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 .
DETAILED ACTION
Art Unit – Location
The Art Unit location of your application in the USPTO may have changed. To aid in correlating any papers for this application, all further correspondence regarding this application should be directed to Art Unit 2682.
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 and 3-7 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an Abstract Idea without significantly more.
The claim(s) recite(s) mental processes and mathematical concepts. This judicial exception is not integrated into a practical application because the abstract idea is implemented using a generic estimation system. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the generic system acquires and calculates without a significant technical improvement.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of a system is generic and lacks detailed improvements to improving the technology of the system.
Step 1. The claims are directed to a Process, Machine, and Article of Manufacture.
Step 2A. Prong 1. The invention comprises an Abstract Idea of: A Mathematical Concept and a Mental Process.
Mathematical Concept of: calculating areas of detected garbage and non-garbage portions weighted by respective distances. Calculating areas, weighting areas by distances, and estimating pollution levels is a mathematical concept. Please refer to MPEP 2106.04(a)(2) I C. e.g. “Mathematical Calculations” which can be done using simple tools such as a pencil and paper.
A Mental Process of: acquiring an image, detecting garbage and non-garbage areas, and acquiring and calculating distances, calculating the number of pieces of garbage, estimating pollution levels, and segmentation can be done by a human. MPEP 2106.04(a)(2) III B. e.g. “A Claim That Encompasses a Human Performing the Step(s) Mentally With or Without a Physical Aid Recites a Mental Process”. A physical aid may be a rangefinder or a simple ruler.
Step 2A Prong 2. There are no additional elements or claimed limitations which are directed to integration into a practical application. There is no technical improvement to the claimed pollution level estimation system 2106.04(d)(1), 2106.05.
Step 2B. Are there additional elements that amount to significantly more?
The claims cite a process which can be performed by a human using mathematical concepts and mental processes, where the inclusion of a estimation system as a substitute for a human is generic and lacks the details for a significant technical improvement or an inventive concept. MPEP 2106.05.
The estimation system is merely a generic machine which is well-understood, routine, and conventional. The additional claim limitation elements in combination with the learning machine do not amount to significantly more because: In combination, the well-understood, routine and conventional functions do not improve the system function and segmentation through machine learning. There appears to be no meaningful technological result from the estimation system.
Allowable Subject Matter
Claims 1 and 3-7 would be allowable if the 101 Abstract Idea rejection listed above is overcome.
The closest reference of record is Li ("CN 111767822) In the Applicant’s independent claim 1, the reference of Li does not teach:
acquire information indicating a distance from an imaging point to each of the garbage portion and non-garbage portion with respect to each of the garbage portion and non-garbage portion in the image and calculates the areas of the garbage portion weighted by the respective distances and estimate a pollution level at the location to be estimated on the basis of the calculated areas.
Li fails to directly anticipate or render the above underlined limitations obvious (to be used with other claimed limitations).
Li teaches capturing an image; but does not teach indicating a distance from an imaging point to the garbage portion as claimed, presumably to weight an image of a garbage object in a distance relative to a nearer garbage object having the same area.
Inaba teaches capturing temporal images; however, Inaba does not teach indicating a distance from an imaging point to the garbage portion as claimed, presumably to weight an image of a garbage object in a distance relative to a nearer garbage object having the same area.
Relevant Prior Art
Non-Patent Literature
Coastal Waste Detection Based on Deep Convolutional Neural Networks
Abstract: Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.
Conclusion
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/TED W. BARNES/ Ph.D. Electrical Engineering
Primary Examiner
Art Unit 2682
/TED W BARNES/Primary Examiner, Art Unit 2682