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 Status
This action is in response to the applicant’s arguments filed on June 17, 2026. Claim(s)
1, 11, and 20 are amended. Thus, claims 1-20 are pending for examination in this application.
Response to Amendment
Applicant’s remarks and amendments filed June 17, 2026, have been entered.
Response to Arguments
Applicant’s arguments filed June 17, 2026, regarding the rejection(s) of claim(s) 1-20 have been fully and completely considered but are moot because the arguments do not apply to the new combination of the references, facilitated by Applicant’s newly submitted amendments, including new prior art— Pio et al, US 20190200089 —being used in the current rejection.
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.
Claim(s) 1-2, 4, 8, 11-12, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421.
Regarding claim 1, Townsend teaches a method for captioning a video, the method comprising (see Townsend, Col 2, Lines 38-44, “methods are disclosed that generate a video summarization that condenses lengthy video data (e.g., over an hour of recording) in a short video summary (e.g., 2-5 minutes) highlighting interesting events that occurred in the video data”):
receiving the video to be captioned (see Townsend, Col 18, Lines 1-5, “the image capture device 110 has completed video capturing at a third time, and the video data 510-3 includes an entirety of the video data to be uploaded from the image capture device 110 to the server(s) 112” );
partitioning the video into a plurality of segments (see Townsend, Col 18, Lines 3-12, “As illustrated in FIG. 5, the image capture device 110 may divide the video data 510-3 into video sections 520-3 (e.g., first video section through tenth video section) and an upload of a majority of the video sections 520-3 is complete at the third time (e.g., first video section through ninth video section are uploaded to the server(s) 112)”);
for each of the segments, generating an image grid comprising a plurality of frames in the segment (see Townsend, Col 3, Lines 19-25, Lines, “After completion of (or during) video capture for individual video sections, the image capture device 110 may send (124) the individual video sections and optional additional data and the server(s) 112 may receive (126) the individual video sections and the optional additional data,” and Col Lines, “The additional data may include … a frame selector statistic or the like. The image capture device 110 may determine the frame selector statistic from the video data (e.g., using every video frame, using a sampling rate of 1 Hz or the like) and the frame selector statistic may identify transitions in the video data. For example, the video data may include three distinct portions, such as a first indoor scene, a second outdoor scene and a third indoor scene, the frame selector statistic may identify the three unique portions by determining a similarity between individual video frames using color, correlation, motion data or the like and may group the video frames into three groups,” the additional data includes the frame selector which may be a plurality of frames in the segment);
for each of the image grids, generating an image grid caption describing the image grid (see Townsend, Fig. 5 and Col 18, Lines 10-18, “while the image capture device 110 uploads the tenth video section to the server(s) 112, the server(s) 112 may have annotated a portion of the video sections 520-3 (e.g., first video section and second video section) and may be annotating the remaining video sections (e.g., third video section through ninth video section),” 520-1-520-3 are considered to be the plurality of segments; 520-3 first-tenth are considered to be the image grids);
and generating a consolidated caption for the video (see Townsend, Col, Lines, “using annotation data to generate video summarizations”).
Townsend does not expressively teach
generating an image grid comprising a composite image of a plurality of frames in the segment;
However, Pio in a similar invention in the same field of endeavor teaches
generating an image grid comprising a composite image of a plurality of frames in the segment (see Pio, Fig. 4 and Paragraph [0033], “In such embodiments, some, or all, of the thumbnail preview images can be stitched together to form one or more composite preview images. For example, a composite preview image can be assembled as a grid (or table) of thumbnail preview images. In some instances, multiple composite preview images may be generated for a content item depending on the length of the content item's playback duration”);
The combination of Townsend and Pio are analogous art because they are both in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for thumbnail previous images to be stitched together to form one or more composite preview images and assembled as a grid of thumbnail preview images as taught in the method of Pio in the method of Townsend to provide thumbnail previews (Pio, Paragraph [0033]).
Townsend in view of Pio does not expressively teach
for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model;
and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions.
