DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office correspondence is in response to the application number 18/828823 filed on September 9, 2024.
Claims 1 – 20 are pending.
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Priority
This Application is a continuation of International Application No. PCT/CN2023/080147 filed on Mar. 7, 2023, which claims priority to Chinese Patent Application No. 202210227347.4 filed on Mar. 8, 2022. The applicant is entitled to a priority date of 3/8/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 06, 2024 was filed on or after the mailing date of the application on September 9, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections 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 – 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims recites:
Claim 1: A knowledge update method, comprising:
sending, by a knowledge provision network element, knowledge information to a network management network element, wherein the knowledge information indicates quality of intent knowledge of a first intent operation;
receiving, by the knowledge provision network element, policy information from the network management network element, wherein the policy information indicates a manner of updating the intent knowledge; and
updating, by the knowledge provision network element, the intent knowledge based on the policy information
Claim 8: A knowledge update method, comprising:
receiving, by a network management network element, knowledge information from a knowledge provision network element, wherein the knowledge information indicates quality of intent knowledge of a first intent operation; and
sending, by the network management network element, policy information to the knowledge provision network element, wherein the policy information indicates a manner of updating the intent knowledge.
Claim 11: A knowledge update method, comprising:
receiving, by a knowledge consumption network element, policy information from a knowledge provision network element, wherein the policy information indicates a manner of updating intent knowledge;
determining, by the knowledge consumption network element, performance evaluation information based on the policy information, wherein the performance evaluation information indicates a change value of a performance indicator of at least one target network device in a target time period and executing the first intent operation and executing no other intent operation other than the first intent operation during the target time period; and
sending, by the knowledge consumption network element, the performance evaluation information to the knowledge provision network element.
Claim 14: A knowledge update method, comprising:
sending, by a knowledge provision network element, knowledge information to a network management network element and receiving, by the network management network element, the knowledge information from the knowledge provision network element, wherein the knowledge information indicates quality of intent knowledge of a first intent operation;
sending, by the network management network element, policy information to the knowledge provision network element;
receiving, by the knowledge prov1s1on network element, the policy information from the network management network element, wherein the policy information indicates a manner of updating the intent knowledge; and
updating, by the knowledge provision network element, the intent knowledge based on the policy information.
Claims 1, 8, 11, and 14 is directed towards certain methods of organizing human activity (in particular moving knowledge information from one part to another, making certain decisions based on policy or performance, all of which can be done within the human mind). Accordingly, the claims recite an abstract idea (Step 2A, prong 1).
This judicial exception is not integrated into a practical application. In particular, the claims recited additional elements: knowledge provision network element, network management network element, knowledge consumption network element, target network device, but the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite knowledge update methods inserting the said elements in a sending, receiving, and updating steps that generally link the use of the judicial exception to a particular technological environment or field of use. The recited limitations are not enough to add significantly more to the claimed method and is an attempt to limit the use of the abstract idea to a particular technological environment for which to apply the underlying abstract concept, which does not add significantly more.
For example, claim 11 recites limitation determining, by the knowledge consumption network element, performance evaluation information based on the policy information, wherein the performance evaluation information indicates a change value of a performance indicator of at least one target network device in a target time period and executing the first intent operation and executing no other intent operation other than the first intent operation during the target time period; which provides performance evaluation information to indicate a change value of a performance indicator of at least one target network device, but this in itself does not impose any meaningful limits in practicing the abstract idea. Even considering all the additional elements, such as the target network device, in combination, the claim is just providing a computerized system to perform the invention, but doesn’t improve the computing technology as the additional elements do not integrate the invention into a practical application. The claims is directed to an abstract idea and with the addition of activity to change a performance indicator that is not sufficient to enable the claim to be patent eligible and not directed to an abstract idea.
The claims as currently recited does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even considering all the additional element in combination, the claims represent a computerized method to perform the invention, but doesn’t improve the computing technology as the additional elements do not integrate the invention into a practical application. The claims are directed to an abstract idea and merely links the judicial exception to a particular technological environment or field of use(MPEP 2106.05(h)) and use generic computer components described at a high level of generality and merely instructions to implement the abstract idea on a computer and/or use a computer as a tool the claim is not patent eligible and directed to an abstract idea MPEP 2106.05(f). (Step 2A, prong2).
Even when incorporating dependent claims 2 – 7, 9 – 10, 12 – 13, and 15 – 20 into the independent claims, there are not additional elements/limitations 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/limitations is drawn to limitations that attempts to limit the use of the abstract idea to a particular technological environment or field of use and/or use a computer as a tool, and includes well-understood, routine, and conventional activities that amount to no more than implementing the abstract idea with a computerized system and/or includes insignificant extra-solution activity . The claims are not patent eligible(Step 2B).
Therein claims 1 – 20 are rejected under 35 USC 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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8 and 14 are rejected under 35 U.S.C. 103 as being un-patentable over Geddes (U.S. 2004/0205182 A1; herein referred to as Geddes) in view of R et al. (U.S. 2021/0028980 A1; herein referred to as R).
