DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/810,601 filed on October 10, 2025. Claims 1-5, 9-13, and 15-20 are pending with the application. Claims 6-8 and 14 have been cancelled. Claims 15-20 have been added.
Examiner Notes
The 112(f) interpretation previously raised has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The applicant states support for the amendments are found in the specification but the specification appears to be silent. The specification is not clear on how the newly claimed limitation of an “receive real-time network operating data; generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information” is described. The specification appears to describe the real-time network operating data relates to the capacities of the network at various dates and times, which are not necessarily correlated to backup jobs. Rather, such operating data is collected to understand fluctuations in, e.g., bandwidth and proxies (Specification Paragraphs 0040 and 0053). The specification also appears to describe separate from data on backup jobs, the AI is better informed by knowing states of the network whether or not backup jobs are running at those times. As with backup operations data, network operating data can include a wide range of metrics (Specification Paragraphs 0037 and 0050). However, the specification does not appear to support receiving real-time network operating data to generate a historical resource parameter schedule comprising date, time, and data capacity information.
All dependent claims that ultimately depend on claims 1 are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 9-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1, the limitations directed towards generate, based on the periodic backup operations data, a historical resource utilization schedule, generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information, train an artificial intelligence (AI) model based on historical the resource utilization schedule and the historical resource parameter schedule, generate, using the AI, and recommend an optimized schedule for the data backup and/or restore operation, is a process that, under its broadest reasonably interpretation, covers performance of these limitation in the mind but for the recitation of generic computer components. That is, other than reciting a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data nothing in the claim precludes these steps from practically being performed in the mind and/or by a human with pen and paper and organizing human activity.
For example, but for the limitations stating a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data, the mention of generate, based on the periodic backup operations data, a historical resource utilization schedule, generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information, train an artificial intelligence (AI) model based on historical the resource utilization schedule and the historical resource parameter schedule, generate, using the AI, and recommend an optimized schedule for the data backup and/or restore operation, encompasses a user observing network data and determining a historical schedule and optimized schedule for data backup. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites abstract ideas.
The judicial exception is not integrated into a practical application by additional elements. In particular, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data. A system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of storing and reading data) such that it amounts to no more than mere instructions to apply the exception. a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is considered by the examiner to be mere data gathering such that it amounts to no more than insignificant extra solution activity, wherein the system gathers data from a model and operating data corresponding to operating states from one or more resources. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional elements, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network e.g., using the internet to gather data, Symantec (see MPEP 2106.05(d))). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. To further elaborate, the additional limitations of a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Claim 1 is not patent eligible.
Claim 10 recites similar limitations as in claim 1. Therefore claim 10 is rejected for the same reasons as set forth above. See claim 1 for analysis.
With respect to claims 2, 11, and 16 the limitations are directed towards wherein the processors are configured to execute further processor-executable routines to estimate a resource usage based on the recommended schedule or a schedule selected by a user for the data backup and/or restore of the backup data. The elements directed to estimate a resource usage based on the recommended schedule or a schedule selected by a user for the data backup and/or restore of the backup further elaborates the abstract idea and the human mind and/or with pen and paper can generate the historical resource utilization schedule based on the periodic operating data. The additional elements the processors are configured to execute further processor-executable routines are interpreted to merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claims 2, 11, and 16 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claims 3 and 12, the limitations are directed towards the processors are configured to execute further processor-executable routines to periodically or continuously retrain the Al model based on an estimated time and an actual time taken for the data backup and/or restore operation. These additional elements merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claims 3 and 12, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claims 4, 9, and 13, the limitations are directed towards wherein the periodic backup operations comprises client device parameters, proxy parameters, file system parameters, data backup system parameters, data backup server parameters, network parameters, parallelization parameters, dataset type, dataset size, day of the week, time of the day, and time taken for data backup and/or restore for the training datasets. These additional elements merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claims 4, 9, and 13, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 5 and 15, the limitations are directed towards wherein the periodic backup operations is generated in real-time and/or based on a pre-defined configuration. These additional elements merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claims 5, does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 17 the limitations are directed towards training the AI model based on periodic backup operations historical data corresponding to data backup and/or restore of one or more training datasets. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can train the AI model based on periodic backup operations historical data corresponding to data backup and/or restore of one or more training datasets. Therefore, claim 17 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 18 the limitations are directed towards estimating a cost for the data backup and/or restore of the backup data based on the estimated resource usage. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can estimate a cost for the data backup and/or restore of the backup data based on the estimated resource usage. Therefore, claim 18 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 19 the limitations are directed towards estimating a resource usage based on a schedule selected by a user for the data backup and/or restore of the backup data. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can estimate a resource usage based on a schedule selected by a user for the data backup and/or restore of the backup data. Therefore, claim 19 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 20 the limitations are directed towards estimating a cost for the data backup and/or restore of the backup data based on the estimated resource usage. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can estimate a cost for the data backup and/or restore of the backup data based on the estimated resource usage. Therefore, claim 20 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 10, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Bedadala et al. (U.S. Publication No.: US 20210034571 A1) hereinafter Bedadala, in view Wertheimer et al. (U.S. Publication No.: US 20150254141 A1) hereinafter Wertheimer, and in further view of Oosaki et al. (U.S. Publication No.: US 20050125467 A1) hereinafter Oosaki.
