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 .
DETAILED ACTION
This action is responsive to the Application filed on 04/15/2026
Claims 1-14 are pending in the case. Claims 1, 11 and 14 are independent claims. Claim 15 have been canceled. Claims 1-6, 8-11 and 14 have been currently amended. Claims 16-21 has been newly added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-14 and 16-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-10 and 16-21 are drawn to a method, claims 11-13 are drawn to an apparatus and claim 14 is drawn to a non-transitory tangible computer-readable medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 11 and 14 are nonverbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A computer-implemented method, comprising:
processing, by an apparatus implementing a first neural network trained to process textual metadata, the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata;
processing, by the apparatus implementing a second neural network trained to process numerical metadata, the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata;
fusing, by the apparatus implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file;
and configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). As well as, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 1 recites in part:
“processing, […], the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata”
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
“processing, […], the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata”
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
“fusing, […], the first vector with the second vector to produce a classification of media content in the media file”
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can with pen and paper combine the first vector with the second vector to classify the media content in the media file.
configuring, […], one or more settings associated with the classification to control reproduction of the media content
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can modify based on the classification settings to control the reproduction of the content.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
“A computer-implemented method, comprising:
[…] by an apparatus implementing a first neural network trained to process textual metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus implementing a second neural network trained to process numerical metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus implementing a third neural network distinct from the first and second neural networks, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus in response to producing the classification, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “apparatus” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
“A computer-implemented method, comprising:
[…] by an apparatus implementing a first neural network trained to process textual metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus implementing a second neural network trained to process numerical metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus implementing a third neural network distinct from the first and second neural networks, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by the apparatus in response to producing the classification, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “apparatus” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 11:
Claim 11 recites: An apparatus, comprising: a memory;
and a processor coupled to the memory, wherein the processor is to:
process, by implementing a first neural network trained to process textual metadata, the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata;
process, by implementing a second neural network trained to process numerical metadata, the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata:
fuse, by implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file; and
configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 11 is directed to an abstract idea, specifically, a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). As well as, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 11 recites in part:
process, […] the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
process, […] the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
fuse, […] the first vector with the second vector to produce a classification of media content in the media file
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can with pen and paper combine the first vector with the second vector to classify the media content in the media file.
configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can modify based on the classification settings to control the reproduction of the content.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 11 recites in part:
“An apparatus, comprising: a memory;
and a processor coupled to the memory, wherein the processor is to” as drafted, amount to computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
[…] by implementing a first neural network trained to process textual metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a second neural network trained to process numerical metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a third neural network distinct from the first and second neural networks, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 11 recites in part:
“An apparatus, comprising: a memory;
and a processor coupled to the memory, wherein the processor is to” as drafted, amount to computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
[…] by implementing a first neural network trained to process textual metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a second neural network trained to process numerical metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a third neural network distinct from the first and second neural networks, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 14:
Claim 14 recites: A non-transitory tangible computer-readable medium storing executable code that, when executed by a processor, cause the processor to:
process, by implementing a first neural network trained to process textual metadata, the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata;
process, by implementing a second neural network trained to process numerical metadata, the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata;
fuse, by implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file; and
configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 14 is directed to an abstract idea, specifically, a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). As well as, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 14 recites in part:
process, […] the textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
process, […] the numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). The text describes mapping symbols (text)[Symbol font/0xE0] numbers, vector construction and numerical representation of information, all of which fall within mathematical relationships, mathematical transformation and data encoding using numerical representation. Even if the exact algorithm is not stated, the essence of the step is applying a mathematical model or calculation to produce a numerical vector. See MPEP § 2106.04(a)(2)(I)(C).
fuse, […] the first vector with the second vector to produce a classification of media content in the media file
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can with pen and paper combine the first vector with the second vector to classify the media content in the media file.
configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
The limitation above is broadly and reasonably interpreted as a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can modify based on the classification settings to control the reproduction of the content.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 14 recites in part:
“A non-transitory tangible computer-readable medium storing executable code that, when executed by a processor, cause the processor to” as drafted, amount to computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
[…] by implementing a first neural network trained to process textual metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a second neural network trained to process numerical metadata, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
[…] by implementing a third neural network distinct from the first and second neural networks, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-10 are dependent on claim 1 and claims 12-13 are dependent on claim 11, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claim 2 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application. Involves, a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. See MPEP § 2106.04(a)(2)(III).
