DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings were received on 03/28/2024. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on the following date(s): 11/05/2025 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are noted below where the generic place holder is in bold and the functional language italicized:
Claim 1:
determined one or more confidence levels falls within its associated evaluation band: at a reclassification unit, collecting said determined one or more confidence levels and said data for reclassification;
Examiner notes that the use of the colon appears to associate the claimed unit with the determining and collecting process which appears to be the case in light of specification paragraph [0026].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, the claim limitation noted above, in the claim interpretation section, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Specifically, the specification merely re-recites claim language and fails to clearly link corresponding hardware structure to the claimed functions, noted in the claim interpretation above. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
The dependent claims that depend on claim 14, fail to resolve the noted deficiency in claim 14.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III; And alternatively directed to a Mathematical concepts – mathematical relationships (see MPEP § 2106.04(a)(2), subsection I))
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
receiving data at a pre-trained neural network; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network)
and upon reclassification, adapting said pre-trained neural network by transfer learning using said reclassification; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; A claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception. Thus, claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
and training a machine learning model to make an agricultural prediction using the labeled plurality of simulated images (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application.
Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 2: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein said reclassification is a machine-based reclassification or a human reclassification. (Deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 3: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising preprocessing said data before said determining of said one or more confidence levels; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and mere instructions to apply the judicial exception using a computer/computing environment as a tool.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 4: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea recited in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein said pre-trained neural network can be a Feedforward Neural Network, a Convolutional Neural Network, a Recurrent Neural Network, a Long Short Term Memory Network, a Gated Recurrent Unit Network, a Transformer Network, a Capsule Network, an Autoencoder, a Variational Autoencoder, or a Graph Neural Network. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 5: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
comprising determining said one or more confidence levels by determining a probability that said data belongs to said associated class. (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 6: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea as analyzed in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein said one or more classes may be mutually exclusive. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation simply link the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 7: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein said one or more evaluation bands may be defined by a deviation from a nominal threshold. (Considered directed to a Mathematical concepts – mathematical relationships (see MPEP § 2106.04(a)(2), subsection I))
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 8: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
comprising generating a new class .
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising generating a new class based on said transfer learning comprising training said new class on said data. (Deemed insufficient to transform the judicial exception into a patentable invention because the recitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 9: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea as analyzed in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein adapting said pre-trained neural network by transfer learning comprises feeding features to said pre-trained neural network, and/or fine-tuning one or more layers of said pre-trained neural network. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 10: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea as analyzed in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein adapting said pre-trained neural network by transfer learning comprises feeding features to said pre-trained neural network, and/or fine-tuning one or more layers of said pre-trained neural network. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application. Specifically, first, the additional limitations directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 11: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation simply link the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).)
wherein adapting said pre-trained neural network by transfer learning comprises optimizing said pre-trained neural network for a performance indicator, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application. Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 12: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
comprising analyzing said unclassifiable data and said associated confidence levels for patterns that suggest novel classes for said unclassifiable data. (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III; And alternatively directed to a Mathematical concepts – mathematical relationships (see MPEP § 2106.04(a)(2), subsection I))
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application.
Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Claim 13: Does the claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
modifying said evaluation bands to adapt said classes based on said data. (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III; And alternatively directed to a Mathematical concepts – mathematical relationships (see MPEP § 2106.04(a)(2), subsection I))
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception and fail to integrate the abstract into practical application.
Specifically, first, the additional limitations are deemed directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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. This claim is not patent eligible.
Regarding claims 14-26, the claims are similar to claims 1-13 respectively and thus rejected under the same rationale.
As shown above, claims 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more” than the recited judicial exception. The claims are therefore directed to an abstract idea.
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.
Claims 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over Baughman et al. (US 20220246130, hereinafter ‘Baug’) in view of Masud et al. (US 20120054184, hereinafter ‘Masud’).