As to claim 1:
Bedadala discloses:
A system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment [Paragraph 0405 teaches improve the efficiency of the information management system by delaying generation of a backup index to a period of time when load on the information management system is reduced. For example, the backup index may be generated after scheduled business hours or when less users are accessing resources of the information management system. The backup index generation may be delayed without losing backup metadata that enables access of data at the network storage system 302 by generating transaction log files that identify the transactions performed with respect to the network storage system 302.], the system comprising:
one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment [Paragraph 0207 teaches information related to trending, predictions, job, cell or component status, risk, service level, costing, etc. can generally be provided to users via user interface 158 in a single integrated view or console (not shown). Report types may include: scheduling, event management, media management and data aging. Available reports may also include backup history. Paragraph 0456 teaches a backup schedule is generated for the client computing device 102 based at least in part on a prediction function or parameter model. Paragraph 0457 teaches the prediction function or parameter model may be generated using a machine learning algorithm. Historical data associated with the client computing device 102, other computing devices of the primary storage subsystem 117 or other computing devices having the same role or classified similarly to the client being device 102 may be supplied to the machine learning algorithm to generate the prediction function.]
train an artificial intelligence (AI) model based on historical the resource utilization schedule and the historical resource parameter schedule [Paragraph 0457 teaches the prediction function or parameter model may be generated using a machine learning algorithm. Historical data associated with the client computing device 102, other computing devices of the primary storage subsystem 117 or other computing devices having the same role or classified similarly to the client being device 102 may be supplied to the machine learning algorithm to generate the prediction function.]
generate, using the AI, and recommend an optimized schedule for the data backup and/or restore operation [Paragraph 0457 teaches based on the profile information and/or other historical data, machine learning algorithm may generate the prediction function, which may be used to predict a backup schedule for a particular client.]
Bedadala discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose generate, based on the periodic backup operations data, a historical resource utilization schedule and receive real-time network operating data; generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information.
Wertheimer discloses:
generate, based on the periodic backup operations data, a historical resource utilization schedule [Paragraph 0032 teaches based on the resource availability of tape library 150, tape management software 134 may generate utilization schedule 136, which defines a distribution of working backup data 146 as archived backup data 156 using tape drives 151 at scheduled times. Utilization schedule 136 may be configured to favor a continuous, load-balanced usage of tape drives 151 to minimize resource contention, optimize hardware utilization, and keep tape drives available for restore jobs or new backup jobs. Note: Generating utilization schedule based on resource availability data which much include a historical observation at data resource usage to determine available resource usage after the resources are used (historical) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala, by incorporating generating utilization schedule based on storage availability data which much include a historical observation at data in storage to determine the amount of storage available after data is stored, as taught by Wertheimer (see Paragraph 0032), because both applications are directed to backup scheduling; incorporating generating utilization schedule based on storage availability data which much include a historical observation at data in storage to determine the amount of storage available after data is stored improves the utilization of backup storage and reduces infrastructure and administration costs (see Wertheimer Paragraph 0031).
