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
Per MPEP 2001.06(b), Examiner notes that he has reviewed all cited prior art in related application 18/155,384.
Obviousness Type Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-9, 11-12, and 14-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8, 10, 12-13, and 15-19 of U.S. Patent No. 12,399,872. Although the claims at issue are not identical, they are not patentably distinct from each other they recite similar subject matter in the same statutory categories. See chart below.
Instant application claim
‘872 Patent claim
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Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1, 9, and 15 each recites identifying one or more entities that are eligible for data migration to a destination database; generating, using a plurality of planning procedures, a data migration plan for the one or more eligible entities, wherein generating the data migration plan includes: mapping, based on data metric values of the one or more eligible entities, different ones of the eligible entities to one or more instances in the destination database, wherein the mapping is further performed based on utilization metric values of the one or more instances; and altering the mappings of one or more entities to instances in the destination database, wherein altering the mappings is based on determining that a normal range of data for entities mapped to instances in the destination database does not meet a threshold normal range. Identifying entities is evaluating and a mental process, generating a data migration plan, mapping entities to database instances, and altering said mappings are each recited broadly and are mental processes accomplishable in the human mind or on paper. Each claim recites an additional element of causing execution of the generated data migration plan for migrating data of the one or more eligible entities from one or more source databases to the destination database, which is an output step and insignificant extra-solution activity. Claim 9 recites a non-transitory computer-readable medium and clam 15 recites at least one processor and a memory having instructions thereon, which are each generic components of a computer system. Examiner notes specification paragraph 0004 states “Moving data from one storage location to another often results in long downtimes for the data and may impact the response times of the computer system to requests to access the database (e.g., to retrieve data) during such a move,” and paragraph 0013 states a system migrating its data encounters many challenges, including “downtime of user data during migration, entity computational capacity, computational costs, data distribution throughout database instances, entity-specific data availability and efficiency constraints, constraints of both the source and destination databases (e.g., in terms of processing and storage capacity), handling different amounts and types of data for multiple entities, etc.” Paragraph 0013 also states “The disclosed techniques attempt to balance the workload migrating user data from one database to another on a large scale while minimizing e.g., computation costs,” and Examiner found further description of techniques for addressing said problems in paragraphs 0014-0015 and 0016-0017. However, the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the output step is routine and conventional activity per Veettil et al (US 20220237206, paragraph 0080, figure 5 executing a data migration plan). The non-transitory computer-readable medium, at least one processor, and memory having instructions thereon are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2 and 10 each recites wherein the destination database is a cloud database, wherein the instances of the destination database are geographically distributed building blocks of the destination database having different processing and storage capacities, and migrating data to a destination database is storing data which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claims 3, 11, and 17 each recites wherein the mapping includes mapping an entity that has a largest data metric value relative to other eligible entities to an instance in the destination database that corresponds to a minimum utilization metric value relative to other instances in the destination database, and mapping the entity is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 4 recites wherein the mapping is further performed based on one or both of capacity thresholds of the one or more instances of the destination database and an anchor identifier assigned to one or more eligible entities, and mapping the entity is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claims 5 and 12 each recites determining, using a multi-dimensional knapsack procedure based on the mapping, a number of migration events for migrating data for the one or more eligible entities from the one or more source databases to the destination database, and determining a number of migration events is evaluating and a mental process; and assigning respective ones of the one or more eligible entities to different ones of the migration events, and assigning entities to events is evaluating and a mental process. Claim 6 recites wherein updating the generated data migration plan includes executing at least a workload balancing procedure included in the plurality of planning procedures based on results of simulating the migration of data according to the generated data migration plan, and executing a workload balancing procedure is executing generic software and involves evaluation of the workload and is a mental process. Claim 7 recites determining the data metric values of the one or more eligible entities, wherein the data metric values are for one or more of the following types of metrics: a balance metric indicating database central processing unit and storage utilization, a constraint metric indicating requirements of an eligible entity on the instances of the destination database, a date and region eligibility metric indicating a location and a date at which data is migratable for an eligible entity, and a database instance capacity threshold, and determining data metric values is evaluating and a mental process.
