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 .
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 02/26/2026 was reviewed and the listed references were noted.
Drawings
The 7 page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-21 are currently pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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 1-3, 6-8, 11, and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20260120487 with filing date of 10/11/2024) in view of Zhou (US 20260017960 with filed date of 07/15/2024).
Consider Claim 1, Li teaches “A computer-implemented method comprising:
obtaining a video that comprises a plurality of video frames;” (Li; [0091]; “The Wolf 200 obtains image data (e.g., one or more frames 206) associated with the video 202.”) “for each of one or more segments of the video, wherein each segment comprises one or more consecutive video frames of the video:” (Li; [0104]; “The sampling process(es) 204 may obtain the frame(s) 206 (e.g., sequential images) from the video”) “providing one or more video frames of the segment as input to a video captioning model to generate a set of one or more respective captions, each describing content depicted in the segment;” (Li; [0078]; “For example, the Wolf functionality 120 may use one or more first neural networks to generate one or more video-level video captions using video data, one or more second neural networks to generate one or more image-level captions using image data (e.g., obtained using the video data)”) “providing at least the set of respective captions as input to a summarization model to generate a corresponding annotation for the segment; and” (Li; [0094]; “The Wolf functionality 120 provides the final first image-level caption(s) 212A to a summarization process 214, which outputs the image-level video caption(s) 216 based at least in part on that input.”)
Li does not explicitly disclose “generating a data sample comprising at least data representing the video and data representing the corresponding annotation for each of the one or more segments of the video; and adding the data sample to a dataset.”. However, in an analogous field of endeavor, Zhou teaches “generating a data sample comprising at least data representing the video and data representing the corresponding annotation for each of the one or more segments of the video; and adding the data sample to a dataset.” (Zhou; [0050]; “…a method for generating a training dataset for a video generation model…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…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.”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Li with the teachings of Zhou to further generate a dataset from the video caption model output. One of ordinary skill in the art would be motivated to combine Li and Zhou to create a dataset of higher quality video dataset with corresponding dense annotations to more effectively train video processing models (Zhou, [0012]). Accordingly, the combination of Li and Zhou discloses the invention of Claim 1.
Consider Claim 2, the combination of Li and Zhou teaches “The method of claim 1, further comprising: generating, from the dataset, training data for training a video processing model; and training the video processing model on the training data.” (Zhou; [005]; “The captioned videos can be utilized for various applications, including use as labeled data in a training dataset for video generation models. Pre-trained video generation models, including both diffusion-based and language model-based models, can be fine-tuned using such training datasets.”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 2 and are incorporated herein by reference. Thus, the method recited in claim 2 is met by Li and Zhou.
Consider Claim 3, the combination of Li and Zhou teaches “The method of claim 2, wherein the video processing model is the video captioning model.” (Zhou; Abstract; “…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”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 3 and are incorporated herein by reference. Thus, the method recited in claim 3 is met by Li and Zhou.
Consider Claim 6, the combination of Li and Zhou teaches “The method of claim 1, wherein the one or more segments of the video are identified from the video by dividing the video into the one or more segments that each meet a threshold duration.” (Zhou; [0024]; “The video 302 can be segmented in various ways. In some implementations, the video 302 is segmented into segments 310 of a predetermined duration. For example, the video 302 can be split into thirty-second clips (with a possible last remaining clip of less than thirty seconds).”) The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 6 and are incorporated herein by reference. Thus, the method recited in claim 6 is met by Li and Zhou.
Consider Claim 7, the combination of Li and Zhou teaches “The method of claim 1, wherein providing one or more video frames of the segment as input to the video captioning model comprises providing the plurality of video frames of the video as input to the video captioning model.” (Li; [0078]; “For example, the Wolf functionality 120 may use one or more first neural networks to generate one or more video-level video captions using video data, one or more second neural networks to generate one or more image-level captions using image data (e.g., obtained using the video data)”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 7 and are incorporated herein by reference. Thus, the method recited in claim 7 is met by Li and Zhou.
