CTNF 18/597,417 CTNF 95316 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims Objections Claim 2 is objected to because of the following informalities: Claim language “segregating, at the one or more servers or the one or more cloud computing devices, the retrieved data related to one or more quality parameters defined in the selected quality procedure into region-specific data” should read “segregating, at the one or more servers or the one or more cloud computing devices, the retrieved data related to the one or more quality parameters defined in the selected quality procedure into region-specific data” in order to provide the appropriate antecedent basis. Claim 4 is objected to because of the following informalities: Claim language “the final quality disposition action is based on a worst-case summation of comparing the retrieved data to one or more tolerances corresponding to two or more quality parameters defined in the selected quality procedure” should read “the final quality disposition action is based on a worst-case summation of comparing the retrieved data to the one or more tolerances corresponding to two or more quality parameters defined in the selected quality procedure” in order to provide the appropriate antecedent basis. Claim 7 is objected to because of the following informalities: Claim language “the quality disposition actions include one or more of sending a report, sending an email and/or a text message, and/or displaying an alert and/or a notification” should read “[[the]] quality disposition actions include one or more of sending a report, sending an email and/or a text message, and/or displaying an alert and/or a notification” in order to provide the appropriate antecedent basis. Claim 9 is objected to because of the following informalities: Claim language “segregate the retrieved data related to the one or more quality parameters defined in the selected quality procedure into region-specific data; and compare the segregated data to the one or more region-specific tolerances” should read “segregate the retrieved data related to the one or more quality parameters defined in the selected quality procedure into region-specific data; and compare the segregated data to the one or more region-specific tolerances” in order to provide the appropriate antecedent basis. 07-29-01 AIA Claim 16 is objected to because of the following informalities: Claim language “segregate the retrieved data related to the one or more quality parameters defined in the selected quality procedure into region-specific data; and compare the segregated data to the one or more region-specific tolerances” should read “segregate the retrieved data related to the one or more quality parameters defined in the selected quality procedure into region-specific data; and compare the segregated data to the one or more region-specific tolerances” in order to provide the appropriate antecedent basis . Appropriate correction is required. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below. Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process/product. The below claim is considered to be a statutory category (process). Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belongs to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity. Independent Claim 1 is copied below, with the limitations belonging to an abstract idea highlighted in bold; the remaining limitations are ‘’additional elements’’. A computer-implemented method for quality control, the method comprising: each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receiving one or more turnup properties corresponding to the turnup event at one or more servers or one or more cloud computing devices; selecting, at the one or more servers or the one or more cloud computing devices, a predefined quality procedure matching one or more of the turnup properties ; retrieving, at the one or more servers or the one or more cloud computing devices, data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure; comparing, at the one or more servers or the one or more cloud computing devices, the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ; and based on the results of the comparing, executing, at the one or more servers or the one or more cloud computing devices, a quality disposition action defined in the respective quality parameter of the selected quality procedure. Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that when recited as such in a claim limitation covers mathematical processes (mathematical relationships, mathematical formulas or equations, mathematical calculations), certain methods of organizing human activity, and mental processes (concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion). For example, the limitations of “ selecting, at the one or more servers or the one or more cloud computing devices, a predefined quality procedure matching one or more of the turnup properties ”, “ comparing, at the one or more servers or the one or more cloud computing devices, the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ”, and “ based on the results of the comparing, executing, at the one or more servers or the one or more cloud computing devices, a quality disposition action defined in the respective quality parameter of the selected quality procedure ” are