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 filed information disclosure statement (IDS) is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gordon (US 20230186906) in view of Benkreira (US 20210005188).
As per claim 1, Gordon teaches one or more processors executing computer program instructions ([0112]) that, when executed, cause operations comprising:
receiving a transcript of a dialogue between a user and an agent, the transcript comprising utterances between the user and the agent ([0004], [0028]- [0029], receiving a transcript of a call between a customer and an agent);
extracting, from the transcript, a set of utterances associated with the user ([0029], extracting utterances and sentences from the call transcript);
providing the set of utterances associated with the user to each of a plurality of sentiment machine learning models configured to output a sentiment value of each utterance of the set of utterances associated with the user ([0028]- [0029], [0077]- [0080], wherein utterances corresponding to the transcript of a dialogue (call) between a user and a first agent are received, provided to sentiment machine learning models for generating corresponding utterances sentiment values);
binning each utterance of the set of utterances associated with the user into one or more bins based on the sentiment values, wherein each bin of the one or more bins correspond to a non-overlapping numerical range of sentiment values ([0045], [0082], wherein a sentiment momentum for the call is generated by assigning respective sentiments of the user and the agent into specific non-overlapping classes of sentiments such as positive, negative, and neutral sentiment);
determining a negative-sentiment probability associated with each bin of the one or more bins by randomly sampling a subset of the set of utterances corresponding to a respective bin of the one or more bins, wherein the negative-sentiment probability indicates that utterances associated with the respective bin of the one or more bins indicate a negative sentiment ([0033]- [0036], determining probability of negative sentiment associated with each of the classified bins based on the number utterances related to negative sentiments);
determining an overall negative-sentiment probability for the transcript based on each determined negative-sentiment probability associated with each bin of the one or more bins ([0035], [0074], wherein said, the sentiment value 904 indicates a call sentiment that represents the overall sentiment of the call);
linking the overall negative-sentiment probability for the transcript to a user identifier associated with the user (Fig. 8 and [0065]- [0069], wherein overall probabilities (positive, negative, neutral) are linked to each of the caller an agent)
Gordon may not explicitly disclose in response to receiving a communication request from the user comprising the user identifier, determining whether the overall negative-sentiment probability linked to the user identifier satisfies a threshold negative-sentiment probability value; and routing the communication request to a second agent in response to the overall negative-sentiment probability satisfying the threshold negative-sentiment probability value. Benkreira in the same field of endeavor teaches a service platform that determines the service performance score based on a sentiment value associated with the topic satisfying a threshold sentiment value (e.g., a sentiment that indicates negative sentiment and/or a certain degree of negative sentiment), and when the sentiment score satisfies the threshold sentiment score, the service platform may enable the communication to be redirected and/or may automatically redirect the communication to another service representative device (e.g., a service representative device of a manager) ([0015], [0050], [0066]- [0068]). Therefore, it would have been obvious at the time the application was filed to use the above features of Benkreira with the system of Gordon, in order to conserve computing resources and/or network resources of the service platform as the most qualified service representative can more quickly and efficiently handle the communication relative to other service representatives (Gordon, [0015]).
As per claim 2, Gordon teaches generating a sentiment value related to each utterance of a set of utterances associated with a user by providing each utterance of the set of utterances to a sentiment machine learning model, wherein the set of utterances is extracted from a transcript of a dialogue between the user and a first agent ([0077]- [0080], wherein a transcript of a dialogue (call) between a user and a first agent is received, and an utterance sentiment value representing sentiment associated with an utterance made during the call is generated by a sentiment neural network);
binning each utterance of the set of utterances associated with the user into a set of bins based on the sentiment values related to a respective utterance of the set of utterances ([0082], wherein a sentiment momentum for the call is generated by assigning respective sentiments of the user and the agent into a specific class of sentiments, positive, negative, or neutral. See also, [0090], wherein sentences are indexed and stored based on sentiments, positive, negative, or neutral);
determining a sentiment probability of each bin of the set of bins by randomly sampling a subset of utterances corresponding to a respective bin of the set of bins, wherein each bin of the set of bins is associated with a range of sentiment values ([0074], wherein each line segment of the graph represent a sentiment probability, within a range, i.e. positive, negative, or neutral, corresponding to each of the customer and agent during the call);
determining an overall sentiment probability for the transcript based on the determined sentiment probability of each bin of the set of bins ([0074], wherein said, the sentiment value 904 indicates a call sentiment that represents the overall sentiment of the call).
Gordon may not explicitly disclose in response to receiving a communication request from the user, routing the communication request to a second agent based on the overall sentiment probability satisfying a threshold sentiment probability. Benkreira in the same field of endeavor teaches a service platform that determines the service performance score based on a sentiment value associated with the topic satisfying a threshold sentiment value (e.g., a sentiment that indicates negative sentiment and/or a certain degree of negative sentiment), and when the sentiment score satisfies the threshold sentiment score, the service platform may enable the communication to be redirected and/or may automatically redirect the communication to another service representative device (e.g., a service representative device of a manager) ([0015], [0050], [0066]- [0068]). Therefore, it would have been obvious at the time the application was filed to use the above features of Benkreira with the system of Gordon, in order to conserve computing resources and/or network resources of the service platform as the most qualified service representative can more quickly and efficiently handle the communication relative to other service representatives (Gordon, [0015]).
As per claim 3, Gordon teaches wherein each utterance associated with the respective bin of the set of bins is associated with the same sentiment probability of that of the respective bin ([0090], indexing and storing sentences based on sentiments).
As per claim 4, Gordon teaches sorting, in descending order, each utterance of the set of utterances based on the associated sentiment probability of a respective utterance of the set of utterances ([0045]); selecting, from the sorted utterances, a number of sentiment probabilities associated with the set of utterances that satisfy a first condition ([0061], selecting one or more utterances with utterance sentiment values that are within a predetermined variance); and determining the overall sentiment probability for the transcript based on the selected number of sentiment probabilities ([0035], [0074], wherein said, the sentiment value 904 indicates a call sentiment that represents the overall sentiment of the call).
As per claim 5, Gordon teaches wherein determining the sentiment probability of each bin of the set of bins further comprises: providing, for each bin of the set of bins, the randomly sampled subset of utterances corresponding to the respective bin of the set of bins, to a machine learning model configured to determine the sentiment probability of the respective bin ([0028]- [0029], [0077]- [0080], wherein utterances corresponding to the transcript of a dialogue (call) between a user and a first agent are received, provided to sentiment machine learning models for generating corresponding utterances sentiment values).
As per claim 6, Gordon obtaining training data comprising (i) a set of training utterances, (ii) a set of labels indicating training sentiment values, wherein each label of the set of labels corresponds to a respective training utterance of the set of training utterances, and (iii) a training sentiment probability label indicating a training sentiment probability associated with the set of training utterances; and providing the training data to a training routine of the machine learning model to train the machine learning model ([0030], the neural network may be trained using labeled examples of a sentence particular words corresponding with a particular sentiment ( e.g., Negative, Neutral, or Positive, expressed in numerical values) as training data. The training may use the dictionary 132 as a part of training data. In some aspects, the sentence sentiment determiner 114 converts words of a sentence into one or more multi-dimensional vectors the multi-dimensional vector(s) as input to the neural network. The trained neural network may output one or more values that collectively indicate sentiment for the sentence. See also, [0079]).
As per claim 7, Gordon may not explicitly disclose selecting, for each bin of the set of bins, the randomly sampled subset of utterances associated with the user corresponding to the respective bin of the set of bins; and receiving a user input indicating labels corresponding to each bin of the set of bins, wherein the labels indicate a user-derived sentiment probability of the respective bin of the set of bins based on the randomly sampled subset of utterances associated with the user corresponding to the respective bin of the set of bins. However, Gordon teaches training a sentiment neural network using labeled examples of a sentence particular words corresponding with a particular sentiment (e.g., Negative, Neutral, or Positive, expressed in numerical values) as training data ([0030]). Furthermore, the service platform of Benkreira may portion user inputs and/or communications into a training set, a validation set, a test set, and/or the like ([0033]). Therefore, it would have been obvious at the time the application was filed for the system of Gordon in view Benkreira to receive a user input indicating labels corresponding to each bin of the set of bins, as claimed. This would save on cost and time for training the sentiment neural network.
As per claim 8, Gordon may not explicitly disclose generating training data based on (i) the randomly sampled subset of utterances corresponding to the respective bin of the set of bins, (ii) the sentiment values related to each utterance of the randomly sampled subset of utterances corresponding to the respective bin of the set of bins, and (iii) the user input indicating a label corresponding to each bin of the set of bins. However, Gordon teaches training a sentiment neural network using labeled examples of a sentence particular words corresponding with a particular sentiment (e.g., Negative, Neutral, or Positive, expressed in numerical values) as training data ([0030]). Furthermore, the service platform of Benkreira may portion user inputs and/or communications into a training set, a validation set, a test set, and/or the like ([0033]). Therefore, it would have been obvious at the time the application was filed for the system of Gordon in view Benkreira to receive a user input indicating labels corresponding to each bin of the set of bins and generate training data, as claimed above. This would save on cost and time for training the sentiment neural network.
As per claim 9, Gordon teaches a computer terminal that includes an operator station where an operator of a call center receives incoming calls from customers. Each call associated with a plurality of utterances and corresponding transcripts. A sentiment analyzer that analyzes conversations that take place during the plurality of incoming calls ([0024]- [0029]). Therefore, the system of Gordon will perform the same steps for every utterance within the transcript. As detailed with regard to claim 2 for the first utterance, Gordon teaches generating a second sentiment value related to each second utterance of a set of second utterances associated with the user by providing each utterance of the set of second utterances to a second sentiment machine learning model ([0077]- [0080], wherein a transcript of a dialogue (call) between a user and a first agent is received, and an utterance sentiment value representing sentiment associated with an utterance made during the call is generated by a sentiment neural network); binning each second utterance of the set of second utterances associated with the user into a set of second bins based on the second sentiment values related to a respective second utterance of the set of second utterances ([0082], wherein a sentiment momentum for the call is generated by assigning respective sentiments of the user and the agent into a specific class of sentiments, positive, negative, or neutral); determining a second sentiment probability of each second bin of the set of second bins by randomly sampling a second subset of utterances corresponding to a second respective bin of the set of second bins, wherein each second bin of the set of second bins is associated with a second range of second sentiment values ([0074], wherein each line segment of the graph represent a sentiment probability, within a range, i.e. positive, negative, or neutral, corresponding to each of the customer and agent during the call); determining a second overall sentiment probability for the transcript based on the determined second sentiment probability of each bin of the set of bins ([0074], wherein said, the sentiment value 904 indicates a call sentiment that represents the overall sentiment of the call); determining a combined overall sentiment probability for the transcript based on (i) the overall sentiment probability for the transcript and (ii) the second overall sentiment probability for the transcript ([0035], determining the overall sentiment probability of the call by combining the averaging the sentiment probability of all utterances within the call). Gordon may not explicitly disclose routing the communication request to the second agent based on the combined overall sentiment probability for the transcript satisfying a threshold combined overall sentiment probability in lieu of the overall sentiment probability for the transcript satisfying the threshold sentiment probability. Benkreira in the same field of endeavor teaches a service platform that determines the service performance score based on a sentiment value associated with the topic satisfying a threshold sentiment value (e.g., a sentiment that indicates negative sentiment and/or a certain degree of negative sentiment), and when the sentiment score satisfies the threshold sentiment score, the service platform may enable the communication to be redirected and/or may automatically redirect the communication to another service representative device (e.g., a service representative device of a manager) ([0015], [0050], [0066]- [0068]). Therefore, it would have been obvious at the time the application was filed to use the above features of Benkreira with the system of Gordon, in order to conserve computing resources and/or network resources of the service platform as the most qualified service representative can more quickly and efficiently handle the communication relative to other service representatives (Gordon, [0015]).
As per claim 10, Gordon may not explicitly disclose wherein routing the communication request to the second agent further comprises: determining that the second agent is associated with an assessment value satisfying a threshold assessment value; in response to determining that the second agent is associated with the assessment value satisfying the threshold assessment value, determining a communication channel identifier associated with the second agent; and routing the communication request to the second agent based on the communication channel identifier. Benkreira in the same field of endeavor teaches a service platform that determines the service performance score based on a sentiment value associated with the topic satisfying a threshold sentiment value (e.g., a sentiment that indicates negative sentiment and/or a certain degree of negative sentiment), and when the sentiment score satisfies the threshold sentiment score, the service platform may enable the communication to be redirected and/or may automatically redirect the communication to another service representative device (e.g., a service representative device of a manager) ([0015], [0050], [0066]- [0068], and more [0072], wherein said, the service platform may identify which service representative is more likely to conserve computing resources and/or network resources associated with the communication by being relatively more suited to engage in the communication with the user). Therefore, it would have been obvious at the time the application was filed to use the above features of Benkreira with the system of Gordon, in order to conserve computing resources and/or network resources of the service platform as the most qualified service representative can more quickly and efficiently handle the communication relative to other service representatives (Gordon, [0015]).
As per claim 11, Gordon teaches linking the overall sentiment probability for the transcript to a user identifier associated with the user, wherein the linking comprises storing the overall sentiment probability for the transcript in a database indicating a mapping between (i) overall sentiment probabilities of transcripts and (ii) user identifiers (Fig. 8 and [0065], wherein shown a mapping between overall sentiment probabilities of transcripts and corresponding speakers).
As per claim 12, Gordon teaches receiving the transcript of the dialogue between the user and the first agent; performing natural language processing on the transcript to determine an identifier associated with the user; and extracting, based on the identifier associated with the user, the set of utterances associated with the user ([0065], wherein natural language processing is necessarily used to identify text within the transcript and speakers IDs).
As per claim 13, Gordon teaches wherein the sentiment value, the sentiment probability, and the overall sentiment probability is associated with a sentiment characteristic of a set of sentiment characteristics ([0068], sentiment probability is associated with sentiment characteristics such as very boring, happy).
As per claim 14, Gordon teaches wherein each bin of the set of bins are associated with a non-overlapping numerical range of sentiment values ([0045]).
As per claims 15-20, Gordon teaches a computer readable medium ([0114]). The remaining steps are rejected under the same rationale as applied to the method steps of rejected claims 2-4, 9-10, and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659