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 .
Response to Arguments
Applicant’s arguments, see Remarks pg. 8, filed 3/3/2026, with respect to the status of the claims are hereby acknowledged.
The applicant’s Remarks, see pg. 8, filed 3/3/2026, with respect to the Double Patenting rejection have been fully considered. The applicant’s arguments regarding holding the double patenting rejections in abeyance are not persuasive. MPEP 804 states: As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated. Therefore, an application must not be allowed unless the required compliant terminal disclaimer(s) is/are filed and/or the withdrawal of the nonstatutory double patenting rejection(s) is made of record by the examiner. See MPEP § 804.02, subsection VI., for filing terminal disclaimers required to overcome nonstatutory double patenting rejections in applications filed on or after June 8, 1995. Therefore, the rejection is maintained.
Applicant’s arguments, see Remarks pg. 8, filed 3/3/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered. The examiner acknowledges that applicant’s arguments are directed to newly amended limitations not previously presented. Therefore, the examiner will set forth a new grounds of rejection in order to address the new limitations.
With respect to applicant’s arguments, the applicant has amended the claims to modify the term “boundary” with “temporal” to recite temporal boundary. In particular, the applicant argues, inter alia, the following:
Among the recitations of claim 1, as amended herein, that are neither taught nor suggested by the proposed combination of Mao and Zhang are: merging the first set of temporal boundaries and a second set of temporal boundaries to generate a target set of temporal boundaries. The Office Action cites to Mao to provide the "merging" claim limitation, alleging that this limitation is disclosed in paragraph [0036]-[0037] of Mao. However, as seen in the corresponding Figure 3 and the cited paragraphs, Mao appears to identify spatial boundaries within a video. Mao is silent regarding temporal boundaries in media. Zhang is likewise silent regarding temporal boundaries and their merging.
The "merging" step recited in the claim cited above is not an obvious extension of Mao or Zhang (or any of the cited references). Paragraphs [0027]-[0028] describe the merging process of the present application. The specification describes that merging may be performed "based on the selected boundaries (e.g., the boundaries selected to be utilized as the target boundaries) being identified as having higher accuracy levels than unselected boundaries (e.g., boundaries not utilized as the target boundaries)." Paragraph [0028] further describes "identifying priorities associated with different types of boundaries." These disclosures demonstrate that merging is a non-trivial step that is not obvious to add to the cited references. Since "merging" is not described in Mao or Zhang, claim 1 is allowable over the applied references.
The other references do not cure these defects of Mao and Zhang. Withdrawal of the 35 U.S.C. § 103 rejection of claim 1 is respectfully requested.
In response to applicant’s argument, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Additionally, on the issue of obviousness, the Supreme Court stated the analysis of a rejection on obviousness grounds need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 418, 82 USPQ2d 1385 (2007). The obvious analysis cannot be confined by a formalistic conception of the words teaching, suggestion, and motivation. Id. at 419. Further, the Court stated that common sense teaches, however, that familiar items may have obvious uses beyond their primary purposes, and in many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle. Id. at 420.
First, with respect to the modification of the term boundary to “temporal boundary,” the examiner notes that the broadest reasonable interpretation of the term “temporal” includes the dictionary definition “of or relating to time as opposed to eternity” and “of or relating to time as distinguished from space; of or relating to the sequence of time or to a particular time.” In view of the broadest reasonable interpretation of the term temporal, the prior art of record to Mao and Zhang render the limitation obvious. The prior art identifies boundaries and/or breakpoints with respect to a timeline of the presentation of video content frames as opposed to spatial content displayed in the video scenes. The applicant argues that the prior art “appears” to identify “spatial boundaries,” however, the prior art discloses the teachings in terms of a timeline and does not use the term “spatial” in their disclosure. All things considered, the examiner will set forth a new grounds of rejection.
Double Patenting Rejection
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Independent claims 1 and 11 dependent claims 2-10 and 12-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Wu et al., US 12149709 B2 Although the claims at issue are not identical, they are not patentably distinct from each other because the claim so the current application are broader in scope and read on the claimed limitations of U.S. Patent No. US 12149709 B2.
Current Application 18/915,204 U.S. Patent No. US 12149709 B2
1. A method comprising: determining media content that includes at least one of video content or audio content; determining a first set of temporal boundaries indicating one or more first locations associated with a first portion of the media content; determining a second set of temporal boundaries indicating one or more second locations associated with a second portion of the media content; merging the first set of temporal boundaries and the second set of temporal boundaries to generate a target set of boundaries; utilizing a boundary generation process on the media content to determine a computer vision/machine learning (CV/ML) temporal boundary report that includes at least one of the first set of temporal boundaries or the second set of boundaries and a CV/ML temporal boundary report log that is based at least in part on the media content; and encoding, based at least in part on the target set of temporal boundaries and the CV/ML temporal boundary report, the media content as encoded media content.