Regarding independent claim 1, Baug teaches a classification method, said method comprising: (in [0004] Embodiments of the present invention disclose a method, computer system, and a computer program product for speech synthesis. The present invention may include generating one or more final voiceprints. The present invention may include generating one or more voice clones based on the one or more final voiceprints. The present invention may include classifying the one or more voice clones into a grouping using a language model…; And in [0030] Referring to FIG. 1, an exemplary networked computer environment 100 in accordance with one embodiment is depicted. The networked computer environment 100 may include a computer 102 with a processor 104 and a data storage device 106 that is enabled to run a software program 108 and a speech synthesis program 110a….)
receiving data at a pre-trained neural network; (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones [receiving data at a pre-trained neural network]. The voice clones may be classified into one of the two or more groupings (e.g., accents) of the speech corpus 114 (e.g., connected speech corpus 114, database 114)…)
determining at said pre-trained neural network one or more confidence levels based on said data, wherein each of said one or more confidence levels is associated with a class and an evaluation band; comparing said determined one or more confidence levels with their associated evaluation bands; (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones. The voice clones may be classified into one of the two or more groupings (e.g., accents) of the speech corpus 114 (e.g., connected speech corpus 114, database 114). The one or more voice clones may be classified into one of the two or more groupings [wherein each of said one or more confidence levels is associated with a class and an evaluation band] (e.g., accents) by the trained language model if the voice clone equals or exceeds [comparing said determined one or more confidence levels with their associated evaluation bands] a confidence level [determining at said pre-trained neural network one or more confidence levels based on said data]. The one or more voice clones may be classified into an unknown grouping by the trained language model if the voice clone is below [comparing said determined one or more confidence levels with their associated evaluation bands] a confidence level [determining at said pre-trained neural network one or more confidence levels based on said data].)
if at least one of said determined one or more confidence levels falls within its associated evaluation band: collecting said determined one or more confidence levels and said data for reclassification; (in [0048] For example, the uncloned voice samples of the connected speech corpus 114 (e.g., speech corpus 114, database 114) may be manually classified into the groupings (e.g., accents) American-English or British-English based on identifying information encoded in the uncloned voice samples. The trained language model may classify the one or more voice clones as either American-English, British-English, or unknown. The trained language model may classify a voice clone as American-English or British-English if the voice clone equals or exceeds a confidence level. The trained language model may classify a voice clone as unknown if the voice clone is below a confidence level [if at least one of said determined one or more confidence levels falls within its associated evaluation band]. As the trained language model classifies more voice clones the trained language model may reclassify unknown voice clones into groupings [if at least one of said determined one or more confidence levels falls within its associated evaluation band: collecting said determined one or more confidence levels and said data for reclassification]… [0055] At 214, the speech synthesis program generates a new archetypal voice. The speech synthesis program 110 may generate the new archetypal voice by blending one or more voice clones. The speech synthesis program 110 may blend the one or more voice clones of an identified subgrouping wherein the one or more voice clones of the subgrouping have a vector difference below the similarity threshold [collecting said determined one or more confidence levels and said data for reclassification]... [0056] The speech synthesis program 110 may blend the one or more voice clones by mathematical operation to generate the new archetypal voice [collecting said determined one or more confidence levels and said data for reclassification]. The archetypal voice being a single voiceprint representative of the subgroup (e.g., cluster) identified… [0057] The speech synthesis program 110 may generate a new archetypal voice by blending multiple subgroupings (e.g., clusters) based on the subgroupings (e.g., clusters) selected by a user. The speech synthesis program 110 may blend one or more voice clones within a subgroup (e.g., cluster) or across multiple subgroups (e.g., clusters) based on the multidimensional vectors of the corresponding final voiceprint [collecting said determined one or more confidence levels and said data for reclassification]. The user of the speech synthesis program 110 may manually select the one or more final voiceprints of which the vectors are to be blended. The user may select two or more subgroups (e.g., clusters) in which the one or more final voiceprint vectors are to be blended…)