Claim 3 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 4 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 5 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 6 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 7 incorporates the rejection of claim 6 and does not integrate the judicial exception into a practical application.
Claim 8 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 9 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 10 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 12 incorporates the rejection of independent claim 11 and does not integrate the judicial exception into a practical application.
Claim 13 incorporates the rejection of independent claim 11 and does not integrate the judicial exception into a practical application.
Claim 16 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 17 incorporates the rejection of dependent claim 16 and does not integrate the judicial exception into a practical application.
Claim 18 incorporates the rejection of dependent claim 17 and does not integrate the judicial exception into a practical application.
Claim 19 incorporates the rejection of dependent claim 18 and does not integrate the judicial exception into a practical application.
Claim 20 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 21 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-4, 9, 11-14 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (Pub No.: 20180032846 A1), hereinafter referred to as Yang, in view of Dal et al (Pub No.: 20180046649 A1), hereinafter referred to as Dal and further in view of Navin et al. (Pub No.: 20190327526 A1), hereinafter referred to as Navin.
With respect to claim 1, Yang disclose :
A computer-implemented method, comprising: processing, by an apparatus implementing a first neural network trained to process textual metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the Nth layer of the CNN, performing a multilayer fusing unit.)
Processing, by the apparatus implementing a second neural network trained to process numerical metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the ninth layer of the CNN, performing a multilayer fusing unit. )
Fusing, by the apparatus implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file (In Fig. 1F and paragraphs [0081-0082], Yang discloses generating respective features vectors using separate convolutional neural networks corresponding to different media modalities and combining (fusing) the generated vectors into a unified representation that is used to classify the media content. The multilayer fusing produces a classification output data.)
With respect to claim 1, Yang does not explicitly disclose:
The textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata
The numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, it is known by Dal to disclose;
(In paragraphs [0135-0134], Dal discloses a 3D model converted into a numerical feature vector of the media document using a trained convolutional network (CNN). The digital media documents, such as images, audio recordings, and videos, often include metadata that includes textual descriptions of the digital media document. (Note: In paragraph [0092], Dal discloses a vector of values or features (commonly referred to as a feature vector).))
(In paragraphs [0098-0099], Dal discloses 2D images supplied to a trained CNN to generate a plurality of feature vectors (extracted from 2D views) associated with the 3D model.)
Yang and Dal are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Yang with classifying video image data as taught by Yang, while computing a feature vector of the media document using a convolutional neural network as taught by Dal. The motivation for doing so would have been to improve video classification accuracy (See [0037] of Yang.)
With respect to claim 1, Yang and Dal does not explicitly disclose:
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, Navin is known to disclose:
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content (In Fig. 5 and paragraph [0051], Navin discloses determining a type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. Navin further discloses applying or adjusting display and audio settings — including brightness, contrast, color, and other settings that influence how the media content is visually or audibly rendered.)
Yang in view of Dal and Navin are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Navin with adjusting setting based on the recommended playback settings as taught by Navin. The motivation for doing so would have been to improve recommended playback settings (See [0060] of Navin.)
Regarding claim 3, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Yang disclose:
The method of claim 1, wherein the first vector comprises first probability values corresponding to classes of the media content (In paragraph [0026], Yang discloses a first layer of a CNN to extract a first set of feature maps and generate classification output data for the training video image data. A class-conditional probability vector associated with the training video image data.)
Regarding claim 4, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Dal disclose:
The method of claim 1, wherein analyzing the numerical metadata comprises: extracting the numerical metadata (In paragraph [0099], Dal disclose one or more feature vectors can be extracted from the 3D model. (Note: In paragraph [0092], Dal disclose vector of values or features (commonly referred to as a feature vector).))
Inputting the numerical metadata to the second neural network to produce the second vector (In paragraph [0099], Dal discloses that each of the 2D images may be supplied to a trained CNN to generate a plurality of feature vectors (extracted from 2D views) associated with the 3D model.)
Regarding claim 9, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Dal disclose:
The method of claim 1, wherein: the first neural network is trained based on training text associated with a set of training media and labels corresponding to the set of training media, anthem second neural network is trained based on training numerical metadata associated with the set of training media and the labels corresponding to the set of training media (In paragraphs [0135-0134], Dal discloses a 3D model converted into a numerical feature vector of the media document using a trained convolutional network (CNN). The digital media documents, such as images, audio recordings, and videos, often include metadata that includes textual descriptions of the digital media document.)
