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 .
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 are 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
1. A method for load-balanced delivery of completed tasks to servers, the method comprising:
establishing a message queue at a first server for communication with a plurality of nodes, wherein the first server maintains a list of tasks to be completed and delivered to the nodes;
monitoring the plurality of nodes to determine their availability;
assigning tasks to an available node of the plurality of nodes using reinforcement learning, wherein a reward is determined based on a completion status of the assigned task for a current state of the available node;
receiving a completed task from the available node;
acknowledging completion of the assigned task by the available node;
and delivering the completed task to a second server.
first server
second server
plurality of nodes
Claim 1 Step 1:
Claim 1 is directed to a method for load-balanced delivery of completed tasks to servers a load counting method, the method comprising: a series of steps, and is therefore directed to a process, which is one of the four statutory categories.
Claim 1 Step 2A, Prong One:
Limitation(s) 1 b,c can be performed in the human mind through observation, evaluation,
judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
Accordingly, claim 1 recites a judicial exception (i.e., an abstract idea).
Claim 1 Step 2A, Prong Two:
Limitation(s) 1 g-i provide(s) mere instructions to implement the limitations which can be
performed in the human mind, i.e., the judicial exception, on a computer, which is not indicative of
integration into a practical application. See MPEP 2106.04(d) and 2106.0S(f).
Limitation(s) 1 a,d-f amount(s) to insignificant extra-solution activity of necessary data
gathering/outputting, as it is merely outputting the result of the judicial exception, which is not
indicative of integration into a
practical application. See MPEP 2106.04(d) and 2106.0S(g).
Claim 1Step 2B:
The combination of these elements amount to a method comprising steps which can
be performed mentally on a computer and comprising a step of insignificant extra-solution and well-
understood, routine and conventional activity. Therefore, these elements, when considered individually
and in combination, fail to add an inventive concept to the claim. Consequently, claim 1 as a whole does
not amount to significantly more than the recited judicial exceptions and the claim is not eligible.
2. The method of claim 1,
wherein a positive reward is assigned based on a successful completion and delivery of the task
by the available node.
Limitation(s) 2 a can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
Limitation(s) 2 b provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
An integration into a practical application is not substantiated from the above.
3. The method of claim 1,
wherein a negative reward is assigned based on an unsuccessful completion of the task
due to node failure.
Limitation(s) 3 a can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
Limitation(s) 3 b provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
An integration into a practical application is not substantiated from the above.
4. The method of claim 1,
further comprising establishing a policy by learning a strategy over time that maximizes a cumulative reward.
Limitation(s) 4 a can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
An integration into a practical application is not substantiated from the above.
5. The method of claim 4,
further comprising updating the policy dynamically based on the rewards obtained from outcomes of task assignments.
Limitation(s) 5 a can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
An integration into a practical application is not substantiated from the above.
6. The method of claim 4,
wherein the strategy is learned using Q-learning or Deep Q-Networks (DQN).
Limitation(s) 6 a provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
An integration into a practical application is not substantiated from the above.
7. The method of claim 1,
further comprising continuously monitoring activity of each node within a plurality of storage nodes.
Limitation(s) 7 a can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
An integration into a practical application is not substantiated from the above.
8. The method of claim 1, further comprising:
detecting that a node has become unavailable;
redirecting the task to another available node;
and detecting when the unavailable node becomes available and directing a new task thereto.
Limitation(s) 8 a-c can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
An integration into a practical application is not substantiated from the above.
9. The method of claim 1,
wherein the first server is a highly available (HA) server
and the second server is a network-attached storage server.
Limitation(s) 9 a,b provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
An integration into a practical application is not substantiated from the above.
The following claim corresponds to claim 1 and therefore inherits the same rejection.
10. A system for load-balanced delivery of completed tasks to servers, the system comprising:
a highly available (HA) server configured to maintain a message queue in communication with a plurality of nodes; and a network-attached storage server configured to communicate with the HA server, wherein the HA server maintains a list of tasks to be completed by at least one of the plurality of nodes and delivered to the network-attached storage server; wherein the HA server is configured to: monitor the plurality of nodes and determine the availability of each node; assign tasks to available nodes using reinforcement learning, wherein a reward is determined based on a completion status of the assigned task relative to a current state of the plurality of nodes; receive completed tasks from the available nodes; acknowledge completion of tasks by the available nodes; and deliver the completed tasks to the network-attached storage server.
The following claim corresponds to claim 2 and therefore inherits the same rejection.