2. The method as recited in claim 1, wherein the first set of temporal boundaries are associated with at least one of a CV/ML device or an instantaneous decoder refresh (IDR) frames placing encoder algorithm.
3. The method as recited in claim 2, wherein the CV/ML temporal boundary report log includes information associated with automated generation of boundaries by the CV/ML device, the automated generation being utilized by the CV/ML device to infer CV/ML generated temporal boundaries utilizing an encode of the media content, the media content being analyzed by the encode for instantaneous decoder refresh (IDR) frame placement, the IDR frame placement being utilized to identify IDR frames and non-IDR frames associated with a third portion of the media content, individual ones of the IDR frames being followed by at least one of the non-IDR frames.
4. The method as recited in claim 1, further comprising: determining a target temporal boundary report that includes the target set of temporal boundaries; and determining a default temporal boundary report that includes the second set of temporal boundaries and a default boundary report log associated with an encode of the media content.
5. The method as recited in claim 4, further comprising: utilizing a second temporal boundary generation process on the media content to generate the default temporal boundary report; and utilizing a third temporal boundary generation process on the media content to generate the target temporal boundary report, the target temporal boundary report being a combination of the CV/ML temporal boundary report and the default boundary report.
6. The method as recited in claim 5, wherein the target temporal boundary report is identified as having a higher level of accuracy than at least one of the CV/ML boundary report or the default temporal boundary report.
7. The method as recited in claim 4, wherein encoding the media content is based at least in part on the target temporal boundary report and the default boundary report.
8. The method as recited in claim 1, further comprising packaging the encoded media content as packaged media content.
9. The method as recited in claim 8, further comprising: generating a manifest link associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link based at least in part on user input received via the destination device; determining that the client request is to stream the packaged media content; and causing streaming of the packaged media content via the destination device.
10. The method of claim 6, wherein encoding the media content comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded media content.
11. A system comprising: one or more processors; memory; and one or more computer-executable instructions stored in the memory and executable by the one or more processors to perform operations comprising: determining media content that includes video content and audio content; determining a first set of temporal boundaries indicating one or more first locations associated with a first portion of the media content; determining a second set of temporal boundaries indicating one or more second locations associated with a second portion of the media content; merging the first set of temporal boundaries and the second set of temporal boundaries to generate a target set of temporal boundaries; utilizing a temporal boundary generation process on the media content to determine a computer vision/machine learning (CV/ML) temporal boundary report; and encoding, based at least in part on the target set of temporal boundaries and the CV/ML temporal boundary report, the media content as encoded media content.
12. The system as recited in claim 11, wherein the CV/ML temporal boundary report includes at least one of the first set of temporal boundaries or the second set of temporal boundaries and a CV/ML temporal boundary report log that is based at least in part on the media content.
13. The system as recited in claim 12, wherein the operations further comprise: determining a target temporal boundary report that includes the target set of temporal boundaries; and determining a default boundary report that includes the second set of temporal boundaries and a default temporal boundary report log associated with an encode of the media content.
14. The system as recited in claim 13, wherein the operations further comprise: utilizing a second temporal boundary generation process on the media content to generate the default temporal boundary report; and utilizing a third temporal boundary generation process on the media content to generate the target temporal boundary report, the target temporal boundary report being a combination of the CV/ML temporal boundary report and the default temporal boundary report.
15. The system as recited in claim 13, wherein the target temporal boundary report is identified as having a higher level of accuracy than at least one of the CV/ML temporal boundary report or the default temporal boundary report log.
16. The system as recited in claim 13, wherein encoding the media content is based at least in part on the target temporal boundary report and the default temporal boundary report.
17. One or more non-transitory computer-readable media storing one or more computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining media content that includes at least one of video content or audio content; determining a first set of temporal boundaries indicating one or more first locations associated with a first portion of the media content; determining a second set of temporal boundaries indicating one or more second locations associated with a second portion of the media content; merging the first set of temporal boundaries and the second set of temporal boundaries to generate a target set of temporal boundaries; utilizing a temporal boundary generation process on the media content to determine a computer vision/machine learning (CV/ML) temporal boundary report; and encoding, based at least in part on the target set of temporal boundaries and the CV/ML temporal boundary report, the media content as encoded media content.