Bedadala and Wertheimer discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose receive real-time network operating data; generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information.
Oosaki discloses:
receive real-time network operating data; generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information [Paragraph 0088 teaches feature information or attribute information can be related to each of the storing areas. Namely, information about backup data to be archived in each of the storing area can be related to the backup data, and archived. The feature information is information showing the features of the backup data, which is, for example, a date when the backup is performed, features of the data at the time of the backup, information about compression (compressed or not, form of the compression, etc.), data capacity, etc. Paragraph 0089 teaches the attribute information is information relating to information on a… backup schedule. Note: The specification is silent on describing a historical resource parameter schedule. Receiving information related to data that is to be backed up, wherein the information includes historical date, time, and capacity information to generate a historical log or historical data structure (real-time network operating data to generate a historical data structure) reads on the claims. The examiner interprets the claimed “receive real-time network operating data; generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information” to be receiving real-time network operating data that includes historical date, time, and capacity information to generate a log or data structure comprising that data.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala and Wertheimer, by incorporating receiving information related to data that is to be backed up, wherein the information includes historical date, time, and capacity information to generate a historical log or historical data structure, as taught by Oosaki (see Paragraph 0088), because the three applications are directed to backup scheduling; incorporating receiving information related to data that is to be backed up, wherein the information includes historical date, time, and capacity information to generate a historical log or historical data structure improves the security level of the data (see Oosaki Paragraph 0040).
Claim 10 recites similar limitations as in claim 1. Therefore claim 10 is rejected for the same reasons as set forth above. See claim 1 for analysis.
As to claim 18:
Bedadala discloses:
The method of claim 11, further comprising estimating a cost for the data backup and/or restore of the backup data based on the estimated resource usage [Paragraph 0207 teaches information related to trending, predictions, job, cell or component status, risk, service level, costing, etc. can generally be provided to users via user interface 158 in a single integrated view or console (not shown). Report types may include: scheduling, event management, media management and data aging. Available reports may also include backup history.]
Claim 20 recites similar limitations as in claim 11. Therefore claim 20 is rejected for the same reasons as set forth above. See claim 11 for analysis.
Claim(s) 2, 11, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Bedadala et al. (U.S. Publication No.: US 20210034571 A1) hereinafter Bedadala, in view Wertheimer et al. (U.S. Publication No.: US 20150254141 A1) hereinafter Wertheimer, view of Oosaki et al. (U.S. Publication No.: US 20050125467 A1) hereinafter Oosaki, and further in view Thomas et al. (U.S. Patent No.: US 10083094 B1) hereinafter Thomas.
As to claim 2:
Bedadala, Wertheimer, and Oosaki discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose estimate a resource usage based on the recommended schedule for the data backup.
Thomas discloses:
The system of claim 1, further comprising: estimate a resource usage based on the recommended schedule for the data backup or a schedule selected by a user for the data backup and/or restore of the backup data [Column 7 Lines 27-36 teaches backup resource predictor 320 can calculate an average throughput (e.g., the amount of data backed up from a source system 120 per second) from previous backup operations (which may be time-weighted or averaged over the most recent n data points for the backup job). Using the calculated average throughput and the current size of the data to be backed up, backup resource predictor 320 can generate an estimated amount of time to complete a backup operation on the identified source systems 120. Column 7 Lines 57-61 teaches scheduler 410 provides proposed backup start times to activity predictor 134, which, as described above, estimates source system resource utilization, backup job resource utilization, and backup job duration. Column 9 Lines 21-30 teaches the backup system schedules a backup job to meet a recovery point objective associated with the backup job. The backup job determines a latest completion time for the backup job based on the timestamp of the most recent backup and the RPO specified for the backup job. Based on the estimated resource primary workload resource utilization and the estimated backup resource utilization, the backup system determines when a backup can be performed to meet the RPO while imposing minimal impact to the primary workload at the targeted source systems 120. Note: A resource predictor (resource usage estimator) that predicts resource utilization (estimate a resource usage) for (based on) proposed scheduling (recommended/selected schedule) data backup, wherein a user can be any operator of the system reads on the claimed a resource usage estimator configured to estimate a resource usage based on the recommended schedule for the data backup.] and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala, Wertheimer, and Oosaki, by incorporating a resource predictor that predicts resource utilization for proposed scheduling data backup, as taught by Thomas (see Column 7 Lines 27-36, Column 7 Lines 57-61, and Column 9 Lines 21-30), because four applications are directed to backup scheduling; incorporating a resource predictor that predicts resource utilization for proposed scheduling data backup provides better, more accurate predictions of activity and available resources at a source system, and consequently make more accurate predictions of the amount of time that a given set of backup operations need for completion (see Thomas Column 5 Lines 48-52).
