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 .
Introduction
This office action is in response to communications filed 01/26/2024. Claims 1-20 are pending and likewise have been examined.
Specification
3. The disclosure is objected to because of the following informalities: The brief description of the drawings does not contain an individual brief description of drawings for drawings 1A-1F. Each drawing 1A-1F requires its own brief description, See MPEP 608.01(f).
Appropriate correction is required.
Claim Rejections - 35 USC § 102
4. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claim(s) 8 12, and 15-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kwak et al. (US 11778098 B1).
Regarding Claim 8:
Kwak teaches a device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to(Col 6, Ln 45-52, above-described methods are embodied in the form of processor-executable code and stored in a computer-readable program medium. Thus, a non-transitory machine-readable medium having machine executable instructions stored thereon that, when executed by one or more processors):
train a machine learning model to analyze data to predict sentiments associated with the data and a measure of incompleteness of the data(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 11, Ln 20-25, machine learning model trained to adjust sentiment scores. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue);
obtain communication data generated during a communication session between a first device and a second device, wherein the communication data is obtained via a network(Col 2, Ln 45-52, call routing system 100 that includes user devices 120a-120c that can call a number to talk to customer service representatives at a customer call center. The calls from the user devices 120a-120c can be sent to or can be managed by the call routing server 140. For example, a call initiated by a user device, such as a mobile device 120a, may be sent to a call routing server 140 via a communication network. Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words);
provide the communication data as an input to the trained machine learning model(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. See combination with additional reference below for neural network);
determine, using the trained machine learning model, a first set of sentiments associated with the first device and a second set of sentiments associated with the second device(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. See combination with additional reference below for neural network);
determine, using the trained machine learning model, a measure of incompleteness of the communication data based on the first set of sentiments and the second set of sentiments(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue. See combination with additional reference below for neural network);
and perform an action based on the measure of incompleteness of the communication data(Col 4, Ln 49-60, call routing server 140 can send a message to a computer associated with a manager of the customer service representative to inform the manager that the call is not going well).
Regarding Claim 12:
Kwak teaches the device of claim 8, wherein the trained machine learning model includes at least one of a recurrent neural network model or a natural language processing model(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words).
Regarding Claim 15:
Kwak teaches the device of claim 8, wherein the one or more processors, to determine the first set of sentiments and the second set of sentiments, are configured to: determining the first set of sentiments and the second set of sentiments using a natural language processing model(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call).
Regarding Claim 16:
Kwak teaches a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a prediction system, cause the prediction system to(Col 6, Ln 45-52, above-described methods are embodied in the form of processor-executable code and stored in a computer-readable program medium. Thus, a non-transitory machine-readable medium having machine executable instructions stored thereon that, when executed by one or more processors):
train a machine learning model to analyze data to predict sentiments associated with the data and a measure of incompleteness of the data(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 11, Ln 20-25, machine learning model trained to adjust sentiment scores. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue);
obtain communication data generated during a communication session between a first device and a second device, wherein the communication data is obtained via a network(Col 2, Ln 45-52, call routing system 100 that includes user devices 120a-120c that can call a number to talk to customer service representatives at a customer call center. The calls from the user devices 120a-120c can be sent to or can be managed by the call routing server 140. For example, a call initiated by a user device, such as a mobile device 120a, may be sent to a call routing server 140 via a communication network. Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words);
provide the communication data as an input to the trained machine learning model(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. See combination with additional reference below for neural network);
determine, using the trained machine learning model, a first set of sentiments associated with the first device and a second set of sentiments associated with the second device(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. See combination with additional reference below for neural network);
determine, using the trained machine learning model, a measure of incompleteness of the communication data based on the first set of sentiments and the second set of sentiments(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue. See combination with additional reference below for neural network);
and perform an action based on the measure of incompleteness of the communication data(Col 4, Ln 49-60, call routing server 140 can send a message to a computer associated with a manager of the customer service representative to inform the manager that the call is not going well).
Claim Rejections - 35 USC § 103
6. 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.
7. Claim(s) 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwak as applied in claim 8 above, and further in view of Intrator et al. (US 20240242033 A1).
Regarding Claim 11:
Kwak teaches the device of claim 8, but does not teach wherein the communication data includes text and emojis, and wherein the one or more processors, to determine the first set of sentiments and the second set of sentiments, are configured to: perform a sentiment analysis using the text; and determine one or more sentiments associated with the emojis.