Consider Claim 8, the combination of Li and Zhou teaches “The method of claim 1, wherein providing one or more video frames of the segment as input to the video captioning model comprises providing only a particular video frame of the consecutive video frames of the segment as input to the video captioning model.” (Li; [0104]; “The sampling process(es) 204 may obtain the frame(s) 206 (e.g., sequential images) from the video 202 (e.g., by sampling two key-frames every second). Next, the Wolf functionality 120 feeds a first frame or Image 1 into at least one of the image-level model(s) 210 to obtain Caption 1 (identified by reference numeral 250 in FIG. 2)….Next, a second frame or Image 2 is selected that has a temporal correlation with Image 1 (e.g., Images 1 and 2 are key frames in the video 202). Then, the Wolf functionality 120 feeds both Caption 1 and Image 2 into at least one of the image-level model(s) 210 to generate Caption 2.”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 8 and are incorporated herein by reference. Thus, the method recited in claim 8 is met by Li and Zhou.
Consider Claim 11, the combination of Li and Zhou teaches “The method of claim 1, wherein generating a data sample comprising at least data representing the video and data representing the corresponding annotation for each of the one or more segments of the video comprises generating a combined annotation by combining each corresponding annotation.” (Zhou; [0050]; “…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.”; Examiner notes Zhou teaches the generation of the consolidated caption from combining “the segment level captions”. (Zhou; [0027])). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 11 and are incorporated herein by reference. Thus, the method recited in claim 11 is met by Li and Zhou.
Consider Claim 16, the combination of Li and Zhou teaches “The method of claim 1, wherein data representing the video comprises the video frames of the video, or a sequence of video tokens representing the video frames of the video.” (Li; [0653]; “In at least one embodiment, a token is a portion of input data….In at least one embodiment, input data 4610 is one or more video frames.”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 16 and are incorporated herein by reference. Thus, the method recited in claim 16 is met by Li and Zhou.
Consider Claim 17, the combination of Li and Zhou teaches “The method of claim 1, wherein data representing, for each of the one or more segments of the video, the corresponding annotation, comprises text, or a sequence of text tokens representing the text.” (Zhou; [0031]; “The captioning process can be performed for each video in the subset of the plurality of videos determined at step 504….The captioning process can include, for each of the video in the subset, partitioning the video into a plurality of segments. The video can be partitioned in various ways. In some implementations, the video uniformly partitioned such that the segments have similar durations.” (emphasis added)). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 17 and are incorporated herein by reference. Thus, the method recited in claim 17 is met by Li and Zhou.
Consider Claim 18, the combination of Li and Zhou teaches “The method of claim 1, further comprising obtaining one or more respective text sequences corresponding to the video, and wherein providing at least the set of respective captions as input to a summarization model to generate a corresponding annotation for the segment comprises providing the set of respective captions and the one or more respective text sequences as input to the summarization model.” (Li; [0102]; “The Wolf functionality 120 may provide the image-level video caption(s) 216, the motion caption(s) 222, the video-level video caption(s) 232, and the annotated caption(s) 234, when present, to the LLM(s) 240, which may summarize this input information to produce one or more Wolf captions 242 as output.”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 18 and are incorporated herein by reference. Thus, the method recited in claim 18 is met by Li and Zhou.
Consider Claim 19, the combination of Li and Zhou teaches “The method of claim 18, wherein the one or more respective text sequences comprise any one or more of: a title of the video, a description of the video, text specifying one or more entities depicted in the video, or a transcript of speech represented in the video.” (Li; [0102]; “The Wolf functionality 120 may provide the image-level video caption(s) 216, the motion caption(s) 222, the video-level video caption(s) 232, and the annotated caption(s) 234, when present, to the LLM(s) 240, which may summarize this input information to produce one or more Wolf captions 242 as output.”). The proposed combination as well as the motivation for combining the Li and Zhou references presented in the rejection of claim 1, apply to claim 19 and are incorporated herein by reference. Thus, the method recited in claim 19 is met by Li and Zhou.
Claim 20 recites a system with elements corresponding to the steps recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Li and Zhou references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Li and Zhou disclose a processor, a memory (Li; [0704]; “In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein.”).
