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 .
Detailed Action
The instant application having Application No. 17/320,575 has claims 1-20 pending filed on 05/14/2021; there are 2 independent claim and 18 dependent claims, all of which are ready for examination by the examiner.
Response to Arguments
This Office Action is in response to applicant’s communication filed on October 16, 2025 in response to PTO Office Action dated July 16, 2025. The Applicant’s remarks and amendments to the claims and/or specification were considered with the results that follow.
Claim Rejections
Applicant's following arguments filed on 10/16/2025 have been fully considered with the results that follow.
Claim Rejections - 35 USC § 103
35 USC § 103 Rejection of claims 1-20
Independent Claims 1 and 11
Claims 1 and 11
CLAIM 1
Applicant argues on pages 3 and 4 in regards to the independent claim 1, “For the limitation ‘ascertaining, by the processor, a quantum change for migrating database components of the source database’, the present invention discloses the process of migrating from the source database to the target database without using the previous dataset to identify migration strategies. The present invention provides a technical advancement over the cited prior art by assessing the source environment only. Bai is silent on analyzing the source database and source application environment. For the limitation ‘the quantum change assess risk, cost, timeline and an impact to dependent components and applications’, Bai discusses determining the cost of migration when engaging the application, the cost of accessing components leveraged by the application, the speed of the application, and the response time of the application. Bai does not mention analyzing the risk of migration. Bai also does not mention calculating the quantum change based on changes in size, dependencies of the source database. Bai is also silent on analyzing small components and identifying the risk based on the complexity of the source database and source environment“.
Examiner respectfully disagrees with arguments on pages 3 and 4 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Bai (Paragraph [0003] Paragraph [0017], Paragraph [0039], Paragraph [0047] and Paragraph [0064]) teaches “ascertaining, by the processor, a quantum change for migrating database components of the source database to a target database (the migration service includes a schema conversion service that attempts to convert, the source schema and code, including views, stored procedures and functions (a determination (ascertaining) of a first set of migration plans is made using an evaluation of the source dataset and a set of legacy features performed by a computer system/server which may include, but are not limited to, one or more processors, a comparison of the formatted source dataset to a set of similarly formatted source datasets of previously migrated applications, the comparison may use a technique known as a support vector machine (SVM) (a quantum change for migrating database components), where the software components include network application server software, application server software; and database software (DBMS) and the comparison may determine a set of migration plan(s) where the application is migrated from the source to the target database)”. Also, Bai (Paragraph [0017]) teaches “the quantum change assess risk, cost, timeline and an impact to dependent components and applications” (Paragraph [0017] (the comparison may use a technique known as a support vector machine (SVM), may include the determination employing a cost measure (the quantum change assess risk and cost) which may include a cost of engaging the application, a cost of accessing components leveraged by the application (an impact to dependent components and applications) , a speed of the application or a response time of the application (timeline)). Bai (Paragraph [0003]) clearly teaches that “the evaluation is made using the source dataset, the evaluation is performed with a cost measure and the application is migrated from the source to the target”. Thus, the argument that “Bai is silent on analyzing the source database and source application environment’ is incorrect. The applicant’s argument that the “Bai does not mention analyzing the risk of migration. Bai also does not mention calculating the quantum change based on changes in size, dependencies of the source database, and complexity of the source database”. The applicant’s argument that the “Bai does not mention analyzing the risk of migration. Bai also does not mention calculating the quantum change based on changes in size, dependencies of the source database, and complexity of the source database” is just out of context and does not match the language of the limitation claimed for this instant invention. As per MPEP 2111.01 (II), “It is improper to import claim limitations from the specifications. Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”. The applicant’s other argument that “The complexity of the source database is identified in order to decompose or break it into domain-based or target databases. Bai is also silent on analyzing small components and identifying the risk based on the complexity of the source database and source environment” is incorrect as the rejection in the office action is based on the combination of prior arts. Higginson (Paragraph [0013], Paragraph [0017] and Paragraph [0087]) clearly teaches the argued limitations. In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For the reasons specified supra, the claim 1 is not allowable.
Applicant argues on page 5 in regards to the independent claim 1, “Bai does not teach about forecasting the assessment statistic or an interactive assessment report using an AI engine that provides details of functional readiness, blockers, and a timeline to complete the migration of the database components of the source database “.
Examiner respectfully disagrees with arguments on page 5 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Bai (Paragraph [0017], Paragraph [0039] and Paragraph [0057]) teaches “forecasting, by the processor, an assessment statistic automatically, wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database (the learning process using one or more processors, may include the formatted source dataset having a future performance data (forecast) and a set of components of the source where the cost measure (the assessment statistic) may include a value factor of engaging the application at the target, a value factor of accessing components leveraged by the application, a performance factor of the application, a cost of engaging the application, a cost of accessing components leveraged by the application, a speed of the application, or a response time of the application (time line of the application) as specified in a migration plan)”. As mentioned in the claim limitation language “ … wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database, Bai explicitly teaches about a value factor of accessing components leveraged by the application, a performance factor of the application, a cost of engaging the application, a cost of accessing components leveraged by the application, a speed of the application, or a response time of the application (time line of the application”. For the reasons specified supra, the applicant’s argument is invalid and the claim1 is not allowable.
Applicant argues on page 6 in regards to the independent claim 1, “Ratnapuri does not discuss the use of a non-intrusive methodology that utilizes the application source code and DDL scripts as the only inputs for assessing the source database. It also does not specifically discuss database migration based on source code analysis and a rules engine. Furthermore, it mentions using AI to recommend the migration strategy, but it is silent on predicting/forecasting the assessment statistic based on the identified services in the source database of the source application environment. The teaching of Ratnapuri is limited to using AI and does not teach about an interactive assessment report for database migration. Therefore, Bai combined with the teaching of Ratnapuri is silent or does not teach key features of the present invention“.
Examiner respectfully disagrees with arguments on page 6 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Ratnapuri (Paragraph [0050] and Paragraph [0093]) teaches “wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic (after the initial interaction, the assessment process may begin with a proprietary suite of application program such as EQMIND, the elements of artificial intelligence that learn over time may be used in order to make one or more migration recommendations here where such learning may be based on other previous system (unrelated) migrations that allow elements of EQMIND evolve over time”. Also, Bai (Paragraph [0017], Paragraph [0039] and Paragraph [0057]) teaches “forecasting, by the processor, an assessment statistic automatically, wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database” as specified supra. Thus, the combination of Bai and Ratnapuri teaches “forecasting, by the processor, an assessment statistic automatically, wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database, wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic”. Related to other applicant’s argument “Ratnapuri does not discuss the use of a non-intrusive methodology that utilizes the application source code and DDL scripts as the only inputs for assessing the source database” the applicant’s argument is out of context related to the language in the claim limitation. As per MPEP 2111.01 (II), “It is improper to import claim limitations from the specifications. Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”. For the reasons specified supra, the claim 1 is not allowable.
