Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 9-18 and 20-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 12407603 B2 to Nikain et al. (“Nikain”) in view of US 10116709 B1 to Kielhofner et al. (“Kielhofner”).
Regarding claim 1, Nikain taught a method comprising:
receiving, by a computer system (consider Fig. 10, specifically element 1002 and column 14, lines 52-62) including a quantum computer (consider column 14, lines 52-column 18, lines 9-13 regarding “quantum dot” “processors”), data related to a multipoint communication between a start point and an end point from one or more data sources; calculating, by the computer system, one or more routes for the multipoint communication between the start point and the end point using the data and based at least in part on a shortest route between the start point and the end point (“network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”; consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28); (consider column 3, lines 53-column 4, line 3 regarding the “routing equipment” “communicatively coupled” “via routes” wherein an “originator of a communication”/”source node” sends a “communication” to a “destination node”) (consider further column 5, lines 62-65, “As depicted, system 200 can include second routing equipment 170 connected to first routing equipment 150 and fourth routing equipment 280 via route 126A and route 226 via network 190”) (consider further column 6, line 50-column 7, line 5, “As discussed with FIGS. 4-5 below, receiving component 214 can, in accordance with one or more embodiments, receive, from the first routing equipment, a current communication for transit via second routing equipment 170 to destination equipment (e.g., fourth routing equipment 280), with second routing equipment 170 being selected by first routing equipment 150 based on the first model, and the second model, other than the first model, describing respective predicted delays from other routing equipment other than the first routing and second routing equipment 185. In another example, in one or more embodiments, computer executable components 220 can include instructions that, when executed by processor 160, can facilitate performance of operations defining, communicating component 216. As discussed with FIGS. 4-5 below, communicating component 216 can, in accordance with one or more embodiments, based on predictive model 172A, can relay the current communication to second routing equipment 170 to transit the current communication to destination equipment, e.g., equipment corresponding to the destination address of a packet forwarded via TCP/IP protocol to a next selected network segment (router, node).”)
optimizing (using “predictive models”/”aggregated predictive model”), using the quantum computer of the computer system, the one or more routes based on the data and a communication status of one or more nodes along the one or more routes (“historical network node performance”; consider column 8, lines 25-28); dynamically selecting, by the computer system, the optimized one or more routes based on timing and performance requirements of the computer system; and executing, by the computer system, the multipoint communication between the start point and the end point using the optimized one or more routes. (consider column 7, line 57-column 8, line 11, “In one approach, first routing equipment 150 can generate a local predictive model of available network routes, e.g., using a component similar to network condition estimating component 212 described with FIG. 2 above. This generated predictive model can be provided using connections 460C and 460E to routing equipment 410 and third routing equipment 185, respectively, where this model can be aggregated with the present predictive model at this equipment. By this process, the aggregated predictive models from second routing equipment 170 and third routing equipment 185 can reach first routing equipment 150, where the resulting aggregated predictive model 315 can predict conditions in both the 460A, 460B, 460C, and 460F route and the 460D, 460E route. In additional embodiment, after a predictive model (e.g., aggregated predictive model 315) is used to select a route (e.g., route 460), routing components along the route can provide feedback 440A-B to the routing equipment that selected the route (e.g., first routing equipment 150). This feedback can be used to incrementally update aspects of different models, e.g., to increase the accuracy of predictions provided.”) (consider further column 8, lines 12-42 regarding “selecting network routes based on predictive models generated and maintained by employing AI/ML approaches”, specifically “Example inputs that can be used to train AI/ML components 510 can include historical network node performance, and feedback 440A-B from specific route selections.”)
Nikain may be interpreted as not expressly teaching wherein the communication status includes an indication of current availability of the one or more nodes.
