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 in view of the following analysis:
Claim 1:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “generating, by the one or more processors, an updated truth source data set by augmenting the truth source data set”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generating a dataset via augmenting an existing dataset. This would be seen as a mental process because a person having ordinary skill of the art would be able to augment the existing dataset and derive a new dataset.
• “generating, by the one or more processors and using a trained model that comprises at least one attention layer, a probability data set that comprises a particular probability that a particular data parameter should be present in the updated truth source data set; and”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generate a probability dataset comprising a particular probability. This would be seen as a mental process because a person having ordinary skill of the art would be able to generate the probability dataset that the correct parameter is present In the updated truth source dataset.
• “generating, by the one or more processors and based at least in part on the probability data set, a probability threshold set corresponding to each data parameter represented in the probability data set.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generating a probability threshold that corresponds to each data parameter in the dataset. This would be seen as a mental process because a person having ordinary skill of the art would be able to identify every data parameter and generate a probability threshold for each parameter in a dataset.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
The additional elements of “identifying, by one or more processors, a truth source data set associated with a plurality of data parameters;” which is 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").
“one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
As discussed above, the additional elements of “utilizing a stratified masking algorithm that masks at least one data parameter from the truth source data set;” by utilizing this component which is recited at a high level of generality and amounts to extra-solution activity of outputting 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 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis
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:
The additional elements of “identifying, by one or more processors, a truth source data set associated with a plurality of data parameters;” which is 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").
“one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
As discussed above, the additional elements of “utilizing a stratified masking algorithm that masks at least one data parameter from the truth source data set;” which is recited at a high level of generality and amounts to extra-solution activity of outputting 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.
Claim 2:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “training, by the one or more processors, a task-specific model to generate at least the probability threshold set” is/are recited at a high-level of generality 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))
• “one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “training, by the one or more processors, a task-specific model to generate at least the probability threshold set” is/are recited at a high-level of generality 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))
• “one or more processors” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “determining, by the one or more processors, that the particular probability corresponding to the particular data parameter satisfies the particular probability threshold corresponding to the particular data parameter; and”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses determining whether a certain threshold is satisfied or not. This would be seen as a mental process because a person having ordinary skill of the art would be able to view the data and the threshold and determine if the probability is satisfied.
• “in response to determining that the particular probability threshold is satisfied, skipping, by the one or more processors, updating of the particular probability data in an updated probability data set.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses skipping updating the probability data. This would be seen as a mental process because a person having ordinary skill of the art would be able to determine if the probability threshold is satisfied and if so, skipping the updating process of the probability data.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “by the one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “by the one or more processors” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “determining, by the one or more processors, that the particular probability corresponding to the particular data parameter does not satisfy the particular probability threshold corresponding to the particular data parameter; and”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses determining whether a certain threshold is met or not. This would be seen as a mental process because a person having ordinary skill of the art would be able to view the probability corresponding to the parameter, and determine whether it exceeds the probability threshold or not.
• “generating, by the one or more processors, updated probability data corresponding to the particular probability threshold by updating the particular probability data corresponding to the particular probability threshold to zero in response to determining that the particular probability will not satisfy.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generating updated probability data in correspondence to whether the probability threshold is satisfied or not. This would be seen as a mental process because a person having ordinary skill of the art would be able to generate updated probability data, by setting the probability data to zero, in response to whether the probability threshold is satisfied or not.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
“by the one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “by the one or more processors” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As discussed above, the additional elements of “generating, by the one or more processors, updated probability data corresponding to the particular probability threshold by updating the particular probability data corresponding to the particular probability threshold to zero in response to determining that the particular probability will not satisfy.” which is recited at a high level of generality and amounts to extra-solution activity of outputting 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.
Claim 5:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “applying, by the one or more processors, a second data set to the task-specific model, wherein the task-specific model is configured to ignore at least one non-imputed data parameter based at least in part on the updated probability data”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses implementing a second data set to the model that ignores at least one non-imputed parameter. This would be seen as a mental process because a person having ordinary skill of the art would be able to apply the second data set and ignore one of the imputed data parameters after considering the updated probability data.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “one or more processors” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “wherein the task-specific model is determined based at least in part on a machine learning task determined to be performed.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses selecting which task-specific model to use. This would be seen as a mental process because a person having ordinary skill of the art would be able to view the task to be performed and determine which task-specific model to use.