Claim 8 recites determining whether to exclude one or more of the eligible entities from the data migration plan, wherein the determining is performed based on the eligible entities being included in a previously generated migration plan, and determining to exclude entries is evaluating and a mental process. Claim 13 recites wherein the data metric values include at least a number of entities allowed to be included within a given migration event, relief cycles of the one or more eligible entities, and locations of the one or more eligible entities, and data metric values are data which is a mental process accomplishable in the human mind or on paper. Claim 14 recites generating, based on executing the generated migration plans, a performance report, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and altering, based on the performance report, one or more of the generated migration plans using one or more of the plurality of planning models, and altering a plan is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 16 recites wherein the one or more source databases and the destination database are local databases that store data for the one or more entities locally to an enterprise server that gathers data for the one or more entities, and storing data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II.
Claim 18 recites wherein the generated data migration plan includes individual migration plans generated for respective ones of the one or more eligible entities, wherein during the generating the individual migration plans impact one another and are executed independently of one another, and generating individual migration plans is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 19 recites convert, using a gear ratio, one or more metrics of the one or more eligible entities on the one or more source databases to expected metric values on the destination database, and converting is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 20 recites wherein the one or more metrics of the eligible entities include database central processing unit (CPU) time and utilization, and metrics are data which is a mental process accomplishable in the human mind or on paper.
Rejections under 35 U.S.C. 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 1-4, 7-9, 11, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Shah et al (US 20210349865), hereafter Shah, in view of Buehne et al (US 12,174,804), hereafter Buehne.
With respect to claims 1, 9, and 15, Shah teaches:
identifying one or more entities that are eligible for data migration to the destination database (paragraph 0024 receiving a request to migrate source data of the CPQ platform to the target CPQ platform, paragraph 0028 source data may include information identifying an entity associated with the source data, an item or one or more objects);
generating, using a plurality of planning procedures, a data migration plan for the one or more eligible entities (paragraph 0034 target mapping may include a set of mapping rules that as individual sets of the source data to target elements of the target CPQ template) where generating the data migration plan includes:
mapping, based on data metric values of the one or more eligible entities, different ones of the eligible entities to one or more instances in the destination database (paragraph 0033, generating a target mapping, paragraph 0026, the request may include information such as information identifying user, user device, source CPQ platform, source data, source data structure storing source data and target CPQ platform); and
causing execution of the generated data migration plan for migrating data of the one or more eligible entities from one or more source databases to the destination database (paragraph 0017, the data migration system may determine using a migration analysis model a target mapping for the source data and migrate the source data from the source platform to the target platform).
Shah does not teach:
wherein the mapping is further performed based on utilization metric values of the one or more instances; and
altering the mappings of one or more entities to instances in the destination database, wherein altering the mappings is based on determining that a normal range of data for entities mapped to instances in the destination database does not meet a threshold normal range.
Buehne teaches these things:
wherein the mapping is further performed based on utilization metric values of the one or more instances (column 20, lines 25-55, collecting performance data, gathering information of detected events, collected performance data selected to provide relevant database metrics for the migration analysis process. Capturing the production workload for a selection of databases such that they can be mapped to a destination environment); and
altering the mappings of one or more entities to instances in the destination database, wherein altering the mappings is based on determining that a normal range of data for entities mapped to instances in the destination database does not meet a threshold normal range (column24 lines 15-20, migration analysis, generate migration plan in the background and periodically transmit performance information to migration infrastructure which can generate preemptive migration plans and analysis, also columns 25-26 lines 56-24, migration infrastructure utilizes pattern data for migration analyses, column 28 lines 16-32 describing thresholds for patterns in databases).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Shah’s data migration system by utilizing a workload balancing procedure and utilization metrics in Buehne because Shah describes (paragraph 0017), determining target mapping for source data-based migration analysis. As such, providing performance data, gathering information of detected events, collected performance data selected to provide relevant database metrics for the migration analysis process and capturing the production workload as described by Buehne (column 20, lines 25-55) would provide for a selection of databases such that they can be mapped to a destination environment in order to conserve computing resources as described by Shah at (paragraph 0017).