In regard to claim 1, Geddes teaches A knowledge update method (see abstract “ . . . A network management system and method is disclosed for providing an intelligent decision support system for assisting human operators monitor, maintain, and diagnose and solve problems with a network. According to one embodiment, a system and method is provided that uses a partial order planner and a knowledge base to implement intelligent decision support using knowledge stored as plan and goal graphs, concept graphs, and/or scripts . . .”) , comprising:
sending, by a knowledge provision network element (e. g. see Fig.2 inference engine 202) knowledge information to a network management network element (e.g. operator monitor 205) (see ¶¶ [0054-0055] “ . . . Inference engine 202 is also coupled to the network management collector 204. This component monitors the communications network using conventional technology such as by sending pings or SNMP requests to network devices. The data collected is incorporated into knowledge base 203 in order to assist the inference engine 202 in performing its functions. Finally, inference engine 202 is coupled to a graphical user interface 201 component that facilitates input and output with a network operator. The graphical user interface 201 output is dependent on the current state of the system as determined by the inference engine 202. . . .” see ¶ [0058] “ . . . The system remains in the all clear state until the network management collector 204 detects a change in the status of a device, or until the inference engine 202 detects a new intention of the network operator. For example, if the network operator begins viewing the firewall 103 log files and logs on to public servers 101, the inference engine may determine that the operator's activity has changed from nominal network operations to respond to security event. In that case, the graphical user interface 201 changes the display to show information that may help the network operator diagnose and resolve security problems . . .” ), wherein the knowledge information indicates quality of intent knowledge of a first intent operation (see Fig. 3 ¶ [0063] “ . . . One embodiment of the present invention is implemented using an inference engine 301 such as the one described in FIG. 3 including one or more planners 302, an intent interpreter 303, an information manager 304, a script performer 305, a knowledge base 306, and a situation assessor 307 . . .” see ¶ [0069] “ . . . to create an intelligent interface, the system monitors a network operator's actions to determine what the operator is trying to accomplish. The intent interpreter does this using a task-analytic decomposition of the purposes of network operators within the communications network domain. This decomposition is represented as a plan and goal graph (PGG), an acyclic, directed graph that represents the hierarchy of possible goals that may be pursued to achieve an intention and the methods (or plans) that can be used to satisfy each goal. Additionally, intent interpreter 303 uses knowledge represented as scripts. These scripts are sequences of primitive actions whose execution may be dependent on the state of the execution context. Other embodiments may use scripts that may include non-primitive actions (e.g., recursive script calls or additional script calls). Scripts represent standard procedures or processes that are routinely used to perform specific network activities described by plan sub-elements. Such standard operational procedures may include standard responses to both normal and abnormal events and operating conditions within a network. The intent interpreter 303 uses reasoning on the PGG to represent problem solving behaviors that are necessary when existing network management procedures defined by scripts are not appropriate for the situation. Using assertions made by the other components of the system together with domain knowledge stored in knowledge base 206, the intent interpreter determines the most likely intent of a network operator. This determined intent is then used to update the information being displayed to the network operator and to generate one or more plans to satisfy the interpreted goals of the operator. . . .”) ;
Geddes fails to explicitly teach,
However R teaches
receiving, by the knowledge provision network element (see Fig. 1 controller device 10) (see ¶ [0004] “ . . Network management systems (NMSs) and NMS devices, also referred to as controllers or controller devices, may support these services such that an administrator can easily create and manage these high-level network configuration services. . . .”), policy information from the network management network element, wherein the policy information indicates a manner of updating the intent knowledge (see ¶ ¶ [0005-0006] “ . . . In particular, user configuration of devices may be referred to as “intents.” An intent-based networking system lets administrators describe the intended network/compute/storage state. User intents can be categorized as business policies or stateless intents. Business policies, or stateful intents, may be resolved based on the current state of a network. Stateless intents may be fully declarative ways of describing an intended network/compute/storage state, without concern for a current network state. Intents may be represented as intent data models, which may be modeled using unified graphs. Intent data models may be represented as connected graphs, so that business policies can be implemented across intent data models. For example, data models may be represented using connected graphs having vertices connected with has-edges and reference (ref) edges. Controller devices may model intent data models as unified graphs, so that the intend models can be represented as connected. In this manner, business policies can be implemented across intent data models. When Intents are modeled using a unified graph model, extending new intent support needs to extend the graph model and compilation logic. ; and
updating, by the knowledge provision network element (see Fig. 1 administrator 12), the intent knowledge based on the policy information (see ¶ ¶ [0028-0029] “ . . . Administrator 12 uses controller device 10 to configure elements 14 to specify certain operational characteristics that further the objectives of administrator 12. For example, administrator 12 may specify for an element 14 a particular operational policy regarding security, device accessibility, traffic engineering, quality of service (QoS), network address translation (NAT), packet filtering, packet forwarding, rate limiting, or other policies. Controller device 10 uses one or more network management protocols designed for management of configuration data within managed network elements 14, such as the SNMP protocol or the Network Configuration Protocol (NETCONF) protocol or a derivative thereof, such as the Juniper Device Management Interface, to perform the configuration. In general, NETCONF provides mechanisms for configuring network devices and uses an Extensible Markup Language (XML)-based data encoding for configuration data, which may include policy data . . . Controller device 10 may be configured to accept high-level configuration data, or intents, from administrator 12 (which may be expressed as structured input parameters . . . “).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the applicant’s application to incorporate systems and methods for the management of network devices using a controller device that maintains and updates knowledge information on the configuration of the network device and previous device-level intent configuration information, as taught by R, into systems and methods for providing intelligent decision support in network management by collecting data and incorporating the data into a knowledge base that can be used to infer the intent for a network device, as taught by Geddes. Such incorporation enables knowledge of the network to be updated from multiple sources and used to predict operational intent of the network.
In regard to claim 8, Geddes teaches A knowledge update method (see abstract as described for the rejection of claim 1 and is incorporated herein), comprising:
receiving, by a network management network element (e.g. operator monitor 205), knowledge information from a knowledge provision network element (e. g. see Fig.2 inference engine 202) (see ¶¶ [0054-0055] as described for the rejection of claim 1 and is incorporated herein) , wherein the knowledge information indicates quality of intent knowledge of a first intent operation (see Fig. 3 ¶ [0063] ¶ [0069] as described for the rejection of claim 1 and is incorporated herein) ;
Geddes fails to explicitly teach,
However R teaches and sending, by the network management network element (see ¶ [0004] as described for the rejection of claim 1 and is incorporated herein), policy information to the knowledge provision network element, wherein the policy information indicates a manner of updating the intent knowledge (see ¶ ¶ [0005-0006], ¶ ¶ [0028-0029] as described for the rejection of claim 1 and is incorporated herein)
The motivation to combine R with Geddes is described for the rejection of claim 1 and is incorporated herein.