Claim 7:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “generating, by the one or more processors and based at least in part on the probability data set, the second probability threshold set corresponding to each data parameter represented in the probability data set”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generating a second probability threshold set that corresponds to each data parameter in the probability dataset. This would be seen as a mental process because a person having ordinary skill of the art would be able to generate a second probability threshold set for each parameter in a dataset.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
“one or more processors” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
“training, by the one or more processors, a second task-specific model to generate at least a second probability threshold set, wherein the second task-specific model comprises at least one second pre-processing layer that learns a second particular probability threshold for each data parameter of the plurality of data parameters; and” is/are recited at a high-level of generality 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 not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “one or more processors” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “wherein identifying the truth source data set comprises combining a first set of data and a second set of data based at least in part on identifiers shared between the first set of data and the second set of data.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses combining two datasets based on identifiers shared between two sets of data. This would be seen as a mental process because a person having ordinary skill of the art would be able to view two sets of data, determine which identifiers they share, and in part combine them accordingly.
Claim 9:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “The computer-implemented method of claim 1, further comprising training the trained model by at least” is/are recited at a high-level of generality 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 “applying, by the one or more processors, at least a subset of the updated truth source data set corresponding to a particular identifier to a transformer model”. No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “The computer-implemented method of claim 1, further comprising training the trained model by at least” is/are recited at a high-level of generality 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 “applying, by the one or more processors, at least a subset of the updated truth source data set corresponding to a particular identifier to a transformer model”. No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (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.
Claim 10:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “wherein the at least one attention layer comprises a set attention block comprising a plurality of layers, wherein at least a subset of the updated truth source data set is processed via the plurality of layers of the set attention block, and wherein attention block output from the set attention block is provided to a parallel linear block that generates a tensor corresponding to the attention block output.” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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 at least one attention layer comprises a set attention block comprising a plurality of layers, wherein at least a subset of the updated truth source data set is processed via the plurality of layers of the set attention block, and wherein attention block output from the set attention block is provided to a parallel linear block that generates a tensor corresponding to the attention block output.” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “wherein the tensor is applied to a sigmoid activation function that outputs the probability that the particular data parameter should be present in the updated truth source data set for each data parameter of the any number of data parameters.”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical performance with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses applying a mathematical function to a tensor to generate an output. This would be seen as a mathematical process because a person having ordinary skill of the art would be able to determine the tensor, apply it to a sigmoid activation function to generate an output that corresponds to the data parameter.
Claim 12:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “generating, by the one or more processors, task-specific results by applying the second parameter data set to a task-specific model trained based at least in part on the probability threshold set.”. As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses generating task-specific results by a. This would be seen as a mental process because a person having ordinary skill of the art would be able to view two sets of data, determine which identifiers they share, and in part combine them accordingly.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
The additional elements of “receiving, by the one or more processors, a second parameter data set;” which is 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 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
The additional elements of “receiving, by the one or more processors, a second parameter data set;” which is 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.
Regarding claim 13:
Step 2A, Prong 1 analysis:
Claim 13 recites the same abstract ideas as claim 1, therefore it is rejected under the same basis.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:” which is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
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:
• “A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to ” This limitation is recited at a high level of generality and amount to 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding claim 14:
Claim 14 recites similarly to claim 2, therefore it is rejected under the same basis.
Regarding claim 15:
Claim 15 recites similarly to claim 3, therefore it is rejected under the same basis.
Regarding claim 16:
Claim 16 recites similarly to claim 4, therefore it is rejected under the same basis.
Regarding claim 17:
Claim 17 recites similarly to claim 5, therefore it is rejected under the same basis.
Regarding claim 18:
Claim 18 recites similarly to claim 6, therefore it is rejected under the same basis.
Regarding claim 19:
Claim 19 recites similarly to claim 7, therefore it is rejected under the same basis.
Regarding claim 20:
Claim 14 recites similarly to claim 1, therefore it is rejected under the same basis.
Claim Rejections - 35 USC § 103
Claims 1-3, 5-6, and 8-20 are rejected under 35 U.S.C. 103 in view of Prieditis et, al. (US20230267164A1, referred to as Prieditis hereinafter) and in further view of Deghani et, al. (US10740433B2, referred to as Deghani hereinafter).
Claim 1:
Prieditis teaches:
identifying, by one or more processors, a truth source data set associated with a plurality of data parameters; (Prieditis)[0007]” In further features, the one or more processors are configured to generate a prediction based on data in at least one row of the data set, where the at least one row of the data set is filled with at least one of the M values.”[0044] ”The computer readable medium 108 stores a data set 116. The data set 116 includes patient data 120, training data 124, and reserved data 128. FIG. 2 includes an example illustration of the data set 116 including examples of the patient data 120, the training data 124, and the reserved data 128.” The claim does not restrict the truth source data set or the data parameters to any specific type of data. Under the BRI, the data set 116 corresponds to the truth source data set. The patient data, training data and reserve data included in the data set corresponds to the plurality of data parameters
generating, by the one or more processors, an updated truth source data set by augmenting the truth source data set (Prieditis)[0061]” the imputation module 416 initializes missing values in the columns of the training data 124 with most likely values for the rows, respectively. For example, for columns including categorical values (T or F, 1 or 0, etc.), the imputation module 416 may initialize missing values randomly to achieve the same frequency distribution as the non-missing values of that column. For values of columns including continuous values, the initial values can be filled in randomly to achieve the same mean and variance of the non-missing values of that column.” Prieditis teaches frequency-distribution aware random initialization of missing values, which is equivalent to augmenting a dataset to generate an updated dataset, containing new data.
generating, by the one or more processors and using a trained model that comprises at least one attention layer, (Prieditis)[0055]” A model generation module 408 generates mathematical models 412 (prediction engines) for each column. The mathematical model 412 for a column generates a predicted value for missing values in that column.” Prieditis teaches of per-column models producing a predicted value per data parameter.
generating, by the one or more processors and based at least in part on the probability data set, a probability threshold set corresponding to each data parameter represented in the probability data set. (Prieditis)[0064-0065]” the error module 420 determines the error values for the columns, respectively, based on the values generated by the models 412 based on the training data 124 and values determined based on the corresponding columns of the reserved data 128. For example, the error module 420 may determine the error value using RSME or another suitable error metric. The error value reflects a performance of the mathematical models 412. At 524, the error module 420 may determine whether the error value is greater than the predetermined value or did not decrease by at least the predetermined amount.” Prieditis teaches per-column performance/error thresholds using one threshold per column which is equivalent to ‘per data parameter’ as taught by the claim. The set of all per-column thresholds is equivalent to ‘probability threshold set corresponding to each data parameter’. The thresholds derive from the predicted (probability) values, teaching the limitation ‘based at least in part on the probability data set’ thus rendering the limitation obvious.
However, Prieditis fails to teach:
utilizing a stratified masking algorithm that masks at least one data parameter from the truth source data set;
a probability data set that comprises a particular probability that a particular data parameter should be present in the updated truth source data set;
However, Deghani teaches:
utilizing a stratified masking algorithm that masks at least one data parameter from the truth source data set; (Deghani)[col. 4 lines 1-4]“ predicting missing target words, in which case the input sequence is one or more preceding natural language sentences; algorithmic tasks, in which case the input sequence can be a sequence of symbols, e.g., integers; program evaluation and memorization tasks, in which case the input is symbols in a computer program; machine-translation tasks, in which case the input is words of a natural language sentence in a first language” Deghani expressly teaches masking during training in which target tokens are hidden so the model must predict them.
a probability data set that comprises a particular probability that a particular data parameter should be present in the updated truth source data set; (Deghani)[Claim 1]”an encoder configured to receive an input sequence of elements each having a respective initial input representation and to revise the input representations by repeatedly applying a same series of encoding operations” [claim 11]” 11. The system of claim 8, wherein the decoder is configured to obtain the per-symbol target distribution at position 1≤pos≤n by applying an affine transformation O from the final state to an output vocabulary size, followed by the softmax: p(y pos |y [1:pos-1] ,H T)=Softmax(OH T).” Deghani teaches of an attention layer substrate and an output probability generated by the SoftMax function.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of a softmax function as taught by Deghani. A person would be motivated to do so as to (Deghani)[Abstract]” employing self-attention to combine information from different parts of sequences.”
Regarding claim 2:
Prieditis teaches:
training, by the one or more processors, a task-specific model to generate at least the probability threshold set, (Prieditis)[0060]” A prediction module 424 generates one or more predicted values based on the patient data 120 with the missing values filled” Prieditis teaches generating data based on the user data, it would be obvious to a person having ordinary skill of the art that based on the type of data collected from the user, the task would also be specified, thus making it task-specific.
Prieditis fails to teach:
wherein the task-specific model comprises at least one pre-processing layer that learns a particular probability threshold for each data parameter of the plurality of data parameters.
However, Deghani teaches:
wherein the task-specific model comprises at least one pre-processing layer that learns a particular probability threshold for each data parameter of the plurality of data parameters. (Dehghani)[col.4 20-30] The system then repeatedly revises the representations using a self-attention process and a transition function for multiple steps . Thus , the system revises the representations , and determines whether a stop - encoding condition is reached for each input element ( 240 ) . In some implementations , the stop - encoding condition for each input element is a minimum number of revision steps T. [Claim 4] “to initialize a matrix H0∈
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m×d with m rows, one for each item of the sequence, with the d elements of the representation of the item in the d columns of the matrix in the row;” Deghani teaches of a learned input/embedding layer known as the matrix H0∈
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m×d with one row per sequence element, learned during training. The ‘pre-processing layer’ taught by the claim would be seen as an obvious design choice by a person having ordinary skill of the art, within the broad class of gated and threshold output layers taught implicitly by both references.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of an attention layer substrate as taught by Deghani. A person would be motivated to do so as to (Deghani)[Abstract]” employing self-attention to combine information from different parts of sequences.”
Regarding claim 3:
Prieditis teaches:
determining, by the one or more processors, that the particular probability corresponding to the particular data parameter satisfies the particular probability threshold corresponding to the particular data parameter; (Prieditis)[0066]” If 524 is true, control returns to 512 to continue the process and update the mathematical models 412 again. If 524 is false, control continues with 528. At 528, the imputation module 416 imputes (fills/stores) missing values in columns of the training data 120 with the values generated by the mathematical models 412 of the columns, respectively. The prediction module 424 may generate one or more predictions based on the training data 120 with the missing values imputed/filled.” Prieditis teaches of an iterative loop that makes a decision. As taught by Prieditis, the error/probability satisfies the threshold taught by the claim.
in response to determining that the particular probability threshold is satisfied, skipping, by the one or more processors, updating of the particular probability data in an updated probability data set. (Prieditis)[0066]” If 524 is true, control returns to 512 to continue the process and update the mathematical models 412 again. If 524 is false, control continues with 528. At 528, the imputation module 416 imputes (fills/stores) missing values in columns of the training data 120 with the values generated by the mathematical models 412 of the columns, respectively. The prediction module 424 may generate one or more predictions based on the training data 120 with the missing values imputed/filled.” Prieditis teaches the control flows to block 528 which corresponds to no further updates, directly teaching the ‘’skip updating” step taught by the claim, teaching the same control-flow path.
Regarding claim 4:
Claim 4 is rejected under 35 U.S.C. 103 in view of Prieditis, in further view of Deghani, and in further view of Jayaraman et, Al. (US10558921B2, referred to as Jayaraman hereinafter)
Prieditis in further view of Deghani fails to teach:
determining, by the one or more processors, that the particular probability corresponding to the particular data parameter does not satisfy the particular probability threshold corresponding to the particular data parameter;
generating, by the one or more processors, updated probability data corresponding to the particular probability threshold by updating the particular probability data corresponding to the particular probability threshold to zero in response to determining that the particular probability will not satisfy.
However, Jayaraman teaches:
determining, by the one or more processors, that the particular probability corresponding to the particular data parameter does not satisfy the particular probability threshold corresponding to the particular data parameter; and (Jayaraman)[Claim 1]” a machine learning classifier that: receives input observations; predicts, for each respective input observation, a set of probabilities that indicate a likelihood of each respective input observation belonging to each respective output category of a plurality of output categories, wherein the set of probabilities are predicted based at least in part on a keyword analysis of the input observations identify one or more keywords within test data, wherein each of the one or more keywords are associated with information technology help desk trouble tickets” Jayaraman teaches of a classifier that outputs a set of probabilities per category (per data parameter) where confidence thresholds are applied, thus determining whether the threshold is met or not.
generating, by the one or more processors, updated probability data corresponding to the particular probability threshold by updating the particular probability data corresponding to the particular probability threshold to zero in response to determining that the particular probability will not satisfy. (Jayaraman)[Claim 1]” ;obtain a plurality of confidence thresholds for predictions made by the machine learning classifier; reclassify, for each confidence threshold of the plurality of confidence thresholds, any of the input observations for which all probabilities of the set of probabilities are less than the confidence threshold into a null category that is not one of the output categories;” Jayaraman teaches of a per-category confidence thresholds, when all probabilities for an observation fall below the threshold, the observation is reclassified into a null category, functionally identical to ‘updating the particular probability data corresponding to the particular probability threshold to zero in response to determining that the particular probability will not satisfy’ thus rendering the claim obvious.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with setting the confidence threshold to null depending on whether an observation falls below the threshold as taught by Jayaraman. A person would be motivated to do so as To support widely-implemented operations, enterprises typically use off-the-shelf software applications. (Jayaraman)[col.4 lines 3-4]
Regarding claim 5:
Prieditis teaches:
applying, by the one or more processors, a second data set to the task-specific model, wherein the task-specific model is configured to ignore at least one non-imputed data parameter based at least in part on the updated probability data. (Prieditis)[0060]” For example, the imputation module 416 fills missing values of a first column in the patient data 120 with the value generated by the one of the mathematical models 412 of the first column. A prediction module 424 generates one or more predicted values based on the patient data 120 with the missing values filled. For example, the prediction module 424 may predict a cost value, predict a likelihood of patients having a condition, or generate another type of predicted value based on the patient data 120 with the missing values filled.” Prieditis teaches a prediction module that operates on a second/production data set using the trained imputation framework. Skipping non-converged values inherently ignores those data parameters in the downstream prediction
Regarding claim 6:
Prieditis teaches:
wherein the task-specific model is determined based at least in part on a machine learning task determined to be performed. (Prieditis)[0060]” A prediction module 424 generates one or more predicted values based on the patient data 120 with the missing values filled. For example, the prediction module 424 may predict a cost value, predict a likelihood of patients having a condition, or generate another type of predicted value based on the patient data 120 with the missing values filled.” Prieditis teaches multiple machine-learning tasks like cost prediction, condition-likelihood prediction, etc., for the downstream prediction module.
Regarding claim 7:
Claim 7 is rejected under 35 U.S.C. 103 over Prieditis, in further view of Deghani, and in further view of Jayaraman and in further view of Baron et, Al. (US11664126B2, referred to as Baron hereinafter).
Prieditis teaches:
generating, by the one or more processors and based at least in part on the probability data set, the second probability threshold set corresponding to each data parameter represented in the probability data set. (Prieditis)[0064-0065]” the error module 420 determines the error values for the columns, respectively, based on the values generated by the models 412 based on the training data 124 and values determined based on the corresponding columns of the reserved data 128. For example, the error module 420 may determine the error value using RSME or another suitable error metric. The error value reflects a performance of the mathematical models 412. At 524, the error module 420 may determine whether the error value is greater than the predetermined value or did not decrease by at least the predetermined amount.” Prieditis teaches per-column performance/error thresholds using one threshold per column which is equivalent to ‘per data parameter’ as taught by the claim. The set of all per-column thresholds is equivalent to ‘probability threshold set corresponding to each data parameter’. The thresholds derive from the predicted (probability) values, teaching the limitation ‘based at least in part on the probability data set’ thus rendering the limitation obvious. It would be obvious to a person having ordinary skill of the art to apply the same teachings about the probability threshold taught by Prieditis with the use of a second probability threshold taught by Baron, thus rendering the limitation obvious.
However, the combination of Prieditis in view of Deghani, in further view of Jayaraman does not teach:
training, by the one or more processors, a second task-specific model to generate at least a second probability threshold set, wherein the second task-specific model comprises at least one second pre-processing layer that learns a second particular probability threshold for each data parameter of the plurality of data parameters;
However, Baron teaches:
training, by the one or more processors, a second task-specific model to generate at least a second probability threshold set, wherein the second task-specific model comprises at least one second pre-processing layer that learns a second particular probability threshold for each data parameter of the plurality of data parameters; (Baron)[Claim 1]” selecting, from a plurality of trained machine learning models and based on the plurality of data categories of biological or behavioral characteristics or treatment history of the particular patient, a first machine learning model and a second machine learning model, the first machine learning model being trained using first clinical data of a first subset of the plurality of data categories and having a first clinical performance metric value, the second machine learning model being trained using second clinical data of a second subset of the plurality of data categories and having a second clinical performance metric value, the second subset of the plurality of data categories being different from the first subset of the plurality of data categories of biological or behavioral characteristics or treatment history, the training comprising:” Baron teaches a first machine learning model with its own learned per-data-category with a performance metric value. Baron goes on to teach a second machine learning model trained on a different subset of data categories with its own learned per-data-category performance metric value, equivalent to the ‘second probability threshold set per data parameter’ taught by the claim. Both models are trained with their own respected parameters set during training.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of a second probability threshold set as taught by Baron. A person would be motivated to do so as to generate a combined prediction result based on the first prediction result, the second prediction result, the first weight and the second weight (Baron)[Abstract].
Regarding claim 8:
Prieditis teaches:
wherein identifying the truth source data set comprises combining a first set of data and a second set of data based at least in part on identifiers shared between the first set of data and the second set of data. (Prieditis)[0048]” One or more data sources 132 store and update the patient data 120. For example, a data source may add a row to the patient data 120 for a patient that does not presently have a row in the patient data 120 is to be stored. The data source stores the data for the patient in the added row and the appropriate column(s) for the data. If data is received for a patient that does presently have a row in the patient data 120, the data source may add the data for the patient to the appropriate column(s) for the data.” Prieditis teaches combining records from multiple data sources keyed by patient identifier. Each row is equivalent to each patient, which is equivalent to one shared identifier, thus rendering the claim obvious.
Regarding claim 9:
Prieditis teaches:
applying, by the one or more processors, at least a subset of the updated truth source data set corresponding to a particular identifier to (Prieditis)[0050]” The analysis module 104 fills the missing values using the training data 124 and the reserved data 128. Once the missing values in the patient data 120 are filled, the analysis module 104 generates one or more predictions based on the (then filled) patient data 120 or the (then filled) training data 124 including the values that were previously missing.” Prieditis teaches of a per-identifier (per-row) training application each row being identified by a patient, using the reserved data as the truth source data set that corresponds to the patient, or in this case the identifier, as taught by the claim.
However, Prieditis fails to teach:
A transformer model.
However, Deghani teaches:
a transformer model. (Deghani)[Claim 1]”an encoder configured to receive an input sequence of elements each having a respective initial input representation and to revise the input representations by repeatedly applying a same series of encoding operations to all the elements of the sequence in parallel for each of multiple time steps of an encoding process, including revising the representations of the elements with each time step in the multiple time steps of the encoding process, for at most a predetermined maximum number of time steps;” Deghani teaches employing “self-attention” to combine information from different parts of sequences.” Claim 1 recites the encoder-decoder transformer architecture, with an encoder that revises input representations by repeatedly applying encoding operations and a decoder that autoregressively decodes a target sequence conditioned on the encoder’s output. Deghani further teaches (claims 4-5) more mechanics obvious to a transformer’s architecture.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of a transformer model as taught by Deghani. A person would be motivated to do so as to (Deghani)[Abstract] employing self-attention to combine information from different parts of sequences.”
