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 Response to Office Action mailed 11 July 2025, filed 29 September 2025, with respect to Claim Rejections under 35 USC §101 have been fully considered and are persuasive. The Claim Rejections under 35 USC §101 of Claim 11 has been withdrawn.
Applicant’s arguments, see Response to Office Action mailed 11 July 2025, filed 29 September 2025, with respect to the rejection(s) of claim(s) 1, 3-12, and 14-20 under 35 USC §102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hue et al. (US 2021/0329297 A1) and Su et al. (WO 2021/168001).
Applicant’s cancellation of claims 2 and 13 render all rejections of those claims moot.
Claim Objections
Claims 1, 11, and 12 are objected to because of the following informalities: The claims recite “obtaining game result information corresponding to optimized coding contents according to the optimized coding contents” in lines 5-6 (claim 1), it may be unclear how the optimized coding contents are according to themselves, or alternatively, if the game result information is both corresponding to and according to the optimized coding content either the corresponding or according may be redundant as something corresponding to something may also be according to it. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “optimized coding unit”, “backward coding unit”, “forward coding unit” in, inter alia, claims l, 11, and 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 3-12, and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (Cost Sensitive Learning Based HEVC Screen Content Intra Coding for Mobile Devices; 21 June 2020).
Regarding Claims 1, 11, and 12, Xu discloses a network device, comprising: a non-transitory computer-readable storage medium as a memory, a processor, a computer program which is stored in the memory and executable by the processor [Xu: § 1 Introduction: applications, such as virtual desktop, wireless displays, cloud gaming, and massive online courses… screen sharing between mobile devices], wherein the processor is configured to perform a video coding method, comprising: performing reinforcement learning network training on an acquired image sequence to obtain optimized coding unit partitioning information corresponding to the image sequence [Xu: FIG. 5: Framework of the proposed reinforcement learning based intra coding scheme; and Screen content videos]; obtaining game result information corresponding to optimized coding contents according to the optimized coding contents, reference coding contents, and a preset evaluation index, wherein the optimized coding contents are obtained according to the optimized coding unit partitioning information, the reference coding contents are obtained according to the image sequence; [Xu: FIG. 5: Reward (Value of targeted optimization function); and § 3.4 Coding policy leaning algorithm: According to their feature values, the classification results can be determined according to Eq. 5. Then the reward can be calculated as in Eqs. 7 and 8. These rewards are used to prepare training samples for the next training episode]; and in response to determining that the game result information meets a preset game condition, determining a coding mode corresponding to the optimized coding unit partitioning information as a video coding mode for the image sequence [Xu: § 3.1 Framework: Since we want to take into account the cost of coding mode selection errors when applying the coding strategy, cost-sensitive binary RL classifiers are designed and RL is utilized to gradually refine the classification models].
Xu may not explicitly disclose the game result information is for indicating that the optimized coding contents are better or worse than the reference coding contents; and wherein the image sequence comprises N frames, and performing reinforcement learning network training on the acquired image sequence to obtain the optimized coding unit partitioning information corresponding to the image sequence comprises: inputting the image sequence to a backward propagation network and processing all frames in the image sequence in order of N to 1 to obtain backward coding unit partitioning information corresponding to the image sequence; inputting the image sequence to a forward propagation network and processing all frames in the image sequence in order of 1 to N to obtain forward coding unit partitioning information corresponding to the image sequence; and inputting the backward coding unit partitioning information and the forward coding unit partitioning information to a fusion network to obtain the optimized coding unit partitioning information corresponding to the image sequence.
However, Huo discloses the game result information is for indicating that the optimized coding contents are better or worse than the reference coding contents [Huo: ¶ [0122]: The coding part 19 is configured to, while matching succeeds, encode an index of the optimized prediction mode for the present coding block in the MPM list based on a context model, and while matching fails, encode the optimized prediction mode for the present coding block by use of a truncated binary code].
Huo may not explicitly disclose wherein the image sequence comprises N frames, and performing reinforcement learning network training on the acquired image sequence to obtain the optimized coding unit partitioning information corresponding to the image sequence comprises: inputting the image sequence to a backward propagation network and processing all frames in the image sequence in order of N to 1 to obtain backward coding unit partitioning information corresponding to the image sequence; inputting the image sequence to a forward propagation network and processing all frames in the image sequence in order of 1 to N to obtain forward coding unit partitioning information corresponding to the image sequence; and inputting the backward coding unit partitioning information and the forward coding unit partitioning information to a fusion network to obtain the optimized coding unit partitioning information corresponding to the image sequence.
