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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/16/2023, 1016/2023, and 12/13/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings were submitted on 06/16/2023. These drawings are reviewed and accepted by the examiner.
Allowable Subject Matter
Claims 21-40 would be allowable if the Double Patenting rejection set forth in this office action is overcome.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding independent claims 21, 29, and 35, the closest prior art the Examiner found was Dimitriadis et al. (US 20140163960 A1), which teaches:
“receive audio content of the call” (par. 0051; ‘FIG. 3 shows an exemplary embodiment of the present disclosure in which the signal 202 is an audio signal 300, generally shown.’);
“identify speech vocal content from the audio content” (par. 0059; ‘For example, according to an embodiment of the device 200, each segment 302 may be analyzed in accordance with its linguistic or lexical properties, and also analyzed in accordance with its paralinguistic properties. That is, each segment 302 may be analyzed based on its plain and ordinary meaning in accordance with its definition, connotation, and/or denotation. Each segment 302 may additionally or alternatively be analyzed in accordance with its acoustic properties, pitch, and/or volume. Of course, the above-listed properties are merely exemplary and the segments 302.sub.1,2, . . . , n may additionally or alternatively be analyzed in accordance with additional or alternative linguistic and paralinguistic properties.’);
“identify speech semantic content from the audio content” (par. 0059; ‘For example, according to an embodiment of the device 200, each segment 302 may be analyzed in accordance with its linguistic or lexical properties, and also analyzed in accordance with its paralinguistic properties. That is, each segment 302 may be analyzed based on its plain and ordinary meaning in accordance with its definition, connotation, and/or denotation. Each segment 302 may additionally or alternatively be analyzed in accordance with its acoustic properties, pitch, and/or volume. Of course, the above-listed properties are merely exemplary and the segments 302.sub.1,2, . . . , n may additionally or alternatively be analyzed in accordance with additional or alternative linguistic and paralinguistic properties.’);
“determine a combined message content, the combined message content including a transformation of the speech semantic content and the speech vocal content” (par. 0060; ‘In the above-discussed embodiment of the present disclosure in which the device 200 determines the emotional state 304 and the confidence score 306 of each segment 302 by analyzing the segment 302 in accordance with a plurality of analyses, the device 200 may determine, for each segment 302, a plurality of emotional states 304.sub.1,2, . . . , n and a plurality of confidence scores 306.sub.1,2, . . . , n of the plurality of emotional states 304.sub.1,2, . . . , n. Each of the plurality of emotional states 304.sub.1,2, . . . , n and the plurality of confidence scores 306.sub.1,2, . . . , n may be determined in accordance with one of the plurality of analyses.’ The emotional state and confidence score reads on combined message content.);
“determine a model control structure from the combined message content” (par. 0056; ‘The emotional states 304.sub.1,2, . . . , n or classes, hereinafter referred to as the emotional states 304.sub.1,2, . . . , n, may include, but are not limited to, "neutral", "indifferent", "satisfied", "frustrated", etc.’); and
“apply the model control structure as a feedback control to influence creation of future audio records on one or more machines, wherein the model control structure is a grade of the audio content, the one or more machines comprising a machine display, the machine display altered to display the grade” (par. 0068; ‘Nevertheless, tracking the current emotional state 402 of the audio signal 300 and determining whether the current emotional state 402 changes to another emotional state 304 as discussed above enables a user or party, such as a company representative or agent, to monitor the current emotional state 402 of another user or party, such as a customer, in real-time. ‘; par. 0069; ‘For example, the device 200 may provide the user-detectable notification only in response to determining that the current emotional state 402 of the audio signal 300 changes from "satisfied" to "angry"’); and
“an alert generator” (par. 0068; ‘The device 200 may, for example, provide a user-detectable notification on a display 212 of the device 200, as shown in FIG. 2.’),
“threshold analyzer configured to generate an alert signal to the communication system based on the metric control meeting a condition” (par. 0069; ‘In embodiments of the present disclosure, the device 200 may provide a notification of any and all emotional state changes 404.’).
However, Dimitriadis does not expressly teach:
“the alert generator comprising: at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call”;
“heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call”; and
“aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control.”
While machine learning models, call classifiers, and natural language processors are well-known in the art, the Examiner deems the prior art of record, whether taken alone or in combination, fails to teach, inter alia, “aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control” in combination with the other claimed features.
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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Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11715459 B2 in view of claims 1-4 of U.S. Patent No. 10861436 B1.
Regarding independent claims 21, 29, and 35, U.S. Patent No. 11715459 B2 teaches: “at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call; heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call; aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control; and a threshold analyzer configured to generate an alert signal to the communication system based on the metric control meeting a condition” (claim 1.; ‘at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call; heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call; aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control; and a threshold analyzer configured to generate an alert signal to the communication system based on the metric control meeting a condition.’).
However, U.S. Patent No. 11715459 B2 does not expressly teach: “receive audio content of the call; identify speech vocal content from the audio content; identify speech semantic content from the audio content; determine a combined message content, the combined message content including a transformation of the speech semantic content and the speech vocal content; determine a model control structure from the combined message content; and apply the model control structure as a feedback control to influence creation of future audio records on one or more machines, wherein the model control structure is a grade of the audio content, the one or more machines comprising a machine display, the machine display altered to display the grade”
These features are similarly taught by U.S. Patent No. 10861436 B1 in claim 1. (see chart below). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the alert generator features of U.S. Patent No. 11715459 B2 with the call processing features of U.S. Patent No. 10861436 B1 given that both applications are in the same field of audio call classification.
Instant claims
U.S. Patent No. 11715459 B2
U.S. Patent No. 10861436 B1
21. (new) A communication system for processing a call, the communication system comprising:an audio analysis system configured to: receive audio content of the call; identify speech vocal content from the audio content; identify speech semantic content from the audio content; determine a combined message content, the combined message content including a transformation of the speech semantic content and the speech vocal content; determine a model control structure from the combined message content; and apply the model control structure as a feedback control to influence creation of future audio records on one or more machines, wherein the model control structure is a grade of the audio content, the one or more machines comprising a machine display, the machine display altered to display the grade; and an alert generator, the alert generator comprising: at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call; heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call; aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control; and a threshold analyzer configured to generate an alert signal to the communication system based on the metric control meeting a condition.
1. An alert generator in a communication system for processing a call, the alert generator comprising: at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call; heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call; aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control; and a threshold analyzer configured to generate an alert signal to the communication system based on the metric control meeting a condition.
1. A method of processing a current call on one more machines, the method comprising: receiving audio content for the current call; generating a first feature vector from the audio content representing speech semantic content of the current call; generating a second feature vector from the audio content representing speech vocal content of the current call; forming a combined feature vector by concatenating the first feature vector and the second feature vector; applying the combined feature vector as an input to a multi-modal direct call grading model to generate a plurality of direct call controls; generating a similarity matrix for the current call from the first feature vector and the second feature vector; applying the similarity matrix and a tree structure of feature matrices for prior calls to a distance function to generate an idiosyncratic call control and a similarity call control; weighting the direct call controls, ideosyncratic call control, and similarity call control to form a weighted call control model, wherein weights for the direct call controls model are based on an inter-correlation of a plurality of models utilized in the multi-modal direct call grading model, the weight increasing with decreasing correlation; and applying the weighted call control model as a feedback control to influence processing of future audio calls by the one or more machines.
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658