With respect to claim 11, Yang disclose :
An apparatus, comprising: a memory; and a processor coupled to the memory, wherein the processor is to: process, by implementing a first neural network trained to process textual metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the Nth layer of the CNN, performing a multilayer fusing unit. In fig. 6 and paragraph [0124], Yang disclose a processor and main central memory.)
Process, by implementing a second neural network trained to process numerical metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the ninth layer of the CNN, performing a multilayer fusing unit. )
Fuse, by implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file (In Fig. 1F and paragraphs [0081-0082], Yang discloses generating respective features vectors using separate convolutional neural networks corresponding to different media modalities and combining (fusing) the generated vectors into a unified representation that is used to classify the media content. The multilayer fusing produces a classification output data.)
With respect to claim 11, Yang does not explicitly disclose:
The textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata
The numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, it is known by Dal to disclose;
(In paragraphs [0135-0134], Dal discloses a 3D model converted into a numerical feature vector of the media document using a trained convolutional network (CNN). The digital media documents, such as images, audio recordings, and videos, often include metadata that includes textual descriptions of the digital media document. (Note: In paragraph [0092], Dal discloses a vector of values or features (commonly referred to as a feature vector).))
(In paragraphs [0098-0099], Dal discloses 2D images supplied to a trained CNN to generate a plurality of feature vectors (extracted from 2D views) associated with the 3D model.)
Yang and Dal are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Yang with classifying video image data as taught by Yang, while computing a feature vector of the media document using a convolutional neural network as taught by Dal. The motivation for doing so would have been to improve video classification accuracy (See [0037] of Yang.)
With respect to claim 11, Yang and Dal does not explicitly disclose:
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, Navin is known to disclose:
Configuring, by the apparatus in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content (In Fig. 5 and paragraph [0051], Navin discloses determining a type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. Navin further discloses applying or adjusting display and audio settings — including brightness, contrast, color, and other settings that influence how the media content is visually or audibly rendered.)
Yang in view of Dal and Navin are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Navin with adjusting setting based on the recommended playback settings as taught by Navin. The motivation for doing so would have been to improve recommended playback settings (See [0060] of Navin.)
Regarding claim 12, Yang in view of Dal and Navin disclose the elements of claim 11. In addition, Navin disclose:
The apparatus of claim 11, wherein the numerical metadata includes data indicating duration, sample rate, video presence, bit depth, and number of channels of the media content (In paragraph [0045], Navin discloses, for example, sports and video games may be presented at a higher frame rate in order to prevent blur, while dramas may have a higher volume due to an increased amount of dialogue.)
Regarding claim 13, Yang in view of Dal and Navin disclose the elements of claim 11. In addition, Navin disclose:
The apparatus of claim 11, wherein the processor is to select an audio setting based on the classification (In paragraph [0019], Navin discloses that the user device may store various content profiles associated with certain settings and select from among them after receiving the identified content profile from the server. )
With respect to claim 14, Yang disclose :
A non-transitory tangible computer-readable medium storing executable code that, when executed by a processor, cause the processor to: process, by implementing a first neural network trained to process textual metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the Nth layer of the CNN, performing a multilayer fusing unit. In fig. 6 and paragraph [0124], Yang disclose a processor and main central memory.)
Process, by implementing a second neural network trained to process numerical metadata, (In Fig. 1F and paragraph [0050], Yang discloses a first layer of the CNN and the ninth layer of the CNN, performing a multilayer fusing unit. )
Fuse, by implementing a third neural network distinct from the first and second neural networks, the first vector with the second vector to produce a classification of media content in the media file (In Fig. 1F and paragraphs [0081-0082], Yang discloses generating respective features vectors using separate convolutional neural networks corresponding to different media modalities and combining (fusing) the generated vectors into a unified representation that is used to classify the media content. The multilayer fusing produces a classification output data.)
With respect to claim 14, Yang does not explicitly disclose:
The textual metadata in a media file to generate a first vector of numerical values corresponding to the textual metadata
The numerical metadata in the media file to generate a second vector of numerical values corresponding to the numerical metadata
Configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, it is known by Dal to disclose;
(In paragraphs [0135-0134], Dal discloses a 3D model converted into a numerical feature vector of the media document using a trained convolutional network (CNN). The digital media documents, such as images, audio recordings, and videos, often include metadata that includes textual descriptions of the digital media document. (Note: In paragraph [0092], Dal discloses a vector of values or features (commonly referred to as a feature vector).))
numerical values corresponding to the numerical metadata(In paragraphs [0098-0099], Dal discloses 2D images supplied to a trained CNN to generate a plurality of feature vectors (extracted from 2D views) associated with the 3D model.)
