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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims recite a method and system, each being one of the four categories of eligible subject matter.
Claims 1 and 12
Step 2A Prong 1: The claims recite the following limitations:
generating, by the data processing hardware, a plurality of camouflage patterns based on the machine learning model (Mental Process); assigning, by the data processing hardware, a rank to each of the camouflage patterns (Mental Process).
In view of P0040 of the specification, generating the plurality of patterns is done by matching the camouflage data with the environment data and assigning the ranks to the patterns is done by ranking the comparisons. Under the broadest reasonable interpretation, this comparison may be done practically in a human’s mind. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claims recite the additional limitations:
obtaining, at data processing hardware, camouflage material data; obtaining, at the data processing hardware, environmental data; generating, by the data processing hardware, the machine learning model based on the camouflage material data and the environmental data;… and training, by the data processing hardware, the machine learning model with a camouflage pattern assigned with a highest rank.
Obtaining camouflage material data and environmental data is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Training the machine learning model with the pattern assigned with the highest rank is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The memory in claim 12 is a generic computing component recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining camouflage material data and environmental data is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Training the machine learning model with the pattern assigned with the highest rank is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The memory in claim 12 is a generic computing component recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The claims are not patent eligible.
Claim 19
Step 2A Prong 1: The claim recites the following limitations:
…and generating, by the data processing hardware, a plurality of camouflage patterns based on the one or more of the camouflage material parameters and the environmental data (Mental Process).
In view of P0040 of the specification, generating the plurality of patterns is done by matching the camouflage data with the environment data. Under the broadest reasonable interpretation, this comparison may be done practically in a human’s mind. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the additional limitations:
obtaining, at data processing hardware, one or more of camouflage material parameters; obtaining, at the data processing hardware, environmental data.
Obtaining camouflage material data and environmental data is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining camouflage material data and environmental data is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). The claim is not patent eligible.
Dependent Claims
Claim 2
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim
recites the following limitations:
wherein the plurality of camouflage patterns includes dynamic camouflage patterns that are moving (Mental Process).
In view of P0040 of the specification, generating the plurality of patterns is done by matching the camouflage data with the environment data. Under the broadest reasonable interpretation, this comparison may be done practically in a human’s mind. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application and the claim does not recite additional elements. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 3
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claim recites the following additional elements:
wherein the camouflage material data includes at least one of: color parameter, artistic pattern parameter, or intended use location parameter.
Obtaining camouflage material data including at least one of a color parameter, artistic pattern parameter, or intended use location parameter is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining camouflage material data including at least one of a color parameter, artistic pattern parameter, or intended use location parameter is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is not patent eligible.
Claim 4
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claim recites the following additional elements:
wherein the environmental data includes at least one of: terrain information, live surrounding image information, time information, geolocation information, weather information, temperature information, light information, and electromagnetic background radiation, or noise information.
Obtaining environmental material data including at least one of terrain information, live surrounding image information, time information, geolocation information, weather information, temperature information, light information, and electromagnetic background radiation, or noise information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining environmental material data including at least one of terrain information, live surrounding image information, time information, geolocation information, weather information, temperature information, light information, and electromagnetic background radiation, or noise information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is not patent eligible.
Claim 5
Step 2A Prong 1: The judicial exceptions of claim 4 are incorporated. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claim recites the following additional elements:
wherein the light information includes at least one of: luminosity information, light source information, or reflected light information.
Obtaining light information including at least one of luminosity information, light source information, or reflected light information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Obtaining light information including at least one of luminosity information, light source information, or reflected light information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is not patent eligible.
Claims 6 and 13
Step 2A Prong 1: The judicial exceptions of claims 1 and 12 are incorporated. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claims recite the following additional elements:
wherein generating the plurality of camouflage patterns based on the machine learning model includes: generating, using a genetic algorithm, at least one camouflage pattern of the plurality of camouflage patterns.
The use of a genetic algorithm, such as a mutation, crossover, or noise algorithm as described in P0039 of the specification of the instant application, is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The use of a genetic algorithm, such as a mutation, crossover, or noise algorithm as described in P0039 of the specification of the instant application, is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are not patent eligible.
