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 .
The present application claims priority to U.S. Provisional Patent Application 63/581,495. The priority claim is acknowledged by the examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/19/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note
The Examiner cites particular columns, paragraphs, figures, 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 also apply. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in its 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.
Claim Objections
Claims 14-20 are objected to because of the following informalities: each claim recites “computer-readable storage device”. The instant specification provides the following disclosure: “Such computer-readable media and/or storage media are distinguished from and non-overlapping with communication media and propagating signals (do not include communication media and propagating signals)” [0148]. While the instant specification defines computer-readable storage media, the instant specification does not clearly define a computer-readable storage device to explicitly exclude signals and carrier waves. For the purposes of examination, the Examiner interprets the claims as a “computer-readable media”, which is defined in the specification as explicitly excluding signals and carrier waves.
Appropriate correction is required.
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 1-3, 5, 7-9, 11, 14-16, and 18 of the current application, hereafter 861, are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-5, 8-9, 11-12, 15-16, and 18-19 of copending U.S. Application No. 18/484,853, hereafter 853, in view of Vladimirskiy (US 20230096984 A1).
18/484,861 (current application)
(18/484,853)
Claim 1: A system, comprising:
a processor;
and a memory device that stores program code structured to cause the processor to:
Claim 1: A system, comprising:
a processor;
and a memory device that stores program code structured to cause the processor to:
allocate resources to a user in
response to the user logging into a database;
determine the user logged out of the database subsequent to the user logging in;
reclaim resources from a user in
response to the user
logging out of
a database;
determine a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
determine a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
calculate a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
calculate a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold:
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold
select from the set the probability having a greatest likelihood;
select from the set the probability having a greatest likelihood;
determine whether
a time of predicted activity in
the time window associated with the selected probability
is within an upcoming predetermined length of time;
in response to the time of predicted activity being determined to be within the upcoming predetermined length of time, maintain the allocation of the resources to the user.
reallocate the resources to the user at
a time associated with
the time window corresponding to the selected probability.
Claim 2: The system of claim 1, wherein to calculate the plurality of probabilities, the program code is further structured to cause the processor to:
calculate the probability for a login pattern corresponding to a time window as a ratio of:
a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 2: The system of claim 1, wherein to calculate the plurality of probabilities, the program code is further structured to cause the processor to:
calculate the probability for a login pattern corresponding to a time window as a ratio of:
a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 3: The system of claim 1, wherein to maintain the allocation of the resources to the user, the program code is further structured to cause the processor to:
predict a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
Claim 5: The system of claim 1, the program code further structured to cause the processor to:
predict a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
and maintain the allocation of the resources to the user during the predicted time period.
and maintain the reallocation of the resources to the user during the predicted time period.
Claim 5: The system of claim 1, wherein to determine the plurality of login patterns, the program code is further structured to cause the processor to:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Claim 4: The system of claim 1, the program code further structured to cause the processor to:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, slide through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Claim 7: A method, comprising:
Claim 8: A method, comprising:
allocating resources to a user in response to the user logging into a database; determining the user logged out of the database subsequent to the user logging in;
reclaim resources from a user in
response to the user
logging out of
a database;
determining a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
determining a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
calculating a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
calculating a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold: selecting from the set the probability having a greatest likelihood;
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold, selecting from the set the probability having a greatest likelihood;
determining whether a time of predicted activity in the time window associated with the selected probability is within an upcoming predetermined length of time; and in response to determining the time of predicted activity to be within the upcoming predetermined length of time, maintaining the allocation of the resources to the user.
reallocating the resources to the user at
a time associated with
the time window corresponding to the selected probability.
Claim 8: The method of claim 7, wherein said calculating comprises: calculating the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 9: The method of claim 8, wherein said calculating comprises: calculating the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 9: The method of claim 7, wherein said maintaining the allocation of the resources to the user comprises:
predicting a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
Claim 12: The method of claim 8, further comprising:
predicting a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
and maintaining the allocation of the resources to the user during the predicted time period.
and maintaining the reallocation of the resources to the user during the predicted time period.