In regard to claim 14, Geddes teaches A knowledge update method (see abstract as described for the rejection of claim 1 and is incorporated herein) , comprising:
sending, by a knowledge provision network element (e. g. see Fig.2 inference engine 202) , knowledge information to a network management network element (e.g. operator monitor 205) (see ¶¶ [0054-0055], ¶ [0058] as described for the rejection of claim 1 and is incorporated herein) and receiving, by the network management network element, the knowledge information from the knowledge provision network element, wherein the knowledge information indicates quality of intent knowledge of a first intent operation (see Fig. 3 ¶ [0063], ¶ [0069] as described for the rejection of claim 1 and is incorporated herein);
Geddes fails to explicitly teach,
However R teaches
sending, by the network management network element, policy information to the knowledge provision network element (see ¶ [0004] as described for the rejection of claim 1 and is incorporated herein) ;
receiving, by the knowledge prov1s1on network element, the policy information from the network management network element, wherein the policy information indicates a manner of updating the intent knowledge (see ¶ ¶ [0005-0006] as described for the rejection of claim 1 and is incorporated herein) ; and
updating, by the knowledge provision network element (see Fig. 1 administrator 12) , the intent knowledge based on the policy information (see ¶ ¶ [0028-0029] as described for the rejection of claim 1 and is incorporated herein).
The motivation to combine R with Geddes is described for the rejection of claim 1 and is incorporated herein.
Claims 2 - 3 11, 15, and 16 are rejected under 35 U.S.C. 103 as being un-patentable over Geddes (U.S. 2004/0205182 A1; herein referred to as Geddes) in view of R et al. (U.S. 2021/0028980 A1; herein referred to as R) as applied to claims 1, 8, and 14 in further view of Abdelkader et al. (U.S. 2024/0214287 A1; herein referred to as Adelkader)
In regard to claim 2 the combination of Geddes, R, and Abdelkader teaches further comprising:
obtaining, by the knowledge provision network element (see Abdelkader Fig. 1 intent specification platform 111), performance evaluation information (e.g. satisfaction) (see Abdelkader ¶ [0026] “ . . . a range of the intent fulfilment satisfaction indicator indicates levels of satisfaction from the lowest to the highest. . .”) indicating a change value of a performance indicator of at least one target network device(see Abdelkader Fig. 1 consumer 120) in a target time period (see Abdelkader ¶ [0066] “ . . consumer 120 requests in step S301 “Increase capacity of X slice by 50%”. Utility function U=f(α.Math.T, β.Math.C, θ.Math.T_SI), where T is the total intent execution time, C is the relative increase in capacity after intent fulfillment, T_SI is the service interruption time, and α+β+θ=1, are weights representing interest of the consumer 120. The intent execution time and service interruption time are evaluated relative to a reference time (of e.g. 1 second), for example, to allow for comparability of the three components . . “)., wherein the target network device executes the first intent operation and executes no other intent operation other than the first intent operation during the target time period (see Abdelkader Fig. 3 ¶ [0063] “ . . . As seen in FIG. 3, consumer 120 of an intent service submits an intent request to IDNMS 110 through intent specification platform 111 and standardized interfaces 121 (step S301). The IDNMS 110 proceeds to fulfil the submitted intent through intent logic execution in intent fulfillment system 112 (step S303) and performs a list of actions affecting specific network resources 130 to achieve the objective of the intent (step S305). After the intent execution, the IDNMS 110 sends an intent fulfilment notification to the consumer 120 informing it that the requested intent has been fully executed (S307), or in other cases failed to execute, was partially executed, etc. . . “) ; and the updating, by the knowledge provision network element, the intent knowledge based on the policy information (see claim 1 which is incorporated herein) comprises:
updating the intent knowledge based on the policy information and the performance evaluation information (see Abdelkader Fig. 3 ¶ [0064] “ . . . Upon receiving the intent fulfilment notification, the consumer 120 sends a data collection request to check the state of the intent objective (step S309). After gathering the relevant data, e.g. KPIs, parameters, etc. (step S311), the consumer 120 calculates its satisfaction level for the fulfillment of the intent by the IDNMS 110 (step S313). According to at least some example embodiments, this satisfaction calculation is performed based on a utility function which is at least one of consumer, intent and service specific, and takes into account relevant KPIs and aspects important for that specific consumer, intent, or service. . . “)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the applicant’s application to incorporate systems and methods to evaluate in a Cognitive Autonomous Networks (CANs) in 5G networks, e.g. radio access networks, and other (e.g. future) generations of wireless/mobile networks and, specifically, the use of intents in managing networks. In particular, relate to intent performance of a device used by a consumer to measure satisfaction, as taught by Abdelkader, into systems and methods for providing intelligent decision support in network management by collecting data and incorporating the data into a knowledge base that can be used to infer the intent for a network device, using a controller device that maintains and updates knowledge information on the configuration of the network device and previous device-level intent configuration information, as taught by the combination of Geddes and R. Such incorporation provides a means to predict changes in intent for a network device.
