DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/07/25 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-18 and 21-22 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims. Although the same art was applied, new explanations were given to account for the amended limitations.
However, the examiner will respond to the applicant’s arguments, with respect to 35 U.S.C. 101. The applicant does not appear to have made arguments, with respect to the art.
The applicant first argues:
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This argument is not persuasive because as discussed below, new claim 21 is directed to data processing that merely uses a computer as a tool to perform an abstract idea. Also, the image analysis, speech recognition, and textual analysis merely serve to generally link the judicial exception to a particular technological environment or field of use. No detail is given as to how the image analysis, speech recognition, or textual analysis are performed. Also, the claim is written as the processor and memory executing the machine-readable instructions to cause to system to perform … The focus of the claim is on the data processing and not on a specific approach to image data, speech recognition, or textual analysis that would be considered a “solution”. Here, the image analysis, speech recognition, and textual analysis are generically recited.
Next, the applicant argues:
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This argument is not persuasive because there is a distinction between an improvement in a judicial exception and an improvement in technology. Here, the claims, as a whole, utilize a computerized data processing technique that is defined by abstract mathematical concepts. The data that results from the data processing stays on the computer. Although the applicant has added the language of “change one or more manufacturing, fabrication, or assembly processes ...” the “change” discussed by the applicant appears to be a computerized data processing change, as opposed to a real-world structural one.
Next, the applicant argues:
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The examiner respectfully disagrees. The applicant’s claims have not identified a specific industry, such as automotive production, oil refinery, widget maker, etc … In their current state, the applicant’s claims would monopolize the claimed data processing technique across all industries.
Next, the applicant argues:
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This argument is not persuasive because here, the user interface is described generically, such that it serves more as a form of displaying the output data, rather than serving as an improved user interface solution. The claims do not positively recite any interaction between the user and the display. The claims are focused on the display itself, rather than the user interaction with the display.
For the above reasons, the rejection is upheld.
Drawings
As stated previously, the drawings filed on 06/19/23 are approved.
Claim Objections
In the amendments of 11/07/25, claim 1, lines 9-10 have cancelled the phrase “using a machine learning model.” In claim 1, lines 16, the language still states, “determine, using the machine learning model, that the first defect data object …” There is now no antecedent basis for the phrase, “using the machine learning model.” However, this phrase was cancelled in corresponding independent claims 8 and 15. The examiner will construe that it was intended for that phrase to also be cancelled in claim 1.
In the amendments of 11/07/25, claim 20 appears to have been cancelled but is delineated by the description of “(Original)”. The examiner will interpret this limitation to be “(Cancelled)”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 and 21-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 8, and 15 have been amended to state, “based on the identification of the one or more manufacturing, fabrication, or assembly issues, change one or more manufacturing, fabrication, or assembly processes to remedy the one or more manufacturing, fabrication, or assembly deficiencies.”
The examiner could not find support for this limitation in the applicant’s disclosure. The examiner only found a few instances of “change” in the applicant’s original specification.
Paragraph 0032 states, “In some implementations, defect cleaning module 122 may be configured to apply filtering parameters only to newly introduced data, only to subsets of data, at specific times, and/or responsive to changes in filtering parameters.”
Paragraph 0126 states, “Although an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments …”
Paragraph 0127 states, “Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made …”
None of these sections appear to provide support for the limitation of, “based on the identification of the one or more manufacturing, fabrication, or assembly issues, change one or more manufacturing, fabrication, or assembly processes to remedy the one or more manufacturing, fabrication, or assembly deficiencies.”
The examiner also looked for variations of the word, “change”, such as “modify” or “adjust.”
The closest support found was in paragraph 0067, which states, “Learning module 130 may further be configured to update feature weights based on newly determined or modified issues. As discussed above, the system may afford a user the opportunity to determine new issues, add defects to existing issues, and remove defects from existing issues. Modification of issues through the creation or confirmation of new issues by a user and the addition of or removal of defects from existing issues by a user, may cause learning module 130 to update feature weights based on the modification … In other examples, learning module may adjust and/or update feature weights as new defects are added to existing issues, as defects are removed from existing issues, and/or as entire issues are dissolved.”
However, nowhere in this section is there mention of manufacturing, fabrication, or assembly. It appears that the modifications/adjustments involve updating feature weights of a learning module, as opposed to some sort of structural change to manufacturing, fabrication, or assembly processes. It is unclear whether the applicant intended for the updating of feature weights to be the claimed “change of one or more manufacturing, fabrication, or assembly processes.”
