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) submitted on 04/17/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 2 and 9-11 rejected under 35 USC 102 as being anticipated by Li et al (Li hereinafter US 20180167619) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments with respect to claim 1 and 9-11 stand rejected under 35 USC 103 as being unpatentable over Ma et al (Ma hereinafter US 20210174152) in view of Cai et al (Cai hereinafter CN 112634268) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1, 7, 10, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bovik et al US 20110274361 A1 in view of Garcia De Blas et al (Garcia hereinafter US 20140337871 A1)
As per claim 1
Bovik teaches A video quality evaluation method (Figure 10 Paragraph [0069] “ determining quality of an image or video based on one or more identified distortion categories “) classifying each video in a video set; (Paragraph [0073] “identifying one or more distortion categories for a particular image or video is performed using a distortion classifier,”) inputting videos of different categories into different preset models and acquiring quality evaluation results of the videos by using the preset models (Paragraph [0037] “using an appropriate quality assessment algorithm to quantify quality based on the distortion.” Paragraph [0038] “ one or more algorithms for blind Image Quality Assessment (IQA) that are designed for different distortions (e.g. JPEG, JPEG2000, Blur, etc.) are available.” Paragraph [0038] “ computer system receives an image or video as an input, classifies it into one of these distortion categories, and then proceeds to determine the quality of the image or video using the methods and algorithms described herein.”) wherein the videos comprise videos of a first category (Paragraph [0057] “In the embodiment of FIG. 6, the image or video is received in block 602. A distortion classifier containing a distorted image statistics signature for two or more distortion categories is provided in block 604. The distortion categories may include white noise, filtering distortions (e.g., Gaussian blurring), compression distortions (e.g., wavelet-transform based compression, MPEG (e.g., MPEG 1, 2 or 4), H.26X (e.g., H.263, H.264), Discrete Cosine Transform compressions such as JPEG compression or JPEG2000 compression), and other distortions including unknown or unclassified distortions” Bovik shows that the received image and or video is classified into one or more distortion categories including MPEG, H.26X, JPEG compression or JPEG2000 compression, white noise, and Gaussian blurring. Any of these video distortion categories constitutes a “first category” )
Bovik does not teach the preset models comprise a measurement mapping evaluation model: before the inputting videos of different categories into different preset models. the method further comprises: acquiring transmission characteristic data of the videos on a video link, the transmission characteristic data comprises transmission related characteristic data of the videos on the video link: and the inputting videos of different categories into different preset models, and acquiring quality evaluation results of the videos by using the preset models. comprises: inputting transmission characteristic data of the videos of the first category into the measurement mapping evaluation model, acquiring a first score of the videos of the first category by using the measurement mapping evaluation model, and outputting the first score after being evaluated by the measurement mapping evaluation model according to the transmission characteristic data.
Garcia teaches the preset models comprise a measurement mapping evaluation model (Figure 4 Paragraph [0024] “assigning a Key Quality Indicator, or KQI, to each KPI by means of analytical models and calculating a global KQI function of a set of KQIs.” Garcia cements a mathematical function relating the measured KPI to its corresponding KQI. Garcia states that “The particular KQI must capture the impact of its associated KPI.“ in paragraph [0065]. Figure 4 shows the correspondence between KPI and KQI for each video viewed.”) before the inputting videos of different categories into different preset models. the method further comprises: acquiring transmission characteristic data of the videos on a video link, the transmission characteristic data comprises transmission related characteristic data of the videos on the video link (Garcia covers this claim limitation by teaching OTT video provided through a network Paragraph [0024] “calculating a Key Performance Indicator, or KPI, from measurable network parameters of said network for each video provided by said video service” and collecting network measurements at “ one point of the network (for instance, one link). Garcia’s disclosed transmission measurements include received video data (bytes) , video bitrate(Paragraph [0045]) instant throughput (Paragraph [0036]-[0045] , reception timing (Paragraph [0060]) and reception of video data packets) the inputting videos of different categories into different preset models, and acquiring quality evaluation results of the videos by using the preset models. comprises: inputting transmission characteristic data of the videos of the first category into the measurement mapping evaluation model (Garcia does this by showing that a measured network parameters are used to calculate a KPI and that the KPI is provided to the analytical model /function that maps out the network derived KPI to a corresponding video quality KQI. Garcia says that “As a function of these KPIs, the QoE is expressed in terms of Key Quality Indicators (KQIs) following another analytical model.” In paragraph [0034]) acquiring a first score of the videos of the first category by using the measurement mapping evaluation model, (Paragraph [0066] “, the scale of KQI ranges from 1 to 5, where score 1 is the worst quality (dreadful) and the value 5 corresponds to the highest quality (ideal).” Garcia shows here that the KPI to KQI model creates a KQI that represents a perception of quality of a video.) outputting the first score after being evaluated by the measurement mapping evaluation model according to the transmission characteristic data. (Garcia states that “As a function of these KPIs, the method is able to obtain the particular KQIs of each video playback. In FIG. 7 the KQI cumulative distribution function was depicted. “ In paragraph [0079] The KPI is derived from the measured transmission characteristics and the corresponding KQI is automatically determined according to the disclosed analytical mapping model)
In a combined teaching Bovik’s received videos are first classified according to their respective distortion categories and evaluated using the quality assessment model appropriate to the identified category. Garcia’s network based quality evaluation is incorporated as one of the quality evaluation models used by Bovik so that for a video of a corresponding category, transmission related measurements obtained from the video link are supplied to Garcia’s analytical measurement mapping model. This maps the network derived KPI to the corresponding KQI quality score. This Bovik/Garcia methodology takes Bovik’s category and corresponding quality model selection while using Garcia’s transmission measurements to KPI to analysis mapping model to KQI score. This is done in order to evaluate video quality affected by network transmission.
