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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 .
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.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan 6/7/2023. It is noted, however, that applicant has not perfected priority. Therefore, effective filing date of application is 7/24/2024.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Regarding claim 1 this part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP §2106.03. The claim recites an apparatus; thus, the claim is directed to a machine which is one of the statutory categories of invention.
Step 2A Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(II) and the October 2019 Update, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The limitations “determine whether or not a scheduling-target job at a current timepoint is to be offloaded to a cloud based on a preset period during which the cloud is to be used, a maximum value of cumulative cost that can be spent to use the cloud during the period, an elapsed time from a starting timepoint of the period to the current timepoint, a cumulative value of costs of jobs for which the cloud has been used during the elapsed time, and a cost in a case in which the scheduling-target job is executed using the cloud, the period, the maximum value, the elapsed time, the cumulative value of costs, and the cost of the scheduling-target job being included in the record information.” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitations as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas. See MPEP §2106.04(a)(2). Accordingly, claim 1 recites a judicial exception (i.e. an abstract idea).
Step 2A, Prong 2, This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
The claim recites the following additional elements “An information processing apparatus comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to,” is recited at a high level of generality (i.e. generic apparatus, memory, processor) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
The additional element “acquire record information from a storage device” does not amount to a practical application, because they are essentially regarding data gathering and applying method for execution. Under step 2B, the courts have identified data gathering as well understood routine and conventional. See MEPE 2106.05d.
Step 2B, This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “An information processing apparatus comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to,” are merely a generic computer or generic computer components to apply the judicial exception which cannot provide an inventive concept.
The claims include additional elements of “acquire record information from a storage device” does not amount an inventive concept, because it fails to meaningfully limit the claim because they are essentially regarding data gathering and applying method for execution. Under step 2B, the courts have identified data gathering as well understood routine and conventional. See MEPE 2106.05d.
Claim 2, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims include additional elements “wherein the one or more processors further:
offloads the scheduling-target job to the cloud if a first threshold that is obtained by dividing the maximum value by the period is greater than a value obtained by dividing, by the elapsed time, a value that is obtained by adding the cost of the scheduling-target job to the cumulative value of costs.” does not integrate the abstract idea into a practical application, nor is significantly more than the abstract idea because it is at best the equivalent of merely adding the words “apply it” to the judicial exception. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
Claim 3 is a dependent claim rejected for the same reasons as claim 2. Furthermore, the claims include additional elements “wherein the one or more processors further: calculates a cloud appropriateness value using the cost when the cloud is used for the scheduling-target job and an index indicative of a quantity of all jobs that are waiting to be scheduled at the current timepoint; and generates a second threshold using the record information, compares the cloud appropriateness value and the second threshold, and, based on a result of the comparison, determines whether or not the scheduling-target job is to be offloaded to the cloud.” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 4, is a dependent claim rejected for the same reasons as claim 3. Furthermore, the claims include additional elements “wherein the generation unit acquires the cloud appropriateness value and the cost for each of a plurality of jobs that have been scheduled during the elapsed time and that are included in the record information, calculates a cumulative value by accumulating the corresponding costs in the order of smaller cloud appropriateness values, and sets, as the second threshold, a value that is obtained by dividing the cumulative value by the elapsed time and that is close to the first threshold.” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 5, is a dependent claim rejected for the same reasons as claim 3. Furthermore, the claims include additional elements “wherein the one or more processors further: in a case in which the index is the number of all of the jobs, calculates the cloud appropriateness value by dividing the number of all of the jobs by the cost, and determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold,” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 6, is a dependent claim rejected for the same reasons as claim 3. Furthermore, the claims include additional elements “wherein the one or more processors further: in a case in which the index is a sum of resource amounts required by all of the jobs, calculates the cloud appropriateness value by dividing the sum of the resource amounts required by all of the jobs by the cost, and determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold,” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 7, is a dependent claim rejected for the same reasons as claim 3. Furthermore, the claims include additional elements “wherein the one or more processors further: in a case in which the index is the number of all of the jobs, calculates the cloud appropriateness value by dividing the number of all of the jobs by a resource amount required by the scheduling-target job, and determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold,” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 8, is a dependent claim rejected for the same reasons as claim 3. Furthermore, the claims include additional elements “wherein the one or more processors further: in a case in which the index is a sum of resource amounts required by all of the jobs, calculates the cloud appropriateness value by dividing the sum of the resource amounts required by all of the jobs by a resource amount required by the scheduling-target job, and determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold,” does not amount to a practical application or an inventive concept, because these are just additional steps to an abstract idea.