The examiner requests that the applicant clarify and/or show where the amended limitation is supported in the applicant’s disclosure. For the purposes of examination, the examiner will interpret any type of change, modification, or adjustment to read on the claimed limitation, regardless of whether it is a data processing change/modification or some sort of more direct manufacturing, fabrication, or assembly process change/modification. It should also be noted that the claims are not clear about the nature of the manufacturing, fabrication, or assembly process changes.
All other claims depend on independent claims 1, 8, and 15. They are also rejected as a result of their dependency.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 10-11, 14, 17-18 and 21-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1 (lines 9-10), 8 (lines 8-9), and 15 (line 7) have deleted the phrase, “using a machine learning model.” However, there is now a lack of antecedent basis for the phrase “the machine learning model”
Instances where this phrase that now lacks antecedent basis are found include:
Claim 1, line 16
Claim 3, lines 3
Claim 4, line 4
Claim 7, line 3
Claim 10, line 3
Claim 11, line 4
Claim 14, line 3
Claim 17, line 3
Claim 18, line 4
Claims 2-7 and 21-22 depend on claim 1 and are also rejected as a result of their dependency.
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 1-18 and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a system, which is a machine. Independent claim 9 is directed to a computer-implemented method, which is a process. Independent claim 16 is directed to a non-transitory computer readable medium, which is a manufacture. All other claims depend on independent claims 1, 9, and 16. As such, claims 1-19 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
determine a defect similarity between the first defect data object and the second defect data object, wherein the defect similarity is determined based on a comparison metric with which to determine the defect similarity, wherein the comparison metric is based on a feature weight (This limitation recites abstract mathematical equations and relationships. The applicant’s disclosure discloses specific equations, such in paragraphs 0038 and 0046. The machine learning model is defined by mathematical concepts.)
determine, using the machine learning model, that the first defect data object and the second defect data object are related based on the comparison metric (This limitation recites abstract mathematical equations and relationships. The applicant’s disclosure discloses specific equations, such in paragraphs 0038 and 0046. The machine learning model is defined by mathematical concepts.)
Independent claims 8 and 15 recite similar abstract limitations.
All other claims depend on independent claims 1, 8, and 15 and also recite abstract concepts, as a result of their dependency. The dependent claims are further directed to various machine learning operations that are defined by mathematical relationships, equations, or calculations.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A system comprising: at least one processor; and at least one memory storing machine-readable instructions, wherein the at least one processor is configured to access the at least one memory and execute the machine-readable instructions (These are generic computer components. Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. See MPEP 2106.05(f))
obtain a first defect data object and a second defect data object from a database (obtaining data from a database to be processed merely adds insignificant extra-solution activity to the judicial exception and is not indicative of integration into a practical application. See MPEP 2106.05(g)).)
perform feature extraction from one or more unstructured fields corresponding to the first defect data object and the second defect data object (This limitation appears to merely be data processing that uses a computer as a tool to perform an abstract idea (Paragraph 0033 of the applicant’s original specification states, “Defect feature extraction module 118 may include programming instruction that cause computer system 110 … to extract feature information.”). It is not indicative of integration into a practical application. (see MPEP 2106.05(f)).)
based on the performed feature extraction (As discussed in the preceding limitation, the feature extraction appears to be computer processing that merely uses a computer as a tool to perform an abstract idea. (see MPEP 2106.05(f)).)
related to an attribute of a manufacturing or an industrial process (This limitation merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). It is not indicative of integration into a practical application.)
store the comparison metric within the database, wherein subsequent feature extraction from the database is based on the stored comparison metric (Storing data in a database merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Also, storing data in a database is a computer operation, and merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
generate and store an issue data object comprising the first defect data object and the second defect data object in the database, the issue data object indicative of a diagnosis of a defect common to the first defect data object and the second defect data object (This limitation is not indicative of integration into a practical application, as it merely uses a computer as a tool to perform an abstract idea. Also, storing data in a database adds insignificant extra-solution activity to the judicial exception.)
obtain one or more additional defect data objects in which respective comparison value scores indicative of a degree to which the additional defect data objects correspond to or are related to the issue data object satisfy a criterion, or remove one or more existing defect data objects in which respective comparison value scores indicative of a degree to which the existing defect data objects correspond to or are related to the issue data object or other existing defect data objects within the issue data objects fail to satisfy the criterion (This limitation is not indicative of integration into a practical application, as it merely uses a computer as a tool to perform an abstract idea. The limitation is directed to computer processing.)
