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 .
1. This communication is responsive to the Amendment filed 3/25/2026.
2. Claims 1-20 are pending in this application. Claims 1, 10 and 17 are independent claims. In the instant Amendment, claims 1, 6, 10, 17 and 19 were amended. This action is made Final.
Claim Rejections - 35 USC § 103
3. 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.
4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Wu et al (“Wu” US 2022/0108086) in view of Vu et al (“Vu” US 2024/0020546) and further in view of Feng et al (“Feng” US 11,443,119).
Regarding claim 1, Wu discloses a method comprising:
receiving, by a processing device, a language model (see fig 2a, 130; e.g., trained model) configured to generate summaries of dialogues (see fig 2a, 150; e.g., dialog summary), the language model trained using training dialogues (see the Abstract; e.g., “A dialogue summary is generated using the generative language model trained using the summary draft.”) and dialogue skeletons generated based on the training dialogues (see the Abstract; e.g., “language model is trained to generate a segment summary for each dialogue segment using a portion of the summary draft that corresponds to at least one dialogue turn in the dialogue segment.”);
receiving, by the processing device, an input including an input dialogue (see fig 2a, 202; also see paragraphs [0023] and [0056]; e.g., input dialogue); and
generating, by the processing device, a summary of the input dialogue using the language model (see fig 2a; e.g., label 150 is output from label 220).
Wu does not expressly disclose using one or more perturbation-based probes based on a textual similarity score between an output of a dialogue state tracking model for the one or more perturbation-based probes and an output for an unedited said training dialogue, the dialogue skeletons used supervision in a prompt transfer approach between a source task and a target task.
However, Vu discloses supervision in a prompt transfer approach between a source task and a target task is well known in the art (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”). It would have been obvious to an artisan before the effective filing date of the present invention to include Vu’s teachings in Wu’s user interface in an effort to provide a user-friendly interface that significantly boosts the performance of prompt tuning across many tasks.
Moreover, Feng discloses using one or more perturbation-based probes based on a textual similarity score between an output of a dialogue state tracking model for the one or more perturbation-based probes and an output for an unedited said training dialogue (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value). It would have been obvious to an artisan before the effective filing date of the present invention to include Aggarwal’s teachings in Wu’s user interface since by removing such an utterance from training data associated with the dialog model in order to generate an adapted model, task completion efficiency using the adapted model is improved in comparison to using the original, un-adapted model (i.e., the model with training data that includes the non-essential utterance).
Regarding claim 2, Vu discloses wherein the source task is a dialogue state tracking task for a particular domain and the target task is a dialogue summarization task for the particular domain (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 3, Vu discloses wherein the generating the summary includes configuring an input sequence to the language model to include a soft prompt generated during training of the language model based in part on the dialogue skeletons (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 4, Vu discloses wherein the prompt transfer approach includes freezing parameters of the language model and learning parameters of the soft prompt (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 5, Vu discloses wherein the prompt transfer approach includes learning a soft prompt for the source task and using the soft prompt from the source task to initialize parameters of a soft prompt for the target task (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 6, Feng discloses wherein the dialogue skeletons include a subset of dialogue turns extracted from training dialogues using the one or more perturbation-based probes (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Regarding claim 7, Feng discloses wherein the one or more perturbation-based probes are configured to generate the dialogue skeletons by determining a sensitivity of a dialogue state tracking model to dialogue turns of the training dialogues (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Regarding claim 8, Feng discloses wherein the subset of dialogue turns includes dialogue turns over a threshold level of sensitivity (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Regarding claim 9, Vu discloses wherein the dialogue skeletons represent an intermediate task-specific medium between the source task and the target task (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Claim 10 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding claim 11, Wu discloses receiving an input including a dialogue; and generating a summary of the dialogue using the trained machine learning model (see claim 1 above).
Regarding claim 12, Vu discloses wherein the source task is a dialogue state tracking task and the target task is a dialogue summarization task (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 13, Feng discloses wherein the one or more perturbation-based probes are configured to determine a sensitivity of the machine learning model to dialogue turns for a particular training dialogue, and wherein the subset of dialogue turns includes dialogue turns over a threshold level of sensitivity (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Regarding claim 14, Vu discloses wherein the machine learning model includes a pretrained language model, and the training includes freezing parameters of the pretrained language model and generating a soft prompt to adjust an input sequence to the pretrained language model (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 15, Vu discloses wherein the training includes using the dialogue skeletons as supervision to refine the soft prompt as part of the prompt transfer for both the source task and the target task (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 16, Vu discloses wherein the training includes using the dialogue skeletons as supervision to refine the soft prompt as part of the prompt transfer for either the source task or the target task (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Claim 17 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding claim 18, Vu discloses wherein the generating the summary includes prepending the soft prompt to an input sequence generated based on the input dialogue (see paragraphs [0011]-[0012], [0044], [0050], [0054], [0141] and [0155]; e.g., “prompt-based transfer learning approach”; “soft prompt”; “a frozen language model (e.g., the parameters of the language model may be fixed as the parameters of the source prompt and/or the target prompt are being learned)”).
Regarding claim 19, Feng discloses wherein the dialogue skeletons include a subset of dialogue turns extracted from training dialogues using the one or more perturbation-based probes (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Regarding claim 20, Feng discloses wherein the one or more perturbation-based probes are configured to generate the dialogue skeletons by determining a sensitivity of a dialogue state tracking model to dialogue turns of the training dialogues (see col. 1, lines 27-44; col. 4, lines 10-19; col. 4, line 62 to col. 5, line 6; and col. 5, lines 7-58; e.g., remove one or more utterances associated with a given conversation management turn from the dialog model when the relevance value of the one or more utterances is determined to be below a given threshold value).
Response to Arguments
5. Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xin, et al (CN 114398906).
7. 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.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHAWN N TILLERY whose telephone number is (571)272-6480. The examiner can normally be reached M-F 9:00a - 5:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L Bashore can be reached at (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHAWN N TILLERY/Primary Examiner, Art Unit 2174
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