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 .
Applicant’s submission dated 03/05/2025 has been received and made of record.
Application 19/071,354 is a continuation of EP24165512.5, filed 03/22/2024.
Claims 1-20 are currently pending in Application 19/071,354.
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.
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, 4-8, 10, 11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Herzig (US 2020/0335124 A1) in view of Bajaj (US 2022/0321443 A1) .
Regarding claim 1, Herzig discloses A computer-implemented method for adapting a network (Herzig: Paragraphs [0004]-[0005] and [0018], “a method may comprise training, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, a neural network model to learn representations that capture characteristics of the graphs of chatbots”), the network comprising a plurality of network nodes, at least one network connection, each network connection connecting two network nodes (Herzig: Figure 2 and Paragraph [0035], “consider that the grid layout defines coordinates for the nodes from left to right and from top to bottom”), and at least one identifier (Herzig: Figure 2 and Paragraph [0035], “consider that the grid layout defines coordinates for the nodes from left to right and from top to bottom”), wherein at least one of the network nodes is a target node (Herzig: Paragraph [0046], “1) a success (s) node may be the last node of the path if it is not a sink node”), wherein each identifier is associated with at least one of the network nodes (Herzig: Figure 2 and Paragraph [0035], “consider that the grid layout defines coordinates for the nodes from left to right and from top to bottom”), wherein a path over the network connections taken by a user through the network comprises at least one of the network nodes (Herzig: Figure 2 and Paragraph [0031], “ the path that is created by the analysis of this utterance starts with the root node 202, then moves to the Make a payment node (1), checking whether this utterance expresses the user intention to make a payment”), wherein the network nodes comprised in the path are selected based on comparing at least one user input with data pertaining to the at least one identifier (Herzig: Figure 2 and Paragraph [0031], “ the path that is created by the analysis of this utterance starts with the root node 202, then moves to the Make a payment node (1), checking whether this utterance expresses the user intention to make a payment”), the computer-implemented method comprising:
receiving data representative of network nodes comprised in the path (Herzig: Paragraph [0043], “ a structure-based representation, may capture which nodes are being visited during a conversation with a user, at which nodes the conversation's turns end”);
determining, based on the data, whether the path comprises at least one target node (Herzig: Paragraph [0046], “1) a success (s) node may be the last node of the path if it is not a sink node”);
and if the path does not comprise at least one target node, (Herzig: Paragraph [0046], “2) A failure (f) node may be the last node of the path if it is a sink node”).
Herzig does not explicitly disclose comparing adapting the network.
However, Bajaj teaches this limitation (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Herzig and Bajaj are analogous art in the same field of endeavor as the instant invention as all are drawn to pathing between nodes. 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; that is, it would have been obvious to incorporate Bajaj’s target adaptation feature into the system of Herzig to allow the dialog flow network to adapt to include a desired destination node.
Herzig-Bajaj teaches 4. The computer-implemented method of claim 1, wherein adapting the network comprises at least one of:
modifying at least one of the at least one identifier;
modifying the association between at least one of the at least one identifier and at least one of the network nodes;
adding at least one network node to the network (Bajaj: Paragraph [0046], “entries are added to the ARC as and when performance metrics are learned for an application session on a particular path”, and Paragraph [0056], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)”);
removing at least one node from the network;
adding at least one network connection to the network (Bajaj: Paragraph [0046], “entries are added to the ARC as and when performance metrics are learned for an application session on a particular path”, and Paragraph [0056], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)”);
and removing at least one network connection from the network.
Herzig-Bajaj teaches 5. The computer-implemented method of claim 1, wherein the network nodes are each assigned to one of at least one level, wherein network nodes within the same level are not directly connected to each other (Herzig: Figure 2 shows network nodes on the same level not directly connected to each other, including (1,2) and (1,1,1,2)).
Herzig-Bajaj teaches 6. The computer-implemented method of claim 1, wherein the at least one network node that is a target node has a single network connection (Herzig: Figure 2 shows network nodes that are target nodes with single network connections, including (4,3) and (4,1,1)).