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to modify Bovik’s category based video quality evaluation system to include Garcia’s network measurement based quality evaluation model. This is because Garcia teaches that measurable network transmission conditions affect the perceived quality of streamed video and can be automatically mapped to a video quality indicator. This modification would have predictably allowed Bovik’s system to account for video quality degradation caused by network transmission in addition to identifying the applicable video quality category. This provides a complete and accurate objective assessment of the quality actually experienced by a user without requiring subjective human evaluation.
As per claim 7
Bovik and Garcia de Blas teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
The modified Bovik/Garcia combination in tandem teaches The video quality evaluation method according to claim 1, wherein the classifying each video in a video set comprises: classifying each video in the video set according to at least one data from functional scenario, video length, number of concurrent access, access type and network environment parameters.
Bovik teaches the classification architecture which classifies a video into a category and use the category appropriate quality evaluation as previously presented.
Garcia teaches that video quality is characterized on a per video basis using network environment parameters (Paragraph [0024] “the method of the invention, in a characteristic manner, comprises calculating a Key Performance Indicator, or KPI, from measurable network parameters of said network for each video” Paragraph [0038] “specific KPIs have been used for each video playback.”) Garcia then identifies network conditions (Paragraph [0038] “ An interruption occurs when the instantaneous throughput received is exceeded by the bitrate of the video.”)
In the combined methodology a person of ordinary skill in the art can see that Bovik’s known video classification operation is modified to use Garcia’s per video network environment parameters as classification criteria. In addition to classifying the video according to Bovik’s distortion characteristics, the system classifies each video according to network conditions that reflect that video’s transmission such as its measured interruption conditions, and applies the corresponding quality evaluation. This creates a system where each per video network environment parameters are obtained from each video. They are then classified according to that parameter and output a corresponding quality evaluation. This is obvious to the skilled artisan because allowing the classification framework to select video quality evaluations that reflects the network environment’s influence on a video enables an evaluation that’s actually based on network condition experienced by each streamed video. It makes transmission induced quality effects accountable in the evaluation. Bovik gives the classification framework and Garcia the network environment criterion.
As per claim 10
Bovik and Garcia De Blas teaches all claim limitations previously rejected in claim 1’s 103 rejection.
Bovik teaches An electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores an instruction able to be executed by the at least one processor, and the instruction is executed by the at least one processor to enable the at least one processor to be able to implement the video quality evaluation method according to claim 1. (Figure 11)
As per claim 11
Bovik and Garcia De Blas teaches all claim limitations previously rejected in claim 1’s 103 rejection.
Claim 11 is the non-transitory computer readable storage medium that executes the method of claim 1. Bovik teaches A non-transitory computer readable storage medium, storing a computer program, the computer program, when executed by a processor, implementing the video quality evaluation method (Paragraph [0085] “ is noted that the above-described embodiments may comprise software. In such an embodiment, program instructions and/or a database (both of which may be referred to as "instructions") that represent the described systems and/or methods may be stored on a computer readable storage medium…a computer readable storage medium may include storage media such as magnetic or optical media…the term computer readable storage medium refers to a non-transitory (tangible) medium,”)
As per claim 17
Bovik and Garcia de Blas teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
The modified Bovik/Garcia combination in tandem teaches The video quality evaluation method according to claim 1, wherein the classifying each video in a video set comprises: classifying each video in the video set according to at least one data from functional scenario, video length, number of concurrent access, access type and network environment parameters.
Bovik teaches the classification architecture which classifies a video into a category and use the category appropriate quality evaluation as previously presented.
Garcia teaches that video quality is characterized on a per video basis using network environment parameters (Paragraph [0024] “the method of the invention, in a characteristic manner, comprises calculating a Key Performance Indicator, or KPI, from measurable network parameters of said network for each video” Paragraph [0038] “specific KPIs have been used for each video playback.”) Garcia then identifies network conditions (Paragraph [0038] “ An interruption occurs when the instantaneous throughput received is exceeded by the bitrate of the video.”)
