DETAILED ACTION
Introduction
1. This office action is in response to Applicant’s submission filed on 06/05/2023. Claims 1-20 are pending in the application and have been examined.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
3. The drawings filed on 06/05/2023 have been accepted and considered by the Examiner.
Nonstatutory Double Patenting
4. 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 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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. 12,175,983. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of patents ‘983 anticipate the instant claims as presented in the chart below. Similarly, system claims 11-20 in the current App. ‘138 are also anticipated and follow likewise the mirror mapping to the corresponding system claims 11-20 in the patent ‘983.
App. No. 18/329,138
1. A computer-implemented method comprising: extracting, by the computer, an enrollment voiceprint for an enrolled speaker using a plurality of enrollment audio features of one or more enrollment speech samples, including a set of enrollment audio features for a keyword sequence occurring in each enrollment speech sample; extracting, by the computer, a test voiceprint for a test speaker using a plurality test audio features of a test speech sample; determining, by the computer, an audio similarity score based upon a difference between the enrollment voiceprint and the test voiceprint; and identifying, by the computer, a content match between the set of enrollment audio features for the keyword sequence and a set of test audio features of the plurality of test audio features of the test speech sample.
2. The method according to claim 1, further comprising identifying, by the computer, the test speaker as the enrolled speaker, in response to determining that the audio similarity score satisfied a threshold.
3. The method according to claim 1, further comprising identifying, by the computer, the test speaker as the enrolled speaker, in response to identifying the content match.
4. The method according to claim 1, wherein the computer obtains the one or more enrollment speech samples and the test speech sample via a telephone channel associated with an agent device.
5. The method according to claim 1, wherein the keyword sequence includes repeated content, and wherein each enrollment speech sample includes an instance of the repeated content.
6. The method according to claim 1, wherein identifying the content match includes applying, by the computer, a modified dynamic time warping process on the set of enrollment audio features containing the keyword sequence and the set of test audio features.
7. The method according to claim 1, further comprising: for each enrollment speech sample, generating, by the computer, a set of enrollment text features by applying a speaker diarization algorithm on the enrollment speech sample; and generating, by the computer, a set of test text features by applying the speaker diarization algorithm on the test speech sample, wherein the computer identifies the content match having the keyword sequence using each set of enrollment text features of the one or more enrollment speech samples and the set of test text features of the test speech sample.
8. The method according to claim 7, further comprising: for each set of enrollment text features, extracting, by the computer, an enrollment entity name associated with the enrolled speaker for the keyword sequence using the enrollment text features; and extracting, by the computer, a test entity name associated with the test speaker for the keyword sequence using the test text features.
9. The method according to claim 1, wherein the computer performs a passive recognition by extracting the plurality of test features from a plurality of test speech samples at a given interval.
10. The method according to claim 1, wherein the enrollment audio features and the test audio features comprise at least one of mel-frequency cepstral coefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), or perceptual linear prediction (PLP).
Pat. No. 12,175,983
1. A computer-implemented method comprising: obtaining, by a computer, an enrollment audio sample having an enrollment utterance of a first speaker and a test audio sample including a test utterance of a second speaker; extracting, by the computer, an enrollment voiceprint for the first speaker using one or more acoustic features of the enrollment utterance of the first speaker, and a first content sequence of one or more expected words using the enrollment utterance; extracting, by the computer, a test voiceprint for the second speaker using the one or more acoustic features of the test utterance of the second speaker, and a second content sequence of one or more test words using the test utterance; determining, by the computer, an acoustic similarity for the enrollment utterance and the test utterance based on comparing the enrollment voiceprint against the test voiceprint; determining, by the computer, a content similarity for the enrollment utterance and the test utterance based on comparing the first content sequence against the second content sequence; and identifying, by the computer, the second speaker as the first speaker based upon the acoustic similarity and the content similarity.
8. The method according to claim 1, wherein determining the acoustic similarity includes detecting, by the computer, a speaker match between the first speaker and the second speaker based upon the test voiceprint of the second speaker having a distance to the enrollment voiceprint of the first speaker satisfying a speaker-match threshold.
6. The method according to claim 1, wherein determining the content similarity includes detecting, by the computer, at least a partial content sequence match between the one or more expected words of the enrollment utterance and the one or more test words of the test utterance.
2. The method according to claim 1, further comprising extracting, by the computer, one or more speech portions including one or more enrollment utterances for the first speaker from the enrollment audio sample.
6. The method according to claim 1, wherein determining the content similarity includes detecting, by the computer, at least a partial content sequence match between the one or more expected words of the enrollment utterance and the one or more test words of the test utterance.
7. The method according to claim 6, wherein the partial content sequence match includes the one or more expected words in the enrollment utterance and the one or more test words in the test utterance, the one or more expected words being the same as the one or more test words.
