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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/06/2025 has been entered.
Response to Arguments
Applicant's amendment filed on 10/06/2025 is acknowledged and the following rejections set forth in the previous Office Action are withdrawal:
The claims 1-11 being rejected under 35 USC §112 (a) or 35 USC §112 (pre-AIA ), first paragraph.
Applicant's arguments filed on 10/06/2025 have been fully considered but they are not persuasive. The Office has thoroughly reviewed Applicants' arguments which are moot in view of the new ground(s) of rejections to be detailed in the following.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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 1-20 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 pre-AIA the applicant regards as the invention.
The amended claim 1 recites “a defect detector including a data store of gray scale vector clusters having an associated anomaly index for at least one product that is considered defect-free and the defect detector including instructions stored on a computer device and configured to match the gray scale data vector for a captured product image to one or more of the gray scale vector clusters to assign an anomaly value to the captured product image corresponding to the anomaly index associated with the one or more gray scale vector clusters matching the gray scale data vector for the captured product image and provide an inspection output that the product is defective based on the anomaly value in comparison to a threshold value.” However, it is unclear what this claim limitation means.
For example, it is unclear what it means by “a data store of gray scale vector clusters having an associated anomaly index for at least one product that is considered defect-free”. Here we have “a data store of gray scale vector clusters”, “an associated anomaly index”, and “at least one product that is considered defect-free”, but the relationship among them is unclear. Is the “anomaly index” associated with “at least one product that is considered defect-free” or “a data store of gray scale vector clusters”? If it is associated with “a data store of gray scale vector clusters”, does it mean all of the “gray scale vector clusters” in the “data store” have the same “anomaly index”? Also, what is the relationship between the “data store of gray scale vector clusters” and the “at least one product that is considered defect-free”? Does it mean all of the “gray scale vector clusters” in the “data store” have the “at least one product that is considered defect-free” or only one or some of the “gray scale vector clusters” in the “data store” have the “at least one product that is considered defect-free”? If all or some of the “gray scale vector clusters” in the “data store” have the “at least one product that is considered defect-free”, does it mean all or some of the “gray scale vector clusters” are defect-free clusters and with the same anomaly index? Is the “associated anomaly index” only for the “at least one product that is considered defect-free”? It is unclear how this claim limitation is limiting the claim due to the lack of clarity for the relationship between “a data store of gray scale vector clusters”, “an associated anomaly index”, and “at least one product that is considered defect-free”.
It is also unclear what it means by “to match the gray scale data vector for a captured product image to one or more of the gray scale vector clusters to assign an anomaly value to the captured product image corresponding to the anomaly index associated with the one or more gray scale vector clusters matching the gray scale data vector for the captured product image and provide an inspection output that the product is defective based on the anomaly value in comparison to a threshold value.” It is unclear what it means by “to match the gray scale data vector for a captured product image to one or more of the gray scale vector clusters” or “the one or more gray scale vector clusters matching the gray scale data vector for the captured product image”. It is unclear what it means by that “the gray scale data vector for the captured product image” belonging to different clusters as it is implied by “the one or more gray scale vector clusters matching the gray scale data vector for the captured product image”. If so, are these clusters having the same anomaly index? If they have the same anomaly index, why are these clusters separate clusters instead of being one cluster? If they have difference anomaly indices, it is then unclear what it means by the captured product image having different anomaly values when they are assigned “to the captured product image corresponding to the anomaly index associated with the one or more gray scale vector clusters”. Also, it is unclear what it means by “to assign an anomaly value to the captured product image corresponding to the anomaly index”. Is the “value” or “image” corresponding to the “index”? Despite