In the combined methodology a person of ordinary skill in the art can see that Bovik’s known video classification operation is modified to use Garcia’s per video network environment parameters as classification criteria. In addition to classifying the video according to Bovik’s distortion characteristics, the system classifies each video according to network conditions that reflect that video’s transmission such as its measured interruption conditions, and applies the corresponding quality evaluation. This creates a system where each per video network environment parameters are obtained from each video. They are then classified according to that parameter and output a corresponding quality evaluation. This is obvious to the skilled artisan because allowing the classification framework to select video quality evaluations that reflects the network environment’s influence on a video enables an evaluation that’s actually based on network condition experienced by each streamed video. It makes transmission induced quality effects accountable in the evaluation. Bovik gives the classification framework and Garcia the network environment criterion.
Applicant is advised that should claim 7 be found allowable, claim 17 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim.
Claim 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bovik et al US 20110274361 A1 in view of Garcia De Blas et al (Garcia hereinafter US 20140337871 A1) in further view of Huang et al (Huang hereinafter WO 2017107774 A1 “METHOD AND DEVICE FOR PROCESSING VIDEO QUALITY INFORMATION”
As per claim 3
Bovik and Garcia teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Bovik nor Garcia teach after the acquiring a first score of the videos of the first category by using the measurement mapping evaluation model, further comprising: backwards deducing and locating abnormal transmission characteristic data of the videos on the video link according to the measurement mapping evaluation model in a case that the first score is less than a first expected score, and/or outputting video quality warning information according to the first score in a case that the first score is less than a first expected score.
Huang teaches after the acquiring a first score of the videos of the first category by using the measurement mapping evaluation model (Detailed description: “A method for processing video quality information is provided…wherein the damage parameter is used as an input of a pre-established mathematical model, and the output of the mathematical model is as the first average opinion value MOS, the pre-established mathematical model is obtained by self-learning according to the relationship between the damage parameter and the MOS of the input data.” Huang shows a compatible processing methodology as the Bovik/Garcia methodology. That being a model that operates on video impairment information to obtain a first quality score (the MOS) ) outputting video quality warning information according to the first score in a case that the first score is less than a first expected score. (Detailed description: “And sending the first MOS to the network management server, where the first MOS is used to provide a basis for whether the network management server sends an alarm, where an alarm is generated when the first MOS is less than a preset threshold.” Detailed description “The first sending module is configured to send the first MOS determined by the determining module to the network management server, where the first MOS is used to provide a basis for whether the network management server sends an alarm, where the first MOS is smaller than a preset threshold An alarm is issued.” Detailed description; “wherein the gateway server issues an alarm when the first MOS is less than a preset threshold, and sends an alarm to prompt the user to observe the quality of the video screen and manage the network transmission status in time.”)
In a combined teaching, the Bovik/Garcia methodology teach the core of claim 1. This includes classification of video and use of transmission related characteristic data with a measurement mapping evaluation model to acquire a quality score. Huang teaches what occurs after the first score is acquired.
Accordingly, a person of ordinary skill in the art, at the time this invention was effectively filed would have found it obvious to modify the Bovik/Garcia methodology to include Huang’s concept of having a warning function. A person of ordinary skill in the art would do this so that the system automatically provides a warning when the determined video quality becomes undesirable. This modification allows the Bovik/Garcia methodology to automatically notify a user when evaluated video quality goes beneath a threshold rather than just calculating the quality score. The predictable advantage here is a timely detection and notification of deficient video quality. This permits the transmission condition to be examined and addressed swiftly.
As per claim 18
Bovik Garcia de Blas, and Huang teach all claim limitations previously rejected in claim 3’s 103 rejection. See claim 3’s 103 rejection.
The modified Bovik/Garcia combination in tandem teaches The video quality evaluation method according to claim 1, wherein the classifying each video in a video set comprises: classifying each video in the video set according to at least one data from functional scenario, video length, number of concurrent access, access type and network environment parameters.
Bovik teaches the classification architecture which classifies a video into a category and use the category appropriate quality evaluation as previously presented.
Garcia teaches that video quality is characterized on a per video basis using network environment parameters (Paragraph [0024] “the method of the invention, in a characteristic manner, comprises calculating a Key Performance Indicator, or KPI, from measurable network parameters of said network for each video” Paragraph [0038] “specific KPIs have been used for each video playback.”) Garcia then identifies network conditions (Paragraph [0038] “ An interruption occurs when the instantaneous throughput received is exceeded by the bitrate of the video.”)
In the combined methodology a person of ordinary skill in the art can see that Bovik’s known video classification operation is modified to use Garcia’s per video network environment parameters as classification criteria. In addition to classifying the video according to Bovik’s distortion characteristics, the system classifies each video according to network conditions that reflect that video’s transmission such as its measured interruption conditions, and applies the corresponding quality evaluation. This creates a system where each per video network environment parameters are obtained from each video. They are then classified according to that parameter and output a corresponding quality evaluation. This is obvious to the skilled artisan because allowing the classification framework to select video quality evaluations that reflects the network environment’s influence on a video enables an evaluation that’s actually based on network condition experienced by each streamed video. It makes transmission induced quality effects accountable in the evaluation. Bovik gives the classification framework and Garcia the network environment criterion.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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