Notice of Pre-AIA or AIA Status
This Non-Final communication is in response to application no. 18/542,344 filed on 12/15/2023. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities: “wherein the result includes a prediction that or a probability…”. Examiner is assuming the “that” is unintended. Appropriate correction is required.
Claims 5, 12, and 18 are objected to because of the following informalities: “to be associated with one or specific entities”. Examiner is assuming the “one or” is intended to be “one or more”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 7, 11, 14, 17 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 11, and 17 all recite the limitation "the generated data". There is insufficient antecedent basis for this limitation in the claim.
Claims 7, 14, and 20 all recite the limitation “the other text string”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a
judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly
more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory (a process, machine, manufacture or composition of
matter.) Claims 1-7 are a process and claims 8-20 are a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
generating, for each data sample of the set of data samples, an embedded vector of data samples; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
identifying a set of reference labels; (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
identifying a subset of the set of data samples that correspond to the reference label using the marked labels of data samples in the set of data samples; (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
performing a clustering technique using the embedded vectors of the data samples in the subset; (This is an abstract idea of a "Mental Process." The "performing a clustering technique" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The clustering technique could be done manually by an individual.)
generating a set of clusters using the embedded vectors of the data samples, wherein the generation results in assigning the embedded vector of each of at least some data samples in the subset to a cluster of the set of clusters; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
selecting, for each cluster of the set of clusters, one or more embedded vectors; and (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
generating representative embedded vectors of the at least some of the set of clusters corresponding to the reference label using a statistical technique and the selected one or more embedded vectors, wherein the statistical technique is configured such that a representation of or weight of selected one or more embedded vectors of each of the at least some of the set of clusters is same; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
accessing a set of data samples; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
accessing, for each data sample of the set of data samples, a marked label, wherein the marked label is one of the sets of reference labels; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional elements “accessing a set of data samples” and “accessing, for each data sample of the set of data samples…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “generating a result…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, recites an additional abstract idea:
selecting, for each cluster of the set of clusters, one or more embedded vectors close to associated centroid. (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
Step 2A Prong 2: claim 2 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 2 does not recite an additional element.
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, recites an additional abstract idea:
generating data corresponding to the reference label using embedded vectors associated with clusters of the set of clusters but not using the selected one or more embedded vectors, wherein the prompt includes or is based on the data corresponding to the generated data. (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
availing, to a user and for each of the set of clusters, the data samples associated with the embedded vectors of the cluster; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
receiving, for each cluster of at least one of the set of clusters, an indication that the data samples associated with the embedded vectors of the cluster do not correspond to the set of reference labels; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, recites an additional abstract idea:
determining a probability threshold, the method includes: (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generating, for each embedded vector associated with the generated data, probabilities using a probabilistic model indicating a likelihood of association of each embedded vector from the generated data with a particular reference label; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generating, for each embedded vector associated with a nil data sample of a set of nil data samples, probabilities using the probabilistic model indicating the likelihood of association of each embedded vector from the set of nil data samples with the particular reference label, wherein a nil data sample do not correspond to the set of reference labels; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
selecting a maximum ratio as a probability threshold for the particular reference label by iteratively comparing the probabilities generated for the embedded vectors associated with the generated data and the probabilities generated for the embedded vectors associated with the nil data samples of the set of nil data samples. (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
Step 2A Prong 2: claim 4 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 4 does not recite an additional element.
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, recites an additional abstract idea:
identifying one or more entity names for querying; (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
detecting a webpage associated with an entity name of the one or more entity names, wherein the webpage is estimated to have been newly generated within a predefined absolute or relative time period and to be associated with one or specific entities; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
extracting a data sample from the webpage. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 5, recites an additional abstract idea:
generating, …, a predicted probability of one or more subsequent data samples associated with the entity name being associated with a particular reference label of the set of reference labels. (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using another machine-learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
generating another result by processing the data sample using another machine-learning model, wherein the other result includes another prediction as to whether or another probability of the other text string corresponding to the given reference label; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
generating a blended result based on the result and the other result. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 7 is ineligible.
With respect to claim 8:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 8. Therefore, claim 8 is ineligible.
With respect to claim 9:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 9. Therefore, claim 9 is ineligible.
With respect to claim 10:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 10. Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 3:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
With respect to claim 9:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-2, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable by Snell (NPL: ‘Prototypical Networks for Few-shot Learning’ (2017)). in view of Cui (NPL: ‘Prototypical Verbalizer for Prompt based Few-shot Tuning’ (2022)).
Regarding claim 1, Snell teaches:
A computer-implemented method comprising: (Conclusion “We have proposed a simple method called Prototypical Networks for few-shot learning based on the idea that we can represent each class by the mean of its examples in a representation space learned by a neural network.”)
accessing a set of data samples; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
generating, for each data sample of the set of data samples, an embedded vector of data samples; (Introduction “In order to do this, we learn a non-linear mapping of the input into an embedding space using a neural network”)
identifying a set of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
accessing, for each data sample of the set of data samples, a marked label, wherein the marked label is one of the sets of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
identifying a subset of the set of data samples that correspond to the reference label using the marked labels of data samples in the set of data samples; (Section 2.2 “Training episodes are formed by randomly selecting a subset of classes from the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points.”)
performing a clustering technique using the embedded vectors of the data samples in the subset; generating a set of clusters using the embedded vectors of the data samples, wherein the generation results in assigning the embedded vector of each of at least some data samples in the subset to a cluster of the set of clusters; (Section 2.3 “Prototype computation can be viewed in terms of hard clustering on the support set, with one cluster per class and each support point assigned to its corresponding class cluster. It has been shown [4] for Bregman divergences that the cluster representative achieving minimal distance to its assigned points is the cluster mean. Thus the prototype computation in Equation (1) yields optimal cluster representatives given the support set labels when a Bregman divergence is used.”)
selecting, for each cluster of the set of clusters, one or more embedded vectors; and generating representative embedded vectors of the at least some of the set of clusters corresponding to the reference label using a statistical technique and the selected one or more embedded vectors, wherein the statistical technique is configured such that a representation of or weight of selected one or more embedded vectors of each of the at least some of the set of clusters is same; (Section 2.2 “Prototypical Networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ. Each prototype is the mean vector of the embedded support points belonging to its class:” The prototype is the representative embedded vector. Examiner is interpreting that the statistical technique is taking the mean of the embedded vectors as the weighted average when the weights of the embedded vectors of the clusters is the same is just the mean.
Snell does not teach:
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels.
However, Cui does:
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].”)
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels. (Section 4.3 inference has equations 8 and 9 which are the probability and predictions for prompt x respectively.)
Snell and Cui are considered analogous art to the claimed invention because they are in the same field of endeavor being prototypical networks and few-shot learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui. One would want to do this to incorporate prompts into their system which can improve accuracy (Cui abstract).Regarding claim 2, Snell in view of Cui teaches claim 1 as outlined above. Snell further teaches:
for each of one or more reference labels of the set of reference labels: selecting, for each cluster of the set of clusters, one or more embedded vectors close to associated centroid. (We can see this in Figure 1.)
Regarding claim 8, Snell teaches:
accessing a set of data samples; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
generating, for each data sample of the set of data samples, an embedded vector of data samples; (Introduction “In order to do this, we learn a non-linear mapping of the input into an embedding space using a neural network”)
identifying a set of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
accessing, for each data sample of the set of data samples, a marked label, wherein the marked label is one of the sets of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
identifying a subset of the set of data samples that correspond to the reference label using the marked labels of data samples in the set of data samples; (Section 2.2 “Training episodes are formed by randomly selecting a subset of classes from the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points.”)
performing a clustering technique using the embedded vectors of the data samples in the subset; generating a set of clusters using the embedded vectors of the data samples, wherein the generation results in assigning the embedded vector of each of at least some data samples in the subset to a cluster of the set of clusters; (Section 2.3 “Prototype computation can be viewed in terms of hard clustering on the support set, with one cluster per class and each support point assigned to its corresponding class cluster. It has been shown [4] for Bregman divergences that the cluster representative achieving minimal distance to its assigned points is the cluster mean. Thus the prototype computation in Equation (1) yields optimal cluster representatives given the support set labels when a Bregman divergence is used.”)
selecting, for each cluster of the set of clusters, one or more embedded vectors; and generating representative embedded vectors of the at least some of the set of clusters corresponding to the reference label using a statistical technique and the selected one or more embedded vectors, wherein the statistical technique is configured such that a representation of or weight of selected one or more embedded vectors of each of the at least some of the set of clusters is same; (Section 2.2 “Prototypical Networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ. Each prototype is the mean vector of the embedded support points belonging to its class:” The prototype is the representative embedded vector. Examiner is interpreting that the statistical technique is taking the mean of the embedded vectors as the weighted average when the weights of the embedded vectors of the clusters is the same is just the mean.
Snell does not teach:
A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels.
However, Cui does:
A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: (in their abstract they link their code so they must have a system with a processor and computer readable storage medium to run the code).
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].”)
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels. (Section 4.3 inference has equations 8 and 9 which are the probability and predictions for prompt x respectively.)
Snell and Cui are considered analogous art to the claimed invention because they are in the same field of endeavor being prototypical networks and few-shot learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui. One would want to do this to incorporate prompts into their system which can improve accuracy (Cui abstract).Regarding claim 9, Snell in view of Cui teaches claim 8 as outlined above. Snell further teaches:
for each of one or more reference labels of the set of reference labels: selecting, for each cluster of the set of clusters, one or more embedded vectors close to associated centroid. (We can see this in Figure 1.)
Regarding claim 15, Snell teaches:
accessing a set of data samples; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
generating, for each data sample of the set of data samples, an embedded vector of data samples; (Introduction “In order to do this, we learn a non-linear mapping of the input into an embedding space using a neural network”)
identifying a set of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
accessing, for each data sample of the set of data samples, a marked label, wherein the marked label is one of the sets of reference labels; (Section 2.1 “In few-shot classification we are given a small support set of N labeled examples S = {(x1,y1),...,(xN,yN)} where each xi ∈ RD is the D-dimensional feature vector of an example and yi ∈ {1,...,K} is the corresponding label.”)
identifying a subset of the set of data samples that correspond to the reference label using the marked labels of data samples in the set of data samples; (Section 2.2 “Training episodes are formed by randomly selecting a subset of classes from the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points.”)
performing a clustering technique using the embedded vectors of the data samples in the subset; generating a set of clusters using the embedded vectors of the data samples, wherein the generation results in assigning the embedded vector of each of at least some data samples in the subset to a cluster of the set of clusters; (Section 2.3 “Prototype computation can be viewed in terms of hard clustering on the support set, with one cluster per class and each support point assigned to its corresponding class cluster. It has been shown [4] for Bregman divergences that the cluster representative achieving minimal distance to its assigned points is the cluster mean. Thus the prototype computation in Equation (1) yields optimal cluster representatives given the support set labels when a Bregman divergence is used.”)
selecting, for each cluster of the set of clusters, one or more embedded vectors; and generating representative embedded vectors of the at least some of the set of clusters corresponding to the reference label using a statistical technique and the selected one or more embedded vectors, wherein the statistical technique is configured such that a representation of or weight of selected one or more embedded vectors of each of the at least some of the set of clusters is same; (Section 2.2 “Prototypical Networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ. Each prototype is the mean vector of the embedded support points belonging to its class:” The prototype is the representative embedded vector. Examiner is interpreting that the statistical technique is taking the mean of the embedded vectors as the weighted average when the weights of the embedded vectors of the clusters is the same is just the mean.
Snell does not teach:
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including:
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels.
However, Cui does:
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including: (in their abstract they link their code so they must have a system with a processor and computer readable storage medium to run the code).
generating a prompt using, for each of the set of reference labels, the embedded vectors of at least some of the set of clusters; and (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].”)
generating a result by processing an input using a machine-learning model, wherein the input includes the prompt and identifies another data sample, and wherein the result includes a prediction that or a probability of the another data sample corresponding to a given reference label of the set of reference labels. (Section 4.3 inference has equations 8 and 9 which are the probability and predictions for prompt x respectively.)
Snell and Cui are considered analogous art to the claimed invention because they are in the same field of endeavor being prototypical networks and few-shot learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui. One would want to do this to incorporate prompts into their system which can improve accuracy (Cui abstract).
Claims 3, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable by Snell in view of Cui and Acharya (US 2023/0196804 A1).
Regarding claim 3, Snell in view of Cui teaches claim 1 as outlined above. Cui teaches
generating data corresponding to the reference label using embedded vectors associated with clusters of the set of clusters but not using the selected one or more embedded vectors, wherein the prompt includes or is based on the data corresponding to the generated data. (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].” Cui does not teach leaving some of the embedded vectors out.)
However, Acharya does teach the rest of the limitations:
availing, to a user and for each of the set of clusters, the data samples associated with the embedded vectors of the cluster; receiving, for each cluster of at least one of the set of clusters, an indication that the data samples associated with the embedded vectors of the cluster do not correspond to the set of reference labels; and ([0121] “The sampled instances 184 are presented to the user in a ranked order to assign labels, a process identified as label querying 194 in FIG. 1. The user can select 186 the sampled instances 184, dismiss 196 the sampled instances, and annotate 198 the sampled instances.” And [0122] “A feedback loop 188 uses the selection 186, the dismissal 196, and the annotation 198 of the sampled instances 184 by the user to modify the sampling priority 174 for subsequent sampling iterations.”)
Snell, Cui and Acharya are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the human-in-the-loop system of Acharya. One would want to do this to for a minimum cost way of improving a models accuracy (Acharya [0011]).
Regarding claim 10, Snell in view of Cui teaches claim 8 as outlined above. Cui teaches
generating data corresponding to the reference label using embedded vectors associated with clusters of the set of clusters but not using the selected one or more embedded vectors, wherein the prompt includes or is based on the data corresponding to the generated data. (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].” Cui does not teach leaving some of the embedded vectors out.)
However, Acharya does teach the rest of the limitations:
availing, to a user and for each of the set of clusters, the data samples associated with the embedded vectors of the cluster; receiving, for each cluster of at least one of the set of clusters, an indication that the data samples associated with the embedded vectors of the cluster do not correspond to the set of reference labels; and ([0121] “The sampled instances 184 are presented to the user in a ranked order to assign labels, a process identified as label querying 194 in FIG. 1. The user can select 186 the sampled instances 184, dismiss 196 the sampled instances, and annotate 198 the sampled instances.” And [0122] “A feedback loop 188 uses the selection 186, the dismissal 196, and the annotation 198 of the sampled instances 184 by the user to modify the sampling priority 174 for subsequent sampling iterations.”)
Snell, Cui and Acharya are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the human-in-the-loop system of Acharya. One would want to do this to for a minimum cost way of improving a models accuracy (Acharya [0011]).
Regarding claim 16, Snell in view of Cui teaches claim 15 as outlined above. Cui teaches
generating data corresponding to the reference label using embedded vectors associated with clusters of the set of clusters but not using the selected one or more embedded vectors, wherein the prompt includes or is based on the data corresponding to the generated data. (Section 3.2 “The vanilla prompt-based tuning converts the downstream task to a cloze-style mask language modeling problem. For example, to formulate the text classification task, we can modify the original input x with a template T (·) = A [MASK] news: to get the prompt input T (x) = A [MASK] news: x. With T (x), M produces the hidden vector at the [MASK] position h[MASK].” Cui does not teach leaving some of the embedded vectors out.)
However, Acharya does teach the rest of the limitations:
availing, to a user and for each of the set of clusters, the data samples associated with the embedded vectors of the cluster; receiving, for each cluster of at least one of the set of clusters, an indication that the data samples associated with the embedded vectors of the cluster do not correspond to the set of reference labels; and ([0121] “The sampled instances 184 are presented to the user in a ranked order to assign labels, a process identified as label querying 194 in FIG. 1. The user can select 186 the sampled instances 184, dismiss 196 the sampled instances, and annotate 198 the sampled instances.” And [0122] “A feedback loop 188 uses the selection 186, the dismissal 196, and the annotation 198 of the sampled instances 184 by the user to modify the sampling priority 174 for subsequent sampling iterations.”)
Snell, Cui and Acharya are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the human-in-the-loop system of Acharya. One would want to do this to for a minimum cost way of improving a models accuracy (Acharya [0011]).
Claims 4, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable by Snell in view of Cui and Mizuta (US 2023/0126191 A1).
Regarding claim 4, Snell in view of Cui teaches claim 1 as outlined above. Neither of them teach the limitations of claim 4. However Mizuta does:
for each of one or more reference labels of the set of reference labels, determining a probability threshold, the method includes: ([0060] “FIG. 8 is a diagram illustrating another way to determine a threshold value that provides the boundary for discriminating between known and unknown classes in the embodiment described above. “)
generating, for each embedded vector associated with the generated data, probabilities using a probabilistic model indicating a likelihood of association of each embedded vector from the generated data with a particular reference label; ([0060] “The diagram of (a) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of known classes and the learned data in each class, and its normal distribution curve C. This normal distribution curve C is a line drawn assuming that the histogram is normally distributed. “ and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
generating, for each embedded vector associated with a nil data sample of a set of nil data samples, probabilities using the probabilistic model indicating the likelihood of association of each embedded vector from the set of nil data samples with the particular reference label, wherein a nil data sample do not correspond to the set of reference labels; and ([0060] “The diagram of (b) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of unknown classes and the learned data in each class.” and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
selecting a maximum ratio as a probability threshold for the particular reference label by iteratively comparing the probabilities generated for the embedded vectors associated with the generated data and the probabilities generated for the embedded vectors associated with the nil data samples of the set of nil data samples. ([0060] “In FIG. 8, cases are illustrated where a threshold value Th3 or Th4, is set as a threshold value that provides a boundary for discriminating between known and unknown classes.”)
Snell, Cui and Mizuta are considered analogous art to the claimed invention because they are in the same field of endeavor being data classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the threshold determination of Mizuta. One would want to do this to prevent data from an unknown class being classified into a known class (Mizuta [0016]).
Regarding claim 11, Snell in view of Cui teaches claim 8 as outlined above. Neither of them teach the limitations of claim 11. However Mizuta does:
for each of one or more reference labels of the set of reference labels, determining a probability threshold, the method includes: ([0060] “FIG. 8 is a diagram illustrating another way to determine a threshold value that provides the boundary for discriminating between known and unknown classes in the embodiment described above. “)
generating, for each embedded vector associated with the generated data, probabilities using a probabilistic model indicating a likelihood of association of each embedded vector from the generated data with a particular reference label; ([0060] “The diagram of (a) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of known classes and the learned data in each class, and its normal distribution curve C. This normal distribution curve C is a line drawn assuming that the histogram is normally distributed. “ and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
generating, for each embedded vector associated with a nil data sample of a set of nil data samples, probabilities using the probabilistic model indicating the likelihood of association of each embedded vector from the set of nil data samples with the particular reference label, wherein a nil data sample do not correspond to the set of reference labels; and ([0060] “The diagram of (b) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of unknown classes and the learned data in each class.” and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
selecting a maximum ratio as a probability threshold for the particular reference label by iteratively comparing the probabilities generated for the embedded vectors associated with the generated data and the probabilities generated for the embedded vectors associated with the nil data samples of the set of nil data samples. ([0060] “In FIG. 8, cases are illustrated where a threshold value Th3 or Th4, is set as a threshold value that provides a boundary for discriminating between known and unknown classes.”)
Snell, Cui and Mizuta are considered analogous art to the claimed invention because they are in the same field of endeavor being data classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the threshold determination of Mizuta. One would want to do this to prevent data from an unknown class being classified into a known class (Mizuta [0016]).
Regarding claim 17, Snell in view of Cui teaches claim 15 as outlined above. Neither of them teach the limitations of claim 17. However Mizuta does:
for each of one or more reference labels of the set of reference labels, determining a probability threshold, the method includes: ([0060] “FIG. 8 is a diagram illustrating another way to determine a threshold value that provides the boundary for discriminating between known and unknown classes in the embodiment described above. “)
generating, for each embedded vector associated with the generated data, probabilities using a probabilistic model indicating a likelihood of association of each embedded vector from the generated data with a particular reference label; ([0060] “The diagram of (a) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of known classes and the learned data in each class, and its normal distribution curve C. This normal distribution curve C is a line drawn assuming that the histogram is normally distributed. “ and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
generating, for each embedded vector associated with a nil data sample of a set of nil data samples, probabilities using the probabilistic model indicating the likelihood of association of each embedded vector from the set of nil data samples with the particular reference label, wherein a nil data sample do not correspond to the set of reference labels; and ([0060] “The diagram of (b) in FIG. 8 illustrates a histogram illustrating the result of the minimum Euclidean distance calculated between the samples of unknown classes and the learned data in each class.” and [0062] “The distribution to be determined is not limited to a normal distribution, and a half-normal distribution or Poisson distribution may be used, or a mixture distribution that takes multimodality into account may be assumed as a probability distribution.” They can use probability distributions)
selecting a maximum ratio as a probability threshold for the particular reference label by iteratively comparing the probabilities generated for the embedded vectors associated with the generated data and the probabilities generated for the embedded vectors associated with the nil data samples of the set of nil data samples. ([0060] “In FIG. 8, cases are illustrated where a threshold value Th3 or Th4, is set as a threshold value that provides a boundary for discriminating between known and unknown classes.”)
Snell, Cui and Mizuta are considered analogous art to the claimed invention because they are in the same field of endeavor being data classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with the threshold determination of Mizuta. One would want to do this to prevent data from an unknown class being classified into a known class (Mizuta [0016]).
Claims 5-6, 12-13, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable by Snell in view of Cui and Sharma (US 2019/0163736 A1).
Regarding claim 5, Snell in view of Cui teaches claim 1 as outlined above. Neither of them teach the limitations of claim 5. However Sharma does:
identifying one or more entity names for querying; ([0045] “ In some implementations, intelligence platform 215 may perform a web search using an entity identifier, and may obtain text to be processed based on the web search.”)
detecting a webpage associated with an entity name of the one or more entity names, wherein the webpage is estimated to have been newly generated within a predefined absolute or relative time period and to be associated with one or specific entities; and ([0045] “Additionally, or alternatively, intelligence platform 215 may perform web searches using different web search engines, and may receive text to be processed based on results associated with the web searches (e.g., may identify particular web pages, such as webpages associated with the top ten results, top twenty results, top one hundred results, or the like). Additionally, or alternatively, intelligence platform 215 may identify a score associated with a web page (e.g., indicating a relevancy of the web page to the entity),” also [0047] “Additionally, or alternatively, intelligence platform 215 may identify particular web pages based on a time indicator (e.g., a time stamp, or the like) associated with the web pages (e.g., may identify web pages that are most recent, updated most recently, updated most frequently, or the like).”)
extracting a data sample from the webpage. ([0045] “receive text to be processed based on web pages associated with scores that satisfy a threshold.”)
Snell, Cui and Sharma are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with identifying websites and extracting information of Sharma. One would want to do this to improve the amount of information the system has access to (Sharma [0011]).
Regarding claim 6, Snell in view of Cui and Sharma teaches claim 1 as outlined above. Cui further teaches:
generating, using another machine-learning model, a predicted probability of one or more subsequent data samples associated with the entity name being associated with a particular reference label of the set of reference labels. (Section 4.3 inference has equation 8 which is the probability for prompt x respectively. And Sharma provides the data as mentioned in the previous claim.)
Regarding claim 12, Snell in view of Cui teaches claim 8 as outlined above. Neither of them teach the limitations of claim 12. However Sharma does:
identifying one or more entity names for querying; ([0045] “ In some implementations, intelligence platform 215 may perform a web search using an entity identifier, and may obtain text to be processed based on the web search.”)
detecting a webpage associated with an entity name of the one or more entity names, wherein the webpage is estimated to have been newly generated within a predefined absolute or relative time period and to be associated with one or specific entities; and ([0045] “Additionally, or alternatively, intelligence platform 215 may perform web searches using different web search engines, and may receive text to be processed based on results associated with the web searches (e.g., may identify particular web pages, such as webpages associated with the top ten results, top twenty results, top one hundred results, or the like). Additionally, or alternatively, intelligence platform 215 may identify a score associated with a web page (e.g., indicating a relevancy of the web page to the entity),” also [0047] “Additionally, or alternatively, intelligence platform 215 may identify particular web pages based on a time indicator (e.g., a time stamp, or the like) associated with the web pages (e.g., may identify web pages that are most recent, updated most recently, updated most frequently, or the like).”)
extracting a data sample from the webpage. ([0045] “receive text to be processed based on web pages associated with scores that satisfy a threshold.”)
Snell, Cui and Sharma are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with identifying websites and extracting information of Sharma. One would want to do this to improve the amount of information the system has access to (Sharma [0011]).
Regarding claim 13, Snell in view of Cui and Sharma teaches claim 12 as outlined above. Cui further teaches:
generating, using another machine-learning model, a predicted probability of one or more subsequent data samples associated with the entity name being associated with a particular reference label of the set of reference labels. (Section 4.3 inference has equation 8 which is the probability for prompt x respectively. And Sharma provides the data as mentioned in the previous claim.)
Regarding claim 18, Snell in view of Cui teaches claim 15 as outlined above. Neither of them teach the limitations of claim 18. However Sharma does:
identifying one or more entity names for querying; ([0045] “ In some implementations, intelligence platform 215 may perform a web search using an entity identifier, and may obtain text to be processed based on the web search.”)
detecting a webpage associated with an entity name of the one or more entity names, wherein the webpage is estimated to have been newly generated within a predefined absolute or relative time period and to be associated with one or specific entities; and ([0045] “Additionally, or alternatively, intelligence platform 215 may perform web searches using different web search engines, and may receive text to be processed based on results associated with the web searches (e.g., may identify particular web pages, such as webpages associated with the top ten results, top twenty results, top one hundred results, or the like). Additionally, or alternatively, intelligence platform 215 may identify a score associated with a web page (e.g., indicating a relevancy of the web page to the entity),” also [0047] “Additionally, or alternatively, intelligence platform 215 may identify particular web pages based on a time indicator (e.g., a time stamp, or the like) associated with the web pages (e.g., may identify web pages that are most recent, updated most recently, updated most frequently, or the like).”)
extracting a data sample from the webpage. ([0045] “receive text to be processed based on web pages associated with scores that satisfy a threshold.”)
Snell, Cui and Sharma are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with identifying websites and extracting information of Sharma. One would want to do this to improve the amount of information the system has access to (Sharma [0011]).
Regarding claim 19, Snell in view of Cui and Sharma teaches claim 18 as outlined above. Cui further teaches:
generating, using another machine-learning model, a predicted probability of one or more subsequent data samples associated with the entity name being associated with a particular reference label of the set of reference labels. (Section 4.3 inference has equation 8 which is the probability for prompt x respectively. And Sharma provides the data as mentioned in the previous claim.)
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by Snell in view of Cui and Galeotti (US 2024/0177000 A1).
Regarding claim 7, Snell in view of Cui teaches claim 1 as outlined above. Neither of them teach the limitations of claim 7. However Galeotti does:
generating another result by processing the data sample using another machine-learning model, wherein the other result includes another prediction as to whether or another probability of the other text string corresponding to the given reference label; and generating a blended result based on the result and the other result. ([0104] “At step 304, probabilistic confidence labels may be received from one or more machine-learning models configured to classify the objects. The outputs from the machine-learning models may not need normalization, although in some non-limiting embodiments they may be normalized as described herein with respect to step 302. In machine-learning model-based confidence labeling, the confidence label can be constructed as a weighted score of outputs from one or more machine-learning models, for example combining the last layers' output either before or after each last layer is passed through a softmax activation layer, to get the probability that an object belongs to a class. The weighting assigned to a machine-learning model may be proportional to the overall accuracy and numerical range of the model's prediction so as to ensure probabilities sum to one. In non-limiting embodiments, other smoothing, modeling, and/or the like techniques are used to achieve a desired format and/or properties.”)
Snell, Cui and Galeotti are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with combing probabilities when making a classification of Galeotti. One would want to do this to improve the performance of the machine learning model (Galeotti [0095]).
Regarding claim 14, Snell in view of Cui teaches claim 8 as outlined above. Neither of them teach the limitations of claim 14. However Galeotti does:
generating another result by processing the data sample using another machine-learning model, wherein the other result includes another prediction as to whether or another probability of the other text string corresponding to the given reference label; and generating a blended result based on the result and the other result. ([0104] “At step 304, probabilistic confidence labels may be received from one or more machine-learning models configured to classify the objects. The outputs from the machine-learning models may not need normalization, although in some non-limiting embodiments they may be normalized as described herein with respect to step 302. In machine-learning model-based confidence labeling, the confidence label can be constructed as a weighted score of outputs from one or more machine-learning models, for example combining the last layers' output either before or after each last layer is passed through a softmax activation layer, to get the probability that an object belongs to a class. The weighting assigned to a machine-learning model may be proportional to the overall accuracy and numerical range of the model's prediction so as to ensure probabilities sum to one. In non-limiting embodiments, other smoothing, modeling, and/or the like techniques are used to achieve a desired format and/or properties.”)
Snell, Cui and Galeotti are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with combing probabilities when making a classification of Galeotti. One would want to do this to improve the performance of the machine learning model (Galeotti [0095]).
Regarding claim 20, Snell in view of Cui teaches claim 15 as outlined above. Neither of them teach the limitations of claim 20. However Galeotti does:
generating another result by processing the data sample using another machine-learning model, wherein the other result includes another prediction as to whether or another probability of the other text string corresponding to the given reference label; and generating a blended result based on the result and the other result. ([0104] “At step 304, probabilistic confidence labels may be received from one or more machine-learning models configured to classify the objects. The outputs from the machine-learning models may not need normalization, although in some non-limiting embodiments they may be normalized as described herein with respect to step 302. In machine-learning model-based confidence labeling, the confidence label can be constructed as a weighted score of outputs from one or more machine-learning models, for example combining the last layers' output either before or after each last layer is passed through a softmax activation layer, to get the probability that an object belongs to a class. The weighting assigned to a machine-learning model may be proportional to the overall accuracy and numerical range of the model's prediction so as to ensure probabilities sum to one. In non-limiting embodiments, other smoothing, modeling, and/or the like techniques are used to achieve a desired format and/or properties.”)
Snell, Cui and Galeotti are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the clustering and embedding of Snell with prompt generation and classification of Cui and with combing probabilities when making a classification of Galeotti. One would want to do this to improve the performance of the machine learning model (Galeotti [0095]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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