DETAILED ACTION
1. This office action is in response to applicant’s communication filed on 05/18/2026 in response to PTO Office Action mailed on 02/17/2026. The Applicant’s remarks and amendments to the claims and/or the specification were considered with the results as follows.
2. In response to the last Office Action, no claims are amended, added or canceled. As a result, claims 1-20 are pending in this office action.
3. The IDS filed on 05/18/2026 has been acknowledged.
Claim Rejections - 35 USC § 112
4. Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11 and 19 recite the limitation “the validation comprises validating the data structure row by row against a predefined sample file structure and recording, in an internal database and logging module, transformation changes including the time of action and the user involved.
The specification describes validating the data structure row by row against a predefined sample file structure is in para. [0033]: “In some embodiments, system 100 can further include an Automated Data Verification and Auditing Module 108. This module is responsible for validating the data structure row by row against a predefined sample file structure. It also provides functionality to check cell values based on user-defined constraints, such as the allowance of empty cells or the precision of decimals”.
The specification describes the internal database and logging module is in para. [0041]: “The system 100 can include Internal Database and Logging Module 116, which keeps a comprehensive log of all transformation changes, including the time of action, the user involved, any relevant comments, etc. This module enhances the system's transparency and traceability, allowing users to audit the transformation processes effectively. Testing Module 114 can be configured to integrate with Internal Database and Logging Module 116 to log all test results for auditing and compliance purposes. A feedback loop between modules can ensure that only verified and accurate transformations are deployed to production”.
However, the specification does not reasonably convey possessions of the claimed combination of (1) validating the data structure row by row against a predefined sample file structure and (2) recording, in the Internal Database and Logging Module, transformation changes including the time of action and the user involved. Accordingly, the claims lack adequate written description support under 35 U.S.C. 112(s)
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, 3-8, 11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Neil (US 2021/0374143 A1) and in view of Wilson (US 2023/0146121 A1) and further in view of Olivieri (US 2007/0185935 A1).
Referring to claim 1, Neill discloses a data processing system for automating an Extract, Transform, Load (ETL) process (See para. [0018], a data processing system is simultaneously processing real-time streaming of multi-dimensional data for dynamic extract, transform, load [ETL] processing), comprising: a server, coupled to a processor (See para. [0041], the system includes at least one processor and a memory storing instructions), and configured to execute instructions that:
extract data from multiple sources (See para. [0017], para. [0018] and para. [0100], the ETL processing extracts data from data sources), the sources selected from one or more of cloud storage, external APIs, and direct file uploads (See para. [0017], the data sources are cloud-based sources, data feeds, database systems, user events or etc.), wherein the extraction is performed a stream mode processing unit (See para. [0082], para. [0090], para. [0100], the ETL process extracts data from data streams) configured to segment data in two or more manageable chunks (See para. [0008] and para. [0131] and Figure 4A and 4B, the system extracts patterns of information and processes the information into relevant ingested datasets or chunks, each incoming dataset fills a time window or reservoir to a fixed sample size);
validate the structural integrity and content accuracy of the extracted data using data integrity algorithms (See para. [0151]-para. [0153] and Figure 5C, the module 530 includes a data-in interface 532 for receiving data and a data-out interface for data model validation and error detection, the comparator is configured to validate data models including validate structures of the received data and classify the received content to an existing data model) , the validation being defined by, the data integrity algorithms based on at least one of: format consistency checks, anomaly detection and data corruption identification (See para. [0153], the module 530’s comparator 540 is configured to validate the data itself that is received to determine that the data matches a predefined template or it correctable to match what is known to be non-erroneous data, note in para. [0203] and Figure 15B, the output data model is validated to ensure the requirements imposed have been satisfied, including logical consistency, data checks, referential integrity and so forth);
transform the extracted data via a transformation processing unit (See para. [0154] and Figure 6, transforms the extracted data from data streams via a data mining module 108), the transformation comprising application of one or more pre-defined and custom transformation templates (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis) […];
format the transformed data for loading into destination systems (See para. [0202] and Figure 15A, the data processing system 100 transforms the one or more data source feeds and accompanying datasets 1502 into a resultant target data model 1506 and set of datasets as shown that can be processed further by the target system).