Claim 11: The method of claim 7, wherein said determining a plurality of login patterns comprises:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Claim 11: The method of claim 8, further comprising:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Claim 14: A computer-readable storage device encoded with program instructions that, when executed by a processor circuit, perform a method comprising:
Claim 15: A computer-readable storage device encoded with program instructions that, when executed by a processor circuit, perform a method comprising:
allocating resources to a user in response to the user logging into a database; determining the user logged out of the database subsequent to the user logging in;
reclaiming resources from a user in
response to the user
logging out of
a database;
determining a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
determining a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity;
calculating a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
calculating a plurality of probabilities corresponding to the determined login patterns for the time windows, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows;
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold: selecting from the set the probability having a greatest likelihood;
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold, selecting from the set the probability having a greatest likelihood;
determining whether a time of predicted activity in the time window associated with the selected probability is within an upcoming predetermined length of time; and in response to determining the time of predicted activity to be within the upcoming predetermined length of time, maintaining the allocation of the resources to the user.
reallocating the resources to the user at a time associated with the time window corresponding to the
selected probability.
Claim 15: The computer-readable storage device of claim 14, wherein said calculating comprises: calculating the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 16: The computer-readable storage device of claim 15, wherein said calculating comprises: calculating the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period.
Claim 16: The computer-readable storage device of claim 14, wherein said maintaining the allocation of the resources to the user comprises:
predicting a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
Claim 19: The computer-readable storage device of claim 15, the method further comprising:
predicting a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability;
and maintaining the allocation of the resources to the user during the predicted time period.
and maintaining the reallocation of the resources to the user during the predicted time period.
Claim 18: The computer-readable storage device of claim 14, wherein said determining a plurality of login patterns comprises:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Claim 18: The computer-readable storage device of claim 15, the method further comprising:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold, sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
Regarding claims 1, 7, and 14:
853 teaches: reclaim resources from a user in response to the user logging out of a database.
Vladimirskiy teaches: allocate resources to a user in response to the user logging into a database (Paragraphs 30 and 37; “As users log in, the number of available sessions decreases.”, “In a second example, the auto-scale logic automatically powers on personal desktop session host VMs when a user login is detected. In one example, in response to a user initiated a login request, the request is received by the connection broker service and the logged in database. The auto-scaling logic system then receives a trigger event or proactively polls the connection broker service and logged in database.” As users log in to the database, a session is necessarily allocated to the user, thus corresponding to allocating resources to a user in response to a user logging into a database);
determine the user logged out of the database subsequent to the user logging in (Paragraph 37; “In one example, in response to a user initiated a login request, the request is received by the connection broker service and the logged in database. The auto-scaling logic system then receives a trigger event or proactively polls the connection broker service and logged in database. When the system recognizes a login attempt for a powered off personal desktop VM, the auto-scaling logic causes the appropriate personal desktop session host VM automatically starts”, where recognizing the login attempt for an existing powered off host necessitates determining that a user had previously logged out of the database prior to logging in before.).853 and Vladimirskiy are considered to be analogous because they are in the same field of resource management. Therefore, it would have been obvious to a person of ordinary skill in the art to have modified the system of 853 to further include the teachings of Vladimirskiy of allocating resources to a user in response to the user logging in, and determine whether the user previously logged out before logging back in. The allocation and reclamation of resources represent reciprocal aspects of resource management in computer systems addressing the same technical field of efficient utilization of shared resources. The additional limitations of allocation upon login and logout detection represent an application of known session management techniques on the resource reclamation system of the copending claims. Incorporating known lifecycle steps into the copending system would yield the predictable result of coordinated allocation and reclamation of resources to the start and end of user sessions.
853 teaches: reallocating the resources to the user at a time associated with the time window corresponding to the selected probability.
Vladimirskiy teaches: scaling resources based on timing conditions (Paragraph 49; “The system continuously monitors the virtual desktop environment for presence of such hosts and scales them in during the allowed time window”).
853 and Vladimirskiy are considered to be analogous because they are in the same field of resource management. Therefore, it would have been obvious to a person of ordinary skill in the art to have modified the system of 853 to further include the teachings of Vladimirskiy of allocating resources based on timing conditions. Maintaining allocation under particular timing conditions is a predictable variation of reallocation at a particular predicted time window and would have been obvious to a person of ordinary skill in the art. Further, incorporating a determination that predicted activity falls within an upcoming threshold and maintaining allocation in response represents application of known methods of timing control on the predictive allocation framework of the copending claim, yielding the predictable result of avoiding premature deallocation and ensuring available resources when predicted use is upcoming.