treated by the Examiner as belonging to mental processes - concept performed in the human mind including an observation, evaluation, judgement, and/or opinion. With regards to the mental steps, according to the 2019 PEG: “If a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind. See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.”); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d. 1314, 1324 (Fed. Cir. 2016) (holding that computer-implemented method for ‘‘anonymous loan shopping” was an abstract idea because it could be ‘‘performed by humans without a computer”); Versata Dev. Grp. v. SAP Am., Inc., 793 F.3d 1306, 1335 (Fed. Cir. 2015) (‘‘Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person's mind.”).” Limitations of “each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receiving one or more turnup properties corresponding to the turnup event at one or more servers or one or more cloud computing devices” and “retrieving, at the one or more servers or the one or more cloud computing devices, data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure” are treated as an extra solution activity recited in generality (e.g., mere data gathering). Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The additional elements: “turnup event”, “production unit”, “plurality of equipment stations”, “production line”, “turnup properties”, “servers”, “cloud computing devices”, and “quality parameters” add extra-solution activities (i.e., mere data gathering, source/type of data to be manipulated) using elements recited at a high level of generality (see MPEP 2106.05(g)); generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The preamble of Claim 1: “A computer-implemented method for quality control, the method comprising:” is a generically recited preamble. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B. Step 2B of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2A, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea. Essentially, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because they are well-understood and conventional in the relevant art of US20230236212 by Ennsbrunner (hereinafter Ennsbrunner) and US20200159183A1 to Ueda (hereinafter Ueda). Therefore, claim 1 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more. The independent claim, therefore, is not patent eligible. Similar limitations comprise the abstract ideas of independent Claims 8 and 15 . With regards to the dependent claims, Claims 2-7, 9-14, and 16-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims and/or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20200159183A1 to Ueda (hereinafter Ueda) in view of US20230236212A1 to Ennsbrunner (hereinafter Ennsbrunner ) . Regarding Claim1: Ueda discloses: “ A computer-implemented method for quality control, the method comprising :” (para 0008 – “ an object thereof is to provide a quality analysis device and a quality analysis method ”; para 0030 – “ The illustrated quality analysis device is configured with a computer , and includes a processor 101 ”); “ each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receiving one or more turnup properties corresponding to the turnup event at one or more servers or one or more cloud computing devices ” (Fig.3; para 0036 – “ The quality data is data indicating states of the products as the objects subjected to the quality analysis, and is thus an aggregation of values acquired every time each of the products is manufactured or inspected (i.e. turnup event , added by examiner). The quality data may be recorded in any type of device and, for example, it is data that is accumulated in an apparatus in the factory line (i.e. turnup property , added by examiner), or a supervision system for controlling an apparatus. It may instead be data that is accumulated in a product management system for managing the test results of the product inspection ”; para 0035 – “ FIG. 3 is an example of the quality data. In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ”, etc. are shown ”); “ retrieving, at the one or more servers or the one or more cloud computing devices, data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure ” (para 0033 – “ The input device 106 is used for inputting the quality data and the apparatus-information data; for inputting parameters such as, a counting target, the comparison condition, the base condition and the like ; and for inputting a start request for processing of the quality data, and the like .”; para 0037 – “ The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired (i.e. retrieved , added by examiner) every time the product is manufactured. The time-series data is a sequence of values obtained through sequential measurements by lapse of time .”); “ comparing, at the one or more servers or the one or more cloud computing devices, the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition (i.e. comparing the retrieved data to tolerances , added by examiner) that are subjected to the quality analysis, and to output, for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition ”; para 0028 – “ a base condition indicating a condition that constitutes a basis of quality analysis ( i.e. quality parameters , added by examiner); and a comparison condition indicating a condition subjected to the quality analysis ”); and “ based on the results of the comparing, executing, at the one or more servers or the one or more cloud computing devices, a quality disposition action defined in the respective quality parameter of the selected quality procedure ” (para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Ueda does not specifically disclose: “ selecting, at the one or more servers or the one or more cloud computing devices, a predefined quality procedure matching one or more of the turnup properties ”. However, Ennsbrunner discloses: “ selecting, at the one or more servers or the one or more cloud computing devices, a predefined quality procedure matching one or more of the turnup properties ” (para 0009 – “ In known quality assessment systems of processing operations, in particular welding processes, it is checked whether specific criteria lie within predetermined limits (i.e. being the predefined quality procedure , added by examiner)… in a welding process, it may be necessary to adapt specific welding parameters due to workpiece tolerances ”; para 0033 – “ For processing the workpiece W, specific target values of the processing parameters P i,soll (x) are selected from a plurality of possible processing parameters P i (x), which are stored, for example, in a database or a memory 9 , with which the workpiece W is processed in order to achieve a desired processing result (interpreted as the turnup property , added by examiner)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Ueda, as taught by Ennsbrunner , in order to improve the quality control to match the specific properties during the turnup event. Regarding Claim 2 : Ueda/ Ennsbrunner combination discloses the method of Claim 1. Ueda further discloses: “ wherein, for a non-homogenous production unit, the method further comprises: segregating, at the one or more servers or the one or more cloud computing devices, the retrieved data related to one or more quality parameters defined in the selected quality procedure into region-specific data ” (para 0035 – “ In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “ Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ” (i.e. the production unit is non-homogeneous , added by examiner), etc. are shown ”; para 0037 – “ FIG. 4 shows an example of the apparatus-information data . In FIG. 4, as an example of the data items of the apparatus-information data, “Facility ID”, “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown. Note that “Apparatus ID” is identification information for each apparatus, “Class ID” is identification information indicating a class of each apparatus, and “Facility ID” is identification information indicating what class of the apparatus the facility is configured with… “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) used for the product manufacturing condition or the product inspection (i.e. parameters defined in the selected quality procedure into region-specific data , added by examiner). The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired every time the product is manufactured ”); and “ comparing, at the one or more servers or the one or more cloud computing devices, the segregated data to one or more region-specific tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0047 – “ The comparison condition and the base condition may be automatically classified by a clustering method as shown in FIG. 7 . Instead, they may be predefined manually or these conditions may be manually written like queries for a database (i.e. using one or more servers , added by examiner). When they are to be defined from the outside, their corresponding values are inputted through the input device 106 in FIG. 2, so that the processor 101 performs processing corresponding to the condition setting unit 3 to thereby cause the auxiliary storage device 102 to store the conditions for analysis ”; para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 3 : Ueda/ Ennsbrunner combination discloses the method of Claim 1. Ueda further discloses: “ determining, at the one or more servers or the one or more cloud computing devices, a final quality disposition action for the production unit defined in the selected quality procedure ” (para 0047 – “ The comparison condition and the base condition may be automatically classified by a clustering method as shown in FIG. 7 . Instead, they may be predefined manually or these conditions may be manually written like queries for a database (i.e. using one or more servers , added by examiner); para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Regarding Claim 4 : Ueda/ Ennsbrunner combination discloses the method of Claim 3. Ueda further discloses: “ wherein the final quality disposition action is based on a worst-case summation of ” (para 0037 – “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) (interpreted as a worst-case summation , added by examiner ) used for the product manufacturing condition or the product inspection ”; para 0035 – “In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “ Pass/Fail Result Indicating Acceptance or Rejection ” (interpreted as the final quality disposition action , added by examiner)) “ comparing the retrieved data to one or more tolerances corresponding to two or more quality parameters defined in the selected quality procedure ” (para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 5 : Ueda/ Ennsbrunner combination discloses the method of Claim 1. Ueda further discloses: “wherein the turnup properties include one or more of an equipment station identifier, a production unit identifier, and/or a customer identifier” (para 0037 – “ In FIG. 4, as an example of the data items (interpreted as the turnup properties , added by examiner) of the apparatus-information data, “Facility ID” (i.e. equipment station identifier , added by examiner), “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown ”). Regarding Claim 6 : Ueda/ Ennsbrunner combination discloses the method of Claim 1. Ueda further discloses: “ wherein the data sources include one or more of a nuclear scanner, an X-ray scanner, an imaging system, a laboratory report, and/or a user-defined calculations ” (para 0064 – “ The data aggregation unit 1 is a processing unit that acquires quality data and apparatus-information data. The data-type classification unit 2 is a processing unit that classifies the quality data and the apparatus-information data acquired by the data aggregation unit 1 , in accordance with a predetermined specific rule (i.e. user-defined calculations , added by examiner)”). Regarding Claim 7 : Ueda/ Ennsbrunner combination discloses the method of Claim 1. Ueda further discloses: “ wherein the quality disposition actions include one or more of sending a report, sending an email and/or a text message, and/or displaying an alert and/or a notification ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition that are subjected to the quality analysis, and to output , for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition. This makes it possible to rapidly extract a trouble factor candidate and to easily predict occurrence of trouble ”). Regarding Claim 8 : Ueda discloses: “ An apparatus for quality control, the apparatus comprising at least one processor and at least one non-transitory memory comprising program code, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least ” (para 0030 – “ The illustrated quality analysis device is configured with a computer, and includes a processor 101 ; an auxiliary storage device 102 ; a memory 103 ”; para 0031 – “ The functions of the respective functional units in the quality analysis device are implemented by software, firmware or a combination of software and firmware . The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units ”): “ each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receive one or more turnup properties corresponding to the turnup event ” (Fig.3; para 0036 – “ The quality data is data indicating states of the products as the objects subjected to the quality analysis, and is thus an aggregation of values acquired every time each of the products is manufactured or inspected (i.e. turnup event , added by examiner). The quality data may be recorded in any type of device and, for example, it is data that is accumulated in an apparatus in the factory line (i.e. turnup property , added by examiner), or a supervision system for controlling an apparatus. It may instead be data that is accumulated in a product management system for managing the test results of the product inspection ”; para 0035 – “ FIG. 3 is an example of the quality data. In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ”, etc. are shown ”); “ retrieve data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure ” (para 0033 – “ The input device 106 is used for inputting the quality data and the apparatus-information data; for inputting parameters such as, a counting target, the comparison condition, the base condition and the like ; and for inputting a start request for processing of the quality data, and the like .”; para 0037 – “ The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired (i.e. retrieved , added by examiner) every time the product is manufactured. The time-series data is a sequence of values obtained through sequential measurements by lapse of time .”); “ compare the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition (i.e. comparing the retrieved data to tolerances , added by examiner) that are subjected to the quality analysis, and to output, for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition ”; para 0028 – “ a base condition indicating a condition that constitutes a basis of quality analysis ( i.e. quality parameters , added by examiner); and a comparison condition indicating a condition subjected to the quality analysis ”); and “ based on results of the comparing, execute a quality disposition action defined in the respective quality parameter of the selected quality procedure ” (para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Ueda does not specifically disclose: “ select a predefined quality procedure matching one or more of the turnup properties ”. However, Ennsbrunner discloses: “ select a predefined quality procedure matching one or more of the turnup properties ” (para 0009 – “ In known quality assessment systems of processing operations, in particular welding processes, it is checked whether specific criteria lie within predetermined limits (i.e. being the predefined quality procedure , added by examiner)… in a welding process, it may be necessary to adapt specific welding parameters due to workpiece tolerances ”; para 0033 – “ For processing the workpiece W, specific target values of the processing parameters P i,soll (x) are selected from a plurality of possible processing parameters P i (x), which are stored, for example, in a database or a memory 9 , with which the workpiece W is processed in order to achieve a desired processing result (interpreted as the turnup property , added by examiner)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Ueda, as taught by Ennsbrunner , in order to improve the quality control to match the specific properties during the turnup event. Regarding Claim 9 : Ueda/ Ennsbrunner combination discloses the apparatus of Claim 8. Ueda further discloses: “ wherein, for a non-homogenous production unit ” (para 0035 – “ In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “ Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ” (i.e. the production unit is non-homogeneous , added by examiner), etc. are shown ”), “ the at least one non-transitory memory and the program code are further configured to, with the at least one processor, cause the apparatus to at least :” (para 0030 – “ The illustrated quality analysis device is configured with a computer, and includes a processor 101 ; an auxiliary storage device 102 ; a memory 103 ”; para 0031 – “ The functions of the respective functional units in the quality analysis device are implemented by software, firmware or a combination of software and firmware . The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units ”): “ segregate the retrieved data related to one or more quality parameters defined in the selected quality procedure into region-specific data ” (para 0037 – “ FIG. 4 shows an example of the apparatus-information data . In FIG. 4, as an example of the data items of the apparatus-information data, “Facility ID”, “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown. Note that “Apparatus ID” is identification information for each apparatus, “Class ID” is identification information indicating a class of each apparatus, and “Facility ID” is identification information indicating what class of the apparatus the facility is configured with… “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) used for the product manufacturing condition or the product inspection (i.e. parameters defined in the selected quality procedure into region-specific data , added by examiner). The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired every time the product is manufactured ”); and “ compare the segregated data to one or more region-specific tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0047 – “ The comparison condition and the base condition may be automatically classified by a clustering method as shown in FIG. 7 . Instead, they may be predefined manually or these conditions may be manually written like queries for a database (i.e. using one or more servers , added by examiner). When they are to be defined from the outside, their corresponding values are inputted through the input device 106 in FIG. 2, so that the processor 101 performs processing corresponding to the condition setting unit 3 to thereby cause the auxiliary storage device 102 to store the conditions for analysis ”; para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 10 : Ueda/ Ennsbrunner combination discloses the apparatus of Claim 8. Ueda further discloses: “ wherein the at least one non-transitory memory and the program code are further configured to, with the at least one processor, cause the apparatus to at least :” (para 0030 – “ The illustrated quality analysis device is configured with a computer, and includes a processor 101 ; an auxiliary storage device 102 ; a memory 103 ”; para 0031 – “ The functions of the respective functional units in the quality analysis device are implemented by software, firmware or a combination of software and firmware . The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units ”): “ determine a final quality disposition action for the production unit defined in the selected quality procedure ” (para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Regarding Claim 11 : Ueda/ Ennsbrunner combination discloses the method of Claim 10. Ueda further discloses: “ wherein the final quality disposition action is based on a worst-case summation of ” (para 0037 – “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) (interpreted as a worst-case summation , added by examiner ) used for the product manufacturing condition or the product inspection ”; para 0035 – “In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “ Pass/Fail Result Indicating Acceptance or Rejection ” (interpreted as the final quality disposition action , added by examiner)) “ comparing the retrieved data to one or more tolerances corresponding to two or more quality parameters defined in the selected quality procedure ” (para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 12 : Ueda/ Ennsbrunner combination discloses the apparatus of Claim 8. Ueda further discloses: “ wherein the turnup properties include one or more of an equipment station identifier, a production unit identifier, and/or a customer identifier ” (para 0037 – “ In FIG. 4, as an example of the data items (interpreted as the turnup properties , added by examiner) of the apparatus-information data, “Facility