CTNF 18/934,568 CTNF 79993 Detailed Action 07-03-01-aia AIA 07-03-01-r-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. See 35 U.S.C. § 100 (note). Art Rejections Anticipation 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1, 15 and 20 are rejected under 35 U.S.C. § 102( a)(1 ) as being anticipated by US Patent Application Publication 2021/0005211 (07 January 2021) (“Stahlmann”) . Claim 1 is drawn to “an audio processing method.” The following table illustrates the correspondence between the claimed method and the Stahlmann reference. Claim 1 The Stahlmann Reference “1. An audio processing method, wherein the method comprises: The Stahlmann reference describes a system and method that performs a corresponding method for processing audio through dynamic range control (DRC) and peak limiting. Stahlmann at Abs., ¶ 36, FIG.1A. “obtaining media data to be processed and audio metadata of the media data to be processed; Stahlmann describes an object audio decoder 100 that similarly receives, or obtains, an encoded input signal 102 from an encoder 150. Id. at ¶¶ 37, 58, FIGs.1A, 1B. Encoded input signal 102 includes both input audio data frames (i.e., media data) and OAMD (i.e., metadata) as claimed. Id. “performing first loudness compensation on the media data to be processed based on the audio metadata to obtain first media data, wherein the first loudness compensation comprises loudness compensation for dynamic range control; Decoder 100 similarly performs DRC on the input audio data frames based on data contained in the OAMD. Id. at ¶ 41. “determining a second loudness compensation value corresponding to peak limiting based on an audio feature of the first media data; and “performing second loudness compensation and peak limiting on the first media data based on the second loudness compensation value to determine target media data for playback.” Decoder 100 also performs a second loudness compensation that includes peak limiting on input audio data frames based on a determined second loudness compensation value. Id. at ¶¶ 57, 58. For example, Stahlmann describes decoder 100 as downmixing input audio frames and generating a peak value (i.e., an audio feature), which is used by a peak limiter 112 to calculate a peak limiting gain, or second loudness compensation value. Id. Table 1 For the foregoing reasons, the Stahlmann reference anticipates all limitations of the claim. Claim 15 is drawn to “an electronic device.” The following table illustrates the correspondence between the claimed device and the Stahlmann reference. Claim 15 The Stahlmann Reference “15. An electronic device, comprising: The Stahlmann reference describes a system and method that performs a corresponding method for processing audio through dynamic range control (DRC) and peak limiting. Stahlmann at Abs., ¶ 36, FIG.1A. “a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the computer instructions, when executed by the processor, cause the processor to: Stahlmann’s computer system 500 likewise includes memory 506, 508 and processor 504 in communication so that processor 504 may execute instructions in one of memories 506, 508, including the actions discussed below. Id. at ¶¶ 175–182, FIG.5. “obtain media data to be processed and audio metadata of the media data to be processed; Stahlmann describes an object audio decoder 100 that similarly receives, or obtains, an encoded input signal 102 from an encoder 150. Id. at ¶¶ 37, 58, FIGs.1A, 1B. Encoded input signal 102 includes both input audio data frames (i.e., media data) and OAMD (i.e., metadata) as claimed. Id. “perform first loudness compensation on the media data to be processed based on the audio metadata to obtain first media data, wherein the first loudness compensation comprises loudness compensation for dynamic range control; Decoder 100 similarly performs DRC on the input audio data frames based on data contained in the OAMD. Id. at ¶ 41. “determine a second loudness compensation value corresponding to peak limiting based on an audio feature of the first media data; and “perform second loudness compensation and peak limiting on the first media data based on the second loudness compensation value to determine target media data for playback.” Decoder 100 also performs a second loudness compensation that includes peak limiting on input audio data frames based on a determined second loudness compensation value. Id. at ¶¶ 57, 58. For example, Stahlmann describes decoder 100 as downmixing input audio frames and generating a peak value (i.e., an audio feature), which is used by a peak limiter 112 to calculate a peak limiting gain, or second loudness compensation value. Id. Table 2 For the foregoing reasons, the Stahlmann reference anticipates all limitations of the claim. Claim 20 is drawn to “a non-transitory computer-readable storage medium.” The following table illustrates the correspondence between the claimed medium and the Stahlmann reference. Claim 20 The Stahlmann Reference “20. A non-transitory computer-readable storage medium, having stored thereon computer instructions that are used to cause a computer to: The Stahlmann reference describes a system and method that performs a corresponding method for processing audio through dynamic range control (DRC) and peak limiting. Stahlmann at Abs., ¶ 36, FIG.1A. Stahlmann’s computer system 500 likewise includes memory 506, 508 and processor 504 in communication so that processor 504 may execute instructions in one of memories 506, 508, including the actions discussed below. Id. at ¶¶ 175–182, FIG.5. “obtain media data to be processed and audio metadata of the media data to be processed; Stahlmann describes an object audio decoder 100 that similarly receives, or obtains, an encoded input signal 102 from an encoder 150. Id. at ¶¶ 37, 58, FIGs.1A, 1B. Encoded input signal 102 includes both input audio data frames (i.e., media data) and OAMD (i.e., metadata) as claimed. Id. “perform first loudness compensation on the media data to be processed based on the audio metadata to obtain first media data, wherein the first loudness compensation comprises loudness compensation for dynamic range control; Decoder 100 similarly performs DRC on the input audio data frames based on data contained in the OAMD. Id. at ¶ 41. “determine a second loudness compensation value corresponding to peak limiting based on an audio feature of the first media data; and “perform second loudness compensation and peak limiting on the first media data based on the second loudness compensation value to determine target media data for playback.” Decoder 100 also performs a second loudness compensation that includes peak limiting on input audio data frames based on a determined second loudness compensation value. Id. at ¶¶ 57, 58. For example, Stahlmann describes decoder 100 as downmixing input audio frames and generating a peak value (i.e., an audio feature), which is used by a peak limiter 112 to calculate a peak limiting gain, or second loudness compensation value. Id. Table 3 For the foregoing reasons, the Stahlmann reference anticipates all limitations of the claim. Obviousness 07-20-aia AIA 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. 07-21-aia AIA Claim s 2–5, 7, 8, 13 and 16–19 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Stahlmann and US Patent Application Publication 2017/0187560 (published 29 June 2017) (“Ng”) . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Stahlmann; Ng; US Patent Application Publication 2024/0276143 (effectively filed 09 February 2023) and US Patent Application Publication 2023/0127001 (published 27 April 2023) (“Das”) . 07-21-aia AIA Claim s 11 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Stahlmann; Ng; US Patent 12,621,604 (effectively filed 29 April 2022) (“Garcia”) and US Patent Application Publication 2008/0056504 (published 06 March 2008) (“Gorges”) . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Stahlmann and US Patent Application Publication 2020/0329330 (published 15 October 2020) (“Mitchell”) . Claim 2 depends on claim 1, and further requires the following: “wherein determining the second loudness compensation value corresponding to peak limiting based on the audio feature of the first media data comprises: “obtaining a target performance requirement; “determining a second loudness compensation value determining way corresponding to the target performance requirement based on a correspondence between a performance requirement and a loudness compensation value determining way corresponding to peak limiting; and “determining the second loudness compensation value based on the second loudness compensation value determining way and the audio feature of the first media data.” Stahlmann describes a peak limiter 112 that performs a peak limiting gain corresponding to the claimed second loudness compensation value. Stahlmann at ¶¶ 57, 58, 73, 85–88. Specifically, Stahlmann extracts an audio feature, such as a peak value and determining the gain based on an inverse of the peak and a comparison between a peak level and a preconfigured peak limiting threshold. Id. While Stahlmann describes a peak limiter 112 that performs a method similar to the one claimed, Stahlmann does not describe the determination of a second loudness compensation value (i.e., peak limiting) way based on a correspondence between a peak limiting way and an obtained target performance requirement. Id. Stahlmann describes the use of a number of peak limiting techniques, but Stahlmann does not describe any technique or need to determine a way, or technique, for peak limiting outside of an initial design choice. Id. at ¶ 63, 88, 91 (describing application of either hard of soft limiting that exhibit different levels of complexity and latency). The Ng reference, like Stahlmann, is drawn to signal processing, including peak limiting. Ng at Abs., ¶¶ 9, 77. Ng reiterates Stahlmann’s teaching that there are multiple types of peak limiting available. Id. Ng