With respect to clam 9, Shah teaches a non-transitory computer-readable medium (paragraph 0005 non-transitory computer-readable medium storing one or more instructions).
With respect to claim 15, Shah teaches at least one processor (paragraph 0089 figure 6, processors 607) and a memory having instructions thereon (paragraph 0089 figure 6, memory having instructions 608).
With respect to claim 2, all the limitations in claim 1 are addressed by Shah and Buehne above. Shah also teaches wherein the destination database is a cloud database, wherein the instances of the destination database are geographically distributed building blocks of the destination database having different processing and storage capacities (paragraph 0003, request to migrate source data associated with source platform to target platform, associated with plurality of target platform, paragraph 0021, the data migration system may be hosted by a cloud computing environment or by one or more server devices, and may be associated with one or more user devices, source CPQ platforms, and/or target platforms (e.g., including the user device, the source CPQ platform, and the target CPQ platform discussed above).
With respect to claims 3, 11, and 17, all the limitations in claims 1, 9, and 15 are addressed by Shah and Buehne above. Shah also teaches wherein the mapping includes mapping an entity that has a largest data metric value relative to other eligible entities to an instance in the destination database that corresponds to a minimum utilization metric value relative to other instances in the destination database (paragraph 0017, as the data migration system may determine using a migration analysis model a target mapping for the source data and migrate the source data from the source platform to the target platform).
With respect to claim 4, all the limitations in claim 1 are addressed by Shah and Buehne above. Buehne also teaches wherein the mapping is further performed based on one or both of capacity thresholds of the one or more instances of the destination database and an anchor identifier assigned to one or more eligible entities (column 20 lines 25-55, collect performance data, gathers information of detected events, collected performance data selected to provide relevant database metrics for the migration analysis process. Capturing the production workload for a selection of databases such that they can be mapped to a destination environment, column 24 lines 15-20, as migration analysis, generate migration plan in the background and periodically transmit performance information to migration infrastructure which can generate preemptive migration plans and analysis).
With respect to claim 7, all the limitations in claim 1 are addressed by Shah and Buehne above. Buehne also teaches determining the data metric values of the one or more eligible entities, wherein the data metric values are for one or more of the following types of metrics: a balance metric indicating database central processing unit and storage utilization, a constraint metric indicating requirements of an eligible entity on the instances of the destination database, a date and region eligibility metric indicating a location and a date at which data is migratable for an eligible entity, and a database instance capacity threshold (column 20 lines 25-55, collect performance data, gathering information of detected events, collected performance data selected to provide relevant database metrics for the migration analysis process. Capturing the production workload for a selection of databases such that they can be mapped to a destination environment, column 24 lines 15-20, migration analysis, generate migration plan in the background and periodically transmit performance information to migration infrastructure which can generate preemptive migration plans and analysis, column 35 lines 1-16 using database capacity thresholds for migration plans).
With respect to claim 8, all the limitations in claim 1 are addressed by Shah and Buehne above. Shah also teaches determining whether to exclude one or more of the eligible entities from the data migration plan, wherein the determining is performed based on the eligible entities being included in a previously generated migration plan (paragraph 0034, the data mapper may include, in the target mapping, information identifying any unmapped element such as, for example, object(s) and/or field(s) of the source data not mapped to the target CPQ template, object(s) and/or field(s) of the target CPQ template not mapped to the source data, and/or the like. In other words, there may be one or more objects, fields, and/or the like of the source data for which there are no corresponding objects, fields, and/or the like in the target CPQ template. Likewise, there may be one or more objects, fields, and/or the like of the target CPQ template for which there are no corresponding objects fields, and/or the like in the source data. The data migration system may determine a mapping for unmapped element(s) using a migration analysis model, as explained in more detail below).