Yang and Dal are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Yang with classifying video image data as taught by Yang, while computing a feature vector of the media document using a convolutional neural network as taught by Dal. The motivation for doing so would have been to improve video classification accuracy (See [0037] of Yang.)
With respect to claim 14, Yang and Dal does not explicitly disclose:
Configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content
However, Navin is known to disclose:
Configure, in response to producing the classification, one or more settings associated with the classification to control reproduction of the media content (In Fig. 5 and paragraph [0051], Navin discloses determining a type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. Navin further discloses applying or adjusting display and audio settings — including brightness, contrast, color, and other settings that influence how the media content is visually or audibly rendered.)
Yang in view of Dal and Navin are analogous pieces of art because both references concern classifications of media documents. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Navin with adjusting setting based on the recommended playback settings as taught by Navin. The motivation for doing so would have been to improve recommended playback settings (See [0060] of Navin.)
Regarding claim 16, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Dal disclose:
The method of claim 1, further comprising performing an operation based on the classification of the media content in the media file (In paragraph [0119], Dal discloses that the feature vector of the media document of the query is supplied to a classifier (e.g., a classifier trained on entries within the database) to generate a classification or classifications (e.g., classifications 656 or classifications)
Regarding claim 17, Yang in view of Dal and Navin disclose the elements of claim 16. In addition, Navin disclose:
The method of claim 16, wherein the performing the operation includes at least one of: selecting an audio setting; storing a record of the classification of the media content in the media file; providing a recommendation to a user with a profile that indicates a preference for media sharing the classification; or tagging the media file to indicate the media class of the media content (The examiner selects: selecting an audio setting: In Fig. 5 and paragraph [0051], Navin discloses determining a content type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. Navin further discloses applying or adjusting display and audio settings — including brightness, contrast, color, and other settings that influence how the media content is visually or audibly rendered.)
Regarding claim 18, Yang in view of Dal and Navin disclose the elements of claim 17. In addition, Navin disclose:
The method of claim 17, wherein the performing the operation includes selecting the audio setting, and wherein the audio setting includes a surround setting, a stereo setting, a speech setting, an amplification setting, or an attenuation setting (The examiner selects: selecting an audio setting: In Fig. 5 and paragraph [0051], Navin discloses determining a content type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. Navin further discloses applying or adjusting display and audio settings — including brightness, contrast, color, and other settings that influence how the media content is visually or audibly rendered.)
Regarding claim 19, Yang in view of Dal and Navin disclose the elements of claim 18. In addition, Navin disclose:
The method of claim 18, wherein the audio setting is the surrounding setting; and wherein the surround setting includes at least one of a spatial filter, more than two speaker channels, a 5.1-channel surround sound, a 7.1-channel surround sound, an object-based audio, or 9.1-channel surround sound, or a synthetic surround sound (The examiner selects two speakers. In paragraph [0027], Navin discloses, the auxiliary components 204 may include surround sound speakers, sound bars, set-top cable boxes, streaming service boxes, and the like.)
Regarding claim 20, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Dal disclose:
The method of claim 1, wherein the textual metadata includes at least one of a content-related descriptor of the media file, a media format for the media file, language of the media file, title of the media file, or an album cover image of the media file (In paragraph [0140], Dal discloses visual information about the product such as a name (or title), a textual description, tags (or keywords), and they are organized in classes (commonly called categories).)
Regarding claim 21, Yang in view of Dal and Navin disclose the elements of claim 1. In addition, Navin disclose:
The method of claim 1, wherein the numerical metadata includes at least one of a format of the media file, a content duration of the media file, a sample rate of the media file, a video presence of the media file, a bit depth of the media file, a number of audio channels of the media file, or a video frame rate of the media file (In paragraph [0045], Navin discloses, for example, sports and video games may be presented at a higher frame rate in order to prevent blur, while dramas may have a higher volume due to an increased amount of dialogue.)
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Dal and Navin and further in view of Ramsl et al. (Pub No.: 20190244094 A1), hereinafter referred to as Ramsl.