Herzig-Bajaj teaches 7. The computer-implemented method of claim 1, wherein the network nodes comprise a preselected node, the method further comprising:
receiving data representative of network nodes comprised in at least two paths (Bajaj: Paragraph [0077], “The first site is coupled to a second site 1022 through two different paths 1024, 1026 as described above.”);
determining a rate at which the at least two paths include the preselected node (Bajaj: Paragraph [0076], “The link utilization on each path, i.e., the capacity minus the traffic rate, is rendered as a number, e.g., 10 and 5. The two or more numbers, depending on the number of available paths, are used as an allocation ratio to allocate packets between the two paths. The initial ratio is 10:10 or 1:1. Later the allocation ratio is adjusted based on the new traffic conditions to 10:5 or 2:1”);
and if the rate exceeds a predefined rate threshold, the method adapting the network (Bajaj: Paragraph [0076], “The link utilization on each path, i.e., the capacity minus the traffic rate, is rendered as a number, e.g., 10 and 5. The two or more numbers, depending on the number of available paths, are used as an allocation ratio to allocate packets between the two paths. The initial ratio is 10:10 or 1:1. Later the allocation ratio is adjusted based on the new traffic conditions to 10:5 or 2:1”).
Herzig-Bajaj teaches 8. The computer-implemented method of claim 1, further comprising:
generating an adapted path based on applying the user inputs to the adapted network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”);
determining whether the adapted path comprises at least one of the target nodes (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”);
and if the adapted path does not comprise at least one of the target nodes and the path comprises at least one of the target nodes (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”), either:
reversing the adaptation of the network;
or adapting the network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Herzig-Bajaj teaches 10. The computer-implemented method of claim 1, wherein the comparing user input with data pertaining to the at least one identifier comprises applying a trained model to the user input and the data pertaining to the at least one identifier (Herzig: Paragraph [0046], “The fallout utterance analyzer may train an intent threshold computation model 508 by receiving the intent threshold computation (504) as inputs and by using a supervised classifier (506) that may be trained using the training data”).
Herzig-Bajaj teaches 11. The computer-implemented method of claim 10, wherein selecting at least one of the network nodes comprised in the path based on the comparison of the user input and the data pertaining to at least one identifier comprises determining a similarity parameter for at least one of the network nodes, the similarity parameter indicative of a similarity between the user input and the data pertaining to the identifier associated with the at least one network node (Herzig: Paragraph [0008], “ a method comprising training, at the computer system, a neural network model to learn representations that capture characteristics of the graphs of chatbots, wherein the captured characteristics include at least a content-based representation based on user utterances that are relevant to the nodes and based on the chatbot response for the nodes…”).
Herzig-Bajaj teaches 13. The computer-implemented method of claim 11, wherein determining the similarity parameter for at least one of the network nodes comprises determining the similarity parameter for each of at least two of the network nodes, the method further comprising selecting one of the network nodes based on the similarity parameter of the one of the network nodes being greater than the at least one similarity parameter of the at least one other of the network nodes (Herzig: Table 2 and Paragraph [0070], “the average distance was calculated using cosine similarity between vector representations for pairs of bots that belong to the domain vs pairs of bots from different domains”).
Herzig-Bajaj teaches 14. The computer-implemented method of claim 1, wherein the network is one of a plurality of networks (Bajaj: Figure 1 and Paragraph [0044], “networks”).
Herzig-Bajaj teaches 15. The computer-implemented method of claim 14, wherein the plurality of networks is part of a global network comprising the plurality of networks and additional nodes and identifiers (Bajaj: Figure 1, “Internet”).
Herzig-Bajaj teaches 16. The computer-implemented method of claim 14, wherein adapting the network comprises consulting another network of the plurality of networks (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”).
Herzig-Bajaj teaches 17. The computer-implemented method of claim 14, wherein adapting the network comprises adding a network connection between the network and another network of the plurality of networks (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”, Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Herzig-Bajaj teaches 18. The computer-implemented method of claim 15, wherein adapting the network comprises using the additional nodes and identifiers of the global network (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”, Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Herzig-Bajaj teaches 20. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to implement the method of claim 1 (Bajaj: Paragraph [0099], “a programmed product, comprising computer readable media tangibly embodying a program of machine-readable instructions executable by a digital data processor to perform the method”; Herzig: Paragraph [0078], “The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Herzig (US 2020/0335124 A1) and Bajaj (US 2022/0321443 A1) as applied to claim 8 above, further in view of Ouellette (US 2012/0182863 A1)
Herzig-Bajaj teaches 19. The computer-implemented method of claim 8.