However, in an analogous art relating to mitigating node and route performance issues within a network, Kielhofner taught wherein a communication status may include an indication of current availability of one or more nodes during the calculation and optimization of one or more routes for a multipoint connection. (consider Fig. 5 and supporting disclosure at column 19, line 52-column 22, line 32, specifically regarding wherein “the system 110 enables evaluation of communication routes/paths from each respective facility 102 (or even each asset 104 and/or network edge device 106 within a facility)” such that “each network edge device 106 generally determines optimized communication paths (routes) from or to each specific facility and/or asset. Said another way, each network edge device 106 assesses an optimal path for each given communication session from the particular edge device's “perspective” of the Internet” so that “aspects of the process 500 assess historical and real time (or virtually real-time) evaluations of the network conditions (e.g., which networks are congested, which nodes are responding in a more efficient manner, etc.)” and “the setup/establishment of the OCS includes automatically (or even manually) populating the OCS directory with information pertaining to nodes existing in available communication networks” wherein “if new nodes are introduced during the setup of a communication session, then the OCS directory 310 is updated with the addresses and communication types (or more generally, information) of such new nodes” and further that “An exemplary schematic showing an OCS directory (usually comprising a list of potential nodes and information relating to such nodes that is used to optimize the communication sessions) was explained earlier in connection with FIG. 3. Also, generally speaking, business rules (or communication session criteria) relate to policies/criteria that govern various aspects of a communication session, such as a bandwidth requirement, a maximum tolerable packet loss rate, one or more allowable applications, number of active on-premise devices simultaneously engaged in communication sessions, a maximum tolerable delay in voice, a maximum tolerable jitter, a minimum video frame rate, allowable geographical locations of intermediate nodes, etc.” wherein “business rules are created by an OCS user or system administrator in relation to accomplishing various evaluations (both current and over time) of network conditions, e.g., which networks are congested, which nodes have failed, which are the best nodes for certain types of communication sessions, etc., and other factors associated with geographically diverse and unpredictable routing infrastructure characteristics of the Internet. Such evaluations, in OCS embodiments, are utilized to determine optimal communication session routes as packets proceed through one or more intermediate nodes in the Internet’ such that “the node generates (at step 510A) and thereafter stores (at step 512A) a prioritized list of nodes that can be used in subsequent communication sessions. In one embodiment, the prioritized list of nodes represent potential preferred “next-hop” nodes for a variety of communication types and specific communication sessions emanating to or received at the node (network edge device)”) (Examiner’s emphasis added.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Nikain to include the taught features of Kielhofner such that the modification includes every element as claimed. Given Nikain' s disclosure of calculation and optimization of routes for a multipoint connection using communication status of one or more nodes along the one or more routes, Kielhofner specifically taught that indications of current availability of one or modes along one or more routes allows for the determination of optimal communication session routes as packets proceed through one or more intermediate nodes in the Internet (consider column 21, lines 37-40). Given this specific advantage in Kielhofner, one skilled in the art would have been motivated to modify the teachings of Nikain with the teachings of Kielhofner such that the communication status of one or more nodes along the one or more routes as taught in Nikain may be further enhanced with an indication of current availability of the one or more nodes as taught in Kielhofner so that the communication status of one or more nodes along the one or more routes can also include an indication of current availability of the one or more nodes as claimed. Therefore, such a modification of the teachings of Nikain with the teachings of Kielhofner would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 2, the combined teachings of Nikain and Kielhofner taught the method of claim 1.
Nikain further taught wherein optimizing the one or more routes includes using machine learning (“AI/ML”) to optimize the one or more routes. (consider column 8, lines 12-42, specifically regarding “selecting network routes based on predictive models generated and maintained by employing AI/ML approaches”)
Regarding claim 3, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain further taught wherein using machine learning includes using a machine learning model including a neural network. (again, consider column 8, lines 12-42, specifically “Additional implementations can include ensemble ML algorithms/methods, including deep neural networks (DNN)”)
Regarding claim 4, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain further taught wherein using machine learning includes using a machine learning model including a long short-term memory (LSTM) network. (again, consider column 8, lines 12-42, specifically “Additional implementations can include ensemble ML algorithms/methods, including” “long short-term memory (LSTM) networks.”)
Claim 9 recites a system comprising a computing system (consider Fig. 10, specifically element 1002) comprising one or more processors (consider column 14, lines 52-62) and a data storage system in communication with the one or more processors (column 18, lines 35-42), wherein the one or more processors includes a quantum computer (consider column 14, lines 52-column 18, lines 9-13 regarding “quantum dot” “processors”) and wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to perform substantially the same limitations as recited in claim 1 and is also rejected under 35 USC § 103 as being unpatentable over the same combined teachings of Nikain and Kielhofner and the same rationale supporting the conclusion of obviousness.
