System and method for real-time identification of dissatisfaction data

1 . A method for automatic real-time identification of dissatisfaction data by utilizing one or more processors along with allocated memory, the method comprising:

establishing a communication link between an application and a cloud platform deployed on a cloud environment, the application including a service layer module, an interface layer module, and a user interface (UI) layer module;

receiving, by the service layer module, real-time stream of call transcript data generated during a call or a chat between a customer and an agent;

calling an application programming interface (API) to invoke the interface layer module to consume the real-time stream of the call transcript data as input data outputted from the service layer module;

implementing a machine learning model that includes predefined complaint data;

concatenating all utterances during the call between the customer and the agent prior to applying the machine learning model;

receiving the call transcript data at the machine learning model;

processing the call transcript data using the machine learning model, the processing comprising:

comparing the call transcript data with one or more relevant portions of the predefined complaint data; and

generating a first similarity score, based on the comparing, that identifies how similar the call transcript data is compared to the predefined complaint data;

automatically identifying the call transcript data as a first dissatisfaction data based on determining that the first similarity score is equal to or more than a predetermined threshold value, wherein the first dissatisfaction data is included as an updated portion of the predefined complaint data;

updating the machine learning model, the updating comprising:

training the machine learning model with at least the updated portion of the predefined complaint data for automatic identification of additional dissatisfaction data and automatic identification of additional similarity score, wherein the additional similarity score is derived from a new call transcript data, as received by the service layer module during a new call between one or more customers and one or more agents of a set of agents; and

updating the predetermined threshold value in response to the training;

after the updating of the machine learning model, receiving one or more additional call transcript data at the machine learning model;

generating, using the machine learning model, an additional similarity score for each of the one or more additional call transcript data, based on individually comparing each of the additional call transcript data to the predefined complaint data, wherein the comparing identifies how similar each of the additional call transcript data is compared to the predefined complaint data;

automatically identifying the at least one of the one or more additional call transcript data as additional dissatisfaction data based on determining that the additional similarity score is equal to or more than the predetermined threshold value, wherein the additional dissatisfaction data is included as part of the updated portion of the predefined complaint data for further updating of the machine learning model;

automatically generating suggestions data that includes complaint data based on the dissatisfaction data to resolve issues raised by the customer;

calling an API to invoke the UI layer module to display the suggestions data onto display screen of an agent computing device utilized by the agent;

receiving agent feedback data on the complaint data from the agent computing device;

storing the agent feedback data onto a database;

consuming the agent feedback data by an analyst computing device utilized by an analyst;

training the machine learning model by applying the agent feedback data onto the machine learning model;

receiving, by the service layer module, the new call or a new chat between the customer and the agent;

implementing the trained machine learning model; and

comparing, in response to the implementing, the new call transcript data with the predefined complaint data to generate the additional similarity score.

2 . The method according to claim 1 , wherein the call transcript data includes real-time stream of voice data received from a telephony device, and the method further comprising:

converting the voice data into text data; and

receiving the text data at the machine learning model.

3 . The method according to claim 1 , wherein the call transcript data includes real-time stream of text data received from a chat platform, and the method further comprising:

generating text data from the call transcript data; and

receiving the text data at the machine learning model.

4 . The method according to claim 1 , in generating the call transcript data in real-time, the method further comprising:

concatenating all utterances during the call between the customer and the agent;

converting the utterances into text data; and

receiving the text data at the machine learning model.

5 . The method according to claim 1 , wherein the machine learning model includes one or more of the following models: a natural language processing (NLP) model and a long short term memory (LSTM) model.

6 . The method according to claim 5 , further comprising:

storing output from the machine learning model onto a database.

7 . The method according to claim 1 , wherein the suggestions data further includes policy data and de-escalation script data.

8 . The method according to claim 1 , wherein the predefined complaint data includes historical logs of complaint data within an organization along with complaint data accessed from public database of complaints, and the method further comprising:

training the machine learning model with the predefined complaint data.

9 . A system for automatic real-time identification of dissatisfaction data, the system comprising:

a processor; and

a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:

establish a communication link between an application and a cloud platform deployed on a cloud environment, the application including a service layer module, an interface layer module, and a user interface (UI) layer module;

cause the service layer module to receive real-time stream of call transcript data generated during a call or a chat between a customer and an agent;

call an application programming interface (API) to invoke the interface layer module to consume the real-time stream of the call transcript data as input data outputted from the service layer module;

implement a machine learning model that includes predefined complaint data;

concatenate all utterances during the call between the customer and the agent prior to applying the machine learning model;

receive the call transcript data at the machine learning model;

process the call transcript data using the machine learning model, the processing comprising:

compare the call transcript data with one or more relevant portions of the predefined complaint data; and

generate a first similarity score, based on the comparing, that identifies how similar the call transcript data is compared to the predefined complaint data;

automatically identify the call transcript data as a first dissatisfaction data based on determining that the first similarity score is equal to or more than a predetermined threshold value, wherein the first dissatisfaction data is included as an updated portion of the predefined complaint data;

update the machine learning model, the update comprising:

train the machine learning model with at least the updated portion of the predefined complaint data for automatic identification of additional dissatisfaction data and automatic identification of additional similarity score, wherein the additional similarity score is derived from a new call transcript data, as received by the service layer module during a new call between one or more customers and one or more agents of a set of agents; and

update the predetermined threshold value in response to the training;

after the updating of the machine learning model, receive one or more additional call transcript data at the machine learning model;

generate, using the machine learning model, an additional similarity score for each of the one or more additional call transcript data, based on individually comparing each of the additional call transcript data to the predefined complaint data, wherein the comparing identifies how similar each of the additional call transcript data is compared to the predefined complaint data;

automatically identify the at least one of the one or more additional call transcript data as additional dissatisfaction data based on determining that the additional similarity score is equal to or more than the predetermined threshold value, wherein the additional dissatisfaction data is included as part of the updated portion of the predefined complaint data for further updating of the machine learning model;

automatically generate suggestions data that includes complaint data based on the dissatisfaction data to resolve issues raised by the customer;

call an API to invoke the UI layer module to display the suggestions data onto display screen of an agent computing device utilized by the agent;

receive agent feedback data on the complaint data from the agent computing device;

store the agent feedback data onto a database;

consume the agent feedback data by an analyst computing device utilized by an analyst;

train the machine learning model by applying the agent feedback data onto the machine learning model;

receive, by the service layer module, the new call or a new chat between the customer and the agent;

implement the trained machine learning model; and

compare, in response to the implementing, the new call transcript data with the predefined complaint data to generate the additional similarity score.

10 . The system according to claim 9 , wherein the call transcript data includes real-time stream of voice data received from a telephony device, and the processor is further configured to:

convert the voice data into text data; and

receive the text data at the machine learning model.

11 . The system according to claim 9 , wherein the call transcript data includes real-time stream of text data received from a chat platform, and the processor is further configured to:

generate text data from the call transcript data; and

receive the text data at the machine learning model.

12 . The system according to claim 9 , in generating the call transcript data in real-time, the processor is further configured to:

concatenate all utterances during the call between the customer and the agent;

convert the utterances into text data; and

receive the text data at the machine learning model.

13 . The system according to claim 9 , wherein the suggestions data further includes policy data and de-escalation script data.

14 . A non-transitory computer readable medium configured to store instructions for automatic real-time identification of dissatisfaction data, wherein, when executed, the instructions cause a processor to perform the following:

establishing a communication link between an application and a cloud platform deployed on a cloud environment, the application including a service layer module, an interface layer module, and a user interface (UI) layer module;

receiving, by the service layer module, real-time stream of call transcript data generated during a call or a chat between a customer and an agent;

calling an application programming interface (API) to invoke the interface layer module to consume the real-time stream of the call transcript data as input data outputted from the service layer module;

implementing a machine learning model that includes predefined complaint data;

concatenating all utterances during the call between the customer and the agent prior to applying the machine learning model;

receiving the call transcript data at the machine learning model;

processing the call transcript data using the machine learning model, the processing comprising:

comparing the call transcript data with one or more relevant portions of the predefined complaint data; and

generating a first similarity score, based on the comparing, that identifies how similar the call transcript data is compared to the predefined complaint data;

automatically identifying the call transcript data as a first dissatisfaction data based on determining that the first similarity score is equal to or more than a predetermined threshold value, wherein the first dissatisfaction data is included as an updated portion of the predefined complaint data;

updating the machine learning model, the updating comprising:

training the machine learning model with at least the updated portion of the predefined complaint data for automatic identification of additional dissatisfaction data and automatic identification of additional similarity score, wherein the additional similarity score is derived from a new call transcript data, as is-received by the service layer module during a new call between one or more customers and one or more agents of a set of agents; and

updating the predetermined threshold value in response to the training;

after the updating of the machine learning model, receiving one or more additional call transcript data at the machine learning model;

generating, using the machine learning model, an additional similarity score for each of the one or more additional call transcript data, based on individually comparing each of the additional call transcript data to the predefined complaint data, wherein the comparing identifies how similar each of the additional call transcript data is compared to the predefined complaint data;

automatically identifying the at least one of the one or more additional call transcript data as additional dissatisfaction data based on determining that the additional similarity score is equal to or more than the predetermined threshold value, wherein the additional dissatisfaction data is included as part of the updated portion of the predefined complaint data for further updating of the machine learning model;

automatically generating suggestions data that includes complaint data based on the dissatisfaction data to resolve issues raised by the customer;

calling an API to invoke the UI layer module to display the suggestions data onto display screen of an agent computing device utilized by the agent;

receiving agent feedback data on the complaint data from the agent computing device;

storing the agent feedback data onto a database;

consuming the agent feedback data by an analyst computing device utilized by an analyst;

training the machine learning model by applying the agent feedback data onto the machine learning model;

receiving, by the service layer module, the new call or a new chat between the customer and the agent;

implementing the trained machine learning model; and

comparing, in response to the implementing, the new call transcript data with the predefined complaint data to generate the additional similarity score.