DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 2/25/2026. Claims 1-20 are pending in this Office Action.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed on 2/25/26 with respect to rejection of claims under 35 USC 103 have been considered and are not persuasive. The examiner respectfully traverses applicant arguments.
Applicant argues: “In stark contrast, Applicant's invention uses a machine learning model to perform a much higher-level, cognitive task: it analyzes features in a captured image (e.g., a barcode, a text label, the physical shape of a product) and then selects an appropriate analytical module-a "tool"- from a library of distinct processing modules. For Example, as recited in claim 5, these "tools" are discrete software processes such as "an optical character recognition bounding region" or "a barcode recognition bounding region." Optimizing the emissivity parameter for an infrared sensor, as taught by Segelmark, is fundamentally different from selecting an OCR tool to analyze a text label. Segelmark does not teach or suggest using a machine learning model to select a discrete processing tool based on image features. Accordingly, Segelmark fails to cure the deficiency of Basak, and the proposed combination fails to teach a key limitation of the independent claims. For at least this reason, the claims are patentable under 35 U.S.C. § 103.”(Applicant’s arguments at page 2)
In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “a machine learning model to perform a much higher-level, cognitive task: it analyzes features in a captured image (e.g., a barcode, a text label, the physical shape of a product) and then selects an appropriate analytical module-a "tool"- from a library of distinct processing modules”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The examiner also respectfully disagrees because one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Claim 1 recites in part the limitation of “identifying, via a machine learning model, a tool for assessing the criterion and settings for the tool based on the feature in the image”. The examiner relies on the combination of references Basak and Segelmark to show the teaching of this limitation. Specifically, Basak teaches the analyzing image and displaying a list of recommended tools as shown in Basak par [0058]-[0063]. However, Basak fails to expressly discloses the use of “ a machine learning” to identity the tool. Segelmark par [0017] teaches using one or more trained learning model to determine or set image settings . The combination of Basak and Segelmark therefore teaches the claim limitation of “identifying, via a machine learning model, a tool for assessing the criterion and settings for the tool based on the feature in the image”.
Applicant argues: “the combination is based on improper hindsight reasoning” (Applicant’s arguments at page 2)
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner also recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivation to combine the references is found in the references themselves (Segelmark) and therefore , the combination Basak and Segelmark to achieve the claimed invention is proper.
Applicant’s remaining arguments with respect to remaining claims are substantially encompassed in the argument above, therefore examiner responds with the same rationale as stated above.
For at least the foregoing reasons, the examiner maintains prior art rejections.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Basak et al.(US Patent Application Publication 2022/0035490 A1, hereinafter “Basak”) and further in view of Segelmark et al.(US Patent Application Publication 2024/0256977 A1, hereinafter “Segelmark”)
As to claim 1, Basak teaches a method, comprising: populating a graphical user interface (GUI) with a first instance of an image of a product captured by a machine vision system; identifying a feature of the product shown in the image that is associated with a criterion for analyzing the product according to a quality assurance test; identifying, via a machine learning model, a tool for assessing the criterion and settings for the tool based on the feature in the image; populating the GUI with a selectable icon that includes a second instance of the image with an overlay produced according to an assessment of the product via the tool configured according to the settings; and in response to receiving a selection of the selectable icon, adding the tool to a job comprising a series of processes for evaluating the product. (Basak par [0058]-[0063] teaches analyzing image and rendering a list of recommended tools. Basak par [0066] taches user selects one or more tools, the user may select the job deployment toggle to upload the machine vision job to a selected device)
Basak fails to expressly teach using machine learning model to identify the tool.
However, Segelmark teaches machine learning model to identify the tool. (Segelmark par [0017] teaches the infrared imaging system may determine/set image settings using one or more trained machine learning model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Basak and Segelmark to achieve the claimed invention. One would have been motivated to make such combination to avoid costly delays in implementing accurate feature classifications and image setting determinations.(Segelmark par [0065])
As to claim 2, Basak and Segelmark teach the method of claim 1, further comprising: in response receiving an adjustment to the settings for the tool, replacing suggested values for the setting with user-specified values to the setting ; and updating the machine learning model based on the user-specified values.( Segelmark par [0022] teaches for any given trained machine learning model, the given trained machine learning model used to determine one or more of the image settings may be further trained through iterative interactions between the predictions by the machine learning model on sensing data, adjustments/corrections from the user, and / or data derived from the predictions and user adjustments / corrections )
As to claim 3, Basak and Segelmark teach the method of claim 1, wherein the settings include activation commands for a light fixture associated with the machine vision system, further comprising: simulating application of the light fixture in the second instance of image. (Basak Fig.5 and par [0064] teaches commonly used tools 518 include brightness and contrast)
As to claim 4, Basak and Segelmark teach the method of claim 1, wherein the product shown in the image is provided in a failure state for the feature according to the criterion, wherein the tool and the settings are suggested with a passing state for the feature.(Basak par [0002] teaches defective product may pass inspection due to the inappropriate tools and/or products meeting quality standards may be incorrectly flagged as defective)
As to claim 5, Basak and Segelmark teach the method of claim 1, wherein the tool is at least one of: an optical character recognition bounding region; a barcode recognition bounding region; a feature presence recognition bounding region; and an alignment verification bounding region including at least two features of the product. (Basak Fig.5 teaches barcode decode tool 516)
As to claim 6, Basak and Segelmark teach the method of claim 1, wherein the tool and the setting are provided in the GUI as a combination for selection. (Basak fig.5, menu 514-518)
As to claim 7, Basak and Segelmark teach the method of claim 1, further comprising: sensing and recommending hardware for at least one of an industrial Ethernet (IE), programmable logic controller, general purpose input output (GPIO), and a file transfer protocol (FTP) server for saving images.(Basak par [0026] teaches image data and / or post image data may be sent to a server for storage )
Claims 8- 14 merely recite a system to perform the method of claims 1- 7 respectively. Accordingly, Basak and Segelmark teach every limitation of claims 8- 4 as indicates in the above rejection of claims 1- 7 respectively.
Claims 15- 20 merely recite a non-transitory computer readable storage device storing instructions when executed by a processor, performs the method of claims 1- 6 respectively. Accordingly, Basak and Segelmark teach every limitation of claims 15- 20 as indicates in the above rejection of claims 1-6 respectively.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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, Viker Lamardo can be reached at 571-270-5871. 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.
/HIEN L DUONG/Primary Examiner, Art Unit 2147