DETAILED ACTION
This action is responsive to the Remarks filed on 01/06/2026. Claims 21, 24, 26, 27, 29,30 32, 34, 35, 37, 39 are pending in the case. Claims 21, 29 and 37 are independent claims.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/06/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to the 101 rejection of claims 21, 24, 26, 27, 29,30 32, 34, 35, 37, 39 on 01/06/2026 have been considered and are not persuasive.
Applicant traverses the rejection noting the claims emphasize technical benefits related to the claimed architecture in combination with enhanced input tokenization.
Examiner disagrees.
As noted in the rejection, the claims describe additional elements which are described in the MPEP as elements which do not integrate into a practical application or significantly more and thus cannot be considered an improvement (see 2106.05(f-h) and 2106.05(d)).
The amendments do not ameliorate prior issues as they appear to iterate the prior limitations by claiming second augmentation step and second processing of model input. As previously noted pre-processing data by way of an abstract idea and applying the data to a named “shared” model does not reflect any supposed improvements to the functioning of the claimed computer functioning.
The rejection is maintained.
Claim Rejections - 35 USC § 101
Claims 21, 24, 26, 27, 29,30 32, 34, 35, 37, 39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Regarding Claim 21, 29 and 37
Under step 1, Claim 21 is directed to a “Computer-implemented method”, which is directed to a process, one of the statutory categories. Claim 29 is directed “A system comprising one or more computers” which is directed to a machine, one of the statutory categories. Claim 37 is directed to “One or more non-transitory computer readable media”, which is directed to an article of manufacture, one of the statutory categories.
The Claim recites the following limitations which are considered abstract ideas: “generating… a first augmented model input for the machine learning model by tokenizing an input text in a source language using a shared vocabulary that includes tokens representing sub-word units that are shared between a plurality of different candidate languages to generate a first tokenized input and prepending a first identifier token that identifies at least a first target language to the first tokenized input”,
“generating… a second augmented model input for the machine learning model by tokenizing the input text in the source language using the shared vocabulary to generate a second tokenized input and prepending a second identifier token that identifies at least a second target language to the second tokenized input:”
“to generate, based on an encoding of the input text, a first model output that is a first translation of the input text into the first target language…
“wherein network parameters of the shared decoder neural network have been determined”
“to generate, based on the encoding of the input text, a second model output that is a second translation of the input text into the second target language”
Under step 2A Prong 1, the aforementioned limitations amount to activities that can be performed in the human mind. The human mind is capable of generating translations of a source sequence into a plurality of different specified target languages. The human mind is capable of determining parameters of a model.
Therefore the claims recite an abstract idea.
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. In particular, the claim recites additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (“by one or more computers…processing, by the one or more computers, the first augmented model input using the machine learning model comprising a shared decoder neural network … the shared decoder neural network is shared between the plurality of different candidate languages and… and wherein the model output is a sequence that includes outputs from the shared vocabulary ... the processing comprising processing respective layer inputs… to generate the model output…. training the machine learning model on training data… processing, by the one or more computers, the second augmented model input using the machine learning model… processing respective layer inputs using the plurality of layers… to generate the second model output. ”) See MPEP 2106.05(f). The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The above limitations describe simply apply input information to generate output information via a certain named model, none of the above limitations describe any specific functionality beyond the result of apply the identified abstract idea (i.e translating into a target language). The above limitations do not describe how the model is shared, sharing a model amounts to simply applying the model generally to translate into different languages. No details of the technology itself are described in the above cited limitations beyond the results of applying the model. Further, training the model on data describes using a computer to perform an existing process. No details about how the training is performed is provided. In addition the limitations “for each candidate language in the plurality of different candidate languages, a respective set of training examples that each include (i) an input text segment in a respective first language, (ii) an output text segment in the candidate language that is a translation of the input text segment into the candidate language, and (iii) an identifier that specifies the candidate language, and wherein the first model output is a first sequence that includes outputs from the shared vocabulary” is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). The limitations only describe details about the training data, and do not reflect any details related to how the training process is performed. In addition, the claim recites additional element(s) “…[the neural network] includes a plurality of layers comprising nonlinear computation units,… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network” that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further, the additional elements of recited above are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. The additional elements of “[the neural network] includes a plurality of layers comprising nonlinear computation units,… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network” are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. According to MPEP 2106.05(d)(I), “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)...The required factual determination must be expressly supported in writing, as discussed in MPEP § 2106.07(a). Appropriate forms of support include one or more of the following: ...