18. The one or more non-transitory computer-readable media as recited in claim 17, wherein the operations further comprise packaging the encoded media content as packaged media content.
19. The one or more non-transitory computer-readable media as recited in claim 18, wherein the operations further comprise: generating a manifest link associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link based at least in part on user input received via the destination device; determining that the client request is to stream the packaged media content; and causing streaming of the packaged media content via the destination device.
20. The one or more non-transitory computer-readable media as recited in claim 17, wherein encoding the media content comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded media content.
1. A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium having computer-executable instructions stored thereon which, when executed on the at least one processor, cause the system to perform operations comprising: receiving, from a third-party device associated with a third-party, a request; identifying, via the request, a first set of boundaries associated with first locations of first frames in media content; identifying a second set of boundaries and a third set of boundaries associated with the media content, the second set of boundaries being associated with second locations of second frames in the media content and being generated utilizing a computer vision/machine learning (CV/ML) device, the third set of boundaries being associated with third locations of third frames in the media content, the third locations being default locations generated utilizing an encoder algorithm for placing instantaneous decoder refresh (IDR) frames; merging a combination of boundaries, including the first set of boundaries, the second set of boundaries, and the third set of boundaries, to generate a target set of boundaries, the target set of boundaries being associated with target locations of target frames in the media content; performing, based on a target boundary report, a CV/ML boundary report, and a default boundary report, an encoding process with the target set of boundaries to encode video content and audio content of the media content as encoded media content, the target boundary report including the target set of boundaries and having a higher level of accuracy than at least one of the CV/ML boundary report or the default boundary report, the CV/ML boundary report including the second set of boundaries, the default boundary report including the third set of boundaries; and packaging the encoded media content as packaged media content, with segments of the audio content being aligned with the target set of boundaries, such that the video content and the audio content are synchronized during playback of the encoded media content.
2. The system of claim 1, wherein merging the first set of boundaries and the second set of boundaries further comprises: performing a first boundary generation process utilized to generate the CV/ML boundary report, the CV/ML boundary report including a CV/ML boundary report log generated based on the media content, the CV/ML boundary report log including information associated with automated generation of boundaries by the CV/ML device, the automated generation being utilized by the CV/ML device to infer CV/ML generated boundaries utilizing a 480p encode of the media content, the media content being analyzed by the 480p encode for IDR frame placement, the IDR frame placement being utilized to identify IDR frames and non-IDR frames in the media content, individual ones of the IDR frames being followed by at least one of the non-IDR frames; performing a second boundary generation process utilized to generate the default boundary report, the default boundary report including a default boundary report log associated with the 480p encode of the media content; and performing a third boundary generation process on the media content utilized to generate the target boundary report, the target boundary report being a combination of the CV/ML boundary report and the default boundary report, the target boundary report being identified as having a higher level of accuracy than at least one of the CV/ML boundary report or the default boundary report log, wherein performing the encoding process further comprises encoding the media content based on the target boundary report, the default boundary report, and the CV/ML boundary report.
3. The system of claim 1, the first set of boundaries including at least one first cue-point, the operations further comprising: receiving, from the third-party device, at least one second cue-point via a selection of a manifest link associated with the encoded video content, wherein: individual ones of at least one first level of precision are associated with first alignment between at least one corresponding first cue-point and at least one corresponding first segment of the media content; individual ones of at least one second level of precision are associated with second alignment between at least one corresponding second cue-point and at least one corresponding second segment of the encoded media content; and the individual ones of the at least one second level of precision are equal to or greater than the individual ones of the at least one first level of precision.
4. The system of claim 1, the operations further comprising: generating a manifest link to obtain a manifest associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link via user input received by the destination device; determining that the client request is to stream the packaged media content; and enabling streaming of the packaged media content via the destination device.
5. The system of claim 1, wherein performing the encoding process further comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded media content.
6. A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium having computer-executable instructions stored thereon which, when executed by the at least one processor, cause the system to perform operations comprising: identifying media content, the media content including video content and audio content; identifying a first set of boundaries, the first set of boundaries being first locations associated with a first portion of the media content associated with a media content identifier, the first set of boundaries being associated with at least one of a computer vision/machine learning (CV/ML) device or an instantaneous decoder refresh (IDR) frames placing encoder algorithm; merging the first set of boundaries and a second set of boundaries to generate a target set of boundaries, the second set of boundaries being second locations associated with a second portion of the media content; performing, based at least in part on a target boundary report, and at least one of a CV/ML boundary report or a default boundary report, an encoding process to encode the media content as encoded media content, the encoding process utilizing the video content, the audio content, and the target set of boundaries, the target boundary report including the target set of boundaries, and at least one of the CV/ML boundary report including the first set of boundaries or the default boundary report including the second set of boundaries; and packaging the encoded media content as packaged media content.
