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. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
DETAILED ACTION
Double Patenting
2. 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
3. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10,540,446. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in the present application are broader and transparently found in the U.S. Patent 10,540,446 with obvious wording variations.
U.S. Patent Application 18/139,650
U.S. Patent 10,540,446
1. A system for generating natural language, the system comprising:
1. A system for generating natural language, the system comprising:
a prospect modeling component, operable to correlate known information about a target prospect personality with a quantitative personality model;
a prospect modeling component, operable to correlate known information about a target prospect personality with a quantitative personality model, the quantitative personality model being expressed as a vector indicating the relative expression of a plurality of relatively mutually orthogonal personality traits;
a neural sequence-to-sequence encoder-decoder, wherein an encoder of the neural sequence-to-sequence encoder-decoder is operable to deconstruct a source text and represent it as a sequence of weights on a pre-built conditional text model; and
a neural sequence-to-sequence encoder-decoder, wherein the encoder is operable to deconstruct a source text and represent it as a sequence of weights on a pre-built conditional text model;
a decoder of the neural sequence-to-sequence encoder-decoder is operable to create a generated text with approximately equal semantic content but differing syntax and word choice;
wherein the decoder is operable to create a generated text with approximately equal semantic content but differing syntax and word choice; and
the syntax and word choice of the generated text varies as a function of the expression of the quantitative personality model, and internal weights associated with the encoder-decoder are updated to reinforce high text similarity and to discourage low text similarity.
2. The system of claim 1, further comprising an evaluator coupled to the encoder and decoder, wherein the evaluator is operable to compare the generated text with a set of measurements made against the source text to create a text similarity evaluation and
wherein the text similarity evaluation is provided to one or both of the encoder and decoder.
wherein the syntax and word choice of the generated text varies as a function of the expression of the quantitative personality model.
2. The system of claim 1, further comprising an evaluator coupled to the encoder and decoder, wherein the evaluator is operable to compare the generated text with a set of measurements made against the source text to create a text similarity evaluation and
wherein the text similarity evaluation is provided to one or both of the encoder and decoder; and
wherein the internal weights associated with one or both of the encoder and decoder are updated to reinforce high text similarity and to discourage low text similarity.
3. The system of claim 1, further comprising a discriminator coupled to the encoder and decoder, wherein the discriminator provides a distinguishability score reflecting a weighted probability that the generated text is human-generated; and
wherein the distinguishability score is provided to one or both of the encoder and decoder; and
wherein the internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
3. The system of claim 1, further comprising a discriminator coupled to the encoder and decoder, wherein the discriminator provides a distinguishability score reflecting a weighted probability that the generated text is human-generated; and
wherein the distinguishability score is provided to one or both of the encoder and decoder; and
wherein the internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
4. The system of claim 1, further comprising an evaluator coupled to the encoder and decoder, wherein the evaluator provides a personality score reflecting the association of the language use in the generated text with the personality model input to the decoder; and
wherein the personality score is provided to the decoder; and
wherein the internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
4. The system of claim 1, further comprising an evaluator coupled to the encoder and decoder, wherein the evaluator provides a personality score reflecting the association of the language use in the generated text with the personality model input to the decoder; and
wherein the personality score is provided to the decoder; and
wherein the internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
5. The system of claim 1, wherein the generated text is provided to a representative of the modeled prospect class; and wherein the response of the representative is used to update the prospect model.
5. The system of claim 1, wherein the generated text is provided to a representative of the modeled prospect class; and wherein the response of the representative is used to update the prospect model.
6. The system of claim 5, wherein the updating of the prospect model is relative to the measured receptiveness of the representative to the text.
6. The system of claim 5, wherein the updating of the prospect model is relative to the measured receptiveness of the representative to the text.
7. The system of claim 5, wherein the updating of the prospect model is relative to the imputed measurement of personality traits.
7. The system of claim 5, wherein the updating of the prospect model is relative to the imputed measurement of personality traits.
8. A computer-implemented method for generating natural language, comprising:
providing a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality;
deconstructing a source text and representing it as a sequence of weights on a prebuilt conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder; and
creating a generated text with approximately equal semantic content but a differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
wherein the syntax and word choice of the generated text varies as a function of the expression of the quantitative personality model, and updating internal weights associated with one or both of the encoder and decoder to reinforce low distinguishability and to discourage high distinguishability.
9. The method of claim 8, further comprising:
comparing, using an evaluator coupled to the encoder and decoder, the generated text with a set of measurements made against the source text to create a text similarity evaluation,
wherein the text similarity evaluation is provided to one or both of the encoder and decoder.
8. A method for generating natural language, comprising:
providing a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality, the quantitative personality model being expressed as a vector indicating a relative expression of a plurality of relatively mutually orthogonal personality traits;
deconstructing a source text and representing it as a sequence of weights on a prebuilt conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder; and
creating a generated text with approximately equal semantic content but differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
wherein the syntax word choice of the generated text varies as a function of the expression of the quantitative personality model.
9. The method of claim 8, further comprising:
comparing, using an evaluator coupled to the encoder and decoder, the generated text with a set of measurements made against the source text to create a text similarity evaluation,
wherein the text similarity evaluation is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce high text similarity and to discourage low text similarity.
