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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 16 December 2024 and 9 October 2025 are being considered by the examiner.
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.
Claim 7 is 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.
Claim 7 recites the limitation "for each sub-region". There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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 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 1, 6, 8-9, 14, 17 and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (“Depression Angle Invariant SAR Target Recognition via Feature Transformation).
Regarding claim 1, Wang et al. disclose a training apparatus comprising:
at least one memory that is configured to store instructions and at least one processor that is configured to execute the instructions (Section II.A, first paragraph explains that the framework use consists of a feature extractor, a feature transformer, and a classifier, which clearly will be run using some kind of computer which has a processor and stored instructions [in a memory] to carry out the neural network teachings as disclosed in Wang et al.) to:
acquire a training data that includes a training image, first angle information, and a ground truth data (Section II.A.2.: “During training…” and Section III.A Datasets), wherein the training image is an image on which an object is captured and which is generated by a sensor, and wherein the first angle information indicates a first incident angle that is an incident angle of the sensor and a first azimuth angle that is an azimuth angle of the object captured on the training image (Section II.A..2. and see Section III.A and B., where the first angle information for a first training image “indicates” a first incident angle [90-depression angle] and also “indicates” an first azimuth angle: “The dataset provides images of various ground targets at different azimuth angles, different depression angles, and different ground backgrounds.”);
input the training image to a feature extracting model to acquire a first feature set that is a set of features extracted from the training image (Section II.A.2., and see Figure 2);
acquire second angle information that indicates a second incident angle and a second azimuth angle, wherein the second incident angle, the second azimuth angle, or both are different from counterparts thereof in the first angle information (Section II.A..2. and see Section III.A and B., where the second angle information for a second training image “indicates” a second incident angle [90-depression angle] and also “indicates” an second azimuth angle: “The dataset provides images of various ground targets at different azimuth angles, different depression angles, and different ground backgrounds.” As said in the quoted section, the angles are different.);
generate a second feature set by performing coordinate transformation on the first feature set “based on” the first angle information and the second angle information (Section II.A.2., and see Figure 2. See also Section III.B., which explains that the images are alternately fed into the model, meaning that the second image feature extraction is after the first feature extraction and thus will be “based on” both the first and second angle information.); and
update the feature extracting model based on the first feature set, the second feature set, and the ground truth data (Section II.B., which explains a loss function used to update the feature extracting model, which is “based on” the first feature set, the second feature set, and the ground truth data.).
Regarding claim 6, Wang et al. disclose the training apparatus according to claim 1, wherein the training image is a radar image that is generated by a radar (Section I: Introduction. SAR images are radar images generated by radar. See Section III.B.).
Regarding claim 8, Wang et al. disclose the training apparatus according to claim 1, wherein the updating of the feature extracting model includes:
inputting the first feature set into a task executing model to acquire a first result of a task (Section II.B., see equation 3, where the first feature set is input into the feature transformer to acquire a first result of a task [category predictor].);
inputting the second feature set into the task executing model to acquire a second result of the task (Section II.B., see equation 3, where the second feature set is input into the feature transformer to acquire a second result of the task [category predictor].);
computing one or more losses based on the first result of the task, the second result of the task, and the ground truth data (Section II.B., see equation 3, the loss equation includes the first and second results c(x) of the task and the ground truth data since the feature vector x is used.); and
updating trainable parameters of the feature extracting model and the task executing model based on the one or more losses (Section II.B., see equation 3, and see Section III.B. Training Details.).
Regarding claim 9, this claim is rejected under the same rationale as claim 1.
Regarding claim 14, this claim is rejected under the same rationale as claim 6.
Regarding claim 17, this claim is rejected under the same rationale as claim 1.
Regarding claim 22, this claim is rejected under the same rationale as claim 6.
Allowable Subject Matter
Claims 2-5, 10-13 and 18-21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The primary reasons for indicating allowable subject mater in claim 2 is the inclusion of the features reciting “wherein the first feature set is represented by a set of cells each of which has a value of features and coordinates in a first coordinate system that is defined using the first incident angle, wherein the second feature set is represented by a set of cells each of which has a value of features and coordinates in a second coordinate system that is defined using the second incident angle, the first azimuth angle, and the second azimuth angle, and wherein the performing of the coordinate transformation on the first feature set includes: performing, for each cell of the first feature set, coordinate transformation from the first coordinate system to the second coordinate system on coordinates of the cell of the first feature set to compute a corresponding cell of the second feature set; and setting the value of the cell of the first feature set to the corresponding cell of the second feature set” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Claim 3 is objected to due to its dependency from claim 2.
The primary reasons for indicating allowable subject matter in claim 4 is the inclusion of the features reciting “wherein the first feature set is represented by a first set of cells each of which has a value of features and coordinates in a first coordinate system that is defined using the first incident angle, wherein the second feature set is represented by a second set of cells each of which has a value of features and coordinates in a second coordinate system that is defined using the second incident angle, the first azimuth angle, and the second azimuth angle, and wherein the generating of the second feature set includes: performing the coordinate transformation on the first feature set to transform the first feature set into a third set of cells in the second coordinate system; and modifying the value of one or more cells of the third set to generate the second feature set” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Claim 5 is objected to due to its dependency from claim 4.
Claim 10 is objected to for the same reasons as claim 2 above.
Claim 11 is objected to due to its dependency from claim 10.
Claim 12 is objected to for the same reasons as claim 4 above.
Claim 13 is objected to due to its dependency from claim 12.
Claim 18 is objected to for the same reasons as claim 2 above.
Claim 19 is objected to due to its dependency from claim 18.
Claim 20 is objected to for the same reasons as claim 4 above.
Claim 21 is objected to due to its dependency from claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma et al. (US 2021/0063565) disclose of SAR imaging geometry for a ship (Figure 2). Sharma et al. further disclose of a training mode (Paragraphs [0087]-[0090].).
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/STEPHEN G SHERMAN/Primary Examiner, Art Unit 2621
9 July 2026
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