However, Zweig in a similar invention in the same field of endeavor teaches
for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model (see Zweig, Paragraph [0064], “the frames may be provided to a trained machine learning model with visual capabilities, such as a multimodal language model, with instructions, and the trained machine learning model may generate captions for the entire video content item, the shots in the video content item, and the change point frames in the video content item,” and Paragraph [0063], “The video captioning system 302 further includes a shot detector 310 that is configured to identify shot boundaries in the video content item. Each shot is a continuous sequence of frames taken by a camera. Edited videos are usually composed of multiple shots stitched together. Identifying individual shots in the video content item helps the captioning system understand how a video content item is changing” the shots in the video content item are considered to be an image grid);
and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions (see Zweig, Paragraph [0064], “the frames may be provided to a trained machine learning model with visual capabilities, such as a multimodal language model, with instructions, and the trained machine learning model may generate captions for the entire video content item”).
The combination of Townsend, Pio, and Zweig are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to caption each shot and for a multimodal language model to generate captions; generate a script by combining one or more captions as taught in the method of Zweig in the method of Townsend in view of Pio to understand how a video content item is changing (Zweig, Paragraph [0064]).
Regarding claim 2, Townsend in view of Pio in view of Zweig further teaches the method of claim 1,
wherein the video has a duration of at least sixty seconds (see Townsend, Col 13, Lines 3-8, “Video summarization may summarize lengthy video data (e.g., an hour of recording) in a short video summary (e.g., 2-5 minutes) highlighting the interesting events that occurred in the video data.”).
The rationale of claim 1 has been applied herein.
Regarding claim 4, Townsend in view of Pio in view of Zweig further teaches the method of claim 1,
wherein each of the segments has a duration of at least thirty seconds (see Townsend, Col 9, Lines 41-45, “each video clip in the video summary may be relatively short (e.g., between 5-60 seconds)”).
The rationale of claim 1 has been applied herein.
Regarding claim 8, Townsend in view of Pio in view of Zweig further teaches the method of claim 1, wherein generating the image grid caption comprises:
inputting each of the plurality of frames of the image grid into the generative multimodal model to generate a plurality of frame captions (see Zweig, Paragraph [0064], “the frames may be provided to a trained machine learning model with visual capabilities, such as a multimodal language model, with instructions, and the trained machine learning model may generate captions for the entire video content item, the shots in the video content item, and the change point frames in the video content item,” and Paragraph [0063], “The video captioning system 302 further includes a shot detector 310 that is configured to identify shot boundaries in the video content item. Each shot is a continuous sequence of frames taken by a camera. Edited videos are usually composed of multiple shots stitched together. Identifying individual shots in the video content item helps the captioning system understand how a video content item is changing” the shots in the video content item are considered to be frames of an image grid);
and combining the plurality of frame captions to generate the image grid caption (see Zweig, Paragraph [0085], “The script generation system 326 generates the script by combining the audio transcript and the captions. The script includes an overall caption for the video content item along with the start and end timing of the video content item, one or more dialogs and one or more captions”).
The rationale of claim 1 has been applied herein.
As per claim 11, Claim 11 claims a computing system for captioning a video, the computing system comprising: processing circuitry and memory storing instructions that, when executed, cause the processing circuitry to: complete the same limitations as Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
Townsend further teaches a computing system comprising: processing circuitry and memory storing instructions (see Townsend, Col 56, Lines 58-64, “The device 102/image capture device 110/server(s) 112 may include one or more controllers/processors 2604 comprising one-or-more central processing units (CPUs) for processing data and computer-readable instructions and a memory 2606 for storing data and instructions”).
As per claim 12, Claim 12 claims the same limitations as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 2.
As per claim 14, Claim 14 claims the same limitations as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 4.
As per claim 18, Claim 18 claims the same limitations as Claim 8 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 8.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 in view of Mondal et al, US 20230153352.
Regarding claim 3, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1,
wherein the video is uniformly partitioned.
