CTNF 18/706,987 CTNF 87732 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim(s) 1-7 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 (similarly claims 6 and 7) recite: “a virtual machine is migration within a base or between the bases at a plurality of bases”. The examiner is unclear if “a base” is part of “the bases at a plurality of bases” or if it’s not part of “the bases”. Claim 1 (similarly claims 5, 6 and 7) recite: “the bases”. There is insufficient antecedent basis for this limitation in the claim. The examiner is unclear if “the bases” at a plurality of bases include all bases at the plurality of bases or if it’s a subset of bases of the plurality of bases. Claim 3 recite: “impact cost of each base”. The examiner is unclear if “each base” is referring to “the bases” or “a base”. If “each base” is referring to “the bases”, the examiner is unclear if “a base” is part of each base or not. Claim 3 recite: “band”. The examiner is unclear if the term “band” should be interpreted as a “bandwidth” or not. The specification recites “bandwidth” [PGPub paragraph 40], thus the separate/distinct term recited as “band” is distinct. Therefore, the examiner is unclear how the term “band” should be interpreted. Claim 3 recite: “a distance and a band between the user and each base”. The examiner is unclear if the distance between the user and each base should be interpreted as distance from a user virtual machine to each base or the location of the user (not the virtual machine) to each base. Claims 2-5 and 7 are rejected based on rejection of its corresponding dependent claim. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-3 and 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mani (Pub 20190286475) in view of Cortez et al. (Pub 20200117494) (hereafter Cortez) . As per claim 1, Mani teaches: An impact cost calculation apparatus for calculating an impact cost in a case in which a virtual machine is migrated within a base or between the bases at a plurality of bases including the virtual machines, the impact cost calculation apparatus comprising: a processor; and a memory storing program instructions that cause the processor to: ([Paragraph 9], The present disclosure describes techniques for determining opportunities for migrating virtual machines to new hosts by (1) identifying actions that indicate that the virtual machine will go offline, (2) determining whether the virtual machine should be migrated to a new host, (3) if so, determining an appropriate destination host, and (4) performing the migration opportunistically so as to reduce impact to the user's access to the current host virtual machine. [Paragraph 4], The disclosed embodiments describe technologies for data centers to migrate virtual machines in a way that reduces the impact on users' continued access to their allocated virtual machines, while allowing data centers to adhere to operational objectives and at the same time improve operating efficiencies. [Paragraph 112], a memory in communication with the one or more processors…) acquire either a login state or a process state, or both the login state and the process state, in each virtual machine provided at the base; and ([Paragraph 14], In some embodiments, the local state data for the virtual machine may be persisted and migrated or otherwise made available to the virtual machine after migration to the new host device. Local and temporary user state data is typically provided using local memory which provides speed and performance advantages as compared to persisted long-term storage. In many cases, the local temporary storage may be used to cache data for running applications or for data that is frequently accessed. When a full migration takes place, the data in the local storage is typically lost to the user and is not persisted. Accordingly, once the virtual machine is migrated and launched at the new host, the temporary data will need to be rebuilt as applications are executed and application data is retrieved and cached. If the local state data is desired after full migration, steps will need to be taken to save and reconstruct the local state data. [Paragraph 30], Data center 100 may include servers 116a, 116b, and 116c (which may be referred to herein singularly as “a server 116” or in the plural as “the servers 116”) that provide computing resources available as virtual machines 118a and 118b (which may be referred to herein singularly as “a virtual machine 118” or in the plural as “the virtual machines 118”). The virtual machines 118 may be configured to execute applications such as Web servers, application servers, media servers, database servers, and the like. Other resources that may be provided include data storage resources (not shown on FIG. 1) and may include file storage devices, block storage devices, and the like. Servers 116 may also execute functions that manage and control allocation of resources in the data center, such as a controller 115. Controller 115 may be a fabric controller or another type of program configured to manage the allocation of virtual machines on servers 116. [Paragraph 44], Referring to FIG. 4, virtual machine 340 may be migrated to an identified physical host 302 when a