(c) A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).” In accordance with the MPEP, the following factual determination is based on the technical publication: Johan et al Artificial neural networks for modelling and control of Non-Linear Systems, (chapter 2 pg 19-35). (1995). Springer New York, NY. Publication date: December 1995 (PTO-892 NPL Doc. U, copy attached). Johan et al. in pg. 19-20 “The best known neural network architecture is the multilayer feedforward neural network (multilayer perceptron). It is a static network that consists of a number of layers: input layer, output layer and one or more hidden layers connected in a feedforward way…the output of the network and the nonlinear operation σ () is taken elementwise” discloses that multilayer feedforward neural networks are the best known neural network (corresponds to routine and conventional) the network (a decoder nominally) has multiple layers (corresponds to plurality of layers) which include element wise nonlinear operation (corresponding to nonlinear computation units), thus rendering the recitation of “…[the neural network] includes a plurality of layers comprising nonlinear computation units,… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network… using the plurality of layers comprising the nonlinear computation units of the shared decoder neural network” in the claim routine and conventional
As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 24
The claim is directed to a process. The claim recites the following limitations “wherein the shared decoder neural network has an attention mechanism.” Under Step 2A Prong 1, these limitations do not describe additional abstract ideas beyond those described in the parent claim.
Furthermore under step 2A Prong 2 and 2B, the claim recites additional element(s) “wherein the shared decoder neural network has an attention mechanism” which only serve to generally link the judicial exception to a particular technology or field of use, see MPEP 2106.05(h). No details to specify the functionality of the attention mechanism are included in the claims. Accordingly, the recited 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, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 26
The claim is directed to a process. The claim recites the following limitations “wherein the machine learning model has been trained on training data comprising a plurality of paired datasets, wherein each of the paired datasets comprises an input dataset of text in a respective input language paired with an output dataset in a respective output language.” Under Step 2A Prong 1, these limitations do not describe additional abstract ideas beyond those described in the parent claim.
Furthermore under step 2A Prong 2 and 2B, the claim recites additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea “wherein the machine learning model has been trained on training data comprising a plurality of paired datasets, wherein each of the paired datasets comprises an input dataset of text in a respective input language paired with an output dataset in a respective output language.” See MPEP 2106.05(f). The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the recited 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, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Examiner notes, that while the claim alludes to the application of a model which has been trained. The claim does not recite the step of training a model to perform translation actively. The use of an existing model to perform translation cannot therefore be an improvement to the model or technology itself, only an application of technology to a particular problem.
Regarding Claim 27
The claim is directed to a process. The claim recites the following limitations “wherein the plurality of paired datasets does not include a pairing of datasets comprising an input dataset in the source language paired with an output dataset in first the target language.” Under Step 2A Prong 1, these limitations do not describe additional abstract ideas beyond those described in the parent claim.
Furthermore under step 2A Prong 2 and 2B, the claim recites additional element(s) “wherein the plurality of paired datasets does not include a pairing of datasets comprising an input dataset in the source language paired with an output dataset in the first target language” that are generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). Accordingly, the recited 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, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 30
The claim is directed to a machine. Under Step 2A Prong 1, these limitations do not describe additional abstract ideas beyond those described in the parent claim.
Furthermore, under step 2A Prong 2 and 2B, the claim recites additional element(s) “an encoder neural network that is shared between the plurality of different candidate languages and that is configured to receive the first augmented model input and the second augmented model input” which only serve to generally link the judicial exception to a particular technology or field of use, see MPEP 2106.05(h). No details to specify the functionality of the attention mechanism are included in the claims. Accordingly, the recited 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, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 32, 34 and 35
The claims are rejected for the reasons set forth in the rejection of the corresponding claims 24, 26 and 27 in connection with claim 29
Regarding Claim 39
The claim is rejected for the reasons set forth in the rejection of claim 30 in connection with claim 37
Conclusion
Claim(s) 21, 24, 26, 27, 29,30 32, 34, 35, 37, 39 have been searched, but have not been rejected with respect to 35 U.S.C. 102 nor 35 U.S.C. 103. Specifically, none of the reference of record either alone or in combination fairly disclose or suggest the limitation:wherein the shared decoder neural network is shared between the plurality of different languages and configured to generate the model output from a shared vocabulary. However, these claims remain rejected under 35 U.S.C. 101.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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