In regard to claim 3, the combination of Geddes, R, and Abdelkader teaches wherein the policy information further indicates a manner of determining the at least one target network device and the target time period (see R ¶ [0028] “ . . . Administrator 12 uses controller device 10 to configure elements 14 to specify certain operational characteristics that further the objectives of administrator 12. For example, administrator 12 may specify for an element 14 a particular operational policy regarding security, device accessibility, traffic engineering, quality of service (QoS), network address translation (NAT), packet filtering, packet forwarding, rate limiting, or other policies. Controller device 10 uses one or more network management protocols designed for management of configuration data within managed network elements 14, such as the SNMP protocol or the Network Configuration Protocol (NETCONF) protocol or a derivative thereof, such as the Juniper Device Management Interface, to perform the configuration. In general, NETCONF provides mechanisms for configuring network devices and uses an Extensible Markup Language (XML)-based data encoding for configuration data, which may include policy data . . .”; see R ¶ [0049] “ . . . at time t2 containing one or more OOB configuration changes (“device config” in the expression for v.sub.2 in FIG. 3). In one or more aspects, the duration of time between each of time t.sub.0, t.sub.1, and t.sub.2 may be the same (or substantially the same) (e.g., 30 seconds, 5 minutes, 30 minutes, 2 hours, or any other interval of time). . . .”) ; and
the obtaining, by the knowledge provision network element, performance evaluation information comprises: determining, by the knowledge provision network element, the performance evaluation information (e.g. satisfaction) based on the policy information (e.g. network control operations) (see Abdelkader ¶ [0039] “ . . . According to at least some example embodiments, step S203 further includes providing, e.g. via interface 140 shown in FIG. 1, a second feedback report which comprises at least one of the following information: an indication of first network control operations to achieve a better or higher level of satisfaction with the fulfillment of the intent, a utility function used for calculating the level of satisfaction indicated by the measurement, and a result of an evaluation of second network control operations as to whether these will achieve a better or higher level of satisfaction with the fulfillment of the intent. . . “); or
sending, by the knowledge provision network element, the policy information (e.g. network control operations) to a knowledge consumption network element and receiving, by the knowledge provision network element, the performance evaluation information(e.g. satisfaction) from the knowledge consumption network element (see Abdelkader ¶ [0041] “ . . . a feedback report is evaluated. The feedback report comprises at least one of the following information: [0042] a measurement that indicates a level of satisfaction with a fulfillment of an intent which has been submitted to an intent-driven network management system, [0043] an identification of a consumer entity (e.g. consumer 120 of FIG. 1) that has submitted the intent, [0044] an identification of the intent for which the first feedback report is being provided, [0045] an identification of a service related to the intent, [0046] an indication of first network control operations to achieve a higher level of satisfaction with the fulfillment of the intent, [0047] a utility function used for calculating the level of satisfaction indicated by the measurement, [0048] a result of an evaluation of second network control operations as to whether these will achieve a higher level of satisfaction with the fulfillment of the intent. . . .”).
The motivation to combine Abdelkader with the combination of Geddes and R is described for the rejection of claim 2 and is incorporated herein. Additionally, Abdelkader uses network control operations which are policies to determine satisfaction levels (e.g. performance evaluations).
In regard to claim 11, Geddes teaches A knowledge update method (see abstract as described for the rejection of claim 1 and is incorporated herein) ,
Geddes fails to explicitly teach,
However R teaches comprising: receiving, by a knowledge consumption network element (see Fig. 1 controller device 10) (see ¶ [0004] “ . . Network management systems (NMSs) and NMS devices, also referred to as controllers or controller devices, may support these services such that an administrator can easily create and manage these high-level network configuration services. . . .”), policy information from a knowledge provision network element, wherein the policy information indicates a manner of updating intent knowledge (see R ¶ ¶ [0005-0006] as described for the rejection of claim 1 and is incorporated herein.) ;
The motivation to combine R with Geddes is described for the rejection of claim 1 and is incorporated herein.
The combination of Geddes and R fails to explicitly teach,
However Adelkader teaches
determining, by the knowledge consumption network element, performance evaluation information (e.g. satisfaction) based on the policy information (see Abdelkader Fig. 3 ¶ [0064] “ . . . Upon receiving the intent fulfilment notification, the consumer 120 sends a data collection request to check the state of the intent objective (step S309). After gathering the relevant data, e.g. KPIs, parameters, etc. (step S311), the consumer 120 calculates its satisfaction level for the fulfillment of the intent by the IDNMS 110 (step S313). According to at least some example embodiments, this satisfaction calculation is performed based on a utility function which is at least one of consumer, intent and service specific, and takes into account relevant KPIs and aspects important for that specific consumer, intent, or service. . . “), wherein the performance evaluation information(e.g. satisfaction) (see Abdelkader ¶ [0026] “ . . . a range of the intent fulfilment satisfaction indicator indicates levels of satisfaction from the lowest to the highest. . .”) indicates a change value of a performance indicator of at least one target network device(see Abdelkader Fig. 1 consumer 120) in a target time period (see Abdelkader ¶ [0066] “ . . consumer 120 requests in step S301 “Increase capacity of X slice by 50%”. Utility function U=f(α.Math.T, β.Math.C, θ.Math.T_SI), where T is the total intent execution time, C is the relative increase in capacity after intent fulfillment, T_SI is the service interruption time, and α+β+θ=1, are weights representing interest of the consumer 120. The intent execution time and service interruption time are evaluated relative to a reference time (of e.g. 1 second), for example, to allow for comparability of the three components . . “) and executing the first intent operation and executing no other intent operation other than the first intent operation during the target time period (see Abdelkader Fig. 3 ¶ [0063] “ . . . As seen in FIG. 3, consumer 120 of an intent service submits an intent request to IDNMS 110 through intent specification platform 111 and standardized interfaces 121 (step S301). The IDNMS 110 proceeds to fulfil the submitted intent through intent logic execution in intent fulfillment system 112 (step S303) and performs a list of actions affecting specific network resources 130 to achieve the objective of the intent (step S305). After the intent execution, the IDNMS 110 sends an intent fulfilment notification to the consumer 120 informing it that the requested intent has been fully executed (S307), or in other cases failed to execute, was partially executed, etc. . . “); and
sending, by the knowledge consumption network element, the performance evaluation information to the knowledge provision network element (see Abdelkader ¶¶ [0041-0048] “ . . . a feedback report is evaluated. The feedback report comprises at least one of the following information: a measurement that indicates a level of satisfaction with a fulfillment of an intent which has been submitted to an intent-driven network management system, an identification of a consumer entity (e.g. consumer 120 of FIG. 1) that has submitted the intent, an identification of the intent for which the first feedback report is being provided, an identification of a service related to the intent, an indication of first network control operations to achieve a higher level of satisfaction with the fulfillment of the intent, a utility function used for calculating the level of satisfaction indicated by the measurement, a result of an evaluation of second network control operations as to whether these will achieve a higher level of satisfaction with the fulfillment of the intent. . . .”).