7. The system of claim 6, the operations further comprising: receiving a boundary request from a third-party device; and identifying, via the boundary request, a boundary request file and the media content identifier, the boundary request file including an encode value indicating a third-party request for tailorable encode utilized to generate the encoded media content, the boundary request file including automated media content insertion location information instructions, the automated media content insertion location information instructions being utilizable to automate processing of the third-party request by a CV/ML device over a 480p encode of the media content with a vertical resolution of 480 pixels.
8. The system of claim 6, wherein merging the first set of boundaries and the second set of boundaries further comprises: performing a first boundary generation process utilized to generate the CV/ML boundary report including the second set of boundaries, the CV/ML boundary report including a CV/ML boundary report log generated based at least in part on the media content, the CV/ML boundary report log including information associated with automated generation of boundaries by the CV/ML device, the automated generation being utilized by the CV/ML device to infer CV/ML generated boundaries utilizing a 480p encode of the media content with a vertical resolution of 480 pixels, the media content being analyzed by the 480p encode for instantaneous decoder refresh (IDR) frame placement, the IDR frame placement being utilized to identify IDR frames and non-IDR frames associated with a third portion of the media content, individual ones of the IDR frames being followed by at least one of the non-IDR frames; performing a second boundary generation process utilized to generate the default boundary report, the default boundary report including a default boundary report log associated with the 480p encode of the media content; and performing a third boundary generation process on the media content utilized to generate the target boundary report, the target boundary report being a combination of the CV/ML boundary report and the default boundary report, the target boundary report being identified as having a higher level of accuracy than at least one of the CV/ML boundary report or the default report log, wherein performing the encoding process further comprises encoding the media content based at least in part on the target boundary report, the default boundary report, and the CV/ML boundary report.
9. The system of claim 6, the operations further comprising: generating a manifest link associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link via user input received by the destination device; determining that the client request is to stream the packaged media content; and enabling streaming of the packaged media content via the destination device.
10. The system of claim 6, wherein performing the encoding process further comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded media content.
11. The system of claim 6, the operations further comprising: generating the CV/ML boundary report and the default boundary report, the CV/ML boundary report and the default boundary report being generated utilizing the video content; generating the target boundary report as a first target boundary report, the first target boundary report being generated utilizing the CV/ML boundary report and the default boundary report; transmitting the first target boundary report to a client device; receiving feedback information from the client device; and generating a second target boundary report based at least in part on the feedback information.
12. The system of claim 6, the operations further comprising: receiving, from a client device, a feedback mechanism identifier; generating the target boundary report; and transmitting the target boundary report to the client device based at least in part on the feedback mechanism identifier, wherein encoding the video content further comprises encoding the video content based at least in part on feedback information received from the client device.
13. The system of claim 6, the operations further comprising: receiving, from a client device, a feedforward mechanism identifier; generating the target boundary report; and refraining from transmitting the target boundary report to the client device based at least in part on the feedforward mechanism identifier, wherein encoding the video content further comprises encoding the video content based at least in part on the target boundary report.
14. A method comprising: identifying video content and audio content associated with the video content; identifying a boundary request received from a third-party device; identifying a computer vision/machine learning (CV/ML) set of boundaries and a default set of boundaries associated with the video content, the CV/ML set of boundaries being first locations associated with a first portion of the video content, the default set of boundaries being second locations associated with a second portion of the video content, the default set of boundaries being associated with a predetermined resolution of the video content; generating a target set of boundaries by merging a combination of boundaries, including the CV/ML set of boundaries and the default set of boundaries, the target set of boundaries being associated with target locations of target frames in the video content and including selected boundaries from the combination of boundaries having higher accuracy levels than remaining unselected boundaries in the combination of boundaries; performing, based at least in part on a target boundary report, and at least one of a CV/ML boundary report or a default boundary report, an encoding process utilizing the target set of boundaries to encode the video content and the audio content as encoded media content, the target boundary report including the target set of boundaries, and at least one of the CV/ML boundary report including the CV/ML set of boundaries or the default boundary report including the default set of boundaries; and packaging the encoded media content as packaged media content.
