DETAILED ACTION
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 1-8 and 10-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When reviewing independent claim 1, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claims 1-8 and 10-17 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101.
The Examiner will analyze Claim 1, and similar rationale applies to independent Claims 7 and 8.
The rationale, under MPEP § 2106, for this finding is explained below. The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a machine since the claim is directed to a tool diagnosis system.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since Claim 1 limitation of wherein the image processor compares an image of the blade of the tool captured subsequent to machining with an image of the blade of the tool captured after the tool is rotated after the image capturing subsequent to machining, and identifies a wear scar [mental process] are directed to an abstract. The claim is related to mental process by having a person to review the image to assess the damage.
If/when the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The additional claim limitations an imaging device to capture an image of a blade of a tool attached to the machining device is nothing more than insignificant extra solution activity.
A tool diagnosis system, comprising: a machining device to machine a workpiece are used to generally apply the abstract idea without limiting how it functions.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the Claims limitation “an image processor to process an image of the blade of the tool” do not amount to significantly more since the claim limitation does not state specific technic to process the image.
Furthermore, the generic computer components or machine learning algorithm of the processor/memory recited as performing generic computer or machine learning functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The Examiner finds that Claims 2-8, 10, and 13-17 does not state significantly more since the claim only recites additional steps for comparing images to identify blade wear scar.
Thus, claims 1-8, and 10-17 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, all claims are rejected under 35 U.S.C. 101.
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, 7, 8, are rejected under 35 U.S.C. 103 as being unpatentable over Schoop (Pub. No. US 2024/0316717) in view of Andersen et al. (Pub. No. US 2022/0143771).
Regarding claims 1, 7, and 8, Schoop teaches a tool diagnosis system, comprising: a machining device (CNC cutting machine) to machine a workpiece [Para. 29 “Reference is now made to FIGS. 1 and 2 which schematically illustrates one possible embodiment of the new and improved intelligent machine vision system 10 for in-process monitoring of wear of a cutting tool T.”; Para. 7 “A CNC cutting machine refers to a cutting machine with “computer numerical control” that is used to perform a subtractive manufacturing process that typically employs computerized controls and machine tools to remove layers of material from a blank or workpiece to produce a custom designed part”]; an imaging device (machine vision camera) to capture an image of a blade (cutting edge) of a tool (cutting tool) attached (mounted) to the machining device (CNC cutting machine) [Para. 2 “This document generally relates to tool wear monitoring systems and methods and, more particularly, to an intelligent machine vision system and related method for efficient and effective monitoring of a cutting tool while mounted in the spindle of a CNC cutting machine as well as to a CNC cutting machine incorporating that intelligent machine vision system” and Para. 18]; and an image processor (machine vision system controller) to process (analyze) an image of the blade (cutting edge) of the tool (cutting tool) [Para. 45 “the machine vision system controller 50 of the machine vision system 10 and the controller C′ of the CNC cutting machine 100 described above may be further adapted to store the cutting tool images taken with the camera 12 and even analyze those images with artificial intelligence to allow for real-time adaptive control and changes to process parameters (feeds and speeds), as well as robust documentation of the state of wear of each tool used to produce a critical component” and Para. 46 “That method may be broadly said to include the steps of: (a) displacing a spindle S of the machine cutting machine M or 100 to position the machine cutting tool T held in the spindle for monitoring with a machine vision camera 12 and (b) collecting at least one image of a first cutting-edge E of the cutting tool”]; wherein the image processor (machine vision system controller) analyzes an image of the blade (cutting edge) of the tool (cutting tool) captured subsequent to machining with an image of the blade (cutting edge) of the tool (cutting tool) captured after the tool is rotated after the image capturing subsequent to machining [Para. 45 “The machine vision system controller 50 of the machine vision system 10 and the controller C′ of the CNC cutting machine 100 described above may be further adapted to store the cutting tool images taken with the camera 12 and even analyze those images with artificial intelligence to allow for real-time adaptive control and changes to process parameters (feeds and speeds), as well as robust documentation of the state of wear of each tool used to produce a critical component”; Para. 46 “That method may be broadly said to include the steps of: (a) displacing a spindle S of the machine cutting machine M or 100 to position the machine cutting tool T held in the spindle for monitoring with a machine vision camera 12 and (b) collecting at least one image of a first cutting edge E of the cutting tool”; Para. 47 “The method may further include the steps of: repositioning the machine cutting tool T held in the spindle S and collecting at least one image of a second cutting edge E of the cutting tool.”; Para. 45 “”], and
identifies a wear scar (worn portion) [Para. 45 “Once the algorithm identifies the worn portion of the tool,….” .
however, Schoop doesn’t explicitly teach comparing images in order to determine to identify a wear scar.
Andersen teaches comparing images in order to determine to identify a wear scar [Para. 116 “The evaluation can be done by comparing the reference information of the individual cutting tool insert 6 (in the same position) with the current one, acquired via the inspection device or it may be done by a direct measurement of the wear”].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schoop’s machine vision system controller for tool-wear image processing by incorporating Andresen’s teaching of compares reference information with current information and analyzing differences between reference picture and current one taken by a camera, so that Schoop’s machine vision system controller compares the cutting-tool images collected during its repositioned cutting-edge imaging sequence. This modification improves Schoop by making wear scar identification expressly comparative across stored cutting-tool images, thereby improving repeatability of automated tool-wear detection and measurement.
