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.
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.
Claims 1–4, 11–15 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Jing et al. (U.S. 9,977,816 B1) in view of Han (U.S. 12,430,610 B2).
Regarding claim 1, Jing discloses a method for ranking objects, comprising:
ranking a set of objects (a set of objects construed as images) according to a predetermined policy; (Per Fig. 1, Jing generates a ranking score of multiple images to measure quality thereof. Jing col. 3 lines 6–12. That is, a ranking score is to be generated for each of the images that defines an objective measurement of quality of the images.)
obtaining a set of object embeddings of the set of objects (See Applicant’s Spec ¶26. Examiner construed embeddings of the set of objects construed as similarity metric.); (Per Fig. 1, Jing discloses a value of similarity metric between images. Ibid. col. 3 lines 21–31. The value of each similarity metric between pairs of images is shown next to a line between the image pairs.)
determining a plurality of similarity scores (a plurality of similarity scores construed as similarity metric values) based on the set of object embeddings. (Per Fig. 6, Jing calculates similarity metric values between images The values are correlated to probabilities with which the images can be analyzed to determine whether they are similar. Ibid. col. 9 lines 39–52. [t]hat can be used to convert similarity metrics into transitional probabilities.)
However, Jing fails to specifically disclose re-ranking the ranked set of objects based on the plurality of similarity scores for display.
In related art, Han discloses re-ranking the ranked set of objects based on the plurality of similarity scores for display. (Per Fig. 1, Han’s processor 140 re-identifies similarities of a product such that his processor determines whether a corresponding feature matches the product. Han col. 17 lines 1–26. [t]he processor 140 may re-identify a similarity between a feature output from the second neural network model and each of the plurality of features included in the database.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Han into the teachings of Jing to analyze a product whether it is similar to one of plurality of similarities is different from others. Ibid.
Regarding claim 12, Jing discloses an electronic device, comprising:
a memory (Fig. 3, a main memory 330) and a processor (Fig. 3, a processor 320);
wherein the memory is configured to store one or more computer instructions which, when executed by the processor, cause the processor to: rank a set of objects according to a predetermined policy; (Per Fig. 1, Jing generates a ranking score of multiple images to measure quality thereof. Jing col. 3 lines 6–12. That is, a ranking score is to be generated for each of the images that defines an objective measurement of quality of the images.)
obtain a set of object embeddings of the set of objects; (Per Fig. 1, Jing discloses a value of similarity metric between images. Ibid. col. 3 lines 21–31. The value of each similarity metric between pairs of images is shown next to a line between the image pairs.)
determine a plurality of similarity scores based on the set of objects embeddings. (Per Fig. 6, Jing calculates similarity metric values between images The values are correlated to probabilities with which the images can be analyzed to determine whether they are similar. Ibid. col. 9 lines 39–52. [t]hat can be used to convert similarity metrics into transitional probabilities.)
However, Jing fails to specifically disclose re-rank the ranked set of objects based on the plurality of similarity scores for display.
In related art, Han discloses re-ranking the ranked set of objects based on the plurality of similarity scores for display. (Per Fig. 1, Han’s processor 140 re-identifies similarities of a product such that his processor determines whether a corresponding feature matches the product. Han col. 17 lines 1–26. [t]he processor 140 may re-identify a similarity between a feature output from the second neural network model and each of the plurality of features included in the database.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Han into the teachings of Jing to analyze a product whether it is similar to one of plurality of similarities is different from others. Ibid.
Regarding claim 20, Jing discloses a non-transitory computer-readable medium comprising instructions stored thereon which, when executed by a processor, cause the processor to:
rank a set of objects according to a predetermined policy; (Per Fig. 1, Jing generates a ranking score of multiple images to measure quality thereof. Jing col. 3 lines 6–12. That is, a ranking score is to be generated for each of the images that defines an objective measurement of quality of the images.)
obtain a set of object embeddings of the set of objects; (Per Fig. 1, Jing discloses a value of similarity metric between images. Ibid. col. 3 lines 21–31. The value of each similarity metric between pairs of images is shown next to a line between the image pairs.)
determine a plurality of similarity scores based on the set of objects embeddings. (Per Fig. 6, Jing calculates similarity metric values between images The values are correlated to probabilities with which the images can be analyzed to determine whether they are similar. Ibid. col. 9 lines 39–52. [t]hat can be used to convert similarity metrics into transitional probabilities.)
