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 § 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xiong et al. CN116452800 (hereinafter “Xiong”, cited in the IDS, a machine translation is provided that is relied upon by the Examiner in the rejection below).
Regarding claim 1, Xiong discloses a method for remote sensing images (see the bottom of page 6, the invention relates to a semantic description method of a remote sensing image)
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comprising: dividing a global remote sensing image into sub-image regions in a predetermined manner (see top of page 7, step 1.1 the image is uniformly divided into a plurality of sub-regions)
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for each sub-image region of the sub-image regions: determining image point features of image points in the sub-image region based on a feature extraction model (see page 7 step 1.2, judging whether the input image contains obvious clustering features through threshold value truncation [threshold value truncation is interpreted as a feature extraction model]);
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identifying image points in the sub-image region based on the image point features (see above step 1.2 which identifies whether the input image contains obvious clustering features);
and determining a cluster center for the identified image points;
(see page 7, step 1.3.2, clustering pseudo-semantic points to obtain a better clustering center)
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and adjusting a remote sensing model based on cluster centers determined for each of the sub- image regions (see page 7 step 2 and 3 generate a single sentence description for the target subregion obtained in the step 1 which the Examiner interprets as adjusting the remote sensing model using the cluster centers).
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Regarding claim 2, Xiong discloses the dividing a global remote sensing image into sub-image regions in a predetermined manner comprises: dividing the global remote sensing image based on a predetermined remote sensing tile-level size (see step 1.1, divided into 16 regions).
Regarding claim 3, Xiong discloses the determining image point features of image points in the sub-image region comprises: obtaining prior knowledge of the sub-image region; and determining the image point features of the image points in the sub-image region based on the prior knowledge (see above step 2.1 the image features are extracted using a Resnet pretrained [prior knowledge] and pixel level features are extracted using FCN).
Regarding claim 4, Xiong discloses dividing the sub-image region into patches (see step 1.1 above); and for each patch, inputting the prior knowledge and the patch into the feature extraction model to obtain image point features of a plurality of image points in the patch (see step 2.1 above).
Regarding claim 5, Xiong discloses wherein a size of the image point is a predetermined pixel-level size (see above step 2.1 the small-scale image of size 256 pixels is input).
Regarding claim 6, Xiong discloses obtaining a first remote sensing image to be processed and first position information of the first remote sensing image (see step 1 inputting a wide remote sensing image to generate a subarea); determining, based on the first position information, a first sub-image region of the sub-image regions having position information that matches the first position information (see step 3.2 the description vocabulary of the relative position is determined); determining a target image point corresponding to the first remote sensing image from first image points comprised in the first sub-image region (see page 7 cited above the target sub-region generation module is used for dividing and extracting key description regions according to the semantic distribution characteristics of the images); determining, based on predetermined correspondences between the first image points and the cluster centers, a cluster center corresponding to the target image point (see page 8, a semantic clustering unit is used for generation a semantic center through clustering)
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; and determining a representation of the first remote sensing image based on the cluster center (see above cited section where the cluster centers are used to generate a single sentence description for the image using LSTM).
Regarding claim 7, Xiong discloses performing feature fusion on the cluster center and the first remote sensing image to obtain the representation of the first remote sensing image (see step 3.1 above carrying out summary merging [fusion] on the descriptions according to the clustering relation).
Claims 8-14 are similarly analyzed to claims 1-7.
Claims 15-20 are similarly analyzed to claims 1-7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of references cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN B STREGE whose telephone number is (571)272-7457. The examiner can normally be reached M-F 9-5 (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571)272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN B STREGE/Primary Examiner, Art Unit 2669
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