DETAILED ACTION
This is responsive to the application filed 13 February 2025.
Claims 1-20 are pending and considered below.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,248,755. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of U.S. Patent No. 12,248,755 anticipate the currently pending claims (see examples in table below).
Further, it is well settled that the omission of an element/step and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Omission of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
Current Claims
Claims of U.S. Patent No. 12,248,755
1. A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising:
obtaining a transcript of a chat between a customer and an agent, the transcript comprising a customer input from the customer and an agent input from the agent;
selecting, based on the agent input, a response from a plurality of responses representing respective potential replies to the customer input;
determining that a similarity score between the agent input and the selected response satisfies a similarity threshold; and
based on determining that the similarity score between the agent input and the selected response satisfies the similarity threshold, using the customer input and the selected response to train a machine learning model.
2. The method of claim 1, wherein the machine learning model is a natural language understanding model.
3. The method of claim 1, wherein determining that the similarity score between the selected response and the agent input satisfies the similarity threshold comprises using an embedding to compare the selected response to the agent input.
4. The method of claim 1, wherein selecting, based on the agent input, the response from the plurality of responses comprises navigating a logic tree.
5. The method of claim 1, wherein: the transcript comprises a first transcript; and the operations further comprise: obtaining a second transcript corresponding to a second conversation between the customer and the agent, the second transcript comprising a second customer input and a second agent input; selecting, based on the second agent input, a second response from the plurality of responses; determining that a second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold; and based on determining that the similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold, discarding the second transcript.
6. The method of claim 1, wherein selecting, based on the agent input, the response from the plurality of responses comprises iterating through the plurality of responses to find the response that most closely matches the agent input.
7. The method of claim 1, wherein: the customer input comprises a first customer input; the agent input comprises a first agent input; the transcript comprises a second customer input and a second agent input; and the operations further comprise: selecting, based on the second agent input, a second response from the plurality of responses; determining that a second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold; and based on determining that the second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold, adding a new response to the plurality of responses based on the second agent input.
8. The method of claim 1, wherein the operations further comprise obtaining a logic model comprising the plurality of responses.
9. The method of claim 8, wherein the logic model comprises a metric indicating a coverage of the logic model.
10. The method of claim 9, wherein the operations further comprise, based on determining that the similarity score between the selected response and the agent input does not satisfy the similarity threshold, reducing the metric.
1. A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising:
receiving a transcript corresponding to a conversation between a customer and an agent, the transcript comprising a customer input and an agent input;
receiving a logic model comprising a plurality of responses, each response of the plurality of responses representing a potential reply to the customer input; selecting, based on the agent input, a response from the plurality of responses of the logic model;
determining that a similarity score between the selected response and the agent input satisfies a similarity threshold; and
based on determining that the similarity score between the selected response and the agent input satisfies the similarity threshold, training a machine learning model using the customer input and the selected response.
2. The method of claim 1, wherein the machine learning model is a natural language understanding (NLU) model.
3. The method of claim 1, wherein determining that the similarity score between the selected response and the agent input satisfies the similarity threshold comprises using an embedding to compare the selected response to the agent input.
4. The method of claim 1, wherein the logic model comprises a logic tree.
5. The method of claim 1, wherein: the transcript comprises a first transcript; and the operations further comprise: receiving a second transcript corresponding to a second conversation between the customer and the agent, the second transcript comprising a second customer input and a second agent input; selecting, based on the second agent input, a second response from the plurality of responses of the logic model; determining that a second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold; and based on determining that the similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold, discarding the second transcript.
6. The method of claim 1, wherein selecting, based on the agent input, the response from the plurality of responses of the logic model comprises iterating through the plurality of responses to find the response that most closely matches the agent input.
7. The method of claim 1, wherein: the customer input comprises a first customer input; the agent input comprises a first agent input; the transcript comprises a second customer input and a second agent input; and the operations further comprise: selecting, based on the second agent input, a second response from the plurality of responses of the logic model; determining that a second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold; and based on determining that the second similarity score between the selected second response and the second agent input fails to satisfy the similarity threshold, adding a new response to the plurality of responses based on the second agent input.
(see claim 1 above)
8. The method of claim 7, wherein the logic model comprises a metric indicating a coverage of the logic model.
9. The method of claim 8, wherein the operations further comprise, based on determining that the second similarity score between the selected second response and the second agent input does not satisfy the similarity threshold, reducing the metric.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The closest prior art of record, Tanaku et al. (US PGPub 2023/0197105) discloses receiving a transcript corresponding to a conversation between a customer and an agent, the transcript comprising a customer input and an agent input (“receiving, by the service layer module, a new call or a new chat between the customer and the agent; implementing the trained machine learning model; comparing, in response to implementing, the new call transcript data with the predefined complaint data”, [0019]); determining that a similarity score between a selected response and the agent input satisfies a similarity threshold (“generating a second similarity score, based on comparing, that identifies how similar the new call transcript data is compared to the predefined complaint data; and automatically identifying the new call transcript data as a second dissatisfaction data based on determining that the second similarity score is equal to or more than the predetermined threshold value”, [0019]); and training a machine learning model (“training the machine learning model with the predefined complaint data”, [0020]).
Tanaku, alone or in combination with the prior art of record, does not disclose selecting, based on the agent input, a response from the plurality of responses of the logic model; determining that a similarity score between the selected response and the agent input satisfies a similarity threshold; and based on determining that the similarity score between the selected response and the agent input satisfies the similarity threshold, training a machine learning model using the customer input and the selected response as claimed in combination with the additional features.
Housman (US PGPub 20180367480) discloses an apparatus, system, and method for optimizing chat-based communications. A message module receives an outgoing message comprising a portion of a conversation between an agent and a user. An outgoing message may be generated in response to an incoming message from the user and received prior to sending the outgoing message to the user. An analysis module analyzes an incoming message and an outgoing message using a predefined machine learning model to identify one or more features of the outgoing message that have an influence on a desired outcome based on the incoming message. An action module generates one or more corrective actions related to the outgoing message based on one or more features that are identified using the machine learning model. One or more corrective actions are intended to increase the likelihood that an outgoing message will result in a desired outcome.
Mazza et al. (US PGPub 2019/0182382) discloses a method for configuring a topic-specific chatbot: clustering, by a processor, a plurality of transcripts of interactions between customers and human agents of a contact center of an enterprise to generate a plurality of clusters of interactions, each cluster of interactions corresponding to a topic, each of the interactions including agent phrases and customer phrases; for each cluster of the plurality of clusters of interactions: extracting, by the processor, a topic-specific dialogue tree for the cluster; pruning, by the processor, the topic-specific dialogue tree to generate a deterministic dialogue tree; and configuring, by the processor, 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.
Salammagari et al. (US PGPub 2020/0082214) discloses a method for facilitating training of agents is disclosed. Raw transcripts representing textual form of interactions between the agents and customers of the enterprise are transformed to generate transformed transcripts. An interaction summary is generated in relation to each transformed transcript. A plurality of intent-based interaction clusters are derived using the interaction summary generated in relation to each transformed transcript. The plurality of interactions are classified based on the plurality of intent-based interaction clusters and an interaction flow map is generated for each intent-based interaction cluster based on the interactions classified into the respective intent-based interaction cluster. The generated interaction flow map is capable of facilitating training of agents for interacting with the customers of the enterprise.
Nogueira Dos Santos et al. (USPN 11,475,067) discloses techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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/SAMUEL G NEWAY/ Primary Examiner, Art Unit 2657
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