Herzig-Bajaj does not explicitly disclose reversing the adaptation comprises reverting the adapted network to one of a plurality of stored versions of the network.
However, Ouellette teaches reverting a network to a stored version of the network (Ouellette: Claim 14).
Herzig-Bajaj and Oullette are analogous art in the same field of endeavor as the instant invention as all are drawn to pathing between nodes. 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; that is, it would have been obvious to incorporate Oullette’s reversion into the system of Herzig-Bajaj to allow for the system to more easily be restored to a previous working state.
Claim(s) 1-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Bajaj (US 2022/0321443 A1) in view of Arya (US 2023/0037894 A1).
Regarding claim 1, Bajaj discloses A computer-implemented method for adapting a network (Bajaj: Abstract, “conditioning traffic through multiple data paths of a Software-Defined Wide Area Network (SD-WAN)”), the network comprising a plurality of network nodes, at least one network connection, each network connection connecting two network nodes (Bajaj: Figure 1 and Paragraph [0034], “the paths are between hub nodes, branch nodes, or spokes or a hybrid path between different types of nodes”), and at least one identifier (Bajaj: Paragraph [0056], “the path traced through the various nodes is added to a list of eligible paths (step 312). Returning back to decision point 304, if the next hops configuration is not found on a node, then a multi-path route look procedure can be used (step 308) to determine the next hops for the node”; the nodes must inherently be labeled/identified for this to occur), wherein at least one of the network nodes is a target node (Bajaj: Paragraph [0013], “monitoring a first and a second path through an SD-WAN to reach a destination node”), wherein each identifier is associated with at least one of the network nodes (Bajaj: Paragraph [0056], “the path traced through the various nodes is added to a list of eligible paths (step 312). Returning back to decision point 304, if the next hops configuration is not found on a node, then a multi-path route look procedure can be used (step 308) to determine the next hops for the node”; the nodes must inherently be labeled/identified for this to occur), wherein a path over the network connections taken by a user through the network comprises at least one of the network nodes (Bajaj: Paragraph [0013], “monitoring a first and a second path through an SD-WAN to reach a destination node”), wherein the network nodes comprised in the path are selected based on comparing (Bajaj: Paragraph [0060], “FIG. 4 is a diagram of an example of traffic conditioning as may be applied by the traffic conditioning module 240 to data that is sent from a first terminal 410, such as an end user terminal, as shown, to a second terminal 412, such as a server, as shown”), the computer-implemented method comprising:
receiving data representative of network nodes comprised in the path (Bajaj: Paragraph [0043], “Active monitoring involves injecting test packets into the network environment through the various paths and measuring performance based on standard performance parameters. Passive monitoring involves monitoring network traffic data over a period of time to understand the performance of different paths. A metric is derived based on the performance and is compared for different available paths to choose the best path”);
determining, based on the data, whether the path comprises at least one target node (Bajaj: Paragraph [0013], “monitoring a first and a second path through an SD-WAN to reach a destination node”);
and if the path does not comprise at least one target node, adapting the network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Bajaj does not explicitly disclose comparing at least one user input.
However, Arya teaches this limitation (Arya: Paragraph [0061], “The flow generator and enhancer 110 may also compute a conversation path and frequency (1406) to evaluate user utterances”).
Bajaj and Arya are analogous art in the same field of endeavor as the instant invention as all are drawn to pathing between nodes. 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; that is, it would have been obvious to incorporate Arya’s utterance evaluations into the system of Bajaj to allow the system to function better in the domain of interactive dialog and chatbots.
Bajaj-Arya teaches 2. The computer-implemented method of claim 1, wherein the path does not comprise at least one target node, the method further comprising:
receiving data indicative of the user inputs (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
determining a final user input based on the data indicative of the user inputs (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
calculating an accuracy parameter indicative of an accuracy of the selecting the network nodes comprised in the path based on at least one of the data representative of network nodes comprised in the path and the data indicative of the user inputs (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
and adapting the network based on at least one of the data representative of network nodes comprised in the path, the data indicative of the user inputs, the final user input, and the accuracy parameter (Arya: Paragraph [0037], “If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190), the auto-generated responses may be sent to the database for updating the training data. If the accuracy of the auto-generated responses may be less than the first predefined threshold (192), the auto-generated responses may be sent to the self-learning engine 112 of the system for updating the auto-generated responses… the self-learning engine 112 and/or the various thresholds may not be limited to the mentioned parameters and several other parameters may be adjusted depending on the requirements of a user and/or on the desired characteristics of the executable chatbot”).