ID” (i.e. equipment station identifier , added by examiner), “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown ”). Regarding Claim 13 : Ueda/ Ennsbrunner combination discloses the apparatus of Claim 8. Ueda further discloses: “ wherein the data sources include one or more of a nuclear scanner, an X-ray scanner, an imaging system, a laboratory report, and/or a user-defined calculations ” (para 0064 – “ The data aggregation unit 1 is a processing unit that acquires quality data and apparatus-information data. The data-type classification unit 2 is a processing unit that classifies the quality data and the apparatus-information data acquired by the data aggregation unit 1 , in accordance with a predetermined specific rule (i.e. user-defined calculations , added by examiner)”). Regarding Claim 14 : Ueda/ Ennsbrunner combination discloses the apparatus of Claim 8. Ueda further discloses: “ wherein the quality disposition actions include one or more of sending a report, sending an email and/or a text message, and/or displaying an alert and/or a notification ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition that are subjected to the quality analysis, and to output , for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition. This makes it possible to rapidly extract a trouble factor candidate and to easily predict occurrence of trouble ”). Regarding Claim 15 : Ueda discloses: “ A computer program product for quality control, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to :” (para 0031 – “ The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units. The processor 101 reads out and executes programs stored in the auxiliary storage device 102 , to thereby implement the functions of the units from the data aggregation unit 1 to the distribution difference calculation unit 4 in FIG. 1. Further, the time-series data is also stored in the auxiliary storage device 102 ”; para 0032 – “ The programs stored in the auxiliary storage device 102 , the quality data and the apparatus-information data, are loaded into the memory 103 , so that the processor 101 reads them thereby to implement the respective functions and to execute respective corresponding processes. The execution result is written in the memory 103 and is then stored as output data in the auxiliary storage device 102 or outputted through the display I/F 105 to an output device exemplified by the display 107 ”) “ each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receive one or more turnup properties corresponding to the turnup event ” (Fig.3; para 0036 – “ The quality data is data indicating states of the products as the objects subjected to the quality analysis, and is thus an aggregation of values acquired every time each of the products is manufactured or inspected (i.e. turnup event , added by examiner). The quality data may be recorded in any type of device and, for example, it is data that is accumulated in an apparatus in the factory line (i.e. turnup property , added by examiner), or a supervision system for controlling an apparatus. It may instead be data that is accumulated in a product management system for managing the test results of the product inspection ”; para 0035 – “ FIG. 3 is an example of the quality data. In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ”, etc. are shown ”); “ retrieve data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure ” (para 0033 – “ The input device 106 is used for inputting the quality data and the apparatus-information data; for inputting parameters such as, a counting target, the comparison condition, the base condition and the like ; and for inputting a start request for processing of the quality data, and the like .”; para 0037 – “ The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired (i.e. retrieved , added by examiner) every time the product is manufactured. The time-series data is a sequence of values obtained through sequential measurements by lapse of time .”); “ compare the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition (i.e. comparing the retrieved data to tolerances , added by examiner) that are subjected to the quality analysis, and to output, for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition ”; para 0028 – “ a base condition indicating a condition that constitutes a basis of quality analysis ( i.e. quality parameters , added by examiner); and a comparison condition indicating a condition subjected to the quality analysis ”); and “ based on results of the comparing, execute a quality disposition action defined in the respective quality parameter of the selected quality procedure ” (para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Ueda does not specifically disclose: “ select a predefined quality procedure matching one or more of the turnup properties ”. However, Ennsbrunner discloses: “ select a predefined quality procedure matching one or more of the turnup properties ” (para 0009 – “ In known quality assessment systems of processing operations, in particular welding processes, it is checked whether specific criteria lie within predetermined limits (i.e. being the predefined quality procedure , added by examiner)… in a welding process, it may be necessary to adapt specific welding parameters due to workpiece tolerances ”; para 0033 – “ For processing the