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 .
Status of Claims
Claims 1-20 are pending of which claims 1, 11 and 20 are in independent form.
Claims 1-20 are rejected on the ground of nonstatutory double.
Claims 1-20 are rejected under 35 U.S.C. 101 (Abstract idea).
Claims 1-20 are rejected under 35 U.S.C. 103.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12430351 B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) ingesting data into remote systems, and more particularly to automating metadata processing for ingesting data.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 20 directed to a non-transitory computer readable medium, which is directed to one of the four statutory subject matters. Independent Claim 1 is directed to a system, including one or more processors and a memory, which is a machine. Independent claim 11 is directed to a method, which is a process. All other claims depend on claims 1, 13 and 26. As such, claims 1-25 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
" apply an unsupervised machine learning process to metadata extracted from a plurality of data sources to categorize the metadata into a plurality of clusters based on similar metadata attributes” as drafted recites a mental process involving analyzing information, identifying similarities among data, and categorizing information based on observed characteristics.
apply one or more review criteria to the plurality of clusters to generate a curated plurality of clusters” as drafted recites a mental process involving evaluating information and selecting or filtering categories according to predetermined criteria.
“train, with a supervised learning technique, a machine learning model with the curated plurality of clusters, the machine learning model being trained to predict a relevant cluster for input metadata based on attributes of the input metadata” as drafted recites a mental process involving learning from the previously evaluated information and making predictive judgement based upon observed characteristics of input information;
“use the trained machine learning model to facilitate data ingestion” as drafted recites using the results or foregoing analysis and prediction to determine how data should be organized or processed, which likewise reflects evaluating and decision making.
The claims are directed to: reviewing metadata, grouping similar information, applying selection criteria, learning from prior analysis, and determining appropriate category for subsequent information. Although the claims recites ML, the claim merely automates these mental processes using generic computing technology and does not recite any specific improvement in ML technology or computer functionality. Accordingly, these limitations fall within the mental process grouping of abstract ideas. Se MPEP 2016.04(a)(2).
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
"a processor" as drafted recites genetic computer components performing generic computer functions; as identified in MPEP 2106.05(f)(2).
“a memory coupled to the processor, the memory storing computer executable instructions” as drafted recites genetic computer memory performing its well-understood function of storing instructions and data, amounting to no more than generic computer implementation of the abstract idea; as identified in MPEP 2106.05(f).
“metadata extracted from a plurality of data sources” as drafted recites insignificant extra-solution activity in the form of gathering and receiving information for subsequent analysis. Collecting metadata merely supplies information to the abstract analytical process and does not integrate the judicial exception into a practical application; as identified in MPEP 2106.05(g).
“apply an unsupervised machine learning process to metadata extracted from a plurality of data sources to categorize the metadata into a plurality of clusters based on similar metadata attributes” as drafted recites the abstract idea itself, namely organizing and classifying information according to the characteristics. Although the limitation references an unsupervised ML process, it merely invokes ML at a high level of generality without reciting any specific ML architecture, clustering algorithms, training techniques, or technological improvement.
“apply one or more review criteria to the plurality of clusters to generate a curated plurality of clusters” as drafted recites the abstract idea itself by evaluating categorized information and selecting preferred categories according to review criteria, without reciting any particular technical mechanism for performing such evaluation.
“train, with a supervised learning technique, a machine learning model …” as drafted recites the abstract idea itself because it merely applies conventional supervised learning techniques to previously selected information in order to make future predictions. The claim does not recite any improvement to ML technology itself, not any unconventional training methodology or model architecture.
“use the trained machine learning model to facilitate data ingestion” as drafted recites the desired result of applying the foregoing analysis but does not recite any specific technological mechanism by which data ingestion is improved. The limitation simply uses the predication generated from the abstract idea to perform a generic data-processing operation.
The additional elements identified above fail to integrate the abstract idea into a practical application because the additional elements, alone or in combination, amount to no more than:
Generic computer components performing generic computer functions;
Insignificant extra-solution activity, including data gathering metadata for analysis; and
Implementation of abstract idea using generic ML techniques recited at a high level of generality.
The claims do not:
Improve the functioning of a computer or processor;
Improve ML technology itself;
Improve metadata processing technology;
Improve database technology or data ingestion mechanisms;
Providing a specific technological solution for ingesting data;
Recite a specific clustering algorithm or supervised learning architecture;
Recite any particular implementation that improves the computer technology.
Instead, the recited computer implementation merely serves as a tool to implement the abstract idea of analyzing metadata, organizing metadata intro cluster, evaluating the resulting clusters, training a predictive model, and using that prediction to facilitate data ingestion.
