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 .
Claims 1-20 of this US application are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/11/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because Figures 5-7 are poor quality and blurred. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
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.
Regarding claim 1, 11 and 17:
Step 1:
Claim 1 recites “A computer-implemented method”. The claim recites a series of steps and therefore is a process.
Claim 11 recites “A system”. The claim recites a computing system comprising one or more processors and a data storage system and therefore is a machine.
Claim 17 recites “A non-transitory computer-readable storage medium” including instructions and therefore is a manufacture.
Step 2A Prong One:
Claims 1, 11 and 17 recite the limitations “constructing” and “identifying” which specifically recite “constructing, by the computer system, a configuration framework for ingesting, conforming, and curation of data processing of the received data;” and “identifying, by the computer system, one or more filters associated with the configuration framework for conforming the received data;” These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting one or more “processors”, a “data storage system” and a “non-transitory computer-readable storage medium”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “constructing” and “identifying” in the context of this claim encompasses a user mentally, and with the aid of pen and paper generating a framework or a structure for processing of received data and determining that filters associated with the framework or a structure for conforming the received data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claims 1, 11 and 17 recite the additional elements “receiving, by a computer system, data from one or more data sources;” “transforming, by the computer system, a format of the received data based at least in part on the one or more filters and using a machine learning model, wherein the machine learning model is trained to select or apply one or more data transformation rules to conform the data to the configuration framework;” and “storing, by the computer system, the transformed data in a data lake configured for centralized processing.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims 1, 11 and 17 recite the limitations “storing, by the computer system, the transformed data in a data lake configured for centralized processing.” The limitation amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). As discussed above, the additional elements of using one or more “processors”, a “data storage system” and a “non-transitory computer-readable storage medium” to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim 2 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim also recites the additional element “the one or more filters comprise user-defined filter conditions for selecting subsets of the received data” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 3 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim also recites the additional element “the machine learning model comprises a neural network” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 4 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim also recites the additional element “the machine learning model comprises a long short-term memory (LSTM) network, a bidirectional encoder representations from transformers (BERT) model, or a natural language processing (NLP) model.” The limitations amount to requiring the use of software to tailor information and provide it to the user on a generic computer (See MPEP 2106.05(f)). The claim is not patent eligible.
Claim 5 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim also recites the additional element “transforming the format of the received data comprises applying multiple data transformation rules selected by the machine learning model” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 6 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim also recites the additional element “providing, by the computer system, confirmation of receipt and correct format of the data based on the configuration framework.” The limitation amounts to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 7 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim also recites the additional element “generating, by the computer system, extended metadata for the transformed data based on the configuration framework” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 8 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim also recites the additional element “the configuration framework is configured to process multiple data feeds without changes to underlying code” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 9 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim also recites the additional element “the configuration framework is configured to provide real-time data processing and batch data processing” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 10 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim also recites the additional element “the machine learning model is trained using historical data transformation outcomes.” The limitation amounts to requiring the use of software to tailor information and provide it to the user on a generic computer (See MPEP 2106.05(f)). The claim is not patent eligible.
Claim 12 is rejected under the same rationale as claim 2.
Claim 13 is rejected under the same rationale as claim 4.
Claim 14 is rejected under the same rationale as claim 5.
Claim 15 is rejected under the same rationale as claim 8.
Claim 16 is rejected under the same rationale as claim 9.
Claim 18 is rejected under the same rationale as claim 2.
Claim 19 is rejected under the same rationale as claim 4.
Claim 20 is rejected under the same rationale as claim 5.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 6-9, 11-12 and 15-18 are rejected under 35 U.S.C. 102(a)(1) as anticipated by Almaraz et al. (US 2021/0073240, hereinafter “Almaraz”) or, in the alternative, under 35 U.S.C. 103 as obvious over Sensharma (US 10,078,683).
Regarding claim 1, Almaraz teaches A computer-implemented method comprising (Almaraz, [0098]: The operations of method 1100 may be implemented by an application server or its components as described herein.):
receiving, by a computer system, data from one or more data sources (Almaraz, [0099]: At 1105, the application server may receive, from a first database, a data queue indicating a set of archiving jobs.);
constructing, by the computer system, a configuration framework for ingesting, conforming, and curation of data processing of the received data (Almaraz, [0100]: At 1110, the application server may identify a set of data records in the first database based on a first batch of the set of archiving jobs, where the set of data records is stored in a first format supported by the first database and a stored data record of the set of data records includes a set of information.);
identifying, by the computer system, one or more filters associated with the configuration framework for conforming the received data (Almaraz, [0101]: At 1115, the application server may filter, at a first abstraction layer and based on identifying the set of data records in the first database, the stored data record of the set of data records to obtain a filtered data record, where the filtered data record includes a subset of the set of information for the stored data record.);
transforming, by the computer system, a format of the received data based at least in part on the one or more filters and using a machine learning model, wherein the machine learning model is trained to select or apply one or more data transformation rules to conform the data to the configuration framework (Almaraz, [0102]: At 1120, the application server may receive, via the first abstraction layer, the filtered data record of the set of data records based on the first batch of the set of archiving jobs. [0103]: At 1125, the application server may transform the received data record from the first format to a second format different from the first format and supported by a second database.); and
storing, by the computer system, the transformed data in a data lake configured for centralized processing (Almaraz, [0104]: At 1130, the application server may send, to the second database via a second abstraction layer, the transformed data record. [0113]: At 1240, the application server may receive, from the second database and in response to sending the transformed data record to the second database, a confirmation message indicating successful storage of the transformed data record at the second database.).
Assuming, arguendo, that Almaraz does not explicitly teach constructing, by the computer system, a configuration framework for ingesting, conforming, and curation of data processing of the received data.
However, Sensharma teaches constructing, by the computer system, a configuration framework for ingesting, conforming, and curation of data processing of the received data (Sensharma, column 8 lines 31-34: In step 604 it utilizes a machine learning algorithm to create a regulatory framework applicable to the relevant data owned by assets linked to CIS 10 via Data Stream Linkages 300.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz with the teaching about the regulatory framework of Sensharma because it would provide structured rules and standards that a network uses to ensure fair access, security compliance, and stable operations, creating a competitive advantage by building market trust and interoperability.
Regarding claim 2, Almaraz in view of Sensharma teaches wherein the one or more filters comprise user-defined filter conditions for selecting subsets of the received data (Almaraz, abstract: The device may receive a data record of the set of data records from the first database, where the data record is filtered by an abstraction layer (e.g., removing some information from the data record such that the filtered data record corresponds to a user's view of the data record). [0090]: The filtering component 920 may retrieve, from the first database and at the first abstraction layer, the first set of fields visible to the tenant for the filtered data record.).
Regarding claim 6, Almaraz in view of Sensharma teaches providing, by the computer system, confirmation of receipt and correct format of the data based on the configuration framework (Almaraz, [0113]: At 1240, the application server may receive, from the second database and in response to sending the transformed data record to the second database, a confirmation message indicating successful storage of the transformed data record at the second database.).
Regarding claim 7, Almaraz in view of Sensharma teaches generating, by the computer system, extended metadata for the transformed data based on the configuration framework (Almaraz, [0061]: discussing about some metadata associated with the data records).
Regarding claim 8, Almaraz in view of Sensharma teaches wherein the configuration framework is configured to process multiple data feeds without changes to underlying code (Almaraz, [0035]: The application server 205 may perform the archiving process in a multi-tenant environment while maintaining communication between two separate and ideologically different databases that do not support equal functionalities such as transactions, rollbacks, constant availability, or a combination thereof. Additionally or alternatively, in some cases, the application server 205 may not be able to establish uninterrupted connections with both the primary database 210-a and the secondary database 210-b concurrently. Thus, the present disclosure provides for moving data between databases of different types in a multi-tenant environment, without any adverse effect on a tenant's day-to-day operations. [0037]: An automated process (e.g., a process based on resource availability, a scheduled process, a periodic or aperiodic re-try process, etc.) may identify one or more data records 220 to write to the secondary database 210-b.).
Regarding claim 9, Almaraz in view of Sensharma teaches wherein the configuration framework is configured to provide real-time data processing and batch data processing (Sensharma, column 5 lines 31-46: The Real-Time Analysis Engine 120 may provide information to a user through the World View Interface 200 regarding the real-time performance of linked assets. For example, a user may input a query regarding the real-time functionality of a certain line of business that utilizes one or more assets connected to the CIS 10. The Real-Time Analysis Engine 120 can utilize machine learning algorithms, pattern detection algorithms, image recognition algorithms, and other algorithms to provide meaningful information regarding a real-time infrastructure event. It may also call on the Historical Analysis Engine 110 to perform historical analysis regarding data related to a specific real-time event. In a preferred embodiment, the Real-Time Analysis Engine 120 can continuously provide up-to-date reports to the user via the World View Interface 200 for a given real-time event or events.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz with the teaching about the regulatory framework of Sensharma because it would provide structured rules and standards that a network uses to ensure fair access, security compliance, and stable operations, creating a competitive advantage by building market trust and interoperability.
Claim 11 is rejected under the same rationale as claim 1. Almaraz also teaches A system comprising: one or more processors; and a data storage system in communication with the one or more processors, the data storage system comprising instructions thereon that, when executed by the one or more processors, cause the one or more processors to… (Almaraz, [0097]: The processor 1030 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a central processing unit (CPU), a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 1030 may be configured to operate a memory array using a memory controller. In other cases, a memory controller may be integrated into the processor 1030. The processor 1030 may be configured to execute computer-readable instructions stored in a memory 1025 to perform various functions (e.g., functions or tasks supporting offloading data to a cold storage database).).
Claim 12 is rejected under the same rationale as claim 2.
Claim 15 is rejected under the same rationale as claim 8.
Claim 16 is rejected under the same rationale as claim 9.
Claim 17 is rejected under the same rationale as claim 1. Almaraz also teaches A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to… (Almaraz, [0097]: In other cases, a memory controller may be integrated into the processor 1030. The processor 1030 may be configured to execute computer-readable instructions stored in a memory 1025 to perform various functions (e.g., functions or tasks supporting offloading data to a cold storage database).).
Claim 18 is rejected under the same rationale as claim 2.
Claims 3-5, 10, 13-14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Almaraz in view of Sensharma and further in view of Walters et al. (US 20220269884, hereinafter “Walters”).
Regarding claim 3, Almaraz in view of Sensharma teaches the method of claim 1 as discussed above. Almaraz in view of Sensharma does not explicitly teach wherein the machine learning model comprises a neural network.
Walters teaches wherein the machine learning model comprises a neural network (Walters, [0021]: In some implementations, the machine learning model may include a neural network (e.g., a long short-term memory (LSTM) network, a recurrent neural network (RNN), a convolutional neural network (CNN)), an encoder, and/or a decoder. [0047]: As shown by reference number 220, the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, or the like.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz and Sensharma with the teaching about the machine learning model of Walters because it would enable computers understand, interpret, and generate human language so people can interact with technology using normal speech or text instead of complex computer code.
Regarding claim 4, Almaraz in view of Sensharma teaches the method of claim 1 as discussed above. Almaraz in view of Sensharma does not explicitly teach wherein the machine learning model comprises a long short-term memory (LSTM) network, a bidirectional encoder representations from transformers (BERT) model, or a natural language processing (NLP) model.
Walters teaches wherein the machine learning model comprises a long short-term memory (LSTM) network, a bidirectional encoder representations from transformers (BERT) model, or a natural language processing (NLP) model (Walters, [0021]: In some implementations, the machine learning model may include a neural network (e.g., a long short-term memory (LSTM) network, a recurrent neural network (RNN), a convolutional neural network (CNN)), an encoder, and/or a decoder. [0022]: In some implementations, as described elsewhere herein, the lineage analysis model may utilize a natural language processing model to identify differences between corresponding sections of a source document. [0027]: In some implementations, the similarity analysis model may be configured to iteratively compare the first section with sections of a second version until the similarity analysis model identifies the corresponding section. The similarity analysis model may be a cosine similarity model and/or a bidirectional encoder representations from transformers (BERT) model.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz and Sensharma with the teaching about the machine learning model of Walters because it would enable computers understand, interpret, and generate human language so people can interact with technology using normal speech or text instead of complex computer code.
Regarding claim 5, Almaraz in view of Sensharma teaches the method of claim 1 as discussed above. Almaraz in view of Sensharma does not explicitly teach wherein transforming the format of the received data comprises applying multiple data transformation rules selected by the machine learning model.
Walters teaches wherein transforming the format of the received data comprises applying multiple data transformation rules selected by the machine learning model (Walters, [0021]: In some implementations, the machine learning model may include a neural network (e.g., a long short-term memory (LSTM) network, a recurrent neural network (RNN), a convolutional neural network (CNN)), an encoder, and/or a decoder. For example, to train the machine learning model, the document management system may generate an LSTM RNN based on the historical documents (or a subset of the historical documents) and/or corresponding lineage data of the set of the historical documents. Additionally, or alternatively, the document management system may generate a CNN encoder based on the historical documents and/or the lineage data and train an LSTM decoder for the CNN encoder. In some implementations, the document management system may generate and/or utilize a transformer encoder based on the historical documents and the corresponding lineage data and train the LSTM decoder according to the transformer encoder.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz and Sensharma with the teaching about the machine learning model of Walters because it would enable computers understand, interpret, and generate human language so people can interact with technology using normal speech or text instead of complex computer code.
Regarding claim 10, Almaraz in view of Sensharma teaches the method of claim 1 as discussed above. Almaraz in view of Sensharma does not explicitly teach wherein the machine learning model is trained using historical data transformation outcomes.
Walters teaches wherein the machine learning model is trained using historical data transformation outcomes (Walters, [0003]: discussing about a method for managing document lineage includes obtaining document lineage training data associated with a plurality of historical documents and corresponding lineage data of independent historical documents of the plurality of historical documents; [0022]: Accordingly, the machine learning model may be trained according to the lineage data and differences between respective sets of versions of the subsets of historical documents. In some implementations, as described elsewhere herein, the lineage analysis model may utilize a natural language processing model to identify differences between corresponding sections of a source document.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method transferring data of Almaraz and Sensharma with the teaching about the machine learning model of Walters because it would enable computers understand, interpret, and generate human language so people can interact with technology using normal speech or text instead of complex computer code.
Claim 13 is rejected under the same rationale as claim 4.
Claim 14 is rejected under the same rationale as claim 5.
Claim 19 is rejected under the same rationale as claim 4.
Claim 20 is rejected under the same rationale as claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Anderson et al. (US 2024/0220285) discloses that a neural network (e.g., a modified BERT encoder or model) is trained on multiple labeled overfly auditing invoices in order to make acceptable loss training predictions by learning the semantic meaning of different invoice fields, which will help later at deployment time to make correct inference predictions ([0061]).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H NGUYEN whose telephone number is (571)270-1766. The examiner can normally be reached Monday-Friday, 8:30am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHONG H NGUYEN/ Primary Examiner, Art Unit 2156
August 6, 2026
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