11. The system of claim 10, wherein the HA server is configured to assign a positive reward for a successful completion and delivery of tasks.
The following claim corresponds to claim 3 and therefore inherits the same rejection.
12. The system of claim 10, wherein the HA server is configured to assign a negative reward for a unsuccessful completion of tasks due to storage node failure.
The following claim corresponds to claim 5 and therefore inherits the same rejection.
13. The system of claim 10, further comprising a reinforcement learning model to establish a policy by learning a strategy over time to maximize a cumulative reward.
The following claim corresponds to claim 5 and therefore inherits the same rejection.
14. The system of claim 13, wherein the reinforcement learning model is configured to dynamically update the policy based on rewards obtained from outcomes of task assignments.
The following claim corresponds to claim 6 and therefore inherits the same rejection.
15. The system of claim 13, wherein the policy is configured to learn a strategy using Q-learning or Deep Q-Networks (DQN).
The following claim corresponds to claim 7 and therefore inherits the same rejection.
16. The system of claim 10, wherein the HA server is configured to continuously monitor activity of each storage node within a plurality of storage nodes.
The following claim corresponds to claim 8 and therefore inherits the same rejection.
17. The system of claim 10, wherein the HA server is further configured to: detect when a storage node becomes unavailable; redirect the task to another available storage node; and detect when the unavailable node becomes available and direct a new task thereto.
The following claim corresponds to claim 1 and therefore inherits the same rejection.
18. A method of configuring a failover mechanism in case a node goes down and becomes unavailable, the method comprising: periodically monitoring, by a highly available (HA) server, a plurality of nodes; sending, by the HA server, a request to each one of the plurality of nodes to determine the availability of any one of the plurality of nodes; marking, by the HA server, that a node is unavailable/down when a node fails to respond to the request, or returns an error; and redirecting, by the HA server, a load to another node of the plurality of nodes that is an available idle active node when a node is down.
19. The method of claim 18,
comprising resuming normal operation of the HA server once the failed node is recovered.
Limitation(s) 19 a provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
An integration into a practical application is not substantiated from the above.
20. The method of claim 18, comprising:
accepting, by the HA server, a processed report as an acknowledgement;
and preventing, by the HA server, redundancy in report delivery.
by the HA server
Limitation(s) 20 b can be performed in the human mind through observation, evaluation, judgement and opinion, with the aid of pen and paper, and is/are therefore reciting a mental process.
Limitation(s) 20 c provide(s) mere instructions to implement the limitations which can be performed in the human mind, i.e., the judicial exception, on a computer.
Limitation(s) 20 a amount(s) to insignificant extra-solution activity of necessary data gathering/outputting.
An integration into a practical application is not substantiated from the above.
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.
Claim(s) 1,8,10,17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27.
1. A method for load-balanced delivery of completed tasks to servers, the method comprising:
Venugopal teaches the following.
establishing a message queue at a first server for communication with a plurality of nodes,
[0024] a central resource manager or scheduler (i.e., which stores data related to the states/reliability of the other nodes).
[0074] FIG. 4 illustrates an exemplary computing environment (e.g., a distributed computing system) 400 according to an embodiment of the present invention. As shown, the system includes a client 402, a resource manager 404, a database 406, and nodes 408-414, which are in operable communication as shown via network infrastructure 416 (e.g., a communications network, the “cloud,” the Internet, etc.).
Here, the central resource manager corresponds to a first server.
monitoring the plurality of nodes to determine their availability;
[0020] The node manager may incorporate (or utilize) an event-driven finite state machine to manage (or describe, encapsulate, etc.) node lifecycles or failures.
[0021] In some embodiments, the state machine encapsulates the node (or failure) lifecycle in which the node(s) transitions (or are determined to transition) between three states (or statuses): “reliable,” “unreliable,” and “unavailable.” In some embodiments, a fourth state of “available” may also be included/utilized for nodes that recently began operation
assigning tasks to an available node of the plurality of nodes using reinforcement learning,
[0024] The node manager may change the state of the node(s) to drive the operation of the system, which may include, for example, migrating applications (or other tasks, data, etc.) from one node to other, healthy (or reliable) nodes
[0081] in other embodiments, different techniques, such as reinforcement learning, may be utilized to determine the current state based on the previous state and a reward function, as will be appreciated by one skilled in the art.
Venugopal does not teach the following.
wherein the first server maintains a list of tasks to be completed and delivered to the nodes;
Abadi does however.
[0021] In another embodiment, the query server maintains a list of task slots existing across worker nodes in the cluster. The list of task slots includes the status of each task slot. The status of each task slot may be busy, free, or blacklisted.
