DETAILED ACTION
Claims 1-20 have been examined.
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 U.S.C. § 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.
The invention, as taught in Claims 1-20, is directed to “mental steps” and “mathematical steps” without significantly more.
The claims recite:
• generate a first output tensor
• input tensor comprising an input image
• first output tensor comprising values to impart an image filter effect to one or more features in the input image
• generate a second output tensor based on the input tensor
• selectively applying the image filter effect to be imparted to the input image based, at least in part, on the second output tensor indicating the one or more features of the input image
Claim 1
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “1. A method comprising…” Therefore, it is a “method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 1 that recite abstract ideas?
YES. The following limitations in Claim 1 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• generate a first output tensor
• input tensor comprising an input image
• first output tensor comprising values to impart an image filter effect to one or more features in the input image
• generate a second output tensor based on the input tensor
• selectively applying the image filter effect to be imparted to the input image based, at least in part, on the second output tensor indicating the one or more features of the input image
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “executing”
(2) A “first neural network”/“second neural network”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “executing” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Claim 1 merely teaches the embodiment where the claimed “model” is in the form of a “neural network”, which is an “additional element”. The neural network is not used to calculate anything at all. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
This “first neural network”/“second neural network” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “executing”
(2) A “first neural network”/“second neural network”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's Specification, paragraph [0066] recites:
[0066] Computing devices such as cloud server 702, smartphone 724, and other such devices that may employ signal processing and/or filtering architectures can take many forms and can include many features or functions including those already described and those not described herein. Figure 8 shows a block diagram of a general-purpose computerized system, consistent with an example embodiment. Figure 8 illustrates only one particular example of computing device 800, and other computing devices 800 may be used in other embodiments. Although computing device 800 is shown as a standalone computing device, computing device 800 may be any component or system that includes one or more processors or another suitable computing environment for executing software instructions in other examples, and need not include all of the elements shown here.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Specification recites:
[0025] Neural networks and layers of Figure 2 may perform respective functions particularly efficiently in part due to isolating functions of feature detection and filtering into different networks, and in part due to improved nonlinearity. More specifically, multiplying values of output tensors of respective neural networks 208 and 212 may introduce a significant nonlinearity, allowing fewer neural network layers having fewer nodes in neural networks of Figure 2 than in a typical signal processing and/or filtering neural network architecture to produce a desired result. Efficiencies gained by a reduced size and improved nonlinearity in combining values of output tensors of neural network layers 208 and 212 may be further enhanced by an ability of processing stage 202 to perform multiple functions at the same time by concurrently executing two different neural networks having different objectives in parallel. Neural network layers 206-208 and 210-212 may in one example, be convolutional neural network layers, but in other examples may be any type of neural network as are commonly known or may become known in the art.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 1 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 2
Claim 2 recites:
2. The method of claim 1, wherein the image filter effect comprises tone mapping, color grading, mesh shading, de-mosaicing or de-noising, super-resolution, or a combination thereof.
Applicant’s Claim 2 merely teaches mathematical image processing functions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 2 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 3
Claim 3 recites:
3. The method of claim 1, wherein the second output tensor comprises coefficients based, at least in part, on detection of at least one of the one more features in the input image.
Applicant’s Claim 3 merely teaches a mathematical quantity called an “image” tensor. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 3 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 4
Claim 4 recites:
4. The method of claim 3, wherein selectively applying the image filter effect to be imparted to the input image further comprises applying the coefficients to the first output tensor to compute residual values, and combining the computed residual values with the input tensor or a tensor derived from the input tensor to impart the image filter effect.
Applicant’s Claim 4 merely teaches mathematical “applying” of coefficients and mathematical “combining”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 4 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 5
Claim 5 recites:
5. The method of claim 1, wherein the input tensor is determined based, at least in part, on image intensity values of one or more image frames.