Applicant argues on page 7 in regards to the independent claim 1, “It is clear that Bai does not teach about database migration from the source to the target database. Bai teaches about locating servers and their dependencies, but it does not talk about scanning the source database to identify dependencies between database components in the form of database links “.
Examiner respectfully disagrees with arguments on page 7 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. As specified supra, Bai (Paragraph [0003]) clearly teaches that “the evaluation is made using the source dataset, the evaluation is performed with a cost measure and the application is migrated from the source to the target”. Thus, the argument that “Bai does not teach about database migration from the source to the target database’ is incorrect. Related to the applicant’s other argument that the “Bai does not talk about scanning the source database to identify dependencies between database components in the form of database links”, Ratnapuri (Paragraph [0017], Paragraph [0080] and Paragraph [0084]) teaches the limitation “scanning, by the processor, the source database for identifying dependencies between the database components in form of database links”. In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For the reasons specified supra, the claim 1 is not allowable.
Applicant argues on pages 8 and 9 in regards to the independent claim 1, “Higginson does not teach about generating a refactored database by making small changes to the database schema. Therefore, a person skilled in the art would not be able to arrive at the method step of creating a refactored database by making small changes without changing the entire database structure “.
Examiner respectfully disagrees with arguments on pages 8 and 9 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Higginson (Paragraph [0013], Paragraph [0017] and Paragraph [0087] teaches “generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components (the processor(s) are used to generate a pre-migration analysis, the pre-migration analysis may include generating migration scripts, where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a database migration includes “slice and dice” of the source databases into small manageable chunks (re-factored), the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases)”. Higgins clearly teaches “generating a refactored database by making small changes to the database schema (the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases). The applicant’s argument is incorrect. Thus, the claim 1 is not allowable.
Applicant argues on page 10 in regards to the independent claim 1, “Higginson discusses starting the migration engine for the migration and determining whether the target database exists on the target server system. If not, the database may be created and formatted on the target system prior to migration. It talks about checking and creating the target database on the target server system. On the other hand, the present invention discusses updating the granular database components of the target database according to the forecasted assessment statistic and the re-factored database structure. A person skilled in the art would not be able to arrive at the method step discussed in the present invention by combining the teachings of Bai, Ratnapuri, and Higginson“.
Examiner respectfully disagrees with arguments on page 10 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Higginson (Paragraph [0017], Paragraph [0092]. Paragraph [0094] and Fig. 8-9 teaches “updating, by the processor, granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure (generate a pre-migration analysis by one or more processors, after the pre-migration analysis is complete, the migration scripts may need to be converted to a new format to run on a migration engine to be configured for the particular migration environment for the target database systems (re-factored database structure), after the migration plan is generated, the migration engine may be started where the migration engine may determine whether the target database exists on the target server system and in case the does not exist, the database may be created and formatted on the target system prior to migration and if the database exists, the scripts are run to migrate the changes of the source database (update) to the target database)”. Higgins clearly teaches “generating a refactored database by making small changes to the database schema (the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases). For a person skilled in the art, it would be obvious to be able to arrive at the method step or the instant limitation discussed by combining the teachings of Bai, Ratnapuri, and Higginson. The applicant’s argument is incorrect. Thus, the claim 1 is not allowable.
Applicant argues on pages 10 and 11 in regards to the independent claim 1, “Higginson discusses performing the migration by converting the migration scripts to a new format to run on a migration engine. The migration scripts may be converted to a migration-specific format. The migration engine may be configured to control the execution of the migration plan to execute the migration scripts in sequence and in parallel. Higginson discusses re-platforming the updated granular database components. Re-platforming is a strategy that involves modifying a system to work optimally in the cloud without rewriting its core architecture. The prior art does not discuss re-platforming; instead, it discusses updating migration scripts to a new format to run on a migration engine for the migration. The present invention method steps are different; therefore, it cannot be considered as teaching the method step of the present invention “.
Examiner respectfully disagrees with arguments on pages 10 and 11 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Higginson (Paragraph [0092] and Paragraph [0093] teaches “migrating, by the processor, the source database to the target database, wherein the migration re-platforms the updated granular database components (in preparation to run the migration plan, the migration scripts may be copied to the target database system, where. in some cases, migration scripts may need to be converted to a new format to run on a migration engine, the migration scripts may be converted to a migration-specific format, the migration engine may be configured to control the execution of the migration plan such that the migration scripts are carried out in the correct sequence where the migration engine can execute the migration scripts in sequence and in parallel as appropriate teaches)“. Higgins clearly teaches “generating a refactored database by making small changes to the database schema (the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases). The applicant’s argument that the “Re-platforming is a strategy that involves modifying a system to work optimally in the cloud without rewriting its core architecture. The prior art does not discuss re-platforming” is just out of context and does not match the language of the limitation claimed for this instant invention. As per MPEP 2111.01 (II), “It is improper to import claim limitations from the specifications. Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”. The applicant’s argument is incorrect. Thus, the claim 1 is not allowable.
Applicant argues on pages 11 and 12 in regards to the independent claim 1, “Wilton does not discuss the migration of the source database to the target database while retaining the database links. It also does not discuss the identification of readiness parameters for database migration. Wilton does not discuss assessment through the use of a source code scanner and the generation of a complexity matrix or an assessment report. It also does not offer detailed reports on database migration impacts from the source to the target based on source code analysis and a rules engine. Additionally, it does not discuss assessing and addressing the impact on application source code that arises from modifications in the source-to-target database. Furthermore, it is silent on fragmenting databases into smaller, domain-oriented databases. Wilton fails to teach any of the method steps used in the present invention for migrating the database from the source environment to the target environment. It would not have been obvious for an ordinary person skilled in the art before the effective filing date to take the teachings of Wilton and apply them to the teachings of Bai, Ratnapuri, and Higginson “.