performance threshold is met. It should be noted that virtual machines may be migrated between physical hosts within the same rack, within the same grouping, between groupings, between data centers, or between any two devices regardless of physical location and specific hardware and/or software configurations.) calculate, for each virtual machine, an impact cost for a user caused by migration of the virtual machine, on the basis of either the login state or the process state or both the login state and the process state. ([Paragraph 4], The disclosed embodiments describe technologies for data centers to migrate virtual machines in a way that reduces the impact on users' continued access to their allocated virtual machines, while allowing data centers to adhere to operational objectives and at the same time improve operating efficiencies. [Paragraph 62], If it is determined that the migration will take longer than the threshold period of time, the migration may be delayed or cancelled. In another example, the SLA may specify that the total downtime for a user's virtual machine, which may be calculated as the total accumulated minutes or percentage of time where the user has no connectivity to the user's virtualized resources, cannot exceed a specified number. If the estimated downtime for the migration does not violate the total accumulated downtime, then the migration may be allowed to proceed.) Although Mani discloses calculating a user impact for migration (i.e. live or offline) of virtual machine(s) within a base(datacenter) or between bases (datacenters). Mani does not explicitly disclose impact is impact cost. Cortez teaches impact cost . ([Paragraph 26], For example, an impact may refer to a metric that indicates a time or duration that a client device is disconnected from or experiences limited connectivity to an application provided by a virtual machine. In one or more embodiments, an impact is measured by an impact score that indicates a level of impact ranging from a low impact (e.g., a less noticeable impact to a customer) to a high impact (e.g., a more noticeable impact to a customer). In addition, as will be described in further detail below, an impact may vary based on a type of application, a size of a virtual machine, or other characteristics of a virtual machine in accordance with one or more embodiments described herein.) It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Mani wherein an user impact of virtual machine migration is calculated based on acquired login state or process state of the virtual machine, into teachings of Cortez wherein impact cost is calculated for migration of virtual machine(s), because this would enhance the teachings of Mani wherein by calculating the impact cost of virtual machine migration, it allows low-impact virtual machine migration to be determined based on the state of login or process, thus reducing end user impact due to migration. [Cortez paragraph 13-15] As per claim 2, rejection of claim 1 is incorporated: Cortez teaches wherein the program instructions cause the processor to increase the impact cost when a process other than an initial process is present in an operation process of the virtual machine, or increase the impact cost when a service is down and a process impacting on the user is operating. ([Paragraph 26], For example, an impact may refer to a metric that indicates a time or duration that a client device is disconnected from or experiences limited connectivity to an application provided by a virtual machine. In one or more embodiments, an impact is measured by an impact score that indicates a level of impact ranging from a low impact (e.g., a less noticeable impact to a customer) to a high impact (e.g., a more noticeable impact to a customer). In addition, as will be described in further detail below, an impact may vary based on a type of application, a size of a virtual machine, or other characteristics of a virtual machine in accordance with one or more embodiments described herein. [Paragraph 57], The predicted brownout time may range from a very small duration of time (e.g., less than one minute) corresponding to a low impact score to a high duration of time (e.g., an hour or more) corresponding to a high impact score…) As per claim 3, rejection of claim 1 is incorporated: Cortez teaches wherein the program instructions cause the processor to calculate, for each virtual machine, an impact cost of each base serving as a migration destination, on the basis of a distance and a band between the user and each base. ([Paragraph 49], This may include identifying a size of a virtual machine, memory access patterns of the virtual machine, sensitivity of the virtual machine, a priority of a customer associated with the virtual machine, or any other virtual machine characteristic(s). [Paragraph 50], The data collection engine 202 can provide any number of virtual machine characteristics to the impact prediction engine 204, which can determine impact scores for any number of virtual machines associated with the virtual machine characteristics… [Paragraph 31], More specifically, the low-impact live-migration system 104 can facilitate live-migration of virtual machines between server