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 .
DETAILED ACTION
Response to Amendment
2. In response to applicant’s amendment received on January 22, 2026, all requested changes to the claims have been entered. New claims 12-22 have been entered.
Response to Argument
3. Applicant’s arguments filed on January 22, 2026 have been fully considered but they are not persuasive.
Applicant’s argue on pages 10-11 of the response In step 2A, the amended Claim 1 also does not relate to abstract concepts. Claim 1 relates to a computer implementation method for video processing, rather than human thinking or pure comparison and judgment determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model. It needs to be implemented through text encoders, image encoders, feature matrices, and equalization methods. The human brain cannot perform processes such as model inference and feature extraction, and determining the audio corresponding to the text report is based on reverse mapping of audio and text alignment based on timestamps, rather than logical judgment by the human brain, but signal or data indexing and mapping.
Examiner disagrees. The claim does not disclose details of , image-text matching model, text encoders, image encoders, feature matrices, and equalization methods. Human is capable of determining a target object from the plurality of video frames by looking at text in the image frames mentally.
Also, Computer to perform generic computer functions that are well -understood, routine and conventional activities previously known to the industry were found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception
With respect to McRO v. Bandai Namco (2016), examiner believed that the instant application and McRO v. Bandai Namco (2016) are not similar.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The following analysis is based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) published on January 7, 2019 (84 Fed. Reg. 50). See also MPEP 2106.04(a)(2)(II).
Regarding claim 1:
Step 1:
Claims 1-3, 5-8 and 10-22 meet step 1 requirement as they are directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter. In this case, “a method” satisfies a “process” category, “a device” satisfies a “machine” category and non-transitory computer readable medium satisfies a “composition of matter” category.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 1 comprise a mental process that can be practicably performed in the human mind therefore, an abstract idea.
Claims 1, 10 and 11, in general is about determining a target video segment
The limitation of “determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model”, “determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model “, “determining a target audio segment matching the target object from the audio data” and “determining a target video segment comprising the target video frame from the video to be processed in the case that a video corresponding to the target audio segment comprises the target video frame”, “converting the audio data into textual information; “determining a text portion comprising a keyword of the target object from the textual information; and determining the audio corresponding to the text portion as the target audio segment.” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step. That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1-3, 5-8 and 10-22 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
The claim recites only one physical element – processor and memory (claim 10) “configured” to perform various tasks. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model”, “determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model “, “determining a target audio segment matching the target object from the audio data” and “determining a target video segment comprising the target video frame from the video to be processed in the case that a video corresponding to the target audio segment comprises the target video frame” the broadest reasonable interpretation (BRI) would have encompassed any forms of calculating inclusive of mental calculations. For example, a human can visually and acoustically identify portion of video. Further, it could be based on the appearance of the individuals, observation of reactions, or even guessing based on alternate metrics.
claims 1, 10 and 11 recite:
obtaining a plurality of video frames in a video to be processed and audio data corresponding to the video to be processed (adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea);
Consequently, the identified additional element taken into consideration individually or in combination of the steps performed fails to amount of significantly more than the abstract idea above.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
With regard to (2b) the Guidance provided the following examples of limitations that may be enough to qualify as “significantly more" when recited in a claim with a judicial exception:
Improvement to another technology or technical field
Improvement to functioning of computer itself and/or applying the judicial exception with, or by use of, a particular machine
Effecting a transformation or reduction of a particular article to a different state or thing.
Adding a specific limitation other that what is well understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application
Meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment.
The Guidance further set forth limitations that were found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
Adding words to “apply it” (or an equivalent) with the judicial exception or mere instructions to implement abstract ideas on a computer
Simply appending well-understood, routine and conventional activities previously known to the industry specified at a high level of generality to the judicial exception, e.g. a claim to an abstract idea requiring no more than a generic
Computer to perform generic computer functions that are well -understood, routine and conventional activities previously known to the industry.
Adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering in conjunction with a law of nature or abstract idea
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Claims 1-3, 5-8 and 10-22 do not recite any additional elements that are not well-understood, routine or conventional.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above identified additional computer components, using instructions to apply the judicial exception, are merely generic computer components that are well-known, routine, and conventional as is evidenced by Bancorp Services v. Sun Life (Fed. Cir. 2012) and Alice Corp. v. CLS Bank (2014).
claims 1, 10 and 11 recite:
obtaining a plurality of video frames in a video to be processed and audio data corresponding to the video to be processed (adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea);
Thus, since claims 1, 10 and 11 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1, 10 and 11 are not eligible subject matter under 35 U.S.C 101. Similar analysis is made for the dependent claims 2, 5-8 and 12-22 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
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 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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2, 5, 7, 10-12, 14, 16, 18, 20, and 22 are rejected under 35 USC 103 as being unpatentable over Patluri et al. (US Patent 11,869,240) in view of Lakhani et al. (US 10,218,954).
With respect to claim 1, Patluri et al. teach obtaining a plurality of video frames in a video to be processed and audio data corresponding to the video to be processed (Fig. 1A, ref label 120 Movies, 122 catalog videos and 124 Game videos; Fig. 7 ref label 702 Receiving a selection of a first video);
determining a target video frame comprising a target object from the plurality of video frames through an image-text matching model (Fig. 1B, ref label 160d Image analysis, col. 8 first para. Image analysis head 160d may perform object detection, object recognition, facial recognition, etc., to determine feature data representing objects present within portions of the video, it is looking for user provided query (text) shows in Fig. 1A ref 103)
determining a target audio segment matching the target object from the audio data (Fig. 1B, ref label 160a Music Analysis and 160b Audio Analysis);
determining a target video segment comprising the target video frame from the video to be processed in the case that a video corresponding to the target audio segment comprises the target video frame (col. 5 lines 48-63, video indicated by the user's request 103 may be parsed by auto-segmentation service 140 in response to the user's request, to determine the semantically-relevant segments; Fig. 1B).
Patluri et al. do not teach expressly that converting the audio data into textual information; determining a text portion comprising a keyword of the target object from the textual information; determining the audio corresponding to the text portion as the target audio segment.
Lakhani et al. teach converting the audio data into textual information (col. 7 lines 22-23, The audio data for each segment can be translated into text in parallel, Fig. 1 ref label 140); determining a text portion comprising a keyword of the target object from the textual information (col. 7 lines 41-49, At 160, the topics generated from an image or a frame and the topics extracted from audio can be combined. The text can be cross-referenced, and topics common to both texts would be given additional weights. At 170, the video-to content engine generates video text, such as text describing the content of the video, using the result of the combined texts and cross reference. For example, key words indicating topic and semantic that appear in both texts can be selected or emphasized. Fig. 1 ref label 160 and 170); determining the audio corresponding to the text portion as the target audio segment (col. 7 lines 49-52, The output can also include metadata that can be time-stamped with frame references. The metadata can include the number of frames, the range of frames, and/or timestamp references);
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to use converted audio text to search corresponding video range in the method of Patluri et al.
The suggestion/motivation for doing so would have been that to have well known method to search video segment.
Therefore, it would have been obvious to combine Jia et al. with Patluri et al. to obtain the invention as specified in claim 1.
With respect to claim 2, Patluri et al. teach determining a keyword of the target object (col. 5 lines 36-44, In another example, for the request 103 "Play Butch & Sam scenes from Crazytown Movie," the user 102 wants to see segments of the movie Crazytown that include both the character Butch and the character Sam. In this example, the category may be "Character" and two attribute values for the Character category may be sent as the input data to the request analyzer service 104. For example, Category:Character Character="Butch" and Category:Character; Character="Sam".); determining the target video frame through the image-text matching model according to the keyword and the plurality of video frames (col. 10 lines 56-58, an object detection algorithm pertaining to image analysis head 160d may execute on individual frames of the video); wherein the image-text matching model is obtained by training sample data (Fig. 4, ref label 418 and 420 training segmentor), and the sample data comprises a sample video frame and a keyword of a sample object (Fig. 4 ref label Image Samples), and a label of the sample video frame is whether the sample video frame comprises an image corresponding to the sample object (col. 11, lines 19-21 The samples may be labeled with various ground truth data representing one or more attributes of the sample ) .
With respect to claim 5, Patluri et al. teach determining partitioning the video to be processed according to photographed objects to obtain at least one partitioned segment , wherein the photographed objects corresponding to different partitioned segments are different (col. 3 lines 60-67, The features may be used to classify different segments of a video as belonging to one or more categories); extracting a preset number of video frames from the at least one partitioned segment respectively to obtain the plurality of video frames. (claim 1, first time code data is determined by the machine learned model; determining second time code data representing a second temporal segment of the first plurality of temporal segments, wherein the first temporal segment and the second temporal segment are separated by a first plurality of intervening frames of the first video).
With respect to claim 7, Patluri et al. teach tracking the target object in the video to be processed according to the target video frame to obtain a starting visual position and an ending visual position of the target object in the video to be processed; determining the target video segment according to the starting visual position and the ending visual position (col. 2 lines 39-47, For example, a (relatively minor) character (e.g., a character 40 named "Butch") may appear in three scenes in a movie and may be represented by the time code data [20:21-23:40], [41:45-42:16], and [54:01-1:01:10]. The three segments represented by the time code data may be represented as the start time and the end time of each particular segment (e.g., 45 [start time, end time]). The time code data may be stored in a segment database for the category Character=Butch for the particular movie.).
Claim 10 is rejected as same reason as claim 1 above.
With respect to processor and memory see Fig. 5 and program see col. 19 lines 50-62.
Claim 11 is rejected as same reason as claim 1 above.
With respect to program see col. 19 lines 50-62.
Claim 12 is rejected as same reason as claim 2 above.
Claim 14 is rejected as same reason as claim 5 above.
Claim 16 is rejected as same reason as claim 7 above.
Claim 18 is rejected as same reason as claim 2 above.
Claim 20 is rejected as same reason as claim 5 above.
Claim 22 is rejected as same reason as claim 7 above.
Claim 3, 13 and 19 are rejected under 35 USC 103 as being unpatentable over Patluri et al. (US Patent 11,869,240) in view of Lakhani et al. (US 10,218,954) and in further view of Yu et al. (US 2024/0378230).
Patluri et al. and Lakhani et al. teach all the limitations of claim 2 as applied above from which claim 3 respectively depend.
Patluri et al. also teach performing text feature extraction on the keyword through a text encoder in the image-text matching model to obtain a text feature matrix; performing image feature extraction on the video frame through an image encoder in the image-text matching model to obtain an image feature matrix (Fig. 1B, ref label 160d Image analysis, col. 8 first para. Image analysis head 160d may perform object detection, object recognition, facial recognition, etc., to determine feature data representing objects present within portions of the video, it is looking for user provided query (text) shows in Fig. 1A ref 103)
Patluri et al. and Lakhani et al. do not teach expressly that calculating a similarity matrix between the text feature matrix and the image feature matrix; determining, according to the similarity matrix, a similarity between each of the video frames in the plurality of video frames and the target object; determining the target video frame according to the similarity.
Yu et al. teach calculating a similarity matrix between the text feature matrix and the image feature matrix; determining, according to the similarity matrix, a similarity between each of the video frames in the plurality of video frames and the target object (Fig. 5 ref label 504 Similarity calculation; para [0059]-[0060], similarity calculation is performed between the input text/image embedding (from block 502) and video embeddings (from block 503) stored in the information retrieval system via non-metric space searching algorithm, relevant videos returned in a sorted order of similarity. In various embodiments, the process not only identifies specific scenes within a video (indicated by key frames), but also retrieves the video containing these scenes. The result may be a ranked list of relevant videos or entities, sorted in descending order of similarity. It is to be appreciated that structured output helps direct users to the most pertinent video content relative to their search input, optimizing the search and retrieval process.);
determining the target video frame according to the similarity (Fig. 5 ref label 505 Relevant Videos).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to use similarity to determine target frame in the method of Patluri et al. and Lakhani et al.
The suggestion/motivation for doing so would have been that to have logic to determine target frame consistently.
Therefore, it would have been obvious to combine USC 103 as being unpatentable over Yu et al. with Patluri et al. and Lakhani et al. to obtain the invention as specified in claim 3.
Claim 13 is rejected as same reason as claim 3 above.
Claim 19 is rejected as same reason as claim 3 above.
Claim 6, 15 and 21 are rejected under 35 USC 103 as being unpatentable over Patluri et al. (US Patent 11,869,240) in view of Lakhani et al. (US 10,218,954) and in further view of El Ghazzal (US 2022/0303632).
Patluri et al. and Lakhani et al. teach all the limitations of claim 1 as applied above from which claim 6 respectively depend.
Patluri et al. and Lakhani et al. do not teach expressly that obtaining a picture of the target object; determining a target video segment comprising the target video frame from the video to be processed in the case that a confidence level of the picture and the target video frame is greater than a preset value.
El Ghazzal teaches obtaining a picture of the target object; determining a target video segment comprising the target video frame from the video to be processed in the case that a confidence level of the picture and the target video frame is greater than a preset value. (para [0043] The segment and playback speed selection module 206 can also rank segments of a media content item based on scores determined for the segments. The ranking of the segments can be used to determine an order for playing the segments of the media content item. A highest ranking segment for a given user can be played out-of-order before other segments. For example, a video comprising three segments can be accessed by a user. In this example, the segment and playback speed selection module 206 can output binary scores of 0, 1, and 1, respectively, for the three segments, where 0 indicates a segment is predicted to be not interesting to the user and where 1 indicates a segment is predicted to be interesting to the user. The segment and playback speed selection module 206 can further output confidence scores of 100, 50, and 100, respectively, for each of the three segments. In this example, based on the confidence scores, the segment and playback speed selection module 206 can determine that the third segment with a score about user interest of 1 and a confidence score of 100 is most likely to be of interest to the user. In this example, although the segment and playback speed selection module 206 determined a score of 1 to both the second segment and the third segment, the third segment is ranked higher because the confidence score for the third segment is higher than the confidence score for the second segment. Ranking of segments of a media content item based on confidence scores will be discussed in further detail with reference to FIGS. 3A-3B)
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to use confidence level of the picture to determine a target video segment in the method of Patluri et al. and Lakhani et al.
The suggestion/motivation for doing so would have been that to provide best possible result.
Therefore, it would have been obvious to combine El Ghazzal with Patluri et al. and Lakhani et al. to obtain the invention as specified in claim 6.
Claim 15 is rejected as same reason as claim 6 above.
Claim 21 is rejected as same reason as claim 6 above.
Claim 8, and 17 are rejected under 35 USC 103 as being unpatentable over Patluri et al. (US Patent 11,869,240) in view of Lakhani et al. (US 10,218,954) and in further view of Casagrande(US 20090307741)
Patluri et al. and Lakhani et al. teach all the limitations of claim 7 as applied above from which claim 8 respectively depend.
Patluri et al. and Lakhani et al. do not teach expressly that performing sentence breaking on the audio data, and determining a sentence breaking starting point adjacent to the starting visual position and a sentence breaking ending point adjacent to the ending visual position; determining target audio information between the sentence breaking sta1ting point and the sentence breaking ending point; determining a video segment corresponding to the target audio information as the target video segment in the case that a video segment corresponding to the target audio information comprises the starting visual position and the ending visual position.
Casagrande teaches performing sentence breaking on the audio data (Fig. 3, para [0037] and [0038], select string 318), and determining a sentence breaking starting point adjacent to the starting visual position (utilizes the negative offset 312 to identify the beginning boundary 308) and a sentence breaking ending point adjacent to the ending visual position (utilizes the positive offset 314 to identify the ending boundary 310); determining target audio information between the sentence breaking starting point and the sentence breaking ending point (provide independence from the absolute presentation times of the video frames associated with the boundaries 308 and 310 within the audio/video stream 300); determining a video segment corresponding to the target audio information as the target video segment in the case that a video segment corresponding to the target audio information comprises the starting visual position and the ending visual position (provide independence from the absolute presentation times of the video frames associated with the boundaries 308 and 310 within the audio/video stream 300).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to adjust stating position and end position of audio/video segment using offset in the method of Patluri et al. and Lakhani et al.
The suggestion/motivation for doing so would have been that to give better user experience without sudden change.
Therefore, it would have been obvious to combine Casagrande with Patluri et al. and Lakhani et al. to obtain the invention as specified in claim 8.
Claim 17 is rejected as same reason as claim 8 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Randolph Chu whose telephone number is 571-270-1145. The examiner can normally be reached on Monday to Thursday from 7:30 am - 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached on (571) 272-7778.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANDOLPH I CHU/
Primary Examiner, Art Unit 2667
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