Applicant’s Claim 5 merely teaches the mathematical calculation of a tensor. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 5 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 6
Claim 6 recites:
6. The method of claim 1, wherein at least one of the first neural network and the second neural network comprise convolutional neural networks.
Applicant’s Claim 6 merely teaches a generic convolutional neural network. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 6 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 7
Claim 7 recites:
7. The method of claim 1, further comprising a third neural network to generate a third output tensor based, at least in part, on the input tensor, wherein the image filter effect to be imparted to the input image is based, at least in part, on at least one of the first output tensor and the third output tensor as selectively determined by the second output tensor.
Applicant’s Claim 7 merely teaches mathematical calculation of a tensor. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 7 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 8
Claim 8 recites:
8. The method of claim 1, further comprising multiplying one or more values in the first output tensor by one or more values in the second output tensor to produce a product tensor, and adding the product tensor to the input tensor or a tensor derived from the input tensor.
Applicant’s Claim 8 merely teaches mathematical multiplication and addition. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 8 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 9
Claim 9 recites:
9. The method of claim 1, wherein the executing the first neural network, executing the second neural network, and modulating the image filter effect are employed to form one or more layers of a larger network architecture.
Applicant’s Claim 9 merely teaches a generic neural network being executed on a generic computer and mathematical “modulation” of the image filter. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 9 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 10
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “10. A computing device, comprising…” Therefore, it is a “device” (or “apparatus”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 10 that recite abstract ideas?
YES. The following limitations in Claim 10 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• generate a first output tensor
• input tensor comprising an input image
• first output tensor comprising values to impart an image filter effect to one or more features in the input image
• generate a second output tensor based on the input tensor
• selectively applying the image filter effect to be imparted to the input image based, at least in part, on the second output tensor indicating the one or more features of the input image
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “execute”/“one or more processors”
(2) A “first neural network”/“second neural network”
(3) A “memory comprising one more storage devices”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “executing” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Claim 10 merely teaches the embodiment where the claimed “model” is in the form of a “neural network”, which is an “additional element”. The neural network is not used to calculate anything at all. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
This “first neural network”/“second neural network” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “memory comprising one more storage devices” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
***
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
This “memory comprising one more storage devices” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “executing”
(2) A “first neural network”/“second neural network”
(3) A “memory comprising one more storage devices”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's Specification, paragraph [0066] recites:
[0066] Computing devices such as cloud server 702, smartphone 724, and other such devices that may employ signal processing and/or filtering architectures can take many forms and can include many features or functions including those already described and those not described herein. Figure 8 shows a block diagram of a general-purpose computerized system, consistent with an example embodiment. Figure 8 illustrates only one particular example of computing device 800, and other computing devices 800 may be used in other embodiments. Although computing device 800 is shown as a standalone computing device, computing device 800 may be any component or system that includes one or more processors or another suitable computing environment for executing software instructions in other examples, and need not include all of the elements shown here.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Specification recites:
[0025] Neural networks and layers of Figure 2 may perform respective functions particularly efficiently in part due to isolating functions of feature detection and filtering into different networks, and in part due to improved nonlinearity. More specifically, multiplying values of output tensors of respective neural networks 208 and 212 may introduce a significant nonlinearity, allowing fewer neural network layers having fewer nodes in neural networks of Figure 2 than in a typical signal processing and/or filtering neural network architecture to produce a desired result. Efficiencies gained by a reduced size and improved nonlinearity in combining values of output tensors of neural network layers 208 and 212 may be further enhanced by an ability of processing stage 202 to perform multiple functions at the same time by concurrently executing two different neural networks having different objectives in parallel. Neural network layers 206-208 and 210-212 may in one example, be convolutional neural network layers, but in other examples may be any type of neural network as are commonly known or may become known in the art.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “memory comprising one more storage devices” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
***
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Further, Applicant’s Specification recites:
[0070] One or more storage devices 812 may be configured to store information within computing device 800 during operation. Storage device 812, in some examples, is known as a computer-readable storage medium. In some examples, storage device 812 comprises temporary memory, meaning that a primary purpose of storage device 812 is not long-term storage. Storage device 812 in some examples is a volatile memory, meaning that storage device 812 does not maintain stored contents when computing device 800 is turned off. In other examples, data is loaded from storage device 812 into memory 804 during operation. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, storage device 812 is used to store program instructions for execution by processors 802. Storage device 812 and memory 804, in various examples, are used by software or applications running on computing device 500 such as image processor 822 to temporarily store information during program execution.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 10 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 11
Claim 11 recites:
11. The computing device of claim 10, wherein the one or more processors are further operable to multiply the first output tensor by the second output tensor to produce the combined output tensor.