Bajaj-Arya teaches 3. The computer-implemented method of claim 1, wherein the path comprises at least one target node, the method further comprising:
receiving data indicative of the user inputs (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”):
calculating an accuracy parameter indicative of an accuracy of the selecting the network nodes comprised in the path based on at least one of the data representative of network nodes comprised in the path and the data indicative of user inputs (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
and adapting the network based on at least one of the data representative of network nodes comprised in the path, the data indicative of user inputs, and the accuracy parameter (Arya: Paragraph [0037], “If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190), the auto-generated responses may be sent to the database for updating the training data. If the accuracy of the auto-generated responses may be less than the first predefined threshold (192), the auto-generated responses may be sent to the self-learning engine 112 of the system for updating the auto-generated responses… the self-learning engine 112 and/or the various thresholds may not be limited to the mentioned parameters and several other parameters may be adjusted depending on the requirements of a user and/or on the desired characteristics of the executable chatbot”).
Bajaj-Arya teaches 4. The computer-implemented method of claim 1, wherein adapting the network comprises at least one of:
modifying at least one of the at least one identifier;
modifying the association between at least one of the at least one identifier and at least one of the network nodes (Arya: Paragraph [0027], “The flow generator and enhancer may prune the first dialog tree to obtain a first pruned dialog tree. The first pruned dialog tree may include a set of links from the multiple links with a frequency value beyond a predetermined threshold. The first pruned dialog tree may be processed to generate an auto-generated conversational dialog flow for upgrading the executable chatbot”);
adding at least one network node to the network (Bajaj: Paragraph [0046], “entries are added to the ARC as and when performance metrics are learned for an application session on a particular path”, and Paragraph [0056], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)”);
removing at least one node from the network (Arya: Paragraph [0027], “The flow generator and enhancer may prune the first dialog tree to obtain a first pruned dialog tree. The first pruned dialog tree may include a set of links from the multiple links with a frequency value beyond a predetermined threshold. The first pruned dialog tree may be processed to generate an auto-generated conversational dialog flow for upgrading the executable chatbot”);
adding at least one network connection to the network (Bajaj: Paragraph [0046], “entries are added to the ARC as and when performance metrics are learned for an application session on a particular path”, and Paragraph [0056], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)”);
and removing at least one network connection from the network (Arya: Paragraph [0027], “The flow generator and enhancer may prune the first dialog tree to obtain a first pruned dialog tree. The first pruned dialog tree may include a set of links from the multiple links with a frequency value beyond a predetermined threshold. The first pruned dialog tree may be processed to generate an auto-generated conversational dialog flow for upgrading the executable chatbot”).
Bajaj-Arya teaches 5. The computer-implemented method of claim 1, wherein the network nodes are each assigned to one of at least one level, wherein network nodes within the same level are not directly connected to each other (Arya: Paragraph [0003], “ a first dialog tree”; branches of a tree are not directly connected to one another).
Bajaj-Arya teaches 6. The computer-implemented method of claim 1, wherein the at least one network node that is a target node has a single network connection (Arya: Paragraph [0003], “ a first dialog tree”; it is understood that the final branch of a tree has a single network connection).