workpiece W, specific target values of the processing parameters P i,soll (x) are selected from a plurality of possible processing parameters P i (x), which are stored, for example, in a database or a memory 9 , with which the workpiece W is processed in order to achieve a desired processing result (interpreted as the turnup property , added by examiner)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Ueda, as taught by Ennsbrunner , in order to improve the quality control to match the specific properties during the turnup event. Regarding Claim 16 : Ueda/Ennsbrunner combination discloses the computer program product of Claim 15. Ueda further discloses: “ wherein the computer-readable program code portions comprise an executable portion configured to :” (para 0031 – “ The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units. The processor 101 reads out and executes programs stored in the auxiliary storage device 102 , to thereby implement the functions of the units from the data aggregation unit 1 to the distribution difference calculation unit 4 in FIG. 1. Further, the time-series data is also stored in the auxiliary storage device 102 ”; para 0032 – “ The programs stored in the auxiliary storage device 102 , the quality data and the apparatus-information data, are loaded into the memory 103 , so that the processor 101 reads them thereby to implement the respective functions and to execute respective corresponding processes. The execution result is written in the memory 103 and is then stored as output data in the auxiliary storage device 102 or outputted through the display I/F 105 to an output device exemplified by the display 107 ”) “ segregate the retrieved data related to one or more quality parameters defined in the selected quality procedure into region-specific data ” (para 0037 – “ FIG. 4 shows an example of the apparatus-information data . In FIG. 4, as an example of the data items of the apparatus-information data, “Facility ID”, “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown. Note that “Apparatus ID” is identification information for each apparatus, “Class ID” is identification information indicating a class of each apparatus, and “Facility ID” is identification information indicating what class of the apparatus the facility is configured with… “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) used for the product manufacturing condition or the product inspection (i.e. parameters defined in the selected quality procedure into region-specific data , added by examiner). The apparatus-information data is data indicating information of the apparatus that handles the products as objects subjected to the quality analysis, and thus comprises a sequence, or time-series data, of values acquired every time the product is manufactured ”); and “ compare the segregated data to one or more region-specific tolerances corresponding to the one or more quality parameters defined in the selected quality procedure ” (para 0047 – “ The comparison condition and the base condition may be automatically classified by a clustering method as shown in FIG. 7 . Instead, they may be predefined manually or these conditions may be manually written like queries for a database (i.e. using one or more servers , added by examiner). When they are to be defined from the outside, their corresponding values are inputted through the input device 106 in FIG. 2, so that the processor 101 performs processing corresponding to the condition setting unit 3 to thereby cause the auxiliary storage device 102 to store the conditions for analysis ”; para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 17 : Ueda/Ennsbrunner combination discloses the computer program product of Claim 15. Ueda further discloses: “ wherein, for a non-homogenous production unit ” (para 0035 – “ In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “Pass/Fail Result Indicating Acceptance or Rejection”, “Temperature”, “Vibration”, “Rotation Speed”, “ Current at Contact 1 ”, “Voltage at Contact 1 ”, “Current at Contact 2 ”, “Voltage at Contact 2 ” (i.e. the production unit is non-homogeneous , added by examiner), etc. are shown ”) “the computer-readable program code portions comprising an executable portion configured to :” (para 0031 – “ The software or the firmware is written as programs and stored in the auxiliary storage device 102 . These programs serve to cause the computer to execute steps or processes of the respective functional units. The processor 101 reads out and executes programs stored in the auxiliary storage device 102 , to thereby implement the functions of the units from the data aggregation unit 1 to the distribution difference calculation unit 4 in FIG. 1. Further, the time-series data is also stored in the auxiliary storage device 102 ”; para 0032 – “ The programs stored in the auxiliary storage device 102 , the quality data and the apparatus-information data, are loaded into the memory 103 , so that the processor 101 reads them thereby to implement the respective functions and to execute respective corresponding processes. The execution result is written in the memory 103 and is then stored as output data in the auxiliary storage device 102 or outputted through the display I/F 105 to an output device exemplified by the display 107 ”) “ determine a