Applicant’s Claim 11 merely teaches mathematical multiplication. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 11 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 12
Claim 12 recites:
12. The computing device of claim 10, wherein the one or more processors are further operable to add the combined output tensor to the input tensor to produce an output image.
Applicant’s Claim 12 merely teaches one or more generic processors. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 12 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 13
Claim 13 recites:
13. The computing device of claim 10, wherein the first output tensor comprises coefficients based, at least in part, on detection of at least one of the one more features in the input image.
Applicant’s Claim 13 merely teaches a mathematical “image” tensor. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 13 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 14
Claim 14 recites:
14. The computing device of claim 10, wherein the input tensor is derived, at least in part, on image signal intensity values of one or more image frames.
Applicant’s Claim 14 merely teaches mathematical calculation of a tensor. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 14 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 15
Claim 15 recites:
15. The computing device of claim 10, wherein the first neural network or the second neural network, or a combination thereof, comprise a convolutional neural network.
Applicant’s Claim 15 merely teaches a generic convolutional neural network. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 15 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 16
Claim 16 recites:
16. The computing device of claim 10, wherein the one or more processors are further operable to execute a third neural network to generate a third output tensor based, at least in part, on the input tensor, wherein the image filter effect to be imparted to the one or more detected features is based, at least in part, on the second output tensor or the third output tensor, or a combination thereof, as selectively determined by the first output tensor.
Applicant’s Claim 16 merely teaches one or more processors that may be operable to perform certain functions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 16 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 17
Claim 17 recites:
17. The computing device of claim 10, wherein the image filter effect applied in the second neural network comprises tone mapping, color grading, mesh shading, de-mosaicing or de-noising, super-resolution, or a combination thereof.
Applicant’s Claim 17 merely teaches mathematical image processing functions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 17 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 18
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “18. A computer-readable medium with instructions stored thereon, the instructions to be executable by one or more processors to cause a computerized system to…” Therefore, it fails to be a “non-transitory computer-readable medium.” Therefore, the answer to the inquiry is: “NO.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 18 that recite abstract ideas?