and upon reclassification, adapting said pre-trained neural network by transfer learning using said reclassification; (in [0045] The deep feed forward neural network may receive as input the one or more final voiceprints and generate as output one or more voice clones [and upon reclassification, adapting said pre-trained neural network by transfer learning using said reclassification]. A voice clone may be the decoded version of a voiceprint. While a voiceprint is may be comprised of one or more multidimensional vectors, the voice clone may be the corresponding audio version of the voiceprint. As will be described in more detail below with respect to step 214, the corresponding final voiceprints of the one or more voice clones may be blended based on a subgrouping (e.g., cluster) or across multiple subgroupings (e.g., clusters). Generating a new archetypal voice by blending multiple subgroupings (e.g., clusters) may be determined based on the subgroupings selected by a user… [0057] The speech synthesis program 110 may generate a new archetypal voice by blending multiple subgroupings (e.g., clusters) based on the subgroupings (e.g., clusters) selected by a user. The speech synthesis program 110 may blend one or more voice clones within a subgroup (e.g., cluster) or across multiple subgroups (e.g., clusters) based on the multidimensional vectors of the corresponding final voiceprint [… using said reclassification]. The user of the speech synthesis program 110 may manually select the one or more final voiceprints of which the vectors are to be blended. The user may select two or more subgroups (e.g., clusters) in which the one or more final voiceprint vectors are to be blended…; And in [0034] At 202, the speech synthesis program trains a language model. The speech synthesis program 110 may train the language model using transfer learning... The speech synthesis program 110 may train the language model, by way of transfer learning [adapting said pre-trained neural network by transfer learning], such as, but not limited to Bidirectional Encoder Representations from transformers (BERT)…)
if at least one of said determined one or more confidence levels is higher than its associated evaluation band, said class associated with said exceeding one or more determined confidence levels is output; if all of said one or more confidence levels are lower than said associated evaluation band, said confidence levels and said data are collected as unclassifiable. (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones. The voice clones may be classified into one of the two or more groupings (e.g., accents) of the speech corpus 114 (e.g., connected speech corpus 114, database 114). The one or more voice clones may be classified into one of the two or more groupings (e.g., accents) by the trained language model if the voice clone equals or exceeds a confidence level. The one or more voice clones may be classified into an unknown grouping [said confidence levels and said data are collected as unclassifiable] by the trained language model if the voice clone is below a confidence level [if all of said one or more confidence levels are lower than said associated evaluation band, said confidence levels and said data are collected as unclassifiable].)
While Baug teaches the use of the classification algorithm for evaluating new classes as noted above and in [0049] The trained language model may utilize multi-class classification in classifying the one or more voice clones. Multi-class classification may refer to those classification tasks that have more than two groupings. The trained language model may utilize one or more algorithms in performing a multi-class classification of the one or more voice clones, such as, but not limited to, k-nearest neighbors, decision trees, naïve bayes, random forest, gradient boosting, amongst others… [0053] The speech synthesis program 110 may utilize one or more clustering algorithms in determining the numerical difference between the vectors generated by mel-frequency, such as, but not limited to, k means clustering, cosine similarity, Euclidean distance, score vector machines, amongst others.
One of ordinary skill would know that classification algorithms for processing information for detecting novel classes use mathematical evaluation-based techniques making determinations as disclosed above in Baug and noted in the Masud reference below.
Masud further teaches determining at said pre-trained neural network one or more confidence levels based on said data, wherein each of said one or more confidence levels is associated with a class and an evaluation band; in [0079] The instances belonging to a class c may be generated by an underlying generative model .theta..sub.c, and the instances in each class may be independently identically distributed [determining at said pre-trained neural network one or more confidence levels based on said data]. Thus, it may be considered that the instances which are close together under some distance metric are supposed to be generated by the same model, i.e., belong to the same class. This may be one basic assumption for nearest-neighbor classifications. An example of the concept of "nearest neighborhood" is as follows: [0080] Definition 1 (.lamda..sub.c,q-neighborhood) [wherein each of said one or more confidence levels is associated with a class and an evaluation band]: .lamda..sub.c,q-neighborhood, or .lamda..sub.c,q(x) of any instance x is the set of q nearest neighbors of x within class c. For example, let there be three classes c.sub.+, and c.sub.-, and c.sub.0, denoted by the symbols "+", "-", and black dots, respectively, as shown in FIG. 5A...