nodes by selectively identifying virtual machines for live-migration as well as identifying a time of live-migration and a destination server node that avoids or otherwise reduces an impact of the live-migration to one or more of the client devices 112a-n having access to the virtual machine(s). As mentioned above, by selectively identifying virtual machines and strategically timing migration of the virtual machines between server nodes, the low-impact live-migration system 104 can significantly reduce connection and accessibility issues (e.g., different types of impact) that live-migrating the virtual machines can have on the client devices 112a-n.) Mani also teaches ([Paragraph 9], The present disclosure describes techniques for determining opportunities for migrating virtual machines to new hosts by (1) identifying actions that indicate that the virtual machine will go offline, (2) determining whether the virtual machine should be migrated to a new host, (3) if so, determining an appropriate destination host, and (4) performing the migration opportunistically so as to reduce impact to the user's access to the current host virtual machine. Such migrations may be referred to as an “opportunistic migration” or a “migration after reboot or restart” in the present disclosure. [Paragraph 17], Based on one or more criteria, a destination computing device for the virtual machine may be identified. Subsequent to shutting down the virtual machine at the current host and prior to rebooting the virtual machine at the new host, the virtual machine may be migrated to the destination computing device when a performance threshold is met. [Paragraph 60], In one example, the criteria may include performance characteristics such as processor speed, or a specified software/hardware configuration. The destination computing device may be identified based on available resources in the data center, such as available virtual machine containers that have not been allocated for other users…) As per claim 5, rejection of claim 1 is incorporated: Mani teaches wherein the program instructions cause the processor to calculate, for each virtual machine, an impact cost of each base on the basis of a time taken for migration of the virtual machine between servers within the base and a time taken for migration of the virtual machine between the bases. ([Paragraph 61], Operation 505 may be followed by operation 507. Operation 507 illustrates migrating the virtual machine to the destination computing device when a performance threshold is met. In one example, the performance threshold may include an estimated time duration for the migration, which may be based on one or more of the estimated size of the virtual machine, the number and types of applications that are associated with the virtual machine, and the amount of data that is associated with the virtual machine.) Cortez also teaches ([Paragraph 26], As used herein, “migration impact” or “impact” refer interchangeably to a predicted impact of accessing a computing container as a result of an interruption in connectivity between a client device and a virtual machine hosted by one or more server nodes. For example, an impact may refer to a metric that indicates a time or duration that a client device is disconnected from or experiences limited connectivity to an application provided by a virtual machine. In one or more embodiments, an impact is measured by an impact score that indicates a level of impact ranging from a low impact (e.g., a less noticeable impact to a customer) to a high impact (e.g., a more noticeable impact to a customer). In addition, as will be described in further detail below, an impact may vary based on a type of application, a size of a virtual machine, or other characteristics of a virtual machine in accordance with one or more embodiments described herein. [Paragraph 52], As mentioned above, the impact prediction engine 204 includes a blackout prediction engine 302. The blackout prediction engine 302 may include an algorithm or prediction model trained to determine a predicted blackout time for a virtual machine based on a corresponding set of virtual machine characteristics associated with the virtual machine. For example, the blackout prediction engine 302 may determine an estimated time-period that a virtual machine will become disconnected from a client device as a result of live-migrating the virtual machine from one server node to another server node on a node cluster. In addition, or as an alternative to determining an estimated time of disconnection, the blackout prediction engine 302 may determine an estimated period of time that the virtual machine is frozen during which no codes or instructions are executed. The predicted blackout time may range anywhere from a very small time-duration (e.g., 0 seconds) corresponding to a low impact score to a high time-duration (e.g., 30+ seconds) corresponding to a high impact score. [Paragraph 74], The destination server node may be on the same node cluster as the server node from which the virtual machine is live-migrating. Alternatively, the destination node may be on a different node cluster within the cloud computing system…) Claim 6 is a method claim corresponding to the apparatus claim 1. Therefore, rejected based on similar rationale. Claim 7 is a non-transitory computer-readable recording medium claim corresponding to the apparatus claim 1. Therefore, rejected based on similar rationale . 