Examiner respectfully disagrees with arguments on pages 11 and 12 in regards to the independent claim 1. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 1. Wilton (Col 7 Lines 49-67] teaches “and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links (the migration service includes a schema conversion service that attempts to convert, without user intervention, the source schema and code, including views, stored procedures and functions (data object code), collectively referred to herein as ‘schema’, to a format compatible with the target DBMS and the target database (in accordance with a design of the target database while retaining the database links) and the conversion functionality may be accessed via the migration service that can be accessed via the migration API and the schema objects in the source database should be converted to a format that is compatible with the target database and the user can use those types of objects within the target database (while retaining the database links)). The applicant’s argument that the “Wilton does not discuss the migration of the source database to the target database while retaining the database links. It also does not discuss the identification of readiness parameters for database migration. Wilton does not discuss assessment through the use of a source code scanner and the generation of a complexity matrix or an assessment report. It also does not offer detailed reports on database migration impacts from the source to the target based on source code analysis and a rules engine. Additionally, it does not discuss assessing and addressing the impact on application source code that arises from modifications in the source-to-target database. Furthermore, it is silent on fragmenting databases into smaller, domain-oriented database” is totally incorrect as either the claimed limitation is taught by Wilton or by the other prior arts as discussed supra. As indicated by Wilton (Abstract), “Data is migrated between a source database and a target database. The source database management system (“DBMS”) remains operational during the migration. A user selects the source DBMS and target DBMS, selects a virtual machine instance to perform the migration in conjunction with a database migration service. After the setup is complete, the virtual machine instance in conjunction with the database migration service performs data type transformations, and other operations, without user intervention. The database migration service also converts, without user intervention, the source schema and code to a format compatible with the target DBMS. The database migration service can also provide recommendations as to a target DBMS that is a suitable target DBMS”. For a person skilled in the art, it would be obvious to be able to arrive at the method step or the instant limitation discussed by combining the teachings of Bai, Ratnapuri, Higginson and Wilton. Thus, the applicant’s argument is incorrect. Moreover, in response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For the reasons specified supra, the claim 1 is not allowable.
CLAIM 11.
Applicant argues on page 21 in regards to the independent claim 11, “Claim 11 is a system claim corresponding to the claim 1 of the present invention and applicant relies on the rationale mentioned above for the step of claim 1 to support the uniqueness of the system architecture and its innovative approach to addressing the challenges associated with migration.“
Examiner respectfully disagrees with arguments on page 21 in regards to the independent claim 11. As specified supra for the independent Claim 1, the combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the independent claim 11. The claim 11 is not allowable.
Dependent Claims 2-10 and 12-20
CLAIMS 2 and 13
Applicant argues on page 12 in regards to the dependent claims 2 and 13, “Wilton discusses a schema conversion service for sequentially converting the source schema to a compatible schema for the target database. The present invention, on the other hand, discusses a continuous integration and deployment framework that automates and manages changes to database schemas and data in a reliable, testable, and repeatable way. Wilton does not mention how the changes are ascertained, and the steps of the continuous integration and deployment framework are different from the cited prior art “.
Examiner respectfully disagrees with arguments on page 12 in regards to the dependent claims 2 and 13. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 2 and 13. Wilton (Col 9 Lines 61-63 and Col 10 Lines 33-48) teaches “wherein the re-factored database structure is generated utilizing a continuous integration and deployment framework and an automated test framework (the schema conversion service compares the schema objects, as well as different syntax and language structures for the code used in stored procedures and functions supported by the source DBMS with the objects and code supported by the target DBMS, the migration manager in conjunction with the schema conversion service, attempts to convert, without user intervention, the schema to a format compatible with the target and when a problem is detected, the migration manager and/or the migration manager attempts to resolve the problem without user interaction (automated test framework)”. . Wilton teaches all aspects of the current limitation. The applicant’s argument are out of context and does not match the language of the limitation claimed for this instant invention. As per MPEP 2111.01 (II), “It is improper to import claim limitations from the specifications. Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”. Thus, the applicant’s argument is incorrect. Thus, the dependent claims 2 and 13 are not allowable.
CLAIMS 3 and 14
Applicant argues on page 13 in regards to the dependent claims 3 and 14, “Bai discusses the similarity test, a cost measure test, a stability test, a source dataset determination step, and a test for more legacy feature vectors within the set of feature vectors, but is silent on identifying potential inhibitors using the AI Engine. The present invention leverages the latest advancements in artificial intelligence to perform the migration tasks intelligently. The method steps of the present invention are different; therefore, it cannot be considered as teaching the method step of the present invention“.
Examiner respectfully disagrees with arguments on page 13 in regards to the dependent claims 3 and 14. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 3 and 14. Bai (Paragraph [0065] and Paragraph [0066]) teaches “wherein the assessment statistic provides at least one inhibitor to complete the migration of the source database (a first comparison between the source feature vector and a legacy feature vector may include a set of operations consisting of a similarity test, a cost measure test, a stability test, a source dataset determination step, and a test for more legacy feature vectors within the set of feature vectors and once a similarity score is developed, the test may conclude with a pass (yes) or a fail (no) (inhibitor to complete the migration of the source database), depending upon if the similarity score meets or fails to meet the similarity threshold, respectively)”. Bai teaches all aspects of the current limitation claimed for the claims 3 and 14. The applicant’s argument are irrelevant and does not match the language of the limitation claimed for this instant invention. Also, the combination of Bai, Ratnapuri, Higginson and Wilton teaches the use of latest advancements in artificial intelligence to perform the migration tasks intelligently. In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Thus, the applicant’s argument is incorrect. The dependent claims 3 and 14 are not allowable.
CLAIMS 4 and 15
Applicant argues on page 14 in regards to the dependent claims 4 and 15, “The present invention clarifies that the source database is being scanned to identify dependencies between it and other database components in the form of existing database links. The scanning of the source database includes connections between the database components of the source database. The prior art does not discuss or teach the method step disclosed in the present invention.“
Examiner respectfully disagrees with arguments on page 14 in regards to the dependent claims 4 and 15. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 4 and 15. Wilton (Col 3 lines 1-4 and Col 3 Lines 32-37) teaches “wherein scanning the source database includes scanning connections between the database components of the source database (the database migration service monitors the source and target database management systems, network connectivity, the migration instance during the migration, the database migration service includes a schema conversion service in one configuration that attempts to convert, without user intervention, the source database schema and code (database components), including views, stored procedures and functions)”. Wilton teaches all aspects of the current limitation claimed for the claims 4 and 15. The applicant’s argument are irrelevant and does not match the language of the limitation claimed for this instant invention. As discussed supra for the independent claim 1, Ratnapuri (Paragraph [0017], Paragraph [0080] and Paragraph [0084]) teaches the limitation “scanning, by the processor, the source database for identifying dependencies between the database components in form of database links”. In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Thus, the applicant’s argument is not only out of context related to the dependent claims 4 and 15 limitation but also incorrect. The dependent claims 4 and 15 are not allowable.