And comparing said determined one or more confidence levels with their associated evaluation bands, in [0081] In one example, let D.sub.c,q(x) be the mean distance from x to .lamda..sub.c,q(x), i.e., … where D(x.sub.i, x.sub.j) may be the distance between the data points x.sub.i and x.sub.j in some appropriate metric. In one example, let c.sub.min be the class label such that D.sub.c.sub.min.sub.,q(x) may be the minimum among all D.sub.c,q(x), i.e., .lamda..sub.c.sub.min.sub.,q(x) is the nearest .lamda..sub.c,q(x) neighborhood (or q-nearest neighborhood or q-NH) of x [comparing said determined one or more confidence levels with their associated evaluation bands]…. [0088] Therefore, in one example if a novel class c appears in the stream, none of the classification models in the ensemble may be able to correctly classify the instances of c [comparing said determined one or more confidence levels with their associated evaluation bands]. The following property of the novel class may result from the q-NH rule. [0089] Property 1: Let x be an instance belonging to a novel class c, and let c' be am existing class. Then according to q-NH rule, D.sub.c,q(x), i.e., the average distance from x to .lamda..sub.c,q(x) is smaller than D.sub.c',q(x), the average distance from x to .lamda..sub.c,q(x), for any existing class c'. In other words, x may be closer to the neighborhood of its own class (cohesion), and farther from the neighborhood of any existing classes (separation).
Masud and Baug are analogous art because both involve developing information retrieval and processing techniques using generative models and classification algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for data classification and novel data class detection techniques using generative models and classification algorithms as disclosed by Masud with the method of developing information retrieval and processing techniques using generative models and classification algorithms, as disclosed by Baug.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Masud and Baug, as noted above. Doing so allows for developing an outlier identifier to determine a set of filtered outliers, in an incoming data stream, using trained models (Masud, [0043]).
Regarding claim 2, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein said reclassification is a machine-based reclassification or a human reclassification. (in [0048] For example, the uncloned voice samples of the connected speech corpus 114 (e.g., speech corpus 114, database 114) may be manually classified into the groupings [wherein said reclassification is a machine-based reclassification or a human reclassification] (e.g., accents) American-English or British-English based on identifying information encoded in the uncloned voice samples… The trained language model may classify a voice clone as unknown if the voice clone is below a confidence level. As the trained language model classifies more voice clones the trained language model may reclassify unknown [wherein said reclassification is a machine-based reclassification or a human reclassification] voice clones into groupings.)
Regarding claim 3, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, comprising preprocessing said data before said determining of said one or more confidence levels; (in [0052] The speech synthesis program 110 may utilize a sound processing technique [comprising preprocessing said data before said determining of said one or more confidence levels], such as, but not limited to, mel-frequency cepstrum (MFC). The speech synthesis program 110 may generate the mel-frequency for each of the voice clones of a grouping using the corresponding final voiceprint. The speech synthesis program 110 may identify the one or more clusters (e.g., subgroupings) by determining a numerical difference between corresponding vectors generated by mel-frequency. A subgrouping (e.g., cluster) may be two or more voice clones within a grouping with a vector difference below a similarity threshold [comprising preprocessing said data before said determining of said one or more confidence levels]..)
Regarding claim 4, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein said pre-trained neural network can be a Feedforward Neural Network, a Convolutional Neural Network, a Recurrent Neural Network, a Long Short Term Memory Network, a Gated Recurrent Unit Network, a Transformer Network, a Capsule Network, an Autoencoder, a Variational Autoencoder, or a Graph Neural Network. (in [0044] At 208, the speech synthesis program generates one or more voice clones. The speech synthesis program 110 may generate the one or more voice clones based on the one or more final voiceprints. Each of the one or more voice clones may have a corresponding final voiceprint. The speech synthesis program 110 may utilize the one or more final voiceprints as input for a deep feed forward neural network… )
Regarding claim 5, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, comprising determining said one or more confidence levels by determining a probability that said data belongs to said associated class. (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones. The voice clones may be classified into one of the two or more groupings (e.g., accents) of the speech corpus 114 (e.g., connected speech corpus 114, database 114). The one or more voice clones may be classified into one of the two or more groupings (e.g., accents) by the trained language model if the voice clone equals or exceeds a confidence level [comprising determining said one or more confidence levels by determining a probability that said data belongs to said associated class as the determined classification groupings confidence levels evaluated as associated with two or more groupings]. The one or more voice clones may be classified into an unknown grouping by the trained language model if the voice clone is below a confidence level.)
