DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without a practical application or significantly more.
Regarding independent claims 1 and 5, these claims recite the following limitations which are found to be abstract ideas not reciting a practical application or significantly more, with claim 5 being exemplary:
receiving input data including one thermal image by an encoder, wherein the encoder is configured to extract feature information including low, mid, and high-level features from the input data; (abstract idea as a mental process, a human mind extracting information by seeing the image)
capturing multi-scale features from the thermal images based on the received feature information, the multi-scale feature extraction module comprising a pyramidal pooling architecture (PPA) module, a convolutional layer, and a plurality of atrous convolutional layers that accurately captures the multi-scale moving objects from the thermal image; (abstract idea as a mathematical process, the use of a pyramidal pooling architecture, convolutional layer… is well known mathematical process in machine learning)
preserving the PPA module a contextual relationship of the thermal image which is essential for moving object detection; (abstract idea as a mathematical process)
providing three max-pooling layers with corresponding strides followed by a plurality of convolutional layers in the PPA module; (abstract idea as a mathematical process)
preserving maximum value by the PPA module in every pooling area to retain the contextual relationship between the pixels in the complex video frames that can handle various challenging scenes; and (abstract idea as a mathematical process)
perform up-sampling by a decoder to project the multi-scale features space to image space, wherein the decoder includes a plurality of stacked transposed convolution layers that project from feature space to image space, predicting a score map and a threshold of 0.9 is applied on the score map to get one binary class labels as background and foreground. (abstract idea as a mathematical process)
This judicial exception is not integrated into a practical application for the following reasons. Claim 1 further recite additional elements: claim 1 contains one or more processors and a memory; They are not sufficient to recite a practical application of the abstract ideas recited in claim 1as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. see MPEP §2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claims 1 and 5 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the claimed “non-transitory computer-readable storage medium” and “processor” perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
The dependent claims are also directed to an abstract idea such that the elements can be done mentally or considered mathematical process.
The claims do not recite additional elements that integrate the judicial exception into a practical application because the claims are directed to thermal video surveillance however none of the elements of the claims are directed to any thermal video surveillance or any elements describing the result of the method relating to thermal video surveillance. Therefore there is no practical application currently claimed.
Appropriate corrections are required.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 9 is drawn to a “a computer program product comprising computer-readable instructions for implementing the method of claim 5. Although the computer program product is implemented on a computer or processing device, the claimed subject matter is “the computer program product” A computer program product is software per se and does not fall within the definition of a process, machine, manufacture, or composition of matter (In re Nuijten), and are therefore non-statutory. Appropriate corrections are required.
Allowable Subject Matter
Claims 1-9 would be allowable if rewritten to overcome the 101 rejections set forth in the current office action.
Regarding independent Claims 1 and 5; the Examiner found neither prior art cited in its entirety, nor based on the prior art, found any motivation to combine any of the said prior art that teaches: “use a multi-scale feature extraction module coupled with the encoder to receive the feature information of the input data that captures the multi-scale features of moving objects from the thermal image based on the received feature information, the multi- scale feature extraction module comprising a pyramidal pooling architecture (PPA) module, a convolutional layer and plurality of atrous convolutional layers that accurately captures the multi-scale moving objects from the thermal image, wherein the PPA module preserves the contextual relationship of the thermal image which is essential for moving object detection, the PPA module comprises three max-pooling layers with corresponding strides followed by a plurality of convolutional layers, and the PPA module retains maximum value in every pooling area to retain the contextual relationship between the pixels in the complex video frames that can handle various challenging scenes; and utilize a decoder that is configured to perform up-sampling to project the multi-scale features space to image space, wherein the decoder includes a plurality of stacked transposed convolution layers that project from feature space to image space, predicting a score map and a threshold of 0.9 is applied on the score map to get one binary class label as background and foreground” in combination with the other limitations of the independent claims.
The dependent claims are allowable due to its dependence to the independent claims.
A closest prior art, Appadurai et al. US2024/0428414 teaches “The encoder/decoder 1035 is used to facilitate semantic segmentation. The semantic segmentation is used to identify regions in a received image from one or more classes of semantically interpretable structures. As discussed above, these layers include several convolutional, pooling, and batch normalizations in the encoder to arrive at a latent space representation. The decoder includes a series of pooling layers, up-sampling layers, and a full connected layer at the terminal point to provide a semantic segmentation from the latent space representation of the echocardiogram image” However Appadurai et al. does not teach the allowable subject matter of Claim 1 and therefore the claims are allowable over Appadurai et al.
A closest prior art, RAJASEKHARAN UNNITHAN et al. US2026/0100013 teaches “obtaining a multispectral image (e.g., a thermal multispectral image) of a first sample of a sample class, said multispectral image corresponding to a first number (n) of component images, each component image associated with a unique spectral band and representing, at each pixel of the particular component image”, Abstract and “The machine learning algorithm may implement an encoder-decoder architecture, optionally comprising one or more of: an encoder-decoder architecture where a series of convolutional and pooling layers are in the encoder path and/or up-sampling and transposed deconvolutional layers are implemented in the decoder path; and a Leaky RELU activation function for introducing non-linearity. The training set may include training images of a same sample type obtained at different temperatures of the sample type”, Paragraph [0007]. However RAJASEKHARAN UNNITHAN et al. does not teach the allowable subject matter of Claim 1 and therefore the claims are allowable over RAJASEKHARAN UNNITHAN et al.
Conclusion
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/MING Y HON/Primary Examiner, Art Unit 2666
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