the above confusion and questions to be clarified by applicant, let’s assume that “the one or more gray scale vector clusters” have the same anomaly index for the sake of discussion. It is unclear what the relationship is between the “anomaly value” assigned to the captured product image and “the anomaly index” as the meaning of the term “corresponding” is unclear. Does “the anomaly index” have a value? Is the value of “the anomaly index” different from the “anomaly value”? For example, if “the anomaly index” is 0.2, it is unclear what “the anomaly value” should be in order for it to be considered as “corresponding to the anomaly index”. Is it 0.2? what about 0.3 or 1 or 10 or 0.01? Since the relationship of the “anomaly value” to “the anomaly index” is unclear besides “corresponding to the anomaly index”, it is unclear what the “anomaly value” of “the captured product image” would be and it thus is unclear what it means by providing “an inspection output that the product is defective based on the anomaly value in comparison to a threshold value”. This lack of clarity is further compounded by applicant’s argument that “the anomaly index” as claimed “relate to the gray scale vectors for a "product that is considered defect-free."” In other words, the “anomaly value” assigned “to the captured product image corresponding to the anomaly index” is an anomaly value “corresponding to the anomaly index” “relate to the gray scale vectors for a "product that is considered defect-free."” If the anomaly value is already “corresponding to the anomaly index” relating “to the gray scale vectors for a "product that is considered defect-free"”, it is unclear what it even means by comparing the anomaly value to a threshold value. Does applicant mean to say that the product can be defective even if the anomaly value of the captured product image corresponds to the anomaly index relating “to the gray scale vectors for a "product that is considered defect-free"?
This long and overly convoluted claim limitation on “defect detector” is very confusing and it is unclear what the claimed defect detector is doing.
The claim 1 is indefinite since the metes and bounds of the claim cannot be defined due to the lack of clarity for the claimed invention.
Claims 12 and 16 have similar issues and all other claims depend on one of claims 1, 12 and 16, and therefore claims 2-20 are rejected for the same reasons as for claim 1.
To advance the prosecution, the following art rejection is provided despite the above issues and in light of applicant’s arguments.
References Cited in Prior Art Rejections
The following references are cited in the prior art rejections set forth below and are referred to as noted.
Zhang et al., "An Automatic Recognition Method for PCB Visual Defects" 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), IEEE, August 15, 2018, pages 138-142, hereinafter Zhang.
Bonewitz et al., US 5926268 A, published on 1999-07-20, hereinafter Bonewitz.
Stoppa et al., US 20180211373 A1, published on 2018-07-26, hereinafter Stoppa.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang. in view of Bonewitz, and further in view of Stoppa.
Regarding claim 1, Zhang discloses a product inspection Zhang: abstract and Fig. 1) comprising:
Zhang: Figs. 2 and 7-9, section IIIB)
Zhang: Figs. 7-9, section IIIB. Gray scale data vector = gray histogram) and
a defect detector including Zhang: last step in Fig. 1, section IV, Table I. Defect classification or the defect detector is implied by the Support Vector Machine (SVM). Gray scale vector (e.g., gray histogram) clusters are implied by the classification process of SVM as well as Figs. 7-9 and results in Table I. Section IV discloses the “anomaly index” of clusters as labels of classification as in Figs. 12-13. The label has values (i.e., 1-6 in Figs. 12-13) to indicate the defective PCB (even with specific defect classifications or detect types) just like the “anomaly index” has values to indicate a product being defective.)
Zhang does not disclose explicitly but Bonewitz teaches, in an analogous art of container defect inspection using image processing, one or more cameras to capture a product image stream comprising a plurality of image frames, image processing instructions stored on a computer device, a data store of gray level data. (Bonewitz: Figs. 1-5, col. 4, lines 22-35, col. 6, lines 44-56, col. 9, lines 22-35, col. 12, lines 1-5.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang’s disclosure with Bonewitz’s teachings by combining the product inspection technique (from Zhang) with the container defect inspection system (from Bonewitz) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the product inspection technique would still work in the way according to Zhang and the container defect inspection system would continue to function as taught by Bonewitz. In fact, the inclusion of Bonewitz's container defect inspection system would provide a practical implementation of the product inspection technique from Zhang.
