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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/3/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/3/2025 have been fully considered but they are not persuasive.
The applicant contends Krishnamurthy and Mazza fails to disclose the limitation “extracting topics from the stored logs; creating clusters from the extracted topics using a topic modeling technique to group semantically similar topics identified across consecutive conversation turns of the stored logs …”.
The examiner disagrees. Regarding the limitation “extracting topics from the stored logs”, as explained in the final rejection mailed 9/25/2025, the recited limitations does not limit what is considered a topic, sub-topic, main topic or topic modeling technique. Due to the breath of the recited limitation, the limitation is interpreted in the broadest reasonable interpretation in light of the specification without reading the specification in the claim. Such limitation is interpreted as per the dictionary definition of the word “topic”. Based on such definition, Fig. 1a of Krishnamurthy shows a diagram of nodes connected, wherein the connection indicates a relationship between nodes. AS per the office action, label event is considered a topic such as “what is the weather like?”, where Fig. 2a-b,3a-d shows similar topics or events are grouped together.
Regarding the limitation “topic modeling technique”, the recited limitation does not specify what is constituted as a topic modeling technique nor recite limitation specifying a specific topic modeling technique. Furthermore, the applicant’s remarks does not provide further reasoning as to why Krishnamurthy does not disclose a topic modeling technique. Due to the breath of the recited limitation, the limitations are interpreted in the broadest reasonable interpretation in light of the specification without reading the specification into the claim. Krishnamurthy discloses clustering of nodes comprising event (topic), plan, result described in conversation logs. Fig. 1a-b,2a-b,3a-d shows the nodes clustered or grouped together in a diagram or graph or ontology. As explained in the office action mailed 9/25/2025 and below, Fig. 1a shows an example of topic modeling technique used to map or graph the multi-turn conversations.
Regarding the limitation “creating clusters from the extracted topics using a topic modeling technique to group semantically similar topics identified across consecutive conversation turns of the stored logs”, Krishnamurthy discloses
“In some examples, conversational computing interfaces may be trained on a large plurality of different variations on one or more dialogues collected by repeatedly collecting multiple different counterfactual alternatives for one or more turns in the dialogue. For example, the counterfactual alternatives may be collected by finding, for an exemplary turn, one or more semantically related turns (e.g., versions of the same turn with utterances by humans and/or utterances output by a conversational computing interface being paraphrased, versions of the same turn including utterances about different related topics, versions of the same turn including alternative plans for responding to an utterance). Semantically related turns may be obtained using any of the above-described techniques, for example, by providing the exemplary turn to a human demonstrator and asking the human demonstrator to define a semantically related turn, and/or by operating a computer model (e.g., a paraphrasing model) to generate the semantically related turn. In some examples, dialogue variations may be collected from human users (for example, from cloud workers or volunteers, such as Amazon Mechanical Turk™ workers).”
The highlighted portions indicate semantically related turns are generated using the above described techniques on utterances by humans and/or utterances output by a conversational computing interface being paraphrased. Based on such disclosure, Fig. 2a-b, 3a-d can be graphs or ontologies of semantically related turns of stored logs or utterances by humans and/or utterances output by a conversational computing interface being paraphrased.
The applicant contends Maaz fails to disclose the limitations as discussed above.
As per the remarks above, Krishnamurthy et al discloses the limitations as discussed above and Mazza discloses the limitation as indicated in the office action below. Please see the office action below.
The applicant contends the dependent claims 3-4,6-9,12-13,15-18 are in condition for allowance at least by virtue of their dependencies on amended independent claims 1,10, and 19, respectively.
The examiner disagrees. Please see the rebuttal above and office action below.
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.
Claim(s) 1,3-4,6-10,12-13,15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthy et al (US Publication No.: 20210043194) in view of Mazza et al (US Publication No.: 20190182382).
Claim 1, Krishnamurthy et al discloses
conversations between at least an agent and at least a user in logs (Fig. 5, label 502,504, Paragraph 18 discloses annotated dialogue of conversation between a human ad a conversational computing interface. Paragraph 4 discloses a plurality of multi-turn conversations.);
extracting topics from the logs (Fig. 1a,1b, label event indicates the topics generated from the logs. Paragraph 4 discloses a plurality of multi-turn conversations.);
creating clusters from the extracted topics (Fig. 1a shows an example of a cluster, wherein the event is considered a topic or sub-topic.) using a topic modeling technique (Fig. 1a shows an example of topic modeling technique used to map or graph the multi-turn conversations.) to group semantically similar topics identified across consecutive conversation turns of the stored logs (Paragraph 95 discloses semantically related turns of utterances output by a conversational computing interface (stored logs) being paraphrased is obtained using any of the disclosed methods. This indicates Fig. 2a,b,3a-3d are mappings of semantically related turns such as similar topics or similar events are grouped together.),
wherein the clusters include main topics and sub-topics based on a hierarchical relationship among the topics (Fig. 1a, label 102 as a topic, label 108,110 as sub-topics of 102. The arrows indicate the relationship between the topic and sub-topic. Depending on the multi-turn conversations, there are multiple main topics and sub-topics within the conversations.),
each main topic being associated with at least two sub-topics (Fig. 1a, label 108,110 are two sub-topics of the main topic.);
generating the conversation flows, modeled as a graph, from the topic clusters (Fig. 1a, 1b shows the conversation flows from the clusters (label event indicates the topic, plan and result indicates the associated actions and response to the event shown in Fig. 1a,1b. Paragraph 29 discloses Fig. 1a shows annotated dialogues in an integrated graph.).,
wherein the graph comprises nodes representing specific topics (Fig. 1a, label 102 as a topic and 108,110 sub-topics.) and edges representing transitions between the nodes (Fig. 1a shows arrows indicating the relationship or edges representing transitions between nodes or topic to sub-topics.).