final quality disposition action for the production unit defined in the selected quality procedure ” (para 0010 – “ This makes it possible to rapidly extract a trouble factor candidate (i.e. quality disposition action , added by examiner) and to easily predict occurrence of trouble ”). Regarding Claim 18 : Ueda/Ennsbrunner combination discloses the computer program product of Claim 17. Ueda further discloses: “ wherein the final quality disposition action is based on a worst-case summation of ” (para 0037 – “Setting List ID” is information for identifying each piece of setting information for the apparatus, such as, reference values (respective upper and lower limit values) (interpreted as a worst-case summation , added by examiner ) used for the product manufacturing condition or the product inspection ”; para 0035 – “In FIG. 3, as an example of the data items of the quality data, “Production Number”, “Date & Time at Which Product Has Been Introduced into Apparatus (that is, Introduction Time)”, “ Pass/Fail Result Indicating Acceptance or Rejection ” (interpreted as the final quality disposition action , added by examiner)) “ comparing the retrieved data to one or more tolerances corresponding to two or more quality parameters defined in the selected quality procedure ” (para 0041 – “ The condition setting unit 3 selects as conditions for analysis, three categories of: data items and respective upper and lower limit values therefor (i.e. region-specific tolerances , added by examiner), subjected to frequency-distribution calculation (counting target); a data item(s) and a value(s) thereof, used for the comparison condition; and a data item(s) and a value(s) thereof, used for the base condition; (Steps ST 1 , ST 2 ) ”; see also para 0047). Regarding Claim 19 : Ueda/Ennsbrunner combination discloses the computer program product of Claim 15. Ueda further discloses: “ wherein the turnup properties include one or more of an equipment station identifier, a production unit identifier, and/or a customer identifier ” (para 0037 – “ In FIG. 4, as an example of the data items (interpreted as the turnup properties , added by examiner) of the apparatus-information data, “Facility ID” (i.e. equipment station identifier , added by examiner), “Class ID”, “Apparatus ID”, “Manufacturing Date & Time”, “Production Number”, “Setting Information in Manufacturing (that is, Setting List ID)”, etc. are shown ”); and “ wherein the data sources include one or more of a nuclear scanner, an X-ray scanner, an imaging system, a laboratory report, and/or a user-defined calculations ” (para 0064 – “ The data aggregation unit 1 is a processing unit that acquires quality data and apparatus-information data. The data-type classification unit 2 is a processing unit that classifies the quality data and the apparatus-information data acquired by the data aggregation unit 1 , in accordance with a predetermined specific rule (i.e. user-defined calculations , added by examiner)”). Regarding Claim 20 : Ueda/Ennsbrunner combination discloses the computer program product of Claim 15. Ueda further discloses: “ wherein the quality disposition actions include one or more of sending a report, sending an email and/or a text message, and/or displaying an alert and/or a notification ” (para 0010 – “ The quality analysis device according to the invention is configured to set the data items, the base condition and the comparison condition that are subjected to the quality analysis, and to output , for each of the data items, data indicating the degree of divergence provided between the base condition and the comparison condition. This makes it possible to rapidly extract a trouble factor candidate and to easily predict occurrence of trouble ”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US5323837 to Stummer (hereinafter Stummer) discloses method of determining unacceptable deviations from process parameters. US5396432 to Saka et al. (hereinafter Saka) discloses versatile production system and method of operating same. US20220026889 to Liatsa et al. (hereinafter Liatsa) discloses method and device for producing a product and computer program product. US20120078410 to Wong et al. (hereinafter Wong) discloses method, system and apparatus for automatic quality control using a plurality of computers. US20170075344 to Spring et al. (hereinafter Spring) discloses dynamically configurable production and/or distribution line control system and method therefor. US20240004355 to Zhao et al. (hereinafter Zhao) discloses system and Method for Building and Deploying Prescriptive Analytics to Predict and Control End Product Quality in Batch Production Monitoring and Optimization. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lyudmila Zaykova-Feldman whose telephone number is (469)295-9269. The examiner can normally be reached 8:30am CT - 5:30pm CT, Monday through Friday. 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, Arleen Vazquez, can be reached on 571-272-2619 . 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. /LYUDMILA ZAYKOVA-FELDMAN/ Examiner, Art Unit 2857 /LINA CORDERO/ Primary Examiner, Art Unit 2857 Application/Control Number: 18/597,417 Page 2 Art Unit: 2857 Application/Control Number: 18/597,417 Page 3 Art Unit: 2857
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