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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/30/2026 has been entered.
Response to Amendment
Applicant’s amendments filed on 03/10/2026 to the claims have overcome claim rejections under 35 U.S.C. 112(b) as preciously set forth in the Final Rejection Office Action mailed on 01/05/2026. Applicant redefines “object” in paragraph [0088] of applicant’s specification (See Remarks: Pages 9-10).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zheng et al (arXiv:2012.15840v1 31 Dec 2020), hereinafter Zheng.
-Regarding claim 1, Zheng discloses a feature extraction method, comprising (Abstract; FIGS. 1-8
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): performing segmentation processing on an object to be processed to obtain a first segmented object, wherein the object is an image, the first segmented object is an image block, each image block includes one or more pixels in the image, and all image blocks form the image (FIG. 1(a), split an image into fixed size patches ...); Page 4, 1st Col., 4th paragraph, “divide an image … into a grid of … patches … uniformly”); obtaining a plurality of element sets for the first segmented object, wherein each element set corresponds to a pixel block (FIG. 1(a), path of 1st patch (left path), linear projection, patch embedding, position embedding; Page 4, 1st Col., 4th paragraph); obtaining a first feature that is obtained by performing feature extraction on a first vector by using a first feature extraction model, wherein the first vector indicates the first segmented object (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph); obtaining a plurality of second features that is obtained by performing feature extraction on a second vector by using a second feature extraction model, wherein the second vector indicates the element sets (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph, “self-attention”; equation (1)); fusing at least two second features based on a first target weight, to obtain a first fused feature (FIG. 1(a)-1(c)), wherein the first target weight is determined based on a first parameter value indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features (FIG. 1; equations (1)-(3); Page 4, 1st Col., 5th paragraph – 2nd Col, 1st paragraph; it is a common knowledge that the parameter associated with self-attention of the transformer indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features); or the first target weight is a second parameter value comprising at least one preset constant; and performing fusion processing on the first feature and the first fused feature to obtain a second fused feature used to obtain a feature of the object (FIG. 1(c); equation (3)).
-Regarding claim 10, Zheng discloses a feature extraction method, comprising (Abstract; FIGS. 1-8): a processor, and a memory processor coupled to the processor to store instructions, which when executed by the processor, cause the electronic device to perform operations comprising (one or more processors and memories has to be used in order to implement Zheng’s method shown in FIG. 2):performing segmentation processing on an object to be processed to obtain a first segmented object, wherein the object is an image, the first segmented object is an image block, each image block includes one or more pixels in the image, and all image blocks form the image (FIG. 1(a), split an image into fixed size patches ...); Page 4, 1st Col., 4th paragraph, “divide an image … into a grid of … patches … uniformly”); obtaining a plurality of element sets for the first segmented object, wherein each element set corresponds to a pixel block (FIG. 1(a), path of 1st patch (left path), linear projection, patch embedding, position embedding; Page 4, 1st Col., 4th paragraph); obtaining a first feature that is obtained by performing feature extraction on a first vector by using a first feature extraction model, wherein the first vector indicates the first segmented object (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph); obtaining a plurality of second features that is obtained by performing feature extraction on a second vector by using a second feature extraction model, wherein the second vector indicates the element sets (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph, “self-attention”; equation (1)); fusing at least two second features based on a first target weight, to obtain a first fused feature (FIG. 1(a)-1(c)), wherein the first target weight is determined based on a first parameter value indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features (FIG. 1; equations (1)-(3); Page 4, 1st Col., 5th paragraph – 2nd Col, 1st paragraph; it is a common knowledge that the parameter associated with self-attention of the transformer indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features); or the first target weight is a second parameter value comprising at least one preset constant; and performing fusion processing on the first feature and the first fused feature to obtain a second fused feature used to obtain a feature of the object (FIG. 1(c); equation (3)).
