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 .
The claim objects regarding to claim 1, 19 and 20 is withdrawn.
Claim Objections
Claims 1, 8, 19, 20 are objected to because of the following informalities: minor typo.
Claim 8 recite “the artificial intelligence” appears “the one or more artificial intelligence features”;
Claim 1, 19 and 20 recite “the generated artificial intelligence features” appear to be “the one or more generated artificial intelligence features”. Please correct any dependent claims if there is any more.
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, 3-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
Were any 101 SME rejections made (Yes/No; if yes, which claims and Step 1 or Step 2)? No
Step 1 analysis:
-Step 1 rejections omitted: No
-Claims 19 is statutory per disclaimer in paragraph [0098].
-Step 1 rejections made: Compliant/Non-compliant? Why? N/A
Step 2 analysis:
Representative Claim Number: 19
-Are there abstract ideas recited in the claim? Yes
-What is the problem applicant is trying to solve?
Disclosed from paragraph [0025]-Conventional AutoML can generate primitive features but is incapable of identifying a more complex feature such as one with a formula, and does not work collaboratively with human users.
-What is applicant’s improvement/invention/inventive concept?
Disclosed from paragraph [0025]-enable the integration of feature engineering provided by a human with feature engineering provided by AI to craft complex features.
[0025] Generally, systems and methods for feature engineering are disclosed. In one example embodiment, an exemplary feature engineering system enables the integration of feature engineering provided by a human with feature engineering generated using artificial intelligence. The exemplary system also enables multiple data scientists (users) to collaboratively create new features. Conventional AutoML automatically generates generally primitive features, often applying a set of rules to existing features to create the new features, such as Sqrt(Xi) (taking the square root of a feature, such as body weight) and PCA(Xi...X.) (principle component analysis of a feature), and typically does not work collaboratively with human users. While a more complex feature, such as body mass index (BMI), may be valuable for model generation, AutoML is generally incapable of identifying a feature containing a formula, such as the formula for BMI (weight/height), as a new feature. In one example embodiment, an interactive automated feature generation system and user interface enables multiple users and an AutoML system to work together in the feature engineering task, including the crafting of complex features. The improved predictive models generated based on the derived feature sets enhance the performance of machine learning systems in performing inferencing in a variety of applications, including natural language processing, self-driving vehicles, and the like.
-Is the improvement in the abstract idea itself? Yes, utilizing ML to perform feature engineering (which is disclosed as being done by human users) or to assist human users with feature engineering is simply using a generically recited tool to perform a mental process.
-Step 2 rejection omitted: No
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 2A prong 1, the claim recites, “identifying a domain of an input dataset” and “identifying one or more archived domain knowledge features corresponding to the identified domain” which encompass mental processes (see paragraph [0036] explaining that the domain can be identified by comparing to other datasets and features archived in a data registry, analyzing data within a dataset, etc.). The claim further recites, “inputting one or more user feature definitions for one or more user features defined by a user”, “generating, using machine learning, one or more artificial intelligence features based on the identified domain;” “processing the generated artificial intelligence features and the identified archived domain knowledge features to generate a set of candidate features for presentation to the user”, “obtaining a selection of a subset of the candidate features from the user via a user interface where features to be utilized and features to be excluded are identified”, “generating a selected subset of the candidate features by integrating the selection of the subset of the candidate features with the user features;” “generating, using the at least one processor, one or more predictive models based on the integrated selection of the subset of the candidate features and the user features;” While the terms “inputting”, “processing”, “obtaining”, “generating” and “generating” imply use of a computer, these steps encompass mental processes (a user defining user feature definitions for one or more user features, generating a set of candidate features for presentation to the user by “processing” (e.g., mentally analyzing) the identified archived domain knowledge features and the user features, “integrating” (e.g., mentally analyzing) the features selected and user defined features and selecting a subset of the candidate features and user defined features), As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0039, 0047, 0051-0052], Fig. 1B, 1C, 1D. Thus, the claim recites abstract ideas.