The motivation to combine Abdelkader with the combination of Geddes and R is described for the rejection of claim 2 and is incorporated herein.
In regard to claim 15, the combination of Geddes, R, and Abdelkader teaches further comprising: obtaining, by the knowledge provision network element (see Abdelkader Fig. 1 intent specification platform 111), performance evaluation information (e.g. satisfaction) (see Abdelkader ¶ [0026] “ . . . a range of the intent fulfilment satisfaction indicator indicates levels of satisfaction from the lowest to the highest. . .”) indicating a change value of a performance indicator of at least one target network device (see Abdelkader Fig. 1 consumer 120) in a target time period (see Abdelkader ¶ [0066] as described for the rejection of claim 2 and is incorporated herein) , and executing the first intent operation and executing no other intent operation other than the first intent operation during the target time period (see Abdelkader Fig. 3 ¶ [0063] as described for the rejection of claim 2 and is incorporated herein); and
the updating, by the knowledge provision network element, the intent knowledge based on the policy information (see claim 1 which is incorporated herein) comprises:
updating, by the knowledge provision network element, the intent knowledge based on the policy information and the performance evaluation information (see Abdelkader Fig. 3 ¶ [0064] as described for the rejection of claim 2 and is incorporated herein).
In regard to claim 16, the combination of Geddes, R, and Abdelkader teaches wherein the policy information further indicates a manner of determining the at least one target network device and the target time period (see R¶ [0028], ¶ [0049] as described for the rejection of claim 3 and is incorporated herein) ; and
the obtaining, by the knowledge provision network element, performance evaluation information comprises: determining, by the knowledge provision network element, the performance evaluation information (e.g. satisfaction) based on the policy information (e.g. network control operations) (see Abdelkader ¶ [0039] as described for the rejection of claim 3 and is incorporated herein) ; or
sending, by the knowledge provision network element, the policy information (e.g. network control operations) to a knowledge consumption network element (see Abdelkader ¶ [0027]” . . . when the satisfaction is inadequate then the IDNMS 110 requests for further information and hints on how to improve the intent fulfillment. For example, the IDNMS 110 asks the consumer 120 to provide a list of pseudo-operations that are normally performed (e.g. legacy network control operations, which are also referred to as “first network control operation” in the following) to achieve the needed outcome and learns therefore to improve future outputs. . . .”) and receiving, by the knowledge consumption network element, the policy information from the knowledge provision network element (see Abdelkader ¶ [0031]” . . . , the IDNMS 110 provides information about the intent fulfillment with a request to the consumer 120 to provide guidance on how the fulfillment can be improved. For example, the IFS 112 shares a list of pseudo-operations (which are also referred to as “second network control operation” in the following) performed and asks the consumer 120 to review it and highlight possible wrong/non-optimal actions. . . “) ;
determining, by the knowledge consumption network element, the performance evaluation information (e.g. satisfaction) based on the policy information (see Abdelkader Fig. 3 ¶ [0064] as described for the rejection of claim 11 and is incorporated herein) ;
sending, by the knowledge consumption network element, the performance evaluation information to the knowledge provision network element (see Abdelkader ¶¶ [0041-0048] as described for the rejection of claim 11 and is incorporated herein) ; and
receiving, by the knowledge provision network element, the performance evaluation information from the knowledge consumption network element (see Abdelkader ¶ ¶ [0054 -0057] “. . . evaluating a second feedback report which comprises at least one of the following information: the indication of first network control operations to achieve the higher level of satisfaction with the fulfillment of the intent, the utility function used for calculating the level of satisfaction indicated by the measurement, the result of the evaluation of second network control operations as to whether these will achieve the higher level of satisfaction with the fulfillment of the intent. . . .”).
The motivation to combine the references is described for the rejection of claim 2 and claim 11 and is incorporated herein.
The motivation to combine Abdelkader with the combination of Geddes and R is described for the rejection of claim 2 and is incorporated herein.
