DETAILED ACTION
This action is in response to the initial filing of application no. 19/003855 on 12/27/2024.
Claims 1 – 20 are still pending in this application, with claims 1, 9 and 17 being independent.
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 .
Allowable Subject Matter
Aside from the non-prior art rejections, it has been determined that the prior art fails to teach or suggest in reasonable combination the limitations recited by the independent claims filed on 12/27/2024.
Independent claims 1, 9 and 17 recite the following
retrieving a dialogue history including a plurality of speech utterances, each speech utterance including one or more words; determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; and generating, based on the determined probabilities, at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type.
After search and consideration of the prior art, the independent claims (with related dependent claims) recite allowable subject matter, particularly: determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; and generating, based on the determined probabilities, at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type. For example
Shi and Huang (“A Deep Sequential Model for Discourse Parsing in Multi-Party Dialogues”) discloses the following limitations recited in the independent claims: retrieving a dialogue history (Figure 1; Introduction, Model Overview, Experiments-Data Preparation and Implementation Details, pg. 7007 - 7012); encoding the plurality of speech utterances such that each speech utterance is associated with a sequence identifier indicating an order of each speech utterance in the dialogue history (the speech utterances are input to a structure representation encoder which generates an encoding/identifier which indicates an arrangement of the utterances according to pattern/method of dependency, Model Overview, Discourse Representations- Structured Global Representations, Experiments- Implementation Details; pg. 7009-7010, 7012); and generating at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type (Figure 2; Model Overview, Link Prediction and Relation Classification, pg. 7009- 7011).
Yet, Shi and Huang fails to teach, “determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples”. Shi and Huang teaches the following: determining a first probability (decoding) between a speech utterance and a target token of a second speech utterance and a second probability (decoding) between the speech utterance and a target token of a dialogue discourse type (relation classification) (Figure 2; Introduction, Model Overview, Experiments-Data Preparation and Implementation Details, pg. 7007 - 7012).
He (Ho on the IDS filed on 12/27/2024) (“Multi-tasking Dialogue Comprehension with Discourse Parsing”) discloses the following limitations recited in the independent claims: retrieving a dialogue history (Figure 4; 4.Experiments – 4.1 Molweni Dataset, 4.4 Results); encoding the plurality of speech utterances such that each speech utterance is associated with a sequence identifier indication an order of each speech utterance in the dialogue history (the speech utterances are encoded with separators which indicate an order of the speech utterance, Figure 2, Figure 3 and Figure 4; 3 Methods- 3.1 Feature Extraction, 4.Experiments – 4.1 Molweni Dataset, 4.4 Results); and generating at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type (3.2 Prediction).
Yet, He fails to teach, “determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples”. He teaches the following: determining a first probability (decoding) between a speech utterance and a target token of a second speech utterance and a second probability (decoding) between the speech utterance and a target token of a dialogue discourse type (relation classification) (Figure 2, Figure 3 and Figure 4; 3 Methods- 3.2 Prediction 4.Experiments – 4.1 Molweni Dataset, 4.4 Results).
Liu (“Improving Multi-Party Dialogue Discourse Parsing via Domain Integration”) discloses the following limitations recited in the independent claims: retrieving a dialogue history (Figure 2; 5 Experimental Result and Analysis); encoding the plurality of speech utterances (3. Dialogue Discourse Parsing – 3.1 Task Definition and 3.2 Transformer-based Discourse Parser, 5 Experimental Result and Analysis); and generating at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type (3. Dialogue Discourse Parsing – 3.1 Task Definition and 3.2 Transformer-based Discourse Parser, 5 Experimental Result and Analysis).
Yet, Liu fails to teach, “determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples”. Liu teaches the following: determining a first probability (decoding) between a speech utterance and a target token of a second speech utterance and a second probability (decoding) between the speech utterance and a target token of a dialogue discourse type (relation classification) (3. Dialogue Discourse Parsing – 3.1 Task Definition and 3.2 Transformer-based Discourse Parser, 5 Experimental Result and Analysis).
