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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/26/2026 has been entered.
Response to Arguments
Applicant's arguments filed 4/23/2026 have been fully considered but they are not persuasive.
Applicant has amended the independent claims to recite “the generating the affinity vector includes aggregating one or more affinity values across a plurality of content items mapped to a same classification code”. The Examiner notes that Paragraph 0086 teaches that once the classification scores for each class label are calculated, the threshold value (affinity value) is applied to the scores (an aggregation) to identify the scene classes that likely apply to the given shot. Therefore, when creating table 26b, each classification score/class label (classification code) for each scene that includes multiple objects (a plurality of content items mapped to a same classification code) is compared (the aggregation process) to the threshold value (affinity value). Therefore, the rejection stands and has been updated below.
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, 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 1-3, 5-11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dunlop et al. (U.S. Patent Application Publication 2013/0259375) in view of Duffy et al. (U.S. Patent No. 11,496,286).
Referring to claim 1, Dunlop discloses receiving, by one or more processors, a set of content items from a device (see Paragraph 0082 for receiving a video frame within a plurality of received video frames).
Dunlop also discloses determining, by the one or more processors, an affinity value for each content item in the set of content items, wherein the affinity value represents a user preference for each content item (see Paragraph 0086 for determining a threshold value that is based on a system operator/user’s preference/affinity to classify scenes in a video content item).
Dunlop also discloses determining, by the one or more processors, a classification code for each content item in the set of content items based on a perceptual value, wherein the perceptual value comprises a vector representing the content item in a perceptual space (see Figure 1, table 26a and Paragraph 0084 for determining which class label, representing a group of content relating to mountains or indoor, is to be used to create the affinity vector described below, based on the affinity value/threshold and further note table 26a in Figure 1 for the correlation between the class labels and scores 32), wherein the generating the affinity vector includes aggregating one or more affinity values across a plurality of content items mapped to a same classification code (see Paragraph 0086 teaches that once the classification scores for each class label are calculated, the threshold value (affinity value) is applied to the scores (an aggregation) to identify the scene classes that likely apply to the given shot. Therefore, when creating table 26b, each classification score/class label (classification code) for each scene that includes multiple objects (a plurality of content items mapped to a same classification code) is compared (the aggregation process) to the threshold value (affinity value)).
Dunlop also discloses creating, by the one or more processors, an affinity vector relating the affinity value to the classification code for each content item in the set of content items (see Paragraphs 0087-0088 and Figure 1 for creating an affinity vector (shot #1) in table 26b using the class labels and threshold value).
Dunlop also discloses transmitting, by the one or more processors, the affinity vector to a server (see Paragraph 0089 and Figure 1 for transmitting the affinity vectors in table 26b from server 10 to server 12).
Dunlop fails to teach obscuring, by the one or more processors, the affinity vector by performing one or more differential privacy operations.
Duffy discloses obscuring, by the one or more processors, the affinity vector by performing one or more differential privacy operations (see Column 3, Lines 25-28).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to modify the vector obscuring process, as taught by Dunlop, using the differential privacy functionality, as taught by Duffy, for the purpose of using user data to generate data sets for use on a client device, while protecting user privacy (see Column 1, Lines 16-18 of Duffy).
Referring to claim 2, Dunlop discloses that the user preference is indicated by a user interaction with a respective content item of the set of content items, wherein the user interaction represents an indication of like or dislike of the respective content item (see Paragraph 0086 for determining a threshold value that is based on a system operator/user’s preference/affinity to classify scenes in a video content item).
Referring to claim 3, Dunlop discloses that the perceptual value represents a projection of each content item onto a lower-order perceptual space (see scores 32 in Figure 1, wherein the scores represent a projection of a content item (Indoor) onto a lower-order perceptual space (score is 0.01 which is a lower order score)).
Referring to claim 5, Dunlop discloses that creating the affinity vector further comprises generating a code-affinity data set that indicates an amount of user affinity for each of the set of content items having an associated classification code (see Figure 1 for the shots 26b containing classes that include one or more classifications that read on a code-affinity data set that indicates an amount of user affinity for each of the set of content items having an associated classification code by each shot having classifications such as sky, snow and forest).
Referring to claim 6, Dunlop discloses that the code-affinity data set includes a vector length of k, wherein k represents a number of individual classification codes (see Figure 1 for shots 26B which contain multiple classifications, wherein k is a length based on how many classifications are includes in each shot).
Referring to claim 7, Dunlop also discloses creating, by the one or more processors, one or more content-type specific affinity vectors and combining, by the one or more processors, at least two of the one or more content-type specific affinity vectors to concatenate different content-types (see table 26b having classes that indicate content-type specific affinity vectors and combining two or more of the content-type specific affinity vectors (Sky, Snow, Forest) in shot #2).
Referring to claim 8, Duffy discloses adding, by the one or more processors, noise to the affinity vector to prevent recognition of individual content items (see Column 8, Lines 58-63).
Referring to claims 9-11 and 13-16, see the rejection of claims 1-3 and 5-8 respectively.
Referring to claims 17-19, see the rejection of claims 1-3, respectively.
Claims 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dunlop et al. (U.S. Patent Application Publication 2013/0259375) in view of Duffy et al. (U.S. Patent No. 11,496,286) in further view of Chaddha (U.S. Patent No. 6,404,923).
Referring to claim 4, Dunlop and Duffy disclose all of the limitations of claim 1, but fail to teach determining the classification code based on the perceptual value using a codebook.
Chaddha discloses determining a classification code based on a perceptual value using a codebook (see Column 3, Line 33 through Column 4, Line 17 and Column 5, Lines 6-10).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to modify the classification system, as taught by Dunlop and Duffy, using the codebook, as taught by Chaddha, for the purpose of providing a more efficient image classification technique (see Column 2, Lines 37-38 of Chaddha).
Referring to claims 16 and 20, see the rejection of claim 4.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON P SALCE whose telephone number is (571)272-7301. The examiner can normally be reached 5:30am-10:00pm M-F (Flex Schedule).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Flynn can be reached at 571-272-1915. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jason Salce/Senior Examiner, Art Unit 2421
Jason P Salce
Senior Examiner
Art Unit 2421
June 25, 2026
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