Claims 4, 9, 12, and 17 are rejected under 35 U.S.C. 103 as being un-patentable over Geddes (U.S. 2004/0205182 A1; herein referred to as Geddes) in view of R et al. (U.S. 2021/0028980 A1; herein referred to as R) in further view of Abdelkader et al. (U.S. 2024/0214287 A1; herein referred to as Adelkader) as applied to claims 2 - 3, 11, and 15 – 16 in further view of Desai et al. (U.S. 2020/0162325 A1; herein referred to as Desai)
In regard to claim 4, the combination of Geddes, R, Adelkader, and Desai teaches wherein the policy information comprises at least one of the following constraint conditions: a quantity of target network devices is greater than or equal to a first threshold; and a length of the target time period is greater than or equal to a second threshold (see Desai ¶ [0136] “ . . . Such network health factor(s) include, but are not limited to, (1) a percentage (which can be a configurable parameter determined based on experiments and/or empirical studies) of client association rejections for client devices (e.g., wireless end points 130A-F) that have attempted to associate with a network node/site over a given time frame (which can be a configurable parameter determined based on experiments and/or empirical studies); (2) a percentage (which can be a configurable parameter determined based on experiments and/or empirical studies) of access points (APs) down in a given site over a given period of time (which can be a configurable parameter determined based on experiments and/or empirical studies); (3) a number (which can be a configurable parameter determined based on experiments and/or empirical studies) of Dynamic Frequency Selection (DFS) Hits over a given time frame (which can be a configurable parameter determined based on experiments and/or empirical studies); (4) a number (which can be a configurable parameter determined based on experiments and/or empirical studies) of coverage holes over a given period of time (which can be a configurable parameter determined based on experiments and/or empirical studies); (5) a percentage (which can be a configurable parameter determined based on experiments and/or empirical studies) of capacity reduction over a given time period (which can be a configurable parameter determined based on experiments and/or empirical studies); (6) a percentage (which can be a configurable parameter determined based on experiments and/or empirical studies) of APs with Quality of Service Enhanced Basic Service Set (QBSS) higher than a threshold (which can be expressed as a percentage and is a configurable parameter determined based on experiments and/or empirical studies); (7) WAN link failures on a given site or list of sites over a period of time (which can be a configurable parameter determined based on experiments and/or empirical studies), etc. . . .”).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the applicant’s application to incorporate systems and methods for automatic provisioning of network components through the use of intent -based networking that measure performance of the network based on the policies implemented, as taught by Desai, into systems and methods for providing intelligent decision support in network management by collecting data and incorporating the data into a knowledge base that can be used to infer the intent for a network device, using a controller device that maintains and updates knowledge information on the configuration of the network device and previous device-level intent configuration information, when deployed in 5G networks, e.g. radio access networks, and other (e.g. future) generations of wireless/mobile networks and, specifically, the use of intents in managing networks as taught by the combination of Geddes, R, and Abdelkader. Such incorporation provides setting limits on the number of devices and time of implementation for gathering data for the knowledge base.
In regard to claim 9, the combination of Geddes, R, Adelkader, and Desai teaches wherein the policy information comprises at least one of the following constraint conditions: a quantity of target network devices is greater than or equal to a first threshold; and a length of a target time period is greater than or equal to a second threshold (see Desai ¶ [0136] as described for the rejection of claim 4 and is incorporated herein).
The motivation to combine the references is described for the rejection of claim 4 and is incorporated herein.
In regard to claim 12, the combination of Geddes, R, Adelkader, and Desai teaches wherein the policy information comprises at least one of the following constraint conditions: a quantity of target network devices is greater than or equal to a first threshold; and a length of the target time period is greater than or equal to a second threshold (see Desai ¶ [0136] as described for the rejection of claim 4 and is incorporated herein).
The motivation to combine the references is described for the rejection of claim 4 and is incorporated herein.
In regard to claim 17, the combination of Geddes, R, Adelkader, and Desai teaches wherein the policy information comprises at least one of the following constraint conditions: a quantity of target network devices is greater than or equal to a first threshold; and a length of the target time period is greater than or equal to a second threshold (see Desai ¶ [0136] as described for the rejection of claim 4 and is incorporated herein).
The motivation to combine the references is described for the rejection of claim 4 and is incorporated herein.
Claims 5 – 7 10, 13, and 18 – 20 are rejected under 35 U.S.C. 103 as being un-patentable over Geddes (U.S. 2004/0205182 A1; herein referred to as Geddes) in view of R et al. (U.S. 2021/0028980 A1; herein referred to as R) in further view of Abdelkader et al. (U.S. 2024/0214287 A1; herein referred to as Adelkader) as applied to claims 2 -3, 11, and 15 – 16 in further view of Wang (U.S. 2023/0245651 A1; herein referred to as Wang).
In regard to claim 5, the combination of Geddes, R, Adelkader, and Wang teaches further comprising:
obtaining, by the knowledge provision network element, confidence information indicating a trustworthiness level of the performance evaluation information (see Wang ¶ [0399] “ . . . The AI system evaluates the user’s input against the existing intents and objectives in the OKB and assigns a confidence score to each potential intent and objective. This confidence score represents the AI system’s level of certainty that a particular intent or objective is the most relevant one for the user’s input. . . “) ; and
the updating, by the knowledge provision network element, the intent knowledge based on the policy information comprises: updating the intent knowledge based on the policy information and the confidence information (see Wang ¶ [0401] “ . . . The AI system then selects the intent and objective with the highest confidence score as the most relevant one and generates a response to the user. The confidence score can also be used to improve the accuracy of the AI system’s future predictions. . . .”; see Wang ¶ [0266] “ . . . The conversational AI agent’s objective is to maximize cumulative reward over time by learning an optimal policy, which is a mapping of states to actions that yield the highest expected reward. The reward system acts as a measure of the desirability of the agent’s actions and helps guide the agent’s learning process. . . “).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the applicant’s application to incorporate systems and methods for using an AI system for enabling contextually relevant communications based on determinizing the most relevant intent for a device, as taught by Wang, into systems and methods for providing intelligent decision support in network management by collecting data and incorporating the data into a knowledge base that can be used to infer the intent for a network device, using a controller device that maintains and updates knowledge information on the configuration of the network device and previous device-level intent configuration information, when deployed in 5G networks, e.g. radio access networks, and other (e.g. future) generations of wireless/mobile networks and, specifically, the use of intents in managing networks as taught by the combination of Geddes, R, and Abdelkader. Such incorporation enables a confidence score to be determined for the knowledge data gathered.
