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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because claim 20 recites one or more computer-readable media. The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. 101 as covering non-statutory subject matter. The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. 101 as covering both non-statutory subject matter and statutory subject matter. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. $ I01 by adding the limitation "non-transitory" to the claim. Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se.
Applicant’s specification paragraph 93 defines computer readable media includes both computer readable storage media and computer readable signal media. Specification paragraph 94 defines storage media is non-transitory, and paragraph 95 defines signal media as transitory. Therefore, claim 20 recites computer readable media covers both transitory and non-transitory and is rejected under 101 non-statutory subject matter.
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.
Claims 1-2, 9-12, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (Gan with multivariate disentangling for controllable hair editing, European Conference on Computer Vision, 31 October 2022, “Guo”) in view of Zhou et al. (GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations, arXiv.org, -- arXiv:2311.02062v2 [cs.GR] 16 Nov 2023, hereinafter “Zhou”).
Regarding claim 1, Guo discloses A method comprising: (Abstract, “we propose an efficiently controllable method that can provide a set of sliding bars to do continuous and fine hair editing.”)
receiving, by a processing device, a shape parameter representative of an overall shape of a hair model and a style parameter representative of local strand details of the hair model; (Abstract, “an encoder disentangles the hair’s major attributes, including color, texture, and shape, to separate latent representations”; page 5, Fig. 2:
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Encoded latent code “ZS” by “Shape Enc ES” corresponding to Hair shape can be regarded as a shape parameter, while the combination of Encoded latent code “ZC” and “ZT” by “Color Enc EC” and “Texture Enc ET” corresponding to Hair color and texture can be regarded as a style parameter; page 7, para. 6, “The texture attribute captures the pattern and regularity of the hair, such as hair-strand thickness, smoothness, curliness, etc.”). Note that: the respective parameters of overall hair shape and style (color / texture) for local strand details are controlled separately or disentangled. Note that: since the steps in the method of claim 1 need to be performed by a certain device, it is obvious to one having ordinary skills in the art that the device can be regarded as a processing device.
generating, using the machine-learning model, wisps based on the style parameter, the wisps providing local strand details for the hair model; and (page 5, Fig. 2: the decoded “Edited Hair Feature X” by “Decoder DX” based on manipulated latent code values can be regarded as wisps based on the style parameters; page 10, para.3, “The encoder of color EC, encoder of texture ET and decoder for both of them DX are all designed as fully connection architecture”). Note that: (1) the decoded hair features as wisps reflect local hair strand details (hair-strand thickness, smoothness, curliness, etc.); and (2) Decoder DX is designed as fully connection architecture with a few layers shown in Fig. 2 above, indicating it is a machine-learning model.
However, Guo fails to disclose, but in the same art of computer graphics, Zhou discloses
generating, using a machine-learning model, guide strands based on the shape parameter, the guide strands providing a sparse representation of the hair model; (Zhou, page 1, col left, para. 1, “Based on this hierarchical latent representation, our proposed pipeline consists of a strand-VAE and a hairstyle-VAE that encode an individual strand and a set of guide hairs to their respective latent spaces”; page 7, col. right, Fig. 6: “hairstyle-VAE reconstruction” columns show a parse representation of the hair model). Note that: a strand-VAE and a hairstyle-VAE are machine learning model that is based on latent code in latent space as a shape parameter and generates a set of guide hairs as a parse representation of the hair model.
generating, by the processing device, the hair model by interpolating the wisps onto the guide strands. (Zhou, page 3, col. right, Fig. 2: “We build a hierarchical representation of human hairstyles. To generate a hair model, we first draw a random vector from the Normal distribution in the hairstyle latent space. The vector is decoded by the hairstyle-VAE to get a low-resolution latent-map, which corresponds to sparse guide strands. Finally, the neural upsampler synthesizes dense hair strands from the sparse guide strands, which are further refined heuristically based on user specification”). Note that: dense strands are synthesized or interpolated from the sparse guide strands using the neural upsampler with higher level hair details as wisps and the “heuristic refinement” applies corresponding heuristic local features to further generate a realistic hair model.
In addition, Zhou also discloses … by a processing device … (Zhou, page 6, col. right, para. 4, “We evaluate the runtime performance of each module on a PC equipped with an Intel Core i9-11900KF CPU and an NVIDIA A100 40GB GPU”).
Guo and Zhou are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply generating guide strands and a hair model by a processing device, as taught by Zhou into Guo. The motivation would have been “Our compact model possesses the capability to represent and generate diverse hairstyles with high visual fidelity.” (Zhou, page 2, col. left, para. 4). The suggestion for doing so would allow to use a processing device to generate guide strands and a hair model with diverse hairstyles and high visual fidelity. Therefore, it would have been obvious to combine Guo with Zhou.
Regarding claim 2, Guo in view of Zhou discloses The method of claim 1, wherein generating the guide strands based on the shape parameter comprises:
generating, using a first neural network of the machine-learning model, a guide texture and a guide mask based on the shape parameter, the guide texture providing a first two-dimensional (2D) texture map of the guide strands in a frequency domain, the guide mask providing a location of the guide strands; and (Zhou, page 3, col. right, para. 3, “To better retain strand-level details like curliness, we parameterize the strands in the frequency domain using the discrete Fourier transform (DFT), and establish the strand latent space based on this frequency representation”; page 4, col. right, para. 4, “Based on the scalp space parameterization, our hairstyle-VAE learns to generate whole hairstyles utilizing the VAE framework. The input and reconstruction target for the hairstyle-VAE consist of both the low-resolution latent-map M𝑙 ∈ R𝑤𝑀 ×ℎ𝑀 ×𝐷𝑠 and the baldness map M𝑏 ∈ R𝑤𝑀 ×ℎ𝑀 ”). Note that: (1) the latent space is in a frequency domain using the discrete Fourier transform (DFT); and (2) regarding the generation of the guide strands based on the shape parameter, hairstyle-VAE as a first neural network of the machine-learning model has the input and reconstruction target of low-resolution latent-map M𝑙 as a guide texture (a first 2D texture map of the guide strands) and M𝑏 as a guide Mask.
generating, using a second neural network of the machine-learning model, an upsampled guide texture based on a concatenation of the guide texture and the guide mask, the upsampled guide texture providing a second 2D texture map for the overall shape of the hair model in the frequency domain. (Zhou, page 5, col. left, paras. 3-4, “Neural Upsampling The hairstyle-VAE produces a low-resolution latent-map that represents sparse guide strands. To further generate a complete hair model with around 150K strands, we then employ a hybrid densification process involving two steps: upsampling and refinement. The upsampling step outputs a high-resolution strand-map with 25K hairs, and the refinement step additionally populates the strands by 6 times.”). Note that: (1) Neural upsampling as a second neural network of the machine-learning model; (2) the upsampled guide texture can regarded as a second 2D texture map in the frequency domain; and (3) it is obvious to one having ordinary skills in the art that both the guide texture and the guide mask need to be upsampled to formulate the upsampled texture for the overall shape of the hair model and a concatenation of two features (the guide texture and the guide mask) is a conventional network layer setup for the purpose.
The motivation to combine Guo and Zhou given in claim 1 is incorporated here.
Regarding claim 9, Guo in view of Zhou discloses The method of claim 1, wherein training the machine-learning model includes fitting the shape parameter and the style parameter (Guo, page 5, Fig. 2: “The encoding module separates hair into distinct latent representations, of which each is formulated as a standard multivariate Gaussian distribution. In the interactive editing module, any manual editing including a set of bars, reference images, painted masks, can be made. The decoding module outputs an edited portrait with these edited latent representations”; page 6 / para. 6 – page 7 / para. 1, “Firstly, the generated image should be realistic. Since the Φ and Ψ are pretrained, we only need to ensure the generated Xˆ and Sˆ same as those from the real images. So, a realism adversarial loss Lreal, i.e., the first term in Eq. (6), is optimized by adversarial training between generated (Xˆ, Sˆ) and (X, S) from real images, same as the adversarial training in conventional GANs. Secondly, the editing should be correct, i.e. the generated image ˆI should be with the edited attributes while other attributes remain unchanged. These are achieved by the reconstruction loss Lrec, i.e. the second term in Eq. (6). Thirdly, the representation ZC, ZT, ZS are expected to follow a standard multivariate Gaussian distribution for continuous and fine editing, which is formulated by distribution loss Ldist, i.e., the third term in Eq. (6).”). Note that: training the machine-leaning model is equivalent to the optimization process to fit the shape parameter (ZS) and the style parameter (ZC / ZT) to a set of hair truth data (images with a variety of hairstyles or hair models).
to a set of 3D hair models. (Zhou, page 1, Fig. 1: “Our method is able to automatically generate diverse high-quality hairstyles from random latent vectors”, and a variety of 3D hair models shown from different perspectives). Note that: for neural network training the hair truth data can be a set of 3D hair models.