[0022] In one implementation, the query server maintains a list of tasks for the query plan. The list of tasks includes the status of each task. The status of each task may be completed, scheduled, or not yet scheduled.
Abadi provides a (first) server maintaining the tasks to be assigned among the nodes of a system. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal as this combination would allow for the centralized storage of tasks to be performed throughout the system, conserving among the nodes.
Venugopal does not teach the following.
wherein a reward is determined based on a completion status of the assigned for a current state of the available node;
Yajnanarayana does however.
[0056] The representations are used by the apparatus 10 to formulate a mapping of resources to tasks, using a reinforcement learning algorithm to find a mapping which optimises a reward function value, the reward function being based on factors including one or more from among number of pending tasks that will be completed by the mapping, a total or average time to completion of the tasks (or a cumulative pendency time of the tasks), net consumed resources, and resource utilisation rate.
[0058] Once the mapping has been formulated, the apparatus 10 performs the assignment of resources 120 to tasks 110. For example, the assignment may be performed via the interface circuitry 16. The interface circuitry may be a node in a network in communication with one or more of the resources 120 in the physical environment 100 via the network. The resources 120 may be instructed by, or controlled by, devices in the network. The network may be, for example, a computer network, or a telecommunications network.
The computer resources performing the tasks can correspond to devices on a network I.E nodes.
receiving a completed task the available node;
[0056] The representations are used by the apparatus 10 to formulate a mapping of resources to tasks, using a reinforcement learning algorithm to find a mapping which optimises a reward function value, the reward function being based on factors including one or more from among number of pending tasks that will be completed by the mapping, a total or average time to completion of the tasks (or a cumulative pendency time of the tasks), net consumed resources, and resource utilisation rate.
The reward function being updated with the completion result necessarily entails the completed task being received.
acknowledging completion of the assigned task by the available node;
[0056] The representations are used by the apparatus 10 to formulate a mapping of resources to tasks, using a reinforcement learning algorithm to find a mapping which optimises a reward function value, the reward function being based on factors including one or more from among number of pending tasks that will be completed by the mapping, a total or average time to completion of the tasks (or a cumulative pendency time of the tasks), net consumed resources, and resource utilisation rate.
The reward function being updated with the completion result necessarily entails the completed task being indicated and therefore acknowledged by the computing resource (available node).
Yajnanarayana provides a reward function based on task completion. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi as this combonation would allow for stronger indications of nodes states/reliability.
Venugopal does not teach the following.
and delivering (…) to a second server.
Horii does however.
[0010] (…) first server includes: a first transaction execution unit for executing the optimistic process; and a transaction log transmission unit for transmitting a result of the execution of the optimistic process to the second server.
Horii provides delivering a result to second server. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, and Yajnanarayana as this combination would allow for balancing the resources of the network.
8. The method of claim 1, further comprising:
Venugopal teaches the following.
detecting that a node has become unavailable;
[0020] The node manager may incorporate (or utilize) an event-driven finite state machine to manage (or describe, encapsulate, etc.) node lifecycles or failures.
[0021] In some embodiments, the state machine encapsulates the node (or failure) lifecycle in which the node(s) transitions (or are determined to transition) between three states (or statuses): “reliable,” “unreliable,” and “unavailable.” In some embodiments, a fourth state of “available” may also be included/utilized for nodes that recently began operation
redirecting the task to another available node;
[0024] The node manager may change the state of the node(s) to drive the operation of the system, which may include, for example, migrating applications (or other tasks, data, etc.) from one node to other, healthy (or reliable) nodes
and detecting when the unavailable node becomes available and directing a new task thereto
[0084] When a node is brought back to the available state (e.g., after failing/being disabled for maintenance and being repaired), the node may be initially set to the unreliable state due to its previous history (e.g., at least for a predetermined amount of time, such as several hours, a few days, etc.). In such instances, no high priority tasks may be scheduled on and/or migrated to the node until its state is returned to reliable (e.g. after operating for the predetermined amount of time, or “probationary” period, and reported as not generating any errors/faults or a relatively low number of errors/faults).
It’s indicated that tasks may be assigned to nodes made available following disablement following a probationary period.