Regarding claim 6, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein said one or more classes may be mutually exclusive. (in [0047] The trained language model may utilize binary classification in classifying the one or more voice clones. Binary classification may refer to those classification tasks that have two groupings [wherein said one or more classes may be mutually exclusive]. The trained language model may utilize one or more algorithms in performing a binary classification of the one or more voice clones, such as, but not limited to, k-nearest neighbor, logistic regression, decision trees, support vector machines, amongst others [wherein said one or more classes may be mutually exclusive]. The trained language model may be trained using the uncloned voice samples, wherein the uncloned voice samples may be classified into two groupings (e.g., accents) based on identifying information encoded in the uncloned voice samples, detailed in step 202 above.)
Regarding claim 7, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein said one or more evaluation bands may be defined by a deviation from a nominal threshold. (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones. The voice clones may be classified into one of the two or more groupings (e.g., accents) of the speech corpus 114 (e.g., connected speech corpus 114, database 114). The one or more voice clones may be classified into one of the two or more groupings (e.g., accents) by the trained language model if the voice clone equals or exceeds a confidence level [wherein said one or more evaluation bands may be defined by a deviation from a nominal threshold defined as the band from zero to the upper confidence level for classifying measured deviation in observed datasets]. The one or more voice clones may be classified into an unknown grouping by the trained language model if the voice clone is below a confidence level. And where the group clusters are associated with different respective nominal values per similarity threshold and vector differences, in [0054] For example, within the American-English grouping two clusters (e.g., subgroupings) may form. The speech synthesis may generate the mel-frequency for each of the voice clones of a each of the two clusters (e.g., subgroupings) using the corresponding final voiceprint. The speech synthesis program 110 may determine based on the numerical difference between the vectors in Cluster 1 that the vector difference is 0.1 and the similarity threshold is 0.3, accordingly the speech synthesis program 110 may determine that Cluster 1 (e.g., subgrouping) may be a subgrouping [wherein said one or more evaluation bands may be defined by a deviation from a nominal threshold]. The subgrouping may be classified as a new accent manually or automatically by the speech synthesis program 110)
Regarding claim 8, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, comprising generating a new class based on said transfer learning comprising training said new class on said data. (in [0034] At 202, the speech synthesis program trains a language model. The speech synthesis program 110 [comprising generating a new class based on said transfer learning comprising training said new class on said data] may train the language model using transfer learning. Transfer learning may be a technique in which a pre-trained model is used but is fine-tuned for a related task. The speech synthesis program 110 may train the language model, by way of transfer learning, such as, but not limited to Bidirectional Encoder Representations from transformers (BERT). The speech synthesis program 110 may utilize a connected speech corpus 114 (e.g., database 114, speech corpus 114) in training the language model. And in [0060] The speech synthesis program 110 [comprising generating a new class based on said transfer learning comprising training said new class on said data] may be able to receive text from a user and generate audio based on the new archetypal voice [….comprising training said new class on said data], the archetypal voice exhibiting the characteristics of the subgrouping (e.g., cluster).… )
Regarding claim 9, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein adapting said pre-trained neural network by transfer learning comprises feeding features to said pre-trained neural network, and/or fine-tuning one or more layers of said pre-trained neural network. (in [0034] At 202, the speech synthesis program trains a language model. The speech synthesis program 110 may train the language model using transfer learning. Transfer learning may be a technique in which a pre-trained model is used but is fine-tuned for a related task [wherein adapting said pre-trained neural network by transfer learning comprises feeding features to said pre-trained neural network, and/or fine-tuning one or more layers of said pre-trained neural network]. The speech synthesis program 110 may train the language model, by way of transfer learning, such as, but not limited to Bidirectional Encoder Representations from transformers (BERT)…)
