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
Receipt is acknowledged of certified copies of papers 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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because taken as a whole, it is directed to the description of a computer program. 35 U.S.C. 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these "four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of § 101 even if the subject matter is otherwise new and useful." In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Non-limiting examples of claims that are not directed to any of the statutory categories include products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. Software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.
Allowable Subject Matter
Claims 1-19 are allowed.
Claim 20 is currently rejected under 35 USC § 101, but are otherwise not subject to any prior art rejections under either 35 U.S.C. § 102 or 35 U.S.C. § 103. Assuming that the foregoing shortcomings of these claims were rectified, these claims would be allowable.
The following is a statement of reasons for the indication of allowable subject matter:
Several of the features of independent claims 1 and 17-20 were known in the art as evidenced by Tang et al (US PG Pub. No. 2023/0075836) which discloses providing a set of groundtruth regions (“positive region proposals”), each groundtruth region comprising an annotation of a feature in training image data and providing a set of ignore regions (“negative example region proposals”), receiving a set of predicted feature regions from the machine-learning model, each predicted feature region comprising a prediction of a feature in the training image data at ¶ [0219]. However, Tang does not use these regions for training the machine learning model; Tang does not disclose, for each predicted feature region which overlaps with a corresponding ignore region and does not overlap with any of the groundtruth regions, ignoring the predicted feature region so that it is not used to train the machine-learning model.
O’Connell et al (US PG Pub. No. 2022/0277172) discloses providing a set of groundtruth regions, each groundtruth region comprising an annotation of a feature in training image data at ¶ [0031], providing a set of ignore regions (“portions of images which are known not to contain ground truths”), determining a loss value based on similarity coefficients at ¶ [0034], ¶ [0041], and training the machine-learning model on a basis of the loss value at ¶ [0041]. However, O’Connell uses the ignore regions in training and does not disclose, for each predicted feature region which overlaps with a corresponding ignore region and does not overlap with any of the groundtruth regions, ignoring the predicted feature region so that it is not used to train the machine-learning model.
Aktaş et al, “Small Object Detection and Tracking from Aerial Imagery” discloses providing a set of ground truth regions, each ground truth region comprising an annotation of a feature in training image data at pp. 690-691, sec. III(F). Aktaş discloses providing a set of ignore regions at p. 690, sec. III(E). Aktaş discloses receiving a set of predicted feature regions from the machine-learning model, each predicted feature region comprising a prediction of a feature in the training image data at pp. 690-691, secs. III(E)-(F). Aktaş discloses, for each predicted feature region which overlaps with a corresponding ground truth region, generating a similarity coefficient (“Improved IoU”) indicative of a similarity between the predicted feature region and the corresponding ground truth region at p. 691, sec. III(F). Aktaş discloses determining a loss value based on the similarity coefficients at pp. 690-691, sec. III(F). Aktaş discloses training the machine-learning model on a basis of the loss value at pp. 690-691, sec. III(F). And, Aktaş discloses, for each predicted feature region which overlaps with a corresponding ignore region and does not overlap with any of the groundtruth regions, ignoring the predicted feature region so that it is not used to train the machine-learning model at p. 690, sec. III(F): “However, ’ignored regions’ and ’others’ labels are not for learning. Instead, the model should omit the predictions that fall into these regions.” But, Aktaş does not disclose, for each predicted feature region which does not overlap with any of the ignore regions and does not overlap with any of the groundtruth regions, training the machine-learning model on a basis of the predicted feature region. Instead, at p. 691, sec. IV(A), Aktaş discloses: “If the proposal and ground truth do not overlap in regular IoU, it is treated as a false positive rather than being included in the loss function. This technique prevents the model from learning some difficult samples.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID F DUNPHY whose telephone number is (571)270-1230. The examiner can normally be reached on 9 am - 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached on (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID F DUNPHY/
Primary Examiner, Art Unit 2668
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