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 .
2. This is in response to the applicant response filed on 06/15/2026. In the applicant’s response, no claim was amended or cancelled. Accordingly, claims 1-20 are pending and being examined. Claims 1, 11, and 20 are independent form.
Non-statutory Double Patenting
3. The non-statutory double patenting rejections, make in the previous office action mailed on 03/16/2026, are STILL MAINTAINED because the respective claims between claims 1-20 of the instant application and claims 1-18 of US Patent 11,948,360 describe the same invention.
Claim Rejections - 35 USC § 101
4. The claim rejections under 35 USC § 101 make in the previous office action mailed on 03/16/2026, are STILL MAINTAINED because the claimed inventions are directed to non-statutory subject matter (an abstract ideal without significantly more). See, the Examiner’s explanations in the section of Response to Arguments.
Claim Rejections - 35 USC § 102
5. 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 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.
6. 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.
(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.
7. Claims 1-8, 11-18, and 20 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Mei et al (US 20120263433, hereinafter “Mei”).
Regarding claim 1, Mei discloses a computer-implemented method for determining representative frames for a media title (the method the device for automatic generation of visual presentations by acquiring key roles from a video. See Abstract), the method comprising:
aggregating a plurality of face embeddings into a plurality of clusters representing a plurality of characters included in the media title (see the face cluster 212 of fig.2 and para.38: the method “detects faces from the key frames [which are extracted from the video 206] and performs face grouping to output a face cluster 212 for each role in the video.” Also see para.24: “The techniques detect faces that appear in the key frames and groups the faces into face clusters according to role.” It should be noticed that: each role (hereinafter a role, a character, a person, a cluster, and a vertex all are interchangeable) is corresponding to a person (i.e., a character) having a corresponding cluster including a plurality of face images of the person as shown by fig.4.);
computing a first interaction score between two characters included in the plurality of characters based on a co-occurrence of the two characters in a set of frames associated with the media title (to determine “a role importance function f(v)” in the video, the generation tool 218 calculates the degree of the vertex v in the community graph, i.e., Degree(v), on the basis of “the sum of the weight of the edges connected to v”, see para.63; wherein the weights (i.e., the impotencies) between each vertex A and B (i.e., each face cluster A and B) is determined by the correlations (i.e., the interaction scores) of two faces a and b occurred in the same scene defined by Eqs.(4)-(5), see para.54-para.55); and
selecting, from the set of frames, a first frame as representative of the media title based, at least in part, on the first interaction score (“the generation tool 218 selects a key frame that contains key roles”, see para.65. As stated in para.63, how key/important a role is determined by “a role importance function f(v)” that is further determined based on the correlations (i.e., the interaction scores) of the character v to the other characters in the scene, as stated in para.54-55. Therefore, a key frame is selected on the basis of the interaction scores between the character v and each of the other characters in the video).
Regarding claim 2, 12, Mei discloses, wherein the first interaction score is computed based on a number of frames included in the set of frames in which a first character of the two characters occurs within a predetermined number of frames of a second character of the two characters (wherein the Degree(v) is determined on the basis of “the sum of the weight of the edges connected to [vertex] v”, see para.63. In other words, the more the number of vertexes (i.e., the clusters, or the key frames) is, the larger the Degree(v) is.).
Regarding claim 3, 13, Mei discloses, further comprising computing a second interaction score between two other characters included in the plurality of characters based on a co-occurrence of the two other characters in the set of frames, wherein the first frame is selected as representative of the media title based on the second interaction score as well (wherein the weights (i.e., the impotencies) between each vertex A and B (i.e., each face cluster A and B) is determined by the correlations (i.e., the interaction scores) of two faces a and b occurred in the same scene defined by Eqs.(4)-(5), see para.54-para.55).
Regarding claim 4, 14, Mei discloses, further comprising: generating a character interaction graph that stores a plurality of interaction scores for a plurality of pairs of characters included in the plurality of characters; and storing the first interaction score in the character interaction graph (see the community graph shown by fig.5, wherein the correlation values, such as 1, 0.22 and the like, between the nodes are the interaction scores between the roles, see para.59).
Regarding claim 5, 15, Mei discloses, wherein the character interaction graph comprises a set of nodes representing the plurality of characters and a set of edges that interconnect the set of nodes and represent interactions between pairs of characters (ibid.).
Regarding claim 6, 16, Mei discloses, further comprising traversing the character interaction graph to retrieve a second interaction score associated with two other characters included in the plurality of characters, wherein the first frame is selected as representative of the media title based on the second interaction score as well (as shown by fig.5, wherein the character 506 is selected as the first key role on the basis of the second key role 508 which is associated two other characters 504 and 510.).