10. The method of claim 8, further comprising:
creating, using a discriminator coupled to the encoder and decoder, a distinguishability score reflecting a weighted probability that the generated text is human-generated,
wherein the distinguishability score is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
10. The method of claim 8, further comprising:
creating, using a discriminator coupled to the encoder and decoder, a distinguishability score reflecting a weighted probability that the generated text is human-generated,
wherein the distinguishability score is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
11. The method of claim 8, further comprising:
creating, using an evaluator coupled to the encoder and decoder, a personality score reflecting an association of the language use in the generated text with the qualitative personality model,
wherein the personality score is provided to the decoder, and
wherein internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
11. The method of claim 8, further comprising:
creating, using an evaluator coupled to the encoder and decoder, a personality score reflecting an association of the language use in the generated text with the qualitative personality model,
wherein the personality score is provided to the decoder, and
wherein internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
12. The method of claim 8, wherein the generated text is provided to a representative of the modeled prospect class, and wherein a response of the representative is used to update the quantitative personality model.
12. The method of claim 8, wherein the generated text is provided to a representative of the modeled prospect class, and wherein a response of the representative is used to update the quantitative personality model.
13. The method of claim 12, wherein updating of the quantitative personality model is relative to a measured receptiveness of the representative to the text.
13. The method of claim 12, wherein updating of the quantitative personality model is relative to a measured receptiveness of the representative to the text.
14. The method of claim 12, wherein updating of the quantitative personality model is relative to an imputed measurement of personality traits.
14. The method of claim 12, wherein updating of the quantitative personality model is relative to an imputed measurement of personality traits.
15. A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to:
provide a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality;
deconstruct a source text and represent it as a sequence of weights on a prebuilt conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder; and
create a generated text with approximately equal semantic content but differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
wherein a syntax word choice of the generated text varies as a function of the expression of the quantitative personality model, and internal weights associated with one or both of the encoder and decoder are updated to reinforce high text similarity and to discourage low text similarity.
16. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
compare, using an evaluator coupled to the encoder and decoder, the generated text with a set of measurements made against the source text to create a text similarity evaluation,
wherein the text similarity evaluation is provided to one or both of the encoder and decoder.
15. A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to:
provide a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality, the quantitative personality model being expressed as a vector indicating a relative expression of a plurality of relatively mutually orthogonal personality traits;
deconstruct a source text and represent it as a sequence of weights on a prebuilt conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder; and
create a generated text with approximately equal semantic content but differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
wherein the syntax word choice of the generated text varies as a function of the expression of the quantitative personality model.
16. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
compare, using an evaluator coupled to the encoder and decoder, the generated text with a set of measurements made against the source text to create a text similarity evaluation,
wherein the text similarity evaluation is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce high text similarity and to discourage low text similarity.
17. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
create, using a discriminator coupled to the encoder and decoder, a distinguishability score reflecting a weighted probability that the generated text is human-generated,
wherein the distinguishability score is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
17. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
create, using a discriminator coupled to the encoder and decoder, a distinguishability score reflecting a weighted probability that the generated text is human-generated,
wherein the distinguishability score is provided to one or both of the encoder and decoder, and
wherein internal weights associated with one or both of the encoder and decoder are updated to reinforce low distinguishability and to discourage high distinguishability.
18. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
create, using an evaluator coupled to the encoder and decoder, a personality score reflecting an association of the language use in the generated text with the qualitative personality model,
wherein the personality score is provided to the decoder, and
wherein internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
18. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to:
create, using an evaluator coupled to the encoder and decoder, a personality score reflecting an association of the language use in the generated text with the qualitative personality model,
wherein the personality score is provided to the decoder, and
wherein internal weights associated with the decoder are updated to reinforce high personality association and to discourage lower personality association.
19. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to provide the generated text to a representative of the modeled prospect class and to update the quantitative personality model based on a response of the representative.
19. The non-transitory computer readable medium of claim 15 further comprising instructions which, when executed by the one or more processors, cause the one or more processors to provide the generated text to a representative of the modeled prospect class and to update the quantitative personality model based on a response of the representative.
20. The non-transitory computer readable medium of claim 19, wherein the instructions comprise instructions which, when executed by the one or more processors, cause the one or more processors to update the quantitative personality model to a measured receptiveness of the representative to the text.
20. The non-transitory computer readable medium of claim 19, wherein the instructions comprise instructions which, when executed by the one or more processors, cause the one or more processors to update the quantitative personality model to a measured receptiveness of the representative to the text.
Response to Arguments
4. Applicant's arguments filed 3/10/26 with regard to the 35 USC 112, second paragraph rejection(s) and claim objections have been fully considered and are persuasive. The 35 USC 112, second paragraph rejection(s) of claims 1-20 and claim objections of claims 8-14 have been withdrawn.
Examiner contacted Applicant’s undersigned attorney via email on 3/19/2026 requesting to file electronic eterminal disclaimer to resolve the double patenting rejection(s), but receive no responses from the Applicant’s undersigned attorney. Examiner respectfully provides the office action.
Conclusion
5. THIS ACTION IS MADE FINAL. 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.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH H NGUYEN whose telephone number is (571)272-7489. The examiner can normally be reached Monday-Friday 7:30AM-3:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571-272-7488. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693
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