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 .
This office action is in response to communication filed on 01/28/2025.
Claims 1-20 present for examination.
Information Disclosure Statement
It is hereby acknowledged that the following papers have been received and placed of record in the file:
Information Disclosure Statement(s) as received on 01/28/2025, 12/08/2025, 02/06/2026, and 05/11/2026 is/are considered by the Examiner.
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 3-5, 7, 8, 16, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 18, and 20 of copending Application No. 18/938064 in view of Dai et al. (US 2022/0141513 A1), hereinafter Dai.
This is a provisional nonstatutory double patenting rejection.
Instant Application
Application (18/938064)
1. A method comprising:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a performance of the target metric based on the bandwidth distribution;
determining a model for selecting a top profile for the plurality of segments, wherein the model includes a set of conditions;
solving the model based on the performance of the target metric, wherein solving the model provides a solution that meets the set of conditions and outputs the top profile for each segment; and
outputting the top profile for each segment for delivery to the client device, wherein the top profile for each segment is used to remove profiles when requesting segments in the plurality of segments during playback of the instance of content.
1. A method comprising:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a plurality of top profile decisions for the plurality of segments, wherein a top profile decision lists a top profile that can be selected for respective segments in the plurality of segments;
determining a performance of the target metric for the plurality of top profile decisions based on the bandwidth distribution;
selecting a top profile decision from the plurality of top profile decisions based on the performance of the target metric for the plurality of top profile decisions; and
using the top profile decision to determine a target quality, wherein the target quality is used to select top profiles for requesting segments in the plurality of segments during playback of the instance of content.
3. The method of claim 1, wherein the target metric is selected from a first metric based on data saved and a second metric based on quality loss.
2. The method of claim 1, wherein the target metric is selected from a first metric based on data saved and a second metric based on quality loss.
4. The method of claim 1, wherein the bandwidth distribution is based on a download bandwidth probability.
3. The method of claim 1, wherein the bandwidth distribution is based on a download bandwidth probability.
5. The method of claim 3, wherein the bandwidth distribution is based on information from the client device.
4. The method of claim 3, wherein the bandwidth distribution is based on information from the client device.
7. The method of claim 1, wherein determining the target metric comprises:
receiving a target value for the target metric.
5. The method of claim 1, wherein determining the target metric comprises:
receiving a target value for the target metric.
8. The method of claim 5, wherein the target value for the target metric is adaptable per instance of content or client device.
6. The method of claim 5, wherein the target value for the target metric is adaptable per instance of content or client device.
16. A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a performance of the target metric based on the bandwidth distribution;
determining a model for selecting a top profile for the plurality of segments, wherein the model includes a set of conditions;
solving the model based on the performance of the target metric, wherein solving the model provides a solution that meets the set of conditions and outputs the top profile for each segment; and
outputting the top profile for each segment for delivery to the client device, wherein the top profile for each segment is used to remove profiles when requesting segments in the plurality of segments during playback of the instance of content.
18. A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a plurality of top profile decisions for the plurality of segments, wherein a top profile decision lists a top profile that can be selected for respective segments in the plurality of segments;
determining a performance of the target metric for the plurality of top profile decisions based on the bandwidth distribution;
selecting a top profile decision from the plurality of top profile decisions based on the performance of the target metric for the plurality of top profile decisions; and
using the top profile decision to determine a target quality, wherein the target quality is used to select top profiles for requesting segments in the plurality of segments during playback of the instance of content.
20. An apparatus comprising:
one or more computer processors; and
a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a performance of the target metric based on the bandwidth distribution;
determining a model for selecting a top profile for the plurality of segments, wherein the model includes a set of conditions;
solving the model based on the performance of the target metric, wherein solving the model provides a solution that meets the set of conditions and outputs the top profile for each segment; and
outputting the top profile for each segment for delivery to the client device, wherein the top profile for each segment is used to remove profiles when requesting segments in the plurality of segments during playback of the instance of content.
