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 .
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.
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/5/26 has been entered.
Response to Arguments
Applicant’s cancellation of claims 9 and 17 render the 112 rejections moot and they are withdrawn.
With respect to the amendment, and upon further review of Aggarwal, Aggarwal at ¶27-30 discloses fabric, material, and type of clothing, which is an attire class (e.g. shirt, pants, etc.). The same paragraphs obtain attribute/value pairs for the items in the images. Although Aggarwal does not explicitly use the word segmentation, the description at ¶26 discusses multiple items of clothing in an image and that each item is identified, which is segmentation of an image for the individual clothing items.
While the examiner appreciates applicant’s argument with respect to Aggarwal, with respect to the “unique conditional classification logic,” the claims merely recite a “based on” which implies any sort of relationship on the label, of clothing to classify. The labeling is used to classify and is therefore “based on” the label. Further, Aggarwal at ¶26-30 discloses varies types of fabrics, patterns, and styles for the clothing that the clothing item is classified into. Applicant is suggested to go into detail with respect to how the unique conditional classification logic operates beyond the “based on” language to potentially overcome this rejection.
Applicant’s remaining arguments with respect to Ge are moot in light of the remapping of the claim elements to Aggarwal.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-4, 6, 10, 12, 14, 18 are rejected under 35 USC 102 as being anticipated by 2021/0073593, Aggarwal (hereafter Aggarwal)
1. A method of an electronic device for on-device lifestyle recommendations, the method comprising: receiving a user input; (Aggarwal ¶63 search term) (Parker
determining a fashion context based on the user input; (Aggarwal ¶66 the query determines the context of the results; note that Aggarwal describes clothing context in ¶26-30)
dynamically clustering fashion objects in at least one image stored in the electronic device based on the fashion context; and (Aggarwal ¶75 clustering; see also ¶133-147)
displaying a lifestyle recommendation comprising the clustered fashion objects. (Aggarwal ¶68-70 recommendations according to the results)
wherein the dynamically clustering the fashion objects in the at least one image comprises: identifying the fashion objects in the at least one image by analyzing the at least one image stored using an artificial intelligence (AI) model; (Aggarwal ¶28 AI)
segmenting the identified fashion objects from the at least one image; (Aggarwal ¶26 multiple items of clothing, each item is identified separately, which is segmentation)
obtaining labels of the segmented fashion objects; (Aggarwal ¶26-30 attribute/value pairs of the clothing item)
performing one of:
based on the labels of the segmented fashion objects being clothes, classifying the segmented fashion objects into a pattern class, a fabric class, and an attire class, and (Aggarwal ¶27-30 pattern, material type, article type)
based on the labels of the segmented fashion objects being fashion accessories, classifying the segmented fashion objects into a fashion accessory class; (Aggarwal ¶27-30 pattern, material type, article type)
generating a fashion knowledge graph comprising different classes of the segmented fashion objects; traversing the fashion context through the fashion knowledge graph; and
dynamically clustering the fashion objects in the different classes obtained based on the traversal. (Aggarwal ¶91-96 nodes (nodes in this context disclose a traversal graph); ¶133 clustering algorithms utilize graphs (nodes for each item and their connections make up a graph) and nodes close together are cluster as known in the art of clustering algorithms)
Claim 10 is rejected under a similar rationale.
3. The method of claim 1, wherein the method further comprises: updating the fashion knowledge graph based on a user action on the recommendation. (Aggarwal ¶122 updating based on user feedback)
Claim 12 is rejected under a similar rationale.
4. The method of claim 1, wherein the method further comprises: updating the fashion knowledge graph based on receiving and analyzing a new image. (Aggarwal ¶84 updating with new images; note each new product added updates the graph (see e.g. ¶26-27 collection of images))
Claim 18 is rejected under a similar rationale.
6. The method of claim 1, wherein the dynamically clustering the fashion objects in the different classes comprises:
determining a weightage of a match between the at least one class of segmented fashion objects and the fashion context; and (Aggarwal ¶54 weight)
dynamically clustering the segmented fashion objects with the assigned tag in the at least one class based on the weightage. (Aggarwal ¶ uses the weight to change how the item is clustered see also ¶80 and ¶117-120)
Claim 14 is rejected under a similar rationale.
Claim Rejections - 35 USC § 103
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 of this title, 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 7 and 15 are rejected under 35 USC 103 as being unpatentable over Aggarwal in view of US 2021/0287091, Ge (hereafter Ge)
7. The method of claim 1, wherein the segmenting the identified fashion objects from the image comprises: determining a feature vector of the image using a Convolution Neural Network (CNN) model; (Aggarwal ¶37 CNN)
Aggarwal does not disclose
determining Region of Interests (ROIs) of the image by providing the feature vector to a Region Proposal Network;
optimizing scales of the ROIs by providing the feature vector and the predicted ROIs to a Feature Pyramid Network (FPN);
refining an alignment of the ROIs; and
determining the segmented fashion objects comprising output masks, labels, and coordinates of the identified fashion objects in the ROIs using a plurality of neural network models.
Ge discloses
determining Region of Interests (ROIs) of the image by providing the feature vector to a Region Proposal Network; (Ge ¶135 Region of interest)
optimizing scales of the ROIs by providing the feature vector and the predicted ROIs to a Feature Pyramid Network (FPN); (Ge ¶135 pyramid)
refining an alignment of the ROIs; and (Ge ¶135 alignment)
determining the segmented fashion objects comprising output masks, labels, and coordinates of the identified fashion objects in the ROIs using a plurality of neural network models. (Ge ¶134-153 describes the usage of masks, labels, coordinates to segment)
It would have been obvious to modify the system of Aggarwal to identify ROIs using a RPN for the purposes of providing and end-to-end deep clothing analysis as taught by Ge (¶140).
Claim 15 is rejected under a similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached on 7-5 Pacific Time, M-Th.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Greg Morse can be reached on 571-272-38383838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ming Shui/
Primary Examiner, Art Unit 2663
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