further teaches and suggests obtaining a set of criteria, or target performance requirements, including implementation complexity, processing latency and peak reduction performance. Id. Ng teaches using the criteria to make a real-time selection of an optimal peak limiting technique, or way, based on current environmental conditions. Id. Read in context of Stahlmann, the Ng reference reasonably teaches and suggests modifying Stahlmann’s system and method to similarly include a mechanism for making a real-time selection of an optimal peak limiting technique. For example, Ng suggests obtaining a set of criteria, including a desired implementation complexity, processing latency and peak reduction performance and choosing an algorithm whose properties best match the criteria. This would have reasonably suggested modifying Stahlmann to select an appropriate peak limiting technique based on complexity, latency and performance. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 3 depends on claim 2, and further requires the following: “wherein a loudness equalization effect in the performance requirement is positively correlated with processing complexity of the loudness compensation value determining way for peak limiting, and loudness equalization processing performance in the performance requirement is negatively correlated with the processing complexity of the loudness compensation value determining way for peak limiting.” The obviousness rejection of claim 2, incorporated herein, shows the obviousness of modifying Stahlmann’s system and method to make a real-time selection of an optimal peak limiting algorithm based on a set of criteria, including implementation complexity (i.e., processing complexity), peak reduction performance (i.e., loudness equalization effect) and processing latency (i.e., loudness equalization processing performance). Cf. Ng at ¶ 77. It is inherent that implementation complexity is positively correlated with peak reduction due to the increased accuracy and time required performance and negatively correlated with processing latency since more processing resources are required. See Stahlmann at ¶¶ 88, 89, 91. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 4 depends on claim 3, and further requires the following: “wherein the peak loudness compensation value determining way comprises a determining way based on a mapping relationship between a peak loudness compensation value and an audio feature, and a determining way based on a peak loudness compensation model, “wherein the peak loudness compensation model comprises the audio feature as an input and comprises the loudness compensation value as an output, “wherein processing complexity of the determining way based on the mapping relationship between the peak loudness compensation value and the audio feature is less than that of the determining way based on the peak loudness compensation model.” Stahlmann describes a peak limiting algorithm that similarly applies a mapping relationship to an input feature, such as audio loudness, and a modeling technique. Stahlmann at ¶¶ 86–88 (describing a hard limiting technique that maps loudness to a minimum value and a modeling technique that performs a soft limit based on a look ahead feature to predict future clipping). The mapping technique is inherently less complex than the model because it merely involves application of a m i n function while the model technique requires derivation of a look-ahead feature and prediction of future peaks. See id. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 5 depends on claim 4, and further requires the following: “wherein in response to the peak loudness compensation value determining way being the determining way based on the mapping relationship between the peak loudness compensation value and the audio feature, determining the second loudness compensation value based on the second loudness compensation value determining way and the audio feature of the first media data comprises: “obtaining a target peak for the peak limiting and a current audio peak in the audio feature to obtain a difference between the current audio peak and the target peak; and “determining the second loudness compensation value based on the difference and the mapping relationship.” Similarly, Stahlmann describes determining a difference between a peak (e.g., 3 dBFS) and a full-scale threshold (i.e., 0 dBFS) and then generating a gain as an inverse of the peak (e.g., -3dBFS). Stahlmann at ¶ 58. The peak limiter then applies a m i n function to select the lesser of the calculated gain and a max allowed gain. Id. at ¶ 63. For example, if a max allowed gain is +3 dBFS and the current peak is 3 dBFS, the m i n function will map the current peak to a gain of -3 dBFS. Id. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 6 depends on claim 4, and further requires the following: “wherein in response to the peak loudness compensation value determining way being the determining way based on the peak loudness compensation model, determining the second loudness compensation value based on the second loudness compensation value determining way and the audio feature of the first media data comprises: “determining a target submodel in the peak loudness compensation model based on a loudness equalization effect in the target performance requirement, wherein the peak loudness compensation model comprises a plurality of submodels, and the submodels have different processing complexity; and “determining the second loudness compensation value based on the target submodel and the audio feature of the first media data.” Stahlmann describes a soft limiting approach that generates a model of an audio signal to predict future peaks based on an audio signal’s loudness and to then generate a peak limiting gain. Stahlmann at ¶ 88. Stahlmann, however, does not describe the claimed determination of a target submodel from a plurality of submodels having different processing complexity. The Bharitkar reference is drawn to peak limiting of audio signals. Bharitkar teaches an alternative peak limiting algorithm that uses a trained machine learning model to predict a peak limiting gain. Bharitkar at Abs., ¶¶ 49–51, 60, 63, FIG.2. Read in light of Sthalmann and Ng, Bharitkar would have reasonably suggested modifying Stahlmann’s system and method to employ a trained machine learning model as an alternative technique for peak limiting. The Das reference is drawn to a machine learning model that includes multiple exit points in each of a plurality of decision layers. Das at Abs., ¶¶ 8, 37, 58, , 85, 86, FIGs.3A, 3B. These exit points create a plurality of sub-models with lower layers of progressively-increasing complexity. See id. Das teaches and suggests determining an exit point, or sub-model, to use based on current conditions, including a desired QoS. Id. Read in light of Stahlmann, Ng and Bharitkar, the teachings of Das reasonably suggest further modifying Stahlmann’s system and method to include a machine learning model that includes multiple exit points that form a plurality of sub-models that are selected for peak limiting based on current conditions (i.e., a QoS). One of ordinary skill would have recognized the use of machine learning and early exiting would provide a scalable peak limiting function that beneficially adapts to real-time conditions. For the foregoing reasons, the combination of the Stahlmann, the Ng, the Bharitkar and the Das references makes obvious all limitations of the claim. Claim 13 depends on claim 2, and further requires the following: “wherein the target performance requirement is determined based on a loudness processing mode of a current playback device or an error range of loudness compensation.” The obviousness rejection of claim 6, incorporated herein, shows the obviousness of modifying Stahlmann’s system and method to choose a real- time peak limiting algorithm based on a set of criteria. Cf. Ng at ¶ 77. Those criteria include complexity, latency and performance. The consideration of performance reasonably suggests considering the amount of error that results (i.e., how well the peak limiting algorithm limits peaks correctly without creating errors, such as over compensating and under compensating). For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 7 depends on claim 1, and further requires the following: “wherein performing first loudness compensation on the media data to be processed based on the audio metadata to obtain first media data comprises: “obtaining a target performance requirement; “determining a target loudness compensation value determining way for dynamic range control corresponding to the target performance requirement based on a correspondence between a performance requirement and a loudness compensation value determining way for dynamic range control; “determining a first loudness compensation value for dynamic range control based on the target loudness compensation value determining way and the audio metadata; and “performing first loudness compensation on the media data to be processed based on the first loudness compensation value to obtain the first media data.” Stahlmann describes a dynamic range controller (DRC) 106 that performs compression corresponding to the claimed first loudness compensation value. Stahlmann at ¶¶ 43–49. Specifically, Stahlmann extracts an audio feature, such as an input volume and maps the input volume to an output gain. Id. Alternatively, Stahlmann uses an algorithm to generate gains. Id. at ¶ 50. While Stahlmann describes a peak limiter 112 that performs a method similar to the one claimed, Stahlmann does not describe the determination of a target loudness compensation value determining way (i.e., DRC) based on a correspondence between a performance requirement and an obtained target performance requirement. Id. Stahlmann describes the use of a number of DRC techniques, but Stahlmann does not describe any technique