Regarding claim 10, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes network data, and the network data includes static information regarding at least one of the one or more nodes. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections” and also “delays caused by individual nodes of a communication route” such as “delays associated with routing nodes can be limited by the physical capacity of the routing nodes, e.g., processing speed of router interfaces”)
Regarding claim 11, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes traffic data, and the traffic data includes ping latency between the start point and the end point. . (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
Regarding claim 12, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes network data, and the network data includes dynamic information regarding at least one of the one or more nodes. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
Regarding claim 13, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes traffic data, and the traffic data includes status of the one or more nodes, telemetry data, and one or more error conditions associated with the one or more routes. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
Regarding claim 14, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes traffic data. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
Nikain may be interpreted as not expressly teaching wherein the traffic data includes an atmospheric or environmental condition along at least a portion of the one or more routes. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
However, in an analogous art relating to mitigating node and route performance issues within a network, Kielhofner taught that atmospheric or environmental conditions along at least a portion of one or more routes in a network were known (consider column 10, lines 39-67).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Nikain to include the taught features of Kielhofner such that the modification includes every element as claimed. Given Nikain’s disclosure of traffic data and the usage of such to optimize and select routes, Kielhofner specifically taught that usage of atmospheric or environmental condition along at least a portion of the one or more routes is useful to re-reroute or further optimize routes in the event of such conditions (again, consider column 10, lines 39-67). Given this specific advantage in Kielhofner, one skilled in the art would have been motivated to modify the teachings of Nikain with the teachings of Kielhofner such that the traffic data as taught in Nikain may be further enhanced by including atmospheric or environmental conditions along at least a portion of the one or more routes as taught in Kielhofner so that the traffic data includes an atmospheric or environmental condition along at least a portion of the one or more routes as claimed. Therefore, such a modification of the teachings of Nikain with the teachings of Kielhofner would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 15, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the data includes traffic data, and the traffic data includes network latency or outages along at least a portion of the one or more routes. (again, consider column 1, lines 14-24, column 6, lines 34-46 and column 8, lines 25-28 regarding “network conditions” including “predicted” “delays” of “routes” using “feedback 440A-B from specific route selections”)
Regarding claim 16, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the one or more nodes include a router or a firewall or other controller solution. (consider column 3, lines 59-62, “It should be noted that, as discussed herein, first routing equipment 150 can also be termed a node, router or device, without deviating from the spirit of embodiments described herein.”)
Regarding claim 17, the combined teachings of Nikain and Kielhofner taught the method of claim 9.
Nikain further taught wherein the one or more nodes include a satellite or an aircraft. (consider further column 17, lines 29-35, “The computer 1002 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone.”)
Claim 18 recites a non-transitory computer-readable storage medium (consider Fig. 10, specifically element 1002 and column 18, lines 35-42), the non-transitory computer-readable storage medium including instructions that, when executed by computers including a quantum computer (consider column 14, lines 52-column 18, lines 9-13 regarding “quantum dot” “processors”), cause the computers to perform substantially the same limitations as recited in claim 1 and and is also rejected under 35 USC § 103 as being unpatentable over the same combined teachings of Nikain and Kielhofner and the same rationale supporting the conclusion of obviousness.
Regarding claim 20, the combined teachings of Nikain and Kielhofner taught the method of claim 18.
Nikain further taught wherein optimizing the one or more routes includes using machine learning (“AI/ML”) to optimize the one or more routes. (consider column 8, lines 12-42, specifically regarding “selecting network routes based on predictive models generated and maintained by employing AI/ML approaches”)
Regarding claim 21, the combined teachings of Nikain and Kielhofner taught the non-transitory computer-readable storage medium of claim 18.
