DETAILED ACTION
This is in response to the application filed on July 30th 2024, in which claims 1-23 are presented for examination.
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 Objections
Claim 1 is objected to because of the following informalities: it recites a network configuration controller … “configured to: … an output to provide output” (last limitation). This is unclear, perhaps “an output to” should be removed so the claim simply recites “configured to: provide output …”. Alternatively, a verb like “generate” could be added before “an output” (e.g. “configured to: generate an output …”) if supported by the disclosure. Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "162" and "164" have both been used to designate “Endpoint” (Fig. 1). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, 7-8, 11-12, 15, 17, 19-20 and 22-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bastug et al. US 2022/0272035 A1.
Regarding claim 1, Bastug discloses:
a network configuration controller (network controller – paragraph 30, Fig. 1 item 120) comprising one or more processors (network controller provides control functions such as provisioning, monitoring etc. – paragraph 30; Bastug discloses an apparatus/computer which comprises a processor – paragraph 3, Fig. 18, paragraph 153; Bastug teaches the computer/controller perform the described functions – see paragraphs 30, 156; thus the network controller comprises a processor) configured to:
monitor a plurality of node-related flow information sets based on flow information corresponding to a plurality of data flows between a first plurality of endpoints and a second plurality of endpoints via a network (network has plurality of flows, and plurality of endpoints – see paragraph 3, Figs. 1-2; network controller performs monitoring function – paragraph 30), the plurality of node- related flow information sets corresponding to a plurality of networking nodes connecting between a plurality of network inputs of the network and a plurality of network outputs of the network, wherein a node-related flow information set corresponding to a networking node of the plurality of networking nodes comprises information corresponding to one or more data flows communicated via the networking node (nodes have inputs and outputs – Figs. 1-2; flow information corresponds to data flows – see paragraph 3 which teaches state information for set of nodes and corresponding set of flows; the state information is an “information set” that comprises information); and
determine a network-configuration setting to configure the network based on the plurality of node-related flow information sets and at least one target End to End (E2E) performance parameter (state information includes requirements such as latency, performance, etc. – see paragraph 3; configure network resources based on the node/flow state information in order to support deterministic connections which have performance guarantees – see paragraphs 30-32; also see Fig. 4 and paragraphs 54-66 which teaches a controller determining configuration/provisioning information based on flow information), the at least one target E2E performance parameter corresponding to an E2E performance of the plurality of data flows between the first plurality of endpoints and the second plurality of endpoints (performance requirement is for the data flows – paragraphs 3, 31-32); and
an output to provide output information based on the network-configuration setting (promote provision/configuration determination for implementation at local nodes/controllers– Figs. 4-5, paragraphs 3, 57, 81).
Regarding claim 3, Bastug discloses the network configuration controller is configured to monitor the flow information (paragraph 30), and to update the network-configuration setting based on a detected real-time change in the plurality of node-related flow information sets (support adaptive deterministic routing – abstract; system is dynamic – paragraph 91).
Regarding claim 4, Bastug discloses the detected real-time change in the plurality of node-related flow information sets comprises a change indicative of an expected degradation in the at least one target E2E performance parameter (indicate expected flow metrics – paragraph 3).
Regarding claim 5, Bastug discloses the network configuration controller is configured to determine a first network-configuration setting based on a first plurality of node-related flow information sets corresponding to first flow information related to a first time frame, and to determine a second network-configuration setting, different from the first network- configuration setting, based on a second plurality of node-related flow information sets, different from the first plurality of node-related flow information sets (multiple flows, each has state information; thus there are multiple configurations/allocations determined – paragraphs 3, 30-32), corresponding to second flow information related to a second time frame subsequent to the first time frame (repeat process for each request – Figs. 4-5).
Regarding claim 7, Bastug discloses the network configuration controller comprises a ML engine trained to generate ML output information based on an ML input, which is based on the plurality of node-related flow information sets, wherein the network configuration setting is based on the ML output information (agent determine paths using neural network that takes input and generates output – paragraphs 3, 103-107, Figs. 7-8 and 10).
Regarding claim 8, Bastug discloses the network configuration controller is configured to determine network topography information corresponding to a network topology of the network based on the plurality of node-related flow information sets (support representation of network topology including discovery of link states for routing – paragraph 146; also see paragraph 96 which teaches the network controller queries are performed based on the topology); wherein the ML input information is based on the network topography information (agent determine paths using neural network that takes input and generates output – paragraphs 3, 103-107, Figs. 7-8 and 10; input is state information – Fig. 8, paragraph 107).