Regarding claim 5, Yang in view of Dal and Navin disclose the elements of claim 1. Yang in view of Dal and Navin do not explicitly disclose:
The method of claim 1, wherein the first vector comprises first probability values and the second vector comprises second probability values corresponding to a same set of classes
However, Ramsl disclose the limitation (In paragraph [0025], Ramsl discloses that a first item vector may indicate that there is a high probability that the first item is associated with the words “blue,” “toy,” “electronic,” and “handheld.” A second item vector may also indicate that there is a high probability that the second item is associated with some or all of the same words as the first item. )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Yang in view of Dal and Navin to include Ramsl, with converting, by the neural network, the first and second textual data to a first vector and a second vector as taught by Ramsl. The motivation for doing so would have been to improve as more data is received and as users confirm or modify the recommendations (See[0018] of Ramsl)
Claim(s) 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Dal and Navin and further in view of Wang et al. (US Patent No. 11,379,710 B1), hereinafter referred to as Wang.
Regarding claim 6, Yang in view of Dal and Navin disclose the elements of claim 1. Yang in view of Dal and Navin do not explicitly disclose:
The method of claim 1, wherein determining the classification of the media content comprises inputting first probability values and second probability values to the third neural network
However, Wang disclose the limitation (In Col. 5, lines 64–67, Wang discloses the first training output data and second training output data are used to train the third neural network.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Yang in view of Dal and Navin to include Wang, with a third neural network based on first training output data and second training output data as taught by Wang. The motivation for doing so would have been to improve the selected model by tuning or optimizing the hyperparameters (See (Col. 45-46) of Wang.)
Regarding claim 7, Yang in view of Dal, Navin and Wang disclose the elements of claim 6. In addition, Navin disclose:
The method of claim 6, wherein the first probability values and the second probability values each include values corresponding to a movie class, a music class, a voice class, an advertisement class, a news class, and a sports class (In Fig. 5 and paragraph [0051], Navin discloses determining a type of rendered media content, such as sports, video games, horror movies, and determining one or more playback settings based on the determined content type. )
Claim(s) 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Dal and Navin and further in view of Huang et al. (Pub No.: 20200090028A1), hereinafter referred to as Huang.
Regarding claim 8, Yang in view of Dal and Navin disclose the elements of claim 1. Yang in view of Dal and Navin do not explicitly disclose:
The method of claim 1, wherein: the third neural network produces third probability values, and determining the classification comprises selecting a class corresponding to a greatest value of the third probability value
However, Huang disclose the limitation (In paragraph [0079], Huang discloses that a first classifier generates a first probability vector, a second classifier generates a second probability vector, and a third probability vector is fused with the first and second probability vector.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Yang in view of Dal and Navin to include Huang, wherein weights of the first classifier are shared with the second classifier as taught by Huang. The motivation for doing so would have been to improve the classification result of the classification device (See [0035] of Huang.)
Regarding claim 10, Yang in view of Dal and Navin disclose the elements of claim 1. Yang in view of Dal and Navin do not explicitly disclose:
The method of claim 1, wherein the third neural network is trained with first training probability values, second training probability values, and labels corresponding to a set of training media
However, Huang disclose the limitation (In paragraph [0079], Huang discloses that a first classifier generates a first probability vector, a second classifier generates a second probability vector and third probability vector is fused with the first and second probability vector.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Yang in view of Dal and Navin to include Huang, wherein weights of the first classifier are shared with the second classifier as taught by Huang. The motivation for doing so would have been to improve the classification result of the classification device (See [0035] of Huang.)
Response to Arguments
Applicant's arguments filed 04/15/2026 have been fully considered but were not persuasive.
Pertaining to the rejection under 101
On page 8, the applicant relies primarily on specification [0035], which states that using multiple machine learning models (including the first, second, and third model) may increase accuracy, reduce errors, lower latency, and reduce processing usage. The applicant concludes that the claims therefore improve another technology or technical field under MPEP 2106.04(d)(1). However, the examiner believes the claim does not recite these improvements or any specific mechanism that necessarily achieves them. Rather, the claim broadly recites processing textual metadata with a first NN, processing numerical metadata with a second NN, fusing the resulting vectors using a third NN, and configuring a setting based on the resulting classification. The alleged improvements reside in the specification rather than in the claimed invention. Accordingly, the claim does not integrate the recited judicial exception into a practical application by reciting a specific improvement to the function of a computer or to another technology to technical under MPEP 2106.04(d)(1).
Arguments are not persuasive and a full 101 analysis is set forth above
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights