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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Republic of Korea on 21 December 2023. It is noted, however, that applicant has not filed a certified copy of the KR10-20023-0188832 application as required by 37 CFR 1.55.
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 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claims are directed towards a “computer-readable recording medium”, and the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106.03.
However, claims directed towards non-transitory computer readable medium which exclude transitory forms of signal transmission may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106.03.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 5 of copending Application No. 18/991,133 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they recite common subject matter.
The following table illustrates exemplary conflicting claim pairs and the corresponding mapping of limitations.
Instant Application (US. App. No. 18/991,100)
Co-Pending Application (US. App. No. 18/991,133)
Claim
Limitations
Claim
Limitations
1
A method of providing iron scrap classification information through image analysis, the method comprising:
1
A method of providing classification information for iron scraps according to an unloading process, the method comprising:
receiving, by a receiving circuit, a loaded state image from a camera in a state where a plurality of iron scraps are loaded onto a loading device;
receiving, by a receiving circuit, a loaded state image from a camera during an unloading process of a plurality of iron scraps loaded onto a loading device;
obtaining, by a processor, layer information that is updated as the unloading process progresses and determined based on a height of the plurality of iron scraps, wherein the height is determined in a stack direction of the plurality of iron scraps loaded onto the loading device;
determining, by the processor, a region of interest based on the loaded state image that is updated as the unloading process progresses;
generating, by a processor, segmented images including a target iron scrap from the loaded state image using a segmentation model, wherein the segmentation model performs segmentation on the target iron scrap, which is one of the plurality of iron scraps;
generating, by the processor, a segmented image of a target iron scrap, which is included in the region of interest, wherein the target iron scrap is one of the plurality of iron scraps;
generating, by the processor, item information and grade information that correspond to the target iron scrap using a classification model, wherein the classification model performs classification on the segmented images and analyzes the classified images on an image-by-image basis; and
generating, by the processor, item information and grade information for the target iron scrap based on the segmented image and the layer information; and
5
The method of claim 1, wherein:
the generating of the segmented image by the processor includes generating the segmented image that includes the target iron scrap from the loaded state image using a segmentation model; and
the generating of the item information and the grade information by the processor includes generating the item information and the grade information for the target iron scrap using a classification model that performs analysis in units of images.
providing, by the processor, iron scrap classification information that includes the item information and the grade information.
providing, by the processor, classification information for the iron scraps, including the item information and the grade information.
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-9, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ogasawara et al. (US 2023/0316489, effectively filed 14 August 2020), herein Ogasawara.
Regarding claim 1, Ogasawara discloses a method of providing iron scrap classification information through image analysis, the method comprising:
receiving, by a receiving circuit (see Ogasawara [0063], where acquisition interface acquires camera images), a loaded state image from a camera in a state where a plurality of iron scraps are loaded onto a loading device (see Ogasawara [0045], where camera images are taken of iron scrap at the point at which the iron scrap is transported by truck);
generating, by a processor (see Ogasawara [0065], where functions are implemented by a processor), segmented images including a target iron scrap from the loaded state image using a segmentation model, wherein the segmentation model performs segmentation on the target iron scrap, which is one of the plurality of iron scraps (see Ogasawara [0051]-[0053], where semantic segmentation is performed to extract the iron scrap parts, where the scrap part is extracted from the scrap in the window of the image center);
generating, by the processor, item information and grade information that correspond to the target iron scrap using a classification model, wherein the classification model performs classification on the segmented images and analyzes the classified images on an image-by-image basis (see Ogasawara [0058]-[0062], where a first and second scrap discrimination model discriminates the grades of scrap included in the extracted scrap image); and
providing, by the processor, iron scrap classification information that includes the item information and the grade information (see Ogasawara [0062], where the grades of scrap included in the scrap image and the ratio of each grade are discriminated based on the scrap images are output).
Although Ogasawara does not explicitly disclose all features within the same embodiment, Ogasawara does provide the various teachings as relevant embodiments and will occur to those skilled in the art, that various combinations of the disclosed embodiments of the invention described herein may be employed in practicing the invention.
Thus, one of ordinary skill in the art, in view of the suggested disclosed teachings of the embodiments of Ogasawara, would have found it obvious and led to combine the disclosed features and arrive at the claimed invention.
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. In this instance, Ogasawara provide the various teachings as relevant embodiments and will occur to those skilled in the art, that various combinations of the disclosed embodiments of the invention described herein may be employed in practicing the invention. One of ordinary skill in the art would have reasonable expectation of success of combining the disclosed features in the same embodiment for employing the disclosed iron scrap discrimination method.
Regarding claim 2, please see the above rejection of claim 1. Ogasawara discloses the method of claim 1, wherein the generating of the item information and the grade information includes:
generating, by the processor, a target iron scrap image representing the target iron scrap by excluding a background region from the segmented images (see Ogasawara [0053]-[0057], where the scrap part extraction model is trained to extract labeled scrap groups from a variety of backgrounds); and
performing, by the processor, the classification on the target iron scrap image to generate the item information and the grade information (see Ogasawara [0062], where the grades of scrap included in the scrap image and the ratio of each grade are discriminated based on the scrap images are output).
Regarding claim 3, please see the above rejection of claim 1. Ogasawara discloses the method of claim 2, further comprising:
receiving, by the receiving circuit, a correct image representing the target iron scrap (see Ogasawara [0061]-[0062], where teacher data pertaining to the selection model is scrap images and used to determine performance data);
determining, by the processor, a percentage of an overlapping region between the correct image and the target iron scrap image (see Ogasawara [0061]-[0062], where teacher data pertaining to the selection model is scrap images and used to determine performance data such as determination accuracy);
obtaining, by the processor, an iron scrap determination accuracy indicating whether the target iron scrap image corresponds to an actual image of iron scrap, when the percentage of the overlapping region exceeds a threshold overlap percentage (see Ogasawara [0057] and Fig. 8, where the label data indicates regions of scrap; see Ogasawara [0062], where performance data pertaining to model selection is determined based on the discrimination results); and
providing, by the processor, the iron scrap determination accuracy as a performance indicator (see Ogasawara [0062], where performance data pertaining to model selection is determined based on the discrimination results).
Regarding claim 4, please see the above rejection of claim 2. Ogasawara discloses the method of claim 2, further comprising:
determining, by the processor, a target weight based on a region size of the target iron scrap image (see Ogasawara [0060], where the area ratio of grades of scrap in the image is calculated, and further converts the area ratio into a weight ratio based on the bulk density of each piece of scrap);
determining, by the processor, a target accuracy for the target iron scrap image (see Ogasawara [0057], where a test set is used to evaluate the accuracy of a trained scrap part extraction model); and
applying, by the processor, the target weight to the target accuracy to determine an accuracy for the classification model (see Ogasawara [0061], where a determination accuracy can be determined from the weight ratio of the grades of scrap in the image).
Regarding claim 5, please see the above rejection of claim 1. Ogasawara discloses the method of claim 1, further comprising:
receiving, by the receiving circuit, a single segmented image captured for a single iron scrap (see Ogasawara [0083]-[0088], where a images of individual detection targets are suggested to be used in creating artificial images, and that iron scrap are a detection target);
generating, by the processor, a synthesized image based on the loaded state image and the single segmented image (see Ogasawara [0083]-[0088], where a detection scrap image is suggested to be combined with background images); and
applying, by the processor, the segmentation model and the classification model to the synthesized image to provide additional iron scrap classification information (see Ogasawara [0083]-[0088], where artificial images created by combining scrap images with background images are suggested to be used as training data).
Regarding claim 6, please see the above rejection of claim 5. Ogasawara discloses the method of claim 5, wherein the generating of the synthesized image includes:
generating, by the processor, a single iron scrap image by excluding a background region from the single segmented image (see Ogasawara [0083]-[0088], where artificial images created by combing scrap images with background images are suggested to be used as training data); and
generating, by the processor, the synthesized image by combining the loaded state image with the single iron scrap image (see Ogasawara [0083]-[0088], where artificial images created by combing scrap images with background images are suggested to be used as training data).
Regarding claim 7, please see the above rejection of claim 6. Ogasawara discloses the method of claim 6, wherein the generating of the synthesized image by combining the loaded state image with the single iron scrap image includes:
determining, by the processor, a number of possible combinations of the single iron scrap image and the loaded state image based on a region size of the single iron scrap image (see Ogasawara [0083]-[0088], where numerous different artificial images can created by combining different scrap images with background images); and
generating, by the processor, the synthesized image based on the number of possible combinations (see Ogasawara [0083]-[0088], where artificial images created by combining scrap images with background images are suggested to be used as training data).
Regarding claim 8, please see the above rejection of claim 1. Ogasawara discloses the method of claim 1, wherein:
the receiving of the loaded state image includes receiving, by the receiving circuit, a loaded state image that is updated by being captured in a state in which positions of the plurality of iron scraps are updated in the loading device (see Ogasawara [0057], where a new image that includes a completely different background can be used to retrain the scrap part extraction model); and
the generating of the segmented images includes generating, by the processor, the segmented images including the target iron scrap from the updated loaded state image using the segmentation model (see Ogasawara [0057], where a new image that includes a completely different background can be used to retrain the scrap part extraction model).