Regarding claims 3, 9, and 16:
The copending claims recite “reallocating” the resources. The claims of the instant application recite “allocating” the resources. The only distinction between the claims is the use of “allocate” instead of “reallocate”. Reallocating resources necessarily includes allocating resources, as reallocation involves allocating resources again or allocating resources following a prior allocation. Thus, reallocation inherently requires an allocation operation. The substitution of “reallocate” for “allocate” represents both a subset of the broader reallocation operation and a predictable variation of the same resource management action yielding the predictable result of assigning resources to a user.
All other limitations set forth in the above comparison table correspond directly to, and are identical in scope to, the limitations recited in the corresponding claims of 18/484,853.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-4, 7, 9-10, 14, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy (US 20230096984 A1), in view of Deore et al. (US 20200034169 A1) hereafter Deore, further in view of Guo et al. (US 20220405134 A1) hereafter Guo.
Regarding claim 1, Vladimirskiy teaches:
A system, comprising: a processor (Paragraph 220; “In the example shown in FIG. 1, the one or more controllers are embodied in a PC-based implementation of a central control processing system utilizing a central processing unit (CPU or processor), memory and an interconnect bus, shown as the user device 102.”);
and a memory device that stores program code (Paragraph 225; “As used herein, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution. Such a medium may take many forms. Non-volatile storage media include, for example, optical disks, magnetic disks, and solid-state drives, such as any of the storage devices in the user device 102 shown in FIG. 1.”) structured to cause the processor to:
allocate resources to a user in response to the user logging into a database (Paragraphs 30 and 37; “As users log in, the number of available sessions decreases.”, “In a second example, the auto-scale logic automatically powers on personal desktop session host VMs when a user login is detected. In one example, in response to a user initiated a login request, the request is received by the connection broker service and the logged in database. The auto-scaling logic system then receives a trigger event or proactively polls the connection broker service and logged in database.” As users log in to the database, a session is necessarily allocated to the user, thus corresponding to allocating resources to a user in response to a user logging into a database);
determine the user logged out of the database subsequent to the user logging in (Paragraph 37; “In one example, in response to a user initiated a login request, the request is received by the connection broker service and the logged in database. The auto-scaling logic system then receives a trigger event or proactively polls the connection broker service and logged in database. When the system recognizes a login attempt for a powered off personal desktop VM, the auto-scaling logic causes the appropriate personal desktop session host VM automatically starts”, where recognizing the login attempt for an existing powered off host necessitates determining that a user had previously logged out of the database prior to logging in before.).
Vladimirskiy does not teach determine a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity.
However, Deore teaches:
determine a plurality of login patterns for the user from historical data of user interactions with the database (Paragraph 45; “the application volumes manager can collect the historical data for user login times across all the users over an extended period of time. The advisory service which may reside on the application volumes manager machine or may be integrated as part of the application volumes manager can use machine learning to determine predictable patterns of login time windows for various users”, teaches collecting historical login data and determining login patterns for users. Interaction with a database is taught in Paragraph 39, “The connection broker 302 may need to populate its database to indicate that the VM has been reserved for the user.”), each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity (Paragraph 45; “The advisory service which may reside on the application volumes manager machine or may be integrated as part of the application volumes manager can use machine learning to determine predictable patterns of login time windows for various users.”, where determined login patterns are associated with defined start and end times as indicated by “The administrator may also be able to provide the work shift timings 408 for users, suggesting the times at which shifts for certain users/groups start and end. The administrator may use these approaches to prioritize certain users or user groups to have faster logins.”. The login time windows are derived from patterns used to prime VMs in advance of user logins, indicating that multiple time windows share learned, predictable start times corresponding to anticipated user activity);
and login patterns, each calculated probability indicative of a likelihood that the user will log into the database during the corresponding time window of the time windows (Paragraph 45; “the application volumes manager can collect the historical data for user login times across all the users over an extended period of time. The advisory service which may reside on the application volumes manager machine or may be integrated as part of the application volumes manager can use machine learning to determine predictable patterns of login time windows for various users”, in which “The administrator may also be able to provide the work shift timings 408 for users, suggesting the times at which shifts for certain users/groups start and end. The administrator may use these approaches to prioritize certain users or user groups to have faster logins” teaches usage of the patterns to anticipate user logins during the corresponding time window.).