Nikain may be interpreted as not expressly teaching wherein the instructions cause the computers to perform further operations of: detecting expiration of a time to live (TTL) timer related to the multipoint communication; and upon detection of the expiration of the TTL timer, calculating, optimizing and selecting a second one or more routes for the multipoint communication between the start point and the end point, however, Nikain did teach the calculating, optimizing and selecting of one or more routes for the multipoint communication between the start point and the end point as shown above. Nikain also taught that changes may occur within the network such that the originally calculated, optimized and selected one or more routes are no longer valid (consider column 1, lines 25-30) and the data may be detected such that the calculating, optimizing and selecting of one or more routes for the multipoint communication between the start point and the end point may be reperformed to determine a second one or more routes. (consider column 8, lines 4-11 and 18-28)
In an analogous art relating to mitigating node and route performance issues within a network and updating of route selections, Kielhofner taught the use of a time to live (TTL) timer (“predetermined time interval”) related to multipoint communication in which upon the expiration of such being detected, determining a second one or more routes for a multipoint communication between a start point and an end point is performed. (consider column 7, lines 12-15, “Generally, network edge devices are operatively connected to OCS server modules to exchange optimal routing information on a periodic or continual basis”) (consider further column 12, lines 33-58, specifically that “According to one aspect, network edge devices 106 are generally used to gather information about the assets 106, servers 120, 122, and conditions of the networks 108 in a periodic manner to assess characteristics of potential communication paths and network conditions. It will be appreciated by one skilled in the art that because of the complex interconnections of a large number of networks with varying network characteristics and the way information is routed therein (e.g., according to a guarantee-less paradigm of the Internet protocol), quality of communication sessions occurring via the networks 108 is generally unpredictable and unreliable. For example, some nodes may be down or damaged, some nodes may have faster computational capabilities over other nodes, some nodes encounter more network congestion than other nodes, etc. Embodiments of the presently-disclosed OCS, among other things, are used to monitor the quality of networks 108 to provide reliable and optimized communication sessions between assets, as will be better understood from the discussions that follow herein. Specifically, and in one embodiment, the OCS monitors and collects real-time and historical network characteristics, applies business rules related to the network characteristics, evaluates various communication session routes/paths, and if necessary modifies communication routes to account for application of business rules and real-time network characteristics.”) (consider further column 16, lines 1-11, “Additionally, according to one aspect, information relating to assets 104 and conditions of the networks 108 is gathered by OCS nodes (e.g., one or more constituent servers in the OCS server module 112), and passed on to the statistics server 218 for analysis. After such analysis is complete, such information is provisioned to the network edge devices 106. The constituent servers in the OCS server module 112 typically gather information relating to assets 104 and conditions of the networks 108 in a periodic manner, e.g., every few minutes, seconds, hours, or some other pre-determined time-interval.”) (consider further column 19, lines 21-34, “At a high level, the processes described and shown in FIG. 4 relate to identifying an optimal path or route for particular communication sessions, and then implementing the steps to carry out those communication sessions via the determined optimal paths. The OCS process in FIG. 4 starts at sub-process 500A (or, alternately sub-process 500B), in which an OCS node performs an optimal path identification process that is described in detail in FIG. 5A (or, alternately in FIG. 5B). In one embodiment, the optimal path identification process is performed periodically (every few minutes, seconds, or according to some other predetermined time interval), irrespective of whether the OCS node is processing a request for an outgoing communication session or accepting a request for an incoming communication session.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Nikain to include the taught features of Kielhofner such that the modification includes every element as claimed. Given Nikain’s disclosure of traffic data and the usage of such to optimize and select routes, Kielhofner specifically taught that usage of a time to live timer (TTL) is used to determine a second one or more routes based only on its expiration (again, consider column 19, lines 21-34). Given this specific advantage in Kielhofner, one skilled in the art would have been motivated to modify the teachings of Nikain with the teachings of Kielhofner such that the calculating, optimizing and selecting of one or more routes for the multipoint communication between the start point and the end point may be reperformed to determine a second one or more routes as taught in Nikain may be further enhanced by using a time to live timer (TTL) to determine a second one or more routes upon its expiration as taught in Kielhofner so that the instructions detect expiration of a time to live (TTL) timer related to the multipoint communication; and upon detection of the expiration of the TTL timer, calculating, optimizing and selecting a second one or more routes for the multipoint communication between the start point and the end point as claimed. Therefore, such a modification of the teachings of Nikain with the teachings of Kielhofner would have yielded nothing more than predictable results to one of ordinary skill in the art.
Claim(s) 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Nikain and Kielhofner as applied to claim 2, and in further view of US 20260030261 A1 to Lenz.
Regarding claim 5, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain and Kielhofner may be interpreted as not expressly teaching wherein using machine learning includes using a machine learning model including bidirectional encoder representations from transformers (BERT), however, Nikain reasonably suggests that machine learning models of various modalities may be used (again, consider column 8, lines 12-42).
In an analogous art relating to the use of machine learning models, Lenz taught that it was well known within the relevant art that bidirectional encoder representations from transformers (BERT) may be used within a machine learning model. (consider paragraph 0010).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to simply substitute the machine learning model modalities taught in Nikain and Kielhofner with the specific bidirectional encoder representations from transformers (BERT) taught in Lenz such that their combination includes every element as claimed. Examiner finds that the teaching within Lenz demonstrates that the substituted elements and their functions were known in the art and one skilled in the art could have simply substituted one known element for another such that the substitution would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 6, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain and Kielhofner may be interpreted as not expressly teaching wherein using machine learning includes using a machine learning model including natural language processing (NLP), however, Nikain reasonably suggests that machine learning models of various modalities may be used (again, consider column 8, lines 12-42).