Regarding claim 9, please see the above rejection of claim 4. Ogasawara discloses the method of claim 4, wherein:
when a number of pixels included in the target iron scrap image is less than a first number, the target weight increases in proportion to a linear function corresponding to a first slope (see Ogasawara [0060], where the area ratio of grades of scrap in the image is calculated, and further converts the area ratio into a weight ratio based on the bulk density of each piece of scrap, where the converted weight ratio can change corresponding to a change in the area ratio);
when the number of pixels is greater than or equal to the first number and less than a second number, the target weight increases in proportion to an exponential function having a base greater than the first slope (see Ogasawara [0060], where the area ratio of grades of scrap in the image is calculated, and further converts the area ratio into a weight ratio based on the bulk density of each piece of scrap, where the converted weight ratio can change corresponding to a change in the area ratio); and
when the number of pixels is greater than or equal to the second number, the target weight increases in proportion to a linear function corresponding to a second slope smaller than the first slope, wherein the first slope and the second slope have positive numbers, and the second number is greater than the first number (see Ogasawara [0060], where the area ratio of grades of scrap in the image is calculated, and further converts the area ratio into a weight ratio based on the bulk density of each piece of scrap, where the converted weight ratio can change corresponding to a change in the area ratio).
Regarding claim 11, it recites an apparatus performing the method of claim 1. Ogasawara teaches a apparatus for performing the method of claim 1 (see Ogasawara [0065], where functions are implemented by a processor). Please see above for detailed claim analysis.
Please see the above rejection for claim 1, as the rationale to combine the teachings of Ogasawara are similar, mutatis mutandis.
Regarding claim 12, see above rejection for claim 11. It is an apparatus claim reciting similar subject matter as claim 2. Please see above claim 2 for detailed claim analysis as the limitations of claim 12 are similarly rejected.
Regarding claim 13, see above rejection for claim 12. It is an apparatus claim reciting similar subject matter as claim 3. Please see above claim 3 for detailed claim analysis as the limitations of claim 13 are similarly rejected.
Regarding claim 14, see above rejection for claim 11. It is an apparatus claim reciting similar subject matter as claim 5. Please see above claim 5 for detailed claim analysis as the limitations of claim 14 are similarly rejected.
Regarding claim 15, it recites a computer-readable recording medium on which a program is recorded to cause a processor to perform the method of claim 1. Ogasawara teach a computer-readable recording medium on which a program is recorded to cause a processor to perform the method of claim 1 (see Ogasawara [0066], where program according to the present embodiment can be recorded on a computer readable recording medium). Please see above for detailed claim analysis.
Please see the above rejection for claim 1, as the rationale to combine the teachings of Ogasawara are similar, mutatis mutandis.
Regarding claim 16, see above rejection for claim 15. It is an apparatus claim reciting similar subject matter as claim 2. Please see above claim 2 for detailed claim analysis as the limitations of claim 16 are similarly rejected.
Regarding claim 17, see above rejection for claim 16. It is an apparatus claim reciting similar subject matter as claim 3. Please see above claim 3 for detailed claim analysis as the limitations of claim 17 are similarly rejected.
Regarding claim 18, see above rejection for claim 15. It is an apparatus claim reciting similar subject matter as claim 5. Please see above claim 5 for detailed claim analysis as the limitations of claim 18 are similarly rejected.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ogasawara as applied to claim 1 above, and further in view of Vaidyanathan et al. (US 2021/0358103), herein Vaidyanathan.
Regarding claim 10, please see the above rejection of claim 1. Ogasawara does not explicitly disclose the method of claim 1, wherein the providing of the iron scrap classification information includes:
obtaining, by the processor, average weight information indicating a cumulative area and/or cumulative number for each item and grade for the item information and grade information that correspond to the target iron scrap among the plurality of iron scraps; and
providing, by the processor, a circular graph showing a cumulative area ratio and/or cumulative number ratio for each item and grade for the target iron scrap in the loaded state image based on the average weight information.
Vaidyanathan teaches in a related and pertinent system and method of identifying potential areas for mineral extraction (see Vaidyanathan Abstract), where a dashboard can provide reports in graphical form such as pie charts (see Vaidyanathan [0054]).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Vaidyanathan to the teachings of Ogasawara such that a dashboard can be used to provide a pie chart of the weight ratio and total weight of each grade of scrap to be traded as suggested by Ogasawara (see Ogasawara [0045]).
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In this instance, Ogasawara teaches that in actual operations, scrap is traded according to the weight ratio and total weight of each grade of scrap. Vaidyanathan teaches that a dashboard can provide reports in graphical form such as pie charts. One of ordinary skill in the art would have reasonable expectation that a dashboard can be used to provide a pie chart of the weight ratio and total weight of each grade of scrap processed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY WING HO CHOI whose telephone number is (571)270-3814. The examiner can normally be reached 9:00 AM to 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VINCENT RUDOLPH can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY CHOI/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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