or need to determine a way, or technique, for DRC outside of an initial design choice. Id. at ¶¶ 43–50 (describing application of either a mapping technique or an algorithmic generation technique). The Ng reference, like Stahlmann, is drawn to signal processing, including peak limiting. Ng at Abs., ¶¶ 9, 77. Ng reiterates Stahlmann’s teaching that there are multiple types of signal processing available. Id. Ng further teaches and suggests obtaining a set of criteria, or target performance requirements, including implementation complexity, processing latency and peak reduction performance. Id. Ng teaches using the criteria to make a real-time selection of an optimal peak limiting technique, or way, based on current environmental conditions. Id. Read in context of Stahlmann, the Ng reference reasonably teaches and suggests modifying Stahlmann’s system and method to similarly include a mechanism for making a real-time selection of an optimal DRC technique. For example, Ng suggests obtaining a set of criteria, including a desired implementation complexity, processing latency and peak reduction performance and choosing an algorithm whose properties best match the criteria. This would have reasonably suggested modifying Stahlmann to select an appropriate DRC technique based on complexity, latency and performance. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 8 depends on claim 7, and further requires the following: “wherein a loudness equalization effect in the performance requirement is positively correlated with processing complexity of the loudness compensation value determining way for dynamic range control, and loudness equalization processing performance in the performance requirement is negatively correlated with the processing complexity of the loudness compensation value determining way for dynamic range control.” The obviousness rejection of claim 7, incorporated herein, shows the obviousness of modifying Stahlmann’s system and method to make a real-time selection of an optimal DRC algorithm based on a set of criteria, including implementation complexity (i.e., processing complexity), DRC performance (i.e., loudness equalization effect) and processing latency (i.e., loudness equalization processing performance). Cf. Ng at ¶ 77. It is inherent that implementation complexity is positively correlated with DRC effect due to the increased accuracy and time required performance and negatively correlated with processing latency since more processing resources are required. See Stahlmann at ¶¶ 88, 89, 91. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 11 depends on claim 8, and further requires the following: “wherein the performance requirement comprises a loudness equalization effect of a first level and a loudness equalization effect of a second level, the first level is lower than the second level, and “in response to the target performance requirement comprising the loudness equalization effect of the second level, determining the first loudness compensation value for dynamic range control based on the target loudness compensation value determining way and the audio metadata comprises: “determining the first loudness compensation value based on a first loudness compensation model for dynamic range control and the audio metadata in response to a length of the media data to be processed being greater than a preset length.” Claim 12 depends on claim 11, and further requires the following: “wherein determining the first loudness compensation value for dynamic range control based on the target loudness compensation value determining way and the audio metadata further comprises: determining the first loudness compensation value based on a second loudness compensation model for dynamic range control and the audio metadata in response to the length of the media data to be processed being less than or equal to the preset length.” Claims 11 and 12 are analyzed together. Garcia teaches and suggests alternatively performing DRC with a trained machine learning model. Garcia at col. 2 l. 57 to col. 3 l. 8. Gorges further teaches and suggests splitting a DRC response into short-term and long-term approaches. Gorges at ¶¶ 22–27, 65–70, FIG.1. If a signal power remains above a threshold for a short period, a first DRC response is applied. Id. If the signal power persists above a threshold for a longer period, a second DRC response is then applied. Id. Read in the context of Stahlmann, the Garcia and Gorges references would have reasonably taught and suggested modifying Stahlmann’s system to alternatively perform DRC with multiple trained machine learning models that apply different effects based on the length of time a signal’s power level remains elevated, as claimed. For the foregoing reasons, the combination of the Stahlmann, the Ng, the Garcia and the Gorges references makes obvious all limitations of the claims. Claim 14 depends on claim 1, and further requires the following: “wherein obtaining the audio