5. The method according to claim 1, wherein determining the content similarity includes: executing, by the computer, a modified dynamic time warping process on at least a portion of the enrollment audio sample; and executing, by the computer, the modified dynamic time warping process on at least a portion of the test audio sample.
3. The method according to claim 1, further comprising extracting, by the computer, one or more speech portions including one or more test utterances for the second speaker from the test audio sample.
4. The method according to claim 3, further comprising executing, by the computer, speaker diarization on the one or more speech portions, the speaker diarization identifying the test utterance associated with the second speaker in the test audio sample.
6. The method according to claim 1, wherein determining the content similarity includes detecting, by the computer, at least a partial content sequence match between the one or more expected words of the enrollment utterance and the one or more test words of the test utterance.
6. The method according to claim 1, wherein determining the content similarity includes detecting, by the computer, at least a partial content sequence match between the one or more expected words of the enrollment utterance and the one or more test words of the test utterance.
10. The method according to claim 1, wherein the one or more expected words of the first content sequence for the enrolled speaker includes at least one of a trigger phrase or an entity name associate with the first speaker.
6. The method according to claim 1, wherein determining the content similarity includes detecting, by the computer, at least a partial content sequence match between the one or more expected words of the enrollment utterance and the one or more test words of the test utterance.
7. The method according to claim 6, wherein the partial content sequence match includes the one or more expected words in the enrollment utterance and the one or more test words in the test utterance, the one or more expected words being the same as the one or more test words
9. The method according to claim 1, wherein an acoustic feature of the one or more acoustic features includes at least one of mel-frequency cepstral coefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), or perceptual linear prediction (PLP).
Allowable Subject Matter
5. Claims 1-20 would be allowable over the prior art of record for at least the following rationale.
Ziv et al., (U.S. Patent Application Publication: 2016/0343373), already of record, hereinafter referred to as ZIV, teaches, see e.g., an architecture comprising how “… audio file 102 provided to the transcription server 104 can exemplarily be a previously recorded audio file or can be a streaming audio file obtained from an ongoing communication between two speakers…,” further “… audio file 102 and the information file 106 are used for a blind diarization at 110. The blind diarization is characterized as such as the identities of the speakers (e.g. agent, customer) are not known and therefore the diarization 110 discriminates between a first speaker (speaker 1) and a second speaker (speaker 2)…,” “… audio file 102 and the information file 106 are used for a blind diarization at 110. The blind diarization is characterized as such as the identities of the speakers (e.g. agent, customer) are not known and therefore the diarization 110 discriminates between a first speaker (speaker 1) and a second speaker (speaker 2)…” Furthermore, as observed “…one or more of a plurality of frame features are computed. In embodiments, each of the features are a probability that the frame contains speech, or a speech probability. Given an input frame that comprises samples x1, x2, . . . , xF (wherein F is the frame size), one or more, and in an embodiment, all of the following features are computed…’ (ZIV paras. 20, 21, 66, 67, Figs. 3-5).
Notwithstanding, ZIV’s teachings still fail to teach or fairly suggest either individually or in a reasonable combination the recited limitations in independent claims 1 and 11 as specifically recited.
Similarly, dependent claims 2-10; and 12-20 further limit allowable independent claims 1 and 11 correspondingly, and thus they would also be found allowable over the prior art of record by virtue of their dependency.
Any comments considered necessary by Applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Witkowski et al., (M. Witkowski, M. Igras, J. Grzybowska, P. Jaciów, J. Gałka and M. Ziółko, "Caller identification by voice," XXII Annual Pacific Voice Conference (PVC), Krakow, Poland, 2014, pp. 1-7, doi: 10.1109/PVC.2014.6845420), already of record, discloses see e.g., “…software for caller identification or to create his characteristic by analysis of his voice. Based on collected speech
PNG
media_image1.png
335
300
media_image1.png
Greyscale
samples, our system aims to identify emergency callers both on-line and off-line. This homeland security project covers speaker recognition (when speaker's speech sample is known), speaker's gender, age detection and recognition of emotions. Proposed system is not limited to bio-metrics. The goal of this application is to provide an innovative, supporting tool for rapid and accurate threat detection and threat neutralization. This complex system will include: a speech signal analysis, an automatic development of speech patterns database and appropriate classification methods.” (See e.g., Witkowski et al., Abstract, Fig. 4).
Please, see for additional references PTO-892.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Edgar Guerra-Erazo whose telephone number is (571) 270-3708. The examiner can normally be reached on M-F 7:30a.m.-5:00p.m. EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bhavesh Mehta can be reached on (571) 272-7453. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EDGAR X GUERRA-ERAZO/Primary Examiner, Art Unit 2656