Bajaj-Arya teaches 7. The computer-implemented method of claim 1, wherein the network nodes comprise a preselected node, the method further comprising:
receiving data representative of network nodes comprised in at least two paths (Bajaj: Paragraph [0077], “The first site is coupled to a second site 1022 through two different paths 1024, 1026 as described above.”);
determining a rate at which the at least two paths include the preselected node (Bajaj: Paragraph [0076], “The link utilization on each path, i.e., the capacity minus the traffic rate, is rendered as a number, e.g., 10 and 5. The two or more numbers, depending on the number of available paths, are used as an allocation ratio to allocate packets between the two paths. The initial ratio is 10:10 or 1:1. Later the allocation ratio is adjusted based on the new traffic conditions to 10:5 or 2:1”);
and if the rate exceeds a predefined rate threshold, the method adapting the network (Bajaj: Paragraph [0076], “The link utilization on each path, i.e., the capacity minus the traffic rate, is rendered as a number, e.g., 10 and 5. The two or more numbers, depending on the number of available paths, are used as an allocation ratio to allocate packets between the two paths. The initial ratio is 10:10 or 1:1. Later the allocation ratio is adjusted based on the new traffic conditions to 10:5 or 2:1”).
Bajaj-Arya teaches 8. The computer-implemented method of claim 1, further comprising:
generating an adapted path based on applying the user inputs to the adapted network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”);
determining whether the adapted path comprises at least one of the target nodes (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”);
and if the adapted path does not comprise at least one of the target nodes and the path comprises at least one of the target nodes (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”), either:
reversing the adaptation of the network;
or adapting the network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Bajaj-Arya teaches 9. The computer-implemented method of claim 2, further comprising:
generating an adapted path based on applying the user inputs to the adapted network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”);
calculating an adapted accuracy parameter indicative of an accuracy of the selecting the network nodes comprised in the adapted path (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
comparing the adapted accuracy parameter to the accuracy parameter (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”);
and if the adapted accuracy parameter is indicative of lower accuracy than the accuracy parameter (Arya: Paragraph [0037], “The response identifier 108 may process the new intent based utterances, using the enterprise documents 154, to generate auto-generated responses. In an example embodiment, the processor may determine an accuracy of at least one of the auto-generated responses corresponding to the newly identified intents. If the accuracy of the auto-generated responses may be equal to or exceeds a first predefined threshold (as shown at 190)…”), either:
reversing the adaptation of the network;
or adapting the network (Bajaj: Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Bajaj-Arya teaches 10. The computer-implemented method of claim 1, wherein the comparing user input with data pertaining to the at least one identifier comprises applying a trained model to the user input and the data pertaining to the at least one identifier (Arya: Paragraph [0046], “The fallout utterance analyzer may train an intent threshold computation model 508 by receiving the intent threshold computation (504) as inputs and by using a supervised classifier (506) that may be trained using the training data”).
Bajaj-Arya teaches 11. The computer-implemented method of claim 10, wherein selecting at least one of the network nodes comprised in the path based on the comparison of the user input and the data pertaining to at least one identifier comprises determining a similarity parameter for at least one of the network nodes, the similarity parameter indicative of a similarity between the user input and the data pertaining to the identifier associated with the at least one network node (Arya: Paragraph [0046], “The in-domain intent based utterances (528) may be further processed (532) to compute similarity score with each question in training file including training main question and its paraphrases for each intent. The results of the processing may result in deriving a close utterance and intent match based on lowest similarity score (540). For the result, a recommended result may be derived using the training files and trained classifier (506). The recommended result may be at least one from a recommended intent, answer or que code, training main question, new variation, new intent, similar training question and recommended question code. In an example embodiment, the recommended result may be obtained using a voting from the similarity measure and the classifier (i.e. based on 504 and 506 respectively). Based on this, if the resultant similarity score may not be within a threshold range then the type of intent may be predicted to be new intent. The processing at 532 may also lead to deriving a closest similarity score to identify (534) the utterances that may have a similarity score beyond intent threshold range i.e. similarity score falling beyond the maximum of the threshold range. The utterances with similarity score beyond the threshold range may be identified as new intent (536) and the utterances with similarity score within the threshold range may be identified as new variation (538)”).
Bajaj-Arya teaches 12. The computer-implemented method of claim 11, further comprising selecting one of the network nodes based on the similarity parameter of the one of network nodes exceeding a predefined similarity threshold (Arya: Paragraph [0046], “The in-domain intent based utterances (528) may be further processed (532) to compute similarity score with each question in training file including training main question and its paraphrases for each intent. The results of the processing may result in deriving a close utterance and intent match based on lowest similarity score (540). For the result, a recommended result may be derived using the training files and trained classifier (506). The recommended result may be at least one from a recommended intent, answer or que code, training main question, new variation, new intent, similar training question and recommended question code. In an example embodiment, the recommended result may be obtained using a voting from the similarity measure and the classifier (i.e. based on 504 and 506 respectively). Based on this, if the resultant similarity score may not be within a threshold range then the type of intent may be predicted to be new intent. The processing at 532 may also lead to deriving a closest similarity score to identify (534) the utterances that may have a similarity score beyond intent threshold range i.e. similarity score falling beyond the maximum of the threshold range. The utterances with similarity score beyond the threshold range may be identified as new intent (536) and the utterances with similarity score within the threshold range may be identified as new variation (538)”).