The claim further recites, “carrying out, using the at least one processor, inferencing using one or more of the one or more predictive models” which the additional elements amount to merely the words “apply it” or are mere instructions to implement an abstract idea or other exception on a computer. MPEP § 2106.05(a)
At Step 2A prong 2, the claim additionally recites, “a memory”, “at least one processor, coupled to said memory, and operative to perform operations comprising”, “generating one or more predictive models based on the selected features”, as well as the previously-discussed “inputting”, “generating”, “processing”, “obtaining”, “generating”, “generating” and “carrying out” steps. The recitations of the memory, at least one processor operative to perform operations, and “processing” steps amount to generically recited computer components that only amount to applying the judicial exception on a generic computer in accordance with MPEP 2106.05(f)(2). The “inputting” and “obtaining” steps amount to insignificant extra-solution activity (data gathering) in accordance with MPEP 2106.05(g) as well as applying the abstract idea with use of generic computer components in accordance with MPEP 2106.05(f)(2) as a generic computer is utilized to perform these mental processes of defining features and selecting a subset of candidate features. “generating, using machine learning, one or more artificial intelligence features based on the identified domain;” “generating a selected subset of the candidate features by integrating the selection of the subset of the candidate features with the user features;” “generating, using the at least one processor, one or more predictive models based on the integrated selection of the subset of the candidate features and the user features;” recited at such a high level, amounts to mere instructions to apply an exception in accordance with MPEP 2106.05(f)(1) (note there is no description of the model or how it is generated), as well as insignificant extra-solution activity in accordance with MPEP 2106.05(g) as it is incidental to the primary process and is merely a nominal/tangential addition to the claim. And lastly, “carrying out, using the at least one processor, inferencing using one or more of the one or more predictive models” recited at such a high level, amounts to mere instructions to apply an exception in accordance with MPEP 2106.05(f)(1) (note there is no description of the model or how the inference is carried out), as well as insignificant extra-solution activity in accordance with MPEP 2106.05(g). Thus, none of these additional elements, taken alone or in combination, incorporate the judicial exception into a practical application.
At Step 2B, the analysis from Step 2A prong 2 is incorporated herein. The “inputting” and “obtaining” steps additionally amount to WURC activities similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)” and “Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”, see MPEP 2106.05(d)(II). Furthermore, “generating, using machine learning, one or more artificial intelligence features based on the identified domain;” “generating a selected subset of the candidate features by integrating the selection of the subset of the candidate features with the user-defined features;” “generating, using the at least one processor, one or more predictive models based on the integrated selection of the subset of the candidate features and the user-defined features;” are also disclosed in the specification as WURC, see [0002], “Feature engineering uses domain knowledge in the extraction of features from data which may then be used, for example, to generate predictive models”. Thus, the additional elements, taken either alone or in combination, do not amount to significantly more than the abstract idea itself.
-Step 2 rejection made: N/A
-Comments on Step 2A prong 1 analysis:
-Comments on Step 2A prong 2 analysis:
-Comments on Step 2B analysis:
Additional comments/feedback (e.g., suggestion of best practices; additional comments/explanation as needed): Please further define how the interference is carried out and what is the inference and how the features are integrated and based on what rule, criteria, format, etc. to help move forward the prosecution.
Note the above 101 rejection could also be applied to at least independent Claims 1 and 20 with similar analysis.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 3
Claim 3 merely recite other additional elements that disclose providing the candidate features to the user for review which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 4-5
Claims 4-5 merely recite other additional elements that disclose user inputting user feature definitions which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 6
Claims 6 merely recite other additional elements that disclose generating primitive features based on archived formulas which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 7
Claims 7 merely recite other additional elements that disclose checking a validity of the features which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 8
Claims 8 merely recite other additional elements that disclose providing the features for the user to review which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 9
Claims 9 merely recite other additional elements that disclose generating new domain knowledge features which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 10
Claims 10 merely recite other additional elements that disclose reformatting the identified archived domain knowledge features which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 11
Claims 11 merely recite other additional elements that disclose storing the features and dataset which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 12
Claims 12 merely recite other additional elements that disclose performing NLP using the predictive models which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 13
Claims 13 merely recite other additional elements that disclose defining the model generation algorithms which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 14
Claims 14 merely recite other additional elements that disclose selecting the model generation algorithms which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 15
Claims 15 merely recite other additional elements that disclose generating a performance score for the predictive model based on the benchmark which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 16
Claims 16 merely recite other additional elements that disclose generating a knowledge graph which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 17
Claims 17 merely recite other additional elements that disclose comparing the datasets and identify similarity which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claims 18
Claims 18 merely recite other additional elements that disclose defining the user feature in a feature input field which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are 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.