In regard to claim 6, the combination of Geddes, R, Abdelkader, and Wang teaches wherein the obtaining, by the knowledge prov1s1on network element, confidence information comprises: receiving, by the knowledge provision network element, the confidence information from the knowledge consumption network element (see Wang ¶ [0404] “ . . The AI systems can use various types of contextual information to generate confidence scores, such as user profile data, historical behavior, location, time of day, weather, and device type. User profile data can include age, gender, occupation, and interests, while historical behavior can include search and purchase history. Location can be determined by GPS or IP address, while weather data can also be used. Time of day and device type can also be considered . . .”).; and
the obtaining, by the knowledge provision network element, performance evaluation information comprises: receiving, by the knowledge provision network element, the performance evaluation information from the knowledge consumption network element when the trustworthiness level is greater than or equal to a third threshold (e.g. objective function) ( see Wang ¶¶ [0268-0269] “ . . An objective function 219 in the AI system 200 is a mathematical representation of the AI system’s goal or the desired outcome it aims to achieve. The objective function quantifies the performance of the AI system by assigning a numerical value to its current state, taking into consideration various factors such as accuracy, efficiency, and other performance metrics. In the context of ML and optimization, the objective function plays an important role in guiding the AI system during training or decision-making processes. The AI system’s goal is to either minimize or maximize the objective function, depending on the specific problem being addressed. For example, in a supervised learning task like regression or classification, the objective function is often a loss function that measures the difference between the predicted output and the actual target values. The AI system’s goal would be to minimize this loss function, thereby improving the accuracy of its predictions . . .”)
The motivation to combine Wang with the combination of Geddes, R, and Abdelkader is described for the rejection of claim 5 and is incorporated herein. Additionally, Wang enables confidence and performance parameters for determining the accuracy of the knowledge being gathered.
In regard to claim 7, the combination of Geddes, R, Abdelkader, and Wang teaches further comprising: receiving, by the knowledge provision network element, first information (e.g. contextual information) from the network management network element (see Wang ¶ [0005] “ . . . The most relevant contextual information to the user is predicted by the AI system. The AI system then transforms the most relevant contextual information into textual form and predicts a set of intents and objectives for user-centered interaction. . . .”) , wherein the first information indicates a manner of determining the confidence information (see Wang ¶ [0416] “ . . . the AI system determines whether any additional information is needed 1904. If the AI system determines that the available contextual information is insufficient or the AI system is unable to determine the user’s intent and objective with a reasonable level of confidence, it may request additional information again or provide alternative options for the user to choose from. . . .”) ; and
the obtaining, by the knowledge provision network element, confidence information comprises: determining, by the knowledge provision network element, the confidence information based on the first information (see Wang ¶ [0432] “ . . . the AI agent would use contextual information to generate a confidence score for the user’s intent and objective. . .”) ; or
sending, by the knowledge prov1s1on network element, the first information to the knowledge consumption network element and receiving, by the knowledge provision network element, the confidence information from the knowledge consumption network element (see Wang ¶ [0406] “ . . . the AI system is using contextual information to make an educated guess about the user’s intent or objective, which can be represented as a confidence score to guide the system’s actions. . . “).
The motivation to combine Wang with the combination of Geddes, R, and Abdelkader is described for the rejection of claim 5 and is incorporated herein. Additionally, Wang uses confidence scores to determine whether to update a knowledge base.
In regard to claim 10, the combination of Geddes, R, Abdelkader, and Wang teaches further comprising:
sending, by the network management network element (see Wang ¶ [0005] “ . . . The most relevant contextual information to the user is predicted by the AI system. The AI system then transforms the most relevant contextual information into textual form and predicts a set of intents and objectives for user-centered interaction. . . .”), first information to the knowledge provision network element, wherein the first information (e.g. contextual information) indicates a manner of determining confidence information (see Wang ¶ [0416] “ . . . the AI system determines whether any additional information is needed 1904. If the AI system determines that the available contextual information is insufficient or the AI system is unable to determine the user’s intent and objective with a reasonable level of confidence, it may request additional information again or provide alternative options for the user to choose from. . . .”) indicating a trustworthiness level of performance evaluation information (see Wang ¶ [0399] “ . . . The AI system evaluates the user’s input against the existing intents and objectives in the OKB and assigns a confidence score to each potential intent and objective. This confidence score represents the AI system’s level of certainty that a particular intent or objective is the most relevant one for the user’s input. . . “) , the performance evaluation information indicating a change value of a performance indicator of at least one target network device in the target time period (see R ¶ [0028] “ . . . Administrator 12 uses controller device 10 to configure elements 14 to specify certain operational characteristics that further the objectives of administrator 12. For example, administrator 12 may specify for an element 14 a particular operational policy regarding security, device accessibility, traffic engineering, quality of service (QoS), network address translation (NAT), packet filtering, packet forwarding, rate limiting, or other policies. Controller device 10 uses one or more network management protocols designed for management of configuration data within managed network elements 14, such as the SNMP protocol or the Network Configuration Protocol (NETCONF) protocol or a derivative thereof, such as the Juniper Device Management Interface, to perform the configuration. In general, NETCONF provides mechanisms for configuring network devices and uses an Extensible Markup Language (XML)-based data encoding for configuration data, which may include policy data . . .”; see R ¶ [0049] “ . . . at time t2 containing one or more OOB configuration changes (“device config” in the expression for v.sub.2 in FIG. 3). In one or more aspects, the duration of time between each of time t.sub.0, t.sub.1, and t.sub.2 may be the same (or substantially the same) (e.g., 30 seconds, 5 minutes, 30 minutes, 2 hours, or any other interval of time). . . .”) and executing the first intent operation and executing no other intent operation other than the first intent operation during the target time period (see Abdelkader Fig. 3 ¶ [0063] “ . . . As seen in FIG. 3, consumer 120 of an intent service submits an intent request to IDNMS 110 through intent specification platform 111 and standardized interfaces 121 (step S301). The IDNMS 110 proceeds to fulfil the submitted intent through intent logic execution in intent fulfillment system 112 (step S303) and performs a list of actions affecting specific network resources 130 to achieve the objective of the intent (step S305). After the intent execution, the IDNMS 110 sends an intent fulfilment notification to the consumer 120 informing it that the requested intent has been fully executed (S307), or in other cases failed to execute, was partially executed, etc. . . “).
