DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1- 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
When considering subject matter eligibility under 35 USC 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.
Specifically, claims 1 - 16 are directed to a method. It hereby falls under one of the four statutory classes of invention.
If the claim does not fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Claims 1 - 16 as mentioned above is a step of observation, evaluation, and judgement that can be practically performed by a human, either mentally or with the use of pen and paper.
The limitation of “minimizing a loss function to train the information elimination model, wherein two input layers, including one input layer from each of the first adversarial network and the second adversarial network, are generated based on an output layer of the information elimination model and the input feature, the first adversarial network comprises: a first generator configured to perform a task; and a first discriminator configured to recognize the first attribute, the second adversarial network comprises: a second generator configured to recognize the first attribute; and a second discriminator, configured to perform the task, and the loss function is associated with a disentangling loss of the two input layers of the first adversarial network and the second adversarial network, a first loss of the first generator, a second loss of the first discriminator, a third loss of the second generator, and a fourth loss of the second discriminator.” in claims 1 - 16, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “a processor, an electronic device”, nothing in the claim element precludes the steps from practically being performed in a human mind.
The mere nominal recitation of a generic a processor, an electronic device do not take the claim limitations out of the mental processes grouping.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim 1 – 16 recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements “receiving an original signal; generating a feature not including first information based on the original signal and an information elimination model, the first information allowing a first attribute to be recognizable; and performing the task based on the feature and the machine learning model; providing the information elimination model, a first adversarial network, and a second adversarial network”.
The limitation “receiving an original signal; generating a feature not including first information based on the original signal and an information elimination model, the first information allowing a first attribute to be recognizable; providing the information elimination model, a first adversarial network, and a second adversarial network”, amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “performing the task based on the feature and the machine learning model”, represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05 (g)).
The claimed “a processor, an electronic device” are recited at a high level of generality and are merely invoked as tool to perform an existing elimination feature.
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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
The insignificant extra-solution activities identified above, which include the data-gathering (receiving, and performing), and providing steps, are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II) (i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAPE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPO2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting (displaying) offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPO2d at 1092- 93). The claims are not patent eligible.
Claims 1- 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor, an electronic device to perform the receiving, performing and providing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Even when considered in combination, these additional elements (a processor, an electronic device) represent mere instruction to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept.
Claims 1- 16 as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5-7, 10 – 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Liao et al. (US PAP 2024/0412749).
As per claims 1, 15, 16, Liao et al. teach a computer-implemented method for training an information elimination model, the information elimination model configured to eliminate, from an input feature, first information that allows a first attribute to be recognizable, the computer-implemented method comprising:
providing the information elimination model, a first adversarial network, and a second adversarial network (“generating and discriminating the target speech through two speech generators and two discriminators based on a CycleGAN mechanism.”; paragraphs 70, 168); and
minimizing a loss function to train the information elimination model, wherein two input layers, including one input layer from each of the first adversarial network and the second adversarial network, are generated based on an output layer of the information elimination model and the input feature (“it can be determined that the value of the first loss function does not meet the model convergence condition; on the contrary, it can be determined that the value of the first loss function meets the model convergence condition; if the value of the second loss function is greater than the second loss threshold, it can be determined that the value of the second loss function does not meet the model convergence condition; on the contrary, it can be determined that the value of the second loss function meets the model convergence condition.”; paragraphs 27 - 35, 141 – 143),
the first adversarial network comprises: a first generator configured to perform a task; and a first discriminator configured to recognize the first (“generating and discriminating the target speech through two speech generators and two discriminators based on a CycleGAN mechanism…first, an automatic speech recognition (Automatic Speech Recognition, ASR) technology is used to analyze a source speech to obtain phonetic posteriorgram (Phonetic Posteriorgram, PPG) features of the source speech, and then the PPG features are input to a conversion model to obtain acoustic speech parameters to be sent to the vocoder, and finally the vocoder converts the source speech into a target speech according to the speech acoustic features.”; paragraphs 66, 70, 168);
the second adversarial network comprises: a second generator configured to recognize the first attribute; and a second discriminator, configured to perform the task (“generating and discriminating the target speech through two speech generators and two discriminators based on a CycleGAN mechanism…first, an automatic speech recognition (Automatic Speech Recognition, ASR) technology is used to analyze a source speech to obtain phonetic posteriorgram (Phonetic Posteriorgram, PPG) features of the source speech, and then the PPG features are input to a conversion model to obtain acoustic speech parameters to be sent to the vocoder, and finally the vocoder converts the source speech into a target speech according to the speech acoustic features.”; paragraphs 66, 70, 168); and
the loss function is associated with a disentangling loss of the two input layers of the first adversarial network and the second adversarial network, a first loss of the first generator, a second loss of the first discriminator, a third loss of the second generator, and a fourth loss of the second discriminator (paragraphs 97, 128 - 131).
