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 .
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21 and 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, interdependencies between columns is not part of the original disclosure. Instead, specification paragraph 54 states, “The structure of the graph may be determined, at least in part, by interdependencies between transformer pipelines. For example, if a first pipeline makes use of a variable that is modeled by a second pipeline, then the second pipeline may be positioned before the first pipeline in the graph.”
Claim Rejections - 35 USC § 112(b)
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 1-8, 10-19, 21-22 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. Exemplary Claim 1 recites the limitation "the training data" in line 4. There is insufficient antecedent basis for this limitation in the claim. For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
Claims 21-22 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. Specifically, the limitation of interdependencies between columns is indefinite because it is unclear exactly what the term columns is referring to. Claim 2 recites columns of the tabular data, but claim 21 and 22 do not depend on claim 2. For examination purposes, the term is interpreted as variables.
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 1-8, 10-19, 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a method claim. Claim 11 is a CRM claim. Claim 12 is a system claim. Therefore, claims 1, 11, and 12 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
generating a directed acyclic graph that includes the plurality of transformer models as nodes, including a branch where multiple transformer pipelines handle a same variable (mental process – user can manually create a DAG that represents the transformers as nodes and includes a branch where multiple transformer pipelines handle a same variable)
traversing the directed acyclic graph to identify a subset of transformers that are combined in order (mental process – user can manually traverse the DAG to select a subset of nodes)
generating an environment (mental process – user can manually create an environment)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
training a plurality of transformer models from tabular data and relationship information about the training data (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) – Examiner’s note: high level recitation of training machine learning models with previously determined tabular and relationship data)
using the subset of transformers (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) – Examiner’s note: high level recitation of implementing a machine learning model(s) such as a transformer(s))
training a machine learning model using reinforcement learning, based on the environment (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))
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
training a plurality of transformer models from tabular data and relationship information about the training data (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) – Examiner’s note: high level recitation of training machine learning models with previously determined tabular and relationship data)
using the subset of transformers (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) – Examiner’s note: high level recitation of implementing a machine learning model(s) such as a transformer(s))
training a machine learning model using reinforcement learning, based on the environment (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))
Conclusion: The claim is not patent eligible.
For Claim 11: The claim recites the additional elements of a computer program product comprising a computer readable storage medium having program instructions embodied therewith. For Claim 12: The claim recites the additional elements of a hardware processor and a memory that stores a computer program product. All of these additional elements are mere instructions to apply the exception using a generic computer component under both Step 2A prong 2 and Step 2B.
Regarding Claims 2-6, 8, 10, 13-17, 19, 21-22: These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. That is, nothing in the claim limitation precludes the step from practically being performed in the mind.
For claims 2, 13: the limitation encompasses the user manually adding new columns of information.
For claims 3, 14: the limitation encompasses the user manually adding new columns of information.
For claim 4, 15: the limitation encompasses the user manually using a DAG that has multiple distinct paths.
For claim 5, 16: the limitation encompasses the user manually traverse the graph in parallel.
For claim 6, 17: the limitation encompasses the user manually using relationship information between the data in columns.
For claim 8, 19: the limitation encompasses the user manually making a decision to use neural network models as some of the transformers.
For claim 10: the limitation encompasses the user manually executing a decision policy.
For claim 21: the limitation encompasses the user manually ordering transformer pipelines based on interdependencies between columns modeled by the transformer pipelines.
For claim 22: the limitation encompasses the user manually positioning a second pipeline, which models a column, before a first pipeline that uses the column.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claims 7, 18: The limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than the additional elements, nothing in the claim limitation precludes the step from practically being performed in the mind.
For claims 7, 18: the limitation includes the additional element of training using distinct combinations of data and types.
These judicial exceptions are not integrated into a practical application. In particular. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to “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). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claim is not patent eligible.
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.
Claim(s) 1, 6, 10-12, 17, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (hereinafter Zhang), U.S. Patent Application Publication 2020/0184272, in view of Trofymov et al. (hereinafter Trofymov), U.S. Patent Application Publication 2020/0209858, further in view of Srinivasa Rao et al. (hereinafter Rao), U.S. Patent Application Publication 2021/0182859, further in view of Padakandla et al. (hereinafter Padakandla), Reinforcement Learning in Non-Stationary Environments.