generate a display interface, wherein the display interface comprises one or more selectable modules that are configured to launch details associated with the comparison metric or the comparison value scores corresponding to any two selected from a group consisting of: the first defect data object, the second defect data object, and the additional defect data objects (This limitation is not indicative of integration into a practical application because the display interface is a computerized form of outputting the processed information, and merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)). It should be noted that although the claim states that the display interface comprises one or more selectable modules, the limitation does not positively recite the user actually interacting with the selectable modules. The limitation is directed to generating a display interface, which is akin to outputting processed data in visual form. As mentioned, it merely uses a computer as a tool to perform an abstract idea. Furthermore, a mere display of processed data in visual form merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The “solution” of the claimed invention is represented by the data processing.)
identify one or more manufacturing, fabrication, or assembly deficiencies based on the issue data object and the one or more additional defect data objects (The identifying here is a data processing step that merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The mention of manufacturing, fabrication, or assembly deficiencies merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. (see MPEP 2106.05(h)).)
based on the identification of the one or more manufacturing, fabrication, or assembly issues, change one or more manufacturing, fabrication, or assembly processes to remedy the one or more manufacturing, fabrication, or assembly deficiencies (The closest support for this limitation that the examiner could find in the applicant’s original specification was in paragraph 0067 of the applicant’s specification, which states, “Modification of issues through the creation or confirmation of new issues by a user and the addition of or removal of defects from existing issues by a user, may cause learning module 130 to update feature weights based on the modification … learning module may adjust and/or update feature weights as new defects are added to existing issues, as defects are removed from existing issues, and/or as entire issues are dissolved.” This is a data processing “change” that merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), as opposed to a positive recitation of a structural change that applies the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)) or that effects a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)). If there is support in the applicant’s disclosure for a positive recitation of a structural change, the applicant is requested to identify where such support is in the specification.)
Independent claims 8 and 15 recite similar limitations that are not indicative of integration into a practical application.
All other claims depend on independent claims 1, 8, and 15 and also recite limitations that are not indicative of integration into a practical application, as a result of their dependency. As stated above, the dependent claims are further directed to the abstract machine learning operations, and many of the operations are merely executed by using a computer as a tool to perform an abstract idea.
New claims 21-22 are further directed to data processing that merely uses a computer as a tool to perform an abstract idea. Also, the image analysis, speech recognition, and textual analysis merely serve to generally link the judicial exception to a particular technological environment or field of use. No detail is given as to how the image analysis, speech recognition, or textual analysis are performed. Also, the claim is written as the processor and memory executing the machine-readable instructions to cause to system to perform … The focus of the claim is on the data processing and not on a specific approach to image data, speech recognition, or textual analysis that would be considered a “solution”.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 7-11, 14-18, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marum et al (US PgPub 20110321007) in view of Ognev et al (US PgPub 20070074149).
With respect to claim 1, Marum et al discloses:
A system (paragraph 0002 state, “Defect tracking systems …”)
at least one processor (paragraph 0013)
at least one memory storing machine-readable instructions, wherein the at least one processor is configured to access the at least one memory and execute the machine-readable instructions (paragraph 0011) to cause the system to:
obtain a first defect data object and a second defect data object from a database (abstract states, “a subset of the different code segments can be predicted utilizing information contained within a database of previously reported defects.”)
determine a defect similarity between the first defect data object and the second defect data object (see defect similarity engine 334; paragraphs 0027 and 0033), wherein the defect similarity is determined based on a comparison metric with which to determine the defect similarity, wherein the comparison metric is based on a feature weight related to an attribute of a manufacturing or an industrial process (paragraph 0022 states, “Any of a variety of statistical analysis methods can be utilized when determining relevancy. For instance, a greater weight can be attributed to segments of similar defects having strong commonalities with the unresolved defects compared to those similar defects with lesser commonalities (as determined by matching defect characteristics, for example).”)
store the comparison metric within the database (paragraph 0050 states, “information can be stored within a database structure …”)
determine that the first defect data object and the second defect data object are related based on the comparison metric (paragraph 0022)
generate and store an issue data object comprising the first defect data object and the second defect data object in the database, the issue data object indicative of a diagnosis of a defect common to the first defect data object and the second defect data object (abstract states, “An unresolved defect can be identified in a computer program product.” The identification of defects is analogous to the claimed generation of an issue data object. Paragraph 0011 discloses various memory to store data.)
