Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a system with structural components (claims 1 - 5), a computer-implemented method (claims 6 - 13), and a non-transitory computer-readable media with structural components (claims 4 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1, 6, and 14,
Step 2A, Prong One
The claim recites in part:
determine the weight vector corresponding to the fine-tuned machine
learning model, wherein the weight vector represents a difference in parameter values between the fine-tuned machine learning model and the base machine learning model;
For example, a person compares the parameters value of the fine-tuned model to the based model, identifies the differences, and records those differences as a weight vector.
determine a selected value for a scaling coefficient used to combine the base machine learning model and the weight vector, wherein a scaling coefficient indicates a weight to be applied to the weight vector during weight vector arithmetic, at least partly by:
For example, a person can select a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
generating a plurality of new models by arithmetic combination of the base machine learning model and the weight vector, wherein different new models of the plurality of new models are generated using different values of the scaling coefficient;
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector based on the scaling coefficient.
selecting the selected value for the scaling coefficient based on the perplexity values for the individual new models, wherein the selected value corresponds to a first new model, of the plurality of new models, generated according to the selected scaling coefficient, and wherein the selected value is selected based on performance of the first new model relative to both the base machine learning model and the fine-tuned machine learning model;
For example, a person compares several options, looks at how well each performs, and selects the adjustment value that produces the best result compared with the original and improved options.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtain a request to modify a base machine learning model according to
a weight vector corresponding to a fine-tuned machine learning model;
obtain the base machine learning model and the fine-tuned machine learning model;
return the first new model in response to the request.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
computing perplexity values for individual new models of the
plurality of new models, wherein the perplexity values for an individual
new model are calculated using parallelized forward passes, and
wherein the perplexity values for the individual new model are
calculated according to a validation data set including both data
corresponding to the base machine learning model and data
corresponding to the fine-tuned machine learning model;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a non-transitory data store and a processor amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of scaling coefficient parallel forward passes amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
obtain a request to modify a base machine learning model according to
a weight vector corresponding to a fine-tuned machine learning model;
obtain the base machine learning model and the fine-tuned machine learning model;
return the first new model in response to the request.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
computing perplexity values for individual new models of the
plurality of new models, wherein the perplexity values for an individual
new model are calculated using parallelized forward passes, and
wherein the perplexity values for the individual new model are
calculated according to a validation data set including both data
corresponding to the base machine learning model and data
corresponding to the fine-tuned machine learning model;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a non-transitory data store and a processor amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of scaling coefficient and parallel forward passes amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2,
Step 2A, Prong One
The claim recites in part:
wherein the arithmetic combination of the base machine learning model and the weight vector comprises subtracting the weight vector from the base machine learning model
For example, a person can “subtract” by modifying the model’s parameters by shifting them in the opposite direction of that vector
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 3,
Step 2A, Prong One
The claim recites in part:
further configure the processor to generate the fine-tuned machine learning model by fine tuning the base machine learning model or fine tuning of a different model.
For example, a person can generate a new model by modifying the model’s parameters.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 4,
Step 2A, Prong One
The claim recites in part:
wherein generating the plurality of new models by arithmetic combination of the base machine learning model and the weight vector comprises generating the plurality of new models by arithmetic combination of the base machine learning model, the weight vector, and one or more additional weight vectors
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 5,
Step 2A, Prong One
The claim recites in part:
wherein generating the plurality of new models by arithmetic combination of the base machine learning model, the weight vector, and one or more
additional weight vectors comprises:
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
determining, for each of the one or more additional weight vectors, a scaling coefficient, wherein the scaling coefficient for each additional weight vector determined independently of other scaling coefficients;
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
determining a selected value for a consolidated scaling coefficient used to
combine the base machine learning model and the consolidated weight vector, at least partly by:
For example, a person can select a scaling coefficient based on the value of the average weight vector (i.e. an increased scaling coefficient based on an increased average weight)
generating a plurality of new consolidated models by arithmetic
combination of the base machine learning model and the consolidated weight vector, wherein different new consolidated models of the plurality of new consolidated models are generated using different values of the consolidated scaling coefficient;
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector based on the consolidated scaling coefficient.
selecting the selected consolidated value for the consolidated scaling coefficient based on the consolidated perplexity values for the individual new consolidated models, wherein the selected consolidated value corresponds to a first new consolidated model, of the plurality of new consolidated models, generated according to the selected consolidated scaling coefficient.