Regarding Claim 1, Zhang discloses a computer-implemented method for generating an environment, comprising:
training a plurality of transformer models [“transformers 228 may include training” ¶29]
generating a directed acyclic graph that includes the plurality of transformer models as nodes [“model definition engine 204 allows users to create machine learning models 124 from subsets of components 122 in component repository 234. More specifically, model definition 204 may obtain a graph-based structure 210 as a representation of a machine learning model 200” ¶31; “Graph-based structure 210 may include a directed acyclic graph (DAG)” ¶32; “Transformers 228 may represent components” ¶29], including a branch where multiple transformer pipelines handle a same variable [Fig. 3; Note: a same variable is handled in 306 for at least the pipelines 302->306->308 and 304->306->308];
traversing the directed acyclic graph to identify a subset of transformers that are combined in order [“use a neural architecture search technique and/or another technique for generating or modifying the machine learning model architectures” ¶39; Note: a search technique involves traversing a graph for modifying it; “adding and/or removing components 242 in machine learning model 200” ¶39]; and
using the subset of transformers [adding and/or removing components 242 in machine learning model 200” ¶39].
However, Zhang fails to explicitly disclose generating an environment.
Trofymov discloses generating an environment [“ML model generates parameters of the environment” Abstract; “generative machine learning models, such as generative adversarial networks ( GANs ) , may be used to dynamically generate objects , surfaces , or scenarios within the virtual environment 108 , including , for example , dynamically generated signs” ¶35].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang and Trofymov before him before the effective filing date of the claimed invention, to modify the method of Zhang to incorporate the goal of generating an environment of Trofymov.
Given the advantage of generating an environment in which to train a reinforcement learning agent, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Zhang fails to explicitly disclose from tabular data and relationship information about the training data.
Rao discloses from tabular data and relationship information about the training data [“Knowledge graph infrastructure 200 includes a search index pipeline 208, a relational database 210, a data warehouse 212, a data import pipeline 216, and deep learning and/or reinforcement learning models 214.” ¶44; “a relational database may be built from the information contained within the knowledge graph. An analytical base table may then be created from the relational database.” ¶62].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, and Rao before him before the effective filing date of the claimed invention, to modify the combination to incorporate the relational database for storing training data of Rao.
Given the advantage of easily accessible and available data, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Zhang fails to explicitly disclose training a machine learning model using reinforcement learning, based on the environment.
Padakandla discloses training a machine learning model using reinforcement learning, based on the environment [“Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment.” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, and Padakandla before him before the effective filing date of the claimed invention, to modify the combination’s generated environment to incorporate reinforcement learning of Padakandla.
Given the advantage of training a policy for optimal decision making in an environment, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 6, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1.
However, Zhang fails to explicitly disclose wherein the relationship information includes relationships between columns of the tabular data.
Rao discloses wherein the relationship information includes relationships between columns of the tabular data [“a relational database 210” ¶44; “a relational database may be built from the information contained within the knowledge graph” ¶62].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, and Padakandla before him before the effective filing date of the claimed invention, to modify the combination to incorporate the relational database of Rao.
Given the advantage of easily accessible and available data, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 10, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1.
However, Zhang fails to explicitly disclose further comprising executing a decision policy using the environment to test the decision policy in new circumstances.
Padakandla discloses further comprising executing a decision policy using the environment to test the decision policy in new circumstances [“Motivated by the real-world applications where changing environment dynamics (and/or rewards, costs) is frequently observed, we focus on developing a model-free RL method that learns optimal policies for nonstationary environments.” pg. 3, lines 1-3; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, and Padakandla before him before the effective filing date of the claimed invention, to modify the combination to incorporate policy training using different environments of Padakandla.
Given the advantage of real-world applications where environments change, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 11 and 12 are rejected on the same grounds as claim 1.
Claim 17 is rejected on the same grounds as claim 6.
Regarding Claim 21, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1. Zhang further discloses wherein generating the directed acyclic graph includes ordering transformer pipelines based on interdependencies between columns modeled by the transformer pipelines [Fig. 3; Note: transformer pipeline 302->306->308 is ordered based on the interdependencies between variables (i.e., columns of data such as vectors or matrices) modeled by the transformer pipelines since, for example, transformer 302 produces embedding 324 which is the input for transformer 306 which produces concatenated embedding 328 which is the input for transformer 308.].
Regarding Claim 22, Zhang, Trofymov, Rao, and Padakandla discloses the method of claim 21. Zhang further discloses wherein ordering transformer pipelines based on interdependencies between columns modeled by the transformer pipelines includes positioning a second pipeline, which models a column, before a first pipeline that uses the column [Fig. 3; Note: transformer pipelines 302->306 and 306->308 are ordered based on the interdependencies between variables (i.e., columns of data such as vectors or matrices) modeled and used by the transformer pipelines since, for example, transformer 302 produces embedding 324 which is the input for transformer 306 which produces concatenated embedding 328 which is the input for transformer 308. Pipeline 302->304 (i.e., second pipeline) is positioned before pipeline 304->306 (i.e., first pipeline).].
Claim(s) 2, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Trofymov, Rao, and Padakandla, further in view of Fan et al. (hereinafter Fan), U.S. Patent Application Publication 2021/0209486.