The motivation to combine Guo and Zhou given in claim 1 is incorporated here.
Regarding claim 10, Guo in view of Zhou discloses The method of claim 1, wherein the method further comprises generating an updated hair model by:
modifying a value of the shape parameter to change the overall shape of the hair model; (Guo, Fig. 1: “Controllable hair editing by using CtrlHair. Given a portrait in the upper left corner of each subfigure, CtrlHair can edit the hair by sliding a set of bars. CtrlHair supports editing of fine-grained factors of an attribute simultaneously and separately”, and sliding bar for “Shape (Bangs)” to modify a value of the shape parameter to change the overall shape of the hair model). Note that: by sliding “Shape (Bangs)” bar, the shape parameter, latent code ZS value is modified to generate the hair shape.
modifying a value of the style parameter to change the local strand details of the hair model; or (Guo, Fig. 1: “Controllable hair editing by using CtrlHair. Given a portrait in the upper left corner of each subfigure, CtrlHair can edit the hair by sliding a set of bars. CtrlHair supports editing of fine-grained factors of an attribute simultaneously and separately”, and sliding bar for “Color (Brightness)” / “Texture (Smoothness)” to modify a value of the style parameter to change the local details of the hair model). Note that: by sliding “Color (Brightness)” / “Texture (Smoothness)” bars, the style parameter, latent code (ZC / ZT) value is modified to generate the local strand details.
modifying the value of the shape parameter to change the overall shape of the hair model and the value of the style parameter to change the local strand details of the hair model. (Guo, Fig. 1: “Controllable hair editing by using CtrlHair. Given a portrait in the upper left corner of each subfigure, CtrlHair can edit the hair by sliding a set of bars. CtrlHair supports editing of fine-grained factors of an attribute simultaneously and separately”, and sliding bar for “Color (Brightness)” / “Texture (Smoothness)” to modify a value of the style parameter to change the local details of the hair model). Note that: by sliding “Shape (Bangs)” and “Color (Brightness)” / “Texture (Smoothness)” bars, the shape parameter, latent code ZS value is modified to generate the hair shape, and latent code (ZC / ZT) value is modified to generate the local strand details.
Regarding claim 11, Guo in view of Zhou discloses The method of claim 1, wherein the method further comprises:
receiving a digital image of a hairstyle; and (Guo, page 5, Fig 2: “Input Image I” of a hairstyle on the leftmost).
determining, using the machine-learning model, a first value of the shape parameter and a second value of the style parameter that results in the hair model that recreates the hairstyle. (Guo, page 5, Fig 2: latent code “ZS” as a first value of the shape parameter, and latent code “ZC / ZT” as a second value of the style parameter that generates a hair model that exports hairstyle shown in the output image I^ on the rightmost; Fig. 1: sliding bars for “Shape (Bangs)” and “Color (Brightness)” / “Texture (Smoothness)”). Note that: the ZS and ZC / ZT can be determined by sliding the “Shape (Bangs)” and “Color (Brightness)” / “Texture (Smoothness)” bars that are corresponding to the hairstyle in the input image to recreate the hairstyle in the output image.
Regarding claim 12, Guo in view of Zhou discloses The method of claim 11, wherein the first value and the second value are optimized to minimize differences when the hair model is projected onto the hairstyle in the digital image. (Guo, page 5, Fig 2: “Input Image I” of a hairstyle on the leftmost and the output image I^ on the rightmost, “Interactive Editing Module” for interactive latent code adjustment, and “
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” projects the hair model defined by corresponding ZS and ZC / ZT onto the hairstyle in the digital image). Note that: (1) the first value (ZS) and the second value (ZC / ZT) can be manually adjusted (sliding the corresponding bars) or optimized to minimize visual differences between the hairstyles in the digital image and in the output image through user interactive editing; and (2) the output image can be regarded as a projection or rendering of the hair model for the hairstyle.