The motivation to combine the references is described for the rejections of claim 2 and claim 5 and is incorporated herein.
In regard to claim 13, the combination of Geddes, R, Abdelkader, and Wang teaches further comprising:
receiving, by the knowledge consumption network element, first information from the knowledge provision network element, wherein the first information (e.g. contextual information) indicates a manner of determining confidence information (see Wang ¶ [0416] “ . . . the AI system determines whether any additional information is needed 1904. If the AI system determines that the available contextual information is insufficient or the AI system is unable to determine the user’s intent and objective with a reasonable level of confidence, it may request additional information again or provide alternative options for the user to choose from. . . .”) indicating a trustworthiness level of the performance evaluation information (see Wang ¶ [0399] “ . . . The AI system evaluates the user’s input against the existing intents and objectives in the OKB and assigns a confidence score to each potential intent and objective. This confidence score represents the AI system’s level of certainty that a particular intent or objective is the most relevant one for the user’s input. . . “);
determining, by the knowledge consumption network element, the confidence information based on the first information; (see Wang ¶ [0432] “ . . . the AI agent would use contextual information to generate a confidence score for the user’s intent and objective. . .”) and
sending, by the knowledge consumption network element, the confidence information to the knowledge provision network element (see Wang ¶ [0406] “ . . . the AI system is using contextual information to make an educated guess about the user’s intent or objective, which can be represented as a confidence score to guide the system’s actions. . . “).
The motivation to combine the references is described for the rejection of claim 5 and is incorporated herein.
In regard to claim 18, the combination of Geddes, R, Adelkader, and Wang teaches further comprising:
obtaining, by the knowledge provision network element, confidence information indicating a trustworthiness level of the performance evaluation information (see Wang ¶ [0399] as described for the rejection of claim 5 and is incorporated herein); and
the updating, by the knowledge provision network element, the intent knowledge based on the policy information comprises: updating, by the knowledge provision network element, the intent knowledge based on the policy information and the confidence information (see Wang ¶ [0401], ¶ [0266] as described for the rejection of claim 5 and is incorporated herein).
The motivation to combine the references is described for the rejection of claim 5 and is incorporated herein.
In regard to claim 19, the combination of Geddes, R, Abdelkader, and Wang teaches wherein the obtaining, by the knowledge provision network element, confidence information comprises:
sending, by the knowledge consumption network element, the confidence information to the knowledge provision network element and receiving, by the knowledge provision network element, the confidence information from the knowledge consumption network element (see Wang ¶ [0404] as described for the rejection of claim 6 and is incorporated herein) ; and
the obtaining, by the knowledge provision network element, performance evaluation information comprises: receiving, by the knowledge prov1s1on network element, the performance evaluation information from the knowledge consumption network element when the trustworthiness level is greater than or equal to a third threshold (e.g. objective function) ( see Wang ¶¶ [0268-0269] as described for the rejection of claim 6 and is incorporated herein).
The motivation to combine the references is described for the rejection of claim 6 and is incorporated herein.
In regard to claim 20, the combination of Geddes, R, Abdelkader, and Wang teaches further comprising: sending, by the network management network element, first information(e.g. contextual information) to the knowledge provision network element and receiving, by the knowledge provision network element, the first information from the network management network element (see Wang ¶ [0005] as described for the rejection of claim 7 and is incorporated herein) , wherein the first information indicates a manner of determining the confidence information (see Wang ¶ [0416] as described for the rejection of claim 7 and is incorporated herein); and
the obtaining, by the knowledge provision network element, confidence information comprises: determining, by the knowledge provision network element, the confidence information based on the first information (see Wang ¶ [0432] “ . . . the AI agent would use contextual information to generate a confidence score for the user’s intent and objective. . .”) ; or
sending, by the knowledge prov1s1on network element, the first information (e.g. contextual information) to the knowledge consumption network element (see Wang ¶ [0005] as described for the rejection of claim 7 and is incorporated herein) ;
determining, by the knowledge consumption network element, the confidence information based on the first information (see Wang ¶ [0404] “ . . . The AI systems can use various types of contextual information to generate confidence scores, such as user profile data, historical behavior, location, time of day, weather, and device type. User profile data can include age, gender, occupation, and interests, while historical behavior can include search and purchase history. Location can be determined by GPS or IP address, while weather data can also be used. Time of day and device type can also be considered . . .”) ;
sending, by the knowledge consumption network element, the confidence information to the knowledge provision network element (see Wang ¶ [0401] “ . . . The AI system then selects the intent and objective with the highest confidence score as the most relevant one and generates a response to the user. The confidence score can also be used to improve the accuracy of the AI system’s future predictions. . . .”) ; and
receiving, by the knowledge provision network element, the confidence information from the knowledge consumption network element (see Wang ¶ [0406] “ . . . the AI system is using contextual information to make an educated guess about the user’s intent or objective, which can be represented as a confidence score to guide the system’s actions. . . “).
The motivation to combine the references is described for the rejection of claim 7 and is incorporated herein.
Conclusion
There are prior art made of record which are not relied upon but are considered pertinent to applicant’s disclosure. They are listed on the PTO-892 accompanying this action.
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/JAMES N FIORILLO/Examiner, Art Unit 2444