DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Amendment filed 27 January 2026
Claims 1-20 are pending.
This Action is Final.
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.
Claims 1, 2, 7, 11, 12, 19, and are rejected under 35 U.S.C. 103 as being unpatentable over Hardjono et al. (US 20090180495) in view of Jasner et al. (US 20220263804).
As per claims 1, 11, and 19, Hardjono et al. discloses a medium with instructions, a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus/computer to perform a method (see paragraphs [0086]-[0088] where the functions and procedures and application programs are examples of instructions) of routing traffic in a network based on trust scores, the method comprising:
assigning trust scores to respective nodes within a network (see paragraphs [0054]-[0058] where each of the routers exchanges a trust report which includes a trust score to determine trust convergence which is assigned to each router; paragraph [0059] shows a plurality of routers, each with an assigned trust score);
determining, based on the trust scores, one or more preferred routes for one or more data packets through the network, the one or more preferred routes starting at a first node and ending at a second node, and the one or more preferred routes being determined based on a respective cumulative metric for each potential route that accounts for the trust scores of nodes along the potential route (see paragraphs [0059]-[0061] and [0084] where, as an example, the trust score for each of the paths between router 815 and 820 are determined and the policy defines which path to select and, as an example, the path with highest sum of integrity/trust scores is selected); and
routing the one or more data packets along the one or more preferred routes based at least in part on load balancing a volume of traffic (see paragraphs [0064], [0066], and [0083] where the data is transmitted to the selected router along the selected path to reach the destination based on both the trust score and load balancing).
While Hardjono et al. discloses the use of cumulative trust scores by aggregating the trust scores along a potential path (see paragraphs [0059]-[0061] and [0084]) and routing based on both trust scores and load balancing (see paragraph [0066], “it is possible to select paths based on both integrity/trust scores and other factors, such as load balancing”), there lacks an explicit teaching of using a weighted combination of the trust scores as part of the calculation.
However, Jasner et al. teaches the use of a weighted combination of scores including various trust/security values and load balancing values as part of a determination of which path to select (see paragraphs [0067]-[0068] and Table 1.)
At a time before the effective filing date of the invention, it would have been obvious to include weight values in the Hardjono et al. calculation.
Motivation, as recognized by one of ordinary skill in the art, to do so would have been to allow for more flexibility in selecting paths.
As per claims 2, 12, and 20, the modified Hardjono et al. and Jasner et al. system discloses determining the one or more preferred routes based on the trust scores further comprises: determining the one or more preferred routes based on the trust scores and based on a degree of data sensitivity of the one or more data packets, such that load balancing is achieved by: routing a first traffic flow of the one or more data packets along a first route, wherein the first traffic flow has a higher degree of the data sensitivity than a second traffic flow, and routing a part of the second traffic flow along a second route that is less trustworthy than the first route, when a packet routing capacity of the first route is exceeded by a combination of the first traffic flow and the second traffic flow (see Hardjono et al. paragraphs [0064]-[0068] where data sensitivity is used as part of the path selection and data that is flagged as sensitive to trustworthiness can be routed based on the integrity/trust scores of routers in the network, and data that is not considered sensitive can be routed based on factors such as load balancing and that when loads become high to transmit the data based on load balancing and Jasner et al. paragraphs [0067]-[0068] and Table 1).
As per claim 7, the modified Hardjono et al. and Jasner et al. system discloses determining the one or more preferred routes based on the trust scores further comprises: optimizing an objective function that comprises a trust term and a load balancing term, the objective function representing scores for potential routes, wherein the trust term for a potential route combines the trust scores of the nodes along the potential route into the cumulative metric, and the load balancing term accounts for bottlenecks and limitations arising from traffic- flow capacities of the potential route (see Hardjono et al. paragraphs [0064]-[0068] where both trust scores and load balancing are considered for path selection based on a policy, i.e. an optimized function for the administrator’s needs and Jasner et al. paragraphs [0067]-[0068] and Table 1).
Claims 3-6, 10, 13-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the modified Hardjono et al. and Jasner et al. system as applied to claims 1 and 11 above, in view of Carnes, III et al. (US 20220272100).