15. The method of claim 14, further comprising: identifying a third-party set of boundaries received from a third-party-device, wherein generating the target set of boundaries further comprises: generating the target set of boundaries from a second combination of the third-party set of boundaries, the CV/ML set of boundaries, and the default set of boundaries, the target set of boundaries being a second consolidation of the selected boundaries from among the combination, based on the selected boundaries being identified as having higher accuracy levels than remaining unselected boundaries in the combination.
16. The method of claim 14, further comprising at least one of: performing a first boundary generation process utilized to generate the CV/ML boundary report; performing a second boundary generation process utilized to generate the default boundary report; and performing a third boundary generation process utilized to generate the target boundary report, wherein the encoding process is based at least in part on the CV/ML boundary report, the default boundary report, and the target boundary report.
17. The method of claim 14, further comprising: generating a manifest link associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link via user input received by the destination device; determining the client request is to stream the packaged media content; and enabling streaming of the packaged media content via the destination device.
18. The method of claim 14, wherein performing the encoding process further comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded video content.
19. The method of claim 14, further comprising: generating the CV/ML boundary report and the default boundary report, the CV/ML boundary report and the default boundary report being generated utilizing the video content; generating the target boundary report as a first target boundary report, the first target boundary report being generated utilizing the CV/ML boundary report and the default boundary report; transmitting the first target boundary report to a client device; receiving feedback information from the client device; and generating a second target boundary report based at least in part on the feedback information.
20. The method of claim 14, further comprising: receiving, from a client device, a feedback mechanism identifier; generating the target boundary report; and transmitting the target boundary report to the client device based at least in part on the feedback mechanism identifier, wherein encoding the video content further comprises encoding the video content based at least in part on feedback information received from the client device.
The independent claims 1 and 11 of the current application are directed, similarly to representative independent claim reciting, “a method comprising: determining media content that includes at least one of video content or audio content; determining a first set of temporal boundaries indicating one or more first locations associated with a first portion of the media content; determining a second set of temporal boundaries indicating one or more second locations associated with a second portion of the media content; merging the first set of temporal boundaries and the second set of temporal boundaries to generate a target set of boundaries; utilizing a boundary generation process on the media content to determine a computer vision/machine learning (CV/ML) temporal boundary report that includes at least one of the first set of temporal boundaries or the second set of boundaries and a CV/ML temporal boundary report log that is based at least in part on the media content; and encoding, based at least in part on the target set of temporal boundaries and the CV/ML temporal boundary report, the media content as encoded media content” and claim 1 of U.S. Patent No. US 12149709 B2 is directed to “a system comprising: at least one processor; and at least one non-transitory computer-readable storage medium having computer-executable instructions stored thereon which, when executed on the at least one processor, cause the system to perform operations comprising: receiving, from a third-party device associated with a third-party, a request; identifying, via the request, a first set of boundaries associated with first locations of first frames in media content; identifying a second set of boundaries and a third set of boundaries associated with the media content, the second set of boundaries being associated with second locations of second frames in the media content and being generated utilizing a computer vision/machine learning (CV/ML) device, the third set of boundaries being associated with third locations of third frames in the media content, the third locations being default locations generated utilizing an encoder algorithm for placing instantaneous decoder refresh (IDR) frames; merging a combination of boundaries, including the first set of boundaries, the second set of boundaries, and the third set of boundaries, to generate a target set of boundaries, the target set of boundaries being associated with target locations of target frames in the media content; performing, based on a target boundary report, a CV/ML boundary report, and a default boundary report, an encoding process with the target set of boundaries to encode video content and audio content of the media content as encoded media content, the target boundary report including the target set of boundaries and having a higher level of accuracy than at least one of the CV/ML boundary report or the default boundary report, the CV/ML boundary report including the second set of boundaries, the default boundary report including the third set of boundaries; and packaging the encoded media content as packaged media content, with segments of the audio content being aligned with the target set of boundaries, such that the video content and the audio content are synchronized during playback of the encoded media content.” In further reviewing the dependent claims 2-5 and 7-13 of U.S. Patent No. US 12149709 B2, all the elements of the current independent claims read on the claims of U.S. Patent No. US 12149709 B2. Therefore, the mere broadening of the limitations of U.S. Patent No. US 12149709 B2 amounts to the mere rearranging of elements to broaden the scope of the parent application.
With respect to the dependent claims 2-10 and 12-20 are further rejected as being dependent on a rejected independent claims.
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:
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.
Claim(s) 1-3, 8, 11-12, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mao; Weidong et al. US 20200288149 A1 (hereafter Mao) and in further view of Zhang; Wenbo et al. US 20210390130 A1 (hereafter Zhang).