However, Jing fails to specifically disclose re-rank the ranked set of objects based on the plurality of similarity scores for display.
In related art, Han discloses re-ranking the ranked set of objects based on the plurality of similarity scores for display. (Per Fig. 1, Han’s processor 140 re-identifies similarities of a product such that his processor determines whether a corresponding feature matches the product. Han col. 17 lines 1–26. [t]he processor 140 may re-identify a similarity between a feature output from the second neural network model and each of the plurality of features included in the database.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Han into the teachings of Jing to analyze a product whether it is similar to one of plurality of similarities is different from others. Ibid.
Regarding claim 2, Jing as modified by Han, discloses the method, wherein before obtaining the set of object embeddings of the set of objects, the method further comprises:
obtaining an image corresponding to an object in the set of objects; and (Per Fig. 7, Jing discloses an input set of objects. Jing col. 12 lines 18–29. [m]ay be performed to generate the transitional probabilities (as calculated in FIG. 5, act 510) for an input set of objects (e.g., images) using click-data.))
generating an object embedding for the object based on the image offline. (Per Fig. 4, Jing’s ranking score generator 415 discloses offline ranking scores while a user is idle. Ibid. col. 5 lines 37–49. [r]anking score generator 415 may generate the ranking scores “offline” (i.e., not in response to a user search query) based on, for example, all of the indexed images or based on subsets of the indexed images.))
Regarding claim 3, Jing as modified by Han, discloses the method, wherein before obtaining the set of object embeddings of the set of objects, the method further comprises:
obtaining a set of features corresponding to an object in the set of objects, the set of features comprising a category of the object; and (Per Fig. 6, Jing discloses a number of different image features. Jing col. 6 line 56 – col. 7 line 7. Ranking score generator 415 may receive the selection of which features to use (act 601).)
generating an object embedding for the object based on the set of features offline. (Per Fig. 4, Jing’s ranking score generator 415 discloses offline ranking scores while a user is idle. Ibid. col. 5 lines 37–49. [r]anking score generator 415 may generate the ranking scores “offline” (i.e., not in response to a user search query) based on, for example, all of the indexed images or based on subsets of the indexed images.))
Regarding claim 4, Jing as modified by Han, discloses the method, wherein ranking the set of objects according to the predetermined policy comprises:
determining a ranking score associated with the predetermined policy for a target object in the set of objects; and (Per Fig. 1, Jing discloses a value of similarity metric between images. Jing col. 3 lines 21–31. The value of each similarity metric between pairs of images is shown next to a line between the image pairs.)
ranking the set of objects based on the ranking score. (Per Fig. 6, Jing calculates similarity metric values between images The values are correlated to probabilities with which the images can be analyzed to determine whether they are similar. Ibid. col. 9 lines 39–52. [t]hat can be used to convert similarity metrics into transitional probabilities.)
Regarding claim 11, Jing as modified by Han, discloses the method, wherein one or more objects of the re-ranked set of objects are displayed through a carousel control in the user interface. (Per Fig. 1, Han’s processor 140 re-identifies similarities of a product such that his processor determines whether a corresponding feature matches the product. Han col. 17 lines 1–26. [t]he processor 140 may re-identify a similarity between a feature output from the second neural network model and each of the plurality of features included in the database.)
Regarding claim 13, it has been rejected in the same manner as claim 2.
Regarding claim 14, it has been rejected in the same manner as claim 3.
Regarding claim 15, it has been rejected in the same manner as claim 4.
Allowable Subject Matter
Claims 5–10 and 16–19 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yoon et al. (U.S. 11,531,866 B2) discloses a clustering target objects.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-16:00 (EST).
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/BENEDICT E LEE/ Examiner, Art Unit 2665
/Stephen R Koziol/ Supervisory Patent Examiner, Art Unit 2665
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