Claim 21 recites one or more non-transitory computer storage media storing a computer program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Zhou references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Li and Zhou discloses a non-transitory computer readable storage medium (Li; [0704]; “In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein.”).
.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 20260120487 with filing date of 10/11/2024) in view of Zhou et. al. (US 20260017960 with filed date of 07/15/2024) in further view of Croitoru et. al. (US 12620225 filed on 04/24/2023).
Consider Claim 4, the combination of Li and Zhou does not explicitly disclose “The method of claim 1, wherein the one or more segments of the video are identified from the video by: obtaining a transcript of speech represented in the video; and identifying the one or more segments based on one or more portions of text in the transcript, each corresponding to a segment of the video.”. However, in an analogous field of endeavor, Croitoru teaches “The method of claim 1, wherein the one or more segments of the video are identified from the video by: obtaining a transcript of speech represented in the video; and identifying the one or more segments based on one or more portions of text in the transcript, each corresponding to a segment of the video.” (Croitoru; Col 1, Lines 17-23; “Embodiments of the present disclosure provide a machine learning model utilizing natural language processing to analyze a user query and identify a video segment relating to the user query from a long video. The long video can be segmented based on transcripts generated from an audio track of the long video through automatic speech recognition using a trained neural network.”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Li and Zhou with the teachings of Croitoru to further segment a video based on the transcript of speech represented in the video. One of ordinary skill in the art would be motivated to combine Li, Zhou, and Croitoru to more easily “search and find particular segments of long videos that relate to subject matter of interest.” (Croitoru; Col. 1 Lines 12-13). Accordingly, the combination of Li, Zhou, and Croitoru discloses the invention of Claim 4.
Consider Claim 5, the combination of Li, Zhou, and Croitoru teaches “The method of claim 4, wherein identifying the one or more segments based on one or more portions of text in the transcript comprises identifying the one or more segments based on a respective timing of the one or more portions of text.” (Croitoru; Col. 11, Lines 4-8; “In various embodiments, after having the timings for the video segments, the corresponding transcript can be identified and used for that timespan, where the transcript portion for the corresponding timespan can be summarize and information extracted.”). The proposed combination as well as the motivation for combining the Li, Zhou, and Croitoru references presented in the rejection of claim 4, apply to claim 5 and are incorporated herein by reference. Thus, the method recited in claim 5 is met by Li, Zhou, and Croitoru.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 20260120487 with filing date of 10/11/2024) in view of Zhou et. al. (US 20260017960 with filed date of 07/15/2024) in further view of Petitpont et. al. (US 20240320952) in further view of Zhang (WO 2018107914).
Consider Claim 9, the combination of Li and Zhou does not explicitly disclose “The method of claim 1, further comprising determining one or more key video frames of the video by: generating, for each video frame of the video, a respective frame embedding; determining, for each consecutive pair of video frames, a respective difference between the respective frame embedding for a first video frame of the consecutive pair, and the respective frame embedding for a second video frame of the consecutive pair; determining, for each consecutive pair of video frames, whether the respective difference meets a threshold difference; and for each consecutive pair of video frames, in response to determining that the respective difference meets a threshold difference, determining that the second video frame of the consecutive pair is a key video frame.”.
However, in an analogous field of endeavor, Petitpont teaches “The method of claim 1, further comprising determining one or more key video frames of the video by: generating, for each video frame of the video, a respective frame embedding;” (Petitpont; [0004]; “In some aspects, the techniques described herein relate to a method, further including: inputting, by the at least one processor, a plurality of video frames into a video frame encoder to output a plurality of video frame vectors; generating, by the at least one processor, an aggregate video frame vector for the plurality of video frame vectors”) “determining, for each consecutive pair of video frames, a respective difference between the respective frame embedding for a first video frame of the consecutive pair, and the respective frame embedding for a second video frame of the consecutive pair;” (Petitpont; [0004]; “…determining, by the at least one processor, a shot similarity value between the aggregate video frame vector and at least one adjacent aggregate video frame vector of an adjacent plurality of video frames in the sequence”) “determining, for each consecutive pair of video frames, whether the respective difference meets a threshold difference; and” (Petitpont; [0004]; “…determining, by the at least one processor, a scene including the plurality of video frames and the adjacent plurality of video frames based at least in part on the shot similarity value exceeding a threshold value.”) “(Petitpont; [0002]) more efficiently. Accordingly, the combination of Li, Zhou, and Petitpont discloses the above described limitations of Claim 9.