Krishnamurthy et al discloses dialogues between at least an agent and a user (Paragraph 18, Fig. 5, label 502,504), but fails to disclose the dialogues is stored in logs.
Mazza et al discloses transcripts from the chat interactions (paragraph 80) and using such transcripts to generate dialogue graphs (Fig. 3, label 370, transcripts). It would be obvious to one skilled in the art before the effective filing date of the application to modify Krishnamurthy et al by incorporating transcripts of the conversations to generate dialogue graphs as disclosed by Mazza et al so to have easy access to conversations via transcripts or logs and improving data processing of the transcripts or logs in order to generate dialog graphs, wherein such dialog graphs will improve access to information in the conversations and reduce the amount of storage used for storing dialogs or conversations.
Krishnamurthy et al discloses generating conversation graphs (Fig. 1a,1b), but fails to disclose utilizing the graph to configure a virtual agent to generate responses in future conversations.
Mazza et al discloses utilizing the graph to configure a virtual agent to generate responses in future conversations (Abstract discloses “a topic-specific chatbot in accordance with the deterministic dialogue tree; and outputting, by the processor, the one or more topic-specific chatbots, each of the topic-specific chatbots being configured to generate, automatically, responses to messages regarding the topic of the topic-specific chatbot from a customer in an interaction between the customer and the enterprise.”). It would be obvious to one skilled in the art before the effective filing date of the application to modify Krishnamurthy et al’s conversation graph generation by using such graph generation or dialog tree to determine response to message in a chatbot as disclosed by Mazza et al so to improve the chatbots ability to respond to a user, hence improving the user’s experience with automated systems such as chatbots.
Claim 3, Krishnamurthy et al discloses the conversation flows are further modeled using process mining (Fig. 1a,b shows the conversation flows indicates process mining or monitoring changes or transitions and uncovering new topics.).
Claim 4, Krishnamurthy et al discloses the graph is a directed cyclic graph (Fig. 1a,1b shows a direct acyclic graph. Paragraph 37 discloses alternatively or additionally, the dialogue store data structure of the conversation flow can represent relationships between turns in any suitable fashion, for example via a hypergraph and/or cyclic graph.).
Claim 6, Krishnamurthy et al discloses the sub-topics and the main topics are modeled as a tree (Fig. 1a, label 108,110 are mapped in graph as a tree.).
Claim 7, Krishnamurthy et al discloses assigning generic names to the clusters (Fig. 1a, label event as the generic name for an event, wherein the event, result and plan indicates the cluster associated with the event.).
Claim 8, Krishnamurthy et al discloses the generic names are intent names (Label event indicates the user’s needs or purpose or intent.).
Claim 9, Krishnamurthy et al discloses the agent is a human agent (Paragraph 93 discloses human demonstrators maybe asked to author a plan by acting as a stand in for an automated agent. This indicates at least an agent can be a human controlling the computing interface to communicate with the user.).
Claim 10 recites similar limitations as recited in claim 1. In addition, Krishnamurthy et al discloses
Preamble: A computer system for generating conversation flows (Fig. 1a,b, 5,4), the computer system comprising
one or more computer processors (Paragraph 48,100 discloses computer processor.), one or more computer readable memories (Paragraph 48 discloses computer storage device and paragraph 101,102 discloses storage subsystem.), one or more computer readable storage devices (Paragraph 101-102), and program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors via the one or more computer readable memories (Paragraph 99-103 discloses software and/or other instructions stored on one or more suitable storage devices and execution by processors.).
Claim 12 recites similar limitations as recited in claim 3.
Claim 13 recites similar limitations as recited in claim 4.
Claim 16 recites similar limitations as recited in claim 7.
Claim 17 recites similar limitations as recited in claim 8.
Claim 18 recites similar limitations as recited in claim 9.
Claim 19 recites similar limitations as recited in claim 1. In addition, Krishnamurthy et al discloses
Preamble: A non-transitory computer readable storage medium having stored thereon computer executable instructions which when executed by one or more processors, cause the one or more processors to carry out operations for generating conversation flows (Fig. 1a,b, 5,4, paragraphs 99-103).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDA WONG whose telephone number is (571)272-6044. The examiner can normally be reached 9-5.
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/LINDA WONG/Primary Examiner, Art Unit 2655