Claim 15 further recites receiving an original signal; generating a feature not including first information based on the original signal and an information elimination model, the first information allowing a first attribute to be recognizable; and performing the task based on the feature and the machine learning model (“first, an automatic speech recognition (Automatic Speech Recognition, ASR) technology is used to analyze a source speech to obtain phonetic posteriorgram (Phonetic Posteriorgram, PPG) features of the source speech”; paragraphs 66, 70, 168).
As per claim 3, Liao et al. further disclose the output layer of the information elimination model comprises a first control signal corresponding to the first adversarial network and a second control signal corresponding to the second adversarial network (paragraphs 5, 70, 134, 168).
As per claim 5, Liao et al. further disclose the first control signal generated by the information elimination model after training is configured to eliminate the first information from the input feature (paragraphs 5, 70, 134, 168).
As per claim 6, Liao et al. further disclose providing a model configured to perform the task and taking the model as the first generator and the second discriminator; and providing a recognition model for the first attribute and taking the recognition model as the first discriminator and the second generator (“an automatic speech recognition (Automatic Speech Recognition, ASR) technology is used to analyze a source speech to obtain phonetic posteriorgram (Phonetic Posteriorgram, PPG) features of the source speech, and then the PPG features are input to a conversion model to obtain acoustic speech parameters to be sent to the vocoder, and finally the vocoder converts the source speech into a target speech according to the speech acoustic features.”; paragraphs 66 – 68).
As per claim 7, Liao et al. further disclose minimizing the loss function to train the information elimination model comprises: keeping a plurality of parameters of the first adversarial network and the second adversarial network unchanged when minimizing the loss function (“updating network parameters of the discriminator network based on the value of the second loss function; [0034] an iterative training step: performing iterative training based on the speech generation network and the discriminator network after the parameters are updated, until the value of the first loss function and the value of the second loss function both meet a model convergence condition; and [0035] a generator determining step: using the trained speech generation network as a speech” paragraphs 32 – 40).
As per claim 10, 11, Liao et al. further disclose the task comprises a speech recognition-related task; the task comprises an automatic speech recognition (automatic speech recognition (“Automatic Speech Recognition, ASR) technology is used to analyze a source speech to obtain phonetic posteriorgram (Phonetic Posteriorgram, PPG) features of the source speech”; paragraph 66).
As per claim 12, Liao et al. further disclose a gradient reversal layer is included before each of the first discriminator and the second discriminator (paragraphs 139 – 143, 157 - 168).
As per claim 13, Liao et al. further disclose each of the second loss and the third loss comprises a recall loss(paragraphs 139 – 143, 157 - 168).
As per claim 14, Liao et al. further disclose each of the first loss and the fourth loss comprises a connectionist temporal classification loss(paragraphs 139 – 143, 157 - 168).
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 4 is rejected under 35 U.S.C. 103 as being unpatentable Liao et al. (US PAP 2024/0412749) in view of CHANGIZ REZAEI et al. (US PAP 2023/0140142).
As per claim 4, Liao et al. do not specifically teach the information elimination model generates the first control signal and the second control signal using a Gumbel-Softmax function.
CHANGIZ REZAEI et al. disclose that the generator of the generative adversarial network generates a plurality of generated neural network architectures responsive to the received search space. The discriminator of the generative adversarial network selects an optimal neural network architecture from among the plurality of generated neural network architectures… Both SNAS and GDAS methods employ the use of Gumbel Softmax when operating on the search space. The search algorithm is more specific in describing the architectures it selects (Abstract, paragraph 9).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Gumbel Softmax as taught by CHANGIZ REZAEI et al. in Liao et al., because that would help improve the existing models (paragraph 54).
Claims 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Liao et al. (US PAP 2024/0412749) in view of Wang et al. (US PAP 2023/0363679).
As per claims 8, 9, Liao et al. do not specifically teach the first attribute comprises an attribute related to vulnerable populations; the first attribute comprises an attribute of dementia.
Wang et al. disclose a novel adversarial training loss is also introduced to obtain identity-independent and stroke-discriminative features… The “cookie theft” task requires the subject to retrieve, think, organize, and express the information, which will end up evaluating the subject's speech ability from both motor and cognitive aspects. Such an analysis has also been successful in identifying subjects with Alzheimer's-related dementia, aphasia, and some other cognitive-communication impairments, and would be suitable for stroke screening purposes (paragraphs 25, 70).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to analyze cognitive disorder as taught by Wang et al. in Liao et al., because that would help identify subjects with Alzheimer's-related dementia, aphasia, and other cognitive-communication impairments. (paragraph 113).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Baraff et al. teach Devices For Real-time Speech Output With Improved Intelligibility. Rottmann teaches Natural Language Processing And Classification.
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/LEONARD SAINT-CYR/ Primary Examiner, Art Unit 2658