Vladimirskiy and Deore are considered to be analogous to the claimed invention because they are in the same field of predicting future system usage to optimize resource allocation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy to incorporate the teachings of Deore and determine a plurality of login patterns for the user from historical data of user interactions with the database, each login pattern of the login patterns corresponding to a respective time window of a plurality of time windows having a same start of predicted activity. A person of ordinary skill in the art would have understood that pattern recognition is a known method to optimize when and where resources should be allocated, and such an implementation would have yielded the predictable result of efficient resource allocation. Further, a person of ordinary skill in the art would have recognized the application of known login-pattern recognition systems would have yielded the predictable result of reliably predicting user logins and capable of being utilized for resource priming and allocation.
Vladimirskiy in view of Deore does not teach calculating a plurality of probabilities for time windows; in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold: select from the set the probability having a greatest likelihood; and determine whether a time of predicted activity in the time window associated with the selected probability is within an upcoming predetermined length of time; and in response to the time of predicted activity being determined to be within the upcoming predetermined length of time, maintain the allocation of the resources to the user.
However, Guo teaches:
calculating a plurality of probabilities for time windows (Paragraph 25; “workload forecasting operator 132 can predict probability parameters of a second set of future workload data for operating the second set of one or more applications, e.g., application 137, at a second number of future time instances”, which explicitly calculates probability parameters for multiple future time instances, corresponding to a plurality of probabilities for multiple time windows);
in response to a set of the calculated probabilities being determined to have a predetermined relationship with a confidence threshold (Paragraph 31; “resource allocation scaler 233 can adjust resource allocation based on a fixed threshold value predictions made by workload forecasting operator 232. For example, resource allocation scaler 233 can adjust resource allocation using rules related to the fixed threshold value predictions”, which teaches comparing predicted values to a fixed threshold and performing a responsive action when the threshold condition is met, corresponding to determining that a set of probabilities has a predetermined relationship with a confidence threshold and responding accordingly):
select from the set the probability having a greatest likelihood (Paragraph 52; “The future resources allocated can be determined based on loop all the possibilit[ies] then select a value that minimizing the total amount of waste and accidents”, where the evaluation of multiple predicted probability parameters and selecting the value that optimizes an objective corresponds to selecting the probability of the greatest likelihood from a set.);
and determine whether a time of predicted activity in the time window associated with the selected probability is within an upcoming predetermined length of time (Paragraphs 25-26; “workload forecasting operator 132 can predict probability parameters of a second set of future workload data for operating the second set of one or more applications, e.g., application 137, at a second number of future time instances” and “Resource allocation unit 141 can determine the future resources allocated to operate the second set of one or more applications, e.g., application 137, for the second number of future time instances. The future resources can be allocated based on the allocated current resources, a lower bound of resources to satisfy a quality of service (QoS) for operating the second set of one or more applications, an upper bound of resources to satisfy the QoS, and the predicted probability parameters.” The association of predicted probability parameters with future time instances corresponds to determining whether the time associated with a selected probability is within a defined upcoming time period.);
and in response to the time of predicted activity being determined to be within the upcoming predetermined length of time, maintain the allocation of the resources to the user (Paragraphs 26 and 32; “resource allocation scaler... can adjust resource allocation using a scaling rule calculated based on a linear function of the prediction of the values on 5-time points in the future”, where resource allocation is adjusted based on predicted values, and that when the predicted usage falls within the scheduled future time, the allocation is maintained or adjusted accordingly, corresponding to maintaining the allocation of resources in response to the predicted user activity occurring within the upcoming time window).
Vladimirskiy, Deore, and Guo are considered to be analogous to the claimed invention because they are in the same field of resource allocation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore to incorporate the teachings of Guo and calculate probabilities for each time window and respond to a set of the calculated probabilities having a relationship with the confidence threshold with the probability having the greatest likelihood and determine whether that time window is within a particular length of time, and maintain the resource allocation in response if it is within the upcoming length of time. A person of ordinary skill in the art would have been motivated to apply the known method of probability forecasting techniques to login pattern determination in order to obtain probability values corresponding to predicted user pattern behavior to achieve the predictable result of being able to select the greatest likelihood time window that a user is likely to log in, yielding optimal resource allocation.