YES. The following limitations in Claim 18 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• generate a first output tensor
• input tensor comprising an input image
• first output tensor comprising values to impart an image filter effect to one or more features in the input image
• generate a second output tensor based on the input tensor
• selectively applying the image filter effect to be imparted to the input image based, at least in part, on the second output tensor indicating the one or more features of the input image
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “executing”
(2) A “first neural network”/“second neural network”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “executing” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Claim 18 merely teaches the embodiment where the claimed “model” is in the form of a “neural network”, which is an “additional element”. The neural network is not used to calculate anything at all. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
This “first neural network”/“second neural network” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “executing”
(2) A “first neural network”/“second neural network”
An “executing” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's Specification, paragraph [0066] recites:
[0066] Computing devices such as cloud server 702, smartphone 724, and other such devices that may employ signal processing and/or filtering architectures can take many forms and can include many features or functions including those already described and those not described herein. Figure 8 shows a block diagram of a general-purpose computerized system, consistent with an example embodiment. Figure 8 illustrates only one particular example of computing device 800, and other computing devices 800 may be used in other embodiments. Although computing device 800 is shown as a standalone computing device, computing device 800 may be any component or system that includes one or more processors or another suitable computing environment for executing software instructions in other examples, and need not include all of the elements shown here.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “first neural network”/“second neural network” is a broad term which is described at a high level. Applicant’s Specification recites:
[0025] Neural networks and layers of Figure 2 may perform respective functions particularly efficiently in part due to isolating functions of feature detection and filtering into different networks, and in part due to improved nonlinearity. More specifically, multiplying values of output tensors of respective neural networks 208 and 212 may introduce a significant nonlinearity, allowing fewer neural network layers having fewer nodes in neural networks of Figure 2 than in a typical signal processing and/or filtering neural network architecture to produce a desired result. Efficiencies gained by a reduced size and improved nonlinearity in combining values of output tensors of neural network layers 208 and 212 may be further enhanced by an ability of processing stage 202 to perform multiple functions at the same time by concurrently executing two different neural networks having different objectives in parallel. Neural network layers 206-208 and 210-212 may in one example, be convolutional neural network layers, but in other examples may be any type of neural network as are commonly known or may become known in the art.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 18 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 19
Claim 19 recites:
19. The computer-readable medium of claim 18, wherein the image filter effect comprises tone mapping, color grading, mesh shading, de-mosaicing or de-noising, super-resolution, or a combination thereof.
Applicant’s Claim 19 merely teaches mathematical image processing functions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 19 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 20
Claim 20 recites:
20. The computer-readable medium of claim 18, wherein the instructions to be further executable by the one or more processors to apply the image filter effect to be imparted to the one or more features based, at least in part, on:
application of coefficients in the second output tensor to the first output tensor to compute residual values, and
combination of the computed residual values with the input tensor to impart the image filter effect.
Applicant’s Claim 20 merely teaches a mathematical “application” and “combination”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 20 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-6, 9, and 18-19 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Tan, et al., Color Image Demosaicking Via Deep Residual Learning, 2017 IEEE International Conference on Multimedia and Expo (ICME), 10 JUL 2017, pp. 793-798 in its entirety. Specifically:
Claim 1
Claim 1’s “executing a first neural network to generate a first output tensor based, at least in part, on an input tensor comprising an input image, the first output tensor comprising values to impart an image filter effect to one or more features in the input image” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “First stage: Estimate the intermediate G channel.” The prior art “first stage” anticipates the “first neural network.” The prior art “intermediate G channel information” anticipates the clamed “first output tensor”. The prior art “initial image” anticipates the claimed “input tensor.”
Claim 1’s “executing a second neural network to generate a second output tensor indicating one or more features of the input image based, at least in part, on the input image; and” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “Second stage: Recover the RGB channels.” The claimed “second output tensor” is anticipated by the prior art “output RGB.” It is “based on” the “input tensor” because it is calculated by the progress of the input tensor through the cascaded networks.
Claim 1’s “selectively applying the image filter effect to be imparted to the input image based, at least in part, on the second output tensor indicating the one or more features of the input image” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “Second stage: Recover the RGB channels.” The output of the prior art from the “residual learning” is “modulated” by the “intermediate R/B” tensor.
Claim 2
Claim 2’s “2. The method of claim 1, wherein the image filter effect comprises tone mapping, color grading, mesh shading, de-mosaicing or de-noising, super-resolution, or a combination thereof” is anticipated by Tan, et al., page 2, right column, first full paragraph, where it recites:
The contribution of this work is summarized as follows. (1) We propose an end-to-end deep residual demosaicking model by taking advantage of the recent development of CNN technologies (2) We design a customized CNN model for CDM, which adopts a two-stage architecture to incorporate the demosaicking domain knowledge. Specifically, the network first constrains the G channel and then restores the full color images with the guidance of tentative G image. (3) We present a new dataset for more comprehensively evaluating the CDM algorithms. Experiments show that our method significantly outperforms state-of-the-arts on the Kodak, McMaster, and the new dataset both quantitatively and qualitatively.
