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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 26 July 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 an abstract idea without significantly more.
Claim 1 subject matter eligibility analysis:
Step 1: claim 1 recites a method thus seems to be directed to a process which is one of the four statutory categories of invention.
Step 2A Prong 1: claim 1 recites “receiving a data processing request…,”identifying a machine learning model for the request….”These operations are processes that under the broadest reasonable interpretation, cover performance of the limitations by a human mind of with the aid of pen and paper. That is other than reciting a “automated implementation of a machine learning model”, nothing in the claim element precludes the operations from practically being performed by a human mind with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind, then it falls within the “Mental Processes’ grouping of abstract idea (concept performed in the human mind including an observation, evaluation, judgment and opinion). The mere nominal recitation of a generic machine learning model does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Step 2A Prong 2: the judicial exception is not integrated into a practical application. Claim 1 recites the additional element “obtaining a structure for a feature vector for the data processing request” , the obtaining step amounts to mere data gathering considered insignificant extra solution activity because it does not impose any meaningful limits on practicing the abstract idea, (see MPEP 2106.05(g)). The recitation of obtaining a structure for a feature vector for the data processing request does not integrate the mental process into a practical application, does not improve any technology or technical field, does not apply the judicial exception with or by use of a particular machine, does not add specific limitation other than what is well-understood, routine, conventional activity in the field, does not add unconventional steps that confine the claim to a particular useful application, does not include other meaningful limitations beyond linking the use of the judicial exception to a particular technological environment.
Step 2B: claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements “generating or configuring a micro-application actor…, structuring at least a portion of the data…inputting the feature vector… performing at least one processing action….” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (See MPEP 2106.05(d)(II) (iv).
Thus claim 1 is rejected under 35 USC 101 as being an abstract idea without significantly more.
Claim 11 subject matter eligibility analysis:
Step 1: claim 11 recites a system comprising memory and processor thus seems to be directed to a machine, one of the four statutory categories of invention.
Step 2A Prong 1: claim 11 recites “receiving a data processing request…,”determining a machine learning model…for the request….”These operations are processes that under the broadest reasonable interpretation, cover performance of the limitations by a human mind of with the aid of pen and paper. That is other than reciting “at least one memory storing instructions” and “at least one processor…”, nothing in the claim element precludes the operations from practically being performed by a human mind with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind, then it falls within the “Mental Processes’ grouping of abstract idea (concept performed in the human mind including an observation, evaluation, judgment and opinion). The mere nominal recitation of a generic machine learning model does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Step 2A Prong 2: the judicial exception is not integrated into a practical application. Claim 11 recites the additional element “hosting at least one micro application actor wherein…” , Note the wherein clause merely describes the micro application actor as including data defining workflow rules and the actor is operable to execute the workflow rules considered generic components performing generic processor computations. The micro application and hosting operation are recited at a high-level of generality (i.e.,as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not improve any technology or technical field, does not apply the judicial exception with or by use of a particular machine, do not add specific limitation other than what is well-understood, routine, conventional activity in the field, do not add unconventional steps that confine the claim to a particular useful application, do not include other meaningful limitations beyond linking the use of the judicial exception to a particular technological environment, do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements “configuring the workflow rules of the at least one micro-application actor…, structuring at least a portion of the data…inputting the feature vector… performing at least one processing action….” are generic computer functions such that it amounts no more than mere instructions to apply the exception using a generic computer component, recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (See MPEP 2106.05(d)(II) (iv).
Thus claim 11 is rejected under 35 USC 101 as being an abstract idea without significantly more.
Claim 20 subject matter eligibility analysis:
Step 1: claim 20 recites a method thus seems to be directed to a process which is one of the four statutory categories of invention.