Regarding claim 7, 17, Mei discloses, further comprising: computing a plurality of prominence scores for the plurality of characters; and selecting the first frame of video content based on at least one prominence score for at least one character as well (see para.65: “the generation tool 218 selects a key frame that contains key roles”; where a key frame is selected on the basis of the interaction scores between the character v and each of the other characters in the video.).
Regarding claim 8, 18, Mei discloses, wherein the first frame of video content is selected based on a weighted combination of the first interaction score and the at least one prominence score (see para.63, wherein “a role importance function f(v)” is further determined on the basis of the correlations (i.e., the interaction scores) of the character v to each of the other characters in the scene, as stated in para.54-55.).
Regarding claim 11, 20, each of them is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 103
8. 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 of this title, 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.
9. Claims 9-10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mei in view of Kulbhushan (WO2019/018434, hereinafter “Kulbhushan”).
Regarding claim 9, Mei discloses the claimed invention except for applying a convolutional neural network to a plurality of faces to generate a plurality of face scores. However, the technique of applying a convolutional neural network to a plurality of faces to generate a plurality of face scores is well-known and widely used in the field of video summarization. As evidence, Kulbhushan teaches neural network-based methods that may generate key images form a video and determining whether the key images are likely to contain characters. See para.15. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Kulbhushan into the teachings of Mei and apply a convolutional neural network to a plurality of faces taught by Kulbhushan to generate key frames from a video taught by Mei. Suggestion or motivation for doing so would have been to generate “actor/person centric thumbnails for media content”. See, Paragraph [0001] in Kulbhushan. Therefore, the claim is unpatentable over Mei in view of Kulbhushan.
Regarding claim 10, the combination of Mei and Kulbhushan discloses the computer-implemented method of claim 9, wherein the first frame of video content is selected based on a weighted combination of the first interaction score and the at least one face score (Kulbhushan , see para.58: “the system may proceed to create a personalized thumbnail by increasing the weight factor of key images or image clusters containing the face of the particular character, resulting in a relatively high probability that the particular character appears or is highlighted in the thumbnail.”).
Regarding claim 19, claim 19 essentially includes the similar elements recited by the combination of claims 9 and 10, thus it is interpreted and rejected for the reasons set forth in the rejections of claims 9 and 10.
Response to Arguments
10. Applicant’s arguments, filed on 03/16/2026, have been fully considered but they are not persuasive.
10-1. Regarding the claim rejections under 35 U.S.C. 101,
Q1. On page 9, the applicant argues:
“By way of illustration, Example 38 in the Subject Matter Eligibility Examples: [...] Although a random value may be generated or computed based on mathematical concepts, Example 38 indicates that "the mathematical concepts are not recited in the claims."
(The emphases added by the examiner.)
First of all, the examiner respectfully points out that Example 38 is a claim about “Simulating an Analog Audio Mixer” and there is no comparability between Example 38 and the instant case. Second, according to the applicant’s specification, see paragraph [0039], “character analysis engine 124 could generate face embedding clusters 212 using an agglomerative clustering technique. The agglomerative clustering technique initially assigns each face embedding to a different face embedding cluster and merges pairs of face embedding clusters 212 that are within a threshold distance from one another. In this example, the threshold could be selected to ensure near-perfect precision for face embeddings 232 that fall within the corresponding distance.” As such, it is apparent that the agglomerative clustering technique recited by claim 1 includes mathematical comparisons and mathematical calculations between two face images. Therefore, each of claims 1, 11, and 20 falls with mathematical concepts grouping of abstract idea at Step 2A-1.
Q2. On page 11, the applicant argues:
“In that regard, the present Application makes clear that the claimed approach improves efficiency, speed, and scalability of systems that identify representative frames in a media title. [...] thereby achieving the outlined improvements to technology.”
The examiner respectfully disagrees with the applicant’s argument. As explained in the rejections of the claims, there no additional element in claim 1 except for mathematical calculations. As to the argument of “improv[ing] efficiency, speed, and scalability, it is nothing more than mere instruction to implement an abstract idea on a generic computer, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Therefore, the claim as a whole does not integrate the judicial exception into a practical application.
10-2. Regarding the claim rejections under 35 U.S.C. 102 and 103, on page 14, the applicant argues:
“Importantly, Mei contains no such teachings. Instead, Mei discloses that "clustering component 324 clusters faces with the same exemplar 326 as a face cluster 212 ... with each cluster containing the images of one role." See Mei, paragraph [0049]. Images of a face are not the same as face embeddings. The meaning of "embeddings," as understood by one of ordinary skill in the relevant art, is "representations of values or objects like text, image and audio that are designed to be consumed by machine learning models and semantic search algorithms."”
The examiner respectfully disagrees with the applicant’s argument. The examiner respectfully points out: a face image of a person disclosed by fig.4 in Mei, includes a set of pixels each of which represents a part of the face of a person and thereby is an embedding of the face of a person. The applicant’s argument therefore is unpersuasive.
Conclusion
11. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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