Claim 3
Claim 3’s “3. The method of claim 1, wherein the second output tensor comprises coefficients based, at least in part, on detection of at least one of the one more features in the input image” is anticipated by Tan, et al., page 2, right column, last partial paragraph, where it recites:
Fig. 1 shows our proposed CNN architecture for CDM. The proposed network contains two basic modules of K-layer CNNs, stacked by convolutional layers, batch normalization and ReLU nonlinearity layers. For each module, the first layer uses 64 filters of size 3×3 to generate 64 feature maps (i.e., the clamed detected “features”), while the last convolutional layer adopts the filter of size 3×3×64 to generate the corresponding output.
These feature maps are part of the calculation of the prior art “Output RGB” which anticipates the claimed “second output tensor.” Therefore, the output is “based on” the prior art feature maps.
Claim 5
Claim 5’s “5. The method of claim 1, wherein the input tensor is determined based, at least in part, on image intensity values of one or more image frames” is anticipated by Tan, et al., page 3, left column, last full paragraph, where it recites:
To sum up, the proposed CNN based CDM model has two distinct characteristics. First, instead of using the CFA image as input, we take the initial images by simple bilinear interpolation as input, and then the residual learning strategy is used to reconstruct the demosaicked images. Second, the proposed model adopts a two-stage scheme to make use of the detailed G channel information to guide the reconstruction of R/B channels.
The prior art “bilinear interpolation” is performed based on the intensity values in the image.
Claim 6
Claim 6’s “6. The method of claim 1, wherein at least one of the first neural network and the second neural network comprise convolutional neural networks” is anticipated by Tan, et al., page 2, right column, last partial paragraph, where it recites:
Fig. 1 shows our proposed CNN architecture for CDM. The proposed network contains two basic modules of K-layer CNNs, stacked by convolutional layers, batch normalization and ReLU nonlinearity layers. For each module, the first layer uses 64 filters of size 3×3 to generate 64 feature maps, while the last convolutional layer adopts the filter of size 3×3×64 to generate the corresponding output.
Claim 9
Claim 9’s “9. The method of claim 1, wherein the executing the first neural network, executing the second neural network, and modulating the image filter effect are employed to form one or more layers of a larger network architecture” is anticipated by Tan, et al., page 2, right column, last partial paragraph, where it recites:
Fig. 1 shows our proposed CNN architecture for CDM. The proposed network contains two basic modules of K-layer CNNs, stacked by convolutional layers, batch normalization and ReLU nonlinearity layers. For each module, the first layer uses 64 filters of size 3×3 to generate 64 feature maps, while the last convolutional layer adopts the filter of size 3×3×64 to generate the corresponding output.
Claim 18
Claim 18’s “execute a first neural network to generate a first output tensor based on an input tensor comprising an input image, the first output tensor comprising values to impart an image filter effect to one or more features in the input image;” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “First stage: Estimate the intermediate G channel.” The prior art “first stage” anticipates the “first neural network.” The prior art “intermediate G channel information” anticipates the clamed “first output tensor”. The prior art “initial image” anticipates the claimed “input tensor.”
Claim 18’s “execute a second neural network to generate a second output tensor Indicating one or more features of the input image based, At least in part, on the input image; and” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “Second stage: Recover the RGB channels.” The claimed “second output tensor” is anticipated by the prior art “output RGB.” It is “based on” the “input tensor” because it is calculated by the progress of the input tensor through the cascaded networks.
Claim 18’s “apply the image filter effect to be imparted to the one or more features based, at least in part, on the One or more features expressed by the second output tensor.” is anticipated by Tan, et al., page 3, Fig. 1, where it shows the box labeled: “Second stage: Recover the RGB channels.” The output of the prior art from the “residual learning” is “modulated” by the “intermediate R/B” tensor.