Neil does not explicitly disclose integrate user-defined transformation logic through an API, the integration allowing customization of data transformations.
Wilson discloses integrate user-defined transformation logic through an API (See para. [0119] and Figure 6, an application programming interface such as representational state transfer REST interface), the integration allowing customization of data transformations, wherein the customization of data transformations is based on the custom transformation templates (See para. [0125]-para. [0135] and Figure 7, data source 700 and UI component definition 702 [e.g. templates] serve as input to platform UI builder 704, which in turn produces metadata that can be used to customize UI component definition 702, note the UI component definition 702 defines the structure and static aspects of the appearance of a UI component, and serve as placeholders for certain aspects of its appearance that are dynamic).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to integrate user-defined transformation logic through an API, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
Wilson and Neil do not explicitly disclose the validation validating the data structure row by row against a predefined sample file structure and recording, in an internal database and logging module, transformation changes including the time of action and the user involved
Olivieri discloses the validation validating the data structure row by row against a predefined sample file structure and recording, in an internal database (See para. [0036] and para. [0045], the processor engine validates new business object to appropriate back-end business component of a back-end application 116, the back-end application validates business object, the processing engine include an object generator may use the configuration file for a spreadsheet type and create a business object for each reached of the spreadsheet based on the contents of the rows that constitute the record) and logging module, transformation changes including the time of action and the user involved (See para. [0005] and para. [0044], logging updated records in a back-end application with respect to user name).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the validation process of Neil to include validating the data structure row by row against a predefined sample file, as taught by Oilvieri. Skilled artisan would have been motivated to reduce the time necessary for users to perform validation when compared to manual entry (See Olivieri, para. [0005). In addition, all of the references (Olivieri, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data customization. This close relation between all of the references highly suggests an expectation of success.
As to claims 3 and 13, Neil discloses wherein the data integrity algorithms comprise error correction mechanisms, configured to address identified data inconsistencies during the extraction process (See para. [0203] and Figure 15B, the output data model is validated to ensure the requirements imposed have been satisfied, including logical consistency, data checks, referential integrity and so forth).
As to claim 4, Neil discloses performing the transformation processing unit utilizes a built-in programming language for defining custom transformation logic (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis) […]; and the stream mode processing unit to manage data chunk sizes and processing parameters (See para. [0082], para. [0090], para. [0100], the ETL process extracts data from data streams, See para. [0008] and para. [0131] and Figure 4A and 4B, the system extracts patterns of information and processes the information into relevant ingested datasets or chunks, each incoming dataset fills a time window or reservoir to a fixed sample size).
Neil does not explicitly disclose performing a no-code realization of the automating, the no-code realization comprising instructions wherein: the transformation processing unit utilizes a built-in programming language for defining custom transformation logic.
Wilson discloses performing a no- code realization of the automating, the no-code realization comprising instructions wherein: the transformation processing unit utilizes a built-in programming language for defining custom transformation logic, the custom transformation logic enabling user to define custom extensions in a low-code environment, allowing for the specification of rules for custom operations and allow users to manage data without programming expertise (See para. [0126]-para. [0135] and Figure 7, the system uses low-code and no-code techniques for defining bindings between data sources and UI components, as well as how data from the data sources are to be transformed for representation in the UI components. Particularly, structured metadata can be used to define such bindings and transformations, and this structured metadata can be created by way of simple arithmetic/logical/string operations or through graphical menus. This allows an individual who has minimal or no formal training in software development to customize UI components for graphical user interfaces).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to perform no-code realization, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson, Wasson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
As to claim 5, Neil discloses select and configure transformations from a set of pre-built templates (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis).
Neil does not explicitly disclose a no-code user interface (UI) configured to allow users to select and configure transformations without programming expertise.
Wilson discloses a no-code user interface (UI) configured to allow users to select and configure transformations from a set of pre-built templates without programming expertise (See para. [0164], allow the user the select and drag representations of UI component definitions to section 1002).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to perform no-code realization, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson, Wasson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
As to claim 6, Neil does not explicitly disclose wherein the API for integrating user-defined transformation logic is compatible with a range of external programming environments.
Wilson discloses the API for integrating user-defined transformation logic is compatible with a range of external programming environments (See para. [0121], the software API is integrated with Java, JavaScript, etc.).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to integrate user-defined transformation logic through an API, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
As to claims 7 and 17, Neil discloses wherein the server comprises a data loading module configured to load the transformed data into two or more different destination systems, the destination systems selected from databases and data warehouses (See para. [0016], dynamically transforming ingested data into a target data format in real-time for consumption by a network of clients or subscribing systems including database or data-lake platforms).
As to claims 8 and 18, Neil discloses wherein the server is further configured to execute instructions for monitoring data flow through an entirety of the ETL process, comprising tracking progress and performing real-time error correction (See para. [0077], the processing system performs predictive machine learning (ML) methods utilizing the ingested data and system as training data to improve said heuristic and ML methods. Generally, notifications and alerts are sent to external systems to monitor the data model and entity quality in real-time as the pipeline operates against all data. Examples of data quality analysis for data and data models, and subsequent error correction, are further described in relation to FIG. 16C. FIG. 16C represents a degenerative implementation of system can be for basic data and data model ingestion, in addition to data and data model quality analysis and correction).
Referring to claims 11 and 19, Neil discloses a computer-implemented method having instructions thereon that, when executed by a computing device, cause the computing device operations (See para. [0086] and Figure 1, the system includes a module 102 sends the incoming data directly to ETL processor for extracting a payload, the module includes one or more ports 102a for receiving the incoming data from data source(s), the module includes one or more processors and can include a local memory accessible by one or more processors), comprising:
identifying data sources for extraction, the data sources selected from one or more of cloud storage, external APIs, and direct file uploads (See para. [0018], a data processing system is simultaneously processing real-time streaming of multi-dimensional data for dynamic extract, transform, load [ETL] processing);
executing a data extraction process from the identified data sources using a server coupled to a processor (See para. [0018], para. [0041], the system includes at least one processor and a memory storing instructions);
performing data integrity checks on the extracted data using data integrity algorithms to ensure structural accuracy and content consistency (See para. [0151]-para. [0153] and Figure 5C, the module 530 includes a data-in interface 532 for receiving data and a data-out interface for data model validation and error detection, the comparator is configured to validate data models including validate structures of the received data and classify the received content to an existing data model);
configuring data transformations based on the validated data, comprising selecting from pre-built transformation templates and defining custom transformations (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis) […];
applying the configured data transformations to the extracted data (See para. [0154] and Figure 6, transforms the extracted data from data streams via a data mining module 108); validating the transformed data to ensure compliance with predefined criteria; formatting the validated, transformed data for loading into target systems (See para. [0202] and Figure 15A, the data processing system 100 transforms the one or more data source feeds and accompanying datasets 1502 into a resultant target data model 1506 and set of datasets as shown that can be processed further by the target system).
Neil does not explicitly disclose integrate user-defined transformation logic through an API, the integration allowing customization of data transformations.
Wilson discloses integrating custom transformation logic into the data transformation process via an API (See para. [0119] and Figure 6, an application programming interface such as representational state transfer REST interface, also see para. [0125]-para. [0135] and Figure 7, data source 700 and UI component definition 702 [e.g. templates] serve as input to platform UI builder 704, which in turn produces metadata that can be used to customize UI component definition 702, note the UI component definition 702 defines the structure and static aspects of the appearance of a UI component, and serve as placeholders for certain aspects of its appearance that are dynamic).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to integrate user-defined transformation logic through an API, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
Wilson and Neil do not explicitly disclose the validation validating the data structure row by row against a predefined sample file structure and recording, in an internal database and logging module, transformation changes including the time of action and the user involved
Olivieri discloses the validation validating the data structure row by row against a predefined sample file structure and recording, in an internal database (See para. [0036] and para. [0045], the processor engine validates new business object to appropriate back-end business component of a back-end application 116, the back-end application validates business object, the processing engine include an object generator may use the configuration file for a spreadsheet type and create a business object for each reached of the spreadsheet based on the contents of the rows that constitute the record) and logging module, transformation changes including the time of action and the user involved (See para. [0005] and para. [0044], logging updated records in a back-end application with respect to user name).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the validation process of Neil to include validating the data structure row by row against a predefined sample file, as taught by Oilvieri. Skilled artisan would have been motivated to reduce the time necessary for users to perform validation when compared to manual entry (See Olivieri, para. [0005). In addition, all of the references (Olivieri, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data customization. This close relation between all of the references highly suggests an expectation of success.
As to claim 14, Neil discloses implementing a built-in programming language for custom transformation logic (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis).
As to claim 15, Neil does not disclose receiving via a user interface input about a selection and/or configuration of one or more intended transformations from pre-built templates.
Wilson discloses receiving via a user interface input about a selection and/or configuration of one or more intended transformations from pre-built templates (See para. [0164], allow the user the select and drag representations of UI component definitions to section 1002).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to perform no-code realization, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson, Wasson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
As to claim 16, Neil does not explicitly disclose integrating custom transformation logic via an API comprises integrating custom transformation logic via the API from one or more external programming environments.
Wilson discloses integrating custom transformation logic via an API comprises integrating custom transformation logic via the API from one or more external programming environments (See para. [0121], the software API is integrated with Java, JavaScript, etc.).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to integrate user-defined transformation logic through an API, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data transformation customization. This close relation between all of the references highly suggests an expectation of success.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Neil (US 2021/0374143 A1) in view of Wilson (US 2023/0146121 A1) and Olivieri (US 2007/0185935 A1) and further in view of Matovinovic (US 2023/0146421 A1).
As to claim 9, Neil does not explicitly disclose a logging module configured to record one or more data change logs for storing changes to data, including timestamps, a nature of the change, and a user identification associated with manual changes, and wherein the logging module enables comprehensive auditability and/or traceability of each change.
Matovinovic discloses a logging module configured to record one or more data change logs for storing changes to data (See para. [0033], storing usage logs includes historical logs of system metadata changes, user device information, a historical item generation log of all items generated in the system), including timestamps (See para. [0033], storing a master timestamp record when data is instantiated, modified or accessed), a nature of the change, and a user identification associated with manual changes (See para. [0033], storing usage logs includes historical logs of system metadata changes, user device information, a historical item generation log of all items generated in the system), and wherein the logging module enables comprehensive auditability and/or traceability of each change. (See para. [0012], para. [0033], the creation, modification and usage [e.g., item generation] are all secured and tracked in a data structure that ensures enhances security and traceability).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to include a logging module, taught by Matovinovic. Skilled artisan would have been motivated to improve the efficiency regarding the auditing an item and enhance security and traceability over transitional solutions to generate, modify and track items (See Matovinovic, para. [0012]). In addition, all of the references (Matovinovic, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as monitoring data in an analytical system. This close relation between all of the references highly suggests an expectation of success.
As to claim 10, Matovinovic discloses a permission management module configured to adjust access to data change logs and to ensure traceability control (See para. [0063], the system has authorization and authentication processes configured to determine whether the user device has access rights or other permissions).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to include a permission management module, taught by Matovinovic. Skilled artisan would have been motivated to improve the efficiency regarding the auditing an item and enhance security and traceability over transitional solutions to generate, modify and track items (See Matovinovic, para. [0012]). In addition, all of the references (Olivieri, Matovinovic, Wasson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as monitoring data in an analytical system. This close relation between all of the references highly suggests an expectation of success.
Claims 2, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Neil (US 2021/0374143 A1) and in view of Wilson (US 2023/0146121 A1) and Olivieri (US 2007/0185935 A1) and further in view of O’Hare (US 2016/0150047 A1).
As to claim 2, Neil does not explicitly disclose dynamically adjusts the size of data chunks based on the size of the data file and system capacity.
O’Hare discloses wherein the stream mode processing unit dynamically adjusts the size of data chunks based on the size of the data file and system capacity (See para. [0165], dynamically adjusting the size of a bock based on an average file size and system performance feedback).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to adjust the size of data chunks based on the size of the data file and system capacity, taught by O’Hare. Skilled artisan would have been motivated to adjust the size of data chunks dynamically to improve system performance (See O’Hare, para. [0165]). In addition, both of the references (Olivieri, O’Hare, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as monitoring a cloud-based storage. This close relation between all of the references highly suggests an expectation of success.
As to claim 12, Neil does not explicitly disclose segmenting data in manageable chunks adjusted dynamically based on file size and/or system capacity.
O’Hare discloses wherein the data extraction process comprises stream mode processing, and wherein the stream mode processing comprises segmenting data in manageable chunks adjusted dynamically based on file size and/or system capacity (See para. [0165], dynamically adjusting the size of a bock based on an average file size and system performance feedback).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to adjust the size of data chunks based on the size of the data file and system capacity, taught by O’Hare. Skilled artisan would have been motivated to adjust the size of data chunks dynamically to improve system performance (See O’Hare, para. [0165]). In addition, all of the references (Olivieri, O’Hare, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as monitoring a cloud-based storage. This close relation between all of the references highly suggests an expectation of success.
As to claim 20, Neil discloses performing the data extraction process comprises performing stream mode processing, wherein the configuration data transformation comprises built in programming language for defining custom transformation logic […] (See para. [0142], para. [0148] and para. [0199], the transformation involved with data-model template matching analysis) […]; and the stream mode processing unit to manage data chunk sizes and processing parameters (See para. [0082], para. [0090], para. [0100], the ETL process extracts data from data streams, See para. [0008] and para. [0131] and Figure 4A and 4B, the system extracts patterns of information and processes the information into relevant ingested datasets or chunks, each incoming dataset fills a time window or reservoir to a fixed sample size).
Neil does not explicitly disclose enabling user to define custom extensions in a low-code environment, allowing for the specification of rules for custom operations and segment data in manageable chunks adjusted dynamically based on file size and/or system capacity.
Wilson discloses enabling user to define custom extensions in a low-code environment, allowing for the specification of rules for custom operations (See para. [0126]-para. [0135] and Figure 7, the system uses low-code and no-code techniques for defining bindings between data sources and UI components, as well as how data from the data sources are to be transformed for representation in the UI components. Particularly, structured metadata can be used to define such bindings and transformations, and this structured metadata can be created by way of simple arithmetic/logical/string operations or through graphical menus. This allows an individual who has minimal or no formal training in software development to customize UI components for graphical user interfaces).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to perform no-code realization, taught by Wilson. Skilled artisan would have been motivated to allow an individual who has minimal or no formal training in software development to customize UI components for GUI (See Wilson, para. [0002]). In addition, all of the references (Olivieri, O’Hare, Wilson and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data customization. This close relation between all of the references highly suggests an expectation of success.
Neil does not explicitly disclose dynamically adjusts the size of data chunks based on the size of the data file and system capacity.
O’Hare discloses wherein the stream mode processing unit dynamically adjusts the size of data chunks based on the size of the data file and system capacity (See para. [0165], dynamically adjusting the size of a bock based on an average file size and system performance feedback).
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to modify the transformation processing unit of Neil to adjust the size of data chunks based on the size of the data file and system capacity, taught by O’Hare. Skilled artisan would have been motivated to adjust the size of data chunks dynamically to improve system performance (See O’Hare, para. [0165]). In addition, all of the references (Olivieri, Wilson, O’Hare and Neil) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as monitoring a cloud-based storage. This close relation between all of the references highly suggests an expectation of success.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUK TING CHOI whose telephone number is (571)270-1637. The examiner can normally be reached Monday-Friday 9am-6pm.
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/YUK TING CHOI/Primary Examiner, Art Unit 2164
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