In the same field of sentiment analysis Intrator teaches wherein the communication data includes text and emojis, and wherein the one or more processors, to determine the first set of sentiments and the second set of sentiments, are configured to: perform a sentiment analysis using the text; and determine one or more sentiments associated with the emojis(Abstract, Ln 1-17, operations which include receiving image data for an image posted on a social networking platform, determining whether there is text data, performing image data extraction operations, analyzing the text data, determining and combining a score for the image and text data, determining an image sentiment or a text sentiment, calculating weighted metrics based on the image sentiment or the text sentiment. Analysis for both first and second sets is taught in primary reference as shown in rejection of claim 8. Para [0065], Ln 1-15, post may include image, video, and/or text data, as well as links, identifiers, hashtags, emojis or other graphics. Para [0026], Ln 1-15, training data may be associated with past social media and networking posts that may include images, text, graphics or emojis, animations (e.g., GIFs), and the like, which may have a corresponding sentiment).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Kwak with the textual and graphical sentiment analysis of Intrator as it improves prediction accuracy(Para [0018], Ln 1-18).
Regarding Claim 19:
Kwak teaches the non-transitory computer-readable medium of claim 16, but does not teach wherein the communication data includes textual data and graphical data, and wherein the one or more instructions, that cause the prediction system to determine the first set of sentiments and the second set of sentiments, cause the prediction system to: perform a sentiment analysis using the textual data; and determine one or more sentiments associated with the graphical data.
In the same field of sentiment analysis Intrator teaches wherein the communication data includes textual data and graphical data, and wherein the one or more instructions, that cause the prediction system to determine the first set of sentiments and the second set of sentiments, cause the prediction system to: perform a sentiment analysis using the textual data; and determine one or more sentiments associated with the graphical data(Abstract, Ln 1-17, operations which include receiving image data for an image posted on a social networking platform, determining whether there is text data, performing image data extraction operations, analyzing the text data, determining and combining a score for the image and text data, determining an image sentiment or a text sentiment, calculating weighted metrics based on the image sentiment or the text sentiment. Analysis for both first and second sets is taught in primary reference as shown in rejection of claim 8).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Kwak with the textual and graphical sentiment analysis of Intrator as it improves prediction accuracy(Para [0018], Ln 1-18).
8. Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwak as applied in claim 8 above, and further in view of Zhou et al. (US 20200250489 A1).
Regarding Claim 14:
Kwak teaches the device of claim 8, but does not teach wherein the one or more processors, to perform the action, are configured to: predicting values for missing data from the communication data; and provide information regarding the values to one or more of the first device or the second device.
In the same field of sentiment analysis, Zhou teaches wherein the one or more processors, to perform the action, are configured to: predicting values for missing data from the communication data; and provide information regarding the values to one or more of the first device or the second device(Para [0056], Ln 1-10, term “influential rule” means a rule to identify situations that the BOT might not provide expected responses to the user. The situations can comprise at least one of: (a) negative sentiment is detected based on user input. Para [0057], Ln 1-10, in response to none of the critical rules is broken, determining whether the human agent is needed. Para [0058], Ln 1-7, transferring recommendation can be sent to the user in response to determining the human agent is needed by one or more processing units).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Kwak with the conversation analysis system of Zhou, as it reduces the need for humans, improving efficiency(Para [0058], Ln 1-11).
9. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwak as applied in claim 16 above, and further in view of Moudy et al. (US 20160300135 A1).
Regarding Claim 20:
Kwak teaches the non-transitory computer-readable medium of claim 16, but does not teach wherein the one or more instructions, that cause the prediction system to determine the first set of sentiments and the second set of sentiments, cause the prediction system to: parse the communication data to identify one or more phrases; and perform a sentiment analysis using the one or more phrases.
In the same field of sentiment analysis, Moudy teaches wherein the one or more instructions, that cause the prediction system to determine the first set of sentiments and the second set of sentiments, cause the prediction system to: parse the communication data to identify one or more phrases; and perform a sentiment analysis using the one or more phrases(Para [0106], Ln 11-19, Such sentiment NLP neural network 660 may be trained to determine user sentiment from text, for example, based on identified keywords, wording, phrases. Para [0120], Ln 1-13, In some embodiments, multimodal sentiment analyzers may receive, parse, and analyze multimodal content feedback data. As described below, various implementations may include video, image, and/or audio capture devices and capabilities, along with processing and synchronization systems to analyze the video, image, and/or audio data and calculate sentiment scores based on multimodal feedback. Kwak teaches sentiment analysis for both first and second sets).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Kwak with the Sentiment Analysis system of Moudy as it can help improve accuracy of sentiment prediction(Para [0007], Ln 1-10).