Regarding claim 14, Guo discloses
receive a shape parameter representative of an overall shape of a hair model and a style parameter representative of local strand details of the hair model; (Guo, Abstract, “an encoder disentangles the hair’s major attributes, including color, texture, and shape, to separate latent representations”; page 5, Fig. 2:
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Encoded latent code “ZS” by “Shape Enc ES” corresponding to Hair shape can be regarded as a shape parameter, while the combination of Encoded latent code “ZC” and “ZT” by “Color Enc EC” and “Texture Enc ET” corresponding to Hair color and texture can be regarded as a style parameter; page 7, para. 6, “The texture attribute captures the pattern and regularity of the hair, such as hair-strand thickness, smoothness, curliness, etc.”). Note that: the respective parameters of overall hair shape and style (color / texture) for local strand details are controlled separately or disentangled.
generate, using a machine-learning model, a geometry texture based on the shape parameter and the style parameter, (Guo,--- page 5, Fig. 2: “The decoding module outputs an edited portrait with these edited latent representations”, and including a two-dimensional (2D) texture map of the overall shape and local strand details for the hair model.
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). Note that: (1) the decoder is a machine learning model; and (2) The output of the decoder can be regarded as a geometry texture based on the manipulated shape parameter (“Z^S”) and the manipulated style parameters (“Z^C / Z^T”).
However, Guo fails to disclose, but in the same art of computer graphics, Zhou discloses
A computing device comprising:
a processing device; and
a computer-readable medium storing instructions that, in response to execution by the processing device, cause the processing device to perform operations including: (Zhou, page 6, col. right, para. 4, “We evaluate the runtime performance of each module on a PC equipped with an Intel Core i9-11900KF CPU and an NVIDIA A100 40GB GPU”). Note that: a PC is a computing device including a processing device with CPU, GPU, and conventionally having corresponding RAMs storing instructions.
the geometry texture including a two-dimensional (2D) texture map of the overall shape and local strand details for the hair model in a frequency domain; and (Zhou, page 3, col. right, para. 3, “To better retain strand-level details like curliness, we parameterize the strands in the frequency domain using the discrete Fourier transform (DFT), and establish the strand latent space based on this frequency representation”; page 4, col. right, para. 4, “Based on the scalp space parameterization, our hairstyle-VAE learns to generate whole hairstyles utilizing the VAE framework. The input and reconstruction target for the hairstyle-VAE consist of both the low-resolution latent-map M𝑙 ∈ R𝑤𝑀 ×ℎ𝑀 ×𝐷𝑠 and the baldness map M𝑏 ∈ R𝑤𝑀 ×ℎ𝑀 ”; page 4, col. right, para. 1, “we define the 2D parameterization of hairs on the scalp surface as a regular UV map, as illustrated in Fig. 3. The strand representations are embedded into the UV map at the positions corresponding to their roots on the scalp. When the strands are represented by frequency codes V, the corresponding UV map is referred to as a strand-map”) Note that: (1) the latent space is in a frequency domain using the discrete Fourier transform (DFT); and (2) in the frequency domain the corresponding UV map as a 2D texture map of the overall shape and local strand details for the hair model.
sample and transform the geometry texture from the frequency domain to a spatial domain to generate the hair model. (Zhou, page 3, col. right, Fig. 2: “strand-VAE”, “neural upsampler”, “frequency domain”, “DFT”, “spatial domain”, “heuristic refinement”, “We build a hierarchical representation of human hairstyles. To generate a hair model, we first draw a random vector from the Normal distribution in the hairstyle latent space. The vector is decoded by the hairstyle-VAE to get a low-resolution latent-map, which corresponds to sparse guide strands. Finally, the neural upsampler synthesizes dense hair strands from the sparse guide strands, which are further refined heuristically based on user specification”, and
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”; page 3, col. right, para. 3, “To better retain strand-level details like curliness, we parameterize the strands in the frequency domain using the discrete Fourier transform (DFT), and establish the strand latent space based on this frequency representation.”). Note that: the output of “strand-VAE” in “frequency domain” can be sampled together with the upsampled data as the output of “neural unsampler”; and (2) the sampled data in “frequency domain” can be regarded as the input to “DFT” to be transformed into the texture data in “spatial domain” for generating the hair model.
Guo and Zhou are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply a computing device and generating the geometry texture including a two-dimensional (2D) texture map of the overall shape and local strand details for the hair model in a frequency domain, as taught by Zhou into Guo. The motivation would have been “To better retain strand-level details like curliness, we parameterize the strands in the frequency domain using the discrete Fourier transform (DFT), and establish the strand latent space based on this frequency representation.” (Zhou, page 3, col. right, para. 3). The suggestion for doing so would allow to better represent the geometry texture of hairstyles in latent space in frequency domain. Therefore, it would have been obvious to combine Guo with Zhou.