Kawano (“Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning”) discloses the following limitations recited in the independent claims: retrieving a dialogue history (Table 1; 4 Experimental Settings – 4.1 Dataset, pg. 22 and 25); encoding the plurality of speech utterances (Figure 1; 3. Neural dialogue structure parser for Multi-Floor Dialogue – 3.1 Hierarchical Encoder, 4 Experimental Settings – 4.1 Dataset and 4.2 Model Settings, 5. Experimental Results, pg. 23 – 27); and generating at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type (3 Figure 1; 3. Neural dialogue structure parser for Multi-Floor Dialogue – 3.2 Antecedent Predictor and 3.4 Relation-Type Predictor, 4 Experimental Settings – 4.1 Dataset and 4.2 Model Settings, 5. Experimental Results, pg. 23 – 27).
Yet, Kawano fails to teach “determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples”. Kawano teaches a determining a first probability (decoding) between a speech utterance and a target token of a second speech utterance and a second probability (decoding) between the speech utterance and a target token of a dialogue discourse type (relation type predictor) (3 Figure 1; 3. Neural dialogue structure parser for Multi-Floor Dialogue – 3.2 Antecedent Predictor and 3.4 Relation-Type Predictor, 4 Experimental Settings – 4.1 Dataset and 4.2 Model Settings, 5. Experimental Results, pg. 23 – 27)
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5, 7 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5 recites the limitation “training the encoder and decoder based in the calculated loss operation. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the encoder is a T5 encoder and the decoder is a T5 decoder".. There is insufficient antecedent basis for this limitation in the claim.
To overcome these rejections, claim 5 and 7 can be amended to recite the following:
Claim 5. The method of claim 1, further comprising: calculating a loss operation that compares a current dialogue context with a target token sequence; and training an encoder and ta decoder based on the calculated loss operation.
Claim 7. The method of claim 5, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
Claims 13 recites the limitation “train the encoder and decoder based in the calculated loss operation. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the encoder is a T5 encoder and the decoder is a T5 decoder".. There is insufficient antecedent basis for this limitation in the claim.
To overcome these rejections, claim 5 and 7 can be amended to recite the following:
Claim 13. The apparatus of claim 9, wherein said computer program code further includes: calculating code configured to cause the at least one processor to calculate a loss operation that compares a current dialogue context with a target token sequence; and training code configured to cause the at least one processor to train an encoder and ta decoder based on the calculated loss operation.
15. The apparatus of claim 13, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
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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The claim mapping for current application and US 12,190,880.
Current Application
1. A method performed by at least one processor, the method comprising: retrieving a dialogue history including a plurality of speech utterances, each speech utterance including one or more words; determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; and generating, based on the determined probabilities, at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type.
2. The method of claim 1, wherein the at least one discourse relation triple includes a first speech utterance from the plurality of utterances and a second speech utterance from the plurality of utterances, wherein second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
3. The method of claim 2, wherein the generating the at least one discourse relation triple is performed such that the second speech utterance is the last speech utterance in the dialogue history.
4. The method of claim 1, wherein the generating the at least one discourse relation triple is performed such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
5. The method of claim 1, further comprising: calculating a loss operation that compares a current dialogue context with a target token sequence; and training the encoder and the decoder based on the calculated loss operation.
6. The method of claim 1, wherein the discourse type is selected from one of a question answer pair, a question elaboration, and an acknowledgement.
7. The method of claim 1, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
8. The method of claim 1, wherein the dialogue history includes at least three or more speakers.
9. An apparatus comprising: at least one memory configured to store computer program code; and at least one processor configured to access said at least one memory and operate as instructed by said computer program code, said computer program code including: retrieving code configured to cause the at least one processor to retrieve a dialogue history including a plurality of speech utterances, each speech utterance including one or more words, determining code configured to cause the at least one processor to determine a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; generating code configured to cause the at least one processor to generate, based on the determined probabilities, at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type.
