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
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.
Claim(s) 1, 6-9 and 14-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2019/0236098 A1 to Bhotika et al., hereinafter, “Bhotika”.
Claim 1. Bhotika teaches A system, comprising: [0020] providing visually similar items to a query item (e.g., an item of interest in an image)
a processor; FIG. 15, element 1502
and a non-transitory memory storing instructions that, when executed, cause the
processor to: FIG. 15, element 1504
[0084] the device can include many types of memory, data storage or computer-readable media, such as a first data storage for program instructions for execution by the at least one processor 1502
receive a plurality of catalog images each associated with at least one catalog item of a plurality of catalog items; [0022] One or more items in the item catalog, are obtained, where at least some of the items have an associated image and are assigned one or more visual attributes from the one or more visual categories.
for a respective catalog item of the plurality of catalog items: [0022] One or more items in the item catalog, are obtained, where at least some of the items have an associated image and are assigned one or more visual attributes from the one or more visual categories.
generate, based on a respective set of catalog images of the plurality of catalog images, respective catalog embeddings representing the respective set of catalog images, [0022] One or more items in the item catalog, are obtained, where at least some of the items have an associated image and are assigned one or more visual attributes from the one or more visual categories. (visual attributes are interpreted as catalog embeddings)
wherein the respective set of catalog images is associated with the respective catalog item; [0022] One or more items in the item catalog, are obtained, where at least some of the items have an associated image…
receive a plurality of query images associated with a query item; [0057] The query image (interpreted as query item) is analyzed 1006, for example utilizing techniques described herein, in order to assign various visual attributes from one or more of the visual attribute categories to the item of interest. A plurality of items (interpreted as plurality of query images), for example in an item catalog as discussed herein and with respect to the example computing environments illustrated in FIG. 13 or 16, are obtained 1008…
generate, based on the plurality of query images, query embeddings representing the plurality of query images; [0057] Of the obtained items, one or more may have one or more visual attributes (interpreted as query embeddings) assigned, for example based on the visual appearance of the one or more images, or on metadata associated with the item.
for a respective query image of the plurality of query images associated with the query item: select, based on a comparison of the respective query image and the plurality of catalog images associated with the plurality of catalog items that meet a similarity criteria, [0057] A visual similarity score is determined 1010 for at least some of the items in the item catalog. According to various embodiments, the visual similarity score comprises a number or other metric capable of being used to compare and rank various items. The visual similarity score in various embodiments indicates a visual similarity of one or more of the items in the catalog to the item of interest…
a candidate set of the plurality of catalog items; [0058] A visual similarity result set is generated 1012 for one or more of the shared visual attributes and is ordered according to the visual similarity score.
generate, based on a comparison of the query embeddings representing the query item and the respective catalog embeddings of the candidate set of the plurality of catalog items, respective similarity scores; [0057] Of the obtained items, one or more may have one or more visual attributes (interpreted as query embeddings) assigned, for example based on the visual appearance of the one or more images, or on metadata associated with the item. A visual similarity score (interpreted as the comparison) is determined 1010 for at least some of the items (interpreted as the respective similarity scores) in the item catalog. [0082]
determine, based on the respective similarity scores, that the query item is similar to a respective catalog item of the candidate set of the plurality of catalog items; [0058]The visual similarity result set (interpreted as the candidate set) in various embodiments includes items of the item catalog having at least one visual attribute matching those of the item of interest (interpreted as the query item).
and in response to determining the query item is similar to the respective catalog item, identify the query item for review. [0058] A user, for example, may then select one or more visual attributes, for example by clicking a user interface element or similar method, resulting in a listing of items in the item catalog having the matching selected one or more visual attributes being generated and ranked according to the items overall visual similarity score.
Claim 6. Bhotika teaches wherein the respective catalog embeddings representing the respective plurality of catalog images associated with the respective catalog item and the query embeddings representing the plurality of query images associated with the query item are generated via an unsupervised machine learning model. [0073] The data set utilized in various embodiments may be trained to classify new data, such as according to FIG. 12 and/or utilizing machine-learning techniques including neural networks and deep neural networks. [-0077]
Claim 7. Bhotika teaches wherein identifying the query item for review includes notifying a user that the query item is similar to one or more catalog items of the plurality of catalog items. [0054] the user will be presented (interpreted as notifying) with the most relevant visually similar catalog items.
Claim 8. Bhotika teaches wherein identifying the query item for review includes preventing the query item from being added to the plurality of catalog items. [0058] if so, then the non-matching items are removed 1018 from the listing prior to the listing being generated for presentation 1016
Claim 9. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
Claim 14. Reviewed and analyzed in the same way as claim 6. See the above analysis and rationale.
Claim 15. Reviewed and analyzed in the same way as claim 7. See the above analysis and rationale.