CLAIM 5
Applicant argues on page 15 in regards to the dependent claim 5, “Higginson does not teach about generating a refactored database by making small changes to the database schema. Therefore, a person skilled in the art would not be able to arrive at the method step of creating a refactored database by making small changes without changing the entire database structure. Therefore, the cited prior art does not teach or provide information that allows the person skilled in the art to arrive at the technical advancement of the present invention.“
Examiner respectfully disagrees with arguments on page 15 in regards to the dependent claim 5. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claim 5. Higginson (Paragraph [0087]) teaches “wherein the re-factored database structure is generated in accordance with the target database while retaining connections between the database components of the source database (the source databases may be divided into individual objects (re-factored database structure), each individual object may be associated with its own migration script and a migration script may include instructions or parameters used to migrate the associated object(s) from the source databases to the target databases)”. Higgins teaches all aspects of the current limitation claimed for the dependent claim 5. The applicant’s arguments are incorrect and does not match the language of the limitation claimed for this instant dependent claim 5. . As discussed supra for the independent claim 1, Higginson (Paragraph [0013], Paragraph [0017] and Paragraph [0087] teaches “generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components”. Thus, the applicant’s argument is incorrect. The dependent claim 5 is not allowable.
CLAIMS 6 and 16
Applicant argues on page 16 in regards to the dependent claims 6 and 16, “Ratnapuri does not discuss the use of a non-intrusive methodology that utilizes the application source code and DDL scripts as the only inputs for assessing the source database. It also does not specifically discuss database migration based on source code analysis and a rules engine. Ratnapuri discusses using AI to recommend a migration strategy, but it is silent on predicting or forecasting the assessment statistic based on the identified services in the source database of the source application environment. The teaching of Ratnapuri is limited to using AI and does not teach about an interactive assessment report for database migration. Therefore, Bai combined with the teaching of Ratnapuri is silent or does not teach key features of the present.“
Examiner respectfully disagrees with arguments on page 16 in regards to the dependent claims 6 and 16. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 6 and 16. Ratnapuri (Paragraph [0050] and Paragraph [0093]) teaches “wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic”. Also, Bai (Paragraph [0017], Paragraph [0039] and Paragraph [0057]) teaches “forecasting, by the processor, an assessment statistic automatically, wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database” as specified supra for the independent claim 1. Thus, the combination of Bai and Ratnapuri teaches “predicting or forecasting the assessment statistic based on the identified services in the source database of the source application environment”. Related to other applicant’s argument “Ratnapuri does not discuss the use of a non-intrusive methodology that utilizes the application source code and DDL scripts as the only inputs for assessing the source database. It also does not specifically discuss database migration based on source code analysis and a rules engine” the applicant’s argument is out of context related to the language in the claim limitation. As per MPEP 2111.01 (II), “It is improper to import claim limitations from the specifications. Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim”. Thus, the applicant’s arguments are not only out of context related to the dependent claims 6 and 16 limitation but also incorrect. The dependent claims 6 and 16 are not allowable.
CLAIMS 8 and 18
Applicant argues on page 17 in regards to the dependent claims 8 and 18, “Wilton teaches how to implement the present invention using a computing device. However, it does not teach about the database script mentioned in the present invention. The present invention teaches about implementing scripts from the source application environment in accordance with the target application environment. This is different from the teaching and application of the prior art.”
Examiner respectfully disagrees with arguments on page 17 in regards to the dependent claims 8 and 18. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 8 and 18. Wilton (Col 3 Lines 66-67, Col 4 Lines 1-6 and Col 10 Lines 17-21) teaches “further comprising: implementing, by the processor, scripts from the source application environment in accordance with the target application environment (the program modules that execute on one or more computing devices, which include routines, programs (scripts), components, data structures and other types of structures that perform particular tasks or implement particular abstract data types and the migration manager may access functionality provided by the schema conversion service to convert the schema objects associated with the source DBMS to schema objects supported by the target DBMS)”. Wilton teaches all aspects of the current limitation claimed for the dependent claims 8 and 18. Thus, the applicant’s arguments are incorrect. The dependent claims 8 and 18 are not allowable.
CLAIMS 9 and 19
Applicant argues on page 18 in regards to the dependent claims 9 and 19, “Higginson does not teach about accessing the application code comprising business logic, links, rule engines, libraries of available environments, standard tools, and coding languages. The prior art discusses one or more server computers 120, 125, 130, which can be general purpose computers and/or specialized server computers (including, merely by way of example, PC servers, UNIX servers, mid-range servers, mainframe computers, rack-mounted servers, etc.). Furthermore, it teaches that one or more of the servers (e.g., 130) may be dedicated to running applications, such as a business application, a web server, or an application server. It discusses running the functionality of the prior art on the server, while the present invention discusses accessing the application code to perform database migration from one source database to the target database.”
Examiner respectfully disagrees with arguments on page 18 in regards to the dependent claims 9 and 19. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 9 and 19. Higginson (Paragraph [0045]) teaches “accessing, by the processor, an application code comprising a business logic, links, rule engines, libraries of available environments, standard tools, and coding languages (the web server can also run any of a variety of server applications and/or mid-tier applications, including HTTP servers, FTP servers, CGI servers, database servers, Java servers, business applications (business logic), and the like, where the server(s) also may be one or more computers which can be capable of executing programs or scripts in response to the user computers and the web application may be implemented as one or more scripts or programs written in any programming language, such as Java™, C, C# or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming/scripting languages)”. Higginson (Paragraph [0092]) and Paragraph [0095]) clearly teach “ accessing the application code to perform database migration from one source database to the target database (in preparation to run the migration plan, the migration scripts may be copied to the target database and may need to be converted to a new target database format to run on a migration engine and any parameter used to generate the migration plan may be changed automatically during execution by the migration engine according to business logic and other constraints provided to the migration engine)”. Thus, Higginson teaches all aspects of the current limitation claimed for the dependent claims 9 and 19. The applicant’s arguments are incorrect. The dependent claims 9 and 19 are not allowable.
CLAIMS 10 and 20
Applicant argues on page 19 in regards to the dependent claims 10 and 20, “The re-factored database structure is a technique used to make small changes to the database schema without altering the semantics or structure. Higginson discusses "slice and dice" the source databases into small manageable chunks. Higginson does not teach about generating a refactored database by making small changes to the database schema. Therefore, a person skilled in the art would not be able to arrive at the method step of creating a refactored database by making small changes without changing the entire database structure.”
