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 § 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 1-5, 8-13, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0285667 A1), hereinafter “Gupta”, and in view Bjornsson et al. (US 2022/0300550 A1), hereinafter “Bjornsson”, and further in view of Bell et al. (“Learning visual similarity for product design with convolutional neural networks”), hereinafter “Bell”.
As per claim 1, Gupta teaches a method for visual content search and creation comprising:
“detecting multiple objects in an image displayed on a user’s workspace” at [0020], [0027] and Figs. 2A-2B;
(Gupta teaches using object detection to detect objects and features in an image)
“attaching a representative text label to detect objects in the image displayed on a user’s workspace” at [0027] and Figs. 2A-2B;
(Gupta teaches attaching tags to the detected objects in the image)
“inferring both a high-level domain and a low-level domain from the representative text labels attached to the detected objects” at [0027], [0044] and Fig. 2A-2B;
(Gupta teaches each of object belongs to a certain class such as humans, building, or cars (i.e., “high-level domain”) and sub-class such man, woman, or kid, baby (i.e., “low-level domain”))
“displaying, through a user interface, initial visual content according to the high-level domain and the low-level domain including the detected objects represented in bounding boxes” at [0027]-[0032] and Fig. 2A-2D;
(Gupta teaches detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images using suitable means for boundary or edge detection, may utilize color detection to identify the boundary between two features, may user material detection to detect the boundary or edge between. The batch search system provides a user interface for viewing, selecting and modifying the tags)
Gupta does not teach “detecting a dynamic, user-controlled selection of a customized region in a bounding box enclosing the detected object in the initial visual content; detecting a component selected by the user in the customized region from the detected object enclosed in the bounding box; retrieving out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component, but serving a different, out-of-domain purpose from the detected object; and display, though the user interface, the retrieved, out-of-domain inspirational visual content” as claimed. However, Bjornsson teaches a method for visual search via free-form visual feature selection including the steps of:
“detecting a dynamic, user-controlled selection of a customized region in a bounding box enclosing the detected object in the initial visual content, detecting a component selected by the user in the customized region from the detected object enclosed in the bounding box” at [0066] and Fig. 2;
(Bjornsson teaches receiving a free-form user input to the user interface that selects a particular sub-portion of one or more objects depicted by the image. The particular sub-portion comprise one or more visual feature, such as decorative features of furniture, particular cuts of sleeves on clothing items. Bjornsson teaches at Fig. 2 a bounding box 210 enclosing the detected table leg. A free-form user input selects a customized regions 214 comprising a particular sub-portion of the detected object)
“retrieving out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component,
(Bjornsson teaches receiving from the visual search system a set of visual search results responsive to visual features included in the particular sub-portion of the one or more object (e.g., content including a particular furniture decorative feature indicated by free-form user input))
Thus, it would have been obvious to one of ordinary skill in the art to combine Bjornsson with Gupta in order to “provide a suer with improved visual search results which are more directly relevant to specific, user-selected visual feature”, as suggested by Bjornsson at [0019].
Gupta and Bjornsson, as combined, does not explicitly teach retrieving out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component, “but serving a different, out-of-domain purpose from an automotive domain of the detected object” as claimed. However, Bell teaches a method for learning visual similarity for product design including the steps of:
“retrieving, out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component, but serving a different, out-of-domain purpose from an automotive domain the detected object” at Section 6.1 and Fig. 14.
(Bell teaches the cross-category search which is “searching only product of a different object category than the query (e.g. finding a table that is visually similar to a chair)”)
“displaying, through the user interface, the retrieved, out-of-domain inspirational visual content” at Fig. 14.
(Bell teaches in response to a query image of a chair, displaying the search results comprising products in different object categories)
Thus, it would have been obvious to one of ordinary skill in the art to combine Bell with Gupta-Bjornsson’s teaching in order to provide so that the search system can provide inspiration, design ideas and recommendation by responding to user’s query with objects from different categories. Bell teaches at Section 1 that “providing automated tools for design suggestion and ideas can be very useful to these user”. For example, a home owner is replacing furniture in their home and wants to find a chair that matches their existing table can send a photo of a chair as query object and retrieve the search result which include matching tables and bookcases, as suggested by Bell at Section 1.
As per claim 2, Gupta-Bjornsson and Bell teach the method of claim 1 discussed above. Gupta also teaches: “detecting the multiple objects comprises automatically recognizing the multiple objects in the image displayed on the user’s workspace using computer vision-based object detection and instance segmentation and/or a natural language processor” at [0027]-[0028].
As per claim 3, Gupta-Bjornsson and Bell teach the method of claim 1 discussed above. Gupta also teaches: “in which attaching the representative text labels comprise using an optimal character recognition (OCR) block and/or a natural language processor (NLP) to attached the representative text label to the detected objects in the image displayed on the user’s workspace” at [0029].
As per claim 4, Gupta-Bjornsson and Bell teach the method of claim 1 discussed above. Gupta also teaches: “displaying the initial visual content comprises searching for visual content images between the high-level domain and the low-level domain, using the search engine” at [0031]-[0033], [0039]-[0040] and Figs. 2C – 2F.
As per claim 5, Gupta-Bjornsson and Bell teach the method of claim 1 discussed above. Gupta also teaches: “further comprising: displaying, through the user interface, a first visual content image; detecting a target image specified by the user from the first visual content image; and displaying a second visual content image retrieved by a search engine based on a perceptual and functional similarity to the target image specified by the user” at [0031]-[0033], [0039]-[0040] and Figs. 2C – 2F.
As per claim 8, Gupta-Bjornsson and Bell teach the method of claim 1 discussed above. Bell also teaches: “the user interface enables the user to specify a region in the bounding boxes and/or unique boundaries of each object to include/exclude in a search by a search engine” at Section 1- Introduction and Figs. 1, 9.
Claims 9-13, 16-20 recite similar limitations as in claims 1-5, 8 and are therefore rejected by the same reasons.
Response to Arguments
Applicant's arguments filed 12/12/2025 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding independent claims 1, 9 and 17, Applicant argued that “FIGURE 14 of Bell was discussed during the interview. The domains shown in FIGURE 14 involve furniture and the other examples provided in Bell involve interior home furniture, rather than an automotive domain. Because Bell involves furniture, such as indoor furniture, Gupta, Bjornsson, and Bell fail to teach or suggest “... retrieving (to retrieve) out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component, but serving a different out-of-domain purpose from an automotive domain of the detected object ...,” as in amended claims 1, 9, and 17.” On the contrary, the meaning of “automotive domain” is unclear and was not defined in the Specification, the term “automotive domain” encompasses anything related to a vehicle, such as engine, seats, carpet, windows, lighting, etc. Bell teaches at Fig. 14 a search engine which receives an image of an object (e.g., a chair) and retrieves other objects that are out-of-domain or in different category, such as “Armchairs”, “Outdoor lighting”, “Floor lamps”, “Rugs”, “Wallpaper”, which are also from automotive domain. For example, in response to a query image including a chair, the search engine can return image of a car’s seat, an exterior light of a car or a rug on the floor of a car. Bell therefore teaches retrieving out-of-domain inspirational visual content, by a search engine, having a perceptual and functional similarity to the selected component, but serving a different out-of-domain purpose from an automotive domain of the detected object, as required by the claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
January 13, 2026
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