Therefore, judicial exception is not integrated into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a computer readable storage medium, computer, memory, and processor, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, pages 2-5 of the published instant specification describe generic off‐the‐shelf computer‐based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2, 8, 12 and 18 (Applying the Trained ML Model to New Metadata),
The claim recites:
Processing new metadata using the trained ML model to determine a predicted cluster, ingesting associated data based on the predicted cluster, and converting metadata attributes to be consistent with data in the predicted cluster (claim 2 and 12).
Automatically extract the new metadata input from a received data file from a data source to be ingested (claims 8 and 18).
This merely refines: applying existing predication model to additional information; classifying newly received metadata; normalizing metadata based on predicted classification; gathering metadata prior to analysis. No technical mechanism is provided for: how metadata extraction is technologically improved; how metadata conversion is performed; how the ingestion process itself is technologically enhanced. These fall under: Mental Process (evaluation, classification, comparison, standardization).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 3-4, and 13-14 (Database Organization and Relationship Analysis),
The claim recites:
Extract the curated plurality of clusters into a relational database format (claim 3 and 13).
Receive a data structure indicating that metadata of a data source present in the relational database format is being altered; and parse the relational database format to determine affected downstream applications (claim 4 and 14).
This merely refines: organizing information into a database format; analyzing relationships between stored information; identifying downstream impacts based on stored metadata. No technical mechanism is provided for: improving RDB technology; improve database parsing; improve downstream dependency analysis. These fall under: Mental Process (evaluation, organization and relationship analysis).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 5, and 15 (Review of Generated Clusters),
The claim recites:
generate an interface to receive input altering a composition of the plurality of clusters, the interface comprising one or more flagged entries for review.
This merely refines: presenting analyzed information for review; allowing use modification of categorized information. No technical mechanism is provided for: improving UI technology; improving computer functionality; improve user interaction. These fall under: Mental Process.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 6, and 16 (Model Retraining),
The claim recites:
receive data indicating errors associated with outputs of the trained machine learning model; retrain the trained machine learning model; and process new metadata with the re-trained machine learning model.
This merely refines: evaluating prediction results; updating the existing prediction model; repeating the same prediction process with additional information. No technical mechanism is provided for: improving ML technology; improving Model training; improve computational efficiency. These fall under: Mental Process (evaluation, learning, predication).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 7, and 17 (Particular Clustering Technique),
The claim recites:
the unsupervised machine learning process employs Kmodes.
This merely refines performing the same clustering analysis using a particular clustering technique. No technical mechanism is provided for: improving Kmodes algorithms; improving clustering technology; improve computer operation. These fall under: Mental Process (evaluation, learning, predication).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 9, and 19 (Particular Metadata Attribute),
The claim recites:
extracted metadata provided to the unsupervised machine learning process comprises at least one of a data source, an attribute identifier, an associated application, an expected data type, and a data value.
This merely refines: specifying the type of information being analyzed, identifying categories of metadata used during classification. No technical mechanism is provided for: improving metadata extraction; improving metadata processing; improve computer functionality. These fall under: Mental Process (evaluation, learning, predication).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claim 10 (Automated Validation),
The claim recites:
provide a validator that is automated, the validator relying on relationships captured by the plurality of curated clusters to validate different data sources in a same cluster of the plurality of curated clusters.
This merely refines: evaluating information using learned relationships; validating data based on previously determined classification. No technical mechanism is provided for: improving validation technology; improving database systems; improve computer functionality. These fall under: Mental Process (evaluation, comparison, decision making).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
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.
Claim(s) 1, 3-5, 7-11, 13-5, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit; Chintan et al. (US 11720600 B1) [Dixit] in view of Polleri; Alberto et al. (US 20210081848 A1) [Pollari] in view of Sherstinsky; Alex et al. (US 10410626 B1) [Sherstinsky].
Regarding claims 1, 11 and 20, Dixit discloses, a system for ingesting data based on processed metadata, the system comprising: a processor; and a memory coupled to the processor, the memory storing computer executable instructions that when executed by the processor cause the processor (see Fig. 1) to:
apply an unsupervised machine learning process to metadata extracted from a plurality of data sources to categorize the metadata into a plurality of clusters based on similar metadata attributes (The metadata of the data assets can include, for example, an entity name, an attribute name, definitions, and/or any other suitable information. With the metadata collected, natural language processing and/or other suitable methods can be used with the metadata to extract classification features therefrom, thereby producing a collection of classification features from each metadata silo, as shown in FIG. 11. Next, unsupervised learning and/or probabilistic modeling can be used to group related classification features into separate buckets, clusters, and/or the like, thereby producing a collection of related classification features. In this manner, the machine learning model can provide suggestions for classifications to be passed on to working groups and/or users that govern or otherwise provide input to enterprise taxonomy [col. 19, ll. 7-21]).
However Dixit does not explicitly facilitate apply one or more review criteria to the generated plurality of clusters to generate a curated plurality of clusters.