20. An apparatus comprising:
one or more computer processors; and
a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles;
determining a target metric and a bandwidth distribution;
determining a plurality of top profile decisions for the plurality of segments, wherein a top profile decision lists a top profile that can be selected for respective segments in the plurality of segments;
determining a performance of the target metric for the plurality of top profile decisions based on the bandwidth distribution;
selecting a top profile decision from the plurality of top profile decisions based on the performance of the target metric for the plurality of top profile decisions; and
using the top profile decision to determine a target quality, wherein the target quality is used to select top profiles for requesting segments in the plurality of segments during playback of the instance of content.
Regarding claims 1, 3-5, 7, 8, 16, and 20, US application 18/938064, hereinafter Huang, in view of US 2022/0141513 A1, hereinafter Dai, disclose the method of outputting top profile for each segment using model in claims 1-6, 18, and 20.
Huang does not explicitly disclose using a model to output top profile.
Dai discloses profile decision generator 304 may analyze the network conditions and select an appropriate profile ladder based on the network conditions ([0043]); generating a profile subset decision using a prediction model; profile decision generator 304 may use the trained profile subset decision prediction model ([0061]).
Therefore, it would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed inventio to incorporate the feature of Dai in system in order to save resources.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the data saved ratio" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 11, claim limitation recites “there is only one and only one top profile” in line 5, which renders the claim vague and indefinite. Examiner is unable to determine the scope of the claim.
Claim 12 recites the limitation "the video quality loss" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 12, claim limitation recites “there is only one and only one top profile” in line 6, which renders the claim vague and indefinite. Examiner is unable to determine the scope of the claim.
Regarding claim 13, claim limitation recites “reformulating the model into a linear programming problem” in line 3, which renders the claim vague and indefinite. It is unclear how a system would reformulate the model without formulating the model.
Claim 14 recites the limitation “wherein reformatting the model into the linear programming problem” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 15-17, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dai et al. (US 2022/0141513 A1), hereinafter Dai.
Regarding claim 1, Dai discloses
A method comprising:
determining an instance of content to be delivered to a client device, wherein the instance of content includes a plurality of segments that is associated with a plurality of profiles ([0023]: content delivery server system 116 delivers segments of video to client 104; the segments may be a portion of the video, such as six seconds of the video; as is known, a video may be encoded in multiple profiles that correspond to different levels, which may be different levels of bitrates and/or quality);
determining a target metric (quality of service (QoS) metrics) and a bandwidth distribution (predicted network bandwidth) ([0042]: predict the network conditions before playback of a respective session; network metrics predictor 302 may output the predicted network bandwidth and also one or more quality of service (QoS) metrics);
determining a performance of the target metric based on the bandwidth distribution ([0034]: predict network metrics for the session; the network metrics may include different metrics that describe aspects of video delivery or playback, such as network bandwidth, a rebuffer ratio, and a failure ratio; network bandwidth may be the available bandwidth that is measured for the session; & [0042]: predict the network conditions before playback of a respective session; network metrics predictor 302 may output the predicted network bandwidth and also one or more quality of service (QoS) metrics);
determining a model (a prediction model) for selecting a top profile for the plurality of segments, wherein the model includes a set of conditions ([0043]: profile decision generator 304 may analyze the network conditions and select an appropriate profile ladder based on the network conditions; & [0061]: generating a profile subset decision using a prediction model; profile decision generator 304 may use the trained profile subset decision prediction model);
solving the model based on the performance of the target metric, wherein solving the model provides a solution that meets the set of conditions and outputs the top profile for each segment ([0061]: in this case, network metrics prediction 604 (e.g., network bandwidth 606-1, rebuffer ratio 606-2, and failure ratio 606-3) are input into profile decision generator 304; then, profile decision generator 304 outputs a profile subset decision; the profile subset decision may be one of the selected profile subsets A, B, C, or D; & [0065]: network metrics prediction 204 has passed all the rules and the highest bitrate distribution in the profile subsets is selected); and
outputting the top profile for each segment for delivery to the client device, wherein the top profile for each segment is used to remove profiles when requesting segments in the plurality of segments during playback of the instance of content ([0043]: the profile subset with a higher bitrate distribution is selected to allow client 104 to request higher bitrates to improve the quality of playback; & [0018]: if network conditions change, the in-session profile adapter can adapt to these changing network conditions to add different profiles that may be more appropriate or remove profiles that may no longer be appropriate; & [0069]: the profile ladder may restrict the number of profiles and in-session profile adapter 110 may remove a profile with a higher bitrate when adding a lower bitrate profile).
