DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. 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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (U.S. patent pub. 2024/0161520 A1).
Regarding claim 1: Li et al. discloses a system for image retrieval (fig. 1 element 100 and paragraphs 0022, 0026, and 0027), comprising:
a processing device connected to a database configured to store a set of images (figs 6 and 7), the processing device including:
a computer vision model including a text encoder configured to extract textual features and a vision encoder configured to extract image features, and generate embeddings used for image retrieval tasks (figs. 2, 3, and 8, and paragraphs 0084-0090); and
a summarization module configured to be trained using a targeted dataset, the summarization module configured to restrict a number of queries per image that are learnable by the computer vision model to a selected number (paragraph 0034, the query number can be fixed or tunable during training).
Regarding claim 2: The system of claim 1, wherein the restricted number of queries results in a restricted number of embeddings that can be used for image retrieval (paragraph 0034, 0086, and 0087).
Regarding claim 3: The system of claim 1, wherein the processing device is included in a vehicle system (paragraph 0070).
Regarding claim 4:The system of claim 1, wherein the summarization module is a summarization head attached to a backbone of the computer vision model (figs. 1-6 and paragraphs 0019-0020 and 0064-0067, the neural network is the backbone).
Regarding claim 5: The system of claim 4, wherein the summarization head includes a cross-attention mechanism having a plurality of head layers, a subset of the plurality of head layers being frozen (fig. 8 and paragraphs 0034-0036).
Regarding claim 6: The system of claim 5, wherein each head layer receives an image feature and generates embeddings from the image feature based on learning weights, the learning weights only applied to head layers that are not frozen (paragraphs 0036 and 0064-0065).
Regarding claim 7: The system of claim 1, wherein the computer vision model is a dense open vocabulary model (abstract and paragraphs 0004 and 0026, large language model is read as dense open vocabulary model.).
Regarding claim 8: The system of claim 7, wherein the computer vision model is a Contrastive Language Image Pre-training (CLIP) model (paragraphs 0023, 0043, and 0087).
Regarding claim 9: See claim 1.
Regarding claim 10: See claim 4.
Regarding claim 11: See claim 5.
Regarding claim 12: See claim 6.
Regarding claim 13: See claim 7.
Regarding claim 14: See claim 8.
Regarding claim 15: See claim 1.
Regarding claim 16: See claim 4.
Regarding claim 17: See claim 5.
Regarding claim 18: See claim 6.
Regarding claim 19: See claim 7.
Regarding claim 20: See claim 8.
Contact Information
3. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on 571-272-4650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
March 7, 2026