Notice of Pre-AIA or AIA Status
The present application, filed on or after February 6, 2024, 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1 does not fall within at least one of the four categories of patent eligible subject matter because it is directed to a computer readable medium (CRM). Claims 2-10 are dependent on claim 1 and do not specify if the CRM is non-transitory. Therefore, claims 2-10 are also directed to non-statutory subject matter. Under the broadest reasonable interpretation, in light of the specification, the CRM comprises computer instructions to implement a method, which does not fall within one of the four categories of invention (see MPEP § 2106.03).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 11,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
identifying a tabular computer model that receives a context of a plurality of context points and an input data point and outputs a classification of the input data point by applying a set of model parameters to the context and the input data point; (Mental process; A human can identify a model that takes tabular data as input and outputs a classification.)
determining a training loss for a set of training points applied to the model with the context and the set of model parameters based on classification of the set of training points relative to respective training labels of the set of training points; (Mental process; A human can determine a training loss for a set of training points and model parameters based on classification relative to class labels.)
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
training the plurality of context points to reduce the training loss with respect to training labels of the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
training the plurality of context points to reduce the training loss with respect to training labels of the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 1 recites the same limitations as claim 11, but under the embodiment of a computer readable medium instead of a method. Thus, claim 1 is rejected under the criteria.
Regarding claim 12,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 12 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the method further comprises applying the computer model with the trained context to a new data point. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the method further comprises applying the computer model with the trained context to a new data point. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 2 recites the same limitations as claim 12, but under the embodiment of a computer readable medium instead of a method. Thus, claim 2 is rejected under the same criteria.
Regarding claim 13,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 13 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the set of model parameters are fixed while training the plurality of context points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the set of model parameters are fixed while training the plurality of context points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 3 recites the same limitations as claim 13, but under the embodiment of a computer readable medium instead of a method. Thus, claim 3 is rejected under the same criteria.
Regarding claim 14,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 14 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the set of model parameters is trained on a plurality of training sets other than the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the set of model parameters is trained on a plurality of training sets other than the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 4 recites the same limitations as claim 14, but under the embodiment of a computer readable medium instead of a method. Thus, claim 4 is rejected under the same criteria.
Regarding claim 15,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 15 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the context points include class labels. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the context points include class labels. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 5 recites the same limitations as claim 15, but under the embodiment of a computer readable medium instead of a method. Thus, claim 5 is rejected under the same criteria.
Regarding claim 16,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 16 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the context points are tabular data. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the context points are tabular data. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 6 recites the same limitations as claim 16, but under the embodiment of a computer readable medium instead of a method. Thus, claim 6 is rejected under the same criteria.
Regarding claim 17,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 17 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the plurality of context points are not in the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the plurality of context points are not in the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 7 recites the same limitations as claim 17, but under the embodiment of a computer readable medium instead of a method. Thus, claim 7 is rejected under the same criteria.
Regarding claim 18,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 18 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein a number of the plurality of context points is smaller than a number of training points in the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein a number of the plurality of context points is smaller than a number of training points in the set of training points. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 8 recites the same limitations as claim 18, but under the embodiment of a computer readable medium instead of a method. Thus, claim 8 is rejected under the same criteria.
Regarding claim 19,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 19 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein a number of the plurality of context points is 100 or less. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein a number of the plurality of context points is 100 or less. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 9 recites the same limitations as claim 19, but under the embodiment of a computer readable medium instead of a method. Thus, claim 9 is rejected under the same criteria.
Regarding claim 20,
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
Claim 20 is dependent upon claim 11 and therefore recites the same abstract idea.
Step 2A Prong 2: Does the claim recite elements that integrate the judicial exception into a practical application?
The method of claim 11, wherein the tabular computer model is a TabPFN model architecture. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: Does the claim recite elements that amount to significantly more than the judicial exception?
The method of claim 11, wherein the tabular computer model is a TabPFN model architecture. The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Claim 10 recites the same limitations as claim 20, but under the embodiment of a computer readable medium instead of a method. Thus, claim 10 is rejected under the same criteria.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over “TabPFN: A Transformer That Solves Tabular Classification Problems in a Second (Hollmann)” in view of “Scaling TabPFN: Sketching and Feature Selection for Tabular Data-Fitted (Feuer)”
Regarding claims 1 and 11:
Hollmann teaches “determining a training loss for a set of training points applied to the model with the context and the set of model parameters based on classification of the set of training points relative to respective training labels of the set of training points (Page 2: The loss of the PFN training thus is the cross-entropy on held-out examples of synthetic datasets.)
and training the plurality of context points to reduce the training loss with respect to training labels of the set of training points. (Section E.1: For each training we tested a set of 3 learning rates, {.001,.0003,.0001}, and used the one with the lowest final training loss. The resulting model contains 25.82 M parameters.)”
Feuer teaches “A method for training a model context for a data set, comprising: identifying a tabular computer model that receives a context of a plurality of context points and an input data point and outputs a classification of the input data point by applying a set of model parameters to the context and the input data point; (Page 1: Prior-Data Fitted Networks such as TabPFN have successfully been demonstrated to classify tabular data based on a training set given as the model input [Hollmann et al., 2023]. Rather than using training data to fit the model parameters, TabPFN gets at inference time a set that contains both labeled and unlabeled samples. It then predicts the ‘missing’ labels directly based on the labeled input samples, rather than exclusively relying on trained model parameters. In this sense, the working of TabPFN resembles the phenomenon of in-context learning that is exhibited by large language models such as GPT.)”
It would have been obvious to one having ordinary skill in the art at the time that the claimed invention was effectively filed to combine the teachings to Hollmann and Feuer, since both references discuss the TabPFN architecture and are thus analogous art. Therefore, one would be able to apply a cross-entropy training loss to a TabPFN model for a set of training points.
Regarding claim 1, Hollmann further teaches “A system for training a model context for a data set, comprising: a processor configured to execute instructions; a computer-readable medium having instructions executable by the processor for:” (Page 3 section 3 teaches that “In the prior-fitting phase, we train the TabPFN once on samples from the prior described in Section 4. To be more precise, we trained a 12-layer Transformer for 18000 batches of 512 synthetically generated datasets each, which required a total of 20 hours on one machine with 8 GPUs (Nvidia RTX 2080 Ti).” The processor or CRM is an inherent component when training any machine learning model)
Regarding claims 2 and 12:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the method further comprises applying the computer model with the trained context to a new data point.” (This is a characteristic of any ML model, thus it is interpreted as a characteristic of the TabPFN model mentioned in both references)
Regarding claims 3 and 13:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the set of model parameters are fixed while training the plurality of context points.” (On page 27 teaches that TabPFN model parameters remain fixed during training.)
Regarding claims 4 and 14:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the set of model parameters is trained on a plurality of training sets other than the set of training points.” (The abstract on page 1 teaches that TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. This prior incorporates ideas from causal reasoning: It entails a large space of structural causal models with a preference for simple structures.)
Regarding claims 5 and 15:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the context points include class labels.” ( The abstract teaches that TabPFN performs in-context learning (ICL), it learns to make predictions using sequences of labeled examples (x, f(x)) given in the input, without requiring further parameter updates.)
Regarding claims 6 and 16:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the context points are tabular data.” (The abstract of page 1 teaches that context points are tabular by nature due to them being drawn from tabular datasets)
Regarding claims 7 and 17:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the plurality of context points are not in the set of training points.” (The abstract on page 1 teaches that TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. Thus, the most plausible interpretation (based on the figures of page 3) is the context points are new samples that were not among the synthetic data used for offline training.)
Regarding claims 8 and 18:
Feuer teaches “The method of claim 11 (or the system of claim 1), wherein a number of the plurality of context points is smaller than a number of training points in the set of training points.” (Pages 2 and 3 teach that context length is not necessarily less than the full training dataset length, but it is a design choice and thus can be set to a length than that of the number of training points.)
Regarding claims 9 and 19:
Feuer teaches “The method of claim 11 (or the system of claim 1), wherein a number of the plurality of context points is 100 or less.” (Pages 2 and 3 teach that experiments on TabPFN performance are done using variable context lengths (100, 500, 1000.)
Regarding claims 10 and 20:
Hollmann teaches “The method of claim 11 (or the system of claim 1), wherein the tabular computer model is a TabPFN model architecture.” (Both Hollmann and Feuer are analogous are and teach TabPFN architecture.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN W FIGUEROA whose telephone number is (571)272-4623. The examiner can normally be reached Monday-Friday, 10AM-6PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MIRANDA HUANG can be reached at (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KEVIN W FIGUEROA
Primary Examiner
Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124
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