Claims 7 and 14
Step 2A Prong 1: The judicial exceptions of claims 1 and 12 are incorporated. The claims recite the following limitations:
generating, by the data processing hardware, a simulated environment for each of the camouflage patterns (Mental Process).
In view of P0043 of the specification of the instant application, generating a simulated environment includes placing a pattern over an environment. A human can practically use their mind to place a pattern over an environment with the aid of the data. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application and the claim does not recite additional elements. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 8 and 15
Step 2A Prong 1: The judicial exceptions of claims 7 and 14 are incorporated. The claims recite the following limitations:
wherein each of the simulated environments includes a corresponding camouflage pattern on an image of environment where the corresponding camouflage pattern is intended to be used (Mental Process).
A human can practically use their mind to overlay a camouflage pattern on top of an image of an environment with the aid of the data. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application and the claim does not recite additional elements. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 9 and 16
Step 2A Prong 1: The judicial exceptions of claims 7 and 14 are incorporated. The claims recite the following limitations:
wherein the respective camouflage pattern is on a random location of the image of environment (Mental Process).
A human can use their mind to practically place a pattern on top of a random location of an image with the aid of the data. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application and the claim does not recite additional elements. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 10 and 17
Step 2A Prong 1: The judicial exceptions of claims 1 and 12 are incorporated. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claims recite the following additional elements:
providing, for display, the camouflage patterns assigned with the highest rank.
Displaying the pattern with the highest rank is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Displaying the pattern with the highest rank is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are not patent eligible.
Claim 20
Step 2A Prong 1: The judicial exceptions of claim 19 are incorporated. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claim recites the following additional elements:
providing, for display, at least one of the camouflage patterns.
Displaying at least one of the camouflage patterns is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. Displaying at least one of the camouflage patterns is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claim is not patent eligible.
Claims 11 and 18
Step 2A Prong 1: The judicial exceptions of claims 1 and 12 are incorporated. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claims recite the following additional elements:
wherein the machine learning model comprises at least one from neural network and generative adversarial network.
The machine learning model comprising at least one from neural network and generative adversarial network is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The machine learning model comprising at least one from neural network and generative adversarial network is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are not patent eligible.
Claim Rejections - 35 USC § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yang et al (Pub. No.: US 20220130099 A1), hereafter Yang.
Regarding claim 19, Yang teaches obtaining, at data processing hardware, one or more of camouflage material parameters (Caustic pattern data in relation to a body of water may be stored in data center 700, P0029, P0043, P0045); obtaining, at the data processing hardware, environmental data (Environmental data such as terrain data and light rays interacting with the surface of water may be stored in data center 700, P0029, P0043, P0045); and generating, by the data processing hardware, a plurality of camouflage patterns based on the one or more of the camouflage material parameters and the environmental data (“Such components can be used to generate high-quality, physically-based water caustics effects in real-time with acceptable performance”, P0045).
Regarding claim 20, Yang teaches the limitations of claim 19 as outlined above. Yang further teaches providing, for display, at least one of the camouflage patterns (User is shown multiple renderings and can choose a display that offers the best system performance, the display with the highest visual quality, or a desired system-quality balance. “rank” here may be interpreted as either the pattern generated with the highest visual quality or with the pattern associated with the highest system performance, P0020, P0028).
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Keski-Valkama et al (Pub. No.: US 20220413502 A1).
Regarding claims 1 and 12, Yang teaches obtaining, at data processing hardware, camouflage material data (Caustic pattern data in relation to a body of water may be stored in data center 700, P0029, P0043, P0045); obtaining, at the data processing hardware, environmental data (Environmental data such as terrain data and light rays interacting with the surface of water may be stored in data center 700, P0029, P0043, P0045); generating, by the data processing hardware, the machine learning model based on the camouflage material data and the environmental data (“a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center 700”, P0043); generating, by the data processing hardware, a plurality of camouflage patterns based on the machine learning model (“Such components can be used to generate high-quality, physically-based water caustics effects in real-time with acceptable performance”, P0045).
Yang does not appear to explicitly teach assigning, by the data processing hardware, a rank to each of the camouflage patterns; and training, by the data processing hardware, the machine learning model with a camouflage pattern assigned with a highest rank.