Claim 7 recites similar limitations as those of claim 1, directed towards a method. Claim 7 is rejected for similar reasons as those of claim 1.
Claim 14 recites similar limitations as those of claim 1, directed towards a computer-readable storage device. Vladimirskiy further teaches:
a computer-readable storage device (Paragraph 225; “Instructions or code for implementing such operations may be in the form of computer instruction in any form (e.g., source code, object code, interpreted code, etc.) stored in or carried by any tangible readable medium.”).
Claim 14 is rejected for similar reasons as those of claim 1.
Regarding claim 3, Vladimirskiy in view of Deore, further in view of Guo teach the system of claim 1. Deore teaches:
wherein to maintain the allocation of the resources to the user, the program code is further structured to cause the processor to:
predict a time period of user activity based on an earliest time and a latest time of log in by the user to the database indicated in the historical data for the time window associated with the selected probability (Paragraph 45; “the application volumes manager can collect the historical data for user login times across all the users over an extended period of time” and “The administrator may also be able to provide the work shift timings 408 for users, suggesting the times at which shifts for certain users/groups start and end. The administrator may use these approaches to prioritize certain users or user groups to have faster logins.” Teaches using historical login data to determine earliest and latest login times for a user within a defined time window, corresponding to predicting the time period of user activity.);
and that the allocation of resources belongs to the user (Paragraph 45; “The administrator may use these approaches to prioritize certain users or user groups to have faster logins.”).
Guo teaches:
and maintain the allocation of the resources during the predicted time period (Paragraph 26; “Resource allocation unit 141 can determine the future resources allocated to operate the second set of one or more applications, e.g., application 137, for the second number of future time instances. The future resources can be allocated based on the allocated current resources, a lower bound of resources to satisfy a quality of service (QoS) for operating the second set of one or more applications, an upper bound of resources to satisfy the QoS, and the predicted probability parameters.” Teaches maintaining future resource allocation in accordance with predicted events, corresponding to maintaining allocation of resources during the predicted activity period.).
Claim 9 recites similar limitations as those of claim 3. Claim 9 is rejected for similar reasons as those of claim 3.
Claim 16 recites similar limitations as those of claim 3. Claim 16 is rejected for similar reasons as those of claim 3.
Regarding claim 4, Vladimirskiy in view of Deore, further in view of Guo teach the system of claim 1. Deore teaches:
wherein the program code is further structured to cause the processor to:
in response to the time of predicted activity being determined to not be within the upcoming predetermined length of time, reclaim the resources (Paragraph 35; “manager 302 may also accept configuration parameters from an administrator regarding the duration of the reclaim timer, i.e. how long the VMs can remain primed without the user logging in. The manager 302 may also establish communication with the connection broker 304 to inform the broker that a VM needs to be allocated to a certain user or user group. After this is completed, the manager 302 may implement the reclaim timer. Upon expiration of the reclaim timer, the manager 302 can contact the management center server and request it to reclaim the VM back into VM pool”, where the monitoring of a predicted activity window and reclamation of allocated VMs when the user does not log in within the configured, thereby being predetermined, time period, corresponds to reclaiming resources when the predicted time of activity is determined to not be within the upcoming predetermined length of time.).
Claim 10 recites similar limitations as those of claim 4. Claim 10 is rejected for similar reasons as those of claim 4.
Claim 17 recites similar limitations as those of claim 4. Claim 17 is rejected for similar reasons as those of claim 4.
Claims 2, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy in view of Deore, further in view of Guo, further in view of Kaizerman et al. (US 20200210809 A1) hereafter Kaizerman.
Regarding claim 2, Vladimirskiy in view of Deore, further in view of Guo teach the system of claim 1. Deore teaches:
a database (Paragraph 39; “The connection broker 302 may need to populate its database to indicate that the VM has been reserved for the user.”).
Vladimirskiy in view of Deore, further in view of Guo does not teach wherein to calculate the plurality of probabilities, the program code is further structured to cause the processor to: calculate the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged in during the time window over a historical time period in the historical data; to a number of days of the historical time period.