Claim 19
Claim 19’s “19. The computer-readable medium of claim 18, wherein the image filter effect comprises tone mapping, color grading, mesh shading, de-mosaicing or de-noising, super-resolution, or a combination thereof.” is anticipated by Tan, et al., page 2, right column, first full paragraph, where it recites:
The contribution of this work is summarized as follows. (1) We propose an end-to-end deep residual demosaicking model by taking advantage of the recent development of CNN technologies (2) We design a customized CNN model for CDM, which adopts a two-stage architecture to incorporate the demosaicking domain knowledge. Specifically, the network first constrains the G channel and then restores the full color images with the guidance of tentative G image. (3) We present a new dataset for more comprehensively evaluating the CDM algorithms. Experiments show that our method significantly outperforms state-of-the-arts on the Kodak, McMaster, and the new dataset both quantitatively and qualitatively.
Claims 4, 7-8, 10-17, and 20 are not rejected under art since, when reading the claims in light of the specification, as per MPEP § 2111.01, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 4. Specifically:
Claim 4’s "...applying the coefficients to the first output tensor..."
Claim 4’s "...combining the computed residual values with the input tensor or a tensor derived from the first input tensor..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 7. Specifically:
Claim 7’s "...as selectively determined by the second output tensor..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 8. Specifically:
Claim 8’s "...multiplying one or more values in the first output tensor by one or more values in the second output tensor to produce a product tensor, and adding the product tensor to the input tensor or a tensor derived from the first input tensor..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 10 (and its dependent clams 11-17.) Specifically:
Claim 10’s "...apply the effect of the second output tensor to the one or more detected features in the first output tensor to produce a combined output tensor..."
Claim 10’s "...apply the combined output tensor to the input tensor..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 20. Specifically:
Claim 20’s "...application of coefficients in the second output tensor to the first output tensor to compute residual values..."
Claim 20’s "...combination of the computed residual values with the input tensor to impart the effect..."
Response to Arguments
Applicant's arguments filed 08 MAY 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues:
Argument 1
Claim Rejection Under 35 U.S.C. § 112
Claims 1-9 and 18-20 were objected to under 35 U.S.C §112, second paragraph, for failing to particularly point out and distinctly claim the subject matter which the applicant regards as the invention. More specifically, the Office Action states that the term "modulate" was used in claims 1-9 and 18-20, but was not adequately defined in the specification to explain its meaning in the context of the claims.
Clams 1, 4, 18, and 20 are amended to replace the term "modulate" with "apply." This amendment is consistent with the examples in the specification, such as the flowchart of Figure 4 and its accompanying description in the specification, and no new matter has been added.
The current Claim rejections under 35 U.S.C. § 112 are WITHDRAWN.
Argument 2
Claims 1-20 were rejected under 35 U.S.C. §101 as being directed to an abstract idea without significantly more than the abstract idea itself.
More specifically, the Office Action states on p. 7 that "The neural network is not used to calculate anything at all. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea." Similar arguments were made with respect to independent claims 10 and 18 and their dependents.
Applicant has therefore amended claims 1, 10, and 18 to reflect certain additional details, such as specifying that the first neural network input tensor comprises an image and the first neural network output tensor comprises values to impart an image filter effect (e.g., filter coefficients, rather than a filtered image) to one or more features in the input image. Similarly, the second neural network is now described as receiving the input tensor image and outputting one or more features of the image. The image filter effect from the first neural network is then selectively applied (i.e., optionally applied) to the image based on the features of the image identified in the second neural network output. As explained in the specification, using separate neural networks to perform these two functions may reduce the overall neural network size relative to applying filter effects to certain features using a single network, and applying this method to an image for filtering to produce a selectively filtered (i.e., optionally filtered) output image as now described in the claims clarifies what is calculated by the neural networks, evidences more than an abstract idea, and provides a practical application.
