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 .
Status of the Claims
Claims 1-3, 6, and 7 have been amended. Claims 1-7 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/03/2024 and 10/07/2025 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: “Allocating Tasks in Edge Computing”. This is a mere suggestion and applicant may use a different title, so long as the title is descriptive and clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-5 recite an apparatus (i.e. machine), claim 6 recites a method (i.e. process), and claim 7 recites a non-transitory computer-readable recording medium (i.e. machine). Therefore claims 1-7 fall within one of the four statutory categories of invention.
Independent claims 1, 6, and 7 recite the limitations of observing task information regarding a task requested by an [end device] and network usage information indicating a usage status of a physical network constructed and modeled by nodes including edge nodes and cloud nodes; calculating an optimal specific node for offloading the task based on a result of the observation; and transferring the task to the specific node. The claimed invention is directed towards controlling the allocation of tasks, and the claim limitations correspond to certain methods of organizing human activity (following rules or instructions, etc.) as evidenced by the limitations detailing observing task information and network usage information and transferring the task to a specific node based on the observation. The claim limitation also directly correspond to mental processes (observation, evaluation, judgment, opinion), since the limitations detail the observation and evaluation of data, and making a decision (judgement/opinion…specifically the allocation of the tasks to specific nodes), based on the observed and evaluated data. The use of a computer to perform these tasks does not take the claims out the judicial exception categories. For instance, MPEP §2106.04(a)(2)(III) discloses that claims can recite a mental process even if they are claimed as being performed on a computer when the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed: 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. This is the case in the applicant’s invention. The claims recite an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section is/are inserted for reading clarity, but are analyzed as “additional elements” under step 2A Prong Two and Step 2B, below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a processor, a memory, and an end device. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claim 3 recites the limitation of calculating the specific node using a [task offloading algorithm] that is based on cooperative multi-agent deep reinforcement learning, based on the result of the observation. The claim limitation is further directed to the abstract idea analyzed above. In addition, the use of the offloading algorithm based off deep reinforcement learning amounts to mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations). The claim recites an abstract idea. The use of the algorithm also amounts to generally linking the judicial exception to a particular field of use (allocating tasks for computing resources). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible
Dependent claims 2, 4, and 5 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2, 4, and 5 are also rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (2023/0153124) in view of Dechene (2022/0245462).
Claim 1: Zhao discloses: A control apparatus comprising:
a processor; (Zhao ¶0020 disclosing the edge server processing the task)
and a memory storing program instructions that cause the processor to: (Zhao ¶0059 disclosing the compute readable storage medium may be a hard disk or internal memory of the device; see also ¶0060 further discloses the processes in the method embodiments can be implemented by instructing relevant hardware through a computer program)
Zhao in view of Dechene discloses:
observe task information regarding a task requested by an end device and network usage information indicating a usage status of a physical network constructed and modeled by nodes including edge nodes and cloud nodes;
Zhao discloses observing task information regarding a task requested by an end device, and a physical network constructed and modeled by nodes including edge nodes and cloud nodes: (Zhao ¶0020 disclosing the terminal device originates and sends a task to the edge server through the access network; ¶0022 a plurality of computing tasks is received by an edge server from a terminal device; ¶0040 each edge server acts as an agent who makes a series of decisions on task offloading (specifically, cloud server selection) over time; for the edge server m, a system state is defined by the observed UL and DL access latencies; the system state observed by the edge server m at time t is specified by a 1×3 J vector; ¶0043 disclosing the agent interacts with the environment by taking actions, observing the reward and system state transition, and updating its knowledge about the environment; ¶0048 the edge servers learn to optimize the task offloading policies via the history of observed latency, hence gradually learn to select the proper cloud server; ¶0019 the edge network computing system includes a terminal device, an access network, a core network, an edge server, and a cloud server; see also ¶0017). Zhao does not appear to explicitly disclose observing task information regarding a task requested by an end device and network usage information indicating a usage status of a physical network constructed and modeled by nodes including edge nodes and cloud nodes. Dechene appears to suggest or disclose this limitation/concept: (Dechene ¶0057 disclosing the agent can respond to routing requests from participating nodes of computer network nodes, and/or can proactively program local node route entries prior to requests; network states, including topology, adjacencies, traffic, and performance data, can be observed via monitoring service; ¶0062 each action (task) can be informed by observations of a state of an environment (e.g., a training or live environment of a computer network)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao to include observing task information regarding a task requested by an end device and network usage information indicating a usage status of a physical network constructed and modeled by nodes including edge nodes and cloud nodes as taught by Dechene. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Zhao in order to allocate hardware resources and reduce computational overhead, etc. (see Dechene ¶0002-¶0004).