obtain one or more additional defect data objects in which respective comparison value scores indicative of a degree to which the additional defect data objects correspond to or are related to the issue data object satisfy a criterion, or remove one or more existing defect data objects in which respective comparison value scores indicative of a degree to which the existing defect data objects correspond to or are related to the issue data object or other existing defect data objects within the issue data objects fail to satisfy the criterion (figure 1, reference 135 and figure 2; paragraph 0019 states, “the unresolved defect can be specified along with additional defect information.”; paragraph 0025 states, “When additional defects are detected, the method 100 can repeat …”)
generate a display interface (paragraph 0048 states, “The user interface 340 can be an interface through which human to machine interactions occur. The user interface 340 can be a graphical user interface (GUI), a voice user interface (VUI), a multi-modal interface, a text user interface, and the like.”), wherein the display interface comprises one or more selectable modules that are configured to launch details associated with the comparison metric or the comparison value scores corresponding to any two selected from a group consisting of: the first defect data object, the second defect data object, and the additional defect data objects (suggested by figure 3 and paragraphs 0043-0044, which state, “User interface 340 permits a user of device 310 to interact with the defect prediction engine 332 and its functions. For example, window 342 represents a screen of user interface 340 showing an unresolved defect 343 and its specifics (e.g., details 344). Window 342 includes an option 345 to help find the problem causing the defect. Selection of option 345 can bring up window 439 … window 349 can include specific recommendations, which may be derived from specifics of the past defects that were previously reported and that have already been repaired or corrected.” Please also note claim 3, which states, “responsive to a user selection of one of the subset of the different code segments, navigating to the corresponding navigational aid …”)
identify one or more manufacturing, fabrication, or assembly deficiencies based on the issue data object and the one or more additional defect data objects (figure 2; paragraph 0026 states, “FIG. 2 shows a flow chart of a method 200 for identifying similar defects …”)
based on the identification of the one or more manufacturing, fabrication, or assembly issues, change one or more manufacturing, fabrication, or assembly processes to remedy the one or more manufacturing, fabrication, or assembly deficiencies (As discussed in the above 112 rejection, any disclosure of changes/modifications/adjustments will be construed to read on the claimed limitation. Paragraph 0030 states, “the resolution score can be adjusted based on change set specific values.” Paragraph 0003 states, “defect tracking systems are often used to report what changes have been made …” Paragraph 0037 states, “The source code manager (SCM) 354 can track and control changes in software/firmware.” Paragraph 0038 states, “Changes can be identified by number, letter code, or other revision number …”))
With respect to claim 1, Marum et al differs from the claimed invention in that it does not explicitly disclose:
perform feature extraction from one or more unstructured fields corresponding to the first defect data object and the second defect data object
based on the performed feature extraction
wherein subsequent feature extraction from the database is based on the stored comparison metric
With respect to claim 1, Ognev et al discloses:
perform feature extraction from one or more unstructured fields corresponding to the first defect data object and the second defect data object (Paragraph 0060 of Ognev states, “when data is located (e.g., stored) in multiple defect data stores, a process for determining which data store or group of data stores, to search can be effected via a machine learning technique.” Paragraph 0063 of Ognev states, “SVM’s are configured via a learning or training phase within a classifier constructor and feature selection module.”)
based on the performed feature extraction (paragraphs 0060 and 0063)
wherein subsequent feature extraction from the database is based on the stored comparison metric (obvious in view of combination; Marum teaches comparison (paragraph 0040) and Ognev teaches feature extraction.)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Ognev et al into the invention of Marum et al. The motivation for the skilled artisan in doing so is to gain the benefit of automating the processes of Marum.
Independent claims 8 and 15 represent variations of claim 1 and are rejected for similar reasons.
With respect to claims 2, 9, and 16, Marum et al, as modified, discloses:
adjust the feature weight or a different feature weight based on the generated issue data object, the additional defect data objects, or the removed existing defect data objects (obvious in view of paragraph 0022 of Marum; it would be obvious to one of ordinary skill in the art to adjust weight based on the defect data.)