In an analogous art relating to the use of machine learning models, Lenz taught that it was well known within the relevant art that natural language processing may be used within a machine learning model. (consider paragraphs 0003-0004).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to simply substitute the machine learning model modality taught in Nikain and Kielhofner with natural language processing taught in Lenz such that their combination includes every element as claimed. Examiner finds that the teaching within Lenz demonstrates that the substituted elements and their functions were known in the art and one skilled in the art could have simply substituted one known element for another such that the substitution would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 7, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain and Kielhofner may be interpreted as not expressly teaching wherein using machine learning includes using a machine learning model including an artificial intelligence (AI)-based knowledge tree, however, Nikain reasonably suggests that machine learning models of various modalities may be used (again, consider column 8, lines 12-42).
In an analogous art relating to the use of machine learning models, Lenz taught that it was well known within the relevant art that an artificial intelligence (AI)-based knowledge tree may be used within a machine learning model. (consider paragraphs 0012 and 0014-0016 regarding “Graph Neural Networks” including “knowledge graphs”).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to simply substitute the machine learning model modality taught in Nikain and Kielhofner with artificial intelligence (AI)-based knowledge trees taught in Lenz such that their combination includes every element as claimed. Examiner finds that the teaching within Lenz demonstrates that the substituted elements and their functions were known in the art and one skilled in the art could have simply substituted one known element for another such that the substitution would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 8, the combined teachings of Nikain and Kielhofner taught the method of claim 2.
Nikain and Kielhofner may be interpreted as not expressly teaching wherein using machine learning includes using a machine learning model including a large language model (LLM), however, Nikain reasonably suggests that machine learning models of various modalities may be used (again, consider column 8, lines 12-42).
In an analogous art relating to the use of machine learning models, Lenz taught that it was well known within the relevant art that large language models (LLM) may be used within a machine learning model. (consider paragraph 0010).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to simply substitute the machine learning model modalities taught in Nikain and Kielhofner with the specific large language model (LLM) taught in Lenz such that their combination includes every element as claimed. Examiner finds that the teaching within Lenz demonstrates that the substituted elements and their functions were known in the art and one skilled in the art could have simply substituted one known element for another such that the substitution would have yielded nothing more than predictable results to one of ordinary skill in the art.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Nikain and Kielhofner as applied to claim 18, and in further view of US 11985534 B2 to Berzin et al. (“Berzin”).
Regarding claim 19, the combined teachings of Nikain and Kielhofner taught the non-transitory computer-readable storage medium of claim 18.
Nikain and Kielhofner may be interpreted as not expressly teaching wherein calculating the one or more routes includes separating the multipoint communication into segments to be transmitted separately using the one or more optimized routes.
However, in an analogous art relating to generation of service-optimized routes, Berzin taught separating the multipoint communication into segments to be transmitted separately using one or more optimized routes. (consider column 8, line 61-column 9, line 18 regarding the usage of “segment routing” in “computing” “paths”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Nikain and Kielhofner to include the taught features of Berzin such that the modification includes every element as claimed. Given Nikain and Kielhofner’s disclosure of calculating routes, Berzin specifically taught that separating the multipoint communication into segments to be transmitted separately using one or more optimized routes in order to route traffic in a low-latency manner (again, consider column 8, line 61-column 9, line 18 regarding the usage of “segment routing” in “computing” “paths”). Given this specific advantage in Berzin, one skilled in the art would have been motivated to modify the teachings of Nikain and Kielhofner with the teachings of Berzin such that the calculating the one or more routes as taught in Nikain and Kielhofner may be further enhanced by separating the multipoint communication into segments to be transmitted separately using one or more optimized routes as taught in Berzin so that the calculating the one or more routes includes separating the multipoint communication into segments to be transmitted separately using the one or more optimized routes as claimed. Therefore, such a modification of the teachings of Nikain and Kielhofner with the teachings of Berzin would have yielded nothing more than predictable results to one of ordinary skill in the art.
Conclusion
An updated search did not reveal additional prior art that is relevant to the claimed invention or to the broader disclosure.
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 G. C. Neurauter, Jr. whose telephone number is (571)272-3918. The examiner can normally be reached Monday-Friday 9am-5pm Eastern Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tonia Dollinger, can be reached at 571-272-4170. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459
Prosecution Insights