As per claims 3 and 13, the modified Hardjono et al. and Jasner et al. system discloses assigning the trust scores to respective nodes within the network further comprises: receiving, at one or more processors, network data representing indicia of trustworthiness at the respective nodes of the network and assigning a determined trust scores to the respective nodes of the network (see Hardjono et al. paragraphs [0059]-[0061] where the trust reports indicate a trustworthiness of the respective node and is used to assign a trust score), but fails to disclose applying the network data of a first node to a first machine learning (ML) model to determine a first trust score, and repeating for each of the respective nodes of the network to determine the trust scores using the ML model.
However, Carnes, III et al. teaches receiving, at one or more processors, network data representing indicia of trustworthiness at the respective nodes of the network; applying the network data of a first node to a first machine learning (ML) model to determine a first trust score, and repeating for each of the respective nodes of the network to determine the trust scores using the ML model; and assigning the determined trust scores to the respective nodes of the network (see paragraphs [0059]-[0061] and [0088]-[0094] where the system uses a ML model to label each device as trusted or suspicious based on the network activity).
At a time before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to include the machine learning model of Carnes, III et al. as part of the trust score determination of the modified Hardjono et al. and Jasner et al. system.
Motivation to do so would have been to provide a faster, more accurate classification process (see Carnes, III et al. paragraph [0060]).
As per claims 4 and 15, the modified Hardjono et al., Jasner et al., and Carnes, III et al. system discloses the first trust score represents a consistency of the first node to stably transmit data through the first node while preserving an integrity of the data (see Hardjono et al. paragraphs [0048], [0056]-[0059] and [0064]-[0068] where the system checks for integrity/trust and Carnes, III et al. paragraph [0094] where the system checks for integrity/trust and consistency with, for example, protocols).
As per claim 5, the modified Hardjono et al., Jasner et al., and Carnes, III et al. system discloses the network data for a given node of the network compromises one or more of: (i) telemetry data from the given node; (ii) packet latency through the given node; (iii) packet loss rate at the given node; (iv) results of deep packet inspection at the given node; (v) a number of successful transactions the given node; (vi) response time of the given node; (vii) incidence of security breaches or data leak at the given node; (viii) a software version installed on the given node; (ix) security patches installed on the given node; (x) detections of potential security threats, vulnerabilities, bugs, or attacks; exposures to other nodes suspected of compromise; (xi) number of connections to neighboring nodes; (xii) time of operating in a proper manner; (xiii) network policies at the given node; (xiv) statistical fluctuations in performance at the given node; (xv) a packet size or distribution of packet sizes, and/or (xvi) anomalous events detected at the given node (see Hardjono et al. paragraph [0043] showing at least quality of service which is telemetry data; Jasner et al. paragraphs [0067]-[0068]; and Carnes, III et al. paragraph [0094] showing suspicious/anomalous event detection).
As per claims 6 and 14, the modified Hardjono et al., Jasner et al., and Carnes, III et al. system discloses the ML model comprises a decision tree model, a regression model, a classifier models, a clustering model, a K-means clustering model, a K-nearest neighbor clustering model, a reinforcement learning model, a Q-learning model, a large language model, and/or a transformer model (see Carnes, III et al. paragraph [0091] showing a classifier model).
As per claim 10, the modified Hardjono et al. and Jasner et al. system discloses determining whether a trust score of a node is with different ranges of values (see Hardjono et al. paragraph [0059] showing the trust score out or 100), but fails to disclose treating the node as compromised by quarantining the node, when the trust score of the node is within the first range of values; treating the node as suspicious, when the trust score of the node is within the second range of values, by adding observability functions to the node that provide additional information regarding potential problems on the node; and treating the node as normal, when the trust score of the node is within the second range of values, by continuing to monitor network data from the node and updating a value of the trust score of the node based on the network data.