Claims 7, 11, 16, and 19 recite similar limitations as in claim 2. Therefore claim 7, 11, 16, and 19 are rejected for the same reasons as set forth above. See claim 2 for analysis.
Claim(s) 3, 5, 12, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Bedadala et al. (U.S. Publication No.: US 20210034571 A1) hereinafter Bedadala, in view Wertheimer et al. (U.S. Publication No.: US 20150254141 A1) hereinafter Wertheimer, view of Oosaki et al. (U.S. Publication No.: US 20050125467 A1) hereinafter Oosaki, and in further of Wang et al. (U.S. Publication No.: US 20210255926 A1) hereinafter Wang.
As to claim 3:
Bedadala, Wertheimer, and Oosaki discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose periodically or continuously retrain the Al model based on the estimated time and actual time taken for the data backup.
Wang discloses:
The system of claim 1, wherein the processors are configured to execute further processor-executable routines to periodically or continuously retrain the Al model based on the estimated time and actual time taken for the data backup [Paragraph 0044 teaches the method 200 can be repeated automatically (e.g., without human intervention) periodically. The archived backup job samples can be continuously updated with performed backup job data each time the method is repeated. The ML models continue to be trained with each repetition of the method based on updated backup job data, to hone the one or more ML models of the first prediction algorithm and the one or more ML models of the second prediction algorithm. Paragraph 0063 teaches at error evaluation module 414, an error (e.g., variance and/or standard deviation) of the first prediction algorithm is determined based on difference between the predicted execution durations and actual execution durations. If the error is beyond a threshold value then the models can be retrained and re-applied at modules 406, 408, 410, 412, and 416. Paragraph 0064 teaches the one or more ML models of the second prediction algorithm 403 can include an AutoRegressive Integrated Moving Average (ARIMA) model. Training can include arranging the archived backup jobs 312 into training dataset 402 so that each job sample includes a job scheduled timestamp and an execution duration. Paragraph 0066 training module 418 can train the ARIMA model with the training dataset 402. Note: Training module continuously training and retraining (continuously retrain) ARIMA models (AI model) with backup archived backup job samples that include actual execution durations (actual time taken for a data backup) and predicted execution durations (estimated time) reads on the claimed the training module is further configured to periodically or continuously retrain the Al model based on the estimated time and actual time taken for a data backup.] and/or restore operation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala, Wertheimer, and Oosaki, by incorporating a training module continuously training and retraining (continuously retrain) ARIMA models (AI model) with backup archived backup job samples that include actual execution durations (actual time taken for a data backup) and predicted execution durations (estimated time), as taught by Wang (see Paragraph 0044, 0063, and 0066), because the four applications are directed to backup scheduling; incorporating receiving information related to data that is to be backed up, wherein the information includes historical date, time, and capacity information to generate a historical log or historical data structure improves accuracy of the predictions (see Wang Paragraph 0050).
Claim 12 and 17 recites similar limitations as in claim 3. Therefore claim 12 and 17 are rejected for the same reasons as set forth above. See claim 3 for analysis.
As to claim 5:
Bedadala, Wertheimer, and Oosaki discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose periodically or continuously retrain the Al model based on the estimated time and actual time taken for the data backup.