Regarding claim 2, Dai discloses the method as described in claim 1. Dai further discloses
solving the model lists one top profile for each segment ([0027]: the manifest lists all the available profiles for each segment).
Regarding claim 3, Dai discloses the method as described in claim 1. Dai further discloses
the target metric is selected from a first metric based on data saved and a second metric based on quality loss ([0068]: current network conditions may include available bandwidth, the rebuffer ratio, and the failure ratio for the current session).
Regarding claim 15, Dai discloses the method as described in claim 1. Dai further discloses
sending the top profile for each segment to the client device, wherein the client device uses the top profile to remove profiles when requesting segments in the plurality of segments during playback ([0043]: the profile subset with a higher bitrate distribution is selected to allow client 104 to request higher bitrates to improve the quality of playback; & [0018]: if network conditions change, the in-session profile adapter can adapt to these changing network conditions to add different profiles that may be more appropriate or remove profiles that may no longer be appropriate; & [0069]: the profile ladder may restrict the number of profiles and in-session profile adapter 110 may remove a profile with a higher bitrate when adding a lower bitrate profile).
Regarding claims 16 and 20, the limitations of claims 16 and 20 are rejected in the analysis of claim 1 above and these claims are rejected on that basis.
Regarding claim 17, the limitations of claim 17 are rejected in the analysis of claim 2 above and this claim is rejected on that basis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Manor et al. (US 2012/0201255 A1), hereinafter Manor.
Regarding claim 4, Dai discloses the method as described in claim 1. Dai does not explicitly disclose
the bandwidth distribution is based on a download bandwidth probability.
However, Manor discloses
the bandwidth distribution is based on a download bandwidth probability ([0040]: allocating bandwidth to a communication session based on a probability distribution function; bandwidth is measured by the bandwidth determiner 22 of the processor 18 over a period of time; a probability distribution function (PDF) is determined by the processor 18 based on the measurements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of Manor to Dai, because Dai discloses selecting profiles based on available bandwidth ([0023]) and Manor further suggests allocating bandwidth to a communication session based on a probability distribution function ([0040]).
One of ordinary skill in the art would be motivated to utilize the teachings of Manor in the Dai system in order to prevent system overloads.
Regarding claim 5, Dai and Manor discloses the method as described in claim 4. Dai further discloses
the bandwidth distribution is based on information from the client device ([0068]: client 104 downloads segments of the video and detects the current network conditions; current network conditions may include available bandwidth, the rebuffer ratio, and the failure ratio for the current session).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Manor, and further in view of Lee et al. (US 2009/0010219 A1), hereinafter Lee.
Regarding claim 6, Dai and Manor disclose the method as described in claim 4. Dai and Manor do not explicitly disclose
the bandwidth distribution is segmented into a plurality of bandwidth distributions based on different features, and
a bandwidth distribution is selected based on a feature associated with the client device.
However, Lee discloses
the bandwidth distribution is segmented into a plurality of bandwidth distributions based on different features ([0025]: divides cell bandwidth into more than one sub-bandwidth), and
a bandwidth distribution is selected based on a feature associated with the client device ([0025]: selecting one of sub-bandwidths for a terminal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of Lee to Dai and Manor, because Dai and Manor disclose selecting profiles based on available bandwidth (Dai: [0023]) and Lee further suggests divide bandwidth and selecting bandwidth for a terminal ([0025]).
One of ordinary skill in the art would be motivated to utilize the teachings of Lee in the Dai and Manor system in order to load balance.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Wright et al. (US 2013/0227111 A1), hereinafter Wright.
Regarding claim 7, Dai discloses the method as described in claim 1. Dai does not explicitly disclose
receiving a target value for the target metric.
However, Wright discloses
receiving a target value for the target metric ([0040]: a target value indicating a target client metric is received).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of Wright to Dai, because Dai discloses selecting profiles based on available bandwidth and quality of service metric ([0034]) and Wright further suggests receive a target value indicating a target client metric ([0040]).
One of ordinary skill in the art would be motivated to utilize the teachings of Wright in the Dai system in order to prioritize services/traffics.