Claims 1, 3-11, 13-14, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (Wu) US 2020/0210881 in view of Lloyd et al. (Lloyd) US 2020/0005045 and Chang et al. (Chang) US 2017/0357698
In regard to claim 1, Wu disclose A method comprising: ([0007][0052] method)
identifying, using at least one processor, a domain of an input dataset; ([0004]-[0007] [0036]-[0037][0049]-[0053] receive data of an asset included in a domain, such as a problem to be solved and identify the domain of the input dataset,)
identifying, using the at least one processor, one or more archived domain knowledge features corresponding to the identified domain; ([0005]-[0007] [0019]-[0020] [0030][0036]-[0037][0048]-[0053] identifying features corresponding to the domain which is the problem to be solved, and “features form the optimal set for a particular problem in a particular domain”)
generating, using machine learning, one or more artificial intelligence features based on the identified domain; (Fig. 4, [0052]-[0055] generating features based on the target domain using ML)
inputting, using the at least one processor, one or more user feature definitions for one or more user features defined by a user; (Fig. 3A, [0019]-[0020][0046]-[0047] 326, user input feature configuration, such as attributes, metrics, on the GUI)
processing, using the at least one processor, the generated artificial intelligence features and the identified archived domain knowledge features to generate a set of candidate features for presentation to the user; (Fig. 3A, Fig. 4, [0038] [0042] [0051]-[0055] submit and generate candidate features to display on the GUI based on generated predictive features of the target domain and stored problem domain features) and
carrying out, using the at least one processor, inferencing using one or more of the one or more predictive models. ([0003][0006]-[0007] [0017]-[0020] predicting or inferring an output (predicting feature) based on the ML model.)
But Wu fail to explicitly disclose “obtaining, using the at least one processor, a selection of a subset of the candidate features from the user via a user interface; generating a selected subset of the candidate features by integrating the selection of the subset of the candidate features with the user features; generating, using the at least one processor, one or more predictive models based on the integrated selection of the subset of the candidate features and the user features;”
Lloyd disclose “obtaining, using the at least one processor, a selection of a subset of the candidate features from the user via a user interface; ([0024]-[0028] selecting subset of features from the user on the GUI)
generating a selected subset of the candidate features by integrating the selection of the subset of the candidate features with the user features; ([0028]-[0039] [0057]-[0060] generate selected subset of features by adding a feature from the user and the user selected subset of features from the existing model spec. to generate a new model spec.)
generating, using the at least one processor, one or more predictive models based on the integrated selection of the subset of the candidate features and the user features; ([0024]-[0039] [0057]-[0060] generating a ML model based on the integrated and selected subset of features and use defined features, for example, user can add a feature to the model specification (with selected subset of features), the resulted feature would include the user added feature and selected subset of features. Note: please further define how the models are generated to move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Lloyd’s feature generation pipeline for ML into Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Lloyd’s method of feature selection for ML model based on user’s input would provide more user selection mechanism into Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that selecting the features by the user to build the ML model would improve the ML model building.
But Wu and Lloyd fail to explicitly disclose “the selection of the subset of the candidate features from the user via the user interface where features to be utilized and features to be excluded are identified;”
Chang disclose the selection of the subset of the candidate features from the user via the user interface where features to be utilized and features to be excluded are identified; ([0010]-[0021] claim 1, subset of the features can be selected and deselected by the user via the GUI)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s feature selection method into Lloyd and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Chang’s method of feature selection for ML model based on user’s input would provide more user selection mechanism into Lloyd and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that selecting the features by the user to build the ML model would improve the ML model building.
In regard to claim 3, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
But Wu and Chang fail to explicitly disclose “wherein the processing further comprises providing the candidate features to the user for review and wherein the selection of the subset of the candidate features further comprises one or more modified features generated by the user.”
Lloyd disclose wherein the processing further comprises providing the candidate features to the user for review and wherein the selection of the subset of the candidate features further comprises one or more modified features generated by the user. ([0024]-[0029] displaying the features on the user interface and selecting the subset of candidate features may include adding a feature to the model. etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Lloyd’s feature generation pipeline for ML into Chang and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Lloyd’s method of feature selection for ML model based on user’s input would provide more user selection mechanism into Chang and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that selecting the features by the user to build the ML model would improve the ML model building.