Keski-Valkama teaches assigning, by the data processing hardware, a rank to each of the camouflage patterns (Training data may be given a score, P0052); and training, by the data processing hardware, the machine learning model with a camouflage pattern assigned with a highest rank (Training data with the highest scores may be used to train the generative model, P0053).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Yang and Keski-Valkama before them, to include Keski-Valkama’s specific teaching of assigning scores to training data and training a model with the highest scored data in Yang’s system of Ray Guided Water Caustics. One would have been motivated to make such a combination of assigning scores to training data and training a model with the highest scored data (see Keski-Valkama P0052-P0053) and showing the user multiple renderings of patterns to let the user choose the pattern with the highest visual quality, highest system performance, or a balance to fit the user’s preferences (see Yang P0020, P0028).
Regarding claim 2, Yang in view of Keski-Valkama teaches the limitations of claim 1 as outlined above. Yang further teaches wherein the plurality of camouflage patterns includes dynamic camouflage patterns that are moving (The generated caustic pattern may be animated, meaning it moves in order to mimic actual, physical content captured by a camera, P0029, P0001).
Regarding claim 3, Yang in view of Keski-Valkama teaches the limitations of claim 1 as outlined above. Yang further teaches wherein the camouflage material data includes at least one of: color parameter, artistic pattern parameter, or intended use location parameter (Camouflage data includes caustic pattern data of bodies of water, P0029).
Regarding claim 4, Yang in view of Keski-Valkama teaches the limitations of claim 1 as outlined above. Yang further teaches wherein the environmental data includes at least one of: terrain information, live surrounding image information, time information, geolocation information, weather information, temperature information, light information, and electromagnetic background radiation, or noise information (Environmental data includes terrain data and light rays interacting with the surface of water, P0029).
Regarding claim 5, Yang in view of Keski-Valkama teaches the limitations of claim 4 as outlined above. Yang further teaches wherein the light information includes at least one of: luminosity information, light source information, or reflected light information (Light data includes light rays interacting with the surface of water, including reflections, P0029).
Regarding claims 6 and 13, Yang in view of Keski-Valkama teaches the limitations of claims 1 and 12 as outlined above. Yang further teaches wherein generating the plurality of camouflage patterns based on the machine learning model includes: generating, using a genetic algorithm, at least one camouflage pattern of the plurality of camouflage patterns (A genomics algorithm may be used to train the machine learning model, P0041).
Regarding claims 7 and 14, Yang in view of Keski-Valkama teaches the limitations of claims 1 and 12 as outlined above. Yang further teaches generating, by the data processing hardware, a simulated environment for each of the camouflage patterns (Simulated environments may be generated for caustic patterns, P0032).
Regarding claims 8 and 15, Yang in view of Keski-Valkama teaches the limitations of claims 7 and 14 as outlined above. Yang further teaches wherein each of the simulated environments includes a corresponding camouflage pattern on an image of environment where the corresponding camouflage pattern is intended to be used (Light photons are placed over an image of the environment (water) in order to approximate patterns based on the rays, resulting in water caustics, P0032).
Regarding claims 9 and 16, Yang in view of Keski-Valkama teaches the limitations of claims 8 and 15 as outlined above. Yang further teaches wherein the corresponding camouflage pattern is on a random location of the image of environment (Random points in the area may be selected during the simulations, P0033).
Regarding claims 10 and 17, Yang in view of Keski-Valkama teaches the limitations of claims 1 and 12 as outlined above. Yang further teaches the method further comprising: providing, for display, the camouflage patterns assigned with the highest rank (User is shown multiple renderings and can choose a display that offers the best system performance, the display with the highest visual quality, or a desired system-quality balance. “rank” here may be interpreted as either the pattern generated with the highest visual quality or with the pattern associated with the highest system performance, P0020, P0028).
Regarding claims 11 and 18, Yang in view of Keski-Valkama teaches the limitations of claims 1 and 12 as outlined above. Keski-Valkama further teaches wherein the machine learning model comprises at least one from neural network and generative adversarial network (Generative model may be a generative adversarial network, P0028).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240314272 A1 (Krantz) teaches a method including camouflage detection with machine learning.
US 10157332 B1 (Gray et al) teaches a system including neural network based image manipulation including camouflage generation.
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/I.M./ Examiner, Art Unit 2141
/TAN H TRAN/ Primary Examiner, Art Unit 2141