Regarding clam 1, “a method comprising: determining media content that includes at least one of video content or audio content; determining a first set of temporal boundaries indicating one or more first locations associated with a first portion of the media content; determining a second set of temporal boundaries indicating one or more second locations associated with a second portion of the media content; merging the first set of temporal boundaries and the second set of temporal boundaries to generate a target set of boundaries; utilizing a boundary generation process on the media content to determine a computer vision/machine learning (CV/ML) temporal boundary report that includes at least one of the first set of temporal boundaries or the second set of boundaries and a CV/ML temporal boundary report log that is based at least in part on the media content; and encoding, based at least in part on the target set of temporal boundaries and the CV/ML temporal boundary report, the media content as encoded media content” Mao teaches all the elements of the claim except a report as claimed wherein para [0037-0045] identifying boundaries of using metadata included with the video wherein number of encoders available to encode scenes and which encoding parameters may be used by specific encoders may be determined; Target resolutions and/or bit rates for subsequent transmission of scenes may be determined; the computing device may determine that each scene should be encoded; See para 33-36 regarding merging boundaries. Mao ([0044] discloses identifying visual elements using a machine learning algorithm. Additionally, with respect to the modification of the term boundary to “temporal boundary,” the examiner notes that the broadest reasonable interpretation of the term “temporal” includes the dictionary definition “of or relating to time as opposed to eternity” and “of or relating to time as distinguished from space; of or relating to the sequence of time or to a particular time.” In view of the broadest reasonable interpretation of the term temporal, the prior art of record to Mao and Zhang render the limitation obvious. The prior art identifies boundaries and/or breakpoints with respect to a timeline of the presentation of video content frames as opposed to spatial content displayed in the video scenes.
Regarding the deficiency of Mao, Zhang teaches generating a set of candidate breakpoints in a media item, and using a machine learning model to score the candidate breakpoints and iteratively select subsets of the candidate breakpoints to be a final set of breakpoints, which are stored as part of a bitstream. See para 11, 22, 29 regarding a list and figure 2. Zhang discloses a target set of boundaries in the form of a final set of breakpoints element 240 and 0-240 in figure 2.
Therefore, it would have been obvious to one having ordinary skill in the art before the time of the applicant’s effective filing date to modify Mao identifying boundary points, the metadata-defined boundaries, and the I-frame-identified boundaries using machine learning by further incorporating known elements of Zhang’s invention for iteratively selecting subsets of target boundaries in order to improve the accuracy of content boundary detection through use of an iterative process that leverages machine learning.
Regarding claim 2, “wherein the first set of temporal boundaries are associated with at least one of a CV/ML device or an instantaneous decoder refresh (IDR) frames placing encoder algorithm” is further rejected on obviousness grounds as discussed in the rejection of claim 1 wherein Mao para [0040] in step 402 of figure 4, determine scene boundaries of media content 300 by associating them with I frame positions.
Regarding claim 3, “wherein the CV/ML temporal boundary report log includes information associated with automated generation of boundaries by the CV/ML device, the automated generation being utilized by the CV/ML device to infer CV/ML generated temporal boundaries utilizing an encode of the media content, the media content being analyzed by the encode for instantaneous decoder refresh (IDR) frame placement, the IDR frame placement being utilized to identify IDR frames and non-IDR frames associated with a third portion of the media content, individual ones of the IDR frames being followed by at least one of the non-IDR frames” is further rejected on obviousness grounds as discussed in the rejection of claims 1-2 wherein Mao teaches all the elements of the claim except a report as claimed wherein para [0037-0045] identifying boundaries of using metadata included with the video wherein number of encoders available to encode scenes and which encoding parameters may be used by specific encoders may be determined; Target resolutions and/or bit rates for subsequent transmission of scenes may be determined; the computing device may determine that each scene should be encoded; See para 33-36 regarding merging boundaries. Mao ([0044] discloses identifying visual elements using a machine learning algorithm. See also Zhang teaches generating a set of candidate breakpoints in a media item, and using a machine learning model to score the candidate breakpoints and iteratively select subsets of the candidate breakpoints to be a final set of breakpoints, which are stored as part of a bitstream. See para 11, 22, 29 regarding a list and figure 2. Zhang discloses a target set of boundaries in the form of a final set of breakpoints element 240 and 0-240 in figure 2.