Applicant asserts that generic input of image data is an “additional detail” (i.e., “additional element”) that is significantly more than the abstract idea. Applicant’s limitation is generic and adds nothing significantly more to the abstract ideas.
Applicant asserts generating a mathematical “output tensor comprises values to impart an image filter effect” (e.g., filter coefficients, rather than a filtered image). The production of these mathematical tensor values does not necessarily improve any technology, nor is it significantly more than the abstract mathematical idea.
Further, Applicant argues that “The image filter effect (i.e., the mathematical tensor values that were previously produced) from the first neural network is then selectively applied (i.e., optionally applied) to the image…”
Note that these mathematical values are “selectively applied,” which means they are optionally applied, and need not be applied at all. This does not necessarily improve any technology, nor is it significantly more than the abstract mathematical idea.
Applicant’s argument is unpersuasive.
The 35 U.S.C § 101 rejections stand.
Argument 3
Applicant believes that the amended claims now more clearly recite processes that recite a practical application with a concrete, useful, tangible result, and that therefore comprise significantly more than an abstract idea in that they provide such a practical application. The pending claims also more clearly result in demonstrated improvements to the efficiency of a technology as explained more fully below, and SO these claims are believed to be patent-eligible under 35 U.S.C. §101.
More specifically, applicant notes that the pending claims integrate any abstract idea present into a practical application, and SO are patent-eligible under §101. John Squires, Director of the USPTO in a December 4, 2025 memorandum distributed to examiners, discussed an Appeals Review Panel decision on In re DesJardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) vacating a PTAB §101 rejection. The PTAB rejection was vacated because "the claims were directed to training a machine learning model on multiple tasks, while preserving prior tasks performed, which properly integrated an otherwise abstract idea into a practical application." The pending claims, as amended, provide a filtered output image, which comprises a clear practical application much as in In re DesJardins.
Firstly, the “useful, concrete, and tangible” standard cited by Applicant originated with the case State Street Bank & Trust Co. v. Signature Financial Group, Inc. The Supreme Court abandoned that case in Bilski v. Kappos. Therefore, the “useful, concrete, and tangible” standard is no longer applicable in 35 U.S.C § 101 doctrine.
Secondly, though Applicant asserts that a “technology” is improved, no specific, non-abstract technology is named by Applicant, thus far.
Applicant’s argument is unpersuasive.
The 35 U.S.C § 101 rejections stand.
Argument 4
The pending claims in the present case are also directed to improvement in the functioning of a specific technical machine and/or process, and provide an improvement to technology much as in In re Desjardins. More specifically, the application as filed discusses how combining outputs of discrete neural networks with different purposes may reduce the input size and overall neural network size needed to achieve a desired result (paragraph 17), thereby significantly improving the performance of image filtering systems that employ the methods and systems described and claimed.
In Deputy Commissioner Charles Kim's memorandum of August 4th, he notes that improvements to a technical field or technology are present where a "claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception" and that "whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome."
The pending claims here recite a particular solution to improving the efficiency of filtering image data. More specifically, the claimed "executing a first neural network to generate a first output tensor based, at least in part, on an input tensor comprising an image, the first output tensor comprising values to impart an image filter effect to one or more features in the image," "executing a second neural network to generate a second output tensor indicating one or more features of the image based, at least in part, on the input tensor image," and "selectively applying the image filter effect to be imparted to the image based, at least in part, on the second output tensor indicating the one or more features of the image" comprises a particular way to achieve the desired outcome, and represents a measurable improvement to the functioning of a computer or a technology.
The improvement provided therefore resides in the performance of a particular machine or process, much as in In re Des.Jardins, in that it provides an improvement to the efficiency of image processing. This amounts to a practical application, just as in In re DesJardins, and is therefore patent eligible under §101.