10. Claim(s) 1 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwak, and further in view of Moudy.
Regarding Claim 1:
Kwak teaches a method performed by a prediction system, the method comprising(Abstract, Ln 1-6, Based on this analysis, a server can determine a sentiment score):
training a……model to analyze data to predict sentiments associated with the data and a measure of incompleteness of the data(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 11, Ln 20-25, machine learning model trained to adjust sentiment scores. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue);
obtaining communication data regarding a communication session between a first device and a second device, wherein the communication data is obtained via a network(Col 2, Ln 45-52, call routing system 100 that includes user devices 120a-120c that can call a number to talk to customer service representatives at a customer call center. The calls from the user devices 120a-120c can be sent to or can be managed by the call routing server 140. For example, a call initiated by a user device, such as a mobile device 120a, may be sent to a call routing server 140 via a communication network. Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words);
providing the communication data as an input to the trained neural network model(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. See combination with additional reference below for neural network);
determining, using the trained neural network model, a first set of sentiments associated with the first device and a second set of sentiments associated with the second device, wherein the first set of sentiments and the second set of sentiments are determined based on the communication data(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. See combination with additional reference below for neural network);
determining, using the trained neural network model, a measure of incompleteness of the communication data based on the first set of sentiments and the second set of sentiments(Col 3, Ln 34-44, call routing server 140 can determine the plurality of sentiment scores based a supervised machine learning technique. In this example, a machine learning model operating on the call routing server 140 can receive the recorded text or audio or both and output a sentiment scores based on the words. Col 3, Ln 15-25, The sentiment scores describe a sentiment of the person or the customer service representative at the various points in time on the call. Col 4, Ln 11-20, difference between the two scores and by comparing the difference to a threshold value, the call routing server 140 can provide a more descriptive measure of how well a call is going as compared to taking an average of the determined sentiment scores. Col 5, Ln 45-55, person who had a negative call experience on his or her first call with a customer service representative will most likely call again to speak to another customer service representative about the same issue. See combination with additional reference below for neural network);
and performing an action based on the measure of incompleteness of the communication data(Col 4, Ln 49-60, call routing server 140 can send a message to a computer associated with a manager of the customer service representative to inform the manager that the call is not going well).
Kwak does not specifically teach a neural network model to analyze and predict sentiment.
In the same field of sentiment analysis, Moudy teaches a neural network model to analyze and predict sentiment(Abstract, Ln 1-12, natural language processing neural networks may be used to determine sentiment scores for the feedback data).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Kwak with the Sentiment neural network of Moudy as it can help improve accuracy of sentiment prediction(Para [0007], Ln 1-10).
Regarding Claim 5:
The combination of Kwak and Moudy teaches the method of claim 1, and Moudy, as previously combined in claim 1, teaches wherein the trained neural network model includes at least one of a recurrent neural network model or a natural language processing model(Abstract, Ln 1-12, natural language processing neural networks may be used to determine sentiment scores for the feedback data).
11. Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kwak and Moudy as applied to claim 1 above, and further in view of Intrator.
Regarding Claim 4:
The combination of Kwak and Moudy teaches the method of claim 1, but does not teach wherein the communication data includes textual data and graphical data, and wherein determining the first set of sentiments and the second set of sentiments comprises: performing a sentiment analysis using the textual data; and determining one or more sentiments associated with the graphical data.
In the same field of sentiment analysis Intrator teaches wherein the communication data includes textual data and graphical data(Abstract, Ln 1-17, operations which include receiving image data for an image posted on a social networking platform, determining whether there is text data, performing image data extraction operations, analyzing the text data, determining and combining a score for the image and text data, determining an image sentiment or a text sentiment, calculating weighted metrics based on the image sentiment or the text sentiment),
and wherein determining the first set of sentiments and the second set of sentiments comprises: performing a sentiment analysis using the textual data; and determining one or more sentiments associated with the graphical data(Abstract, Ln 1-17, operations which include receiving image data for an image posted on a social networking platform, determining whether there is text data, performing image data extraction operations, analyzing the text data, determining and combining a score for the image and text data, determining an image sentiment or a text sentiment, calculating weighted metrics based on the image sentiment or the text sentiment. Analysis for both first and second sets is taught in primary reference as shown in rejection of claim 1).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Kwak and Moudy with the textual and graphical sentiment analysis of Intrator as it improves prediction accuracy(Para [0018], Ln 1-18).
12. Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kwak and Moudy as applied to claim 1 above, and further in view of Zhou.
Regarding Claim 7:
The combination of Kwak and Moudy teaches the method of claim 1, but does not teach wherein performing the action comprises: providing a notification regarding the measure of incompleteness of the communication data to one or more of the first device or the second device.
In the same field of sentiment analysis, Zhou teaches wherein performing the action comprises: providing a notification regarding the measure of incompleteness of the communication data to one or more of the first device or the second device(Para [0056], Ln 1-10, term “influential rule” means a rule to identify situations that the BOT might not provide expected responses to the user. The situations can comprise at least one of: (a) negative sentiment is detected based on user input. Para [0057], Ln 1-10, in response to none of the critical rules is broken, determining whether the human agent is needed. Para [0058], Ln 1-7, transferring recommendation can be sent to the user in response to determining the human agent is needed by one or more processing units).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Kwak and Moudy with the conversation analysis system of Zhou, as it reduces the need for humans, improving efficiency(Para [0058], Ln 1-11).
Allowable Subject Matter
13. Claim 2-3, 6, 9-10, 13 and 17-18 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.
14. The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 2:
The combination of Kwak and Moudy teaches the method of claim 1, but does not teach wherein training the neural network model comprises: obtaining training data; identifying one or more categories of data of the training data; identifying a dictionary for each category of the one or more categories; determining whether each identified dictionary includes a complete dataset; and determining a measure of incompleteness of each dataset of each identified dictionary.
Nasir et al. “A New Paradigm to Analyze Data Completeness of Patient Data”, teaches determining completeness of patient data by comparing patient data in a database(Pg 748, 3.2, Data Analysis, Para 1, Ln 1-5), however, this instance of data incompleteness, while more similar to applicants specification, is very different from the determination of an incomplete customer service call, of the primary reference Kwak, and it would not have been obvious to combine the references, nor would the resulting combination make sense.
For these reasons the prior art of record alone or in combination does not teach the limitations of claim 2.
Regarding Claim 3:
The combination of Kwak and Moudy teaches the method of claim 1, but does not teach further comprising: determining one or more matches between the first set of sentiments and the second set of sentiments, wherein determining the measure of incompleteness of the communication data comprises: determining the measure of incompleteness of the communication data based on the one or more matches.
Nasir et al. “A New Paradigm to Analyze Data Completeness of Patient Data”, teaches determining completeness of patient data by comparing patient data in a database(Pg 748, 3.2, Data Analysis, Para 1, Ln 1-5), however, this instance of data incompleteness, while more similar to applicants specification, is very different from the determination of an incomplete customer service call, of the primary reference Kwak, and it would not have been obvious to combine the references, nor would the resulting combination make sense.
For these reasons the prior art of record alone or in combination does not teach the limitations of claim 3.
Regarding Claim 6:
The combination of Kwak and Moudy teaches the method of claim 1, but does not teach wherein the communication data includes textual data and a plurality of data classification identifiers, wherein determining the first set of sentiments and the second set of sentiments comprises: performing a sentiment analysis using the textual data to determine the first set of sentiments and the second set of sentiments, and wherein determining the measure of incompleteness comprises: determining one or more matches between the first set of sentiments and the plurality of data classification identifiers, and determining the measure of incompleteness based on determining the one or more matches.
Nasir et al. “A New Paradigm to Analyze Data Completeness of Patient Data”, teaches determining completeness of patient data by comparing patient data in a database(Pg 748, 3.2, Data Analysis, Para 1, Ln 1-5), however, this instance of data incompleteness, while more similar to applicants specification, is very different from the determination of an incomplete customer service call, of the primary reference Kwak, and it would not have been obvious to combine the references, nor would the resulting combination make sense.
For these reasons the prior art of record alone or in combination does not teach the limitations of claim 6.
Claims 9 and 17 contain similar limitations as Claim 2 and therefore also contain allowable subject matter for the same reasons.
Claims 10 and 18 contain similar limitations as Claim 3 and therefore also contain allowable subject matter for the same reasons.
Claim 13 contains similar limitations as Claim 6 and therefore also contains allowable subject matter.
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aggarwal et al. (US 20230223133 A1).
Determination of Sentiment mismatch between determined sentiment and life situation.
Zimmerman (US 10158758 B2).
Determination of both customer and agents sentiment during communication.
Gurupur et al. “Machine Learning Analysis for Data Incompleteness”.
Data incompleteness determination using machine learning.
Gurupur et al. “THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction”.
Data incompleteness determination using sentiment prediction neural network. Shares inventor with instant application, not prior art.
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/ALEXANDER G MARLOW/ Assistant Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658