10. The apparatus of claim 9, wherein the at least one discourse relation triple includes a first speech utterance from the plurality of utterances and a second speech utterance from the plurality of utterances, wherein the second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
11. The apparatus of claim 10, wherein the generating code is further configured to cause the at least one processor to generate the at least one discourse relation triple such that the second speech utterance is the last speech utterance in the dialogue history.
12. The apparatus of claim 9, wherein the generating code is further configured to cause the at least one processor to generate the at least one discourse relation triple such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
13. The apparatus of claim 9, wherein said computer program code further includes: calculating code configured to cause the at least one processor to calculate a loss operation that compares a current dialogue context with a target token sequence; and training code configured to cause the at least one processor to train the encoder and the decoder based on the calculated loss operation.
14. The apparatus of claim 9, wherein the discourse type is selected from one of a question answer pair, a question elaboration, and an acknowledgement.
15. The apparatus of claim 9, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
16. The apparatus of claim 9, wherein the dialogue history includes at least three or more speakers.
17. A non-transitory computer readable medium having instructions stored therein, which when executed a processor cause the processor to execute a method comprising: retrieving a dialogue history including a plurality of speech utterances, each speech utterance including one or more words; determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; and generating, based on the determined probabilities, at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including at least two speech utterances from the dialogue history and a dialogue discourse type.
18. The non-transitory computer readable medium of claim 17, wherein the at least one discourse relation triple includes a first speech utterance from the plurality of utterances and a second speech utterance from the plurality of utterances, wherein second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
19. The non-transitory computer readable medium of claim 18, wherein the generating the at least one discourse relation triple is performed such that the second speech utterance is the last speech utterance in the dialogue history.
20. The non-transitory computer readable medium of claim 17, wherein the generating the at least one discourse relation triple is performed such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
US 12,190, 880
1. A method performed by at least one processor, the method comprising: retrieving a dialogue history including a plurality of speech utterances, each speech utterance including one or more words; encoding the plurality of speech utterances such that each speech utterance is associated with a sequence identifier indicating an order of each speech utterance in the dialogue history; and decoding the encoded plurality of speech utterances to generate at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including a first sequence identifier of a first speech utterance from the plurality of speech utterances, a second sequence identifier of a second speech utterance from the plurality of speech utterances, and a dialogue discourse type.
2. The method of claim 1, wherein the second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
3. The method of claim 2, wherein the decoding the encoded plurality of speech utterances is performed such that the second speech utterance is the last speech utterance in the dialogue history.
4. The method of claim 1, wherein the decoding the encoded plurality of speech utterances is performed such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
5. The method of claim 1, further comprising: calculating a loss operation that compares a current dialogue context with a target token sequence; and training the encoder and the decoder based on the calculated loss operation.
6. The method of claim 1, wherein the discourse type is selected from one of a question answer pair, a question elaboration, and an acknowledgement.
7. The method of claim 1, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
8. The method of claim 1, wherein the dialogue history includes at least three or more speakers.
9. An apparatus comprising: at least one memory configured to store computer program code; and at least one processor configured to access said at least one memory and operate as instructed by said computer program code, said computer program code including: retrieving code configured to cause the at least one processor to retrieve a dialogue history including a plurality of speech utterances, each speech utterance including one or more words, encoding code configured to cause the at least one processor to encode the plurality of speech utterances such that each speech utterance is associated with a sequence identifier indicating an order of each speech utterance in the dialogue history, and decoding code configured to cause the at least one processor to decode the encoded plurality of speech utterances to generate at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including a first sequence identifier of a first speech utterance from the plurality of speech utterances, a second sequence identifier of a second speech utterance from the plurality of speech utterances, and a dialogue discourse type.
