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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The independent claim 1 (exemplary claim) recites:
1. An image processing method, comprising:
obtaining a target image; - pre/post activity
splitting the target image into at least one sub-image based on a similarity of complexity in the target image; - mental activity
processing the at least one sub-image by
and
obtaining an output image based on the processed at least one sub-image. – pre/post activity
Step Analysis
1: Statutory Category?
Yes. The claim recites a series of steps and, therefore, is a process.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim recites the limitations of splitting and processing sub-image to find an output. The splitting and processing limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by at least one decoder,” nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “by at least one decoder” language, the claim encompasses the user manually process the sub-images to find the output or what’s in the sub-image.
This limitation is a mental process.
2A - Prong 2: Integrated into a Practical Application?
No. The claim recites one additional element:
that at least one decoder is used to perform both the limitation steps. The at least one decoder in steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic decoder/processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to the abstract idea.
2B: Claim provides an Inventive Concept?
No. As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The claim is ineligible.
The dependent claims 2-9 and 15 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore are ineligible.
Claims 10-13 are ineligible for the same reasons as set forth in the analysis for the claims 1-9.
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 1-15 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Tsunoda et al (US Pub. 2019/0005356) in view of Multi-scale self-guided attention for medical image segmentation, by Sinha et al (IDS document).
With respect to claim 1 as best understood, Tsunoda discloses an image processing method, comprising:
obtaining a target image, (see paragraph 0006, wherein …apparatus includes an image acquisition unit configured to acquire a first image captured by an imaging unit…);
splitting the target image into at least one sub-image based on a similarity of complexity in the target image, (see paragraph 0006, wherein …segmentation unit configured to segment each of the first image and the second image into a plurality of segments…classify the plurality of segments…into a first field having a relatively high reliability and a second field having a relatively low reliability…);
[processing the at least one sub-image by at least one decoder corresponding to the at least one sub-image]; and
obtaining an output image based on the processed at least one sub-image, (see paragraph 0006, wherein …determine categories …based on the feature quantities acquired…), as claimed.
However, Tsunoda fails to explicitly disclose processing the at least one sub-image by at least one decoder corresponding to the at least one sub-image, as claimed.
Sinha teaches processing the at least one sub-image by at least one decoder corresponding to the at least one sub-image, (see section III. Method D. Guiding attention, page 4, right hand column, wherein …we integrate an encoder-decoder network that compresses the input features F into a compacted representation in the latent space [46]…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of processing images using computer. The teaching of Sinha to process the image using a decoder can be incorporated into Tsunoda system as suggested (see figure 2, numerical 205 signal processing unit), for suggestion, and modifying the system yields a image segmentation system (see Ashish Abstract), for motivation.
With respect claim 2 as best understood, combination of Tsunoda and Sinha further discloses wherein the splitting of the target image into the at least one sub-image comprises: splitting the target image into at least one grid of equal size; determining the similarity of the complexity between adjacent grids among the at least one grid; and grouping the at least one grid into the at least one sub-image based on the similarity of the complexity between the adjacent grids, (see paragraph 0073, wherein … the re-imaging unit 407 performs re-imaging by using suitable imaging parameters in a low-reliability field based on the reliability for each segment acquired in the reliability determination step S505. In this case, the re-imaging unit 407 performs re-imaging by adjusting the imaging parameters for each group of segments, as with the low-reliability fields A 805 and B 806 in FIG. 8C…), as claimed.
With respect claims 3 and 4 as best understood, combination of Tsunoda and Sinha further discloses wherein the determining the similarity of the complexity between the adjacent grids comprises: obtaining feature information associated with a position of a fine object by performing convolution on the target image; and determining the similarity of the complexity between the adjacent grids based on the feature information and self-attention network; and wherein the feature information comprises a mask map indicating at least one of a location of an easily-lost region, and fine feature maps indicating fine features of the target image, (see Tsunoda paragraph 0006, wherein …acquire feature quantities from each of the plurality of segments formed by segmenting the first image and the second image, respectively, a calculation unit configured to calculate a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the plurality of segments of the first image…; and see Sinha description for figure 1 on page 3, wherein …Four feature maps with different sizes – acquired from the outputs of [Res-2,Res-3,Res-4,Res-5]–are employed. The guided attention modules will generate attentive features at multiple scales, removing noisy areas and helping the network to emphasize the regions that are more relevant to the semantic classes), as claimed.
