DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 27, 34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 27 and 34 recite “wherein the machine learning model is indicated to the machine transcription service,” which is not adequately described in the specification, as originally filed.
Paragraph [0029] describes a transcription job request 310 as specifying job type, data source, redaction type, redaction sensitivity, and transcription destination, but does not describe indicating “a machine learning model.”
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) 21-25, 28-32, 35-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Behre et al. (US 2023/0352009 A1, “Behre”) in view of DiPaulo et al. (US 2023/0353606 A1, “DiPaulo”).
As to claims 21, 28, 35, Behre discloses a system (Fig. 1), comprising:
a plurality of computing devices, respectively comprising a processor and a memory, wherein the plurality of computing devices implement a machine transcription service of a provider network (plurality of third party systems 120 utilize a computing system 110 to produce output transcription data, para. 0030-0032);
wherein the machine transcription service is configured to:
obtain audio data to generate a text transcription of the audio data (input audio data 141 is to be converted to text data or transcription data, para. 0036-0039);
apply an automatic speech recognition technique to the audio data to generate text data (ASR model 146 configured to generated transcription data from input audio, para. 0031);
apply a number of inverse text normalization models to the text data to generate normalized text data, the number of inverse text normalization models being determined for the text transcription of the audio data (apply an inverse text normalization model 222, para. 0062);
apply a machine learning model, trained to recognize profanity for redaction, to the normalized text data to identify one or more portions of the normalized text data for redaction (apply disfluency handling model 220 that removes disfluencies, para. 0062);
remove the identified one or more portions of the normalized text data (para 0062); and
provide the redacted text data to a destination (final sentences 234 are transmitted to the user/remote systems 232, para. 0066).
Behre differs from claims 21, 28, 35 in that it does not disclose the above underlined limitation.
DiPaulo teaches the use of machine learning trained to identify and remove objectionable, harmful, or inappropriate content in a video conference, such as swearing, offensive language, etc., including a separate model for identifying harmful content in text (Abstract, para. 0010-0011, 0075-0076, 0086). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Behre with the above teaching of DiPaulo in order to improve the trust and safety of received content, as taught by DiPaulo (para. 0001).
As to claims 22, 29, 36, Behre in view of DiPaulo teaches: wherein to remove the identified one or more portions of the normalized text data, the machine transcription service is configured to apply a character string indicating the removed one or more portions of the normalized text data (DiPaulo: harmful content may be obfuscated, para. 0011).
As to claims 23, 30, 37, Behre in view of DiPaulo teaches: wherein the machine learning model, trained to recognize profanity is applied in accordance with a request received at the machine transcription service to apply profanity recognition (DiPaulo: harmful content can be identified and removed according to moderation settings configured by a user, para. 0013, 0065, 0081).
As to claims 24, 31, 38, Behre in view of DiPaulo teaches: wherein the destination displays the redacted text data (harmful content may be presented to a moderator for editing, para. 0089).
As to claims 25, 32, 39, Behre in view of DiPaulo teaches: wherein the machine transcription service is further configured to apply a rules-based capitalization technique to the normalized text data (Behre: capitalization model 226, para. 0065).
Claim(s) 26, 33, 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Behre in view of DiPaulo, as applied to claims 21, 28, 35 above, and further in view of Zarecki et al. (US 2022/0292218 A1, “Zarecki”).
Behre in view of DiPaulo differs from claims 26, 33, 40 in that it does not teach: wherein the destination is specified in a request received at the machine transcription service.
Zarecki teaches requesting a redacted transcript from a redaction system and including an intended destination for the redacted transcript (para. 0020). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Behre in view of DiPaulo with the above teaching of Zarecki in order to provide for transcript requests after the conference.
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.
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.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,182,498 in view of DiPaulo.
Claim 21 (present application)
Claim 1 (US 12,182,498)
A system, comprising:
A system, comprising:
a plurality of computing devices, respectively comprising a processor and a memory, wherein the plurality of computing devices implement a machine transcription service of a provider network;
at least one processor; and a memory, storing program instructions that when executed by the at least one processor, cause the at least one processor to implement a machine transcription system,
wherein the machine transcription service is configured to:
configured to:
obtain audio data to generate a text transcription of the audio data;
obtain text data generated from one or more automated speech recognition techniques for redaction;
apply an automatic speech recognition technique to the audio data to generate text data;
text data generated from one or more automated speech recognition techniques for redaction;
apply a number of inverse text normalization models to the text data to generate normalized text data, the number of inverse text normalization models being determined for the text transcription of the audio data;
cause application of a number of inverse text normalization models to the text data to generate normalized text data based, at least in part, on a predicted normalization by a first inverse text normalization model for the text data;
apply a machine learning model, trained to recognize profanity for redaction, to the normalized text data to identify one or more portions of the normalized text data for redaction;
cause application of a machine learning model, trained to recognize text for redaction, to identify one or more portions of the normalized text data for redaction;
remove the identified one or more portions of the normalized text data; and
redact the identified one or more portions of the normalized text data; and
provide the redacted text data to a destination.
provide the redacted text data to a destination.
Claim 1 of the patent differs from claim 21 of the present application in that it does not recite: apply a machine learning model, trained to recognize profanity for redaction.
DiPaulo teaches the use of machine learning trained to identify and remove objectionable, harmful, or inappropriate content in a video conference, such as swearing, offensive language, etc., including a separate model for identifying harmful content in text (Abstract, para. 0010-0011, 0075-0076, 0086). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify claim 1 of the patent with the above teaching of DiPaulo in order to improve the trust and safety of received content, as taught by DiPaulo (para. 0001).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cai et al. (US 2023/0385320 A1) teach a censor model trained to identify offensive content.
Springer et al. (US 2023/0353704 A1) teach redacting profanity from conference content.
Tolle et al. (US 2023/0254350 A1) teach filtering comments containing profanity.
Zhu et al. (US 2021/0375289 A1) teach using inverse text normalization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stella L Woo whose telephone number is (571)272-7512. The examiner can normally be reached Monday - Friday, 8 a.m. to 5 p.m.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached at 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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STELLA L. WOO
Primary Examiner
Art Unit 2693
/Stella L. Woo/ Primary Examiner, Art Unit 2693
Prosecution Insights