 shows an “image view” with options for “list view” and “select images,” and a “filter by” feature that allows users to filter images based on specific criteria. Labels for potential defects are also present, including “crack,” “other,” “product splashing/splatter,” “weed/mark,” “other particle-fiber,” “other particle-non-fiber,” with checkboxes next to each category, ¶ 0068).
As per claim 14, Kaul et al disclose for the respective quality parameter being a non-conformance assessment of a faulty product, the quality data includes a major category value and a product quality value, and wherein the predefined condition includes at least one of: the major category value being yes; and the product quality value being yes (i.e., The analytics system 120 may use one or more machine-learning models to determine the properties of the product (e.g., detect and classify deviations from product specifications), ¶ 0020. The GUI 400 shows a record table of status of a batch. There are several columns in the table, including “S.No,” “Row,” “rule,” “batch ID,” “start time,” “end time,” totally inspected,” “totally accepted,” “totally rejected,” and “comments.” “S. No” stands for serial number, marking the sequence of records. “Row” contains numerical values that could possibly indicate a sorting or grouping mechanism, ¶ 0071).
Claims 8, 10 and 17 are rejected based upon the same rationale as the rejection of claims 1, 4 and 7, respectively, since they are the method claims corresponding to the system claims.
Claims 18 and 20 are rejected based upon the same rationale as the rejection of claims 1 and 4, respectively, since they are the computer-readable medium claims corresponding to the system claims.
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.
Claims 2, 5, 6, 9, 11, 15, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaul et al (US 20240354929 A1), in view of Yang et al (US 20250335860 A1).
As per claim 2, Kaul et al disclose the plurality of quality parameters includes a deviation associated with a faulty product, a corrective and preventive action (CAPA) taken for a faulty product, and a non-conformance assessment of a faulty product (i.e., The analytics system 120 may use one or more machine-learning models to determine the properties of the product (e.g., detect and classify deviations from product specifications), ¶ 0020, The analytics system 120 initiates 660 a corrective action based on the grouped defects. The corrective action may include creating a report to record the defect and a batch the defect was found in, issuing a notification to alert a user (e.g., a human inspector), isolating or quarantining the affected batch to prevent it from being distributed or used further, and conducting an investigation. This investigation may include additional analysis of images taken of the batch before the detection of the defect to determine its cause or pinpoint where the defect may have originated, among other actions, ¶ 0079).
Kaul et al does not disclose the plurality of quality parameters includes a complaint received against a faulty product.
Yang et al disclose by counting and analyzing the number of customer complaints, the product quality inspector can identify key issues about which the customers complain about, and work out targeted measures for product quality improvement, thereby improving customer experience and brand reputation (¶ 0052).
Kaul et al and Yang et al are concerned with effective product quality management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the plurality of quality parameters includes a complaint received against a faulty product in Kaul et al, as seen in Yang et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, Kaul et al disclose obtain, for each of a plurality of historically recalled product batches manufactured by the organization, historical product data corresponding to each historically identified faulty product associated with the historically recalled product batch, wherein the historical product data corresponding to each historically identified faulty product includes historical quality data describing a quality concern raised for the historically identified faulty product, and wherein the historical quality data includes a quality parameter, from the plurality of quality parameters, indicating the quality concern raised for the historically identified faulty product (i.e., the image query module 260 uses cloud services and microservices architecture to enable users to conduct searches for images. Searches can be based on a wide range of criteria, including location, building, production line, specific machines, product types, batches, and time frames. This flexibility allows for pinpoint identification of images across even vast historical datasets, enabling users to refine their search further to include specific machines, camera setups, and defect types, ¶ 0050).
Kaul et al does not disclose for each historically recalled product batch, determine a corresponding historical count value for each of the plurality of quality parameters, wherein the corresponding historical count value is indicative of a number of historically identified faulty products, associated with the historically recalled product batch, tagged with the respective quality parameter; group the plurality of historically recalled product batches into one or more recalled batch groups based on a product type of products associated with each of the plurality of historically recalled product batches, wherein each of the one or more recalled batch groups includes historically recalled product batches of same product type; and for each product type, determine for each of the respective quality parameters, the corresponding pre-determined threshold count value using a threshold determination model and the corresponding historical count value determined for the historically recalled product batches grouped into the recalled batch group corresponding to the product type.