Claim 16. Reviewed and analyzed in the same way as claim 8. See the above analysis and rationale.
Claim 17. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0236098 A1 to Bhotika et al., hereinafter, “Bhotika” in view of US 2025/0371596 A1 to Adamek et al., hereinafter, “Adamek”.
Claim 2. Bhotika fails to explicitly teach the respective catalog embeddings maximize a cosine similarity between the respective set of catalog images, and the respective catalog embeddings minimize the cosine similarity of the respective set of catalog images and another respective set of catalog images associated with another catalog item of the plurality of catalog items. Adamek, in the similar field of querying a product by extracting embeddings based on a neural network to retrieve a similar result (product), teaches wherein the respective catalog embeddings maximize a cosine similarity between the respective set of catalog images, [0129] This comparison can be performed by using a similarity metric such the very well-known cosine similarity that measures the similarity using the cosine of the angle between two vectors in the multidimensional embedding space.
[0145] During training, shown in FIG. 6(a), both encoders are optimized to maximize similarity between images-text pairs representing the same concepts and minimize similarity between unrelated pairs. More specifically, the embedding space is learned by jointly training both encoders (the text encoder and the image encoder) to maximize the cosine similarity of image and text embeddings of the N real image-text pairs in the batch while minimizing the cosine similarity of the embeddings of the N2−N incorrect pairings [CLIP].
and the respective catalog embeddings minimize the cosine similarity of the respective set of catalog images and another respective set of catalog images associated with another catalog item of the plurality of catalog items. [0145] During training, shown in FIG. 6(a), both encoders are optimized to maximize similarity between images-text pairs representing the same concepts and minimize similarity between unrelated pairs. More specifically, the embedding space is learned by jointly training both encoders (the text encoder and the image encoder) to maximize the cosine similarity of image and text embeddings of the N real image-text pairs in the batch while minimizing the cosine similarity of the embeddings of the N2−N incorrect pairings [CLIP].
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhotika with the teachings of Adamek [0131] to simply take the highest found similarity between the query and reference images representing a given catalog part. These similarities can be used to produce the ranking of the catalog parts.
Claim 10. Reviewed and analyzed in the same way as claim 2. See the above analysis and rationale.
Claim 18. Reviewed and analyzed in the same way as claim 2. See the above analysis and rationale.
Claim(s) 3, 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0236098 A1 to Bhotika et al., hereinafter, “Bhotika” in view of US 2023/0115551 A1 to Jin et al., hereinafter, “Jin”.
Claim 3. Bhotika fails to explicitly teach generating the respective similarity scores includes generating a similarity matrix based on a Hadamard product between the query embeddings and each respective catalog embedding of the respective catalog embeddings of the candidate set of the plurality of catalog items. Jin, in the field of calculating a similarity metric between a query and key feature (embeddings), teaches wherein generating the respective similarity scores includes generating a similarity matrix based on a Hadamard product between the query embeddings and each respective catalog embedding of the respective catalog embeddings of the candidate set of the plurality of catalog items. [0041] A similarity score, which may also be referred to as a similarity measure, between a query and a key may be determined by determining the Hadamard product of query and key matrices. Alternative similarity measures may be used, such as dot product or cosine similarity.
[0070] Using these sets of features (interpreted as query embeddings), similarity scores may be computed between regions within the first modality and the first phrase within the second modality. Similarity scores may be computed by determining the Hadamard product of query and key matrices
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhotika with the teachings of Jin [0039] to accurate determinations of similarities between the features (embeddings).
Claim 11. Reviewed and analyzed in the same way as claim 3. See the above analysis and rationale.
Claim 19. Reviewed and analyzed in the same way as claim 3. See the above analysis and rationale.
Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0236098 A1 to Bhotika et al., hereinafter, “Bhotika” in view of US 2023/0115551 A1 to Jin et al., hereinafter, “Jin” and in further view of US 2023/0004590 A1 to Li et al., hereinafter, “Li”.
Claim 4. Bhotika and Jin fail to explicitly teach generating a respective similarity score is based on averaging row-wise minimums for each row of the similarity matrix to reduce each similarity matrix to a single scalar value. Li, in the field of analyzing feature vectors, teaches wherein generating a respective similarity score is based on averaging row-wise minimums for each row of the similarity matrix to reduce each similarity matrix to a single scalar value. [0077] the clustering analysis may comprise applying hierarchical clustering, which may reduce the number of dimensions through methods such as singular value decomposition. Examiner understands singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhotika with the teachings of Li [0051] because there is a need for improved methods for AI-augmented automated analysis of documents.
Claim 12. Reviewed and analyzed in the same way as claim 4. See the above analysis and rationale.
Allowable Subject Matter
Claims 5 and 13 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DELOMIA L GILLIARD whose telephone number is (571)272-1681. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661
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