Claim 9, is an independent method claim rejected for the same reasons as claim 1.
Claim 10, is an independent medium claim rejected for the same reasons as claim 1. In addition, the claim recites two additional elements –a non-transitory medium that includes a program recorded thereon, and a computer--. The medium and computer are recited at a high-level of generality (i.e., as a generic component) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Unnikrishnan et al. (U.S. PG PUB 2021/0157644).
Regarding claim 1, Unnikrishnan teaches an information processing apparatus comprising:
at least one memory storing instructions (see Fig. 1, 34); and
at least one processor configured to execute the instructions to (see Fig. 1, 16):
acquire record information from a storage device (see ¶[0070] “The cloud resource specification inventory 508 contains information on the various cloud resource bundles available for provisioning from cloud providers 406, including the maximum number of instances, their cost and detailed resource specifications thereof.”); and
determine whether or not a scheduling-target job at a current timepoint is to be offloaded to a cloud based on a preset period during which the cloud is to be used (see ¶[0056] “Cloud bursting is the operation of offloading workloads from local hosts to cloud hosts. Essentially, when workload resource demand exceeds the capacity of resources in a local cluster, additional cloud hosts are provisioned and added to the cluster to meet the resource demand.”), a maximum value of cumulative cost that can be spent to use the cloud during the period (see ¶[0070] “The cloud resource specification inventory 508 contains information on the various cloud resource bundles available for provisioning from cloud providers 406, including the maximum number of instances, their cost and detailed resource specifications thereof.”), an elapsed time from a starting timepoint of the period to the current timepoint (see ¶[0066] “Finally, the data structures 700 may additionally include a cloud hosts release queue 708 of cloud hosts which are to be released within a given time interval from the cluster 402.”), a cumulative value of costs of jobs for which the cloud has been used during the elapsed time (see ¶ [0080] “A larger value for the sub-aspects is desirable, so for computing the scores, the maximum value of the particular sub-aspect among all the combinations is considered. For example, to compute a score for the processor cores sub-aspect, the total number of cores in a combination is divided by the maximum value of total number of cores among all feasible resource bundle combinations.”), and a cost in a case in which the scheduling-target job is executed using the cloud, the period, the maximum value, the elapsed time, the cumulative value of costs (see ¶[0081] “Cost: Total cost incurred for a specific combination of resource bundles for a normalized billing interval (e.g., hourly). The total cost is computed by aggregating the cost of all the individual resource bundles in the combination, and can be expressed as: Total Cost= Σ.sub.k=1.sup.k=nAk*Ck”), and the cost of the scheduling-target job being included in the record information (see ¶0079] “Depending on the workload resource demand constraint (soft or hard) and which subset of resource bundle combinations remains with possible candidates, the cloud allocation optimizer orders the first and/or second subset by each combination's respective level of goodness at step 912. The level of goodness for a combination of cloud resource bundles can be defined based on several aspects such as a) compute power; b) cost; and c) distribution/co-location of resources and their relative desirability for a customer.”).
Because Unnikrishnan discloses multiple embodiments and implementations, and all the findings may be disclosed in different embodiments/implementations, obviousness rejection is made. One of ordinary skill in the art at the time of the invention would be able to combine different embodiments adjacent to each other in the prior art and does not require a leap of inventiveness. Unnikrishnan discloses that these embodiments/implementations are used to build and maintain infrastructure to accommodate spikes in resource usage that occur only occasionally, the cloud bursting environment enables the offload of workloads from the local infrastructure (cluster 402) to cloud hosts 404, and therefore the additional infrastructure is paid for only when it is needed, thereby reducing the total cost of ownership (see ¶[0057] of Unnikrishnan).