Polleri discloses, apply one or more review criteria to the generated plurality of clusters to generate a curated plurality of clusters (Unsupervised machine learning may be better able to detect misclassifications in these circumstances, relying upon deviation from unsupervised machine learning-generated clusters and/or deviation of unsupervised machine learning-generated features, rather than application of trained classification models ¶ [0074]. Also see ¶ [0080]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Polleri's system would have allowed Dixit to facilitate apply one or more review criteria to the generated plurality of clusters to generate a curated plurality of clusters. The motivation to combine is apparent in the Dixit reference, because there is a need to improve techniques for generating and managing a library of machine learning applications.
However neither one of Dixit, Polleri or Ma explicitly facilitates the clusters defining attributes or parameters of metadata for the plurality of data sources as being related; train, with a supervised learning technique, a machine learning model with the curated plurality of clusters, the machine learning model being trained to predict a relevant cluster for categories of input metadata.
Sherstinsky discloses, train, with a supervised learning technique, a machine learning model with the curated plurality of clusters, the machine learning model being trained to predict a relevant cluster for categories of input metadata (Human input used to improve the quality of data generated by the unsupervised machine learning system before that data is used to train the supervised machine learning system [Abstract]. cluster confirmation logic configured for an expert to confirm a cluster identified by the unsupervised machine learning system; supervised machine learning logic configured to be trained using the confirmed cluster and to classify new customer service inquiries as belonging to one of the clusters of the customer service requests; progressive training logic configured to train multiple instances of the supervised machine learning logic using subsets of the customer service inquiries and associated answers [col. 5, ll. 25-col. 6, ll. 57]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Sherstinsky's system would have allowed Dixit, and Polleri to facilitate the clusters defining attributes or parameters of metadata for the plurality of data sources as being related; train, with a supervised learning technique, a machine learning model with the curated plurality of clusters, the machine learning model being trained to predict a relevant cluster for categories of input metadata. The motivation to combine is apparent in the Dixit, and Polleri’s reference, because there is a need to improve information management and maintenance buy reducing cost.
Regarding claims 3 and 13, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, extract the curated plurality of clusters into a relational database format (Polleri: a machine learning based NLP engine may learn to understand and categorize the natural language conversations from the end users and to extract necessary information from the conversations to be able to take precise actions, such as performing a transaction or looking up data from a backend system of record. In certain embodiments, the NLU processing or portions thereof is performed by digital assistant 406 itself ¶ [0132]. Also see ¶ [0210], [0305]. The data model provides entities that will become tables in a Relational Database Management System (RDBMS), and the attributes will become columns with specific data types and constraints, and the relationships will be identifying and non-identifying foreign key constraints.…. An ontology consists of classes hierarchically arranged in a taxonomy of subclass-superclass, slots with descriptions defining value constraints, and values for these slots. A knowledge base is defined when the ontology is complete, and when individual instances of these elements (classes and slots) are defined and any restrictions added or refined ¶ [0332]).
Regarding claims 4 and 14, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, receive a data structure indicating that metadata of a data source present in the relational database format is being altered; and parse the relational database format to determine affected downstream applications (Dixit: updating the portion of the hierarchical classification structure of the leader subtree based on an update to the metadata [claims 1 and 12]).
Regarding claims 5 and 15, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, wherein applying one or more review criteria comprises the instructions causing the processor to: generate an interface to receive input altering a composition of the plurality of clusters, the interface comprising one or more flagged entries for review (Polleri: In some embodiments, classifying the input as associated with the class in the plurality of classes may include classifying, by a binary classification model associated with the root node, the input as belonging to classes associated with a first child node of the root node, where the first child node may be on a second layer of the tree structure. In some embodiments, classifying the input as belonging to the classes associated with the first child node of the root node may include: determining, by the binary classification model, a first value indicating a likelihood that the input belongs to the classes associated with the first child node of the root node ¶ [0223]-[0224]).
Regarding claims 8 and 18, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, automatically extract the new metadata input from a received data file from a data source to be ingested (Dixit: In some embodiments, a method includes extracting metadata associated with a set of assets from a set of electronic sources [col. 2, ll. 43-col. 3, ll. 47]. Also [Abstract])
Regarding claims 9 and 19, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, wherein the extracted metadata provided to the unsupervised machine learning process comprises at least one of a data source, an attribute identifier, an associated application, an expected data type, and a data value (Dixit: Further, the metadata 121 includes attributes and a value for each attribute [col. 5, ll. 60-61]. Also see [col. 6, ll. 5-11], [col. 6, ll. 24-30], [col. 7, ll. 18-24]).
Regarding claim 10, the combination of Dixit, Polleri, and Sherstinsky explicitly facilitates, provide a validator that is automated, the validator relying on relationships captured by the plurality of curated clusters to validate different data sources in a same cluster of the plurality of curated clusters (Polleri: Enrichment engine 660 may perform validation and enrichment on the collected events and other information and write them to database 670 ¶ [0202]. Also see ¶ [0286]).