10. The apparatus of claim 9, wherein the second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
11. The apparatus of claim 10, wherein the decoding code is further configured to cause the at least one processor to decode the encoded plurality of speech utterances such that the second speech utterance is the last speech utterance in the dialogue history.
12. The apparatus of claim 9, wherein the decoding code is further configured to cause the at least one processor to decode the encoded plurality of speech utterances such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
13. The apparatus of claim 9, wherein said computer program code further includes: calculating code configured to cause the at least one processor to calculate a loss operation that compares a current dialogue context with a target token sequence; and training code configured to cause the at least one processor to train the encoder and the decoder based on the calculated loss operation.
14. The apparatus of claim 9, wherein the discourse type is selected from one of a question answer pair, a question elaboration, and an acknowledgement.
15. The apparatus of claim 9, wherein the encoder is a T5 encoder and the decoder is a T5 decoder.
16. The apparatus of claim 9, wherein the dialogue history includes at least three or more speakers.
17. A non-transitory computer readable medium having instructions stored therein, which when executed a processor cause the processor to execute a method comprising: retrieving a dialogue history including a plurality of speech utterances, each speech utterance including one or more words; encoding the plurality of speech utterances such that each speech utterance is associated with a sequence identifier indicating an order of each speech utterance in the dialogue history; and decoding the encoded plurality of speech utterances to generate at least one discourse relation triple corresponding to the dialogue history, the at least one discourse relation triple including a first sequence identifier of a first speech utterance from the plurality of speech utterances, a second sequence identifier of a second speech utterance from the plurality of speech utterances, and a dialogue discourse type.
18. The non-transitory computer readable medium of claim 17, wherein the second speech utterance occurs after the first speech utterance such that the second speech utterance is a response to the first speech utterance.
19. The non-transitory computer readable medium of claim 18, wherein the decoding the encoded plurality of speech utterances is performed such that the second speech utterance is the last speech utterance in the dialogue history.
20. The non-transitory computer readable medium of claim 17, wherein the decoding the encoded plurality of speech utterances is performed such that a discourse relation triple is generated for each speech utterance included in the plurality of speech utterances.
Claims 1- 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 20 of U.S. Patent No. 12,190,880 in view of Raiskin (US 2020/0320163) and further in view of Balakrishnan et al. (“Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue”) (“Balakrishnan”). Although the claims at issue are not identical, they are not patentably distinct from each other.
As shown above, the limitations of claims 1 – 20 of US 12,190,880 recite the 1-limitations of claims 1 – 20 of the current application, respectively except for, “determining a probability between each speech utterance and a target token sequence of linearized discourse-relation triples; and generating, based on the determined probabilities, the at least one discourse relation triple”.
However, Raiskin discloses a system and method comprising an encoder and a decoder (Abstract), wherein the decoder performs decoding by determining a probability between an input and a target token sequence ([0011] [0012]).
Additionally, Balakrishnan discloses a system and method for performing sentence level discourse parsing (Abstract), wherein discourse related output generated by a model further comprises linearized output (2. Towards More Expressive Meaning Representations, 2.2 Tree-Structure MRs; 4 Model, 4.1 Seq2Seq with Linearized Trees, pg. 833 and 835).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention recited by the limitations of 1 – 20 of US 12,190,880 in the same way that Raiskin’s invention has been improved to achieve the following, predictable results for the purpose of efficiently automating the process of performing discourse parsing: the decoding further comprises determining a probability between each speech utterance and a target token sequence of discourse-relation triples, wherein the at least one discourse relation triple is generated based on the probability.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of the limitations of claims 1 – 20 of US 12,190,880 and Raiskin’s in the same way that Balakrishnan’s invention has been improved to achieve the following, predictable results for the purpose of efficiently automating the process of performing discourse parsing: the target token sequence further comprises linearized discourse-relation triples.
Thus, claims 1 – 20 of the current application and claims 1 – 20 of US 12,190,880 are obvious variants.
Conclusion
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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