-Regarding claim 19, Zheng discloses a non-transitory computer-readable storage medium having instructions stored therein, which when executed a processor, cause an electronic device to perform operations comprising (Abstract; FIGS. 1-8; one or more processors and memories has to be used in order to implement Zheng’s method shown in FIG. 1): a processor, and a memory processor coupled to the processor to store instructions, which when executed by the processor, cause the electronic device to perform operations comprising (one or more processors and memories has to be used in order to implement Huang’s method shown in FIG. 2): performing segmentation processing on an object to be processed to obtain a first segmented object, wherein the object is an image, the first segmented object is an image block, each image block includes one or more pixels in the image, and all image blocks form the image (FIG. 1(a), split an image into fixed size patches ...); Page 4, 1st Col., 4th paragraph, “divide an image … into a grid of … patches … uniformly”); obtaining a plurality of element sets for the first segmented object, wherein each element set corresponds to a pixel block (FIG. 1(a), path of 1st patch (left path), linear projection, patch embedding, position embedding; Page 4, 1st Col., 4th paragraph); obtaining a first feature that is obtained by performing feature extraction on a first vector by using a first feature extraction model, wherein the first vector indicates the first segmented object (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph); obtaining a plurality of second features that is obtained by performing feature extraction on a second vector by using a second feature extraction model, wherein the second vector indicates the element sets (FIG. 1(a), path of 1st patch (left path), a transformer layer and its input and output; Page 4, 1st Col., 5th paragraph, “self-attention”; equation (1)); fusing at least two second features based on a first target weight, to obtain a first fused feature (FIG. 1(a)-1(c)), wherein the first target weight is determined based on a first parameter value indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features (FIG. 1; equations (1)-(3); Page 4, 1st Col., 5th paragraph – 2nd Col, 1st paragraph; it is a common knowledge that the parameter associated with self-attention of the transformer indicating a similarity between each of the at least two second features and a target second feature, and the target second feature is any one of the at least two second features); or the first target weight is a second parameter value comprising at least one preset constant; and
performing fusion processing on the first feature and the first fused feature to obtain a second fused feature used to obtain a feature of the object (FIG. 1(c); equation (3)).
-Regarding claims 2, 11, and 20, Huang in view of Zheng teaches the method of claim 1, the device of claim 10, and the non-transitory computer-readable storage medium of claim 19. Zheng further discloses obtaining a third feature obtained by performing feature extraction on a third vector by using the first feature extraction model, the third vector indicates a second segmented object comprising some elements in the object; and the performing fusion processing on the first feature and the first fused feature to obtain the second fused feature comprises: fusing the first feature and the third feature based on a second target weight, to obtain a third fused feature, wherein the second target weight is determined based on a third parameter value indicating a similarity between the third feature and the first feature; or the second target weight is a fourth parameter value comprising at least one preset constant; and performing fusion processing on the third fused feature and the first fused feature to obtain the second fused feature (Zheng: FIG . 1(a)-1(c), 2nd patch).
-Regarding claims 3 and 12, Zheng discloses the method of claim 1 and the device of claim 10. Zheng further discloses wherein the first vector indicates the first segmented object carrying first position information of the first segmented object in the to be processed object (Zheng: FIG. 1(a), position embedding; FIG. 8).
-Regarding claims 4 and 13, Zheng discloses the method of claim 1 and the device of claim 10. Zheng further discloses wherein each second vector indicates some elements in the first segmented object carrying second position information of some elements in the first segmented object (Zheng: FIG. 1(a), position embedding; Page 4, 1st Col., 4th paragraph).
-Regarding claims 5 and 14, Zheng discloses the method of claim 1 and the device of claim 10. Zheng further discloses performing end-to-end concatenation processing on the first feature and the first fused feature to obtain the second fused feature (Zheng: FIG. 1; Page 4, 2nd Col., 1st paragraph, “MSA is an extension with m independent SA operations and project their concatenated outputs”).
-Regarding claims 6 and 15, Zheng discloses the method of claim 1 and the device of claim 10. Zheng further discloses performing a target operation on the first feature and the first fused feature to obtain the second fused feature, wherein the target operation comprises at least one of addition or multiplication (Zheng: FIG. 1(c), element sum).
-Regarding claims 7 and 16, Huang discloses the method of claim 6 and the device of claim 15. Huang further discloses when there are a plurality of first fused features, performing end-to-end concatenation processing on the plurality of first fused features to obtain a concatenated feature; mapping the concatenated feature to a feature of a target length determined based on a length of the first feature; and performing addition processing on the first feature and the feature of the target length to obtain the second fused feature (Zheng: FIG. 1; Page 4, 2nd Col., 1st paragraph).
-Regarding claims 8 and 17, Zheng discloses the method of claim 1 and the device of claim 10. Zheng further discloses wherein the fusing at least two second features based on the first target weight, to obtain the first fused feature comprises: inputting the at least two second features into a target model, wherein an output of the target model is the first fused feature, the target model comprises one of a self-attention network transformer, a convolutional neural network (CNN), or a recurrent neural network (RNN), and when the target model is the transformer, the first target weight is determined based on an inner product between each of the at least two second features and the target second feature, or when the target model is the CNN or the RNN, the first target weight is the second parameter value (Zheng: FIG. 1(a)-1(c)).
-Regarding claims 9 and 18, Zheng discloses the method of claim 1 and the device of claim 10. Huang further discloses wherein the object is the image, the first vector indicates a first segmented image comprising some pixels in the image, the second vector indicates some pixels in the first segmented image, and the second fused feature is used to obtain a feature of the image (Zheng: FIG. 1(a)).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664
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