However, Mondal in a similar invention in the same field of endeavor teaches
wherein the video is uniformly partitioned (see Mondal, Paragraph [0100], “the video may be evenly partitioned into n clips (where n is the maximum number of clips permitted) and then the clips may be processed as previously described with respect to FIG. 3A”).
The combination of Townsend, Pio, Zweig, and Mondal are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for the video to be evenly partitioned into n clips as taught in the method of Mondal in the method of Townsend in view of Pio in view of Zweig to enable CBVR [content-based video retrieval] to be used for more practical applications (Mondal, Paragraph [0004]).
As per claim 13, Claim 13 claims the same limitations as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 3.
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 Moura et al, US 20220164580.
Regarding claim 5, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1,
wherein generating the image grid comprises uniformly sampling the plurality of frames from the segment.
However, Moura in a similar invention in the same field of endeavor teaches
wherein generating the image grid comprises uniformly sampling the plurality of frames from the segment (see Moura, Paragraph [0048], “For each untrimmed video … Each video into an average of 100 video segments and 8 frames are sampled for each segment (i.e., T=100, t=8).”).
The combination of Townsend, Pio, Zweig, and Moura are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for the video to be separated into an average of 100 video segments and 8 frames are sampled for each segment as taught in the method of Moura in the method of Townsend in view of Pio in view of Zweig to reduce the annotation of both the large amount of data and action locations (Moura, Paragraph [0049]).
Regarding claim 6, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1,
wherein generating the image grid comprises sampling at least six frames from the segment.
However, Moura in a similar invention in the same field of endeavor teaches
wherein generating the image grid comprises sampling at least six frames from the segment (see Moura, Paragraph [0048], “8 frames are sampled for each segment”).
The combination of Townsend, Pio, Zweig, and Moura are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for 8 frames to be sampled for each segment as taught in the method of Moura in the method of Townsend in view of Pio in view of Zweig to reduce the annotation of both the large amount of data and action locations (Moura, Paragraph [0049]).
As per claim 15, Claim 15 claims the same limitations as Claim 5 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 5.
As per claim 16, Claim 16 claims the same limitations as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 6.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 in view of Pang et al, CN116994327A.
Regarding claim 7, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1,
wherein each of the image grids comprises an image containing the plurality of frames.
However, Pang in a similar invention in the same field of endeavor teaches
wherein each of the image grids comprises an image containing the plurality of frames (see Paragraph [n0032], “For example, for a video V (i.e., a sequence of video images), it can first be divided into M video segments of equal duration, for example, represented as {S<sub>1</sub>,S<sub>2</sub>,...,S<sub>k</sub>}, where K is an integer greater than 2. Then, each video segment is randomly downsampled, and multiple frames of video images are randomly sampled from it to form a sampled segment. The multiple video frames constituting the sampling segment can be consecutive image frames or interval image frames arranged in chronological order according to timestamps,” the sampled segment is considered to be the image grid; the consecutive image frames are considered to be an image containing a plurality of frames).
The combination of Townsend, Pio, Zweig, and Pang are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for a video to be divided into M video segments of equal duration and the video segment is downsampled to form a sampled segment which is considered to be the image grid and for the sampled segment to be consecutive image frames or interval images as taught in the method of Pang in the method of Townsend in view of Pio in view of Zweig to improve action recognition efficiency (Pang, Paragraph [n0004]).
As per claim 17, Claim 17 claims the same limitations as Claim 7 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 7.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 in view of Lee et al, US 20230306056.
Regarding claim 9, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1, further comprising
generating a training dataset that includes a labeled data pair comprising the video and the consolidated caption.
However, Lee in a similar invention in the same field of endeavor teaches
generating a training dataset that includes a labeled data pair comprising the video and the consolidated caption (see Lee, Paragraph [0136], “token encoder 1112 may be trained based on segmented video-caption pairs for the VCMR task dataset and original video-caption pairs for the VR task dataset. Here, segments of a video included in the training data may be segmented based on their ground truth (GT) start and end time steps for each query (caption) annotation”).