Regarding claim 8, “further comprising packaging the encoded media content as packaged media content” is further rejected on obviousness grounds as discussed in the rejection of claims 1-3 wherein Mao para [0030] discloses encoding visual elements with their associated audio content, and differentiating elements having audio content associated therewith (e.g. a newscaster), from silent elements (e.g. a stockticker).)
Claim(s) 4-7, 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mao; Weidong et al. US 20200288149 A1 (hereafter Mao) and in further view of Zhang; Wenbo et al. US 20210390130 A1 (hereafter Zhang) and in further view of Effinger; Charles et al. US 10841666 B1 (hereafter Effinger) and in further view of Oyman; Ozgur US 20160165185 A1 (hereafter Oyman).
Regarding claim 4, “further comprising: determining a target temporal boundary report that includes the target set of temporal boundaries; and determining a default temporal boundary report that includes the second set of temporal boundaries and a default boundary report log associated with an encode of the media content” is further rejected on obviousness grounds as discussed in the rejection of claims 1-3 wherein Mao do not use the terms target and default with respect to the reports but Zhang teaches generating a set of candidate breakpoints in a media item, and using a machine learning model to score the candidate breakpoints and iteratively select subsets of the candidate breakpoints to be a final set of breakpoints, which are stored as part of a bitstream. See para 11, 22, 29 regarding a list and figure 2. Zhang discloses a target set of boundaries in the form of a final set of breakpoints element 240 and 0-240 in figure 2. See also Zhang (pars. [0022], [0023]) disclosing methods to score the result provided by ML-based boundary determination algorithms.
In an analogous art, Effinger teaches (col. 5, lines 52-64; col. 7, lines 40-54) (pars. [0022], [0023]), disclosing methods to score the result provided by ML-based boundary determination algorithms.
In an analogous art, Oyman figure 3 discloses “SOP offer indicating: arbitrary and/or predefined ROI signaling support; actual transmitted ROI signaling; a description of each offered predefined ROI.”
Therefore, it would have been obvious to one having ordinary skill in the art before the time of the applicant’s effective filing date to modify Mao and Zhang for identifying boundary points, the metadata-defined boundaries, and the I-frame-identified boundaries using machine learning and for iteratively selecting subsets of target boundaries in order to improve the accuracy of content boundary detection through use of an iterative process that leverages machine learning and/or analyze the media content (e.g., using machine learning and/or one or more graphics processing algorithms) to determine scene boundaries of the media content wherein a “default report” is generated from metadata by further incorporating known elements of Effinger’s and Oyman for the sender/client feedback information for ROI definition disclosed in Oyman into the scene classification and encoding system of Mao in view of Zhang, in order to improve efficiency of bandwidth usage by transmitting only those portions (ROI) of a video frame that have been requested by the client.
Regarding claim 5, “further comprising: utilizing a second temporal boundary generation process on the media content to generate the default temporal boundary report; and utilizing a third temporal boundary generation process on the media content to generate the target temporal boundary report, the target temporal boundary report being a combination of the CV/ML temporal boundary report and the default boundary report” is further rejected on obviousness grounds as discussed in the rejection of claims 1-4 wherein Zhang teaches generating a set of candidate breakpoints in a media item, and using a machine learning model to score the candidate breakpoints and iteratively select subsets of the candidate breakpoints to be a final set of breakpoints, which are stored as part of a bitstream. See para 11, 22, 29 regarding a list and figure 2. Zhang discloses a target set of boundaries in the form of a final set of breakpoints element 240 and 0-240 in figure 2. See also Effinger teaches (col. 5, lines 52-64; col. 7, lines 40-54) (pars. [0022], [0023]), disclosing methods to score the result provided by ML-based boundary determination algorithms. See also Oyman figure 3 discloses “SOP offer indicating: arbitrary and/or predefined ROI signaling support; actual transmitted ROI signaling; a description of each offered predefined ROI.”
Regarding claim 6, “wherein the target temporal boundary report is identified as having a higher level of accuracy than at least one of the CV/ML boundary report or the default temporal boundary report” is further rejected on obviousness grounds as discussed in the rejection of claims 1-5 wherein Effinger (col. 5, lines 52-64; col. 7 lines 40-54) and Zhang (abstract; pars. [0022], [0023]), where the terms higher score is interpreted as the content insertion point to a higher level of accuracy.