For example, a person compares several options, looks at how well each performs, and selects the adjustment value that produces the best result compared with the original and improved options.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
computing consolidated perplexity values for individual new
consolidated models of the plurality of new consolidated models
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
computing consolidated perplexity values for individual new
consolidated models of the plurality of new consolidated models
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 7,
Step 2A, Prong One
The claim recites in part:
wherein the additional machine learning model is at least one of the base machine learning model or distinct from the base machine learning model, and wherein the request specifies at least one of the weight vector or a fine-tuned machine learning model from which the weight vector is generated.
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 8,
Step 2A, Prong One
The claim recites in part:
wherein generating the plurality of new models by arithmetic combination of the base machine learning model and the weight vector comprises:
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
generating the plurality of new models by arithmetic combination of the base machine learning model, the weight vector, and one or more additional weight vectors,
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
determining, for each of the one or more additional weight vectors, a scaling coefficient, wherein the scaling coefficient for each additional weight vector determined independently of other scaling coefficients;
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased average weight)
determining a consolidated weight vector by weighted average of the weight vector and the one or more additional weight vectors, each weight vector weighted in the weighted average according to a corresponding scaling coefficient; and
For example, a person can select a scaling coefficient based on the value of the average weight vector (i.e. an increased scaling coefficient based on an increased average weight)
combining the base machine learning model and the consolidated weight vector
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector based on the consolidated scaling coefficient.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 9,
Step 2A, Prong One
The claim recites in part:
determining a selected value for a consolidated scaling coefficient used to combine the base machine learning model and the consolidated weight vector, at least partly by:
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
generating a plurality of new consolidated models by arithmetic combination of the base machine learning model and the consolidated weight vector, wherein different new consolidated models of the plurality of new consolidated models are generated using different values of the consolidated scaling coefficient;
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
selecting the selected consolidated value for the consolidated scaling coefficient based on the consolidated perplexity values for the individual new consolidated models, wherein the selected consolidated value corresponds to a first new consolidated model, of the plurality of new consolidated models, generated according to the selected consolidated scaling coefficient.
For example, a person can select a scaling coefficient based on the value of the average weight vector (i.e. an increased scaling coefficient based on an increased average weight)
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
computing consolidated perplexity values for individual new consolidated models of the plurality of new consolidated models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
computing consolidated perplexity values for individual new consolidated models of the plurality of new consolidated models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 10,
Step 2A, Prong One
The claim recites in part:
generating the weight vector
For example, the concept of vectors (mathematical descriptions of shapes, lines, and curves) existed long before computers, and humans have been producing them in analog form for centuries.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 11,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 6, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the request specifies a task or domain, and wherein obtaining the weight vector comprises identifying the weight vector by reference to metadata associating the weight vector with the task or domain.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the request specifies a task or domain, and wherein obtaining the weight vector comprises identifying the weight vector by reference to metadata associating the weight vector with the task or domain.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 12,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 6, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
storing the weight vector as two decomposed low-rank adaptation (LoRA) matrices.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
storing the weight vector as two decomposed low-rank adaptation (LoRA) matrices.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 13,
Step 2A, Prong One
The claim recites in part:
verifying that accuracy of the first new model satisfied a threshold value prior to returning the first new model in response to the request.
For example, a person can compare a model against a threshold to determine if its accuracy is correct (higher than the threshold) or incorrect.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 15,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 14, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
storing the weight vector as two decomposed low-rank adaptation (LoRA) matrices.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
storing the weight vector as two decomposed low-rank adaptation (LoRA) matrices.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 16,
Step 2A, Prong One
The claim recites in part:
further cause the computing system to verify that accuracy of the first new model satisfied a threshold value prior to returning the first new model in response to the request.
For example, a person can compare a model against a threshold to determine if its accuracy is correct (higher than the threshold) or incorrect.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 17,
Step 2A, Prong One
The claim recites in part:
Wherein generating the plurality of new models by arithmetic combination of the base machine learning model and the weight vector comprises generating the plurality of new models by arithmetic combination of the base machine learning model, the weight vector, and one or more additional weight vectors
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 18,
Step 2A, Prong One
The claim recites in part:
wherein generating the plurality of new models by arithmetic combination of the base machine learning model, the weight vector, and one or more additional weight vectors comprises:
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
determine, for each of the one or more additional weight vectors, a scaling coefficient, wherein the scaling coefficient for each additional weight vector determined independently of other scaling coefficients;
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased average weight)
determine a consolidated weight vector by weighted average of the weight vector and the one or more additional weight vectors, each weight vector weighted in the weighted average according to a corresponding scaling coefficient; and
For example, a person can select a scaling coefficient based on the value of the average weight vector (i.e. an increased scaling coefficient based on an increased average weight)
combine the base machine learning model and the consolidated weight vector.
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector based on the consolidated scaling coefficient.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 19,
Step 2A, Prong One
The claim recites in part:
the computing system, further cause the computing system to determine a selected value for a consolidated scaling coefficient used to combine the base machine learning model and the consolidated weight vector, at least partly by:
For example, a person can determine a scaling coefficient based on the value of the weight vector (i.e. an increased scaling coefficient based on an increased weight)
generating a plurality of new consolidated models by arithmetic combination of the base machine learning model and the consolidated weight vector, wherein different new consolidated models of the plurality of new consolidated models are generated using different values of the consolidated scaling coefficient;
For example, a person can generate a new model by modifying the model’s parameters by shifting them in the opposite or same direction of that vector
selecting the selected consolidated value for the consolidated scaling coefficient based on the consolidated perplexity values for the individual new consolidated models, wherein the selected consolidated value corresponds to a first new consolidated model, of the plurality of new consolidated models, generated according to the selected consolidated scaling coefficient.
For example, a person can select a scaling coefficient based on the value of the average weight vector (i.e. an increased scaling coefficient based on an increased average weight)
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
computing consolidated perplexity values for individual new consolidated models of the plurality of new consolidated models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
computing consolidated perplexity values for individual new consolidated models of the plurality of new consolidated models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 20,
Step 2A, Prong One
The claim recites in part:
when executed by the computing system, further configure the computing system to generate the fine-tuned machine learning model by fine tuning the base machine learning model or fine tuning of a different model, wherein the arithmetic combination of the base machine learning model and the weight vector comprises adding the weight vector to the base machine learning model.
For example, a person can “add” by modifying the model’s parameters by shifting them in the same direction of that vector.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mathur et al (US 2015/0293908) teaches system and method for estimating parameters for features of a translation scoring function for scoring candidate translations in a target domain are provided. Given a source language corpus for a target domain, a similarity measure is computed between the source corpus and a target domain multi-model, which may be a phrase table derived from phrase tables of comparative domains, weighted as a function of similarity with the source corpus. The parameters of the log-linear function for these comparative domains are known. A mapping function is learned between similarity measure and parameters of the scoring function for the comparative domains. Given the mapping function and the target corpus similarity measure, the parameters of the translation scoring function for the target domain are estimated. For parameters where a mapping function with a threshold correlation is not found, another method for obtaining the target domain parameter can be used.
Mathur et al not disclose or suggest obvious determine a selected value for a scaling coefficient used to combine the base machine learning model and the weight vector, wherein a scaling coefficient indicates a weight to be applied to the weight vector during weight vector arithmetic, at least partly by: generating a plurality of new models by arithmetic combination of the base machine learning model and the weight vector, wherein different new models of the plurality of new models are generated using different values of the scaling coefficient; computing perplexity values for individual new models of the plurality of new models, wherein the perplexity values for an individual new model are calculated using parallelized forward passes, and wherein the perplexity values for the individual new model are calculated according to a validation data set including both data corresponding to the base machine learning model and data corresponding to the fine-tuned machine learning model; and selecting the selected value for the scaling coefficient based on the perplexity values for the individual new models, wherein the selected value corresponds to a first new model, of the plurality of new models, generated according to the selected scaling coefficient, and wherein the selected value is selected based on performance of the first new model relative to both the base machine learning model and the fine-tuned machine learning model; and return the first new model in response to the request, in combination with the rest of the claimed limitations.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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