Claim 17 is corresponding to claim 9. Therefore, claim 17 is rejected for the same rationale for claim 9.
Claim 18 is corresponding to claim 10. Therefore, claim 18 is rejected for the same rationale for claim 10.
Claim 19 is corresponding to the combination of claims 11-12. Therefore, claim 19 is rejected for the same rationale for claims 11-12.
Claim 20 reciting “One or more computer-readable media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations comprising:”, is corresponding to the method of claim 1. Therefore, claim 20 is rejected for the same rationale for claim 1.
In addition, Guo in view of Zhou discloses One or more computer-readable media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations comprising: (Zhou, page 6, col. right, para. 4, “We evaluate the runtime performance of each module on a PC equipped with an Intel Core i9-11900KF CPU and an NVIDIA A100 40GB GPU”). Note that: (1) a PC is a computing device including a processing device with CPU, GPU, and conventionally having corresponding RAMs storing instructions; and (2) RAMs are One or more computer-readable media storing instructions that, responsive to execution by the processing device.
Guo and Zhou are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply computer-readable media storing instructions, as taught by Zhou into Guo. The motivation would have been “We evaluate the runtime performance of each module on a PC equipped with an Intel Core i9-11900KF CPU and an NVIDIA A100 40GB GPU” (Zhou, page 6, col. right, para. 4). The suggestion for doing so would allow to use computer-readable media storing instructions to perform operations. Therefore, it would have been obvious to combine Guo with Zhou.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Guo, Zhou, and Sklyarova et al. (Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles, arXiv.org, arXiv:2312.11666v1 [cs.CV] 18 Dec 2023, hereinafter “Sklyarova”).
Regarding claim 13, Guo in view of Zhou discloses The method of claim 1, wherein
However, Guo in view of Zhou fails to disclose, but in the same art of computer graphics, Sklyarova discloses
the hair model is combined with a text prompt to a generative model to generate, based on the text prompt, a digital image or digital video of a human with a hairstyle having the overall shape and the local strand details of the hair model. (Sklyarova, Title, “Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles”; page 4, Figure 2: “Hairstyle H”, “Caption P”, “Latent hair map”, “Diffusion Model”, “Decoder”, “Generated hair map”, “woman with afro hairstyle”, “Generated hairstyle H^”, and “Figure 2. Overview. We present our new method for text-guided and strand-based hair generation. For each hairstyle H in the training set, we produce latent hair maps Z and annotate them with textual captions P using off-the-shelf VQA systems [26] and our custom annotation pipeline. Then, we train a conditional diffusion model D [16] to generate the guiding strands in this latent space and use a latent upsampling procedure to reconstruct dense hairstyles that contain up to a hundred thousand strands given textual descriptions. The generated hairstyles are then rendered using off-the-shelf computer graphics techniques [9]”). Note that: a hair or hairstyle or model H is combined with a text prompt (“Caption P”), and based on a textual description (“woman with afro hairstyle”), generates a digital image on the rightmost of Figure 2 with a hairstyle having the overall shape and the local strand details of the hair model.
Guo in view of Zhou, and Sklyarova, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply the hair model combined with a text prompt to generate a digital image, as taught by Sklyarova into Guo in view of Zhou. The motivation would have been “Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles” (Sklyarova, Title). The suggestion for doing so would allow to use the hair model to be combined with a text prompt to generate a digital image. Therefore, it would have been obvious to combine Guo, Zhou, and Sklyarova.
Allowable Subject Matter
Claim 3-8 and 15-16 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding dependent claim 3, in the context of claim as a whole, the prior art either alone or in combination does not teach or suggest the additional elements of: “generating, using a third neural network of the machine-learning model, a residual texture based on the style parameter, the residual texture providing a third 2D texture map of the local strand details in the frequency domain.”
Claims 4-8 depend from claim 3.
Regarding dependent claim 15, in the context of claim as a whole, the prior art either alone or in combination does not teach or suggest the additional elements of: “generating, using a third neural network of the machine-learning model, a residual texture based on the style parameter, the residual texture providing a third 2D texture map of the local strand details in the frequency domain.”
Claim 16 depends from claim 15.
Conclusion
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/Biao Chen/
Patent Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611