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 Objections
Claims 73 and 74 are objected to because of the following informalities: the limitation ‘nontransitory’ in line 1 of claims 73 and 74 should be spelled ‘non-transitory’. Appropriate correction is required. Claim 74 depends on claim 73.
Claim 70 recites the limitations: ‘…uttlen, speechbinary, timedlife, earninsc, and lastgentstime…’. It is not clear the spelling and/or definitions of these limitations. Appropriate action is required.
Claim 71 recites the limitations: ‘…uttlen, speechbinary, timedlife, earninsc, and lastgentstime, lastgentetime, lastcalleretime, lastcallerstime, ecminsa, scminae, timedifs, timedife, list(range(0,300), timedifs, and samince…’. It is not clear the spelling and/or definitions of these limitations. Appropriate action is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 58-72 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.
Claim 58 recites the limitation "content-independent detection" in line 1. It is not clear what this limitation is defined as. Appropriate action is required. Claims 59-72 depend on claim 58.
Claim 58 recites the limitation "per-call feature vectors" in line 6. It is not clear what this limitation is defined as. Appropriate action is required. Claims 59-72 depend on claim 58.
Claim 70 recites the limitations: ‘…uttlen, speechbinary, timedlife, earninsc, and lastgentstime…’. It is not clear the definitions of these limitations. Appropriate action is required.
Claim 71 recites the limitations: ‘…uttlen, speechbinary, timedlife, earninsc, and lastgentstime, lastgentetime, lastcalleretime, lastcallerstime, ecminsa, scminae, timedifs, timedife, list(range(0,300), timedifs, and samince…’. It is not clear the definitions of these limitations. Appropriate action is required.
Claim 73 recites the limitation "per-call feature vectors" in line 6. It is not clear what this limitation is defined as. Appropriate action is required. Claim 74 depends on claim 73.
Claim 75 recites the limitation "per-call feature vectors" in line . It is not clear what this limitation is defined as. Appropriate action is required. Claims 76-77 depend on claim 75.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 58-77 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pat. No. 8,885,798 by Peterson (Note: Peterson is a common inventor to instant application).
Regarding claim 58, Peterson discloses a computer-implemented method of providing content-independent detection (i.e. detect event operation) of dropped customer service calls (i.e. caller-call center dialogue) to an interactive platform (i.e. call center) (Figure 4; col. 9, line 65 – col. 10, line 40), comprising:
receiving a batch of recorded calls (i.e. audio records) for analysis, the recorded calls comprising recorded audio of customer service calls from a human user (i.e. caller) to the interactive platform (i.e. call center) (col. 10, line 60 – col. 11, line 47), and call
metadata (i.e. data) for the recorded calls (col. 11, lines 31-47);
featurizing the recorded calls into per-call feature vectors (i.e. fields), comprising extracting
features (i.e. metrics of interest) that are independent of content of the recorded calls (col. 11, line 31 – col. 12, line 67);
using a machine learning (ML) device (i.e. software toolkit program) to detect dropped calls based on the per-call feature vectors (col. 9, lines 26-46; col. 13, lines 23-56);
providing the dropped calls to a human analyst (i.e. a user or administrator or analyst) (col. 13, lines 1-56);
receiving, from the human analyst, a recommendation to improve the interactive
platform based on the dropped calls (col. 4, line 64 – col. 5, line 6; col. 9, lines 3-15; col. 12, lines 7-22); and
implementing the recommendation on the interactive platform (col. 9, lines 3-15; col. 12, lines 15-22).
Regarding claim 59, Peterson discloses the computer-implemented method of claim 58, wherein the interactive platform is an interactive voice platform (IVP) (col. 8, lines 59-63).
Regarding claim 60, Peterson discloses the computer-implemented method of claim 58, wherein the call metadata comprise metadata from a telephone carrier (col. 11, lines 40-43).
Regarding claim 61, Peterson discloses the computer-implemented method of claim 58, wherein featurizing the recorded calls comprises separating the recorded calls into channels (col. 4, lines 59-64; col. 6, line 65 – col. 7, line 18; col. 8, line 50 – col. 9, line 2).
Regarding claim 62, Peterson discloses the computer-implemented. method of claim 61, wherein the channels comprise a caller channel and a call center channel (col. 4, lines 59-64; col. 6, line 65 – col. 7, line 18; col. 8, line 50 – col. 9, line 2) .
Regarding claim 63, Peterson discloses the computer-implemented method of claim 61, wherein featurizing the recorded calls further comprises tokenizing the recorded calls into discrete utterances based on per-channel silence (col. 4, lines 59-64; col. 9, line 65 – col. 10, line 22).
Regarding claim 64, Peterson discloses the computer-implemented method of claim 63, wherein featurizing the calls comprises classifying non-speech utterances on only one channel (col. 4, lines 59-64; col. 9, line 65 – col. 10, line 22).
Regarding claim 65, Peterson discloses the computer-implemented. method of claim 58, wherein featurizing the recorded calls comprises tokenizing the recorded calls into discrete utterances based on silence (col. 4, lines 59-64; col. 9, line 65 – col. 10, line 22).
Regarding claim 66, Peterson discloses the computer-implemented. method of claim 65, wherein featurizing the recorded calls comprises classifying some speech utterances into one or more high-level classes based on content (col. 10, lines 8-22).
Regarding claim 67, Peterson discloses the computer-implemented method of claim 66, wherein the one or more high-level classes are the only features based on language content (col. 9, line 65 – col. 10, line 22).
Regarding claim 68, Peterson discloses the computer-implemented method of claim 66, wherein the one or more high-level classes comprise an operator greeting (col. 12, lines 50-67).
Regarding claim 69, Peterson discloses the computer-implemented method of claim 58, further comprising training the ML model on a large set of recorded calls with dropped calls tagged (col. 9, lines 26-46; col. 13, lines 23-56).
Regarding claim 70, Peterson discloses the computer-implemented method of claim 58, wherein featurizing the recorded calls comprises extracting, from the recorded calls, features channel, termination, uttlen, speechbinary, timedife, eaminsc, and lastagentstime (col. 11, lines 31-47; col. 11, line 64 – col. 12, line 22).
Regarding claim 71, Peterson discloses the computer-implemented method of claim 58, wherein featurizing the recorded calls comprises extracting, from the recorded calls, at least two features selected from a list consisting of channel, termination, uttlen, speechbinary, timedife, eaminsc, lastagentstime, lastagentetime, lastcalleretime, lastcallerstime, ecminsa, scminae, timedifs, timedife, list(range(0,300), timedifs, and samince (col. 11, lines 31-47; col. 11, line 64 – col. 12, line 22).
Regarding claim 72, Peterson discloses the computer-implemented method of claim 71, further comprising excluding, from the list, at least two features that are highly statistically coordinated with one another (col. 11, lines 31-47; col. 11, line 64 – col. 12, line 22).
Regarding claim 73, Peterson discloses one or more tangible, nontransitory computer-readable storage media having stored thereon executable instructions to (col. 11, lines 11-36; col. 15, lines 9-34):
receive a batch of recorded calls (i.e. audio records) for analysis, the recorded calls comprising recorded audio of customer service calls from a human user (i.e. caller) to an interactive voice platform (IVP) (i.e. call center) (col. 10, line 60 – col. 11, line 47), and
call metadata (i.e. data) for the recorded calls (col. 11, lines 31-47);
featurize the recorded calls into per-call feature vectors (i.e. fields), comprising extracting
features (i.e. metrics of interest) that are independent of verbal content of the recorded calls (col. 11, line 31 – col. 12, line 67);
provide a detection software module (i.e. software toolkit program) to detect dropped calls based on the per-call feature vectors (col. 9, lines 26-46; col. 13, lines 23-56);
provide the dropped calls to a human analyst (i.e. a user or administrator or analyst) (col. 13, lines 1-56);
receive, from the human analyst, a recommendation to improve the IVP based on the
dropped calls (col. 4, line 64 – col. 5, line 6; col. 9, lines 3-15; col. 12, lines 7-22); and
implement the recommendation on the IVP (col. 9, lines 3-15; col. 12, lines 15-22).
Regarding claim 74, Peterson discloses the one or more tangible, nontransitory computer-readable storage media of claim 73, wherein the detection software module includes a machine learning (ML) routine (col. 9, lines 26-46; col. 13, lines 23-56).
Regarding claim 75, Peterson discloses a computing apparatus, comprising:
a hardware platform comprising a processor circuit and a memory; and
instructions encoded within the hardware platform to instruct the processor circuit to (col. 11, lines 11-36; col. 15, lines 9-34):
receive a batch of recorded calls (i.e. audio records) for analysis, the recorded calls comprising recorded audio of customer service calls from a human user (i.e. caller) to an interactive voice platform (IVP) (i.e. call center) (col. 10, line 60 – col. 11, line 47), and
call metadata (i.e. data) for the recorded calls (col. 11, lines 31-47);
featurize the recorded calls into per-call feature vectors (i.e. fields), comprising extracting
features (i.e. metrics of interest) that are independent of verbal content of the recorded calls (col. 11, line 31 – col. 12, line 67);
provide a detection software module (i.e. software toolkit program) to detect dropped calls based on the per-call feature vectors (col. 9, lines 26-46; col. 13, lines 23-56);
provide the dropped calls to a human analyst (i.e. a user or administrator or analyst) (col. 13, lines 1-56);
receive, from the human analyst, a recommendation to improve the IVP based on the
dropped calls (col. 4, line 64 – col. 5, line 6; col. 9, lines 3-15; col. 12, lines 7-22); and
implement the recommendation on the IVP (col. 9, lines 3-15; col. 12, lines 15-22).
Regarding claim 76, Peterson discloses the computing apparatus of claim 75, further comprising a virtualization infrastructure (col. 9, lines 26-46; col. 13, lines 23-56).
Regarding claim 77, Peterson discloses the computing apparatus of claim 75, further comprising a containerization infrastructure (col. 9, lines 26-46; col. 13, lines 23-56).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 Form.
*U.S. Patent No. 8,150,020 by Blanchard et al discloses providing reports of caller hang-up rates for analysis by human experts (see Abstract and col. 6, lines 10-14)
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/LISA HASHEM/Primary Examiner, Art Unit 2692
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