10. A system for load-balanced delivery of completed tasks to servers, the system comprising: a highly available (HA) server configured to maintain a message queue in communication with a plurality of nodes; and a network-attached storage server configured to communicate with the HA server, wherein the HA server maintains a list of tasks to be completed by at least one of the plurality of nodes and delivered to the network-attached storage server; wherein the HA server is configured to: monitor the plurality of nodes and determine the availability of each node; assign tasks to available nodes using reinforcement learning, wherein a reward is determined based on a completion status of the assigned task relative to a current state of the plurality of nodes; receive completed tasks from the available nodes; acknowledge completion of tasks by the available nodes; and deliver the completed tasks to the network-attached storage server.
17. The system of claim 10, wherein the HA server is further configured to: detect when a storage node becomes unavailable; redirect the task to another available storage node; and detect when the unavailable node becomes available and direct a new task thereto.
Claim(s) 2,3,4,11,12,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20180121766 A1, McCord, 2018-01-22.
2. The method of claim 1,
Venugopal in view of Adabi, Yajnanarayana, and Horii teaches the following as described above.
based on a successful completion and delivery of the task by the available node.
Venugopal in view of Adabi, Yajnanarayana, and Horii does not teach the following.
wherein a positive reward is assigned
McCord does however.
[0053] (…) Reinforcement learning may be used to determine the most efficient distribution of imperative and contingent work items among human and virtual bot workers, using both positive- and negative-reinforcement learning to “zero in” on optimized work allocation and assigning tasks to humans and virtual bots according to which is more suited to the work needed.
McCord provides positive values being assigned in a reward function . It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, and Horri as this combination would allow for stronger indications of nodes states/reliability.
3. The method of claim 1,
Venugopal in view of Adabi, Yajnanarayana, and Horii teaches the following as described above.
based on an unsuccessful completion of the task due to node failure.
Venugopal in view of Adabi, Yajnanarayana, and Horii does not teach the following.
wherein a negative reward is assigned
McCord does however.
[0056] Reward values may also be used to provide negative reinforcement learning, for example by defining negative reward values to train away from certain states or outcomes.
McCord provides negative values being assigned in a reward function. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, and Horri as this combination would allow for learning system to train away from failed executions.
4. The method of claim 1,
Venugopal in view of Adabi, Yajnanarayana, and Horii does not teach the following.
further comprising establishing a policy by learning a strategy over time that maximizes a cumulative reward.
McCord does however.
[0078] (…) In one aspect, the SLIO (self-learning interaction optimizer) 200 finds an action policy that has a maximum value of expectation (mean) value of net accumulated reward (total return) over a time horizon in presence of uncertainty of different scenarios.
McCord provides finding (establishing) a policy to maximize a cumulative reward. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, and Horri as this combination would optimize the system with respect to on the desired objectives.
11. The system of claim 10, wherein the HA server is configured to assign a positive reward for a successful completion and delivery of tasks.
12. The system of claim 10, wherein the HA server is configured to assign a negative reward for a unsuccessful completion of tasks due to storage node failure.
13. The system of claim 10, further comprising a reinforcement learning model to establish a policy by learning a strategy over time to maximize a cumulative reward.
Claim(s) 5,14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20180121766 A1, McCord, 2018-01-22 and US 20240227829 A1, Bonasera, 2023-01-11.
5. The method of claim 4,
Venugopal in view of Adabi, Yajnanarayana, Horii, and McCord does not teach the following.
further comprising updating the policy dynamically based on the rewards obtained from outcomes of task assignments.
Bonasera does however.
[0012] Moreover, the method further comprises iteratively adjusting the plurality of training hyperparameters by way of the at least one updated activation function of the initial policy, and iteratively determining an updated trajectory action based on the plurality of training hyperparameters, the at least one updated activation function, and the trajectory reward value.
Bonasera provides iteratively adjusting (updating dynamically) an aspect of a policy. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, Horri, and McCord as this combination would allow different polices to be compared against one another until an optimal result is achieved.
14. The system of claim 13, wherein the reinforcement learning model is configured to dynamically update the policy based on rewards obtained from outcomes of task assignments.
Claim(s) 6,15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20180121766 A1, McCord, 2018-01-22 and US 20110215903 A1, Yang , 2010-03-04.
6. The method of claim 4,
Venugopal in view of Adabi, Yajnanarayana, Horii, and McCord does not teach the following.
wherein the strategy is learned using Q-learning or Deep Q-Networks (DQN).
Yang does however.
[0097] the policies can be learned using reinforcement learning, such as Q-learning
Yang provides a policy learned via Q learning. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, Horri, and McCord as this combination would confer an established and adaptable method for learning/optimization that can operate without needing a model to be provided.
15. The system of claim 13, wherein the policy is configured to learn a strategy using Q-learning or Deep Q-Networks (DQN).
Claim(s) 7,16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20030110263 A1, Shillo, 2002-10-23.
7. The method of claim 1,
Venugopal in view of Adabi, Yajnanarayana, and Horii does not explicitly teach the following.
further comprising continuously monitoring activity of each node within a plurality of storage nodes.
Shillo does however.
[0022] The performance of each storage node is dynamically monitored
Shillo provides nodes that are dynamically (continuously) monitored. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, Horri, as this combination would in turn allow faults to be tasks to be reassigned continuously, preventing downtime within the system.
16. The system of claim 10, wherein the HA server is configured to continuously monitor activity of each storage node within a plurality of storage nodes.
Claim(s) 9,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20140026000 A1, Ma, 2012-11-14.
9. The method of claim 1,
Venugopal in view of Adabi, Yajnanarayana, and Horii does not teach the following.
wherein the first server is a highly available (HA) server
Ma does however.
[0033] FIG. 1 is a schematic diagram of a highly available server system based on cloud computing according to an embodiment of the present invention, as shown in FIG. 1, the server system comprises a central control management server 11, a cloud host server 12, a storage server 13 and at least one application server 14.
and the second server is a network-attached storage server.
[0033] FIG. 1 is a schematic diagram of a highly available server system based on cloud computing according to an embodiment of the present invention, as shown in FIG. 1, the server system comprises a central control management server 11, a cloud host server 12, a storage server 13 and at least one application server 14.
Ma provides a highly available server as well as an associated storage server. It would have been obvious to one skilled in art to introduce these elements into the design of Venugopal in view of Adabi, Yajnanarayana, Horri, as this combination would confer a central server adapted to maintain functionality in event of partial breakage as well as a server designated for storage that would enable conservation of memory resources elsewhere in the system.
The following claim corresponds to claim 1, 8, and 9 and therefore inherits the same rejections
18. A method of configuring a failover mechanism in case a node goes down and becomes unavailable, the method comprising: periodically monitoring, by a highly available (HA) server, a plurality of nodes; sending, by the HA server, a request to each one of the plurality of nodes to determine the availability of any one of the plurality of nodes; marking, by the HA server, that a node is unavailable/down when a node fails to respond to the request, or returns an error; and redirecting, by the HA server, a load to another node of the plurality of nodes that is an available idle active node when a node is down.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20140026000 A1, Ma, 2012-11-14 and US 20010027453 A1, Suto, 2001-03-29.
19. The method of claim 18,
Venugopal in view of Adabi, Yajnanarayana, Horii, and Ma teaches the following as described above.
HA server
once the failed node is recovered.
Venugopal in view of Adabi, Yajnanarayana, Horii, and Ma does not teach the following.
comprising resuming normal operation of the (…) server (…)
Suto does however.
[0102] After the completion of the above restoring process, the clients 30, 32 are connected again to the server 14, and the servers 12, 14 are activated again to resume operation of the entire distributed data processing system 10.
Suto provides a resumption of server functionality. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, Horri,and Ma as this combination would allow for continuous functionality of task management operations when nodes are recovered.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210109830 A1, Venugopal, 2019-10-14 in view of US 20140108861 A1, Abadi, 2013-10-15, 20220166676 A1, Yajnanarayana, 2019-03-23, and US 20080163221 A1, Horii, 2007-12-27 as well as in view of US 20140026000 A1, Ma, 2012-11-14 and US 20220035792 A1, Gschwind, 2020-07-29.
20. The method of claim 18, comprising:
Venugopal in view of Adabi, Yajnanarayana, Horii, and Ma teaches the following as described above.
by the HA server
Venugopal in view of Adabi, Yajnanarayana, Horii, and Ma does not teach the following.
accepting, (…), a processed report as an acknowledgement;
and preventing, (…), redundancy in report delivery.
Gschwind does however.
[0063] The request may, for example, be a request to store the record. In another example, the request may be a matching request for matching the record with records of the dataset 107. In both cases, a matching of the record with records of the dataset 107 may be required, e.g., before storing a received record it may be matched with existing ones to prevent storing duplicate records.
Gschwind provides matching records (reports) to prevent storing duplicate (redundant) entries. It would have been obvious to one skilled in art to introduce this element into the design of Venugopal in view of Adabi, Yajnanarayana, Horri,and Ma as this combination would allow for the conservation of memory resources.
Conclusion
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/LUCAS DONALD ABSHER/
Examiner, Art Unit 2194
/KEVIN L YOUNG/Supervisory Patent Examiner, Art Unit 2194
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