In the above rejection, despite the 112 issues raised in the previous section, the last limitation on defect detection, including such features as clustering and comparing an anomaly to a threshold, is interpreted as implied by the classification process disclosed by Zhang including SVM. In case that applicant disagrees with this interpretation, examiner provide the following obviousness argument. The claimed defect detection features differing from Zhang’s defect classification process including SVM, such as technique for clustering vectors (e.g., k-means clustering) or comparing anomaly value to a threshold, are well known and commonly practiced in the image processing art. Examiner takes an Official Notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang {modified by Bonewitz}’s disclosure with the Official Notice’s teachings by combining the product inspection system (from Zhang {modified by Bonewitz}) with the well-known techniques including vector clustering and threshold comparison (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the product inspection system would still work in the way according to Zhang {modified by Bonewitz} and the well-known techniques including vector clustering and threshold comparison would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's well-known techniques including vector clustering and threshold comparison would provide a practical implementation of the product inspection system from Zhang {modified by Bonewitz}.
The only difference between the claimed invention and the disclosure from combining Zhang with Bonewitz and the Official Notice (ON), or Zhang {modified by Bonewitz and ON}, is that Zhang {modified by Bonewitz and ON} does not disclose a defect-free classification or type for the objects or products captured in the image frames even though Zhang discloses 6 classifications or types of defects. However, it is well known and commonly practices in the art of image processing involving defect detection to have a defect-free classification as evidenced by the prior art reference of Stoppa. (Stoppa: [0010, 0014, 0026, 0030, 0140-0142, 0143]. “[0030] The memory may further store instructions that, when executed by the processor, cause the processor to assign the classification to the object in accordance with the plurality of features by: comparing each of the features to a corresponding previously observed distribution of values of the feature; assigning the clean classification in response to determining that all of the values of the features are within a typical range; and assigning a defect classification for each feature of the plurality of features that are in outlier portions of the corresponding previously observed distribution.” “[0140] … The training set includes examples of clean (e.g., defect free objects) as well as examples of defective objects with labels of the types of defects present in those examples.” “[0143] During operation, the trained CNN may be applied to extract a feature vector from a scan of an object under inspection. The feature vector may include color, texture, and shape detected in the scan of the object. The classifier may assign a classification to the object, where the classifications may include being defect-free (or “clean”) or having one or more defects.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang {modified by Bonewitz and ON}’s disclosure with the Stoppa’s teachings by combining the product inspection system (from Zhang {modified by Bonewitz and ON}) with the technique of having defect and defect-free classifications (from the Stoppa) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the product inspection system would still work in the way according to Zhang {modified by Bonewitz and ON} and the technique of having defect and defect-free classifications would continue to function as taught by the Stoppa. In fact, the inclusion of the Stoppa's technique of having defect and defect-free classifications would provide a practical and alternative implementation of the product inspection system from Zhang {modified by Bonewitz and ON} and would enable a better and more flexible product inspection system due to the alternative implementation made possible by the technique from Stoppa.
Therefore, it would have been obvious to combine Zhang with Bonewitz and the Official Notice (ON) and Stoppa to obtain the invention as specified in claim 1.
Regarding claim 2, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 1 wherein the one or more cameras capture an image stream including a plurality of product image frames and the vector generator creates a plurality of gray scale data vectors for the product image frames and the defect detector matches the plurality of gray scale data vectors to the gray scale vector clusters and assigns the anomaly values to the plurality of product image frames and uses the anomaly values from the product image frames to provide the inspection output. (Zhang: Figs. 1-2 and 9. Sections IIIB and IV) (Bonewitz: Figs. 3 and 4A-B, col. 5, lines 14-16, col. 6, lines 44-56, col. 9, lines 22-35)
Regarding claim 3, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 2 wherein the plurality of product image frames correspond to a plurality of products movable along a conveyor path and the system includes product tracking and separation functions to separate image frames into product files for each of the plurality of products. (Bonewitz: Figs. 3 and 4A-B, col. 6, lines 44-56, col. 9, lines 22-35)
Regarding claim 4, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 2 wherein the inspection system includes a conveyor assembly to move the product along a conveyor path and the conveyor assembly includes at least one product holder having a rotation mechanism to rotate the product and the one or more cameras are supported along the conveyor assembly to capture the image stream including the plurality of product image frames of a circumference of the product as the product rotates in the product holder. (Bonewitz: Fig. 6; col. 6, line 57 to col. 7, line 7; col. 7, line 67 to col. 8, line 17.)