Regarding claim 11, Bastug discloses the ML engine comprises a Deep Reinforcement Learning (DRL) engine (use deep reinforcement learning techniques – paragraph 91) configured to generate the ML output information comprising action information based on the ML input comprising observation information and reward information (rewards/reward function – paragraphs 3 and 106), wherein the observation information is based on the plurality of node-related flow information sets (state information – paragraph 3), the reward information is based on the at least one target E2E performance parameter (performance requirements – paragraphs 30-32), the network-configuration setting is based on the action information (use deep reinforcement learning agents to provision the deterministic flow – paragraph 93-94, Fig. 7).
Regarding claim 12, Bastug discloses the network configuration controller is configured to determine network topography information corresponding to a network topology of the network based on the plurality of node-related flow information sets (support representation of network topology including discovery of link states for routing – paragraph 146; also see paragraph 96 which teaches the network controller queries are performed based on the topology); and to determine the network-configuration setting based on the network topography information (deterministic paths are based on state information and network topology – see paragraphs 3, 28 and Figs. 1-2).
Regarding claim 15, Bastug discloses the network configuration controller is configured to determine network topology information corresponding to a network topology of the network based on the plurality of node-related flow information sets (support representation of network topology including discovery of link states for routing – paragraph 146; also see paragraph 96 which teaches the network controller queries are performed based on the topology), and to determine the network topography information based on the network topology information, wherein the network topology information comprises routing information corresponding to active data flow routes between the plurality of networking nodes (represent topology including active data flows between nodes – Figs. 1-3 and paragraph 83).
Regarding claim 17, Bastug discloses the network-configuration setting comprises one or more node specific parameter settings corresponding to one or more networking nodes of the plurality of networking nodes (nodes receive individual configuration settings – paragraph 57, Fig. 4).
Regarding claim 19, Bastug discloses wherein the at least one target E2E performance parameter comprises a Job Completion Time (JCT) (in a score based reward system, an expected completion time is compared to the actual completion time – see Fig. 12, paragraph 135).
Regarding claim 20, Bastug discloses the target E2E performance parameter comprises at least one of a usage efficiency, quality of experience, quality of service, delay, bandwidth, or power consumption of the network (performance measures include latency, jitter, and reliability requirements – paragraphs 3, 31; thus they comprise at least quality and delay).
Regarding claim 22, it is a system that corresponds to the apparatus of claim 21. The corresponding limitations are rejected for the same reasons. Bastug also discloses a network comprising a plurality of networking nodes connecting between a plurality of network inputs of the network and a plurality of network outputs of the network (paragraph 3, Figs. 1-2); and a network controller configured to control the network based on the network configuration setting (network controller controls the network (i.e. provisions/allocates) based on the determined settings – paragraph 30; also see Fig. 4, paragraphs 57-59).
Regarding claim 23, it is a system claim that corresponds to the apparatus of claim 7; thus it is rejected for the same reasons.
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) 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bastug.
Regarding claim 2, Bastug discloses the node-related flow information set … identifies a network input and a network output corresponding to the flow … and next-hop information to identify a next-hop node corresponding to the flow (deterministic path is from source to destination including each hop along the path – paragraph 32; includes interfaces for a route – paragraph 113). Bastug does not explicitly disclose source/destination address information or source/destination port information, or data length information. But such information is extremely well-known routine and conventional in the art. So it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information set of Bastug to include address, port and length/size data. This is merely the incorporation of a well-known technique (e.g. all IP packets include this information) in order to yield a predictable result.
Regarding claim 13, Bastug discloses the network topography information comprises a topography map to map [flows] to a plurality of ingress-egress [pairs] (map topology including nodes, flows and pairs – see Figs. 1-3 and paragraph 83), the plurality of ingress-egress pairs corresponding to a plurality of ingress … of the plurality of network nodes and a plurality of egress … of the plurality of networking nodes (this is just the definition of a pair). Bastug does not explicitly disclose data sizes or ports. But such information is extremely well-known routine and conventional in the art. So it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the topology of Bastug to include port and length/size data. This is merely the incorporation of a well-known technique (e.g. all IP packets include this information) in order to yield a predictable result.
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bastug in view of Sanchez Charles et al. US 2020/0136957 A1.
Regarding claim 9, Bastug does not explicitly disclose the network configuration controller is configured to determine size-reduced network topography information by reducing a size of the network topography information based on a size of the ML input, wherein the ML input is based on the size-reduced network topology information. But this is taught by Sanchez as an autonomous network controller that uses AI/ML to optimize network routing by analyzing topology and reducing a search space for the ML model by reducing/eliminating some network nodes (i.e. “reduced representation”, see abstract, Figs. 1, 3 and paragraphs 14 and 28; paragraph 29 discloses the reduced size can be based on the resources for the ML model). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bastug with the topology size reduction taught by Sanchez for the purpose of determining network routes. Sanchez teaches this optimizes the network automatically and lightens the load on the AI/ML model (paragraph 10).
