DETAILED ACTION
This non-final office action is responsive to application 18/290,346 as submitted on November 13th 2023.
Claim status is currently pending and under examination for claims 1-11 of which independent claims are 1, 10 and 11.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 9 is objected to because of the following informalities:
In Claim 9, lines 3-4, “learn a part layers of the learned student model” should read “learn a part of layers of the learned student model”
Appropriate correction is required.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 10 and 11
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, independent claim 1, under the broadest reasonable interpretation, recites the following limitations that are abstract ideas:
add a pseudo-label to the unlabeled data, by using a teacher model learned by using the labeled data; (mental process)
evaluate the pseudo-label added to the unlabeled data, by using an evaluation model learned by using at least one of the labeled data and the unlabeled data, (mental process)
and output the pseudo-label that reaches a predetermined evaluation criterion, as an evaluation pseudo-label; (mental process)
The “add” step involves identifying a pseudo-label to assign to unlabeled data which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of adding a pseudo-label at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “add” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “evaluate” step involves determining if a pseudo-label is correctly assigned to unlabeled data which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of evaluating a pseudo-label at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “evaluate” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “output” step involves identifying if a pseudo-label should be assigned to unlabeled data based on reaching an evaluation criterion which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of outputting a pseudo-label at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “output” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
Therefore, the independent claim recites a judicial exception. Independent claims 10 and 11 recite similar limitations corresponding to claim 1, therefore the same subject matter eligibility analysis is applied.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the judicial exception recited above is not integrated into a practical application. The claims recite the following additional elements, but these additional elements are not sufficient to integrate the judicial exception into a practical application:
at least one memory that is configured to store instructions; (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
and at least one processor that is configured to execute the instructions to; (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
input labeled data and unlabeled data; (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
add a pseudo-label to the unlabeled data, by using a teacher model learned by using the labeled data; (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
evaluate the pseudo-label added to the unlabeled data, by using an evaluation model learned by using at least one of the labeled data and the unlabeled data, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
learn a student model, by using the labeled data, and pseudo-labeled data obtained by adding the evaluation pseudo-label to the labeled data; (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
and output the learned student model. (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
A non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded, (Claim 11) (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
The “input” step is recited at a high-level of generality such that the limitation amounts to no more than mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The “add” step requires a “teacher model” to add a pseudo-label. The teacher model is used to apply the recited judicial exception without placing any limitation on how the teacher model operates. The limitation amounts to mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The “evaluate” step requires an “evaluation model” to evaluate a pseudo-label. The evaluation model is used to apply the recited judicial exception without placing any limitation on how the evaluation model operates. The limitation amounts to mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The “learn” step is recited at a high-level of generality such that the limitation amounts to no more than mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The “output” step is recited at a high-level of generality such that the limitation amounts to no more than mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The remaining additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to “apply” an exception using a generic component. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f).
Therefore, the above limitations do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception.
In regards to the “teacher model” in the “add” step, the limitations are recited so generically such that they amount to no more than mere instructions to “apply” the judicial exception on a computer using generic computer components. Mere instructions to apply a judicial exception cannot provide an inventive concept. See MPEP § 2106.05(f).
In regards to the “evaluation model” in the “evaluate” step, the limitations are recited so generically such that they amount to no more than mere instructions to “apply” the judicial exception on a computer using generic computer components. Mere instructions to apply a judicial exception cannot provide an inventive concept. See MPEP § 2106.05(f).
In regards to the “input”, “learn”, and “output” steps and the remaining additional elements, the limitations are recited so generically such that they amount to no more than mere instructions to “apply” the judicial exception on a computer using generic computer components. Mere instructions to apply a judicial exception cannot provide an inventive concept. See MPEP § 2106.05(f).
Therefore, independent claims 1, 10 and 11 are not patent eligible.
Dependent Claims 2-9
The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than a judicial exception.
Claim limitation
Examiner analysis
2. The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to convert the unlabeled data and the labeled data, into domains that are common to each other.
This is a mental process akin to a human evaluation/judgment/observation.
3. The information processing system according to claim 1, wherein the evaluation model is learned by using only the unlabeled data.
This is merely additional information about one or more previously identified mental processes.
4. The information processing system according to claim 1, wherein the evaluation model is learned by using a part of the labeled data and is then learned by using the unlabeled data.
This is merely additional information about one or more previously identified mental processes.
5. The information processing system according to claim 1, wherein the evaluation model is learned by using only the labeled data.
This is merely additional information about one or more previously identified mental processes.
6. The information processing system according to claim 1, wherein the evaluation model is learned by using a difference between an output of the teacher model and a label added to the labeled data.
This is merely additional information about one or more previously identified mental processes.
7. The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to evaluate the pseudo-label by using a plurality of evaluation models that are separately learned.
This is merely additional information about one or more previously identified mental processes.
8. The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to learn the teacher model by using the labeled data,
and re-learn the teacher model by using an evaluation result of the evaluation model.
This is merely additional information about one or more previously identified mental processes.
9. The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to learn a part layers of the learned student model, by using the labeled data.
This is merely additional information about one or more previously identified mental processes.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
The following are the references relied upon in the rejections below:
Li (US 20210264106 A1)
Claims 1, 3-7 and 9-11 are rejected under 35 U.S.C. 102a(2) as being anticipated by Li.
Regarding Claim 1, Li teaches:
An information processing system comprising ([0003] “a data processing system”):
at least one memory that is configured to store instructions ([0003] “memory stores executable instructions”);
and at least one processor that is configured to execute the instructions to input labeled data and unlabeled data ([0003] processor, executable instructions
[0037] “The teacher models 330 may include multiple teachers which may be trained using different features, datasets (labeled or unlabeled)”);
add a pseudo-label to the unlabeled data, by using a teacher model learned by using the labeled data (The Examiner interprets “pseudo-label” according to its broadest reasonable interpretation (BRI) in view of the Applicant’s specification as encompassing “soft label”. This interpretation is consistent with the illustrative descriptions in the Applicant’s specification at [0024], (see excerpt below).
Applicant’s written description at [0024] “The "pseudo-label" is a pseudo-correct answer label, and is generated by a model that is learned by using the labeled data.”
[0037] “multiple teachers which may be trained using different features, datasets (labeled or unlabeled)”
[0042] “the teacher models may be executed with the training data set 455 to generate the resulting training data set 460. … The resulting training data set 460 may be referred to as a transfer data set which contains soft labeled training data. The soft labeled training data may provide the probability of a class for each label. The probability may be calculated by utilizing the logit function which may also be regularized by adding noise to the function. In another example, the probability distribution may be obtained by using a softmax function”
[0039] “during the training process small probabilities are assigned to incorrect answers. Even when these probabilities are very small, some of them are much larger than others. The relative probabilities of incorrect answers contain important information about the differences among incorrect answers”
A teacher model is trained by using a labeled dataset. The teacher model produces a soft label which provides the probability of a class for each sample in a training data set. Small probabilities of a soft label are used to represent incorrect answers (classes), therefore a soft label contains probabilities that are pseudo-correct answer labels and are generated by a teacher model trained on labeled data (and therefore soft labels are pseudo-labels).);
evaluate the pseudo-label added to the unlabeled data, by using an evaluation model learned by using at least one of the labeled data and the unlabeled data ([0039] “in deep neural networks, the normal training objective is to maximize the average log probability of the correct answers. However, during the training process small probabilities are assigned to incorrect answers. Even when these probabilities are very small, some of them are much larger than others. The relative probabilities of incorrect answers contain important information about the differences among incorrect answers. Accordingly, a set of accurate teacher models may be trained to learn the small probabilities among incorrect labels of a training data set.”
[0037] “the teacher models 330 are deep neural network (DNN) teachers such as traditional semi-supervised neural network (NN) teachers”
A set of accurate teacher models (‘evaluation model’) are trained to learn a soft label (‘pseudo label’) that is comprised of small probabilities of incorrect labels and maximized log probabilities of correct labels (answers). By learning small probabilities of incorrect labels, the models are able to distinguish (learn the differences) between incorrect and correct answers (and therefore evaluating the soft label (pseudo-label) by using an evaluation model).),
and output the pseudo-label that reaches a predetermined evaluation criterion, as an evaluation pseudo-label ([0039] “This learned information may then be used to train a student model that learns from both the original labels and the soft labels generated by the teacher models to improve its accuracy without adding significant model parameters”
In a soft-label (‘pseudo label’), small probabilities are assigned to incorrect answers and probabilities are maximized for correct answers (see [0039]). Therefore, small probabilities are a predetermined evaluation criterion since small probabilities represent wrong answers and maximized probabilities (which are larger than small probabilities) are treated as correct answers. Correct answers reach a predetermined evaluation criterion (they surpass the small probabilities / wrong answers), and correct answers therefore are ‘evaluation pseudo-labels’ used to train a student model (and therefore outputting evaluation pseudo-labels).);
learn a student model, by using the labeled data, and pseudo-labeled data obtained by adding the evaluation pseudo-label to the labeled data ([0043] “Once the soft labeled training data is generated from the teacher models, the resulting training data set 460 can be provided to a student training mechanism 480 to train the student model 485.”
[0039] “train a student model that learns from both the original labels and the soft labels generated by the teacher models to improve its accuracy without adding significant model parameters”
Soft labels (‘pseudo-labels’) are comprised of correct answers (‘evaluation pseudo-labels’), see [0039] and are used along with original labels (‘labeled data’) to train a student model (therefore adding evaluation pseudo-labels to labeled data).);
and output the learned student model ([0048] “the student model may distill knowledge from each of the trained teacher models in addition to the finetuned pretrained model. By distilling knowledge from these complex models and utilizing that knowledge in making predictions, the student model may be to provide highly accurate results while having a simple structure.”
[0018] “knowledge is distilled from the more complex training models to train a smaller and simpler student model that is not only smaller in size and easier to deploy and operate but can also provide more accurate results.”).
Regarding Claim 3, Li teaches:
The information processing system according to claim 1, wherein the evaluation model is learned by using only the unlabeled data (A set of accurate teacher models (‘evaluation model’) are trained to learn small probabilities of incorrect labels and maximize the log probabilities of correct labels (see [0039]).
[0037] “The teacher models 330 may include multiple teachers which may be trained using different features, datasets (labeled or unlabeled) or hyperparameters.”).
Regarding Claim 4, Li teaches:
The information processing system according to claim 1, wherein the evaluation model is learned by using a part of the labeled data and is then learned by using the unlabeled data (A set of accurate teacher models (‘evaluation model’) are trained to learn small probabilities of incorrect labels and maximize the log probabilities of correct labels (see [0039]).
[0041] “a training mechanism 430 for training each of the teacher models which may then utilize the information to train each teacher model. In one example, this may involve training several semi-supervised DNN models”).
Regarding Claim 5, Li teaches:
The information processing system according to claim 1, wherein the evaluation model is learned by using only the labeled data (A set of accurate teacher models (‘evaluation model’) are trained to learn small probabilities of incorrect labels and maximize the log probabilities of correct labels (see [0039]).
[0037] “The teacher models 330 may include multiple teachers which may be trained using different features, datasets (labeled or unlabeled) or hyperparameters.”).
Regarding Claim 6, Li teaches:
The information processing system according to claim 1, wherein the evaluation model is learned by using a difference between an output of the teacher model and a label added to the labeled data ([0039] “in deep neural networks, the normal training objective is to maximize the average log probability of the correct answers. However, during the training process small probabilities are assigned to incorrect answers. Even when these probabilities are very small, some of them are much larger than others. The relative probabilities of incorrect answers contain important information about the differences among incorrect answers. Accordingly, a set of accurate teacher models may be trained to learn the small probabilities among incorrect labels of a training data set”
A set of accurate teacher models (‘evaluation model’) are trained to learn small probabilities of incorrect labels and maximize the log probabilities of correct answers. By learning small probabilities of incorrect labels, the models are able to learn the differences between incorrect and correct answers (labels added to labeled data), and therefore the evaluation model is learned by using a difference between an output of a teacher model and a label added to the labeled data.).
Regarding Claim 7, Li teaches:
The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to evaluate the pseudo-label by using a plurality of evaluation models that are separately learned ([0039] “a set of accurate teacher models may be trained to learn the small probabilities among incorrect labels of a training data set”
[0037] “The teacher models 330 may include multiple teachers which may be trained using different features, datasets (labeled or unlabeled) or hyperparameters.”
A set of accurate teacher models (plurality of evaluation models) are trained using different features, datasets, and hyperparameters, therefore the set of accurate teacher models are separately learned and are used to evaluate pseudo-labels (soft labels).).
Regarding Claim 9, Li teaches:
The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to learn a part layers of the learned student model, by using the labeled data ([0038] “train a light-weight student model 350. In one implementation, the student model 350 may be a shallow neural network model (e.g., having one or two layers). Thus, the student model is configured to distill knowledge from the deep neural network models and the pretrained NLP model to provide a shallow neural network student model at minimal loss of accuracy.”
[0043] “the student model may be a semi-supervised DNN model with a modified training objective. The modified training objective may be achieved by using the weighted sum of the original label (training data set 455) and the soft target label (the resulting training data set 460). … By utilizing the hyper parameters 465, model type 470, target weight 475, original training data set 455 and the resulting training data set 460, the student model 485 may be trained to provide more accurate results while having a smaller structure.”).
Regarding Claim 10, the rejection of Claim 1 is incorporated. The difference in scope being:
An information processing method comprising ([0012] “an example method for training a student model”).
Regarding Claim 11, the rejection of Claim 1 is incorporated. The difference in scope being:
A non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded, the information processing method including ([0005] “a non-transitory computer readable medium on which are stored instructions that when executed cause a programmable device to train a first ML model”).
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.
The following are the references relied upon in the rejections below:
Bhatt (US 20160253597 A1)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Bhatt.
Regarding Claim 2, Li teaches: The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to, however Li does not teach converting unlabeled and labeled data into domains that are common to each other, which is taught by Bhatt:
convert the unlabeled data and the labeled data, into domains that are common to each other ([Abstract] “An adaptation method includes using a first classifier trained on projected representations of labeled objects from a first domain to predict pseudo-labels for unlabeled objects in a second domain, based on their projected representations.”
[0015] “an adaptation method includes learning a transformation based on features extracted from objects in first and second domains. A similarity is computed between the first and second domains. Original representations of labeled objects in the first domain and unlabeled objects in the second domain are projected with the learned projection. A first classifier is trained on the projected representations of the objects from the first domain and respective labels. Pseudo-labels for the projected representations of the unlabeled objects are predicted with the first classifier.”
[0029] “the exemplary method facilitates this adaptation in a content-aware manner by seamlessly unifying the similarity between the two domains in the adaptation setting. This is also useful in practical scenarios where there are multiple candidate source domains to learn from and method is able to identify the best source domain from which to learn.”
[0030] “efficiently adapt classifier models trained on one domain to perform well for classification on different domains, without requiring any labeled data from the target domain”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the training method of Li with the domain adaptation technique disclosed by Bhatt to adapt labeled and unlabeled data into a common domain. By adapting labeled and unlabeled data into a common domain, a classifier trained on a source domain can be used to generate pseudo-labels for the unlabeled data, thereby saving time and computer resources since a separate model does not need to be trained to predict labels for unlabeled data from a different domain.
The following are the references relied upon in the rejections below:
Sharifi (US 20230036764 A1)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Sharifi.
Regarding Claim 8, Li teaches:
The information processing system according to claim 1, wherein the at least one processor is configured to execute the instructions to learn the teacher model by using the labeled data ([0037] “multiple teachers which may be trained using different features, datasets (labeled or unlabeled)”),
However, Li does not teach re-learning a teacher model by using an evaluation result of an evaluation model, which is taught by Sharifi:
and re-learn the teacher model by using an evaluation result of the evaluation model ([0067] “Further, the teacher output data 208, the student output data 212, and/or the ground truth training data 210 can include data extracted from hidden layers of the teacher machine-learned model 202 and/or student machine-learned model 204. Parameters of the teacher machine-learned model 202 and/or student machine-learned model 204 can be adjusted based on comparisons of the teacher output data 208, student output data, and/or ground truth training data 210”
[0054] “train a teacher model 140 based on a set of training data 162. The training data 162 can include, for example, labeled and/or unlabeled training examples.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the training method of Li with the teacher training technique disclosed by Sharifi to re-train a teacher model based on comparing ground truths and model-generated outputs. By re-training a teacher model based on comparing ground truths and model-generated outputs, a teacher model can be updated to generate outputs that match ground truths, thereby improving the accuracy of both a teacher and student model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fukuda et al. (US 20200034702 A1) teaches training multiple teacher models and selecting the most accurate teacher model to train a student model.
Luong et al. (US 20220083840 A1) teaches iteratively training a teacher and student model by replacing the teacher model with the student model once cross entropy loss is minimized.
Nagano et al. (US 20220188622 A1) teaches searching a pool of candidate soft labels generated by a teacher model to identify soft labels that are similar to a generated soft label, and then using the similar soft labels to train a student model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEDRO J MORALES whose telephone number is (571)272-6106. The examiner can normally be reached 8:30 AM - 6:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MIRANDA M 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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/PEDRO J MORALES/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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