With respect claim 5 as best understood, combination of Tsunoda and Sinha further discloses wherein the grouping the at least one grid into the at least one sub-image comprises: identifying whether the similarity of the complexity between the adjacent grids is greater than or equal to a threshold; identifying whether a shape obtained by merging the adjacent grids is a rectangle based on identifying that the similarity of the complexity between the adjacent grids is greater than or equal to the threshold; and grouping the adjacent grids based on the shape being a rectangle, (see Tsunoda paragraph 0002, wherein … In Semantic Segmentation, categories “Body”, “Building”, “Sky”, and “Car” are defined by a user with respect to fields “person”, “building”, “sky”, and “car”, respectively, in the image [this is read as shape of any kind]. Then, category recognition for a local field in the image is performed based on feature quantities of the field; and paragraph 0006, wherein …classify the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability, and …to determine categories for the first field and the second field based on the feature quantities acquired from the first image and the feature quantities acquired from the second image [this is read as grouping the adjacent grids per the shaps]), as claimed.
With respect claims 6 and 7 as best understood, combination of Tsunoda and Sinha further discloses wherein the processing the at least one sub-image comprises: determining encoding information corresponding to each of the at least one sub-image; and determining network information for the at least one decoder corresponding to each of the at least one sub-image based on the encoding information; and wherein the network information comprises at least one of an output resolution, a number of layers, and a number of channels of network, (see Sinha page 2, left hand column, wherein ….Specifically, we propose a multi-scale guided attention network for medical image segmentation. First, the multi-scale approach generates stacks at different resolutions containing different semantics. While lower-level stacks focus on local appearance, higher-level stacks will encode global representations. This multi-scale strategy encourages that attention maps generated at different resolutions encode different semantic information…), as claimed.
With respect claim 8 as best understood, combination of Tsunoda and Sinha further discloses wherein the encoding information comprises at least one of a pooling feature, a semantic probability distribution feature, and a shape feature of the at least one sub-image, (see Sinha page 2, left hand column, wherein … Then, at each scale, a stack of attention modules will gradually remove noisy areas and emphasize those regions that are more relevant to the semantic descriptions of the targets. Each attention module contains two independent self-attention mechanisms, which focus on modelling position and channel feature dependencies, respectively. This duple allows to model wider and richer contextual representations and improve dependencies between channel maps, resulting in enhanced feature representations…), as claimed.
With respect claim 9 as best understood, combination of Tsunoda and Sinha further discloses wherein the obtaining the feature information associated with the position of the fine object comprises: performing a convolution operation on the target image based on convolution kernels corresponding to each direction, and wherein the each direction for the convolution kernels are determined based on information of adjacent points and a center point of the convolution kernels, (see Sinha page 4, left hand column, wherein … Thus, the position attention module selectively aggregates global context to the learned features, guided by the spatial attention map; and page 4 right hand column, wherein … At the end of both attention modules, the new generated features are fed into a convolutional layer before performing an element-wise sum operation to generate the position-channel attention features), as claimed.
Claims 10-14 as best understood are rejected for the same reasons as set forth in the rejections for claims 1, 2, 3, 6 and 9, because claims 10-14 as best understood are claiming subject matter of similar scope as claimed in claims 1, 2, 3, 6 and 9 respectively.
Claim 15 as best understood is rejected for the same reasons as set forth in the rejections for claim 1, because claim 15 as best understood is claiming subject matter of similar scope as claimed in claim 1.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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