Yang et al disclose a historical quality data influence coefficient is specifically analyzed as follows: a product defect rate, a product return rate, and a number of customer complaints during a set historical quality inspection period are extracted according to the historical quality-related information, where the product defect rate can be calculated and obtained by randomly sampling a certain number of products from a production line or a warehouse, and recording a number of defective products; the product return rate can be calculated and obtained by logging into a product sales backend management page and obtaining a number of product return orders during the set historical quality inspection period (¶¶ 0042-0043).
A mathematical relationship between historical quality data and historical quality compliance data can be established using a regression analysis method, and a regression coefficient can be calculated to quantify the influence of historical quality data, so as to obtain the historical quality data influence coefficient. In this embodiment, the historical quality data influence coefficient is obtained through comprehensive analysis of the product defect rate, the product return rate, and the number of customer complaints, which are used to determine a numerical value of the historical quality data influence coefficient (¶¶ 0045-0046).
Kaul et al and Yang et al are concerned with effective product quality management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include for each historically recalled product batch, determine a corresponding historical count value for each of the plurality of quality parameters, wherein the corresponding historical count value is indicative of a number of historically identified faulty products, associated with the historically recalled product batch, tagged with the respective quality parameter; group the plurality of historically recalled product batches into one or more recalled batch groups based on a product type of products associated with each of the plurality of historically recalled product batches, wherein each of the one or more recalled batch groups includes historically recalled product batches of same product type; and for each product type, determine for each of the respective quality parameters, the corresponding pre-determined threshold count value using a threshold determination model and the corresponding historical count value determined for the historically recalled product batches grouped into the recalled batch group corresponding to the product type in Kaul et al, as seen in Yang et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6, Kaul et al disclose detect that a new product batch associated with the organization is recalled, wherein the new product batch is not a part of the plurality of historically recalled product batches; and modify the corresponding pre-determined threshold count value based on new product data corresponding to new faulty products within the new product batch (i.e., GUIs allow users to manage and analyze batches of products, with the ability to track, search, and classify product defects through captured images. Users can also select specific images, which could be used for further analysis, reporting, or machine learning applications as model training or quality assurance checks, ¶ 0067).
As per claim 11, Kaul et al does not disclose for the respective quality parameter being a complaint received for a faulty product, the quality data includes a complaint type value, a risk assessment and ranking value, and a product status value, and wherein the predefined condition includes at least one of: the complaint type value being product and the risk assessment and ranking value being high; and the product status value being product returned.
Yang et al disclose the number of customer complaints can directly reflect customer feedback on the product; and when the number of customer complaints increases continuously during the historical quality inspection period, it means that the product quality does not meet expectations, and the quality problems during production have not been solved promptly (¶ 0047). By counting and analyzing the number of customer complaints, the product quality inspector can identify key issues about which the customers complain about, and work out targeted measures for product quality improvement, thereby improving customer experience and brand reputation (¶ 0052).
Kaul et al and Yang et al are concerned with effective product quality management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the respective quality parameter being a complaint received for a faulty product, the quality data includes a complaint type value, a risk assessment and ranking value, and a product status value, and wherein the predefined condition includes at least one of: the complaint type value being product and the risk assessment and ranking value being high; and the product status value being product returned in Kaul et al, as seen in Yang et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9, 15 and 16 are rejected based upon the same rationale as the rejection of claims 2, 5 and 6, respectively, since they are the method claims corresponding to the system claims.
Claim 19 is rejected based upon the same rationale as the rejection of claim 2, since it is the computer-readable medium claim corresponding to the system claim.
Conclusion
The prior art made of record and not relied upon, listed in the PTO-892, considered pertinent to applicant's disclosure, discloses product quality management.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 May 15, 2026
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