metadata of the media data to be processed comprises: “obtaining a frequency response curve of a target playback device, wherein the frequency response curve is used to determine a target loudness; and/or “obtaining a cutoff frequency of the target playback device, wherein the cutoff frequency is used to determine a filtered loudness compensation value corresponding to signal energy of data to be filtered in the media data to be processed, the data to be filtered is media data with a frequency lower than the cutoff frequency in the media data to be processed, the target playback device is configured to perform loudness processing on the media data to be processed based on the filtered loudness compensation value after filtering the data to be filtered from the media data to be processed, and the audio metadata comprises the target loudness and the filtered loudness compensation value.” The Stahlmann reference describes obtaining metadata (OAMD) about the loudspeakers used in Stahlmann’s system in order to determine how to apply DRC and peak limiting. Stahlmann at ¶¶ 45, 58. Stahlmann, however, does not describe obtaining a frequency response curve of each loudspeaker. The Mitchell reference, however, teaches and suggests obtaining a frequency response for each loudspeaker in a system to control DRC. Mitchell at ¶¶ 27, 29, 34. This would have reasonably suggested modifying Stahlmann’s system and method to similarly include and obtain loudspeaker frequency response information in OAMD. Stahlmann’s system would then consider the frequency response in determining the loudness of the system. For the foregoing reasons, the combination of the Stahlmann and the Mitchell references makes obvious all limitations of the claim. Claim 16 depends on claim 15, and further requires the following: “wherein the computer instructions for determining the second loudness compensation value corresponding to peak limiting based on the audio feature of the first media data further cause the processor to: “obtain a target performance requirement; “determine a second loudness compensation value determining way corresponding to the target performance requirement based on a correspondence between a performance requirement and a loudness compensation value determining way corresponding to peak limiting; and “determine the second loudness compensation value based on the second loudness compensation value determining way and the audio feature of the first media data.” Stahlmann describes a peak limiter 112 that performs a peak limiting gain corresponding to the claimed second loudness compensation value. Stahlmann at ¶¶ 57, 58, 73, 85–88. Specifically, Stahlmann extracts an audio feature, such as a peak value and determining the gain based on an inverse of the peak and a comparison between a peak level and a preconfigured peak limiting threshold. Id. While Stahlmann describes a peak limiter 112 that performs a method similar to the one claimed, Stahlmann does not describe the determination of a second loudness compensation value (i.e., peak limiting) way based on a correspondence between a peak limiting way and an obtained target performance requirement. Id. Stahlmann describes the use of a number of peak limiting techniques, but Stahlmann does not describe any technique or need to determine a way, or technique, for peak limiting outside of an initial design choice. Id. at ¶ 63, 88, 91 (describing application of either hard of soft limiting that exhibit different levels of complexity and latency). The Ng reference, like Stahlmann, is drawn to signal processing, including peak limiting. Ng at Abs., ¶¶ 9, 77. Ng reiterates Stahlmann’s teaching that there are multiple types of peak limiting available. Id. Ng further teaches and suggests obtaining a set of criteria, or target performance requirements, including implementation complexity, processing latency and peak reduction performance. Id. Ng teaches using the criteria to make a real-time selection of an optimal peak limiting technique, or way, based on current environmental conditions. Id. Read in context of Stahlmann, the Ng reference reasonably teaches and suggests modifying Stahlmann’s system and method to similarly include a mechanism for making a real-time selection of an optimal peak limiting technique. For example, Ng suggests obtaining a set of criteria, including a desired implementation complexity, processing latency and peak reduction performance and choosing an algorithm whose properties best match the criteria. This would have reasonably suggested modifying Stahlmann to select an appropriate peak limiting technique based on complexity, latency and performance. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 17 depends on claim 16, and further requires the following: “wherein a loudness equalization effect in the performance requirement is positively correlated with processing complexity of the loudness compensation value determining way for peak limiting, and loudness equalization processing performance in the performance requirement is negatively correlated with the processing complexity of the loudness compensation value determining way for peak limiting.” The obviousness rejection of claim 2, incorporated herein, shows the obviousness of modifying Stahlmann’s system and method to make a real-time selection of an optimal peak limiting algorithm based on a set of criteria, including implementation complexity (i.e., processing complexity), peak reduction performance (i.e., loudness equalization effect) and processing latency (i.e., loudness equalization processing performance). Cf. Ng at ¶ 77. It is inherent that implementation complexity is positively correlated with peak reduction due to the increased accuracy and time required performance and negatively correlated with processing latency since more processing resources are required. See Stahlmann at ¶¶ 88, 89, 91. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 18 depends on claim 17, and further requires the following: “wherein the second loudness compensation value determining way comprises a determining way based on a mapping relationship between a peak loudness compensation value and an audio feature, and a determining way based on a peak loudness compensation model, “wherein the peak loudness compensation model comprises the audio feature as an input and comprises the loudness compensation value as an output, “wherein processing complexity of the determining way based on the mapping relationship between the peak loudness compensation value and the audio feature is less than that of the determining way based on the peak loudness compensation model.” Stahlmann describes a peak limiting algorithm that similarly applies a mapping relationship to an input feature, such as audio loudness, and a modeling technique. Stahlmann at ¶¶ 86–88 (describing a hard limiting technique that maps loudness to a minimum value and a modeling technique that performs a soft limit based on a look ahead feature to predict future clipping). The mapping technique is inherently less complex than the model because it merely involves application of a m i n function while the model technique requires derivation of a look-ahead feature and prediction of future peaks. See id. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Claim 19 depends on claim 18, and further requires the following: “wherein in response to the peak loudness compensation value determining way being the determining way based on the mapping relationship between the peak loudness compensation value and the audio feature, the computer instructions for determining the second loudness compensation value based on the second loudness compensation value determining way and the audio feature of the first media data further cause the processor to: “obtain a target peak for the peak limiting and a current audio peak in the audio feature to obtain a difference between the current audio peak and the target peak; and “determine the second loudness compensation value based on the difference and the mapping relationship.” Similarly, Stahlmann describes determining a difference between a peak (e.g., 3 dBFS) and a full-scale threshold (i.e., 0 dBFS) and then generating a gain as an inverse of the peak (e.g., -3dBFS). Stahlmann at ¶ 58. The peak limiter then applies a m i n function to select the lesser of the calculated gain and a max allowed gain. Id. at ¶ 63. For example, if a max allowed gain is +3 dBFS and the current peak is 3 dBFS, the m i n function will map the current peak to a gain of -3 dBFS. Id. For the foregoing reasons, the combination of the Stahlmann and the Ng references makes obvious all limitations of the claim. Summary Claims 1–8 and 11–20 are rejected under at least one of 35 U.S.C. §§ 102 and 103 as being unpatentable over the cited prior art. 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 . 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention. Issues Under 35 U.S.C. § 112 Written Description 07-30-01 The following is a quotation of the first paragraph of 35 U.S.C. § 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. 07-31-01 Claims 9–12 are rejected under 35 U.S.C. § 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention. Claim 9 depends on claim 8, and further requires the following: “wherein the performance requirement comprises a loudness equalization effect of a first level and a loudness equalization effect of a second level, the first level is lower than the second level, and “in response to the target performance requirement comprising the loudness equalization effect of the first level, determining the first loudness compensation value for dynamic range control based on the target loudness compensation value determining way and the audio metadata comprises: “obtaining a slope in dynamic range control parameters and a starting point of a dynamic range in the audio metadata in response to a length of the media data to be processed being greater