The pending claims as amended therefore are not merely abstract ideas as they integrate any abstract idea present into a practical application and they reflect an improvement to the functioning of a computer or to another technology or technical field. The claims are therefore significantly more than an abstract idea under §101, and reexamination and withdrawal of this rejection is therefore respectfully requested.
Applicant argues that “… combining outputs of discrete neural networks with different purposes may reduce the input size and overall neural network size needed to achieve a desired result…”
Though it may reduce a network size that is needed, it also may not reduce the network size that is needed.
Further, a larger network size may also operate on the same data, thus, there need not be any improvement to the network.
Further, In re Desjardins applies to the improvement of machine learning models…not to the argued “image filtering systems.” Therefore, Desjardins may not be applied that way.
Applicant’s argument is unpersuasive.
The 35 U.S.C § 101 rejections stand.
Argument 5
Claim Rejection Under 35 U.S.C. § 102
Claims 1-3, 5-6, 9, and 18-19 were rejected under 35 U.S.C. §102 as being anticipated by Tan, et al., (Color Image Demosaicking Via Deep Residual Learning, 2017 IEEE International Conference on Multimedia and Expo (ICME), 10 JUL 2017, pp. 793-798; hereinafter "Tan").
Applicants note that claims 4, 7-8, 10-17, and 20 were not rejected under art since, when reading the claims in light of the specification, as per MPEP § 2111.01, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified. Applicants have therefore introduced limitations from these claims indicated as allowable under §102/103 (largely as reflected in claim 10 as originally filed) into the independent claims 1, 10, and 18 as discussed in greater detail herein.
Applicant did not include the key limitations that overcome the art of record. For instance, the limitation that:
"apply the effect of the second output tensor to the one or more detected features in the first output tensor to produce a combined output tensor"
Applicant’s argument is unpersuasive.
The 35 U.S.C § 102 rejections stand.
Argument 6
Tan appears to discuss a convolutional neural network trained to perform de-mosaicing on a color image (the Abstract). More specifically, Tan appears to recognize that the number of green pixels captured by a typical image sensor (Bayer pattern image sensor) is twice the number of red and blue pixels, and so the green color channel of a captured image may be restored before reconstructing red and blue channels (p. 1, second column, second paragraph). The cited Figure 1 (copied below) and accompanying description appear to support this, showing separate processing of an input image's green channel from red and blue channels to generate separate intermediate green and intermediate red/blue channels. These different color channels are then used in a second state to recover the red, green, and blue channels of an output image.
Note also that the neural networks of depth K layers appears to be the same for both the Input G and the Input R/B channels, and operate sequentially such that the second stage operates on output from the first stage.
In contrast, the pending claims as amended now recite "executing a first neural network to generate a first output tensor based, at least in part, on an input tensor comprising an image, the first output tensor comprising values to impart an image filter effect to one or more features in the image," "executing a second neural network to generate a second output tensor indicating one or more features of the image based, at least in part, on the input tensor image," and "selectively applying the image filter effect to be imparted to the image based, at least in part, on the second output tensor indicating the one or more features of the image" (emphasis added). More specifically, separately processing different color channels in Tan using sequential neural networks where a second stage processes the output of a first stage does not anticipate calculating a filter effect in one network and identifying features of the image in a separate network from the same input image, as is recited in the pending claims.
Because these amended claim limitations are not found in the cited art, reexamination and allowance of these pending claims, is respectfully requested.
Applicant argues that Tan, et al., “does not anticipate calculating a filter effect in one network and identifying features of the image in a separate network from the same input image…” Actually, this is in fact taught in Fig. 1 of Tan, et al., where it shows two cascaded CNN modules of K-layers. The first filters the input image into intermediate RGB representations. The second network reconstructs the RGB channels (Note that each RGB channel is a “feature” of the input image.)
Applicant’s argument is unpersuasive.
The 35 U.S.C § 102 rejections stand.
Conclusion
THIS ACTION IS MADE FINAL. 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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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
13 AUG 2026
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