Bajaj-Arya teaches 13. The computer-implemented method of claim 11, wherein determining the similarity parameter for at least one of the network nodes comprises determining the similarity parameter for each of at least two of the network nodes, the method further comprising selecting one of the network nodes based on the similarity parameter of the one of the network nodes being greater than the at least one similarity parameter of the at least one other of the network nodes (Arya: Paragraph [0046], “The in-domain intent based utterances (528) may be further processed (532) to compute similarity score with each question in training file including training main question and its paraphrases for each intent. The results of the processing may result in deriving a close utterance and intent match based on lowest similarity score (540). For the result, a recommended result may be derived using the training files and trained classifier (506). The recommended result may be at least one from a recommended intent, answer or que code, training main question, new variation, new intent, similar training question and recommended question code. In an example embodiment, the recommended result may be obtained using a voting from the similarity measure and the classifier (i.e. based on 504 and 506 respectively). Based on this, if the resultant similarity score may not be within a threshold range then the type of intent may be predicted to be new intent. The processing at 532 may also lead to deriving a closest similarity score to identify (534) the utterances that may have a similarity score beyond intent threshold range i.e. similarity score falling beyond the maximum of the threshold range. The utterances with similarity score beyond the threshold range may be identified as new intent (536) and the utterances with similarity score within the threshold range may be identified as new variation (538)”).
Bajaj-Arya teaches 14. The computer-implemented method of claim 1, wherein the network is one of a plurality of networks (Bajaj: Figure 1 and Paragraph [0044], “networks”).
Bajaj-Arya teaches 15. The computer-implemented method of claim 14, wherein the plurality of networks is part of a global network comprising the plurality of networks and additional nodes and identifiers (Bajaj: Figure 1, “Internet”).
Bajaj-Arya teaches 16. The computer-implemented method of claim 14, wherein adapting the network comprises consulting another network of the plurality of networks (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”).
Bajaj-Arya teaches 17. The computer-implemented method of claim 14, wherein adapting the network comprises adding a network connection between the network and another network of the plurality of networks (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”, Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Bajaj-Arya teaches 18. The computer-implemented method of claim 15, wherein adapting the network comprises using the additional nodes and identifiers of the global network (Bajaj: Figure 1, “Internet”, and Paragraph [0044], “networks”, Figure 3 and Paragraph [0053], “Once the destination node is reached, the path traced through the various nodes is added to a list of eligible paths (step 312)… If a destination node is not reached, the path/hop is excluded from the list of eligible paths/hops (step 314)”).
Bajaj-Arya teaches 20. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to implement the method of claim 1 (Bajaj: Paragraph [0099], “a programmed product, comprising computer readable media tangibly embodying a program of machine-readable instructions executable by a digital data processor to perform the method”).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Bajaj (US 2022/0321443 A1) and Arya (US 2023/0037894 A1) as applied to claim 8 above, further in view of Ouellette (US 2012/0182863 A1)
Bajaj-Arya teaches 19. The computer-implemented method of claim 8.
Bajaj-Arya does not explicitly disclose reversing the adaptation comprises reverting the adapted network to one of a plurality of stored versions of the network.
However, Ouellette teaches reverting a network to a stored version of the network (Ouellette: Claim 14).
Bajaj-Arya and Oullette are analogous art in the same field of endeavor as the instant invention as all are drawn to pathing between nodes. 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; that is, it would have been obvious to incorporate Oullette’s reversion into the system of Bajaj-Arya to allow for the system to more easily be restored to a previous working state.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Terry (US 2019/0207876 A1) describes a system for updating AI conversation flow diagrams.
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/IMAD HUSSAIN/Primary Examiner, Art Unit 2453