Zhao, as modified above, disclose the following limitations :
calculate an optimal specific node for offloading the task based on a result of the observation; and (Zhao Fig 2, S220 and ¶0043 Using a deep Q-learning neural network (DQN) with experience replay to select a could server to offload a portion of the plurality of computing tasks; ¶0043 details a reinforcement learning (RL) agent aiming to learn from the environment and take action to maximize its long-term cumulative reward;¶0055 disclosing the algorithms testing every action at the beginning of each run and selects the sever which has the shortest latency; selecting the suboptimal sever when the best original link is congested or select new optimal sever when a small random perturbation occurs in the system; ¶0056 the constant step-size c-greedy algorithm shows good performance on adaptively selecting the best servers for incoming tasks even the distribution of the latency in the system changes)
transfer the task to the specific node. (Zhao Fig. 2, S230 Sending the portion of the plurality of computing tasks to the cloud server and forwarding results of the portion of the plurality of computing tasks received from the cloud server to the terminal device)
Claims 6 and 7: Claims 6 and 7 are directed to a method and non-transitory computer-readable medium, respectively. Claims 6 and 7 recite limitations that are parallel in nature as those addressed above for claim 1, which is directed towards an apparatus. Claims 6 and 7 are therefore rejected for the same reasons as set forth above for claim 1. Furthermore, claim 7 recites:
(Claim 7): A non-transitory computer-readable recording medium having stored therein a program causing a computer to execute a method comprising: (Zhao ¶0060 disclosing that all or part of the processes in the foregoing method embodiments can be implemented by instructing relevant hardware through a computer program. The computer program may be stored in the computer-readable storage medium, and when being executed, the computer program implements the processes of the foregoing method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random-access memory (RAM))
Claim 2: The control apparatus according to claim 1,
wherein the program instructions cause the processor to calculate an optimal route between the nodes by aggregating traffic demand information between the nodes based on the result of the observation, and transfer the task to the specific node via the optimal route.
Zhao discloses offloading a portion of tasks for different nodes, but does not appear to explicitly disclose that the program instructions cause the processor to calculate an optimal route between the nodes by aggregating traffic demand information between the nodes based on the result of the observation, and transfer the task to the specific node via the optimal route. Dechene appears to suggest or disclose this limitation/concept: (Dechene ¶0057 the network control system can allow the trained AI agent to integrate with network nodes, such as computer network nodes, in an SDN model; network control system 315 can include an AI agent routing service 316, a control service 317, and/or a monitoring service; AI agent routing service 316 can include an AI agent that can trained to make routing decisions for computer network nodes; agent can use non-AI routing techniques, such as Shortest-Path Forwarding (SPF) (optimal); network states, including topology, adjacencies, traffic, and performance data, can be observed via monitoring service; ¶0062 each action can be informed by observations of a state of an environment (e.g., a training or live environment of a computer network); observations (e.g., an observation 422) can be taken before and/or after an action; ¶0063 an action (e.g., 421) can be choosing to route through the public internet or instead through a MPLS (Multiprotocol Label Switching) network; one or more observations (e.g., 422) of environment 440 can include a link identifier, a current bandwidth, and an available bandwidth in the network, and such state information can be stored in state; see also ¶0064 regarding the shortest path through a network providing the best reward). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao to include that the program instructions cause the processor to calculate an optimal route between the nodes by aggregating traffic demand information between the nodes based on the result of the observation, and transfer the task to the specific node via the optimal route as taught by Dechene. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Zhao in order to allocate hardware resources and reduce computational overhead, etc. (see Dechene ¶0002-¶0004).
Claim 3: The control apparatus according to claim 1, wherein the program instructions cause the processor to calculate the specific node using a task offloading algorithm that is based on cooperative multi-agent deep reinforcement learning, based on the result of the observation. (Zhao ¶0004 disclosing the disclosure provides a task scheduling/offloading method in the edge network to handle task distribution, offloading and management by applying a deep reinforcement learning model; the task offloading problem is formulated as a multi-agent reinforcement learning problem; see also ¶0018 and ¶0024; ¶0043 each edge server includes a reinforcement learning (RL) agent aiming to learn from the environment and take action to maximize its long-term cumulative reward; the objective of an RL algorithm is to find the optimal policy, which determines the strategy of taking actions under certain system states; ¶0052 the deep reinforcement learning algorithm is applied to the edge network computing system)
Claim 4: The control apparatus according to claim 1, wherein the task information includes at least one of information on a required computing resource demand, a traffic demand, or a maximum allowable delay time. (Zhao ¶0017 when data traffics and computation loads vary in the cloud computing network, any offload decision made without considering the time-varying network conditions may keep forwarding heavy data traffic to a node (i.e., a cloud server) that encounters congestion and may further aggravate the congestion at the node, thus the task offloading method consistent with the present disclosure senses the network conditions and actively avoids congested or busy cloud servers to reduce a task offloading cost)
Claim 5: The control apparatus according to claim 1,
wherein the network usage information is information regarding network topology or bandwidth.
Zhao discloses observing the network environment, but does not appear to explicitly disclose network usage information including is information regarding network topology or bandwidth. Dechene appears to suggest or disclose this limitation/concept: (Dechene ¶0056 the reinforcement learning (RL) training environment can be based on a simulated digital-twin network topology provided by digital twin service 322, and can augmented with synthetic network traffic provided by network traffic service 323; different configuration items such as network topologies, synthetic network traffic, training scenarios (such as node addition or failure), and AI hyper-parameters can be adjusted to customize (e.g., optimize) the agent model, such as through policy service; see also ¶0057; ¶0063 an action (e.g., 421) can be choosing to route through the public internet or instead through a MPLS (Multiprotocol Label Switching) network; one or more observations (e.g., 422) of environment 440 can include a link identifier, a current bandwidth, and an available bandwidth in the network, and such state information can be stored in state). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao to include that the network usage information is information regarding network topology or bandwidth as taught by Dechene. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Zhao in order to allocate hardware resources and reduce computational overhead, etc. (see Dechene ¶0002-¶0004).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
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