than a preset length; “performing loudness estimation based on a target loudness, the slope, and the starting point to determine an estimated loudness; and “determining the first loudness compensation value based on a difference between the target loudness and the estimated loudness.” Claim 10 depends on claim 9, and includes the same limitations. The claims include performing a specific type of dynamic range control in the case the length of the media data to be processed is greater than a preset length. The Specification provides an example length of 5. (Spec. at ¶ 108). This is a facially incomplete description of an invention. Time is not a dimensionless measure that can be expressed with a simple numeral. A complete invention would have included an example amount of time, such as 5 seconds. Further, it is not reasonably clear from the Specification how the length of the media data is measured. Does it refer to an active portion above a certain threshold, or simply the length of a media stream? Without these descriptions, one of ordinary skill in the art would not have reasonably understood the inventors of being in possession of an invention that selects between different DRC algorithms based on a length of time. Additionally, it is facially unclear how audio loudness can be estimated from a target loudness, a DRC slope and a starting point of a dynamic range. The Spec. at ¶ 106 provides a formula, but the formula does not include any input for any audio feature. Without any features extracted from current audio, one of ordinary skill would not have reasonably understood the formula as describing a proper loudness estimation of audio, and not reasonably believed Applicant was in possession of the claimed invention at the time this Application was effectively filed. For the foregoing reasons, claims 9 and 10 are rejected for lack of written description. Claim 11 depends on claim 8, and further requires the following: “wherein the performance requirement comprises a loudness equalization effect of a first level and a loudness equalization effect of a second level, the first level is lower than the second level, and “in response to the target performance requirement comprising the loudness equalization effect of the second level, determining the first loudness compensation value for dynamic range control based on the target loudness compensation value determining way and the audio metadata comprises: “determining the first loudness compensation value based on a first loudness compensation model for dynamic range control and the audio metadata in response to a length of the media data to be processed being greater than a preset length.” Claim 12 depends on claim 11, and includes the same limitations. Like claims 9 and 10, claims 11 and 12 require adjusting DRC processing based on the length of media data, which is not supported by the Specification in a way that would have reasonably conveyed possession of the claimed invention to one of ordinary skill in the art at the time this Application was effectively filed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WALTER F BRINEY III whose telephone number is (571)272-7513. The examiner can normally be reached M-F 8 am-4:30 pm. 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, Carolyn Edwards can be reached at 571-270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Walter F Briney III/ Walter F Briney IIIPrimary ExaminerArt Unit 2692 6/11/2026 Application/Control Number: 18/934,568 Page 2 Art Unit: 2692 Application/Control Number: 18/934,568 Page 3 Art Unit: 2692 Application/Control Number: 18/934,568 Page 4 Art Unit: 2692 Application/Control Number: 18/934,568 Page 5 Art Unit: 2692 Application/Control Number: 18/934,568 Page 6 Art Unit: 2692 Application/Control Number: 18/934,568 Page 7 Art Unit: 2692 Application/Control Number: 18/934,568 Page 8 Art Unit: 2692 Application/Control Number: 18/934,568 Page 9 Art Unit: 2692 Application/Control Number: 18/934,568 Page 10 Art Unit: 2692 Application/Control Number: 18/934,568 Page 11 Art Unit: 2692 Application/Control Number: 18/934,568 Page 12 Art Unit: 2692 Application/Control Number: 18/934,568 Page 13 Art Unit: 2692 Application/Control Number: 18/934,568 Page 14 Art Unit: 2692 Application/Control Number: 18/934,568 Page 15 Art Unit: 2692 Application/Control Number: 18/934,568 Page 16 Art Unit: 2692 Application/Control Number: 18/934,568 Page 17 Art Unit: 2692 Application/Control Number: 18/934,568 Page 18 Art Unit: 2692 Application/Control Number: 18/934,568 Page 19 Art Unit: 2692 Application/Control Number: 18/934,568 Page 20 Art Unit: 2692 Application/Control Number: 18/934,568 Page 21 Art Unit: 2692 Application/Control Number: 18/934,568 Page 22 Art Unit: 2692 Application/Control Number: 18/934,568 Page 23 Art Unit: 2692 Application/Control Number: 18/934,568 Page 24 Art Unit: 2692 Application/Control Number: 18/934,568 Page 25 Art Unit: 2692
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