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 .
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).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,073,851. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the present application define an invention that is merely an obvious variation of the invention claimed in the patent for the following reasons. Comparing the claims, such as claim 1 of the present application and the patent, it is clear that all the elements of the claim 1 are found in claim 1 of the patent. The difference is claim 1 of the patent comprises the additional elements including, wherein generating the first utterance output includes one or more of: applying video data of the first utterance representation to a first video processing part of the machine learning system to generate first video-based output; applying acoustic data of the first utterance representation to a first acoustic processing part of the machine learning system to generate first acoustic-based output; and applying text data of the first utterance representation to a first textual processing part of the machine learning system to generate first text-based output, that are not found in claim 1, therefore represents a species of the generic invention of the application claims. Since it has been held that the generic invention is anticipated by the species, claim 1 of the present application is anticipated by claim 1 of the patent.
Claims 2-20 are, respectively, anticipated by claims 2-20 of the patent.
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1. A method to generate a conversation analysis, the method comprising: receiving multiple utterance representations, wherein each utterance representation represents a portion of a conversation performed by at least two users, wherein one utterance representation represents a particular verbalized statement from one user; and generating a first utterance output by applying a first plurality of utterance representations, which is associated with a first user and which is of the multiple utterance representations, to a machine learning system in order to generate conversational analysis indicators corresponding to each utterance in the plurality of utterance representations, wherein the conversation analysis indicators are generated in order to track the state of the conversation over time; wherein the machine learning system includes memory functionality integration such that an internal state of the machine learning system computationally tracks utterances.
1. A method to generate a conversation analysis, the method comprising: receiving multiple utterance representations, wherein each utterance representation represents a portion of a conversation performed by at least two users, wherein one utterance representation represents a particular verbalized statement from one user, and wherein each utterance representation is associated with one or more of: video data, acoustic data, and text data; and generating a first utterance output by applying a first utterance representation, that is associated with a first user and that is of the multiple utterance representations, to a machine learning system, wherein generating the first utterance output includes one or more of: applying video data of the first utterance representation to a first video processing part of the machine learning system to generate first video-based output; applying acoustic data of the first utterance representation to a first acoustic processing part of the machine learning system to generate first acoustic-based output; and applying text data of the first utterance representation to a first textual processing part of the machine learning system to generate first text-based output; wherein the machine learning system includes memory functionality integration such that an internal state of the machine learning system computationally tracks utterances.
Examiner’s Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to
fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
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) 1-2, 8-9 and 15-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vig et al. (US 2018/0113854).
As to claim 1, Vig teaches a method to generate a conversation analysis, the method comprising: receiving multiple utterance representations 102, wherein each utterance representation represents a portion of a conversation performed by at least two users (agent and customer), wherein one utterance representation represents a particular verbalized statement from one user (Pars.9, 26-28, 37); and
generating a first utterance output by applying a first plurality of utterance representations, which is associated with a first user (customer) and which is of the multiple utterance representations, to a machine learning system (Fig.4, 408) in order to generate conversational analysis indicators 106/Fig.2 corresponding to each utterance in the plurality of utterance representations, wherein the conversation analysis indicators are generated in order to track the state of the conversation over time (Fig.2; Pars.6-7, 26-27, 31-32; Figs.5-6); wherein the machine learning system includes memory functionality integration such that an internal state of the machine learning system computationally tracks utterances (Pars.13, 43-51).
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As to claim 2, Vig teaches generating a second utterance output by applying a second plurality of utterance representations, of the multiple utterance representations, to the machine learning system, wherein the second plurality of utterance representations is associated with a second user (agent utterance 102) and corresponds to a first time window that also corresponds to the first plurality of utterance representations (Figs.1-2, 5-6).
Regarding claims 8-9 and 15-16, the corresponding system and instruction comprising the steps similar to claims 1-2, are analogous therefore rejected as being anticipated by Vig et al. for the foregoing reasons.
Allowable Subject Matter
Claims 3-7, 10-14 and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 3, 10 and 17 are allowable, because Vig doesn’t teach generating first combined speaker features for the first time window by combining the first utterance output and the second utterance output.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657