Examiner respectfully disagrees with arguments on page 19 in regards to the dependent claims 10 and 20. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claims 10 and 20. Higginson (Paragraph [0013], Paragraph [0017], Paragraph [0055] and Paragraph [0087]) teaches “wherein the source database of an application environment migrates to the target database comprising the steps of: a source to target database re-development, a source to target database re-factoring, a source to target database re-hosting, and a source to target database re-platforming (the processor(s) are used to generate a pre-migration analysis, the pre-migration analysis may include generating migration scripts, where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a migration can involve multiple steps, the source databases and the target databases may be subjected to a pre-migration analysis for categorizing objects, identifying objects that require special handling during migration, identifying invalid objects, analyzing the usage and criticality of objects, and/or the like, the source system analysis may also identify large database objects that need further configuration to achieve efficiencies and storage using existing technologies, the pre-migration analysis may use data collected from a cloud-based database modeling service to help the customer configure a migration plan that minimizes downtime, maximizes transfer speeds, ensures data integrity and validation (re-platforming), where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a database migration includes “slice and dice” of the source databases into small manageable chunks (re-factored), the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases). The applicant’s argument “Higginson does not teach about generating a refactored database by making small changes to the database schema” is incorrect. Thus, Higginson teaches all aspects of the current limitation claimed for the dependent claims 10 and 20. The dependent claims 10 and 20 are not allowable.
CLAIMS 7 and 17
Applicant argues on page 20 in regards to the dependent claims 7 and 17, “Apte does not discuss the migration of the source database to the target database while retaining the database links. It also does not discuss the identification of readiness parameters for database migration. Additionally, Apte is silent on forecasting an assessment statistic that provides at least one of the functional readiness, a blocker, and a timeline to complete the migration of the database components of the source database. Furthermore, it does not discuss using the AI engine to identify services in the source database of the source application environment. Instead, it discusses analyzing the infrastructure for migrating the server from the source to the target.”
Examiner respectfully disagrees with arguments on page 20 in regards to the dependent claims 7 and 17. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233), Wilton et al (US Patent 10803031) and Apte et al (US PGPUB 20170192758) teaches all the limitations of the dependent claims 7 and 17. Apte (Paragraph [0031], Paragraph [0078] and Paragraph [0082]) teaches “migrating, by the processor, dictionaries and keywords from the source application environment in accordance with the target application environment (the legacy migration module(s) may include one or more workbenches and/or toolkits that may contain one or more applications, tools, and/or modules such as, but not limited to, a language migration toolkit, the language migration toolkit includes a data dictionary that is a list of the programming language specific keywords or tokens and once tokens are extracted from the source language, a data dictionary will get updated where the equivalent target language statements are mapped). The applicant’s argument that “Apte does not discuss the migration of the source database to the target database while retaining the database links. It also does not discuss the identification of readiness parameters for database migration. Additionally, Apte is silent on forecasting an assessment statistic that provides at least one of the functional readiness, a blocker, and a timeline to complete the migration of the database components of the source database. Furthermore, it does not discuss using the AI engine to identify services in the source database of the source application environment” is just out of context and does not match the language of the limitation claimed for the dependent claims 7 and 17. In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For the reasons specified supra, the independent claims 1 and 11, the dependent claims 7 and 17 are not allowable.
CLAIM 12
Applicant argues on page 21 in regards to the dependent claim 12, “Bai discusses determining the cost of migration when engaging the application, the cost of accessing components leveraged by the application, the speed of the application, and the response time of the application. Bai does not mention analyzing the risk of migration. Bai also does not mention calculating the quantum change based on changes in size, dependencies of the source database.“
Examiner respectfully disagrees with arguments on page 21 in regards to the dependent claim 12. The combination of Bai et al (US PGPUB 20160112510), Ratnapuri Thiruchelvan (US PGPUB 20230196237), Higginson et al (US PGPUB 20180293233) and Wilton et al (US Patent 10803031) teaches all the limitations of the dependent claim 12. Higginson (Paragraph [0091]) teaches “wherein the quantum change for migrating the source database to the target database is calculated on the basis of the size of the source database (a scheduler system may be configured to identify an order of migrating database objects with migration scripts where the scheduler system may identify order based on factors such as the size of the source database objects to be migrated and/or the dependencies of the database objects)”. Higgins teaches all aspects of the current limitation claimed for the dependent claim 12. The applicant’s arguments against Bai are moot. The dependent claim 12 is not allowable.
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 1-6, 8-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bai et al (US PGPUB 20160112510), hereinafter Bai in view of Ratnapuri Thiruchelvan (US PGPUB 20230196237) hereinafter Ratnapuri and in further view of Higginson et al (US PGPUB 20180293233) hereinafter Higginson and Wilton et al (US Patent 10803031) hereinafter Wilton.
As per claim 1:
Bai teaches:
“A method comprising” (Paragraph [0016] (a computer-implemented method includes))
“assessing, by a processor of an application server, a source database of a source application environment” (Paragraph [0003], Paragraph [0017] and Paragraph [0039] (a determination of a first set of migration plans is made using an evaluation including cost measure (assessing) of the source dataset and a set of legacy features performed by a computer system/server which may include, but are not limited to, one or more processors))
“ascertaining, by the processor, a quantum change for migrating database components of the source database to a target database” (Paragraph [0003] Paragraph [0017], Paragraph [0039], Paragraph [0047] and Paragraph [0064] (a determination (ascertaining) of a first set of migration plans is made using an evaluation of the source dataset and a set of legacy features performed by a computer system/server which may include, but are not limited to, one or more processors, a comparison of the formatted source dataset to a set of similarly formatted source datasets of previously migrated applications, the comparison may use a technique known as a support vector machine (SVM) (a quantum change for migrating database components), where the software components include network application server software, application server software; and database software (DBMS) and the comparison may determine a set of migration plan(s) where the application is migrated from the source to the target database ))
“the quantum change assess risk, cost, timeline and an impact to dependent components and applications” (Paragraph [0017] (the comparison may use a technique known as a support vector machine (SVM), may include the determination employing a cost measure (the quantum change assess risk and cost) which may include a cost of engaging the application, a cost of accessing components leveraged by the application (an impact to dependent components and applications) , a speed of the application or a response time of the application (timeline)))
“forecasting, by the processor, an assessment statistic automatically, wherein the assessment statistic provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database” (Paragraph [0017], Paragraph [0039] and Paragraph [0057] (the learning process using one or more processors, may include the formatted source dataset having a future performance data (forecast) and a set of components of the source where the cost measure (the assessment statistic) may include a value factor of engaging the application at the target, a value factor of accessing components leveraged by the application, a performance factor of the application, a cost of engaging the application, a cost of accessing components leveraged by the application, a speed of the application, or a response time of the application (time line of the application) as specified in a migration plan)).
Bai does not EXPLICITLY disclose: wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic; scanning, by the processor, the source database for identifying dependencies between the database components in form of database links; generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links; updating, by the processor, granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating, by the processor, the source database to the target database, wherein the migration re-platforms the updated granular database components.