Regarding claim 10, Bastug does not disclose the size-reduced network topography information comprises network topography information corresponding to a subset of networking nodes, the ML output information corresponding to the subset of networking nodes. But this is taught by Sanchez as the size reduced topology has nodes removed/eliminated and thus is a subset of nodes. Furthermore, the ML output corresponds to the subset because the eliminated nodes cannot be selected (see abstract, paragraphs 28-29 and Figs. 1-3). The motivation to combine is the same as that given above.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Bastug in view of Gray et al. US 2011/0305143 A1.
Regarding claim 14, Bastug does not explicitly disclose the network topography information comprises a plurality of statistical ingress data sizes corresponding to the plurality of ingress ports, and a plurality of statistical egress data sizes corresponding to the plurality of egress ports, wherein a statistical ingress data size corresponding to an ingress port is based on statistical data flow sizes mapped to ingress-egress port pairs comprising the ingress port, wherein a statistical egress data size corresponding to an egress port is based on statistical data flow sizes mapped to ingress-egress port pairs comprising the egress port. But this is taught by Gray as a network topology discovery mechanism (Fig. 1, abstract) that discovers and records the statistical data sizes corresponding to ingress/egress ports (Figs. 2-4, paragraphs 42 and 62-63). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bastug with the statistical ingress/egress data size corresponding to the ports as taught by Gray for the purpose of improving network communication. Gray suggests knowing data size is important to prevent loops (paragraphs 25-27) and improve performance (paragraphs 28-29).
Claim(s) 6, 16, 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Bastug in view of Lee et al. US 2020/0280518 A1.
Regarding claim 6, Bastug discloses the network configuration controller is configured to determine a … state of the plurality of data flows via the network based on the plurality of node related flow information sets and determine the network configuration setting based on the … state of the plurality of data flows via the network and the at least one target E2E performance parameter (network controller uses state information and performance requirements to determine network configuration settings as explained above – see abstract, paragraphs 3, 30-32, Figs. 1-2 and 4-5). Bastug does not explicitly disclose “predicting” a state of the flow or determine configuration settings based on “the predicted” state. But this is clearly taught by Lee as using network/node state information in a probabilistic function to predict congestion and then make configuration adjustments based on the prediction (paragraphs 25 and 29-30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bastug to make predictions as taught by Lee for the purpose of avoiding congestion. Lee teaches that by predicting congestion, proactive actions can be taken to reduce congestion and delay (paragraphs 24-27).
Regarding claim 16, Bastug does not explicitly disclose determining at least one ingress-port setting, the ingress-port setting comprising one of ECN or maximum buffer queue size setting, wherein the at least one ingress-port setting is configured such that any Priority Flow Control event based on a PFC setting is not to occur before an ingress-port event based on the at least one ingress-port setting. But this is taught by Lee as using ECN (ECN - paragraph 16, Fig. 1). Lee further teaches improving pause times with the use of ECN to avoid PFC which causes issues such as head-of-line blocking; thus the port is configured such that ECN occurs before any PFC event (see paragraphs 23 and 25-28). It would have been obvious to one of ordinary skill in the art to modify Bastug to use ECN as taught by Lee. ECN is very well-known in the art and yields predictable results. Also, Lee explicitly suggests this reduces congestion and delay (paragraph 27).
Regrading claim 18, Bastug does not explicitly disclose a node-specific parameter setting comprises at least one of PFC, ECN, or maximal buffer queue size. But this is taught by Lee as discussed above. The motivation to combine is the same.
Regarding claim 21, Bastug does not explicitly disclose the network comprises a network connecting between a plurality of processors of an AI training cluster. But this is taught by Lee as the network is connected to a system including “accelerators” which are a plurality of processors (e.g. “cluster”) for use by AI (paragraphs 88, 90 and Fig. 9). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bastug with the plurality of processors for AI as taught by Lee. One of ordinary skill in the art would easily recognize that using a plurality of processors for AI would provide benefits over a single processor. Furthermore, Bastug itself already discloses using neural network and also suggests functions may be implemented “one or more processors” (paragraph 157).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Francini et al. US 2021/0250301 A1 discloses configuring networks to support delay guarantees, the network including a plurality of endpoints and flows (abstract, paragraph 19, Fig. 1).
Clemm et al. US 2020/0052979 A1 discloses performing network SLO and KPI validation for network flows between nodes (abstract, paragraph 36, Fig. 1).
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/JASON D RECEK/Primary Examiner, Art Unit 2458