With respect to claims 3, 10, and 17, Marum et al, as modified, discloses:
generate or provide training data for the machine learning model based on the generated issue data object, the additional defect data objects, or the removed existing defect data objects (obvious in view of applying machine learning teachings of Ognev to data of Marum)
With respect to claims 4, 11, and 18, Marum et al, as modified, discloses:
obtain an approval of the issue data object (note “Approvals” in figure 3, reference 342)
generate or provide training data for the machine learning model based on the approval of the issue data object (obvious in view of combination; Marum teaches approvals and Ognev teaches machine learning)
With respect to claims 7 and 14, Marum et al, as modified, discloses:
train the machine learning model based on the generated issue data object, the additional defect data objects, or the removed existing defect data objects (obvious in view of combination; Ognev teaches machine learning and training (see paragraphs 0062-0063 of Ognev))
With respect to claim 21, Marum et al, as modified, discloses:
wherein the at least one processor is configured to access the at least one memory and execute the machine-readable instructions to cause the system to perform image analysis on image data, speech recognition on audio data, and textual analysis on textual data within the one or more unstructured fields (Marum paragraph 0017 states, “Computer code products can consist of millions of lines of code, which can be organized in different interactive structural units … applications.” Ognev paragraph 0008 states, “It is to be understood and appreciated that this open and extensible framework can be applied to a wide range of applications where such analysis is needed and/or desired.” In view of Marum and Ognev explicitly disclosing that its principles could be applied to a wide variety of different application contexts, it would be obvious to one of ordinary skill in the art to configure the processor and memory to cause the system to perform each of the claimed applications, which are generic and common applications.)
With respect to claim 22, Marum et al, as modified, discloses:
wherein the at least one processor is configured to access the at least one memory and execute the machine-readable instructions to cause the system to perform feature extraction from one or more corresponding fields or structured features of the first defect data object and the second defect data object (obvious in view of combination; figure 11 of Ognev shows processing unit and access to memory. Ognev discloses feature extraction, and Marum discloses defect data objects. Ognev also discloses defect analysis (abstract).)
Claim(s) 5-6 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marum et al (US PgPub 20110321007) in view of Ognev et al (US PgPub 20070074149), as applied to claims 1-4, 7-11, 14-18, and 21-22 above, and further in view of Caldwell et al (US PgPub 20100174691).
With respect to claims 5 and 12, Marum et al, as modified, discloses:
A system (as applied to claims 1 and 8 above)
With respect to claims 5 and 12, Marum et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein the determining of the defect similarity is based on the feature weight and an other feature weight corresponding to an other attribute, wherein the attribute has a higher feature weight compared to the other attribute and the feature weight is evaluated prior to the other feature weight
With respect to claims 5 and 12, Caldwell et al discloses:
wherein the determining of the defect similarity is based on the feature weight and an other feature weight corresponding to an other attribute, wherein the attribute has a higher feature weight compared to the other attribute and the feature weight is evaluated prior to the other feature weight (paragraph 0054 states, “the method or the system for automatically classifying a defect into a category determines whether the first defect is to be categorized into the first category in some embodiments. In some embodiments, the method or the system for automatically classifying a defect into a category may identify a threshold weight or a threshold score for a keyword …”; The threshold weight is construed to anticipate “an other feature weight”. This would appear to align with paragraph 0044 of the applicant’s specification, which states, “a predetermined threshold comparison value score may be used …” Please note that the examiner could not find any recitation of “an other feature weight” in the specification; it was only mentioned in the claims. The examiner is interpreting the threshold comparison value score of paragraph 0044 to serve as support for this limitation. If this is not the case, the examiner requests the applicant show where this limitation is supported in the specification.)
With respect to claims 5 and 12, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Caldwell et al into the invention of modified Marum et al. The motivation for the skilled artisan in doing so is to gain the benefit of providing a standard for automatic defect categorization.
With respect to claims 6 and 13, Marum et al, as modified, discloses:
halt an evaluation of the other feature weight if the evaluation of the feature weight fails to satisfy the criterion or a different criterion (paragraph 0075 of Caldwell states, “where the method or the system for automatically classifying a defect into a category determines that the result score or resultant weight causes the defect … the method or system stops the current iteration …”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Weiss et al (US PgPub 20180300865) discloses a method for predicting defects in assembly units.
Hu et al (US PgPub 20180300434) discloses pattern centric process control.
Matsushita et al (US PgPub 20040255198) discloses a method for analyzing fail bit maps of wafers and apparatus therefor.
Tanaka et al (US PgPub 20030054573) discloses a method for manufacturing semiconductor devices and method and its apparatus for processing detected defect data.
Ratner et al (US PgPub 20200380664) discloses a method and apparatus for rapid inspection of subcomponents of manufactured component.
Shigeta (US PgPub 20210319661) discloses a management system for substitute currency for gaming.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M 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.
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/LEONARD S LIANG/Examiner, Art Unit 2857 03/21/26