Step 2A Prong 1: claim 20 recites “obtaining a structure for a feature vector…,”identifying a machine learning model for the instruction ….”These operations are processes that under the broadest reasonable interpretation, cover performance of the limitations by a human mind of with the aid of pen and paper. That is other than reciting a “computer-implemented”, “providing a platform” “machine learning model”, nothing in the claim element precludes the operations from practically being performed by a human mind with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind, then it falls within the “Mental Processes’ grouping of abstract idea (concept performed in the human mind including an observation, evaluation, judgment and opinion). The mere nominal recitation of a generic machine learning model does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Step 2A Prong 2: the judicial exception is not integrated into a practical application. Claim 20 recites the additional element “operating at least one micro-application actor hosted by the platform…executing the workflow rules by obtaining a structure for a feature vector for the instruction” , the micro-application actor hosted by the platform executing workflow rules and obtaining step amount to mere data gathering considered insignificant extra solution activity because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(g)). The execution of the workflow rules by the micro-application actor is mere generic computer operations performed by generic computer components. The recitation of obtaining a structure for a feature vector for the instruction does not integrate the mental process into a practical application, does not improve any technology or technical field, does not apply the judicial exception with or by use of a particular machine, does not add specific limitation other than what is well-understood, routine, conventional activity in the field, does not add unconventional steps that confine the claim to a particular useful application, does not include other meaningful limitations beyond linking the use of the judicial exception to a particular technological environment.
Step 2B: claim 20 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements “generating or configuring a further micro-application actor to have workflow rules that include structuring at least a portion of the data…inputting the feature vector… performing at least one processing action…” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (See MPEP 2106.05(d)(II) (iv).
Thus claim 20 is rejected under 35 USC 101 as being an abstract idea without significantly more.
Claims 2, 12 merely require generating a plurality of feature vectors and training the model using the plurality of feature vectors, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 3, 4 merely further describe the identifying of the machine learning model, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 5, 15 merely recite receiving a prediction target for the request, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 6, 16 merely add determining the structure from the data source, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 7, 8, merely further describe the identifying of the at least one data source, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 9, 18 merely recite the structure is obtained via a graphical user interface, considered insignificant extra solution activities (MPEP 2106.05(g).
Claim 10 merely further describes how the request is received, considered insignificant extra solution activities (MPEP 2106.05(g).
Claims 13, 14 merely further describe the determining of the machine learning model, considered insignificant extra solution activities (MPEP 2106.05(g).
Claim 17 merely further describe the identifying of the at least one data source, considered insignificant extra solution activities (MPEP 2106.05(g).
Claim 19 merely further describes the received request, considered insignificant extra solution activities (MPEP 2106.05(g).
As discussed above, although the dependent claims are more detailed than their parent claims, none seem to amount to significantly more than the judicial exception. No claim is patentable.
Claim Rejections - 35 USC § 112
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 1-20 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.
Claim 1 line 10 “a portion of the data” is ambiguous. Note the claim merely mentions “data” in “a data processing request”, “at least one data source”, therefore, it is not clear what applicant meant by “a portion of the data”.
Claim 1 lines 5 and 10 both recite “a feature vector”. It is not clear whether they are related.
Claim 1 line 3 up from last line “inputting the feature vector” is unclear. How is “the feature vector” related to “a structure for a feature vector” recited at line 5 and “a feature vector” recited at line 10?
Claim 2 line 2 “the identified structure” lacks antecedent basis.
Claim 3 line 3 “the structure” lacks antecedent basis.
Claim 4 last line “the structure” lacks antecedent basis.
Claim 5 “a prediction target for the request” is unclear. Is “the request” referring back to the “data processing request” recited in parent claim 1?. According to parent claim 1, the data processing request already includes an identification of at least one data source. Therefore it is not clear what “a prediction target” meant.
Claim 6 line 2 “the data” lacks antecedent basis.
Claim 7 “the identification of the at least …” lacks antecedent basis.
Claim 8 “the identification of the at least…” lacks antecedent basis.
Claim 9 does not cure the deficiencies of its parent claim.
Claim 10 “the request” is unclear. Is it referring back to the “data processing request” recited in parent claim 1?.
Claim 11 line 6 up from last line “a portion of the data” is unclear. how is “‘the data” related to the first data at lines 10-12?
Claim 11 line 4 up from last line “inputting the feature vector” is unclear. How is “the feature vector” related to “a structure for a feature vector” recited at lines 9-10 up from last line?
Claim 12 line 2 “the identified structure” lacks antecedent basis.
Claim 13 does not cure the deficiencies of parent claim 11.
Claim 14 “the structure” lacks antecedent basis.
Claim 15 “a prediction target for the request” is unclear. Is “the request” referring back to the “data processing request” recited in parent claim 11?. According to parent claim 11, the data processing request already includes an identification of at least one data source. Therefore it is not clear what “a prediction target” meant.
Claim 16 line 2 “the data” lacks antecedent basis.
Claim 17 “the identification of the at least …” lacks antecedent basis.
Claim 18 does not cure the deficiencies of parent claim 11.
Claim 19 line 1, “the request” is unclear. Is it referring back to the data processing request recited in parent claim 11?
Claim 20 preamble recites a method of providing a platform for developing context for data. However the body of the claim seems to merely include operating a micro-application actor hosted by the platform to execute workflow rules to generate or configure a further micro-application actor to have workflow rules.
Are the workflow rules at line 4 same as the workflow rules at line 12?
What define the workflow rules at lines 4 and 12?
Note lines 3-6 recite “at least one micro-application actor including data defining workflow rules that includes an instruction to obtain one or more predicted future state of data from at least one data source”.
Lines 11-12 recite “generating or configuring a further micro-application actor to have workflow rules that include…”
What is the subject of the verb “include” in each limitation related to the workflow rules?
How is “a further micro-application actor to have workflow rules” at lines 11-12 related to “at least one micro-application actor including data defining workflow rules” at lines 3-4?
The specification merely repeats the claims language without further explanations.
Art rejection is not being applied because the limitations cannot be ascertained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al (US 20200004891 A1) teach presenting multiple user interface (UI) elements associated with operators including a machine learning (ML) operator and a visualization operator, and determining (1002) a workflow that describes an input data source and an execution order for the ML operator and the visualization operator. The workflow is determined based on a selection of the data source through the UI, a selection of the ML operator and the visualization operator, and an indication of the execution order for the ML operator and the visualization operator through the UI. A visual depiction of the workflow is presented (1004) in the UI, and the workflow is executed (1006) in the execution order against data included in the data source.
Minkin et al (US 20180165604 A1) teach systems and methods for automating data science machine learning using analytical workflows are disclosed that provide for user interaction and iterative analysis including automated suggestions based on at least one analysis of a dataset. Application of the principles described can be considered and variously applied in the fields of scientific discovery, forecasting, and modeling highly complex functions, for instance in predictive analysis. In some embodiments, they can be broken down or separated by methodology including symbolic reasoning (rules/production systems), reinforcement learning (RL), recommenders, and others. Techniques such as rule conflict resolution and the merging of knowledge-based and data-driven methodologies can be performed in novel ways while reactive distributed agents and messaging to achieve workflow inferencing can be implemented.
Doddi et al (US 10262271 B1) teach systems and methods for implementing and using a data modeling and machine learning lifecycle management platform that facilitates collaboration among data engineering, development and operations teams and provides capabilities to experiment using different models in a production environment to accelerate the innovation cycle. Stored computer instructions and processors instantiate various modules of the platform. The modules include a user interface, a collector module for accessing various data sources, a workflow module for processing data received from the data sources, a training module for executing stored computer instructions to train one or more data analytics models using the processed data, a predictor module for producing predictive datasets based on the data analytics models, and a challenger module for executing multi-sample hypothesis testing of the data analytics models.
Driscoll et al (US 20190019106 A1) teach a system includes a repository storing trained machine learning models and metadata corresponding to the trained machine learning models. The system provides an interface for performing operations on the trained machine learning models and corresponding metadata stored in the repository, and evaluates trained machine learning models that are linked to the data sources and stored in the repository to obtain resulting predictions for a payload of records of data contained in the data sources. A method includes configuring one or more data sources, ingesting training data from the configured data sources, identifying a scenario for prediction, training one or more machine learning models on the ingested training data for the identified scenario, reviewing performance of the trained machine learning models and storing the trained machine learning models and corresponding metadata in A model repository, and dynamically creating a user interface for interacting with the stored trained machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UYEN T LE whose telephone number is (571)272-4021. The examiner can normally be reached M-F 9-5.
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, Ajay M Bhatia can be reached at 5712723906. 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.
/UYEN T LE/Primary Examiner, Art Unit 2156 13 December 2025