Regarding claim 7, “wherein encoding the media content is based at least in part on the target temporal boundary report and the default boundary report” is further rejected on obviousness grounds as discussed in the rejection of claims 1-6 wherein Effinger (col. 5, lines 52-64; col. 7 lines 40-54) and Zhang (abstract; pars. [0022], [0023]), where the terms higher score is interpreted as the content insertion point to a higher level of accuracy and Oyman figure 3 discloses “SOP offer indicating: arbitrary and/or predefined ROI signaling support; actual transmitted ROI signaling; a description of each offered predefined ROI.”
Claim(s) 9, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mao; Weidong et al. US 20200288149 A1 (hereafter Mao) and in further view of Zhang; Wenbo et al. US 20210390130 A1 (hereafter Zhang) and in further view of Wu; Yongjun et al. US 10951960B2 (hereafter Wu).
Regarding claim 9, “further comprising: generating a manifest link associated with the packaged media content; transmitting the manifest link to a destination device; receiving, from the destination device, a client request indicating a selection of the manifest link based at least in part on user input received via the destination device; determining that the client request is to stream the packaged media content; and causing streaming of the packaged media content via the destination device” wherein Mao teaches an invention comprising video encodingusing MPEG (para 58, 68) the invention does not use the term manifest but a person of ordinary skill in the art would have understood that manifests are a typical component of MPEG video transmission utilizing URL links.
In an analogous art, Wu teaches the deficiency of Mao and Zhang (col. 2:52-67 to col. 4:1-52 - retrieval of these fragments can be much later, in the context of a VOD access, or nearly instantaneous (near real-time), in the context of streaming. In some implementations, a corresponding index file, commonly referred to as a manifest, may be provided to help retrieve these fragments. For example, the manifest may contain fragment data, such as a pointer, link (e.g., URL), redirect, or timing data associated with each fragment that ultimately allows an end user device, such as a media player, to receive each fragment at the appropriate time. The manifest may also include fragment sequence data, as well as various media data, such as a computer program or instruction for encoding or decoding the fragments (commonly known as a CODEC), a fragment title, etc.).
Therefore, it would have been obvious to one having ordinary skill in the art before the time of the applicant’s effective filing date to modify Mao and Zhang for identifying boundary points, the metadata-defined boundaries, and the I-frame-identified boundaries using machine learning and for iteratively selecting subsets of target boundaries in order to improve the accuracy of content boundary detection through use of an iterative process that leverages machine learning and/or analyze the media content (e.g., using machine learning and/or one or more graphics processing algorithms) to determine scene boundaries of the media content wherein a “default report” is generated from metadata by further incorporating known elements of Wu for generating aa manifest to help a client device retrieve these fragments wherein the manifest may contain fragment data, such as a pointer, link (e.g., URL), redirect, or timing data associated with each fragment that ultimately allows an end user device, such as a media player, to receive each fragment at the appropriate time.
Claim(s) 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mao; Weidong et al. US 20200288149 A1 (hereafter Mao) and in further view of Zhang; Wenbo et al. US 20210390130 A1 (hereafter Zhang) and in further view of Effinger; Charles et al. US 10841666 B1 (hereafter Effinger) and in further view of Oyman; Ozgur US 20160165185 A1 (hereafter Oyman) and in further view of Kieft; Alexander J. et al. US 20200322401 A1 (hereafter Kieft).
Regarding claim 10, “wherein encoding the media content comprises: encoding subtitles content as encoded subtitles content; and encoding thumbnails content as encoded thumbnails content, the encoded subtitles content and the encoded thumbnails content being temporally aligned with the encoded media content” Mao, Zhang, Effinger, Oyman are silent with respect to subtitles as claimed.
However, Kieft discloses in an analogous art directed maintaining synchronicity among different elements of a live media stream, including synchronizing thumbnails and caption information. See [0017]. Therefore, it would have been obvious to one having ordinary skill in the art before the time of the applicant’s effective filing date to modify Mao, Zhang, Effinger, and Oyman for identifying boundary points, the metadata-defined boundaries, and the I-frame-identified boundaries using machine learning and for iteratively selecting subsets of target boundaries in order to improve the accuracy of content boundary detection through use of an iterative process that leverages machine learning and/or analyze the media content (e.g., using machine learning and/or one or more graphics processing algorithms) to determine scene boundaries of the media content by further incorporating known elements of Kieft to encode captions/subtitle content, as well as thumbnails, and to temporally align these elements with the rest of a media content stream, as disclosed in, and to incorporate these elements. Doing so would have entailed simply combining the prior art elements respectively disclosed in Mao, Zhang, and in Kieft, without changing their respective functions, and the combination would have yielded nothing more than predictable results for one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc. See 2143.1.A. 550 U.S. at 416, 82 USPQ2d at 1395.
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.
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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