However, Su discloses wherein the image sequence comprises N frames, and performing reinforcement learning network training on the acquired image sequence to obtain the optimized coding unit partitioning information corresponding to the image sequence comprises: inputting the image sequence to a backward propagation network and processing all frames in the image sequence in order of N to 1 to obtain backward coding unit partitioning information corresponding to the image sequence; inputting the image sequence to a forward propagation network and processing all frames in the image sequence in order of 1 to N to obtain forward coding unit partitioning information corresponding to the image sequence; and inputting the backward coding unit partitioning information and the forward coding unit partitioning information to a fusion network to obtain the optimized coding unit partitioning information corresponding to the image sequence [Su: ¶ [0019]: In contrast, under techniques as described herein, neural network based solutions can be used to provide much better fitting in each of the forward and backward paths, taking advantage of the fact that neural networks are universal function approximators. Moreover, layer-wise structures of neural networks can be used to concatenate both the forward and backward paths together to form an end-to-end video delivery and/or consumption system comprising the neural networks in both the forward and backward paths. Feedbacks such as errors or costs from the backward path can be provided to the forward path under the joint forward and backward path optimization approach. A cost function (or a loss function) in a joint forward and backward path optimization problem can be set up in a way that comprises separate cost contributions (or separate loss contributions) from the forward reshaping path and from the backward reshaping. These separate cost contributions in the loss function can be assigned or weighted with different weighting factors so as to adjust qualities of the forward and backward paths according to a desired tradeoff. As a result, operational parameters for these neural networks used in the end-to-end video delivery and/or consumption system can be obtained as an overall solution to a joint optimization problem of the concatenated forward and backward paths].
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the reinforced machine learning of Su with the processing of Xu in order to provide improved output as well as the determination of Hou in order to provide improved modes of processing, improving output.
Regarding Claims 3 and 14, Xu in view of Huo and Su discloses all the limitations of Claims 1 and 12, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein inputting the image sequence to the backward propagation network and processing all frames in the image sequence in order of N to 1 to obtain the backward coding unit partitioning information corresponding to the image sequence comprises: traversing all frames in the image sequence in order of N to 1 in a following way: obtaining first sub-coding unit partitioning information of an ith frame according to image information of the ith frame and first sub-coding unit partitioning information of a (i+1)th frame; and according to first sub-coding unit partitioning information of each frame, obtaining the backward coding unit partitioning information corresponding to the image sequence; where i represents the ith frame in the image sequence [Xu: § 1 Introduction: In [30], two classifiers were designated to determine whether the current CU is split into sub-CUs and whether SCC modes or traditional intra modes are performed for unsplit CU; and Su: ¶ [0019]].
Regarding Claims 4 and 15, Xu in view of Huo and Su discloses all the limitations of Claims 3 and 14, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein inputting the image sequence to the forward propagation network and processing all frames in the image sequence in order of N to 1 to obtain the forward coding unit partitioning information corresponding to the image sequence comprises: traversing all the frames in the image sequence in order of 1 to N in a following way: obtaining second sub-coding unit partitioning information of a jth frame according to image information of the jth frame and second sub-coding unit partitioning information of a (j-1)th frame; and according to second sub-coding unit partitioning information of each frame, obtaining the forward coding unit partitioning information corresponding to the image sequence; where j represents the jth frame in the image sequence [Xu: § 1 Introduction: In [30], two classifiers were designated to determine whether the current CU is split into sub-CUs and whether SCC modes or traditional intra modes are performed for unsplit CU].
Regarding Claims 5 and 16, Xu in view of Huo and Su discloses all the limitations of Claims 4 and 15, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein inputting the backward coding unit partitioning information and the forward coding unit partitioning information to the fusion network to obtain the optimized coding unit partitioning information corresponding to the image sequence comprises: acquiring the first sub-coding unit partitioning information of each frame from the backward coding unit partitioning information, and acquiring the second sub-coding unit partitioning information of each frame from the forward coding unit partitioning information; obtaining sub-optimized coding unit partitioning information of each frame according to the first sub-coding unit partitioning information and the second 6 sub-coding unit partitioning information of each frame; and obtaining the optimized coding unit partitioning information corresponding to the image sequence according to the sub-optimized coding unit partitioning information of each frame [Xu: §3.1 Framework: After the action is executed, the interpreter feeds back information about the new state s1+ of the environment and reward ri1+1(value of targeted optimization function) associated with the performed action. The feedback information is utilized in gradually updating the model of cost sensitive binary classifiers].
Regarding Claims 6 and 17, Xu in view of Huo and Su discloses all the limitations of Claims 1 and 11, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein obtaining the game result information corresponding to the optimized coding contents according to the optimized coding contents, the reference coding contents, and the preset evaluation index comprises: comparing the optimized coding contents with the reference coding contents according to the preset evaluation index, and determining a relative quality degree between the optimized coding contents and the reference coding contents; and obtaining the game result information corresponding to the optimized coding contents according to the relative quality degree between the optimized coding contents and the reference coding contents [Xu: §4 Experimental results: The coding performance is compared with the anchor that exhaustively searches through all the coding options in SCM 8.7. The video sequences used for coding policy learning are listed in Table 1, where TGM, M, and CC represent text and graphics with motion, mixed content, and camera-captured content, respectively].
Regarding Claims 7 and 18, Xu in view of Huo and Su discloses all the limitations of Claims 6 and 17, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein the game result information comprises success result information and failure result information, the optimized coding contents comprise several sub-optimized coding contents, and the reference coding contents comprise several sub-reference coding contents; and obtaining the game result information corresponding to the optimized coding contents according to the relative quality degree between the optimized coding contents and the reference coding contents comprises: in response to determining that a sub-optimized coding content is better than a corresponding sub-reference coding content, obtaining success result information corresponding to the sub-optimized coding content; and in response to determining that a sub-reference coding content is better than a corresponding sub-optimized coding content, obtaining failure result information corresponding to the sub-optimized coding content [Xu: §4 Experimental Results: A subset using a cropped window on the first frames of each of these sequences are used as training data. Training samples are generated and randomly selected according to the reward values].
Regarding Claims 8 and 19, Xu in view of Huo and Su discloses all the limitations of Claims 7 and 18, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein the preset game condition comprises a winning probability threshold; and determining, in response to determining that the game result information meets the preset game condition, the coding mode corresponding to the optimized coding unit partitioning information as 7 the video coding mode for the image sequence comprises: counting all success result information and all failure result information corresponding to the sub-optimized coding contents; dividing the success result information by a sum of the success result information and the failure result information to obtain a success-result winning probability; and in response to determining that the success-result winning probability is not less than the winning probability threshold, determining the coding mode corresponding to the optimized coding unit partitioning information as the video coding mode for the image sequence [Xu: 3.3 Cost Sensitive binary classifier design: The rate-distortion cost of the CU can be represented by min i,ai,t!=0(Rd;). Rdi is the coding cost (D + IR) of coding modes associated with ai, where R, D and A are the coding bit, the reconstruction distortion, and the Lagrange multiplier, respectively… The rewards corresponding to performing and skipping a particular action s can be defined as follows.; and Eqs. 7 and 8].
Regarding Claims 9 and 20, Xu in view of Huo and Su discloses all the limitations of Claims 7 and 18, respectively, and is analyzed as previously discussed with respect to those claims.
Furthermore, Xu in view of Huo and Su discloses wherein the optimized coding unit partitioning information comprises reinforcement learning prediction information and success sub-coding unit partitioning information corresponding to the success result information, and the reinforcement learning prediction information is configured to represent a game success prediction probability corresponding to the optimized coding unit partitioning information; and the method further comprises: in response to determining that the game result information does not meet the preset game condition, determining a network training weight according to the game result information, prediction error information, and the success sub-coding unit partitioning information, where the prediction error information is a difference between the game result information and the reinforcement learning prediction information; and performing the reinforcement learning network training on the image sequence again according to the network training weight [Xu: § 3.3 Cost sensitive binary classifier design: Oi is the vector consists of bias and weight coefficients for feature vector x for classifier I, ai=1 and ai=0 mean the corresponding coding mode is evaluated a; and skipped respectively].
Regarding Claim 10, Xu in view of Huo and Su discloses all the limitations of Claim 1 and is analyzed as previously discussed with respect to that claim.
Furthermore, Xu in view of Huo and Su discloses wherein before performing the reinforcement learning network training on the acquired image sequence, the method further comprises: acquiring video image data; and obtaining the image sequence corresponding to the video image data according to the video image data [Xu: FIG. 5: Screen content videos; and coded bitstream].
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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/JONATHAN R MESSMORE/Primary Examiner, Art Unit 2482
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