However, Kaizerman teaches:
wherein to calculate the plurality of probabilities, the program code is further structured to cause the processor to: calculate the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged in during the time window over a historical time period in the historical data; to a number of days of the historical time period (Paragraph 38; “In operation 230 features may be extracted from the time series data. Features may include statistics on the time series data in a time window or time period, e.g., average, standard deviation, percentiles, ratios, counts of events, etc. Examples may include average bets in a time period, e.g., one hour, one day, etc., average session length (e.g., game) length in a time period, number of logins in a time period, ratios of the above features, e.g., average bets to average wins ratio, etc.”).
Vladimirskiy, Deore, Guo, and Kaizerman are considered to be analogous to the claimed invention because they are in the same field of time series event detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo to incorporate the teachings of Kaizerman and calculate the probability for a login pattern corresponding to a time window as a ratio of: a number of days the user was logged into the database during the time window over a historical time period in the historical data; to a number of days of the historical time period. A person of ordinary skill in the art would have understood that the implementation of the known method of frequency-based ratio calculations are a commonly utilized statistical technique for estimating likelihood from historical event data, and applying this known method on historical login data would have yielded the predictable result of quantifying user behavior patterns.
Claim 8 recites similar limitations as those of claim 2. Claim 8 is rejected for similar reasons as those of claim 2.
Claim 15 recites similar limitations as those of claim 2. Claim 15 is rejected for similar reasons as those of claim 2.
Claims 5, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa et al. (US 7292960 B1) hereafter Srinivasa.
Regarding claim 5, Vladimirskiy in view of Deore, further in view of Guo teach the system of claim 1. Guo teaches:
wherein to determine the plurality of login patterns, the program code is further structured to cause the processor to:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold (Paragraph 31; “resource allocation scaler 233 can adjust resource allocation based on a fixed threshold value predictions made by workload forecasting operator 232. For example, resource allocation scaler 233 can adjust resource allocation using rules related to the fixed threshold value predictions, which may be based on infrastructure-level metrics, such as CPU utilization measured by the number of nodes. Processing operator 234 can operate applications, e.g., vSystem application, on the cluster of nodes 224, which is a Kubernates cluster. Resource allocation scaler 233 can scale up and down the number of nodes within the cluster of nodes 224 based on the usage of the workloads by various applications” teaches evaluating predicted values and reacting when the threshold, corresponding to the confidence threshold, is not met.).
While Deore implies a sliding window algorithm (Paragraph 45; “collect the historical data for user login times... [and] determine predictable patterns of login time windows”, where iterating through login time data corresponds to sliding through the historical data by a predetermined time increment to determine the next time window), Vladimirskiy in view of Deore, further in view of Guo does not explicitly teach sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window.
However, Srinivasa teaches:
sliding through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window. (Col. 6, lines 53-58, Fig. 1; “evaluation 131 of the quality of the preliminary prediction rules involves using the sliding window over the same historical data, analyzing the temporal series of the event classes to find an analysis time window containing a set of related events, determining whether a target event is present in the next sequential time window”).
Vladimirskiy, Deore, Guo, and Srinivasa are considered to be analogous to the claimed invention because they are in the same field of event detection and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo to incorporate the teachings of Srinivasa and slide through the historical data by a predetermined time increment according to a sliding window algorithm to determine a next time window. A person of ordinary skill in the art would have recognized that incrementing over time windows is a known method in the art and the implementation of such would yield the predictable result of parsing a particular amount of data at a given time.
Claim 11 recites similar limitations as those of claim 5. Claim 11 is rejected for similar reasons as those of claim 5.
Claim 18 recites similar limitations as those of claim 5. Claim 18 is rejected for similar reasons as those of claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang et al. (US 20240160482 A1) hereafter Jiang, further in view of Smith et al. (US 11755372 B2) hereafter Smith.
Regarding claim 6, Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa teach the system of claim 5. Deore teaches:
A database (Paragraph 39; “The connection broker 302 may need to populate its database to indicate that the VM has been reserved for the user.”)