However, in an analogous art, Ratnapuri teaches:
“wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic” (Paragraph [0050] and Paragraph [0093] (after the initial interaction, the assessment process may begin with a proprietary suite of application program such as EQMIND, the elements of artificial intelligence that learn over time may be used in order to make one or more migration recommendations here where such learning may be based on other previous system (unrelated) migrations that allow elements of EQMIND evolve over time)).
“scanning, by the processor, the source database for identifying dependencies between the database components in form of database links” (Paragraph [0017], Paragraph [0080] and Paragraph [0084] (for migrating source local computer network applications to a target cloud operating environment using a migration planning and assessment (MPA) application, discovering (scanning) source local computer network applications that may qualify for migration to the cloud based on the discovery step, after conducting readiness assessment and after arriving at a determination that the selected target assets are in a condition for migration, may install and/or deploy one or more automated or partially automated discovery modules on the source network to determine elements and dependencies relevant to the migration effort and the interface may present the user with various options, including the ability to upload selected dependency data (in form of links on interface))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Ratnapuri and apply them on teachings of Bai for “wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic; scanning, by the processor, the source database for identifying dependencies between the database components in form of database links”. One would be motivated as the operations assimilation and analysis module may include any suitable recursive analysis or artificial intelligence program and Machine Learning for synthesizing input in order to prepare the source network for migration to the cloud in module (Ratnapuri, Paragraph [0120]).
Bai and Ratnapuri do not EXPLICITLY disclose: generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links; updating, by the processor, granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating, by the processor, the source database to the target database, wherein the migration re-platforms the updated granular database components.
However, in an analogous art, Higginson teaches:
“generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components” (Paragraph [0013], Paragraph [0017] and Paragraph [0087] (the processor(s) are used to generate a pre-migration analysis, the pre-migration analysis may include generating migration scripts, where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a database migration includes “slice and dice” of the source databases into small manageable chunks (re-factored), the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases))
“updating, by the processor, granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure” (Paragraph [0017], Paragraph [0092]. Paragraph [0094] and Fig. 8-9 (generate a pre-migration analysis by one or more processors, after the pre-migration analysis is complete, the migration scripts may need to be converted to a new format to run on a migration engine to be configured for the particular migration environment for the target database systems (re-factored database structure), after the migration plan is generated, the migration engine may be started where the migration engine may determine whether the target database exists on the target server system and in case the does not exist, the database may be created and formatted on the target system prior to migration and if the database exists, the scripts are run to migrate the changes of the source database (update) to the target database))
“migrating, by the processor, the source database to the target database, wherein the migration re-platforms the updated granular database components” (Paragraph [0092] and Paragraph [0093] (in preparation to run the migration plan, the migration scripts may be copied to the target database system, where. in some cases, migration scripts may need to be converted to a new format to run on a migration engine, the migration scripts may be converted to a migration-specific format, the migration engine may be configured to control the execution of the migration plan such that the migration scripts are carried out in the correct sequence where the migration engine can execute the migration scripts in sequence and in parallel as appropriate)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Higginson and apply them on teachings of Bai and Ratnapuri for “generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; updating, by the processor, granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating, by the processor, the source database to the target database, wherein the migration re-platforms the updated granular database components”. One would be motivated as the source databases and the target databases may be subjected to a pre-migration analysis where this analysis may be useful for categorizing objects, identifying objects that require special handling during migration, identifying invalid objects, analyzing the usage and criticality of objects, and/or the like (Hihhinson, Paragraph [0054]).
Bai, Ratnapuri and Higginson do not EXPLICITLY disclose: and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links.
However, in an analogous art, Wilton teaches:
“and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links” (Col 7 Lines 49-67 (the migration service includes a schema conversion service that attempts to convert, without user intervention, the source schema and code, including views, stored procedures and functions (data object code), collectively referred to herein as ‘schema’, to a format compatible with the target DBMS and the target database (in accordance with a design of the target database while retaining the database links) and the conversion functionality may be accessed via the migration service that can be accessed via the migration API and the schema objects in the source database should be converted to a format that is compatible with the target database and the user can use those types of objects within the target database (while retaining the database links))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Wilton and apply them on teachings of Bai, Ratnapuri and Higginson for “and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links”. One would be motivated as the data changes to the source database that occur during the migration are migrated to the target by the database migration service, the source database remains operational during the migration process. (Wilton, Col 2 Lines 63-67).
As per claim 2:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Wilton further teaches:
“wherein the re-factored database structure is generated utilizing a continuous integration and deployment framework and an automated test framework” (Col 9 Lines 61-63 and Col 10 Lines 33-48 (the schema conversion service compares the schema objects, as well as different syntax and language structures for the code used in stored procedures and functions supported by the source DBMS with the objects and code supported by the target DBMS, the migration manager in conjunction with the schema conversion service, attempts to convert, without user intervention, the schema to a format compatible with the target and when a problem is detected, the migration manager and/or the migration manager attempts to resolve the problem without user interaction (automated test framework))).
As per claim 3:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Bai further teaches:
“wherein the assessment statistic provides at least one inhibitor to complete the migration of the source database” (Paragraph [0065] and Paragraph [0066] (a first comparison between the source feature vector and a legacy feature vector may include a set of operations consisting of a similarity test, a cost measure test, a stability test, a source dataset determination step, and a test for more legacy feature vectors within the set of feature vectors and once a similarity score is developed, the test may conclude with a pass (yes) or a fail (no) (inhibitor to complete the migration of the source database), depending upon if the similarity score meets or fails to meet the similarity threshold, respectively)).
As per claim 4:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Wilton further teaches:
“wherein scanning the source database includes scanning connections between the database components of the source database” (Col 3 lines 1-4 and Col 3 Lines 32-37 (the database migration service monitors the source and target database management systems, network connectivity, the migration instance during the migration, the database migration service includes a schema conversion service in one configuration that attempts to convert, without user intervention, the source database schema and code (database components), including views, stored procedures and functions)).
As per claim 5:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Higginson further teaches:
“wherein the re-factored database structure is generated in accordance with the target database while retaining connections between the database components of the source database” (Paragraph [0087] (the source databases may be divided into individual objects (re-factored database structure), each individual object may be associated with its own migration script and a migration script may include instructions or parameters used to migrate the associated object(s) from the source databases to the target databases)).
