DETAILED ACTION
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 .
Response to Amendment
The Amendment filed on 03/04/2026 has been entered. Claims 1-18 remain pending in the application.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 are directed to a method and claims 10-18 are directed to a medium. Therefore, the claims are eligible under Step 1 for being directed to a process and a manufacture respectively.
Independent claims 1 and 10:
Step 2A Prong 1:
Claims recite:
heuristically labeling, by the generative model computer program and using the plurality of user-defined label functions, each of the plurality of records with one of a non-labeled value, a bad quality value, or a good quality value - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation of calculating using mathematical methods to label records;
representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation of calculating using mathematical methods to represent labeled records in a matrix;
performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
receiving, by a generative model computer program, a plurality of records from a database - the steps recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
receiving, by the generative model computer program, a plurality of user-defined label functions - the steps recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
outputting, by the generative model computer program, a labeled dataset for the plurality of records - the steps recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
receiving, by a generative model computer program, a plurality of records from a database - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
receiving, by the generative model computer program, a plurality of user-defined label functions - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
outputting, by the generative model computer program, a labeled dataset for the plurality of records - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 2 and 11:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the record comprises a code snippet - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the record comprises a code snippet - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 3 and 12:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the record comprises an email or a news article - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the record comprises an email or a news article - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 4 and 13:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the probabilistic generative latent variable models comprises Factor Analysis - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the probabilistic generative latent variable models comprises Factor Analysis - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 5 and 14:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the probabilistic generative latent variable models comprises a Gaussian process latent variable model - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the probabilistic generative latent variable models comprises a Gaussian process latent variable model - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 6 and 15:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the probabilistic generative latent variable models comprises a Variational Inference Factor Analysis model - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the probabilistic generative latent variable models comprises a Variational Inference Factor Analysis model - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 7 and 16:
Step 2A Prong 1:
Claims recite:
labeling, by the generative model computer program, each of the plurality of records with a plurality of alternate label functions - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation of calculating using mathematical methods to label records; and
wherein the matrix further comprises the plurality of records that are labeled with the alternate label functions - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation of calculating using mathematical methods to represent labeled records in a matrix.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claims 8 and 17:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein at least one of the alternate label functions is based on coding standards - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein at least one of the alternate label functions is based on coding standards - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 9 and 18:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein at least one of the user-defined label functions is defined by subject matter expert - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein at least one of the user-defined label functions is defined by subject matter expert - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7-10 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over SOLMAZ et al. (hereinafter SOLMAZ), US 20240249848 A1, in view of Moreira et al. (hereinafter Moreira), US 20230133410 A1.
Regarding independent claim 1, SOLMAZ teaches a method for weak supervision classification with probabilistic generative latent variable models comprising ([0009]; [0085] FIG. 3 shows a method and a system in accordance with an embodiment of the present invention. The embodiment provides an environmental monitoring and enables feature programming for feature learning and data programming for weak supervision):
receiving, by a generative model computer program, a plurality of records from a database ([0093] Step 1 (cf. FIG. 3): (IoT) sensor data collection from various devices connected with a set of sensors is performed. The devices and sensors are considered to be heterogeneous in their nature and they may produce noisy and sparse measurements. Possible devices are previously listed as possible system components. The data collected from the (IOT) sensors such as image data (e.g., from drones or cameras), or wireless data (e.g., from WiFi scanners or mobile devices such as smartphones) are considered as the “raw” data without the ground-truth labels associated with them. For instance, for the case of the environmental monitoring, a ground-truth might be the times of social interactions that might lead to disease transmission);
receiving, by the generative model computer program, a plurality of ([0064] a knowledge base may provide situation labeling functions; [0122] Step 8 (cf. FIG. 3): Abstract situation labeling functions are small code snippets that are abstract functions, which can be taken from the knowledge base);
heuristically labeling, by the generative model computer program and using the plurality of ([0073] a knowledge base provides different labeling functions that are heuristics for labeling data points with low accurate and low coverage. Once applied to an unlabeled dataset, each labeling function computes a label for each data point of the dataset. Different labeling functions might produce contrasting labels for the same data points. A labeling function might also return abstain if the conditions of the heuristic are not matched; Fig. 9; [0125] Step 9 (cf. FIG. 3): Generative models can be used for weak supervision and probabilistic predictions/labels of possible disease transmissions. The generative model uses the signals that are combined in a “labelling matrix” from the previous step for learning the structures. FIG. 9 illustrates a labelling matrix. Specifically, FIG. 9 shows a labeling matrix including the values predicted by n situation labelling functions (LFs) on each unlabeled raw data point. The values of the matrix of FIG. 9 are illustrated as binary predictions for simplicity, whereas −1 represents no output from an LF for the given data point);
representing, by the generative model computer program, the plurality of records that are labeled with the ([0074] Applying the labeling functions to a training dataset generates a matrix that is fed to a generative model. The generative model decides for each data point a single label);
performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model ([0047] The generative model feeds a discriminative classifier model with probabilistic labels/predictions for the sensor features, wherein the probabilistic labels/predictions of the generative model are used for training the discriminative classifier model. Then, based on an optimization procedure, a subset of the sensor features is determined, in particular by a feature selection optimizer entity; [0126] Discriminative model: The outputs of the generative model may then fed to a discriminative (machine learning) classifier model that would do the final prediction on the raw IoT sensor data, which might or might not labeled by the generative model. The main benefit of the discriminative classifier model is the generalization to a larger dataset compared to the generative model, which is bounded by the coverage of the labeling functions).
SOLMAZ does not explicitly teach the plurality of label functions are user-defined label functions; and outputting, by the generative model computer program, a labeled dataset for the plurality of records.
However, in the same field of endeavor, Moreira teaches the plurality of label functions are user-defined label functions ([0030] A set of labeling functions (e.g., created by the human experts) map data attributes, such as data features or business rules, to the set of concept labels. These labeling functions can be further transformed into concept-specific features in a subsequent machine learning pipeline; Fig. 3; [0040] The Domain Expert(s): (1) define a concept taxonomy; (2) create a set of labeling functions (which use the available primitives in the data to generate concept labels);); and outputting, by the generative model computer program, a labeled dataset for the plurality of records ([0077] FIG. 5 is a flow diagram illustrating an embodiment of a process to generate a labeled dataset).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of using a set of labeling functions created by the human experts to generate a labeled dataset as suggested in Moreira into SOLMAZ’s system because both of these systems are addressing applying labeling functions to the unlabeled data to obtain the “weak” labels. This modification would have been motivated by the desire for an efficient solution to generate weak labels automatically due to high costs of manual labeling (Moreira, [0018]-[0020]).
Regarding dependent claim 7, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SOLMAZ further teaches, further comprising:
labeling, by the generative model computer program, each of the plurality of records with a plurality of alternate label functions ([0073] a knowledge base provides different labeling functions that are heuristics for labeling data points with low accurate and low coverage. Once applied to an unlabeled dataset, each labeling function computes a label for each data point of the dataset. Different labeling functions might produce contrasting labels for the same data points. A labeling function might also return abstain if the conditions of the heuristic are not matched); and
wherein the matrix further comprises the plurality of records that are labeled with the alternate label functions ([0074] Applying the labeling functions to a training dataset generates a matrix that is fed to a generative model).
Regarding dependent claim 8, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 7 that is incorporated. SOLMAZ teaches wherein at least one of the alternate label functions is based on coding standards ([0122] Step 8 (cf. FIG. 3 ): Abstract situation labeling functions are small code snippets that are abstract functions, which can be taken from the knowledge base).
Regarding dependent claim 9, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Moreira further teaches wherein at least one of the user-defined label functions is defined by subject matter expert ([0030] A set of labeling functions (e.g., created by the human experts) map data attributes, such as data features or business rules, to the set of concept labels; Fig. 3; [0040] The Domain Expert(s): (1) define a concept taxonomy; (2) create a set of labeling functions (which use the available primitives in the data to generate concept labels)).
Regarding independent claim 10, it is a medium claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. SOLMAZ further teaches a non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps ([0039])
Regarding dependent claim 16, it is a medium claim that corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Regarding dependent claim 17, it is a medium claim that corresponding to the method of claim 8. Therefore, it is rejected for the same reason as claim 8 above.
Regarding dependent claim 18, it is a medium claim that corresponding to the method of claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over SOLMAZ, in view of Moreira as applied in claims 1 and 10, further in view of Ni et al. (hereinafter Ni), US 20200371778 A1.
Regarding dependent claim 2, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SOLMAZ and Moreira does not teach wherein the record comprises a code snippet.
However, in the same field of endeavor, Ni teaches wherein the record comprises a code snippet ([0058] FIG. 5 is a flowchart illustrating an example method 500 of utilizing a trained machine learning model to map to-be-updated source code snippets to a latent space that contains reference embeddings).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of applying data associated with a source code snippet as input across a machine learning model to generate a new source code embedding in a latent space as suggested in Ni into SOLMAZ and Moreira’s system because both of these systems are addressing training machine learning models to generate embeddings. This modification would have been motivated by the desire for automatically identifying, recommending, and/or effecting these changes, the time and expense of manually changing numerous source code snippets to properly reflect changes to related software technologies across a dependency graph may be reduced or even eliminated (Ni, [0003]).
Regarding dependent claim 11, it is a medium claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over SOLMAZ, in view of Moreira as applied in claims 1 and 10, further in view of Wu et al. (hereinafter Wu), US 20230102892 A1.
Regarding dependent claim 3, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SOLMAZ and Moreira does not teach wherein the record comprises an email or a news article.
However, in the same field of endeavor, Wu teaches wherein the record comprises an email or a news article (Fig. 5; [0042] Transformer-based machine learning algorithm module 510 receives unlabeled text data in a freeform format from any source (such as an online computer system or other online system). For example, as described herein, the unlabeled text data may be from web pages, social media applications, text messaging, emails, chats, etc.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of the unlabeled text data may be from web pages, social media applications, text messaging, emails, chats, etc as suggested in Wu into SOLMAZ and Moreira’s system because both of these systems are addressing annotating text data and determining text data categories for text data in freeform or unknown formats. This modification would have been motivated by the desire for improving computer system functionality and efficiency via mechanisms for labelling text data from various sources (Wu, [0002]).
Regarding dependent claim 12, it is a medium claim that corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Claims 4-6 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over SOLMAZ, in view of Moreira as applied in claims 1 and 10, further in view of Ghojogh et al (hereinafter Ghojogh), "Factor analysis, probabilistic principal component analysis, variational inference, and variational autoencoder: Tutorial and survey." arXiv preprint arXiv:2101.00734 (2021).
Regarding dependent claim 4, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SOLMAZ and Moreira does not teach wherein the probabilistic generative latent variable models comprises Factor Analysis.
However, in the same field of endeavor, Ghojogh teaches wherein the probabilistic generative latent variable models comprises Factor Analysis (pages 4-6, Section 3. Factor Analysis).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of the theory and details of factor analysis as suggested in Ghojogh into SOLMAZ and Moreira’s system because both of these systems are addressing generative models. This modification would have been motivated by the desire for using the simplest and most fundamental generative models (Ghojogh, page 4, Section 3.2).
Regarding dependent claim 5, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SOLMAZ and Moreira does not teach wherein the probabilistic generative latent variable models comprises a Gaussian process latent variable model.
However, in the same field of endeavor, Ghojogh teaches the probabilistic generative latent variable models comprises a Gaussian process latent variable model (pages 4-6, Section 3. Factor Analysis).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of the theory and details of factor analysis as suggested in Ghojogh into SOLMAZ and Moreira’s system because both of these systems are addressing generative models. This modification would have been motivated by the desire for using the simplest and most fundamental generative models (Ghojogh, page 4, Section 3.2).
Regarding dependent claim 6, the combination of SOLMAZ and Moreira teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SOLMAZ and Moreira does not teach wherein the probabilistic generative latent variable models comprises a Variational Inference Factor Analysis model.
However, in the same field of endeavor, Ghojogh teaches wherein the probabilistic generative latent variable models comprises a Variational Inference Factor Analysis model (pages 4-6, Section 3. Factor Analysis).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of the theory and details of factor analysis as suggested in Ghojogh into SOLMAZ and Moreira’s system because both of these systems are addressing generative models. This modification would have been motivated by the desire for using the simplest and most fundamental generative models (Ghojogh, page 4, Section 3.2).
Regarding dependent claim 13, it is a medium claim that corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 14, it is a medium claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding dependent claim 15, it is a medium claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Response to Arguments
Applicant's arguments filed 03/04/2026 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response.
(1) Regarding Claim Rejections Under 35 U.S.C. 101, Applicant alleges claim 1 has been amended to include the additional element of "heuristically labeling, by the generative model computer program and using the plurality of user-defined label functions, each of the plurality of records with one of a non-labeled value, a bad quality value, or a good quality value." Thus, information from the heuristic labeling function is used to perform probabilistic latent variable model analysis on the matrix of labeled records and then to output a labeled dataset for the plurality of records. These elements together recite a meaningful way of using the alleged judicial exception beyond generally linking the use of the judicial exception to a particular technological environment.
As to point (1), Examiner respectfully disagrees. The claim does not limit the plain meaning of “labeling using the plurality of user-defined label functions” which, as explained in the specification, includes a set of heuristic rules that scan for specific keywords inside text, and categorize it accordingly. The unlabeled data is scanned with the labelling functions to create a sparse binary matrix. The sparse input matrix (labelling matrix) created by the labelling functions may be considered to contain all the information needed for a robust model creation. In other words, the labelling matrix can be interpreted as the sufficient statistics of the non-parametric machine learning model. ([0025]; [0030]). The claimed labeling using the plurality of user-defined label functions encompasses performing mathematical calculations. Thus, the broadest reasonable interpretation of “labeling using the plurality of user-defined label functions” encompasses mathematical concepts. One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a). The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. While the disclosure states that “embodiments may build a data framework that allows the user and SMEs to generate pseudo-labels with the help of labelling functions (LFs). These LFs may be a set of heuristic rules that scan for specific keywords inside text, and categorize it accordingly. So, instead of SMEs spending time going through records item-by-item, they propose some rules that classify the data based on patterns”, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea rather than to any technology. See MPEP 2106.05(a). Any purported improvements are provided by the judicial exception alone, i.e. mathematical calculations/relationships, thus the claim as a whole does not integrate the judicial exception into a practical application nor provide significantly more than the judicial exception. Thus, the claims are patent ineligible and are rejected under 35 U.S.C. 101 as detailed in the rejections set forth above.
(2) Applicant’s prior art arguments with respect to the pending claims have been considered but they are moot in view of the new ground(s) of rejections presented above. Although a new ground of rejection has been used to address additional limitations that have been added to claims, a response is considered necessary for several of the Applicant’s arguments since the references on record, SOLMAZ, will continue to be used to meet several of the claimed limitations. According to the specification, the label functions may heuristically scan the code snippet. For example, the output can be a non-labeled value (e.g., −1), a bad quality value (e.g., 0), or a good quality value (e.g., 1). Note that these values are exemplary only and other values may be used as is necessary and/or desired ([0041]). Examiner notes that the claims place no limitations on what these values should represent. Thus, SOLMAZ’s disclosure of value −1 represents no output from an LF for the given data point is considered to teach a non-labeled value (e.g., −1) of claim 1.
Similar arguments have been presented for claim 10 and thus, Applicant’s arguments are not persuasive for the same reasons. The respective dependent claims are rejected by virtue of depending on their respective independent claims 1 and 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Quader et al. (US 20210209412 A1) discloses using automated weak supervision to label training data that is used to train a machine learning model.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER WELCH can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMY P HOANG/ Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights