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 statement(s) (IDS) submitted on: 4-4-2025, 7-31-2025, 11-18-2025, and 5-19-2026 are acknowledged by the examiner.
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 obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over: claims 1-20 of U.S. Pat. No. 11,765,267 and claims 1-20 of U.S. Pat. No. 12,166,919.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the cited patent mentioned above discloses:
‘…accessing, by one or more processors, transcript data representing a conversation held in turns among at least a first party and a second party; identifying, by the one or more processors, a portion of the transcript data, the identified portion representing multiple pairs of turns that each correspond to a same topic in the conversation held among at least the first and second parties; and causing, by the one or more processors, presentation of a label of the identified portion, the presented label indicating the same topic that corresponds to each of the multiple pairs of turns…’ along with the other limitations of claim 1 in the instant application;
‘…labelling the identified portion of the transcript of the conversation with the label that indicates the same topic that corresponds to each of the multiple pairs of turns…’ along with the other limitations of claim 2 in the instant application;
‘…the labelling of the identified portion of the transcript data includes selecting the label from a set of labels specified by a configuration file of a user…’ along with the other limitations of claim 3 in the instant application;
‘…the causing of the presentation of the label of the identified portion includes: generating a user interface (UI) that presents the identified portion of the transcript data with the label of the identified portion; and providing the generated UI to a device configured to present the generated UI…’ along with the other limitations of claim 4 in the instant application;
‘…the generated UI further presents a counter that indicates how many times the same topic appears labeled in the transcript data…’ along with the other limitations of claim 5 in the instant application;
‘…training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript data includes inputting the transcript data into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that represents the multiple pairs of turns that each correspond to the same topic…’ along with the other limitations of claim 6 in the instant application;
‘…the label that indicates the same topic is an output label of a machine-learning model; training data on which basis the machine-learning model is trained includes training transcripts that each include training portions that correspond to training labels indicative of training topics; and the identifying of the portion of the transcript data includes identifying the output label that indicates the same topic in output of the trained machine-learning model…’ along with the other limitations of claim 7 in the instant application;
‘…accessing transcript data representing a conversation held in turns among at least a first party and a second party; identifying a portion of the transcript data, the identified portion representing multiple pairs of turns that each correspond to a same topic in the conversation held among at least the first and second parties; and causing presentation of a label of the identified portion, the presented label indicating the same topic that corresponds to each of the multiple pairs of turns…’ along with the other limitations of claim 8 in the instant application;
‘…labelling the identified portion of the transcript of the conversation with the label that indicates the same topic that corresponds to each of the multiple pairs of turns…’ along with the other limitations of claim 9 in the instant application;
‘…the labelling of the identified portion of the transcript data includes selecting the label from a set of labels specified by a configuration file of a user…’ along with the other limitations of claim 10 in the instant application;
‘…the causing of the presentation of the label of the identified portion includes: generating a user interface (UI) that presents the identified portion of the transcript data with the label of the identified portion; and providing the generated UI to a device configured to present the generated UI…’ along with the other limitations of claim 11 in the instant application;
‘…the generated UI further presents a counter that indicates how many times the same topic appears labeled in the transcript data…’ along with the other limitations of claim 12 in the instant application;
‘…training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript data includes inputting the transcript data into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that represents the multiple pairs of turns that each correspond to the same topic…’ along with the other limitations of claim 13 in the instant application;
‘…the label that indicates the same topic is an output label of a machine-learning model; training data on which basis the machine-learning model is trained includes training transcripts that each include training portions that correspond to training labels indicative of training topics; and the identifying of the portion of the transcript data includes identifying the output label that indicates the same topic in output of the trained machine-learning model…’ along with the other limitations of claim 14 in the instant application;
‘…accessing transcript data representing a conversation held in turns among at least a first party and a second party; identifying a portion of the transcript data, the identified portion representing multiple pairs of turns that each correspond to a same topic in the conversation held among at least the first and second parties; and causing presentation of a label of the identified portion, the presented label indicating the same topic that corresponds to each of the multiple pairs of turns...’ along with the other limitations of claim 15 in the instant application;
‘…labelling the identified portion of the transcript of the conversation with the label that indicates the same topic that corresponds to each of the multiple pairs of turns...’ along with the other limitations of claim 16 in the instant application;
‘…the labelling of the identified portion of the transcript data includes selecting the label from a set of labels specified by a configuration file of a user...’ along with the other limitations of claim 17 in the instant application;
‘…the causing of the presentation of the label of the identified portion includes: generating a user interface (UI) that presents the identified portion of the transcript data with the label of the identified portion; and providing the generated UI to a device configured to present the generated UI...’ along with the other limitations of claim 18 in the instant application;
‘…the generated UI further presents a counter that indicates how many times the same topic appears labeled in the transcript data...’ along with the other limitations of claim 19 in the instant application; and
‘…training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript data includes inputting the transcript data into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that represents the multiple pairs of turns that each correspond to the same topic...’ along with the other limitations of claim 20 in the instant application.
Claims 1- 20 of U.S. Pat. No. 11,765,267 and disclose the claimed invention in the pending claims:
Claim 1: A method of presenting machine-labeled segments of a transcript, the method comprising: training, by one or more processors, a machine-learning (ML) model based on training segments of training transcripts, each training segment being associated with one or more training labels; accessing, by the one or more processors, a conversation transcript that includes text of a conversation held in turns between a first party and a second party; identifying, by the trained ML model and based on the conversation transcript, a conversation segment including multiple pairs of turns all corresponding to a common topic between the first party and the second party; labeling, by the trained ML model, the identified conversation segment based on the common topic that corresponds to the multiple pairs of turn included in the identified conversation segment; and generating a user interface (UI) for presentation on a client device, the UI presenting the conversation transcript with the identified and labeled conversation segment that includes the multiple pairs of turns held between the first party and the second party and corresponding to the common topic.
Claim 2: The method of claim 1, wherein features of the trained ML model comprise at least one of turns identified in the conversation, names, locations of the names in the conversation transcript, values of parameters in the conversation, or a call sentiment for the conversation.
Claim 3: The method of claim 1, wherein a configuration file associated with a user specifies a set of labels that includes the one or more conversation labels associated with the conversation segments.
Claim 4: The method of claim 3, further comprising: detecting, in the UI, a selection of one or more words of text within the conversation transcript; and in response to the detecting of the selection, presenting, in the UI, the set of labels for association with the selected one or more words.
Claim 5: The method of claim 3, further comprising: presenting, in a window of the UI, the set of labels with counters of how many times each label in the set is associated with the conversation transcript.
Claim 6: The method of claim 3, wherein a first label from the set of labels is associated with a parameter, and wherein the trained ML model identifies a value of the parameter within the conversation transcript.
Claim 7: The method of claim 3, further comprising: accessing the configuration file that specifies the set of labels.
Claim 8: The method of claim 1, wherein a first label among the one or more conversation labels indicates an interest-rate quote being presented in the conversation, and wherein a value of the interest-rate quote is extracted from the conversation transcript and associated with the first label.
Claim 9: The method of claim 1, further comprising: presenting, in the UI, an option to set a value for an outcome of the conversation, the value being selected from a group consisting of no answer, left message, not interested, and application started.
Claim 10: The method of claim 1, further comprising: presenting, in the UI, an option to set a value for a sentiment of the conversation, the value being selected from a group consisting of positive, negative, and neutral.
Claim 11: A system to present machine-labeled segments of a transcript, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to perform operations comprising: training a machine-learning (ML) model based on training segments of training transcripts, each training segment being associated with one or more training labels; accessing a conversation transcript that includes text of a conversation held in turns between a first party and a second party; identifying, by the trained ML model and based on the conversation transcript, a conversation segment including multiple pairs of turns all corresponding to a common topic between the first party and the second party; labeling, by the trained ML model, the identified conversation segment based on the common topic that corresponds to the multiple pairs of turn included in the identified conversation segment; and generating a user interface (UI) for presentation on a client device, the UI presenting the conversation transcript with the identified and labeled conversation segment that includes the multiple pairs of turns held between the first party and the second party and corresponding to the common topic.
Claim 12: The system of claim 11, wherein features of the trained ML model comprise at least one of turns identified in the conversation, names, locations of the names in the conversation transcript, values of parameters in the conversation, or a call sentiment for the conversation.
Claim 13: The system of claim 11, wherein the operations further comprise: detecting, in the UI, a selection of one or more words of text within the conversation transcript; and in response to the detecting of the selection, presenting, in the UI, a set of labels for association with the selected one or more words.
Claim 14: The system of claim 11, wherein the operations further comprise: presenting, in a window of the UI, a set of labels with counters of how many times each label in the set is associated with the conversation transcript.
Claim 15: The system of claim 14, wherein a first label from the set of labels is associated with a parameter, and wherein the trained ML model identifies a value of the parameter within the conversation transcript.
Claim 16: A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising: training a machine-learning (ML) model based on training segments of training transcripts, each training segment being associated with one or more training labels; accessing a conversation transcript that includes text of a conversation held in turns between a first party and a second party; identifying, by the trained ML model and based on the conversation transcript, a conversation segment including multiple pairs of turns all corresponding to a common topic between the first party and the second party; labeling, by the trained ML model, the identified conversation segment based on the common topic that corresponds to the multiple pairs of turn included in the identified conversation segment; and generating a user interface (UI) for presentation on a client device, the UI presenting the conversation transcript with the identified and labeled conversation segment that includes the multiple pairs of turns held between the first party and the second party and corresponding to the common topic.
Claim 17: The non-transitory computer-readable medium of claim 16, wherein features of the trained ML model comprise at least one of turns identified in the conversation, names, locations of the names in the conversation transcript, values of parameters in the conversation, or a call sentiment for the conversation.
Claim 18: The non-transitory computer-readable medium of claim 16, wherein the operations further comprise: detecting, in the UI, a selection of one or more words of text within the conversation transcript; and presenting, in the UI, a set of labels for association with the selected one or more words.
Claim 19: The non-transitory computer-readable medium of claim 16, wherein the operations further comprise: presenting, in a window of the UI, a set of labels with counters of how many times each label in the set is associated with the conversation transcript.
Claim 20: The non-transitory computer-readable medium of claim 19, wherein a first label from the set of labels is associated with a parameter, and wherein the trained ML model identifies a value of the parameter within the conversation transcript.
Claims 1- 20 of U.S. Pat. No. 12,166,919 disclose the claimed invention in the pending claims:
Claim 1: A method comprising: accessing, by one or more processors, a transcript of a conversation held in turns between a first party and a second party; identifying, by the one or more processors, a portion of the transcript of the conversation, the identified portion including multiple pairs of turns that all correspond to a common topic within the conversation between the first and second parties; and generating, by the one or more processors, a user interface (UI) that presents the identified portion with a corresponding label indicative of the common topic within the conversation between the first and second parties.
Claim 2: The method of claim 1, further comprising: labelling the identified portion of the transcript of the conversation with the label that indicates the common topic.
Claim 3: The method of claim 2, wherein: the labelling of the identified portion of the transcript of the conversation includes selecting the label indicative of the common topic from a set of labels specified by a configuration file of a user.
Claim 4: The method of claim 1, further comprising: training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript of the conversation includes inputting the transcript of the conversation into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that includes the multiple pairs of turns that all correspond to the common topic.
Claim 5: The method of claim 1, further comprising: providing the generated UI to a client device configured to present the generated UI that presents the identified portion with the corresponding label indicative of the common topic.
Claim 6: The method of claim 1, wherein: the generating of the UI generates the UI to further present a counter that indicates how many times the common topic appears labeled in the transcript of the conversation.
Claim 7: The method of claim 1, wherein: the transcript of the conversation is a conversation transcript; the identifying of the portion identifies a conversation portion of the conversation transcript; the label indicative of the common topic is an output label indicative of the common topic; training data on which basis a machine-learning model is trained includes training transcripts that each include training portions that correspond to training labels indicative of training topics; and the identifying of the conversation portion includes identifying the output label indicative of the common topic in output of the trained machine-learning model.
Claim 8: A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising: accessing a transcript of a conversation held in turns between a first party and a second party; identifying a portion of the transcript of the conversation, the identified portion including multiple pairs of turns that all correspond to a common topic within the conversation between the first and second parties; and generating a user interface (UI) that presents the identified portion with a corresponding label indicative of the common topic within the conversation between the first and second parties.
Claim 9: The system of claim 8, wherein the operations further comprise: labelling the identified portion of the transcript of the conversation with the label that indicates the common topic.
Claim 10: The system of claim 9, wherein: the labelling of the identified portion of the transcript of the conversation includes selecting the label indicative of the common topic from a set of labels specified by a configuration file of a user.
Claim 11: The system of claim 8, wherein the operations further comprise: training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript of the conversation includes inputting the transcript of the conversation into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that includes the multiple pairs of turns that all correspond to the common topic.
Claim 12: The system of claim 8, wherein the operations further comprise: providing the generated UI to a client device configured to present the generated UI that presents the identified portion with the corresponding label indicative of the common topic.
Claim 13: The system of claim 8, wherein: the generated UI further presents a counter that indicates how many times the common topic appears labeled in the transcript of the conversation.
Claim 14: The system of claim 8, wherein: the transcript of the conversation is a conversation transcript; the identifying of the portion identifies a conversation portion of the conversation transcript; the label indicative of the common topic is an output label indicative of the common topic; training data on which basis a machine-learning model is trained includes training transcripts that each include training portions that correspond to training labels indicative of training topics; and the identifying of the conversation portion includes identifying the output label indicative of the common topic in output of the trained machine-learning model.
Claim 15: A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising: accessing a transcript of a conversation held in turns between a first party and a second party; identifying a portion of the transcript of the conversation, the identified portion including multiple pairs of turns that all correspond to a common topic within the conversation between the first and second parties; and generating a user interface (UI) that presents the identified portion with a corresponding label indicative of the common topic within the conversation between the first and second parties.
Claim 16: The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: labelling the identified portion of the transcript of the conversation with the label that indicates the common topic.
Claim 17: The non-transitory computer-readable medium of claim 16, wherein: the labelling of the identified portion of the transcript of the conversation includes selecting the label indicative of the common topic from a set of labels specified by a configuration file of a user.
Claim 18: The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: training a machine-learning model based on training data to identify labels for pairs of turns, each label among the labels indicating a topic of a corresponding pair of turns among the pairs of turns; and wherein: the identifying of the portion of the transcript of the conversation includes inputting the transcript of the conversation into the trained machine-learning model, an output of the trained machine-learning model identifying the portion that includes the multiple pairs of turns that all correspond to the common topic.
Claim 19: The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: providing the generated UI to a client device configured to present the generated UI that presents the identified portion with the corresponding label indicative of the common topic.
Claim 20: The non-transitory computer-readable medium of claim 15, wherein: the generated UI further presents a counter that indicates how many times the common topic appears labeled in the transcript of the conversation.
For these reasons, pending claims 1-20 are rejected. Claims 2-14 depend on claim 1. Claims 16-20 depend on claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 Form.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to LISA HASHEM whose telephone number is 571-272-7542. The examiner can normally be reached on Monday and Thursday 10 a.m. - 7 p.m. EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fan Tsang can be reached on 571-272-7547.
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/LISA HASHEM/ Primary Examiner, Art Unit 2653
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