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit in view of Polleri in view of Sherstinsky in view of in view of Ma; Jian et al. (US 20220035348 A1) [Ma].
Regarding claims 2 and 12, the combination of Dixit, Polleri, and Sherstinsky teaches all the limitation of claims 1 and 11.
However neither one of Dixit, Polleri or Sherstinsky explicitly facilitates in response to receiving a new metadata input process the new metadata input using the trained machine learning model to determine a predicted cluster for the new metadata input; and ingest data associated with the new metadata input based on the predicted cluster, wherein ingesting the data comprises converting at least one metadata attribute for at least a portion of the data associated with the new metadata input to be consistent with other data predicted as being from the predicted cluster.
Ma discloses, in response to receiving a new metadata input process the new metadata input using the trained machine learning model to determine a predicted cluster for the new metadata input; and ingest data associated with the new metadata input based on the predicted cluster, wherein ingesting the data comprises converting at least one metadata attribute for at least a portion of the data associated with the new metadata input to be consistent with other data predicted as being from the predicted cluster (1) load the alignment metadata datasheet available at step 1307 if they are not loaded already; 2) divide the alignment metadata datasheet into the training dataset and the validation dataset; 3) preprocess the training metadata, such as centering, scaling, categorical handling for alignment-guidance pseudo variables if the alignment-guidance pseudo variables enhancement is enabled, eliminating outliers, etc.; 4) build the seed model with the training dataset with the supervised machine learning algorithm selected ¶ [0195]. Also see ¶ [0217]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Ma's system would have allowed Dixit, Polleri, and Sherstinsky to facilitate in response to receiving a new metadata input process the new metadata input using the trained machine learning model to determine a predicted cluster for the new metadata input; and ingest data associated with the new metadata input based on the predicted cluster, wherein ingesting the data comprises converting at least one metadata attribute for at least a portion of the data associated with the new metadata input to be consistent with other data predicted as being from the predicted cluster. The motivation to combine is apparent in the Dixit, Polleri, and Sherstinsky’s reference, because developing and deploying advanced modeling, monitoring, and control systems in batch production processes is desirable and very beneficial to many manufacturers.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit in view of Polleri in view of Sherstinsky in view of CELLA; Charles Howard et al. (US 20210182996 A1) [Cella].
Regarding claims 6 and 16, the combination of Dixit, Polleri, and Sherstinsky teaches all the limitation of claims 1 and 11.
However neither one of Dixit, Polleri, or Sherstinsky explicitly facilitates receive data indicating errors associated with outputs of the trained supervised machine learning model; retrain the trained supervised machine learning model based on the received data indicating errors; and process new metadata with the re-trained supervised machine learning model.
Cella discloses, receive data indicating errors associated with outputs of the trained supervised machine learning model; retrain the trained supervised machine learning model based on the received data indicating errors; and process new metadata with the re-trained supervised machine learning model (the machine learning system 2002 may train/reinforce the models using the collected data to improve the accuracy of the models (e.g., minimize the error value of the model). The machine learning system may execute machine-learning algorithms on the collected data (e.g., training data, outcome data, and/or simulation data) to obtain the model ¶ [0635]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Cella's system would have allowed Dixit, Polleri, and Sherstinsky to facilitate receive data indicating errors associated with outputs of the trained supervised machine learning model; retrain the trained supervised machine learning model based on the received data indicating errors; and process new metadata with the re-trained supervised machine learning model. The motivation to combine is apparent in the Dixit, Polleri, and Sherstinsky reference, because there is a need for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations.
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit in view of Polleri in view of Sherstinsky in view of Kuduva; Kamalesh Kuppusamy et al. (US 20240028925 A1) [Kuduva].
Regarding claims 7 and 17, the combination of Dixit, Polleri, and Sherstinsky teaches all the limitation of claim 1 and 11.
However, neither one of Dixit, Polleri, or Sherstinsky explicitly facilitates wherein the unsupervised machine learning process employs Kmodes.
Kuduva discloses, wherein the unsupervised machine learning process employs Kmode (the first ML models 126 are trained to perform K-modes clustering on categorical features corresponding to event sequences ¶ [0026]. Also see ¶ [0035], [0054] and [0058]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Kuduva's system would have allowed Dixit, Polleri, and Sherstinsky to facilitate wherein the unsupervised machine learning process employs Kmodes. The motivation to combine is apparent in the Dixit, Polleri, and Sherstinsky reference, because there is a need to improve leveraging artificial intelligence and machine learning in combination with data mining to recommend sequences of actions to complete performance of structured processes.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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, Boris Gorney can be reached at (571)270-5626. 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.
7/25/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
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