Wang discloses:
The system of claim 1, wherein the periodic backup operation data is generated in real-time [Paragraph 0050 teaches archived backup job data is logged during backup of the same clients that current backup jobs are to be performed upon, so that the archived backup jobs data 312 accurately represent the environment (and possible changes) of the backup agent capacity predictor. Paragraph 0051 teaches a backup job execution duration is influenced by numerous factors and variations inherent in a client, backup server and target storage domains. Based on observation and analysis of archived backup job data, some data variables are found to influence backup job execution duration, and thus are treated as machine learning features. Such features can include, for example: asset type, Job start time, Target storage, Backup size, and Backup recurrence. Note: Logging (generated) current (real-time) job observed start times (operating data) into archived backup jobs reads on the claimed wherein the operating data is generated in real-time.] and/or based on a pre-defined configuration.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala, Wertheimer, and Oosaki, by incorporating a logging (generated) current (real-time) job observed start times (operating data) into archived backup jobs, as taught by Wang (see Paragraph 0050 and 0051), because the four applications are directed to backup scheduling; incorporating a logging (generated) current (real-time) job observed start times (operating data) into archived backup jobs improves accuracy of the predictions (see Wang Paragraph 0050).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Wang et al. (U.S. Publication No.: US 20210255926 A1) hereinafter Wang, in view Thomas et al. (U.S. Patent No.: US 10083094 B1) hereinafter Thomas, in view of Bykov et al. (U.S. Publication No.: US 20190354399 A1) hereinafter Bykov, and further in view of Kumbhari (U.S. Patent No.: US 8489834 B1) hereinafter Kumbhari.
As to claim 15:
Kumbhari discloses:
The system of claim 1, wherein the periodic backup operations data is generated based on a pre-defined configuration [Column 12 Lines 15-19 teach in the example shown in FIG. 3, the storage data transfer operation is a periodic backup. The periodic backup transfers data through the switch fabric between one or more storage devices at scheduled times, intervals, and/or according to specific events.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bedadala, Wertheimer, and Oosaki, by incorporating a logging (generated) current (real-time) job observed start times (operating data) into archived backup jobs, as taught by Wang (see Paragraph 0050 and 0051), because the four applications are directed to backup scheduling; incorporating a logging (generated) current (real-time) job observed start times (operating data) into archived backup jobs improves accuracy of the predictions (see Wang Paragraph 0050).
Allowable Subject Matter
Claim 4, 9, and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and upon overcoming the 101 rejection.
Response to Arguments
Applicant’s arguments with respect to the 103 rejection of claim 1-5, 9-13, and 15-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant presents the following arguments in October 10, 2025 remarks pages 8-9:
“…claims do not recite a judicial exception but rather involve certain concepts in the context of a technological implementation..”
Examiner respectfully presents the following response to Applicant’s remarks:
Applicant’s arguments have been fully considered but they are not persuasive. Regarding independent claim 1 but for the limitations stating a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data, the mention of generate, based on the periodic backup operations data, a historical resource utilization schedule, generate, based on the real-time network operating data, a historical resource parameter schedule comprising date, time, and data capacity information, train an artificial intelligence (AI) model based on historical the resource utilization schedule and the historical resource parameter schedule, generate, using the AI, and recommend an optimized schedule for the data backup and/or restore operation, encompasses a user observing network data and determining a historical schedule and optimized schedule for data backup. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, therefore examiner maintains the claim recites abstract ideas.
The judicial exception is not integrated into a practical application by additional elements. In particular, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data. A system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of storing and reading data) such that it amounts to no more than mere instructions to apply the exception. a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is considered by the examiner to be mere data gathering such that it amounts to no more than insignificant extra solution activity, wherein the system gathers data from a model and operating data corresponding to operating states from one or more resources. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional elements, a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network e.g., using the internet to gather data, Symantec (see MPEP 2106.05(d))). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. To further elaborate, the additional limitations of a system to optimize scheduling of a data backup and/or restore operation of a backup data set in a network environment, the system comprising, one or more memories storing processor-executable routines and one or more processors communicatively coupled to the memories, wherein the processors are configured to execute the processor-executable routines to: receive periodic backup operations data corresponding to resource utilization for prior data backup and/or restore operations in the network environment, and receive real-time network operating data does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. The examiner maintains claim 1 is not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EARL ELIAS/Examiner, Art Unit 2169
/YU ZHAO/Primary Examiner, Art Unit 2169