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 § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 11, 13 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dong (US 2020/0322684).
Regarding claim 1, Dong discloses a method for cross-type recommendation, comprising:
determining a first type of media content interacted with a user (acquiring the content of a short video viewed by the user, the content may include an ID, images and audios of the short video; see at least paragraphs 0029 and 0036);
determining a first media feature of the first type of media content (acquiring fingerprint features for the short video; see at least paragraphs 0030-0032 and 0056-0059); and
recommending a second type of media content to the user based on the first media feature,
wherein the first type of media content and the second type of media content belong to different
types of media content and share a same feature space (recommending a long video to the user based on matching fingerprint features of the short video with the long video; see at least paragraphs 0044-0045 and 0084-0086).
Regarding claim 11, Dong discloses the method of claim 1, wherein recommending the second type of media content to the user based on the first media feature comprises:
in response to the first type of media content being a fragment of the second type of media
content, displaying a recommendation icon on a display interface of the first type of media content (displaying a prompt message to promote a TV series when the short video of an episode is watched; see at least paragraphs 0034-0038 and 0042).
Claim 13 is rejected on the same grounds as claim 1.
Claim 20 is rejected on the same grounds as claim 1.
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.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dong in view of Barlaskar (US 2019/0163752) and further in view of Zhou (CN-116994171, which is an Applicant provide reference. The English translation is provided for the Applicant’s convenience).
Regarding claim 2, Dong discloses the method of claim 1, wherein the first type of media content is a video (short video; see at least the rejection of claim 1), and the second type of media content is a live streaming, and initial features of the first type of media content and the second type of media content are converted into the same feature space, and the initial features comprise at least one of a picture feature, a speech recognition feature, or a character recognition feature (fingerprint features for the short video; see at least paragraphs 0030-0032 and 0056-0059), but is not clear about that the second type of content is a live streaming and a joint encoder.
Barlaskar discloses that a second type of content is a live streaming; an online system is configured to recommend a live video to a target user by presenting the live video in the target user’s newsfeed of other location; see at least the Abstract.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Dong by the teachings of Barlaskar by having the above limitations so to be able to recommend a live video to a target user during the streaming; see at least paragraph 0005.
Dong in view of Barlaskar are not clear about a joint decoder.
Zhou discloses a joint decoder; see at least page 6, Fifth paragraph.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Dong in view of Barlaskar by the teachings of Zhou by having the above limitations for the purpose of video understanding; see at least the Abstract.
Claim 14 is rejected on the same grounds as claim 2.
Claims 3-10 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dong in view of Du (US 2022/0083813).
Regarding claim 3, Dong discloses the method of claim 1, and determining the first media feature of the first type of media content and determining the first media feature of the first type of media content, as in the rejection of claim but is not clear about using a content understanding model.
Du discloses the above missing limitation; a learning classification model that does feature extraction; see at least paragraphs 0043 and 0057.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Dong by the teachings of Du by having the above limitations so to be able to generate a classification model; see at least the Abstract.
Regarding claim 4, Dong in view of Du disclose the method of claim 3, further comprising at least one of:
training the content understanding model based on a plurality of first type of media content
labeled with video labels (the video identifier of Dong; see at least the rejection of claim 1 in combination with the training of Du; see at least the rejection of claim 3);
training the content understanding model based on a plurality of second type of media
content labeled with live streaming labels (alternative language); or
training the content understanding model based on a first type of media content labeled
with a video genre label and a second type of media content labeled with a live streaming genre
label (alternative language).
Regarding claim 5, Dong in view of Du disclose the method of claim 4, wherein training the content understanding model based on the plurality of first type of media content labeled with the video labels comprises:
inputting a first type of training media content labeled with a video label into the content
understanding model;
generating a video label of the first type of training media content; and
adjusting a parameter of the content understanding model based on the generated video
label and the labeled video label (Dong’s first type of content and the labels, i.e. identifier in combination with Du’s training; see at least the rejection of claim 3 and Figs. 2-3).
Regarding claim 6, Dong in view of Du disclose the method of claim 4, wherein training the content understanding model based on the plurality of second type of media content labeled with the live streaming labels comprises:
inputting a second type of training media content labeled with a live streaming label into
the content understanding model;
generating a live streaming label of the second type of training media content; and
adjusting a parameter of the content understanding model based on the generated live
streaming label and the labeled live streaming label (alternative language from claim 4).
Regarding claim 7, Dong in view of Du disclose the method of claim 4, wherein training the content understanding model based on the first type of media content labeled with the video genre label and the second type of media content labeled with the live streaming genre label comprises:
inputting a plurality of training media content labeled with genre labels into the content
understanding model;
generating genre labels of the training media content; and
adjusting a parameter of the content understanding model based on the generated genre
labels and the labeled genre labels (alternative language from claim 4).
Regarding claim 8, Dong in view of Du disclose the method of claim 4, further comprising:
training the content understanding model based on a plurality pairs of media content
classified into a positive sample combination and a negative sample combination, the positive
sample combination comprising a first type of media content related to a user and a second type
of media content related to the first type of media content, and the negative sample combination
comprising the second type of media content and other first type of media content which is not
related to the second type of media content (the combination of Dong’s first type of content and second type of content; see at least the rejection of claim 1 and Du’s negative sample set and positive sample set; see at least paragraphs 0039-0040 and 0050).
Regarding claim 9, Dong in view of Du disclose the method of claim 8, wherein training the content understanding model based on the plurality pairs of media content classified into the positive sample combination and the negative sample combination comprises:
inputting the positive sample combination into the content understanding model;
generating feature vector pairs of the positive sample combination;
determining a similarity among the generated feature vector pairs of the positive sample
combination; and
adjusting a parameter of the content understanding model based on the determined
similarity (the combination of Dong’s first type of content and second type of content and the similarity; see at least the rejection of claim 1 and Du’s negative sample set and positive sample set; see at least paragraphs 0039-0040 and 0050).
Regarding claim 10, Dong in view of Du disclose the method of claim 8, wherein training the content understanding model based on the plurality pairs of media content classified into the positive sample combination and the negative sample combination comprises:
inputting the negative sample combination into the content understanding model;
generating feature vector pairs of the negative sample combination;
determining a similarity among the generated feature vector pairs of the negative sample
combination; and
adjusting a parameter of the content understanding model based on the determined
similarity (the combination of Dong’s first type of content and second type of content and the similarity; see at least the rejection of claim 1 and Du’s negative sample set and positive sample set; see at least paragraphs 0039-0040 and 0050).
Claim 15 is rejected on the same grounds as claim 3.
Claim 16 is rejected on the same grounds as claim 4.
Claim 17 is rejected on the same grounds as claim 5.
Claim 18 is rejected on the same grounds as claim 6.
Claim 19 is rejected on the same grounds as claim 7.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dong in view of Bataller (US 2018/0004760).
Regarding claim 12, Dong discloses the method of claim 11, further comprising:
in response to the first type of media content being a fragment of the second type of
media content and in response to the first type of media content being related to the second type
of media content, displaying a recommendation icon on a display interface of the first type of
media content; displaying a prompt message to promote a TV series when the short video of an episode is watched; see at least paragraphs 0034-0038 and 0042, but is not clear about when the content is not being a fragment of a second media content.
Bataller discloses a content-based video recommendation and discloses identifying other videos that have a similar or related content to a video and recommending them, not specifically being a fragment of a video; see at least paragraphs 0023, 0029 and 0031.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Dong by the teachings of Bataller by having the above limitations so to be able to provide video recommendation; see at least the Abstract.
Conclusion
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/YASSIN ALATA/Primary Examiner, Art Unit 2426