The reasoning and motivation to combine are similar to those of claim 1.
Regarding claim 5, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 4 wherein the inspection system includes a plurality of product holders coupled to a rotating platform and the one or more cameras are supported relative to the rotating platform to capture the product image stream for the product on the rotating platform. (Bonewitz: Figs. 4 and 6; col. 6, line 57 to col. 7, line 7; col. 7, line 67 to col. 8, line 17. The container rotator 198-200 may hold and rotate more than one containers between 198 and 200 in Fig. 6.)
Regarding claim 6, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 1 wherein the gray scale value of the gray scale pixels is a gray scale range between 0-255 where 0 represents a white gray scale value and 255 represents a black gray scale value. (Bonewitz: col. 9, lines 25-27: “memory 152 stores the images generated by camera 168 as arrays of 512.times.512 pixels having 256 gray levels.”)
Regarding claim 7, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 1 wherein the data store of gray scale vector clusters is created using a training set of product images and clustering algorithms to cluster gray scale data vectors for the training set of product images with similar attributes and the anomaly index for the gray scale vector clusters is assigned using deviation measures of the gray scale vector clusters relative to other gray scale vector clusters. (Zhang: last step in Fig. 1, section IV, Table I. Defect classification or the defect detector is implied by SVM. Gray scale vector (gray histogram) clusters are implied by the classification process of SVM as well as Figs. 7-9 and results in Table I.)(The Official Notice on the technique of vector clustering)
Regarding claim 8, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 7 wherein the training set of product images includes product images for at least one of a set of defective product and a set of non-defective product. (Zhang: Figs. 2-4, section II.)
Regarding claim 9, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 7 wherein the gray scale data vectors of the training set of product images are clustered using Zhang: last step in Fig. 1, section IV, Table I. Defect classification or the defect detector is implied by SVM. Gray scale vector (gray histogram) clusters are implied by the classification process of SVM as well as Figs. 7-9 and results in Table I.)(The Official Notice on the technique of vector clustering)
Zhang {modified by Bonewitz and ON and Stoppa} does not disclose explicitly k-means clustering, which is, however, well known and commonly practiced in the image processing art. Examiner takes a further Official Notice to this fact.
The reasoning and motivation to combine are similar to those of claim 1.
Regarding claim 10, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 1 wherein the product image includes a plurality of image cells and the vector generator creates a plurality of gray scale data vectors for the gray scale pixels in each of the image cells and the defect detector matches each of the plurality of gray scale data vectors with the one or more gray scale vector clusters in the data store. (Zhang: Figs. 7-9, Table I. Defect classification is component by component or region by region. Each component such as a capacitor is interpreted as the claimed “cell”.)
Regarding claim 11, Zhang {modified by Bonewitz and ON and Stoppa} discloses the inspection system of claim 1 wherein the one or more cameras provide a product image stream including a plurality of product image frames for a plurality of products movable along a conveyor path and the system includes product tracking and separation features to separate product image frames for sequential product to compile a product image file including a plurality of product image frames associated with each of the plurality of products. (Bonewitz: Figs. 1-6, col. 4, lines 22-35, col. 6, lines 44-56, col. 9, lines 22-35, col. 12, lines 1-5)
Claims 12-15 are similarly rejected as claims 1-4.
Claims 16-20 are similarly rejected as claims 1-3, 8 and 10-11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FENG NIU whose telephone number is (571)272-9592. The examiner can normally be reached on Monday - Friday, 8am-5pm PT.
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/FENG NIU/Primary Examiner, Art Unit 2669