In regard to claim 4, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose wherein the user feature definitions are inputted via the user interface (UI). (Fig. 3A, [0019]-[0020][0046]-[0047] 326, user input feature configuration, such as attributes, metrics, are inputted via the GUI)
In regard to claim 5, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose wherein the user feature definitions are inputted via a programmatic interface (PI). (Fig. 3A, [0019]-[0020][0046]-[0047] 326, user input feature configuration, such as attributes, metrics, are inputted via the GUI)
In regard to claim 6, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose further comprising generating one or more primitive features based on archived formulas, wherein the set of candidate features further comprises the primitive features. ([0051]-[0055][0061] retrieve the previously used features based on the from a cross-domain feature space stored based on the structure and predicted candidate features include the previously used features)
In regard to claim 7, Wu, Lloyd and Chang disclose The method of claim 6, the rejection is incorporated herein.
Wu disclose wherein the processing further comprises checking a validity of one or more of the primitive features, the domain knowledge features, and the user features. ([0045]-[0055] use metrics to evaluating the features, etc. such as accuracy, precision, etc.)
In regard to claim 8, Wu, Lloyd and Chang disclose The method of claim 6, the rejection is incorporated herein.
But Wu fail to explicitly disclose “wherein the processing further comprises providing the primitive features, the artificial intelligence features, the archived domain knowledge features, and the user features to a user for review and obtaining a modified set of features from the user.”
Lloyd disclose wherein the processing further comprises providing the primitive features, the artificial intelligence features, the archived domain knowledge features, and the user features to a user for review and obtaining a modified set of features from the user. ([0024]-[0029] displaying the features on the user interface and modifying features may include adding a feature, delete a feature, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Lloyd’s feature generation pipeline for ML into Chang and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Lloyd’s method of feature selection for ML model based on user’s input would provide more user selection mechanism into Chang and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that selecting the features by the user to build the ML model would improve the ML model building.
In regard to claim 9, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose wherein the identifying the one or more archived domain knowledge features further comprises generating one or more new domain knowledge features by analyzing metadata for one or more archived datasets obtained from a dataset registry that are similar to the input dataset. ([0019]-[0027][0036]-[0042] generating new features by analyzing attributes of the received dataset for the similar assets)
In regard to claim 10, Wu, Lloyd and Chang The method of claim 1, the rejection is incorporated herein.
But Wu and Chang fail to explicitly disclose “wherein the processing further comprises reformatting the artificial intelligence features, the identified archived domain knowledge features and the user features to comply with a unified format supported by an evaluation module.”
Llody disclose wherein the processing further comprises reformatting the artificial intelligence features, the identified archived domain knowledge features and the user features to comply with a unified format supported by an evaluation module. ([0054]-[0066] feature values follow a similar name-term-value format and transform the features to be represented in a vector format)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Lloyd’s feature generation pipeline for ML into Chang and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Lloyd’s feature format would provide more formatting standard selection into Chang and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing formatting standard would improve the ML model building.
In regard to claim 11, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose further comprising storing the features, the input dataset, and the identified domain in a feature store and/or a dataset registry. ([0006]-[0007] [0020]-[0022][0028]-[0030] stored features in a data store)
In regard to claim 13, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose further comprising defining, by the user, one or more model generation algorithms for the generation of the predictive models. ([0052]-[0055] choose a list of algorithms types via a user interface for generating the model)
In regard to claim 14, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose further comprising selecting one or more model generation algorithms for the generation of the predictive models. ([0052]-[0055] choose a list of algorithms types via a user interface for generating the model)
In regard to claim 17, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose wherein the identifying the domain of the input dataset further comprises comparing the input dataset to datasets in a dataset registry and accessing an identity of a domain corresponding to a similar existing dataset in the dataset registry. ([0005]-[0007] [0019]-[0023] [0027][0030][0036]-[0037][0048]-[0053] identify the similar assets in the target domain based on the analysis of the data attributes corresponding to an identified subject with comparison)
In regard to claim 18, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
Wu disclose wherein the inputting of the one or more feature definitions via the user interface (UI) further comprises defining the corresponding user feature in a feature input field. (Fig. 3A, [0019]-[0020] [0045]-[0047] user can input attributes and input description of the feature in the input field, 322)
But Wu and Chang fail to explicitly disclose “and a corresponding textual description in a feature description input field.”
Llody disclose and a corresponding textual description in a feature description input field. ([0028]-[0029] feature description input field corresponding to the feature on the GUI)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Lloyd’s feature generation pipeline for ML into Chang and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Lloyd’s feature description input field would provide more user inputting field into Chang and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing user inputting field for the model would facilitate the ML model building based on user’s desire and therefore improve user experience using the system.