The combination of Townsend, Pio, Zweig, and Lee are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for a token encoder to be trained based on segmented video-caption pairs where the segments of a video are based on start and end time steps for each (caption) annotation as taught in the method of Lee in the method of Townsend in view of Pio in view of Zweig to be capable of comprehensively recognizing and processing various information, such as video and temporal context, stepping forward from the existing video retrieval technology specialized for visual information (Lee, Paragraph [0005]).
As per claim 19, Claim 19 claims the same limitations as Claim 9 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 9.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 in view of Chen et al, US 20250247586.
Regarding claim 10, Townsend in view of Pio in view of Zweig does not expressively teach the method of claim 1,
wherein the video does not include a scene cut.
However, Chen in a similar invention in the same field of endeavor teaches
wherein the video does not include a scene cut (see Chen, Paragraph [0026], “A two-step post-processing algorithm handles 1) long videos 206 with the transition, such as fade-in and fade-out effects, which cannot be detected as a cut-scene, or 2) unedited footage that does not contain any cut-scenes”).
The combination of Townsend, Zweig, Pio, and Chen are analogous art because they are all in the same field of endeavor of video captioning of long videos. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for long videos 206 to not contain any cut-scenes as taught in the method of Chen in the method of Townsend in view of Pio in view of Zweig for the video to be semantically consistent (Chen, Paragraph [0025]).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al, US 20250247586 in view of Townsend et al, US 10230866 in view of Pio et al, US 20190200089 in view of Zweig et al, US 20250336421 in view of Lee et al, US 20230306056.
Regarding claim 20, Chen teaches,
a method for generating a training dataset for a video generation model, the method comprising (see Chen Paragraph [0021], “captioning pipelines that automatically annotate video data with subtitles to create large-scale datasets generated by ASR”):
receiving a video dataset comprising a plurality of videos (see Chen, Paragraph [0021], “a Panda-70M dataset, is generated. Some samples of the Panda-70M dataset are shown at 102 in FIG. 1. The Panda-70M dataset includes high-resolution videos from an open (public) domain source with rich captions averaging 13.2 words per caption”);
filtering the video dataset based on at least one predetermined criterion to determine a subset of the plurality of videos (see Chen, Paragraph [0021], “By using different models to caption a subset of videos,” and Paragraph [0022], “First, a semantics-aware video splitting algorithm 204 cuts long videos 202 into semantically consistent clips 206 while striking a balance between semantics coherence and the duration of the video clips”);
for each of the videos in the subset of the plurality of videos, performing a captioning process by: partitioning the video into a plurality of segments (see Chen, Paragraph [0026], “splitting is performed based on cut-scene detection. In an example, a long video 202 is split into video clips 206 using a tool for detecting shot changes in videos that automatically splits the video into separate clips (e.g., PySceneDetect),” video clips is considered to be a plurality of segments);
Chen does not expressively teach
for each of the segments, generating an image grid comprising a composite image of a plurality of frames in the segment;
for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model;
and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions;
However, Townsend in a similar invention in the same field of endeavor teaches
for each of the segments, generating an image grid comprising (see Townsend, Col 3, Lines 19-25, Lines, “After completion of (or during) video capture for individual video sections, the image capture device 110 may send (124) the individual video sections and optional additional data and the server(s) 112 may receive (126) the individual video sections and the optional additional data,” and Col Lines, “The additional data may include … a frame selector statistic or the like. The image capture device 110 may determine the frame selector statistic from the video data (e.g., using every video frame, using a sampling rate of 1 Hz or the like) and the frame selector statistic may identify transitions in the video data. For example, the video data may include three distinct portions, such as a first indoor scene, a second outdoor scene and a third indoor scene, the frame selector statistic may identify the three unique portions by determining a similarity between individual video frames using color, correlation, motion data or the like and may group the video frames into three groups,” the additional data includes the frame selector which may be a plurality of frames in the segment);
for each of the image grids, generating an image grid caption describing the image grid (see Townsend, Fig. 5 and Col 18, Lines 10-18, “while the image capture device 110 uploads the tenth video section to the server(s) 112, the server(s) 112 may have annotated a portion of the video sections 520-3 (e.g., first video section and second video section) and may be annotating the remaining video sections (e.g., third video section through ninth video section),” 520-1-520-3 are considered to be the plurality of segments; 520-3 first-tenth are considered to be the image grids);
and generating a consolidated caption for the video (see Townsend, Col, Lines, “using annotation data to generate video summarizations”);
The combination of Chen and Townsend are analogous art because they are both in the same field of endeavor of video captioning of long videos. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to for a frame selector statistic to group the video frames of each section into groups; to annotate the video sections individually; and generate video summarizations based on annotations as taught in the method of Townsend in the method of Chen for the video to improve how the videos are generated and shared (Townsend, Col 1, Lines 10-11).