Regarding claim 10, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein adapting said pre-trained neural network by transfer learning comprises training said pre-trained neural network on said reclassified data. (in [0048] ... The trained language model may classify a voice clone as unknown if the voice clone is below a confidence level. As the trained language model classifies more voice clones the trained language model may reclassify unknown voice clones into groupings [comprises training said pre-trained neural network on said reclassified data]… [0055] At 214, the speech synthesis program generates a new archetypal voice. The speech synthesis program 110 may generate the new archetypal voice by blending one or more voice clones. The speech synthesis program 110 may blend the one or more voice clones of an identified subgrouping wherein the one or more voice clones of the subgrouping have a vector difference below the similarity threshold [comprises training said pre-trained neural network on said reclassified data]… [0057] The speech synthesis program 110 may generate a new archetypal voice by blending multiple subgroupings (e.g., clusters) based on the subgroupings (e.g., clusters) selected by a user. The speech synthesis program 110 may blend one or more voice clones within a subgroup (e.g., cluster) or across multiple subgroups (e.g., clusters) based on the multidimensional vectors of the corresponding final voiceprint [wherein adapting said pre-trained neural network by transfer learning comprises training said pre-trained neural network on said reclassified data]. The user of the speech synthesis program 110 may manually select the one or more final voiceprints of which the vectors are to be blended… And in [0043] The speech synthesis program 110 may derive a final voiceprint for each of the one or more initial voiceprints by adjusting (e.g., fine tuning) each of the one or more initial voiceprints [wherein adapting said pre-trained neural network by transfer learning comprises training said pre-trained neural network on said reclassified data]. The speech synthesis program 110 may fine tune each of the or more initial voiceprints utilizing a machine learning framework, such as, but not limited to, a generative adversarial network (GAN)... The speech synthesis program 110 may utilize this feedback mechanism to adapt the transfer learning model within an active learning paradigm [wherein adapting said pre-trained neural network by transfer learning comprises training said pre-trained neural network on said reclassified data]…; And fine-tuning based on transfer learning in [0034] At 202, the speech synthesis program trains a language model. The speech synthesis program 110 may train the language model using transfer learning. Transfer learning may be a technique in which a pre-trained model is used but is fine-tuned for a related task [wherein adapting said pre-trained neural network by transfer learning comprises training said pre-trained neural network on said reclassified data]…)
Regarding claim 11, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, wherein adapting said pre-trained neural network by transfer learning comprises optimizing said pre-trained neural network for a performance indicator, wherein said performance indicator is a learning rate, a rate of false positives, a measure of accuracy, a recall rate, an error function, and/or an inference time. (in [0043] ... The speech synthesis program 110 may utilize the one or more initial voiceprints as input and the generator neural network may propose each of the initial voiceprints to the discriminator neural network. The discriminator neural network may attempt to determine if the voiceprint is a voiceprint or uncloned voice sample. If the discriminator gets the answer wrong, the initial voiceprint may be fine tuned by adjusting the attention masks and incrementally feeding back error [wherein adapting said pre-trained neural network by transfer learning comprises optimizing said pre-trained neural network for a performance indicator, wherein said performance indicator is ]. The speech synthesis program 110 may utilize this feedback mechanism to adapt the transfer learning model within an active learning paradigm. The speech synthesis program 110 may continue to fine tune a voiceprint until specified halting criteria is reached. The speech synthesis program 110 may derive the final voiceprint for each of the one or more initial voiceprints upon reaching the specified halting criteria [wherein adapting said pre-trained neural network by transfer learning comprises optimizing said pre-trained neural network for a performance indicator]. )
Regarding claim 12, the rejection of claim 1 is incorporated and Baug in combination with Masud further teaches method of claim 1, comprising analyzing said unclassifiable data and said associated confidence levels for patterns that suggest novel classes for said unclassifiable data. (in [0046] At 210, the speech synthesis program 110 classifies the one or more voice clones [comprising analyzing said unclassifiable data and said associated confidence levels for patterns that suggest novel classes for said unclassifiable data]. The speech synthesis program 110 may utilize the trained language model, described in Step 202 above, in classifying the one or more voice clones…[0047] ... The trained language model may be trained using the uncloned voice samples, wherein the uncloned voice samples may be classified into two groupings (e.g., accents) based on identifying information encoded in the uncloned voice samples, detailed in step 202 above... [0048] … The trained language model may classify the one or more voice clones as either American-English, British-English, or unknown. The trained language model may classify a voice clone as American-English or British-English if the voice clone equals or exceeds a confidence level. The trained language model may classify a voice clone as unknown if the voice clone is below a confidence level. As the trained language model classifies more voice clones the trained language model may reclassify unknown voice clones into groupings [comprising analyzing said unclassifiable data and said associated confidence levels for patterns that suggest novel classes for said unclassifiable data]. )