With respect to claim 11, all the limitations in claim 1 are addressed by Shah and Buehne above. Buehne also teaches wherein the mapping includes mapping an entity that has a largest data metric value relative to other eligible entities to an instance in the cloud database that corresponds to a minimum utilization metric value relative to other instances in the cloud database (column 20 lines 25-55, collect performance data, gathering information of detected events, collected performance data selected to provide relevant database metrics for the migration analysis process. Capturing the production workload for a selection of databases such that they can be mapped to a destination environment, column24 lines 15-20, as migration analysis, generate migration plan in the background and periodically transmit performance information to migration infrastructure which can generate preemptive migration plans and analysis).
With respect to claim 16, all the limitations in claim 15 are addressed by Shah and Buehne above. Buehne also teaches wherein the one or more source databases and the destination database are local databases that store data for the one or more entities locally to an enterprise server that gathers data for the one or more entities (column 7 lines 22-32, distributed system 100 may also include one or more databases. Databases may reside in a variety of locations. By way of example, one or more of databases may reside on a non-transitory storage medium local to (and/or resident in) server. Alternatively, databases may be remote from server and in communication with server via a network-based or dedicated connection. In one set of embodiments, databases may reside in a storage-area network (SAN). Similarly, any necessary files for performing the functions attributed to server may be stored locally on server and/or remotely, as appropriate).
With respect to claim 18, all the limitations in claim 15 are addressed by Shah and Buehne above. Buehne also teaches wherein the generated data migration plan includes individual migration plans generated for respective ones of the one or more eligible entities, wherein during the generating the individual migration plans impact one another and are executed independently of one another (column 26 lines 17-20, for example, the one or more migration control engines may migrate two or more database systems in parallel with a combination of different migration methods. The parallel/simultaneous migrations of databases and column 26, lines 25-39, in some embodiments, migration method qualification could entail a migration scoring system where databases are scored according to any one or combination of the various disclosed herein. The migration scoring system could be correlated to the category scheme in some embodiments, such that certain scores satisfying certain score thresholds correspond to certain categories, each of which may be mapped to one or more particular migration methods. Some embodiments may score databases with numerical expressions. Various embodiments may determine a migration score based on any one or more suitable quantifiers. In some embodiments, a migration score may be cumulative of individual scores based on matching each type of the characteristics. With migration scores determined, categorizations may be made based on the scores).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shah and Buehne and further in view of Sharma et al (US 20220086241), hereafter Sharma.
With respect to claim 14, all the limitations in claim 9 are addressed by Shah and Buehne above. The combination of Shah and Buehne does not teach:
generating, based on executing the generated migration plans, a performance report; and
altering, based on the performance report, one or more of the generated migration plans using one or more of the plurality of planning models.
Sharma teaches these things:
generating, based on executing the generated migration plans, a performance report (paragraph 0079 migration plan with a generated assessment); and
altering, based on the performance report, one or more of the generated migration plans using one or more of the plurality of planning models (paragraph 0079 modifies the plan if needed based on the generated assessment).
It would have been obvious to have combined this performance report and altering functionality in Sharma with the techniques for migration plans in Shah and Buehne to improve the migration plan via an assessment, making the plan more user-friendly.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Shah and Buehne and further in view of Vichare et al (US 20230056042), hereafter Vichare.
With respect to claim 5 and 12, all the limitations in claims 1 and 9 are addressed by Shah and Buehne above. The combination of Shah and Buehne does not teach:
determining, using a multi-dimensional knapsack procedure based on the mapping, a number of migration events for migrating data for the one or more eligible entities from the one or more source databases to the destination database ; and
assigning respective ones of the one or more eligible entities to different ones of the migration events.
Vichare also teaches these things:
determining, using a multi-dimensional knapsack procedure based on the mapping, a number of migration events for migrating data for the one or more eligible entities from the one or more source databases to the destination database (paragraph 0148 using knapsack-type procedures to determine migration events involving cloud performance tiers); and
assigning respective ones of the one or more eligible entities to different ones of the migration events (paragraph 0148 using knapsack-type procedures to determine migration events involving cloud performance tiers).
It would have been obvious to have combined the techniques for migration plans in Shah and Buehne with the use of knapsack-style techniques in Vichare to optimize said migration plans using machine learning as Shah does (paragraph 0023).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 6/23/26
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