Chen in view of Townsend does not expressively teach
generating an image grid comprising a composite image of a plurality of frames in the segment;
However, Pio in a similar invention in the same field of endeavor teaches
generating an image grid comprising a composite image of a plurality of frames in the segment (see Pio, Fig. 4 and Paragraph [0033], “In such embodiments, some, or all, of the thumbnail preview images can be stitched together to form one or more composite preview images. For example, a composite preview image can be assembled as a grid (or table) of thumbnail preview images. In some instances, multiple composite preview images may be generated for a content item depending on the length of the content item's playback duration”);
The combination of Chen, Townsend and Pio are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for thumbnail previous images to be stitched together to form one or more composite preview images and assembled as a grid of thumbnail preview images as taught in the method of Pio in the method of Chen in view of Townsend to provide thumbnail previews (Pio, Paragraph [0033]).
Chen in view of Townsend in view of Pio does not expressively teach
for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model;
and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions;
However, Zweig in a similar invention in the same field of endeavor teaches
for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model (see Zweig, Paragraph [0064], “the frames may be provided to a trained machine learning model with visual capabilities, such as a multimodal language model, with instructions, and the trained machine learning model may generate captions for the entire video content item, the shots in the video content item, and the change point frames in the video content item,” and Paragraph [0063], “The video captioning system 302 further includes a shot detector 310 that is configured to identify shot boundaries in the video content item. Each shot is a continuous sequence of frames taken by a camera. Edited videos are usually composed of multiple shots stitched together. Identifying individual shots in the video content item helps the captioning system understand how a video content item is changing” the shots in the video content item are considered to be an image grid);
and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions (see Zweig, Paragraph [0064], “the frames may be provided to a trained machine learning model with visual capabilities, such as a multimodal language model, with instructions, and the trained machine learning model may generate captions for the entire video content item”);
The combination of Chen, Townsend, Pio, and Zweig are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to caption each shot and for a multimodal language model to generate captions; generate a script by combining one or more captions as taught in the method of Zweig in the method of Chen in view of Townsend in view of Pio to understand how a video content item is changing (Zweig, Paragraph [0064]).
Chen in view of Townsend in view of Pio in view of Zweig does not expressively teach
and generating labeled data to be included in the training dataset by pairing each of the videos in the subset of the plurality of videos with its associated consolidated caption.
However, Lee in a similar invention in the same field of endeavor teaches
and generating labeled data to be included in the training dataset by pairing each of the videos in the subset of the plurality of videos with its associated consolidated caption (see Lee, Paragraph [0136], “token encoder 1112 may be trained based on segmented video-caption pairs for the VCMR task dataset and original video-caption pairs for the VR task dataset. Here, segments of a video included in the training data may be segmented based on their ground truth (GT) start and end time steps for each query (caption) annotation.”).
The combination of Chen, Townsend, Pio, Zweig, and Lee are analogous art because they are all in the same field of endeavor of video processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for a token encoder to be trained based on segmented video-caption pairs where the segments of a video are based on start and end time steps for each (caption) annotation as taught in the method of Lee in the method of Chen in view of Townsend in view of Pio in view of Zweig to be capable of comprehensively recognizing and processing various information, such as video and temporal context, stepping forward from the existing video retrieval technology specialized for visual information (Lee, Paragraph [0005]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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