As per claim 6:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Ratnapuri further teaches:
“wherein the re-factored database structure is identified by the AI Engine” (Paragraph [0092] and Paragraph [0093] (a migration strategy may be determined that provides for the best path to the cloud for the identified asset(s) including refactoring or re-architecting the database, addressing weaknesses and optimizing for the selected cloud environment and using artificial intelligence that learn over time may be used in order to make on or more migration recommendations)).
As per claim 8:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Wilton further teaches:
“further comprising: implementing, by the processor, scripts from the source application environment in accordance with the target application environment” (Col 3 Lines 66-67, Col 4 Lines 1-6 and Col 10 Lines 17-21 (the program modules that execute on one or more computing devices, which include routines, programs (scripts), components, data structures and other types of structures that perform particular tasks or implement particular abstract data types and the migration manager may access functionality provided by the schema conversion service to convert the schema objects associated with the source DBMS to schema objects supported by the target DBMS)).
As per claim 9:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Higginson further teaches:
“accessing, by the processor, an application code comprising a business logic, links, rule engines, libraries of available environments, standard tools, and coding languages” (Paragraph [0045] (the web server can also run any of a variety of server applications and/or mid-tier applications, including HTTP servers, FTP servers, CGI servers, database servers, Java servers, business applications (business logic), and the like, where the server(s) also may be one or more computers which can be capable of executing programs or scripts in response to the user computers and the web application may be implemented as one or more scripts or programs written in any programming language, such as Java™, C, C# or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming/scripting languages)).
As per claim 10:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Higginson further teaches:
“wherein the source database of an application environment migrates to the target database comprising the steps of: a source to target database re-development, a source to target database re-factoring, a source to target database re-hosting, and a source to target database re-platforming” (Paragraph [0013], Paragraph [0017], Paragraph [0055] and Paragraph [0087] (the processor(s) are used to generate a pre-migration analysis, the pre-migration analysis may include generating migration scripts, where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a migration can involve multiple steps, the source databases and the target databases may be subjected to a pre-migration analysis for categorizing objects, identifying objects that require special handling during migration, identifying invalid objects, analyzing the usage and criticality of objects, and/or the like, the source system analysis may also identify large database objects that need further configuration to achieve efficiencies and storage using existing technologies, the pre-migration analysis may use data collected from a cloud-based database modeling service to help the customer configure a migration plan that minimizes downtime, maximizes transfer speeds, ensures data integrity and validation (re-platforming), where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a database migration includes “slice and dice” of the source databases into small manageable chunks (re-factored), the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases)).
As per claim 11:
Bai teaches:
“A system comprising” (Paragraph [0016] (a computer-implemented system includes))
“a processor” (Paragraph [0039] (one or more processors))
“and a memory coupled to the processor, wherein the processor executes a plurality of modules stored in the memory, and wherein the plurality of modules comprising” (Paragraph [0039] and Paragraph [0042] (a system memory, and a bus that couples various system components including system memory to processor and the memory may include at least one program product having a set of program modules that are configured to carry out the functions))
“an assessment module, for assessing a source database of a source application environment” (Paragraph [0003], Paragraph [0017] and Paragraph [0039] (a determination of a first set of migration plans is made using an evaluation including cost measure (assessing) of the source dataset and a set of legacy features performed by a computer system/server which may include, but are not limited to, one or more processors))
“and ascertaining a quantum change for migrating database components of the source database to a target database” (Paragraph [0003] Paragraph [0017], Paragraph [0039], Paragraph [0047] and Paragraph [0064] (a determination (ascertaining) of a first set of migration plans is made using an evaluation of the source dataset and a set of legacy features performed by a computer system/server which may include, but are not limited to, one or more processors, a comparison of the formatted source dataset to a set of similarly formatted source datasets of previously migrated applications, the comparison may use a technique known as a support vector machine (SVM) (a quantum change for migrating database components), where the software components include network application server software, application server software; and database software (DBMS) and the comparison may determine a set of migration plan(s) where the application is migrated from the source to the target database))
“the quantum change assessing risk, cost, timeline and an impact to dependent components and applications” (Paragraph [0017] (the comparison may use a technique known as a support vector machine (SVM), may include the determination employing a cost measure (the quantum change assess risk and cost) which may include a cost of engaging the application, a cost of accessing components leveraged by the application (an impact to dependent components and applications) , a speed of the application or a response time of the application (timeline)))
“wherein the assessment module forecasts an assessment statistic automatically that provides at least one of a functional readiness, a blocker and a timeline to complete the migration of the database components of the source database” (Paragraph [0017], Paragraph [0039] and Paragraph [0057] (the learning process using one or more processors, may include the formatted source dataset having a future performance data (forecast) and a set of components of the source where the cost measure (the assessment statistic) may include a value factor of engaging the application at the target, a value factor of accessing components leveraged by the application, a performance factor of the application, a cost of engaging the application, a cost of accessing components leveraged by the application, a speed of the application, or a response time of the application (time line of the application) as specified in a migration plan)).
Bai does not EXPLICITLY disclose: wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic; a re-factor module, for scanning the source database for identifying dependencies between the database components in form of database links; generating a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links; a re-platform module, for updating granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating the source database to the target database, wherein the migration re-platforms the updated granular database components.
However, in an analogous art, Ratnapuri teaches:
“wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic” (wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic” (Paragraph [0050] and Paragraph [0093] (after the initial interaction, the assessment process may begin with a proprietary suite of application program such as EQMIND, the elements of artificial intelligence that learn over time may be used in order to make one or more migration recommendations here where such learning may be based on other previous system (unrelated) migrations that allow elements of EQMIND evolve over time)).
“a re-factor module, for scanning the source database for identifying dependencies between the database components in form of database links” ( (Paragraph [0017], Paragraph [0080] and Paragraph [0084] (for migrating source local computer network applications to a target cloud operating environment using a migration planning and assessment (MPA) application, discovering (scanning) source local computer network applications that may qualify for migration to the cloud based on the discovery step, after conducting readiness assessment and after arriving at a determination that the selected target assets are in a condition for migration, may install and/or deploy one or more automated or partially automated discovery modules on the source network to determine elements and dependencies relevant to the migration effort and the interface may present the user with various options, including the ability to upload selected dependency data (in form of links on interface)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Ratnapuri and apply them on teachings of Bai for “wherein an AI engine identifies services in the source database of the source application environment to forecast the assessment statistic; a re-factor module, for scanning the source database for identifying dependencies between the database components in form of database links”. One would be motivated as the operations assimilation and analysis module may include any suitable recursive analysis or artificial intelligence program and Machine Learning for synthesizing input in order to prepare the source network for migration to the cloud in module (Ratnapuri, Paragraph [0120]).