However, Carnes, III et al. teaches determining whether a trust score of a node is within a first range of values, a second range of values, or a third range of values; treating the node as compromised by quarantining the node, when the trust score of the node is within the first range of values; treating the node as suspicious, when the trust score of the node is within the second range of values, by adding observability functions to the node that provide additional information regarding potential problems on the node; and treating the node as normal, when the trust score of the node is within the second range of values, by continuing to monitor network data from the node and updating a value of the trust score of the node based on the network data (see paragraphs [0047]-[0053] where end points classified as level 3 are quarantined, level 2 classified devices are observed in a simulated network to determine the appropriate level, and the level 1 and level 0 are considered normal, but are continually monitored).
At a time before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to include the classification of devices from Carnes, III et al. in the modified Hardjono et al. and Jasner et al. system.
Motivation to do so would have been to allow modifiable zones based on different organization’s risk policy (see Carnes, III et al. paragraph [0047]).
Claims 8, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the modified Hardjono et al. and Jasner et al. system as applied to claims 7 and 11 above, in view of Yan et al. (Reputation-Based Truth Discovery With Long-Term Quality of Source in Internet of Things).
As per claims 8 and 16, the modified Hardjono et al. and Jasner et al. system discloses the limitations of or substantially similar to claim 7 (see Hardjono et al. paragraphs [0064]-[0068] and Jasner et al. paragraphs [0067]-[0068] as applied above with respect to claim 7), but fails to explicitly disclose the cumulative metric is an LP-norm of the trust scores of the nodes along the potential route.
However, Yan et al. teaches the use of LP-norm as a metric for truth discovery (see page 5415 – left column).
At a time before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to use the LP-norm of Yan et al. to calculate the cumulative metric of the modified Hardjono et al. and Jasner et al. system.
Motivation to do so would have been that LP-norm is a popular regularization for the optimization (see Yan et al. page 5415 – left column).
As per claim 17, the modified Hardjono et al., Jasner et al., and Yan et al. system discloses the objective function is a weighted combination comprising the trust term and the load balancing terms, and weights for the weighted combination are based on a degree of data sensitivity and/or data criticality of the one or more data packets (see Hardjono et al. paragraphs [0060]-[0066] showing the use of trust and load balancing based on data sensitivity and Yan et al. page 5415 – left column showing the use of weights in an optimization function).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the modified Hardjono et al. and Jasner et al. system as applied to claims 1 and 11 above, in view of Morris (US 20090252161).
As per claims 9 and 18, the modified Hardjono et al. and Jasner et al. system discloses determining the one or more preferred routes in a piecewise manner (see Hardjono et al. paragraphs [0060]-[0061]), but fails to explicitly disclose this piecewise manner is done by determining, for each local region of the network, one or more preferred route segments through a local region of the network; and forming the one or more preferred routes by integrating the one or more preferred route segments from a first local region with the one or more preferred route segments from one or more other local regions.
However, Morris teaches determining the one or more preferred routes piecewise by: determining, for each local region of the network, one or more preferred route segments through a local region of the network; and forming the one or more preferred routes by integrating the one or more preferred route segments from a first local region with the one or more preferred route segments from one or more other local regions (see paragraphs [0025]-[0028] where a path is determined based on regions of the paths and their corresponding trust levels).
At a time before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to include the region based trust in the modified Hardjono et al. and Jasner et al. system.
Motivation, as recognized by one of ordinary skill in the art, to do so would have been to allow the system additional flexibility to control where sensitive data is transmitted.
Response to Arguments
Applicant's arguments filed 27 January 2026 have been fully considered but they are not persuasive. Applicant argues that the cited prior art does not teach determining preferred routes based on joint considerations of trust and load balancing (see Response pages 10-11). The Examiner respectfully disagrees in that Hardjono et al. explicitly discloses, “it is possible to select paths based on both integrity/trust scores and other factors, such as load balancing” in paragraph [0066] which discloses using both trust and load balancing as considerations for preferred paths. Furthermore, as put forth above Jasner et al. additional discloses the use of both trust and load balancing for determining paths.
Applicant’s remaining arguments are moot in view of the above response and/or the new grounds of rejection put forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: the remaining references put forth on the PTO-892 form are directed towards routing traffic based on trust scores.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J PYZOCHA whose telephone number is (571)272-3875. The examiner can normally be reached Monday-Thursday 7:30am-5:00pm.
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/Michael Pyzocha/ Primary Examiner, Art Unit 2409