07-21-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mani in view of Cortez and further in view of Chen et al. (Pub 20220334870) (hereafter Chen) . As per claim 4, rejection of claim 1 is incorporated: Although Mani in view of Cortez discloses calculating an impact cost of a plurality of virtual machines. Mani in view of Cortez do not explicitly disclose wherein the program instructions cause the processor to calculate an impact cost of a plurality of cooperating virtual machines , on the basis of an inter-virtual machine coupling degree for the plurality of virtual machines. Chen teaches cooperating virtual machines , on the basis of an inter-virtual machine coupling degree for the plurality of virtual machines. ([Paragraph 62], In the illustrative example, the execution priority can indicate the task priority. For example, Container A can have a priority such as P_D. In this example, P is production and D is a numeric value which indicates the business priority. The numeric value can be automatically adjusted according to execution feedback. As another example, another factor can be an impact of the migration of a container. For example, the Container A has the impact as H, which means high resource demanding and will have a desired improvement after migration. In this case, the impact on application performance is great. [Paragraph 102], In generating tasks to move containers, migration tasks generator 404 uses a number of different pieces of information to generate the tasks to move containers. For example, migration tasks generator 404 can use at least one of a current physical host computer, a target physical host computer, containers identified for movement, a time slot, a priority, a method, a routine strategy, a migration rule, a cost, an impact, a risk, an owner, or other suitable types of information. [Paragraph 98], Container dependency analyzer 406 can identify a dependency relationship between two or more containers. In this illustrative example, the identification of the dependency between two or more containers can be performed by container dependency analyzer 406 using container communications information collected by container affinities monitoring and monitor 414. This information is analyzed by migration manager 318 to determine the container affinities. [Paragraph 99], Container dependency analyzer 406 can be used to identify containers that should be moved together when one of the containers in the dependency relationship is selected for migration. For example, if quality of service improvement analyzer 402 determines that Container A for Application Z can be moved to improve the performance of Application Z, service improvement analyzer 402 can also determine that Container A has a dependency with Container B and Container E using container dependency analyzer 406. As a result, quality of service improvement analyzer 402 can identify Container A, Container B, and Container E for movement to improve one or more performance metrics for Application Z.) It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Mani and Cortez wherein an user impact cost of virtual machine migration is calculated based on acquired login state or process state of the virtual machine, into teachings of Chen wherein an impact cost of inter-virtual machines coupling degree is calculated, because this would enhance the teachings of Mani and Cortez wherein by calculating the impact cost of inter-virtual machines that are coupled, allows identification of virtual machine(s)/container(s) to be migrated during low impact time slot and/or to identify virtual machine(s)/container(s) which would provide most impact by migrating the virtual machine(s)/container(s). [Chen paragraph 61-63] Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONG U KIM whose telephone number is (571)270-1313. The examiner can normally be reached 9:00am - 5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bradley Teets can be reached at 5712723338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DONG U KIM/Primary Examiner, Art Unit 2197 Application/Control Number: 18/706,987 Page 2 Art Unit: 2197 Application/Control Number: 18/706,987 Page 3 Art Unit: 2197 Application/Control Number: 18/706,987 Page 4 Art Unit: 2197 Application/Control Number: 18/706,987 Page 5 Art Unit: 2197 Application/Control Number: 18/706,987 Page 6 Art Unit: 2197 Application/Control Number: 18/706,987 Page 7 Art Unit: 2197 Application/Control Number: 18/706,987 Page 8 Art Unit: 2197 Application/Control Number: 18/706,987 Page 9 Art Unit: 2197 Application/Control Number: 18/706,987 Page 10 Art Unit: 2197 Application/Control Number: 18/706,987 Page 11 Art Unit: 2197 Application/Control Number: 18/706,987 Page 12 Art Unit: 2197 Application/Control Number: 18/706,987 Page 13 Art Unit: 2197 Application/Control Number: 18/706,987 Page 14 Art Unit: 2197 Application/Control Number: 18/706,987 Page 15 Art Unit: 2197
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