Regarding claim 10:
Deghani teaches:
wherein the at least one attention layer comprises a set attention block comprising a plurality of layers, wherein at least a subset of the updated truth source data set is processed via the plurality of layers of the set attention block, and wherein attention block output from the set attention block is provided to a parallel linear block that generates a tensor corresponding to the attention block output. (Deghani)[Claim 1]” an encoder configured to receive an input sequence of elements each having a respective initial input representation and to revise the input representations by repeatedly applying a same series of encoding operations to all the elements of the sequence in parallel for each of multiple time steps of an encoding process, including revising the representations of the elements with each time step in the multiple time steps of the encoding process, for at most a predetermined maximum number of time steps; and” [Claim 4]” to compute representations Ht at time step t, for time steps t from 1 through T, a depth of iteration, iteratively, by applying a multihead dot product self-attention mechanism followed by a recurrent transition function.” Deghani recites a stack of repeated attention sub-layers, equivalent to the ‘plurality of layers’ taught by the claim, to form the attention block. The patient-row data (the subset) is fed in as the source sequence and is processed through all T iterations of the attention stack, equivalent to the data flow aspect taught by the claim.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of an attention layer substrate as taught by Deghani. A person would be motivated to do so to (Deghani)[Abstract]” employing self-attention to combine information from different parts of sequences.”
Regarding claim 11:
Deghani teaches:
wherein the tensor is applied to a sigmoid activation function that outputs the probability that the particular data parameter should be present in the updated truth source data set for each data parameter of the any number of data parameters. (Deghani)[Claim 11]” the decoder is configured to obtain the per-symbol target distribution at position 1≤pos≤n by applying an affine transformation O from the final state to an output vocabulary size, followed by the softmax: p(y pos |y [1:pos1] ,H T)=Softmax(OH T).” Deghani teaches a softmax over the output vocabulary producing a per-token probability. For binary present/absent prediction per data parameter, softmax reduces to a sigmoid, a routine substitution that would be known to a person having ordinary skill of the art, as the two are mathematically similar.
It would be obvious to a person having ordinary skill of the art to combine the generation of predicted values as taught by Prieditis with the use of a softmax function as taught by Deghani. A person would be motivated to do so as to (Deghani)[Abstract]” employing self-attention to combine information from different parts of sequences.”
Regarding claim 12:
Prieditis teaches:
receiving, by the one or more processors, a second parameter data set; and (Prieditis)[0063]” the imputation module 416 imputes (fills) the missing value of each of columns in the training data 124 based on the values of the columns with non-missing values, the present mathematical model for that column, and the most recent values of the mathematical models of the other columns.” Prieditis teaches a prediction module that generates task-specific results (costs, condition likelihood) by applying the production data set (‘second parameter data set’) to the trained model, as taught by the claim.
generating, by the one or more processors, task-specific results by applying the second parameter data set to a task-specific model trained based at least in part on the probability threshold set. (Prieditis)[0063]” the imputation module 416 imputes (fills) the missing value of each of columns in the training data 124 based on the values of the columns with non-missing values, the present mathematical model for that column, and the most recent values of the mathematical models of the other columns.” Prieditis teaches a prediction module that generates task-specific results (costs, condition likelihood) by applying the production data set (‘second parameter data set’) to the trained model, as taught by the claim.
Regarding claim 13:
Claim 13 recites similarly to claim 1, therefore it is rejected under the same basis.
Regarding claim 14:
Claim 14 recites similarly to claim 2, therefore it is rejected under the same basis.
Regarding claim 15:
Claim 15 recites similarly to claim 3, therefore it is rejected under the same basis.
Regarding claim 16:
Claim 16 recites similarly to claim 4, therefore it is rejected under the same basis.
Regarding claim 17:
Claim 17 recites similarly to claim 5, therefore it is rejected under the same basis.
Regarding claim 18:
Claim 18 recites similarly to claim 6, therefore it is rejected under the same basis.
Regarding claim 19:
Claim 19 recites similarly to claim 7, therefore it is rejected under the same basis.
Regarding claim 20:
Claim 14 recites similarly to claim 1, therefore it is rejected under the same basis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYAAN AYAZ SHEIKH whose telephone number is (571)272-4643. The examiner can normally be reached MON-FRI 7:30-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AYAAN AYAZ SHEIKH/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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