Guo teaches:
wherein the program code is further structured to cause the processor to:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold for all time windows determined from the historical data (Paragraphs 31-32; “resource allocation scaler 233 can adjust resource allocation based on a fixed threshold value predictions made by workload forecasting operator 232. For example, resource allocation scaler 233 can adjust resource allocation using rules related to the fixed threshold value predictions, which may be based on infrastructure-level metrics, such as CPU utilization measured by the number of nodes. Processing operator 234 can operate applications, e.g., vSystem application, on the cluster of nodes 224, which is a Kubernates cluster. Resource allocation scaler 233 can scale up and down the number of nodes within the cluster of nodes 224 based on the usage of the workloads by various applications” teaches evaluating predicted values and reacting when the threshold, corresponding to the confidence threshold, is not met. The threshold functions as the confidence boundary for evaluating predicted workload levels over future time instances, corresponding to time windows. When none of the predicted values for future time instances satisfy the threshold condition under the 5-time point rule, the system determines that the condition is not met, corresponding to a response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold for all time windows determined from the historical data.).
Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa does not teach determine whether the historical data covers a predetermined historical time period; in response to the historical data being determined to cover the predetermined historical time period, reclaim the resources, the database being considered idle; and in response to the historical data being determined to not cover the predetermined historical time period, logically pause the resources.
However, Jiang teaches:
determine whether the historical data covers a predetermined historical time period (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.”, which monitors resource usage over time and evaluates whether inactivity persists beyond a predefined threshold period. Scanning usage and comparing idle time to a defined threshold corresponds to determining whether collected historical usage data covers a predetermined historical time period.);
in response to the historical data being determined to cover the predetermined historical time period, reclaim the resources, the system being considered idle (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.” The act of pruning the resources thus frees the resources for later usage, thereby corresponding to reclaiming the resources if the system is considered as idle.);
and in response to the historical data being determined to not cover the predetermined historical time period, prune the resources (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.” The claimed limitation differs from the previous limitation in that it conditions resource reclamation on whether the time period is not covering the threshold. Modifying the comparison logic to trigger reclamation when the measured time is within or outside a predefined time period represents a predictable variation in implementation of the same idle resource management technique which would be within the grasp of a person of ordinary skill in the art as reversing or adjusting a threshold condition represents a routine design choice dependent on objectives. Accordingly, the claimed limitation represents an obvious variant of the technique taught by Jiang at Paragraph 65.).
Vladimirskiy, Deore, Guo, Srinivasa, and Jiang are considered to be analogous to the claimed invention because they are in the same field of event detection and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa to incorporate the teachings of Jiang and determine whether the data covers a predetermined time period, and if so, reclaim the resources and consider the database as idle. A person of ordinary skill in the art would have recognized that tracking the length of time a resource remains in active and reclaiming resources after a defined idle period is a known method in the art which would yield the predictable result of improving system efficiency and reducing waste.
Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa does not teach a logical pause of the resources.
However, Smith teaches:
logically pausing the resources (Col. 49, lines 58-60; “The shutdown instructions 672 can include instructions to shutdown (e.g., suspend) one or more of the cloud computing environments 602.”).
Vladimirskiy, Deore, Guo, Srinivasa, Jiang, and Smith are considered to be analogous to the claimed invention because they are in the same field of event monitoring and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang to incorporate the teachings of Smith and substitute pruning resources for suspending, corresponding to logically pausing, resources. Substituting pausing for pruning represents a simple and predictable modification of resource management techniques yielding the expected result of reducing resource consumption during idle periods while maintaining faster availability.
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang.
Regarding claim 12, Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa teach the method of claim 11. Deore teaches:
A database (Paragraph 39; “The connection broker 302 may need to populate its database to indicate that the VM has been reserved for the user.”)
Guo teaches:
wherein the program code is further structured to cause the processor to:
in response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold for all time windows determined from the historical data (Paragraphs 31-32; “resource allocation scaler 233 can adjust resource allocation based on a fixed threshold value predictions made by workload forecasting operator 232. For example, resource allocation scaler 233 can adjust resource allocation using rules related to the fixed threshold value predictions, which may be based on infrastructure-level metrics, such as CPU utilization measured by the number of nodes. Processing operator 234 can operate applications, e.g., vSystem application, on the cluster of nodes 224, which is a Kubernates cluster. Resource allocation scaler 233 can scale up and down the number of nodes within the cluster of nodes 224 based on the usage of the workloads by various applications” teaches evaluating predicted values and reacting when the threshold, corresponding to the confidence threshold, is not met. The threshold functions as the confidence boundary for evaluating predicted workload levels over future time instances, corresponding to time windows. When none of the predicted values for future time instances satisfy the threshold condition under the 5-time point rule, the system determines that the condition is not met, corresponding to a response to no calculated probabilities being determined to have the predetermined relationship with the confidence threshold for all time windows determined from the historical data.).
Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa does not teach determine whether the historical data covers a predetermined historical time period; in response to the historical data being determined to cover the predetermined historical time period, reclaim the resources, the database being considered idle; and in response to the historical data being determined to not cover the predetermined historical time period, logically pause the resources.
However, Jiang teaches:
determine whether the historical data covers a predetermined historical time period (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.”, which monitors resource usage over time and evaluates whether inactivity persists beyond a predefined threshold period. Scanning usage and comparing idle time to a defined threshold corresponds to determining whether collected historical usage data covers a predetermined historical time period.);
in response to the historical data being determined to cover the predetermined historical time period, reclaim the resources, the system being considered idle (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.” The act of pruning the resources thus frees the resources for later usage, thereby corresponding to reclaiming the resources if the system is considered as idle.).
Vladimirskiy, Deore, Guo, Srinivasa, and Jiang are considered to be analogous to the claimed invention because they are in the same field of event detection and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa to incorporate the teachings of Jiang and determine whether the data covers a predetermined time period, and if so, reclaim the resources and consider the database as idle. A person of ordinary skill in the art would have recognized that tracking the length of time a resource remains in active and reclaiming resources after a defined idle period is a known method in the art which would yield the predictable result of improving system efficiency and reducing waste.
Claim 19 recites similar limitations as those of claim 12. Claim 19 is rejected for similar reasons as those of claim 12.
Claims 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang, further in view of Smith.
Regarding claim 13, Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang teaches the method of claim 11. Jiang teaches:
in response to the historical data being determined to not cover the predetermined historical time period, prune the resources (Paragraph 65; “It may also be enabled to scan the usage of resource pool and prune the resource if the idle time may be larger than the defined threshold. This information may be provided to the database 940.” The claimed limitation differs from the previous limitation in that it conditions resource reclamation on whether the time period is not covering the threshold. Modifying the comparison logic to trigger reclamation when the measured time is within or outside a predefined time period represents a predictable variation in implementation of the same idle resource management technique which would be within the grasp of a person of ordinary skill in the art as reversing or adjusting a threshold condition represents a routine design choice dependent on objectives. Accordingly, the claimed limitation represents an obvious variant of the technique taught by Jiang at Paragraph 65.).
Vladimirskiy, Deore, Guo, Srinivasa, and Jiang are considered to be analogous to the claimed invention because they are in the same field of event detection and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa to incorporate the teachings of Jiang and determine whether the data covers a predetermined time period, and if so, reclaim the resources and consider the database as idle. A person of ordinary skill in the art would have recognized that tracking the length of time a resource remains in active and reclaiming resources after a defined idle period is a known method in the art which would yield the predictable result of improving system efficiency and reducing waste.
Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang does not explicitly teach a logical pause of the resources.
However, Smith teaches:
logically pausing the resources (Col. 49, lines 58-60; “The shutdown instructions 672 can include instructions to shutdown (e.g., suspend) one or more of the cloud computing environments 602.”).
Vladimirskiy, Deore, Guo, Srinivasa, Jiang, and Smith are considered to be analogous to the claimed invention because they are in the same field of event monitoring and response. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vladimirskiy in view of Deore, further in view of Guo, further in view of Srinivasa, further in view of Jiang to incorporate the teachings of Smith and substitute pruning resources for suspending, corresponding to logically pausing, resources. Substituting pausing for pruning represents a simple and predictable modification of resource management techniques yielding the expected result of reducing resource consumption during idle periods while maintaining faster availability.
Claim 20 recites similar limitations as those of claim 13. Claim 20 is rejected for similar reasons as those of claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lui et al. (US 20130080641 A1) discloses a rule set to apply to users for resource allocation and prioritization at run-time.
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/KENNETH P TRAN/Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196