Regarding claim 8, Dai and Wright disclose the method as described in claim 7. Dai and Wright further disclose
the target value for the target metric is adaptable per instance of content or client device (Wright: [0040]: a target value indicating a target client metric is received). Therefore, the limitations of claim 8 are rejected in the analysis of claim 7 above, and the claim is rejected on that basis.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Iyer et al. (US 2022/0116284 A1), hereinafter Iyer.
Regarding claim 9, Dai discloses the method as described in claim 1. Dai does not explicitly disclose
selecting the model based on the target metric, wherein a first target metric uses a first model and a second target metric uses a second model.
However, Iyer discloses
selecting the model based on the target metric, wherein a first target metric uses a first model and a second target metric uses a second model ([0067]: selects the model that will best satisfy the requirements of the selected quality of service (QoS) objective for prioritization, for use by the target hardware platform; & [0084]: dynamically select and/or adjust an optimized model for use based on a current state and/or model utilization metrics of the target hardware platform).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of Iyer to Dai, because Dai discloses using model to determine profile subset decision ([0060] and Iyer further suggests select model that will best satisfy the requirements of QoS ([0067]).
One of ordinary skill in the art would be motivated to utilize the teachings of Iyer in the Dai system in order to select the most relevant model to improve accuracy.
Regarding claim 10, Dai and Iyer disclose the method as described in claim 9. Dai and Ilyer further disclose
the first model includes a first set of conditions for use in determining the top profile (Ilyer: [0023]:the model includes internal parameters that guide how input data is transformed into output data, such as through a series of nodes and connections within the model to transform input data into output data); and
the second model includes a second set of conditions for use in determining the top profile (Ilyer: [0023]:the model includes internal parameters that guide how input data is transformed into output data, such as through a series of nodes and connections within the model to transform input data into output data). Therefore, the limitations of claim 10 are rejected in the analysis of claim 9 above, and the claim is rejected on that basis.
Regarding claim 18, the limitations of claim 18 are rejected in the analysis of claim 9 above and this claim is rejected on that basis.
Claim(s) 13-14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Wei et al. (US 10,897,654 B1), hereinafter Wei.
Regarding claim 13, Dai discloses the method as described in claim 1. Dai does not explicitly disclose
reformulating the model into a linear programming problem; and
solving the linear programming problem, wherein an output of the model comprises a top profile for each segment in the plurality of segments.
However, Wei discloses
reformulating the model into a linear programming problem (Col. 16, lines 1-4: algorithms including but not limited to least-square, linear programming, and variants of heuristic-based search algorithms can be used to perform convex optimization); and
solving the linear programming problem, wherein an output of the model comprises a top profile for each segment in the plurality of segments (Col. 16, lines 8-10: the cost function may sequentially select the encoding profiles (e.g., profiles 471, 472, and 473)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of Wei to Dai, because Dai discloses using model to determine profile subset decision ([0060] and Wei further suggests using linear programming algorithm (Col. 16, lines 1-4).
One of ordinary skill in the art would be motivated to utilize the teachings of Wei in the Dai system in order to optimize resource allocation.
Regarding claim 14, Dai and Wei disclose the method as described in claim 13. Dai further discloses
converting the set of conditions into a second set of conditions for the linear programming problem ([0052]: the actual bandwidth experienced during a session is known and can be correlated to the session features); and
inputting the performance of the target metric into the second set of conditions to solve the linear programming problem ([0052]: the training process may input session features 504 into the model and train the model’s parameters based on the output of the model and the ground truth of the bandwidth and the QoS metrics).
Regarding claim 19, the limitations of claim 19 are rejected in the analysis of claim 13 above and this claim is rejected on that basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nuzman et al. (US 2008/0080374 A1). The prediction of each total bandwidth usage is based on probability distributions for bandwidth usages by the various communications sessions ([0053]).
Vichare et al. (US 2024/0112069 A1). Select instance of the AI model based upon rule ([0012]).
Prasad (US 2020/0053591 A1). Select machine learning model from a set of machine learning models based on performance metrics ([0020]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAYLEE J HUANG whose telephone number is (571)272-0080. The examiner can normally be reached Monday-Friday 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H Hwang can be reached at 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Kaylee Huang
07/24/2026
/KAYLEE J HUANG/Primary Examiner, Art Unit 2447
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