Regarding claim 13, the rejection of claim 1 is incorporated and Masud further teaches method of claim 1, modifying said evaluation bands to adapt said classes based on said data. (in [0086] The illustrative embodiments may use the following definition: [0087] Definition 3 (Existing class and Novel class): Let L be the current ensemble of classification models. A class c is an existing class if at least one of the models L.sub.i.epsilon.L has been trained with the instances of class c [modifying said evaluation bands to adapt said classes based on said data]. Otherwise, c is a novel class… [0115] In line 1 of Process 3 below, Fpseudopoints may be created using the F-outliers as explained earlier. For each classifier L.sub.i.epsilon.L, q-NSC'(h) of every Fpseudopoint h (line 4) may be computed. If the total weight of the Fpseudopoints having positive q-NSC'( ) is greater than q, then L.sub.i may vote for novel class (line 7). If all classifiers vote for a novel class, then a novel class may have really appeared (line 9). Once novel class is declared, the instances of the novel class may be found. This may be performed as follows: suppose h is an Fpseudopoint having positive q-NSC'(h) with respect, to all classifiers L.sub.i.epsilon.L (note that q-NSC'(h) may be computed with respect to each classifier separately) [modifying said evaluation bands to adapt said classes based on said data]. Therefore, all F-outlier instances belonging to h may be identified as novel class instances. …. [0149] Evaluation may be performed as follows: initial models may be built in each method with the first init_number instances. In the illustrative experiments, we may set init_number=3S (first three chunks). From the 4.sup.th chunk onward, the performances of each method may be evaluated on each data, point using the time constraints. The models may be updated [modifying said evaluation bands to adapt said classes based on said data] with a new chunk whenever all data points in that chunk are labeled.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Masud and Baug for the same reasons disclosed above.
Regarding claims 14-26, the claims are similar to claims 1-13 respectively and thus rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tangari et al. (US 20240062021): teaches determining at said pre-trained neural network one or more confidence levels based on said data, wherein each of said one or more confidence levels is associated with a class and an evaluation band in [0105] In certain embodiments, the intent classifier 242 determines, for each skill bot registered with the master bot, a confidence score indicating a likelihood that the skill bot can handle an utterance (e.g., the non-explicitly invoking utterance 234 received from EIS 230). The intent classifier 242 may also determine a confidence score for each system level intent (e.g., help, exit) that has been configured. If a particular confidence score meets one or more conditions, then the skill bot invoker 240 will invoke the bot associated with the particular confidence score. For example, a threshold confidence score value may need to be met. Thus, an output 245 of the intent classifier 242 is either an identification of a system intent or an identification of a particular skill bot. In some embodiments, in addition to meeting a threshold confidence score value, the confidence score must exceed the next highest confidence score by a certain win margin. Imposing such a condition would enable routing to a particular skill bot when the confidence scores of multiple skill bots each exceed the threshold confidence score value.
Sewak et al. (US 20240370484): teaches in [0203] The classification levels disclosed herein were at times binary levels as label and anti-label. The techniques described herein are capable of processing multinomial levels to provide a multinomial label classifier.
Albasiri et al. (US 20240427990): teaches determining at said pre-trained neural network one or more confidence levels based on said data, wherein each of said one or more confidence levels is associated with a class and an evaluation band in [0070] In at least one embodiment, one or more tests may be developed for the rule-based algorithm based on an evaluation of a set of outputs for a set of test inputs 566. The outputs may be compared to an expected set of outputs and evaluated against one or more thresholds, such as a minimum word error rate or some other criteria. It may be determined whether the threshold is exceeded and/or whether the error is within a range or threshold 568. If the error it outside the range, then the rules may be modified 570. If the error is within the range, then parameters associated with the rules may be stored 572 and the rule-based algorithm may then be made available responsive to a request 574. In this manner, different modular rule-based algorithms may be generated and implemented for different semiotic classes of different languages.
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/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
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