In regard to claim 19, claim 19 is an apparatus claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 20, claim 20 is a computer program product claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claims 12, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (Wu) US 2020/0210881, Lloyd et al. (Lloyd) US 2020/0005045 and and Chang et al. (Chang) US 2017/0357698 as applied to claim 1, further in view of Conort et al. (Conort) US 2022/0076164.
In regard to claim 12, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
But Wu, Lloyd and Chang fail to explicitly disclose “wherein carrying out the inferencing using one or more of the one or more predictive models comprises performing natural language processing using one or more of the one or more predictive models.”
Conort disclose wherein carrying out the inferencing using one or more of the one or more predictive models comprises performing natural language processing using one or more of the one or more predictive models. ([0424]-[00426] natural language data derived which means the natural language data are recognized and processed)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Conort’s feature engineering for ML models into Chang, Lloyd and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Conort’s feature engineering with natural language data for ML models would provide more inputting data into Chang, Lloyd and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing natural language input for the model would facilitate the ML model building and therefore improve user experience using the system.
In regard to claim 15, Wu, Lloyd and Chang disclose The method of claim 1, the rejection is incorporated herein.
But Wu, Lloyd and Chang fail to explicitly disclose “further comprising generating a performance score for each generated predictive model based on one or more benchmark datasets.”
Conort disclose further comprising generating a performance score for each generated predictive model based on one or more benchmark datasets. ([0187]-[0196] [0213] ranking with score for the models based on a prediction problem or dataset)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Conort’s feature engineering for ML models into Chang, Lloyd and Wu’s invention as they are related to the same field endeavor of feature engineering using ML. The motivation to combine these arts, as proposed above, at least because Conort’s feature engineering with rating of the ML models would provide more comparing mechanism into Chang, Lloyd and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing comparing mechanism for the model would facilitate the ML model building and therefore improve user experience using the system.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (Wu) US 2020/0210881 , Lloyd et al. (Lloyd) US 2020/0005045 and and Chang et al. (Chang) US 2017/0357698 as applied to claim 1, further in view of Martinez et al. (Martinez) US 2019/0095806
In regard to claim 16, Wu and Lloyd disclose The method of claim 1, the rejection is incorporated herein.
But Wu and Lloyd fail to explicitly disclose “further comprising generating a knowledge graph that relates columns of the input dataset to tags that represent a concept and crafting a derivative machine learning feature based on the concept, wherein the set of candidate features further comprises the derivative machine learning feature.”
Martinez disclose further comprising generating a knowledge graph that relates columns of the input dataset to tags that represent a concept, accessing remote information using at least one of the tags related by the knowledge graph, and crafting a derivative machine learning feature based on the accessed remote information and concept, wherein the set of candidate features further comprises the derivative machine learning feature. ([0007]-[0009] [0027]-[0045][0053]-[0065][0068]-[0078] generating a knowledge graph corresponding to the columns of data to functional labels which represent functional description and derive and draw the derived features based on the labels (the functional label describing one or more system or subsystem, see Fig. 1, such as “GPS” or “INS” and “HVAC” etc. they are concepts and data structure in the database are obtained by using the functional labels) and features include derivative features with a graph-based CNN (a ML) for learning structural relationship between the nodes of a graph to generating a knowledge graph, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Martinez’s feature graph into Chang, Lloyd and Wu’s invention as they are related to the same field endeavor of feature generating. The motivation to combine these arts, as proposed above, at least because Martinez’s feature graph would provide more information cluster method into Chang, Lloyd and Wu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more information cluster method would facilitate the ML model building and therefore improve user experience using the system.
Response to Arguments
Applicant's arguments filed on 1/12/2026 regarding to claim 1, 3-20 have been fully considered but they are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101, please see the updated rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20210034988 A1 2021-02-04 Adel-Vu et al.
DEVICE AND METHOD FOR MACHINE LEARNING AND ACTIVATING A MACHINE
Adel-Vu disclose A device and method for activating a machine or for machine learning or for filling a knowledge graph. Training data are made available, including texts having labels with regard to a structured piece of information. A system for classification is trained using the training data, the system for classification including an attention function that weighs individual vector representations of individual parts of a sentence as a function of weights, a classification of the sentence is determined as a function of an output of the attention function. The machine is activated in response to the input data or a knowledge graph is filled with information, i.e., expanded or built anew, in response to input data… see abstract.
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 XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143