Bai and Ratnapuri do not EXPLICITLY disclose: generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links; a re-platform module, for updating granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating the source database to the target database, wherein the migration re-platforms the updated granular database components.
However, in an analogous art, Higginson teaches:
“generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components” (Paragraph [0013], Paragraph [0017] and Paragraph [0087] (the processor(s) are used to generate a pre-migration analysis, the pre-migration analysis may include generating migration scripts, where the migration scripts transfer the plurality of objects from the one or more source databases to the one or more target databases, a database migration includes “slice and dice” of the source databases into small manageable chunks (re-factored), the source databases may be divided into individual objects (including database components), and each individual object (re-factored database structure) may be associated with its own migration script and the migration script may be used to migrate the associated object(s) from the source databases to the target databases))
“a re-platform module, for updating granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure” (Paragraph [0017], Paragraph [0092]. Paragraph [0094] and Fig. 8-9 (generate a pre-migration analysis by one or more processors, after the pre-migration analysis is complete, after the pre-migration analysis is complete, the migration scripts may need to be converted to a new format to run on a migration engine to be configured for the particular migration environment for the target database systems (re-factored database structure), after the migration plan is generated, the migration engine may be started where the migration engine may determine whether the target database exists on the target server system and if not, the database may be created and formatted on the target system prior to migration and if the database exists, the scripts are run to migrate the changes of the source database (update) to the target database))
“and migrating the source database to the target database, wherein the migration re-platforms the updated granular database components” (Paragraph [0092] and Paragraph [0093] (in preparation to run the migration plan, the migration scripts may be copied to the target database system, where. in some cases, migration scripts may need to be converted to a new format to run on a migration engine, the migration scripts may be converted to a migration-specific format, the migration engine may be configured to control the execution of the migration plan such that the migration scripts are carried out in the correct sequence where the migration engine can execute the migration scripts in sequence and in parallel as appropriate)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Higginson and apply them on teachings of Bai and Ratnapuri for “generating, by the processor, a re-factored database structure, wherein the re-factored database structure is generated by converting the database components of the source database to a format of the target database, breaking the converted database components of the source database into granular components; a re-platform module, for updating granular database components of the target database as per the forecasted assessment statistic and the re-factored database structure; and migrating the source database to the target database, wherein the migration re-platforms the updated granular database components”. One would be motivated as the source databases and the target databases may be subjected to a pre-migration analysis where this analysis may be useful for categorizing objects, identifying objects that require special handling during migration, identifying invalid objects, analyzing the usage and criticality of objects, and/or the like (Hihhinson, Paragraph [0054]).
Bai, Ratnapuri and Higginson do not EXPLICITLY disclose: and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links.
However, in an analogous art, Wilton teaches:
“and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links” (Col 7 Lines 49-67 (the migration service includes a schema conversion service that attempts to convert, without user intervention, the source schema and code, including views, stored procedures and functions (data object code), collectively referred to herein as ‘schema’, to a format compatible with the target DBMS and the target database (in accordance with a design of the target database while retaining the database links) and the conversion functionality may be accessed via the migration service that can be accessed via the migration API and the schema objects in the source database should be converted to a format that is compatible with the target database and the user can use those types of objects within the target database (while retaining the database links))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Wilton and apply them on teachings of Bai, Ratnapuri and Higginson for “and transforming a procedural data object code to data service application programming interface (API) in accordance with a design of the target database while retaining the database links”. One would be motivated as the data changes to the source database that occur during the migration are migrated to the target by the database migration service, the source database remains operational during the migration process. (Wilton, Col 2 Lines 63-67).
As per claim 12:
Bai, Ratnapuri, Higginson and Wilton teach the system as specified in the parent claim 11 above.
Higginson further teaches:
“wherein the quantum change for migrating the source database to the target database is calculated on the basis of the size of the source database” (Paragraph [0091] (a scheduler system may be configured to identify an order of migrating database objects with migration scripts where the scheduler system may identify order based on factors such as the size of the source database objects to be migrated and/or the dependencies of the database objects)).
As per claim 13, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 2 above.
As per claim 14, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 3 above.
As per claim 15, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 4 above.
As per claim 16, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 6 above.
As per claim 18, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 8 above.
As per claim 19, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 9 above.
As per claim 20, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 10 above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bai et al (US PGPUB 20160112510), hereinafter Bai in view of Ratnapuri Thiruchelvan (US PGPUB 20230196237) hereinafter Ratnapuri and in further view of Higginson et al (US PGPUB 20180293233) hereinafter Higginson, Wilton et al (US Patent 10803031) hereinafter Wilton and Apte et al (US PGPUB 20170192758) hereinafter Apte
As per claim 7:
Bai, Ratnapuri, Higginson and Wilton teach the method as specified in the parent claim 1 above.
Bai, Ratnapuri, Higginson and Wilton do not EXPLICITLY disclose: migrating, by the processor, dictionaries and keywords from the source application environment in accordance with the target application environment.
However, in an analogous art, Apte teaches:
“migrating, by the processor, dictionaries and keywords from the source application environment in accordance with the target application environment” (Paragraph [0031], Paragraph [0078] and Paragraph [0082] (the legacy migration module(s) may include one or more workbenches and/or toolkits that may contain one or more applications, tools, and/or modules such as, but not limited to, a language migration toolkit, the language migration toolkit includes a data dictionary that is a list of the programming language specific keywords or tokens and once tokens are extracted from the source language, a data dictionary will get updated where the equivalent target language statements are mapped)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Apte and apply them on teachings of Bai, Ratnapuri, Higginson and Wilton for ““migrating, by the processor, dictionaries and keywords from the source application environment in accordance with the target application environment”. One would be motivated as during Language migration, the transcoder migrates the legacy source code into the parser reads the source token and replace the line with mapped target language syntax to generate the target language and to ensure 100% functional equivalence by simulating the program execution of the migrated application. (Apte, Paragraph [0078]).
As per claim 17, the claim is rejected based upon the same rationale given for the parent claim 11 and the claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pal et al, (US PGPUB 20210271463), A method of application environment migration assesses a source application code of a source application environment, ascertains a quantum change for migrating the source application code to a target application code and forecasts an assessment statistic that provides at least one functional readiness and a timeline to complete the migration of the source application code. Further, scans the source application code for identifying a business logic and generates a re-factored code for the source application code by breaking the source application code into macro-services and repackaging the macro-services in accordance with the target application code while retaining the business logic.
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL K DEWAN whose telephone number is (571) 272-2196. The examiner can normally be reached Mon-Fri 8:00 AM – 5:00 PM (EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TONY MAHMOUDI can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300..
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/Kamal K Dewan/
Examiner, Art Unit 2163
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163