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 .
Response to Arguments
Applicant's arguments filed 08/17/2026 have been fully considered but they are not persuasive. On page 6 applicant argues that the current claim does not teach “contour detection” and the examiner respectfully disagrees. Clearly Cella ¶[1775] teaches, by having the toolpath be a contour, a contour must be detected. The same rejection applies here, since that tool must operate with image analysis to be able to detect that path in a contour manner, in that target image of the toolpath.
Applicant also argues, that Cella does not teach” the detecting the region specifies a range of the specific shape in one direction of the detection target image and an intersecting direction with respect to the one direction from the contour of the specific shape detected in the detecting the contour, and detects specified the range as a region of the specific shape”. The Examiner respectfully disagrees. Cella teaches, ¶|1560] "Region-based CNN (R-CNN) methods are used to extract a region of interest ROI, the region of interest would be “the region” claimed, and the “boundary” would then create a shape. And Cella teaches in ¶[1775] the contour following the path of a tool which would then be the contour. Applicant further did not make the amendment that was discussed. And further applicant has deleted subject matter that Cella clearly had and supported the subject matter of Cella reading on the claimed limitations.
Claim 5 is now objected as allowable. The rest of the claims remained rejected with the previous prior art.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella (US 2022/0197306).
As per claims 1 and 8, Cella teaches, a profile detecting method and apparatus comprising: detecting a specific shape included in a detection target image from the detection target image including the specific shape using a model that has learned a learning image including the specific shape and information regarding the specific shape included in the learning image (Cella, ¶[0107] “[0107] In embodiments, the artificial intelligence system is configured to automatically classify and cluster parts, such as ones that may be additively manufactured, such as based on similarity of attributes, including physical attributes, shapes, functional attributes, material attributes, performance attributes, economic attributes, and others.” In order to classify the shape detection of that shape takes place, and the artificial intelligence system represents a model that has learned a learning image including the specific shape and information regarding the specific shape included in the learning image ); and outputting shape information of the detected specific shape (Cella, ¶[0554] “a set of properties of a physical asset may include a type of the physical asset, the shape and/or dimensions of the asset” this represents outputting shape information of the detected specific shape), wherein the detecting includes: detecting a contour of the specific shape included in the detection target image from the detection target image ( Cella, ¶[1775] “Various types of toolpath strategies and algorithms, such as zigzag, contour,” this represents detecting contour ¶[0051] “to facilitate improved AI-based object recognition, boundary detection,” this represents detecting border of a film by having boundary detection), and detecting a region having the specific shape included in the detection target image from the detection target image (Cella, ¶[1792] Region Based CNNS (RCNNS) and Object Detection
[1792] In embodiments, artificial intelligence and machine learning systems in the data processing system of the autonomous additive manufacturing platform 10110 may enable automatic classification and clustering of 3D printed parts and products. In embodiments, artificial intelligence and machine learning systems in the data processing system of the autonomous additive manufacturing platform 10110 may enable automatic classification and clustering of malicious defects in the additive manufacturing process.” This represents detecting a region having the specific shape); at least one of the detecting the contour (Cella, ¶[1775] “Various types of toolpath strategies and algorithms, such as zigzag, contour,” this represents detecting contour ¶[0051] “to facilitate improved AI-based object recognition, boundary detection,” this represents detecting border of a film by having boundary detection); and the detecting the region specifies a range of the specific shape in one direction of the detection target image and an intersecting direction with respect to the one direction from the contour of the specific shape detected in the detecting the contour, and detects specified the range as a region of the specific shape (Cella, ¶[1560] “Region-based CNN (R-CNN) methods are used to extract regions of interest (ROI), where each ROI is a rectangle that may represent the boundary of an object in image. Conceptually, R-CNN operates in two phases. In a first phase, region proposal methods generate all potential bounding box candidates in the image.” This represents region specifies a range of the specific shape in one direction of the detection target image and an intersecting direction with respect to the one direction from the contour of the specific shape detected in the detecting the contour, and detects specified the range as a region of the specific shape by having Region-based CNN (R-CNN) methods are used to extract regions of interest (ROI)).
As per claim 2, Cella teaches, the profile detecting method according to claim 1, wherein the learning image and the detection target image are images of a cross-section of a semiconductor substrate in which a plurality of recesses indicating a cross-section of a via or a trench is arranged as the specific shape (Cella, ¶[1738] “a new layer of material, which is sintered to form the next cross-section of the object” And this describes photolithography, which is the foundational process used to create the layered cross-sections of a semiconductor chip.).
As per claim 3, Cella teaches, the profile detecting method according to claim 1, further comprising: measuring a dimension of the detected specific shape, wherein the outputting outputs the measured dimension (Cella, [0554] “For example, a set of properties of a physical asset may include a type of the physical asset, the shape and/or dimensions of the asset, the mass of the asset, the density of the asset,” this represents measured dimensions).
As per claim 4, Cella teaches, the profile detecting method according to claim 1, wherein the model learns information regarding the learning image and a contour of the specific shape included in the learning image, and the detecting the contour detects the contour of the specific shape included in the detection target image from the detection target image using the model (Cella, ¶[1775] “Various types of toolpath strategies and algorithms, such as zigzag, contour” This represents contour detects the contour of the specific shape ).
As per claim 6, Cella teaches, the profile detecting method according to claim 1, wherein the outputting selects a detection target region from a plurality of regions including the specific shape on an image to output only shape information indicating a feature of interest (Cella, ¶[01794] “dimensions (overall dimensions, or dimensions of specific features), feature angles, feature areas, surface finish (e.g., degree of light reflectivity, number of pits and/or scratches per unit area), and the like.” This represents a feature of interest).
As per claim 7, the profile detecting method according to claim 1, wherein the outputting selects a detection target region from a plurality of regions including a recess on an image, and outputs only a recess feature amount indicating a feature of interest (Cella, ¶[1741] “For each layer, the laser beam traces a cross-section of the part pattern on the surface of the liquid resin. Exposure to the ultraviolet laser light cures and solidifies the pattern traced on the resin and joins it to the layer below. In embodiments, the SLA process may involve multiple UV lasers, allowing for switching and/or simultaneous work on different target locations and/or different material types.” This represents recess feature amount indicating a feature of interest, by having the analysis of the cross-section).
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 SANTIAGO GARCIA whose telephone number is (571)270-5182. The examiner can normally be reached Monday-Friday 9:30am-5:30pm.
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, Chineyere Wills-Burns can be reached at (571) 272-9752. 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.
/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
Prosecution Insights