Regarding claims 4 and 15, Schoop teaches wherein the imaging device is located in the machining device [Para. 40 “Reference is now made to FIG. 5 which schematically illustrates a CNC cutting machine 100 incorporating the intelligent machine vision system 10′ of the type generally described above”].
Regarding claims 6 and 17, Schoop teaches a brush (air jet) or an air outlet located in the machining device (CNC cutting machine) to clean the blade of the tool [Para. 40 “Reference is now made to FIG. 5 which schematically illustrates a CNC cutting machine 100 incorporating the intelligent machine vision system 10′ of the type generally described above.”; “That cleaning may be performed by (a) displacing the cutting tool T held in the spindle S into an air stream emanating from an air jet 46 that blasts the workpiece chips and the coolant from the cutting tool, (b) displacing the cutting tool held in the spindle into a brush 42 that whisks the workpiece chips and the coolant from the cutting tool or (c) displacing the cutting tool held in the spindle into a brush and an air stream to whisk and blast the workpiece chips and the coolant from the cutting tool”].
Claims 2, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Schoop (Pub. No. US 2024/0316717) in view of Andersen et al. (Pub. No. US 2022/0143771) further in view of Steinberg et al. (Pub. No. US 2005/0068450).
Regarding claims 2 and 13, Schoop in view of Andersen doesn’t explicitly the claim limitation.
However, Steinberg wherein the image processor identifies, through the comparison, a pattern (dust artifact regions) displaced in the images (sequentially displaced images) as adherent matter adhering to the blade of the tool, and removes the adherent matter from the images through image processing (in-painting) [Para. 143, 190, 153, and 155].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schoop in view of Andersen’s machine vision system controller, as configured by Andersen to compare cutting tool images by incorporating Steinberg’s comparison-based identification of pattern, this modification improves Schoop by reducing false wear scar identification caused by transient matter in the images.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Schoop (Pub. No. US 2024/0316717) in view of Andersen et al. (Pub. No. US 2022/0143771) further in view of Sato et al. (Pub. No. US 2015/0293524).
Regarding claims 3 and 14, Schoop in view of Andersen doesn’t explicitly teach the claim limitations.
Sato teaches alert generator to compare the remaining service life of the tool with a number of machining cycles and a machining distance for machining the workpiece, and when the remaining service life of the tool is shorter than a service life for the number of machining cycles and the machining distance for machining the workpiece, the alert generator generates an alert to prompt tool replacement [Para. 401 and 369].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schoop in view of Andersen’s remaining-useful-life algorithm, as configured with Andersen’s tool-lifetime and machining-cycle logic, by incorporating Sato’s machining distance usage-history measurement, thereby improving the timing of tool replacement before the remaining service life becomes insufficient for the planned machining work.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Schoop (Pub. No. US 2024/0316717) in view of Andersen et al. (Pub. No. US 2022/0143771) further in view of Sullivan (Pub. No. US 2007/0267039).
Regarding claims 5 and 16, Schoop in view of Andersen doesn’t explicitly teaches an ultrasonic cleaner located in the machining device to clean the blade of the tool.
Sullivan teaches an ultrasonic cleaner (ultrasonic cleaner) located in the machining device to clean (loosen and remove impurities) the blade of the tool [Para. 6, and 241].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schoop in view of Andersent to teach the claim limitations, feature as taught by Sullivan; because the modification improves schoop by removing fine chips and coolant residues before cutting edge imaging, thereby improving the reliability wear scar detection.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Schoop (Pub. No. US 2024/0316717) in view of Andersen et al. (Pub. No. US 2022/0143771) further in view of ERYUKSEL et al. (Pub. No. US 2022/0402086).
Regarding claims 10, 11, and 12, Schoop teaches a machine learning (deep learning) to learn a remaining service life of the tool using, as training data, a processed image (cutting tool images) of the blade of the tool, a machining condition (process parameters (feeds and speeds)) of the machining device, and specifications of the tool and the workpiece [Para. 4 and 45]; an inferrer to input a processed image of the blade of the tool, a machining condition of the machining device, and specifications of the tool and the workpiece into the trained model to output the remaining service life of the tool.
Schoop in view of Andersent doesn’t explicitly teach a model generator to generate a trained model through using, as training data a processed image and specifications of the tool and the workpiece.
ERYUKSEL teaches a model generator to generate a trained model through using, as training data (data repository), a processed image (images (G)) and specifications of the tool and the workpiece [Para. 257, 260, 240].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schoop in view of Andersent to teach the claim limitations, feature as taught by ERYUKSEL; because the modification improves schoop by converting tool-wear image analysis and remaining service life estimation into an explicitly trained-model workflow.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOLOMON G BEZUAYEHU whose telephone number is (571)270-7452. The examiner can normally be reached on Monday-Friday 10 AM-7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O’Neal Mistry can be reached on 313-446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SOLOMON G BEZUAYEHU/ Primary Examiner, Art Unit 2666
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