Regarding claim 2, Unnikrishnan teaches wherein the one or more processors further:
offloads the scheduling-target job to the cloud if a first threshold that is obtained by dividing the maximum value by the period is greater than a value obtained by dividing, by the elapsed time, a value that is obtained by adding the cost of the scheduling-target job to the cumulative value of costs (see ¶[0082] “Cost Score: Every combination is assigned a cost score between 0 and 1. To calculate the cost score, the cost of the current combination is compared to the minimum cost of all the feasible combinations subsequent to the filtering of step 908. A score of 1 is assigned for the combination with minimum cost, since low cost is desirable. The cost score of other combinations is calculated by dividing the minimum cost by the cost of the current combination. Expensive combinations are thereby assigned lower scores.”).
Regarding claim 3, Unnikrishnan teaches wherein the one or more processors further:
calculates a cloud appropriateness value using the cost when the cloud is used for the scheduling-target job and an index indicative of a quantity of all jobs that are waiting to be scheduled at the current timepoint (see ¶[0075] “Next, in step 908, budget constraints (either soft or hard constraints) are taken into consideration by the cloud allocation optimizer 504. In some embodiments, the cloud allocation optimizer 504 first computes the total cost of each resource bundle combination, and sorts the combinations according to their respective total cost. If the budget constraints are hard set, the cloud allocation optimizer 504 then filters out combinations whose total cost exceeds the remaining safe budget (i.e., the RemainingBudgetSafeEstimate) as computed for the time window. Conversely, if the budget constraints are soft, the cloud allocation optimizer 504 filters out combinations whose total cost exceeds the sum of the remaining safe budget and the budget overflow limit (i.e., the RemainingBudgetSafeEstimate+BudgetOverflowLimit). If the filtering, whether by considering hard or soft budget constraints, results in an empty set of combinations, the cloud allocation optimizer 504 does not provision any cloud resources from cloud providers 506.”); and
generates a second threshold using the record information (see ¶[0069] “BudgetThresholdLimit: A budget threshold limit beyond which the system needs to carefully regulate the usage of the remaining budget to prevent overshooting the budget limit”), compares the cloud appropriateness value and the second threshold, and, based on a result of the comparison, determines whether or not the scheduling-target job is to be offloaded to the cloud (see ¶[0097] “comparing the aggregated cost of the current combination with the minimum cost of all the feasible combinations,”).
Regarding claim 4, Unnikrishnan teaches wherein the generation unit acquires the cloud appropriateness value and the cost for each of a plurality of jobs that have been scheduled during the elapsed time and that are included in the record information, calculates a cumulative value by accumulating the corresponding costs in the order of smaller cloud appropriateness values (see ¶[0081] “Cost: Total cost incurred for a specific combination of resource bundles for a normalized billing interval (e.g., hourly). The total cost is computed by aggregating the cost of all the individual resource bundles in the combination, and can be expressed as: Total Cost=Σ.sub.k=1.sup.k=nAk*Ck”), and sets, as the second threshold, a value that is obtained by dividing the cumulative value by the elapsed time and that is close to the first threshold (see ¶[0082] “Cost Score: Every combination is assigned a cost score between 0 and 1. To calculate the cost score, the cost of the current combination is compared to the minimum cost of all the feasible combinations subsequent to the filtering of step 908. A score of 1 is assigned for the combination with minimum cost, since low cost is desirable. The cost score of other combinations is calculated by dividing the minimum cost by the cost of the current combination. Expensive combinations are thereby assigned lower scores.”).
Regarding claim 5, Unnikrishnan teaches wherein the one or more processors further:
in a case in which the index is the number of all of the jobs, calculates the cloud appropriateness value by dividing the number of all of the jobs by the cost (see ¶[0080] “For example, to compute a score for the processor cores sub-aspect, the total number of cores in a combination is divided by the maximum value of total number of cores among all feasible resource bundle combinations.”), and
determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold (see ¶[0087] “Upon determining a score for each sub-aspect (i.e., from 0 to 1 for each characteristic), a score assignment table 1100 is generated for all remaining combinations of resource bundles, as illustrated in FIG. 11. The table 1100 includes the combination tuple 1000 and a scoring assignment for each sub-aspect as described, and is used to select the most optimal resource combination according to its respective level of goodness according to the aggregate of each sub-aspect scored. Thus, returning to the method 900, the cloud allocation optimizer 504, in a final step 914, selects the optimal combination and generates a cloud resource request 650 according to the combination having the highest score.”).
Regarding claim 6, Unnikrishnan teaches wherein the one or more processors further:
in a case in which the index is a sum of resource amounts required by all of the jobs, calculates the cloud appropriateness value by dividing the sum of the resource amounts required by all of the jobs by the cost (see ¶[0080] “For example, to compute a score for the processor cores sub-aspect, the total number of cores in a combination is divided by the maximum value of total number of cores among all feasible resource bundle combinations.”), and
determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold (see ¶[0087] “Upon determining a score for each sub-aspect (i.e., from 0 to 1 for each characteristic), a score assignment table 1100 is generated for all remaining combinations of resource bundles, as illustrated in FIG. 11. The table 1100 includes the combination tuple 1000 and a scoring assignment for each sub-aspect as described, and is used to select the most optimal resource combination according to its respective level of goodness according to the aggregate of each sub-aspect scored. Thus, returning to the method 900, the cloud allocation optimizer 504, in a final step 914, selects the optimal combination and generates a cloud resource request 650 according to the combination having the highest score.”).
Regarding claim 7, Unnikrishnan teaches wherein the one or more processors further:
in a case in which the index is the number of all of the jobs, calculates the cloud appropriateness value by dividing the number of all of the jobs by a resource amount required by the scheduling-target job (see ¶[0080] “For example, to compute a score for the processor cores sub-aspect, the total number of cores in a combination is divided by the maximum value of total number of cores among all feasible resource bundle combinations.”), and
determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold (see ¶[0087] “Upon determining a score for each sub-aspect (i.e., from 0 to 1 for each characteristic), a score assignment table 1100 is generated for all remaining combinations of resource bundles, as illustrated in FIG. 11. The table 1100 includes the combination tuple 1000 and a scoring assignment for each sub-aspect as described, and is used to select the most optimal resource combination according to its respective level of goodness according to the aggregate of each sub-aspect scored. Thus, returning to the method 900, the cloud allocation optimizer 504, in a final step 914, selects the optimal combination and generates a cloud resource request 650 according to the combination having the highest score.”).
Regarding claim 8, Unnikrishnan teaches wherein the one or more processors further:
in a case in which the index is a sum of resource amounts required by all of the jobs, calculates the cloud appropriateness value by dividing the sum of the resource amounts required by all of the jobs by a resource amount required by the scheduling-target job (see ¶[0080] “For example, to compute a score for the processor cores sub-aspect, the total number of cores in a combination is divided by the maximum value of total number of cores among all feasible resource bundle combinations.”), and
determines whether or not the scheduling-target job is to be offloaded to the cloud based on the cloud appropriateness value and the second threshold (see ¶[0087] “Upon determining a score for each sub-aspect (i.e., from 0 to 1 for each characteristic), a score assignment table 1100 is generated for all remaining combinations of resource bundles, as illustrated in FIG. 11. The table 1100 includes the combination tuple 1000 and a scoring assignment for each sub-aspect as described, and is used to select the most optimal resource combination according to its respective level of goodness according to the aggregate of each sub-aspect scored. Thus, returning to the method 900, the cloud allocation optimizer 504, in a final step 914, selects the optimal combination and generates a cloud resource request 650 according to the combination having the highest score.”).
Regarding claim 9, is an independent method claim corresponding with apparatus claim 1, and is rejected for the same reasons.
Regarding claim 10, is an independent medium claim corresponding with apparatus claim 1, and is rejected for the same reasons. In addition, Unnikrishnan teaches a non-transitory computer readable recording medium that includes a program recorded thereon (see Fig. 1, 34 storage).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.Matsuyama et al. (US PG PUB 2016/0048413) teaches a plurality of information processing apparatuses and a management apparatus to control the information processing apparatuses. Each of the plurality of information processing apparatuses outputs a resource usage quantity variation with respect to a job at a predetermined time interval. The management apparatus generates an execution history containing an attribute of the job and the resource usage quantity variation every time the job is executed, estimates a resource usage quantity of a new job, based on resource usage quantity variations contained in an execution history of a reference job matching the new job in terms of the attribute within a predetermined degree, and specifies the information processing apparatus to be assigned the new job, based on the estimated resource usage quantity.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARINA YUN whose telephone number is (571)270-7848. The examiner can normally be reached Mon, Tues, Thurs, 9-4 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Young can be reached on (571) 270-3180. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Carina Yun
Patent Examiner
Art Unit 2194
/CARINA YUN/Examiner, Art Unit 2194
Prosecution Insights