Regarding Claim 2, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1.
However, Zhang fails to explicitly disclose further comprising transforming the tabular data to introduce new columns to add time-dependent information to each row of the tabular data, before training the plurality of transformer models.
Fan discloses further comprising transforming the tabular data to introduce new columns to add time-dependent information to each row of the tabular data, before training the plurality of transformer models [“the preprocessing 304 may include feature engineering such as e.g., associating a feature to the data value. In one embodiment, this may include adding another data column to the preprocessed time series data table ( or parameter to the data structure if a data structure is used) for the determined feature.” ¶35; “2) time series features such as e.g., rolling windows and lagged values with different lags” ¶35].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, and Fan before him before the effective filing date of the claimed invention, to modify the combination to incorporate adding columns for time-dependent information of Fan.
Given the advantage of collecting and using historic data for a more accurate result, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 13 is rejected on the same grounds as claim 2.
Claim(s) 3, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Trofymov, Rao, Padakandla, and Fan, further in view of Jiang et al. (hereinafter Jiang), Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting.
Regarding Claim 3, Zhang, Trofymov, Rao, Padakandla, and Jiang disclose the method of claim 2.
However, Zhang fails to explicitly disclose further comprising determining a lookback number for each original column in the tabular data, wherein transforming the tabular data includes adding a number of new columns for each original column equal to the lookback number for the respective original column.
Fan discloses further comprising determining a lookback number for each original column in the tabular data, wherein transforming the tabular data includes adding a number of new columns for each original column equal to the lookback number for the respective original column [“the preprocessing 304 may include feature engineering such as e.g., associating a feature to the data value. In one embodiment, this may include adding another data column to the preprocessed time series data table ( or parameter to the data structure if a data structure is used) for the determined feature.” ¶35; “2) time series features such as e.g., rolling windows and lagged values with different lags” ¶35].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, and Fan before him before the effective filing date of the claimed invention, to modify the combination to incorporate adding columns for time-dependent information of Fan.
Given the advantage of collecting and using historic data for a more accurate result, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Zhang fails to explicitly disclose further comprising determining a lookback number for each original column in the tabular data, wherein transforming the tabular data includes adding a number of new columns for each original column equal to the lookback number for the respective original column.
Jiang discloses further comprising determining a lookback number for each original column in the tabular data, wherein transforming the tabular data includes adding a number of new columns for each original column equal to the lookback number for the respective original column [“a sliding lookback window and use the data within this lookback window to predict the future case” §3.2.2; “Vk-l,…,Vk-1, where l represents the length of the lookback window” §4.3].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, Fan, and Jiang before him before the effective filing date of the claimed invention, to modify the combination to incorporate the lookback information of Jiang.
Given the advantage of collecting and using historic data for a more accurate result, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 14 is rejected on the same grounds as claim 3.
Claim(s) 4-5, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Trofymov, Rao, and Padakandla, further in view of Kramer et al. (hereinafter Kramer), The Combining DAG: A Technique for Parallel Data Flow Analysis.
Regarding Claim 4, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1.
However, Zhang fails to explicitly disclose wherein the directed acyclic graph includes multiple distinct graphs, with no dependencies between transformer models of respective distinct graphs.
Kramer discloses wherein the directed acyclic graph includes multiple distinct graphs, with no dependencies between transformer models of respective distinct graphs [“the computation of data flow for independent paths, or independent portions of the paths, is being carried out in parallel” §II ¶3].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, and Kramer before him before the effective filing date of the claimed invention, to modify the combination to incorporate the distinct graphs of Kramer.
Given the advantage of independent paths which allows for parallelism and faster processing, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 5, Zhang, Trofymov, Rao, Padakandla, and Kramer disclose the method of claim 4.
However, Zhang fails to explicitly disclose wherein traversing the directed acyclic graph includes traversing the multiple distinct graphs in parallel.
Kramer discloses wherein traversing the directed acyclic graph includes traversing the multiple distinct graphs in parallel [“the computation of data flow for independent paths, or independent portions of the paths, is being carried out in parallel” §II ¶3].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, and Kramer before him before the effective filing date of the claimed invention, to modify the combination to incorporate the parallelism of Kramer.
Given the advantage of independent paths which allows for parallelism and faster processing, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 15-16 are rejected on the same grounds as claims 4-5, respectively.
Claim(s) 7 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Trofymov, Rao, and Padakandla, further in view of Seijo-Pardo et al. (hereinafter Pardo), Ensemble feature selection: Homogeneous and heterogeneous approaches.
Regarding Claim 7, Zhang, Trofymov, Rao, and Padakandla disclose the method of claim 1.
However, Zhang fails to explicitly disclose wherein each of the plurality of transformer models is trained using a distinct combination of tabular data and transformer type.
Pardo discloses wherein each of the plurality of transformer models is trained using a distinct combination of tabular data and transformer type [(i) homogeneous, i.e., using the same feature selection method with different training data and distributing the dataset over several nodes; and (ii) heterogeneous, i.e., using different feature selection methods with the same training data.” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, and Pardo before him before the effective filing date of the claimed invention, to modify the combination to incorporate the distinct features sets of Pardo.
Given the advantage of increased performance of using varied data, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 18 is rejected on the same grounds as claim 7.
Claim(s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Trofymov, Rao, Padakandla, and Pardo further in view of Clark et al. (hereinafter Clark), What does BERT look at? An Analysis of BERT’s Attention.
Regarding Claim 8, Zhang, Trofymov, Rao, Padakandla, and Pardo disclose the method of claim 7.
However, Zhang fails to explicitly disclose wherein at least some of the plurality of transformer models are implemented as neural network models.
Clark discloses wherein at least some of the plurality of transformer models are implemented as neural network models [“Large pre-trained neural networks such as BERT” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Zhang, Trofymov, Rao, Padakandla, Pardo, and Clark before him before the effective filing date of the claimed invention, to modify the combination to incorporate the neural network models for transformers.
Given the advantage of using a known model type for accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 19 is rejected on the same grounds as claim 8.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the §101 rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that the claims recite a practical application. Examiner disagrees for at least the following reasons.
As Applicant correctly states, the test for finding a technological improvement is to see if the disclosure provides sufficient details of the improvement and if the claims reflect the disclosed improvement.
Regarding the first point, the specification (specifically the paragraphs cited by Applicant, ¶2-3, 20-23, 43-45, 51) indicates that the improvement is merely the automation of the generation of environments. For example, specification paragraph 20 recites that the invention is directed to “address the difficulty of generating training environments by hand.” However, these environments can be generated by hand. A person having ordinary skill in the art knows that these environments were generated by hand prior to the boon of AI which allows many tasks to now be automated. Additionally, Applicant’s assertion that generation of the environments is a part of the total training process is explicitly contradicted by the specification in paragraph 22 which states that, “[a]lthough reinforcement learning is specifically contemplated as a use for the automatically generated environments, it should be understood that they may be put to any appropriate purpose.” These are two separate processes. Merely adding training at the end of the claim does not intrinsically tie them together.
Regarding the second point, any alleged improvement is not reflected in the claims. Applicant asserts that the claims promote diversity and complexity; however, this blanket statement is not tied to anything in the claim. Instead, it merely appears that automating the creation of a lot of environments could potentially provide diverse learning for reinforcement learning. However, a lot of environments created by hand would do the same thing. Automation on a computer is the only difference.
Lastly, the dependent claims’ inclusion of time-dependent data is part of the abstract idea. In order for a practical application to be found, the additional elements must integrate the abstract idea into a practical application. The claims have been considered as a whole, as has been explained in the rejections addressing both the abstract and the additional elements limitations. Furthermore, the Ex parte Desjardins opinion does not alter the result of the 101 analysis in this situation. The claims are viewed at a level of generality commensurate with the breadth of the claim language.
Accordingly, for at least these reasons, the rejections are maintained.
Regarding the prior art rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) in claim 3 the combination of reference does not disclose training of models using tabular data in claim 1, and that 2) in claim 3 the references do not suggest that the number of columns that are added is equal to the lookback number. Examiner disagrees for at least the following reasons.
First, as outlined in the rejection, Zhang discloses training a plurality of transformer models in at least paragraph 29, while Rao discloses tabular data and relationship information in the form of a data structure such as a relational database in at least paragraph 44 that is used in a machine learning model in at least paragraph 62. Applicant appears to differentiate between raw data and embedding data (i.e., data ready for processing by a model). The claim does not make such a differentiation. Merely, the training of the models is from the tabular data. This indicates any type of connection, no matter now attenuated. A person having ordinary skill in the art understands that training a model from specific data involves taking the raw data and embedding it into a vector that can be inputted in to the model. Rao uses the tabular data for machine learning, and the combination with Zhang would logically use the tabular data with the machine learning model in Zhang. It’s the combination of reference which disclose the limitation.
Second, Fan discloses adding columns to a data structure in at least paragraph 35, and Jiang discloses a sliding window in at least §§ 3.2.2 and 4.3, in which length of the window is a lookback number. A person having ordinary skill in the art understands that utilizing a sliding window of Jiang requires the additional data viewed within the length of the window to stored compared to using just a single point in time. Fan discloses adding additional columns of data. It is the combination of Fan and Jiang which disclose the limitation.
For at least these reasons, the rejections are maintained.
Conclusion
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 ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 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, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148