The combination of Li, Zhou, and Petitpont does not explicitly disclose “for each consecutive pair of video frames, in response to determining that the respective difference meets a threshold difference, determining that the second video frame of the consecutive pair is a key video frame.”. However, in an analogous field of endeavor, Zhang teaches “for each consecutive pair of video frames, in response to determining that the respective difference meets a threshold difference, determining that the second video frame of the consecutive pair is a key video frame.” (Zhang; Pg. 2; “Extracting a video frame in the to-be-processed video as a key frame, which is referred to as a current key frame, and extracting a next key frame by sequentially: sequentially selecting a video frame after the current key frame in the to-be-processed video and the current key Comparing the frame to obtain a second similarity, if the second similarity between the second video frame and the current key frame satisfies a third preset condition and is closest to the current key frame, extracting the second video frame And using the second video frame as the current key frame…”).
Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Li, Zhou, and Petitpont with the teachings of Zhang to further select the second frame of the pair as the key frame. One of ordinary skill in the art would be motivated to combine Li, Zhou, Petitpont, and Zhang to allow for similarity comparison between the next consecutive frame to the second video frame to further group similar frames together for more efficient video processing (Zhang; Pg. 2, Para. 9). Accordingly, the combination of Li, Zhou, Petitpont, and Zhang discloses the invention of Claim 9.
Consider claim 10, the combination of Li, Zhou, Petitpont, and Zhang teaches “The method of claim 9, wherein providing one or more video frames of the segment as input to the video captioning model comprises: identifying one or more of the key video frames as belonging to the segment; and” (Li; [0091]; “The Wolf 200 obtains image data (e.g., one or more frames 206) associated with the video 202. For example, the Wolf 200 may use at least one sampling process 204 to sample the frame(s) 206 from the video 202. By way of a non-limiting example, the Wolf 200 may use the sampling process(es) 204 to sample one or more key frames from the video 202.”) “providing one or more of the identified key video frames as input to the video captioning model.” (Li; [0104]; “The sampling process(es) 204 may obtain the frame(s) 206 (e.g., sequential images) from the video 202 (e.g., by sampling two key-frames every second). Next, the Wolf functionality 120 feeds a first frame or Image 1 into at least one of the image-level model(s) 210 to obtain Caption 1 (identified by reference numeral 250 in FIG. 2). One or more of the image-level model(s) 210 may generate, as one or more of the image-level caption(s) 212, detailed scene-level information and/or the object location(s) 218. The image-level model(s) 210 may receive an image (or frame) as input and output an image-level caption (e.g., the Caption 1) that includes scene-level information and/or one or more object locations. Next, a second frame or Image 2 is selected that has a temporal correlation with Image 1 (e.g., Images 1 and 2 are key frames in the video 202).”). The proposed combination as well as the motivation for combining the Li, Zhou, Petitpont, and Zhang references presented in the rejection of claim 9, apply to claim 10 and are incorporated herein by reference. Thus, the method recited in claim 10 is met by Li, Zhou, Petitpont, and Zhang.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 20260120487 with filing date of 10/11/2024) in view of Zhou et. al. (US 20260017960 with filed date of 07/15/2024) in further view of Xie et. al. (CN 115840835 published on 03/24/2023).
Consider Claim 12, the combination of Li and Zhou does not explicitly disclose “The method of claim 11, wherein the combined annotation comprises one or more indices, each identifying a corresponding segment of the video.”. However, in an analogous field of endeavor, Xie teaches “The method of claim 11, wherein the combined annotation comprises one or more indices, each identifying a corresponding segment of the video.” (Xie; “Firstly, the edge server for each video segment…generating a first address index of the video segment; then constructing the corresponding relation between the abstract information of the video segment and the first address index of the video segment, so as to obtain the corresponding relation between the abstract information of the video segment and the first address index of the video segment.”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Li and Zhou with the teachings of Xie to further to generate indices to each video segment. One of ordinary skill in the art would be motivated to combine Li, Zhou, and Xie to more efficiently recall a video segment and its corresponding annotation. Accordingly, the combination of Li, Zhou, and Xie discloses the invention of Claim 12.
Consider Claim 13, the combination of Li, Zhou, and Xie teaches “The method of claim 12, wherein generating a data sample comprising at least data representing the video and data representing the corresponding annotation for each of the one or more segments of the video” (Zhou; [0050]; “…a method for generating a training dataset for a video generation model…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…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.”) “further comprises including, at each of the one or more indices,” (Xie; “…generating a first address index of the video segment; constructing the corresponding relation between the abstract information of the video segment and the first address index of the video segment”) “data representing the corresponding segment of the video identified by the index.” (Xie; “…the abstract information can be text summary of the corresponding video segment, at this time, the edge server can be based on the existing video abstract generating technology, for example, using generative adversarial network Generative Adversarial Networks, GAN), end to end target detection model to generate video caption (VideoCapable), automatically generating text summary of each video segment.”). The proposed combination as well as the motivation for combining the Li, Zhou, and Xie references presented in the rejection of claim 12, apply to claim 13 and are incorporated herein by reference. Thus, the method recited in claim 13 is met by Li, Zhou, and Xie.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Li et. al. (US 20260120487 with filing date of 10/11/2024) in view of Zhou et. al. (US 20260017960 with filed date of 07/15/2024) in further view of Xie et. al. (CN 115840835 published on 03/24/2023) in further view of Polumbus et. al. (US 20110069230).
Consider Claim 14, the combination of Li, Zhou, and Xie teaches “The method of claim 12, wherein generating a data sample comprising at least data representing the video and data representing the corresponding annotation for each of the one or more segments of the video” (Zhou; [0050]; “…a method for generating a training dataset for a video generation model…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…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.”) “further comprises including, at each of the one or more indices, data representing a corresponding audio signal of the corresponding segment of the video identified by the index.” (Polumbus; [0048]; “The language model is used to process the output of the acoustic model to put the word sounds taken from the audio into the most likely string of words that would have been built from those logical word sounds. An indexing engine processes the audio, using the output of the acoustic model engine and the language model in order to produce time-indexed text.”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Li, Zhou, and Xie with the teachings of Polumbus to further store the corresponding audio signal to the index of the corresponding video segment. One of ordinary skill in the art would be motivated to combine Li, Zhou, Xie, and Polumbus to “…enable search engines like Google.RTM. to index not merely the title of a video, but the entirety of what was said during the video as well as any associated metatags relating to contents of the video. Further, because the entire media file is indexed, a search can request a particular scene or occurrence within the event recorded by the media file, and the exact moment within the media relevant to the search can be accessed and played for the requester.” (Polumbus; [0008]). Accordingly, the combination of Li, Zhou, Xie, and Polumbus discloses the invention of Claim 14.
Consider Claim 15, the combination of Li, Zhou, Xie, and Polumbus “The method of claim 14, wherein data representing the corresponding audio signal comprises one or more audio samples of the corresponding audio signal, or a sequence of audio tokens representing the audio samples of the corresponding audio signal.” (Polumbus; [0044]; “The acoustic processing translates the audio signal into a sequence of logical word sounds (e.g., similar to phonemes). Acoustic models based on sample recordings may also be used to help the acoustic model engine better process the audio into the most likely word sounds.”). The proposed combination as well as the motivation for combining the Li, Zhou, Xie, and Polumbus references presented in the rejection of claim 14, apply to claim 15 and are incorporated herein by reference. Thus, the method recited in claim 15 is met by Li, Zhou, Xie, and Polumbus.
Conclusion
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/ANNIE H PHAM/Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
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