Patent Publication Number: US-11657076-B2

Title: System for uniform structured summarization of customer chats

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to, and the benefit of, Indian Provisional Patent Application No. 202011015288, filed on Apr. 7, 2020, and entitled “SYSTEM FOR UNIFORM STRUCTURED SUMMARIZATION OF CUSTOMER CHATS.” 
     BACKGROUND 
     Typically, enterprises engage in customer service or support over voice and chat channels. Typically, customer service chats are task-oriented dialogue towards achieving a customer&#39;s request or question. However, customer service chats on chat platform are typically unstructured human generated textual data that may include unwanted information. Analysis of this textual data would give valuable insights into an enterprise and the enterprises customer care. 
     SUMMARY OF DESCRIBED SUBJECT MATTER 
     In some embodiments, the present disclosure provides a method, at least having steps of receiving, by a processor, a corpus of digital chats records between a plurality of customers and a plurality of customer service representatives of an enterprise, wherein each digital chat record of the corpus of digital chat records is a chat data structure comprising chat content data and chat metadata of a digital chat record. Also, wherein each chat metadata comprises a task identifier corresponding to a task of a plurality of tasks that a customer interacted about with a customer service representative during the digital chat of the corpus of digital chat records. 
     The method further comprising grouping, by the processor, the corpus of digital chat records into subgroups of tasks of the plurality of tasks, wherein each subgroup corresponds to one task of the plurality of tasks; generating, by the processor via a keyword chat module, for each subgroup of digital chat records, a plurality of task keywords and a plurality of task related words from the chat content data of each digital chat record based at least in part on a tokenization analysis of the corpus of digital chat records; and generating, by the processor, for each subgroup of digital chat records, an expandable template data structure based at least in part on the plurality of task keywords and the plurality of task related words. 
     The method further comprises receiving, by the processor, a digital chat record; determining, by the processor, from the metadata of the digital chat record the task identifier corresponding to the task of the plurality of tasks; extracting, by the processor, a plurality of chat utterances from the chat content data from the digital chat record based at least in part on the plurality of task keywords associated with the task and the plurality of task related words associated with the task; and extracting, by the processor, a plurality of chat snippets from the plurality of chat utterances based at least in part on a data proximity window in relation to a task keyword. 
     The method further comprising ranking, by the processor via a template scoring module, the plurality of chat utterances and the plurality of chat snippets based at least in part on the expandable template data structure associated with the task identifier to: 
     i) assign a ranking score to each chat utterance and each chat snippet, and 
     ii) form a plurality of ranked chat utterances and a plurality of ranked chat snippets. Modifying, by the processor, the expandable template data structure to form an adjusted template data structure based at least in part on the plurality of ranked chat utterances and the plurality of ranked chat snippets; populating, by the processor, the adjusted template data structure with a subset of the ranked chat utterances from the plurality of ranked chat utterances and a subset of the ranked chat snippets from the plurality of the ranked chat snippets based at least in part on the ranking score to form a chat summary data structure for the digital chat record; and storing, by the processor, the chat summary data structure for the digital chat record in a database. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ one or more illustrative embodiments. 
         FIG.  1    shows a block diagram of a computer-based system for extracting chat summary data structures, in accordance with some embodiments of the present disclosure; 
         FIG.  2 A  shows an example view of a digital chat record on a chat platform of an enterprise in accordance with some embodiments of the present disclosure; 
         FIG.  2 B  shows an example of a human readable chat summary data structure in accordance with some embodiments of the present disclosure; 
         FIG.  2 C  shows an example of a Structured Query Language (SQL) compatible format summary in accordance with some embodiments of the present disclosure; 
         FIG.  3    shows a flowchart for a method of generating expandable template data structures, task keywords and task related words for a corpus of digital chats records in accordance with some embodiments of the present disclosure; 
         FIG.  4    illustrates a flowchart of a method for extracting and generating chat summary data structures from a digital chat record in accordance with some embodiments of the present disclosure; 
         FIG.  5    illustrates a plurality of expandable template data structures that each represent one task of a plurality of tasks regarding customer interaction with a customer service representative in accordance with some embodiments of the present disclosure; 
         FIG.  6    is an illustrative example of some tasks and task keywords generated for those tasks in accordance with some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Various detailed embodiments of the present disclosure, taken in conjunction with the accompanying figures, are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative. In addition, each of the examples given relating to the various embodiments of the present disclosure is intended to be illustrative, and not restrictive. 
     As used and referenced herein, an “enterprise” and its logical linguistic relatives and/or derivates, means a business, company or firm that engages in commerce with customers or consumers. 
     As used and referenced herein, “normalized” or “normalization” may refer to computations executed by a computer-based system on, for example, utterances to obtain results that do not depend on the length of the utterances or other characteristics of the utterance. 
     As used and referenced herein, the terms “clustering” and “cluster analysis” and their logical linguistic relatives and/or derivatives, may refer to a computer-based analysis including grouping a set of objects in such a way that objects in the same cluster (group) are more similar (in some sense) to each other than to those in other clusters (groups). 
     As used and referenced herein, the term “dynamically” and the term “automatically,” and their logical linguistic relatives and/or derivatives, may refer to computer-based events and/or actions that can be triggered and/or occur without any human intervention. 
     As used and referenced herein, the terms “computer module” and “module” may refer to at least one software component and/or a combination of at least one software component and at least one hardware component designed/programmed/configured to interact with other software and/or hardware components and/or compute system outputs. 
     As used and referenced herein, the term, “chat” or “digital chat” may refer to the process of communicating, interacting and/or exchanging messages over the Internet or other suitable computer network. A “chat” or “digital chat” can involve two or more users that communicate through a chat-enabled service or other suitable software. 
     As used and referenced herein, the term “chat snippets” may refer to part or segments extracted from a chat-based communication between one or more users. 
     As used and referenced herein, the term “digital chat record” may refer to a digital record of, for example, a customer service chat (e.g., electronic textual data) that is a task-oriented dialogue towards achieving a customer-related or user-related objective (e.g., answering customer&#39;s question, performing customer&#39;s request, etc.). 
     As used and referenced herein, the term “customer” may refer to one or more users of the computer based system  100 . 
     As used and referenced herein, the term “task” may refer to chat interactions between one or more users of the system  100 . Such interactions may include requested actions or comments made by a customer to a customer service representative associated with, for example, an enterprise. A “task may also refer to chat-based communication or interactions made towards achieving a customer-related or user-related objective. 
     As used and referenced herein, the term “task type” may refer to a category or class of task associated with a chat-based communication between one or more users. Some examples of task types can include request, questions, comments, or other suitable type of category or class associated with one or more aspects of an enterprise. 
     Embodiments of the present disclosure herein describe a system and method for extracting chat summary data structures of digital chats records captured on a chat platform of an enterprise. Each digital chat record is a data structure of an interaction between, for example, a customer and a customer service representative of the enterprise. The following embodiments provide technical solutions and/or technical improvements that overcome technical problems, drawbacks and/or deficiencies in the technical field of searching and structuring customer service chats. As explained in more detail below, technical solutions and/or technical improvements herein include aspects of improved uniform structured formats for chat platforms, as well as creating computer searchable chat data structures equipped to be mined or used to generate insights regarding an enterprise. 
     Some embodiments of the present disclosure are directed to a technical problem of computing raw data from millions of digital chats records on a chat platform of an enterprise that are unstructured and may content unwanted information. Thus, meaningful analysis or insights into enterprise customer service information may not be mined. In some embodiments, the present disclosure has the technical solution, of at least, generating rich structured representations of the digital chat records in a human readable chat summary data structures and a SQL compatible format summary. In some embodiments, the present disclosure has the technical solution of generating chat summary data structures for digital chat records that are organized, structured, uniformed, expandable, and computer searchable. In some embodiments, the present disclosure has the technical solution of generating chat summary data structures configured to allow optimal searching of digital chat records with speed and efficiency. 
       FIG.  1    is a block diagram of a computer based system  100  (hereinafter, chat summarization system) for extracting chat summary data structures from digital chat records that represent a digital chat record of an interaction captured on the chat platform between a customer and a customer service representative in accordance with an illustrative embodiment of the present disclosure. In some embodiments, chat summary system  100  may include a computer  101 , such as a server, communicating  103  over a communication network  105  with any number of remote computers. In some embodiments, the chat summary system communicates with a chat platform  107  of an enterprise. 
     As shown in inset  109 , the computer  101  may include a processor  111 . In some embodiment, processor  111  may be configured to execute code, which may include a keyword module  113  for generating task keywords and task related words, a data cleaning module  115  for preparing digital chat records for extraction and ranking, an extraction module  117  for extracting specific chat content data for a chat summary data structure of the digital chat records, and a template scoring module  119  for ranking extracted chat utterances and extracted chat snippets of the digital chat records. The computer  101  may further include an input/output device  121 , a communication circuitry interface  123  for communicating  103  over communication network  105 , and a non-transitory memory  125 . Input/output devices  121  may include, but is not limited to, a display  127 , a keyboard  129  and/or a mouse  131 . In some embodiments memory  125  may be configured to store, such as in a database, for example, including but not limited to, expandable template data structures  133  ready to be modified and/or populated for each task, and chat summary data structures  135  of the digital chat records. The communication circuitry interface also allows chat summary system  100  to be part of a network of computers (for example a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or a network of networks, for example the Internet.) 
     In some embodiments, Read-Only-Memory (ROM) (not shown) may be used and include a non-volatile storage device, such as, e.g., a magnetic disk hard drive, a solid-state drive, flash memory, or other non-volatile memory and combinations thereof. In some embodiments, system memory  125  may include a memory such as, e.g., random access memory (RAM) including dynamic RAM and/or static RAM, among other memory devices and combinations thereof. In some embodiments, memory  125  may store data resulting from processing operations, a cache or buffer of data to be used for processing operations, operation logs, error logs, security reports, among other data related to the operation of chat summary system  100 . 
     In some embodiments, a browser application of the present disclosure may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web based language, including, but not limited to Standard Generalized Markup Language (SMGL), such as HyperText Markup Language (HTML), a wireless application protocol (WAP), a Handheld Device Markup Language (HDML), such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, and the like. In some embodiments, a member device of chat summary system  100  may be specifically programmed by either Python, Java, .Net, QT, C, C++ and/or other suitable programming language. 
       FIG.  2 A  illustrates an example of digital chat records  200  that is a digital chat record captured on a chat platform of an enterprise. In some embodiments this digital chat record is not organized, uniform, expandable and/or computer searchable. The digital chat record  200  comprises customer chat lines  201  and customer service representative chat lines  203 . The customer chat line and the customer service representative chat line may be referred to generically as chat lines. 
       FIG.  2 B  illustrates an example of a generated human readable chat summary data structure  202 . The human readable chat summary data structure, in some embodiments has a summary title  205  and relevant chat utterances  207 . 
       FIG.  2 C  illustrates one example of a Structured Query Language (SQL) compatible format summary  204  for the chat summary data structure that is a computer searchable chat summary data structure. In some embodiments, the searchable summary format can be a SQL compatible format. Both  FIGS.  2 B and  2 C  are explained in more detail below. In some embodiments, the SQL compatible format summary may be, but is not limited to, a JavaScript Object Notation (JSON), Extensible Markup Language (XML), or other suitable data exchange formats used to exchange data between platforms. 
       FIG.  3    is a flowchart of a method  300  of using chat summary system  100 , as seen in  FIG.  1   , to generate an expandable template data structures, task keywords and task related words for a corpus of digital chat records, in accordance with one or more embodiments of the present disclosure. Method  300  may include receiving  301  a corpus of digital chat records between a plurality of customers and a plurality of customer service representatives of an enterprise. In some embodiments, each digital chat record of the corpus is a chat data structure comprising chat content data and chat metadata. In some embodiments, the chat metadata is a set of data that describes or provides information about the chat content data and the chat content data is words and/or phrases of words used in a dialogue between customers and customer service representatives. In some embodiments, the chat metadata comprises a task identifier that corresponds to a specific task involved with the interaction between the customer and the customer service representative. 
     Method  300  may include grouping  303  the corpus of digital chat records into subgroups of tasks. In some embodiments, there are subgroups of known tasks and each digital chat record is identified by the metadata task identifier as belonging to a subgrouping of tasks, such that each digital chat record of the corpus of digital chat records is received by chat summary system  100  already identified in the subgroup of tasks. Method  300  may include generating  305  using a keyword module  113 , task keywords and task related words from a corpus of digital chat records of the same task, such that there are task keywords and task related words for each task. In some embodiments, the task keywords and the task related words are generated automatically. 
     In some embodiments, the keyword module of the chat summary system may be configured to receive chat content data and input the chat content data into an algorithm that may be configured to map each of the words and/or phrases of words into a plurality of embedding vectors in an n-dimensional textual embedding space. Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing where words or phrases are mapped to vectors of real numbers. The n-dimensional textual embedding space may also be referred to as a sentence embedding space, or a discourse-based sentence embedding space. The chat content data may be represented as an n-dimensional embedding vector in the textual embedding space. Each location in the n-dimensional textual embedding space may correspond to words, combination of words, a discourse of words, or any combination thereof. The processor may identify distance between embedding vectors in textual embedding space by using Euclidean (geometrical), Manhattan, and/or Levenshtein distance in n-space for example or other suitable computation. 
     In some embodiments, the keyword module  113  may be configured to execute code for a TF-IDF or term frequency-inverse document frequency algorithm to generate the task keywords and the task related words. Term frequency-inverse document frequency is a term-weighting factor scheme of computational linguistic algorithms configured to reflect how important a word is. A weighting factor is a weight given to a data point to assign it a lighter, or heavier, importance in a group. A weighting factor is usually used for calculating a weighted mean, to give less (or more) importance to group members. The TF-IDF value increases proportionally to the number of times a word appears in a document, digital chat record, and is offset by the number of documents, digital chat records, in the corpus that contain the word, and this may adjust for the fact that some words appear more frequently in a general corpus. 
     In some embodiments, the keyword module  113  may be configured to execute code for unigram, bigram, and trigram models of the corpus of digital chat records of the same task to generate task keywords and task related words. Unigrams, bigrams, and trigrams are different types of n-grams. An n-gram of size  1  is referred to as a “unigram”; size  2  is a “bigram” and size  3  is a “trigram”. An n-gram is a contiguous sequence of n items from a given sample of text or speech collected from a corpus of text or speech. The items may be phonemes, syllables, letters, words or base pairs depending on the linguistic computation. In some embodiments, the keyword module  113  may be configured to execute an algorithm as seen in Table 1 and is configured for task keyword and task related word generation. As an example, but not limited to the list, sample task keywords generated from the algorithm of Table 1 are seen in  FIG.  6   . 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
             
            
               
                   
                 procedure Auto 
               
               
                   
                 Keyword Inputs: 
               
               
                   
                 corpus, task 
               
               
                   
                 Initialize: 
               
               
                   
                  Keywords, Q ← ∅ 
               
               
                   
                  Related Words, R ← ∅ 
               
               
                   
                 Step #1: 
               
               
                   
                  U ← Unigrams sorted 
               
               
                   
                  by TF-IDF 
               
               
                   
                  B ← Bigrams sorted by 
               
               
                   
                  TF-IDF 
               
               
                   
                   T ← Trigrams sorted 
               
               
                   
                  by TF-IDF 
               
               
                   
                 Step #2 
               
               
                   
                  Q = unigrams 
               
               
                   
                  containing task R = top 
               
               
                   
                  2% of B ∪ top 1% of T 
               
               
                   
                  R = unique tokens of R ∪ top N of U 
               
               
                   
                 return Q, R 
               
               
                   
               
            
           
         
       
     
     The algorithm of Table 1 uses inputs of the corpus of digital chat records from a task and the keywords Q are an empty set as are the related words R. U stands for Unigrams, B for Bigrams and T for Trigrams each sorted by TF-IDF. Q is equal to unigrams that contain the task. R is set to be the top 2 percent of B and a union with the top 1 percent of T. R is then set as a unique token of R and a union to N of U, where N can be configured to correspond to a predetermined number or percentage. The unique token of R is using tokenization analysis from computational linguistic. Tokenization analysis is the process of demarcating and sometimes classifying sections of a string of input characters. The resulting tokens can be used as a sub-task of parsing input. 
     Method  300  may include generating expandable template data structures  307  for each subgroup of tasks based on the task keywords and the task related words generated by keyword module  113 . In some embodiments, each expandable template data structure specifies the fields to be extracted from the digital chat record for that task. 
       FIG.  4    is a flowchart of a method  400  of using chat summary system  100  as seen in  FIG.  1    to generate chat summary data structures for one or more digital chat records, in accordance with one or more embodiments of the present disclosure. Method  400  may include receiving  401  a digital chat record from the chat platform for generating a chat summary data structure. In some embodiments, the digital chat record used to generate the chat summary data structure may be from the corpus of digital chat records used in method  300  above. In some embodiments, the digital chat record used to generate the chat summary data structure may be a new digital chat record received by chat summary system  100 . 
     Method  400  may include determining  403  the task subgrouping of the digital chat record from the metadata of the chat. In some embodiments, the digital chat record is preprocessed or data cleaned before an extraction process. In some embodiments, the data cleaning is done in data cleaning module  115 . In some embodiments, the data cleaning module  115  may be configured to execute code for removal of stop words which are commonly used words in a language for example, including but not limited to, “the” and “is”. In some embodiments, the data cleaning module  115  may be configured to execute code to remove one-word occurrences in a chat line such as customer chat line  201  or customer service representative chat line  203 . In some embodiments, the data cleaning module  115  may be configured to execute code for Named Entity Resolution (NER), which is a differentiation between similar chat content data. In some embodiments, the data cleaning module  115  may be configured to execute code wherein content data associated with the customer or customer chat lines  201  are separated from content data associated with the customer service representative of the enterprise or customer service representative chat lines  203 . 
     Method  400  may include extracting  405  using extraction module  117 , a plurality of chat utterances from the chat content data based on task keywords associated with the task. In some embodiments, the extraction module  117  may be configured to execute code to extract chat utterances based on Term Frequency-Inverse Document Frequency. In some embodiments, the extraction module  117  may be configured to execute code and extract chat utterances based on word morphological similarity. Word morphological similarity is a method of using word parts, for example, prefixes, suffixes, roots, and stems and compare their similarities. 
     In some embodiments, the extraction module  117  may be configured to execute code for extraction using word semantic similarity. Word semantic similarity is a method that determines similarity by comparing word vectors in multi-dimensional meaning representations of a word. In some embodiments, the extraction module  117  may be configured to execute code for an algorithm to generate word vectors. In some embodiments, the word vectors may be generated using word2vec algorithm which is a group of related models that are shallow, two-layer neural networks that can be trained to reconstruct linguistic contexts of words, t is understood that other analogous algorithms can be equally used. 
     Method  400  may include extracting chat snippets  407  by extraction module  117 . In some embodiments, the extraction module  117  may be configured to execute code wherein the chat snippets in the chat content data are identified by a proximity window to task keywords. In some embodiments, the proximity window can be set at 2 to 4 or other suitable range. In some embodiments, the proximity window is set at 2. A proximity window is also known as a context location and when “t” is the word compared to, or in the instant case “t” is a task keyword, and when the proximity window is set at 2, the locations are (t−2), (t−1), (t+1) and (t+2). 
     Method  400  may include determining  409  a type of task. In some embodiments, there are two types of tasks that are determined. In some embodiments, one type of task is an action task type for example, including but not limited to, a credit card charge dispute or a profile update on a customer account. In some embodiments, a second type of task is an inquiry task type, for example, including but not limited to, a statement balance on a bank or transaction account. In some embodiments of chat summary system  100 , the digital chat records are separated by types of tasks. 
     Method  400  may include ranking  411  by a template scoring module  119 , the chat utterances and the chat snippets. In some embodiments, the ranking assigns a ranking score to each chat utterance and each chat snippet. In some embodiments, the ranking forms a plurality of ranked chat utterances and a plurality of ranked chat snippets. In some embodiments, the ranked chat utterance and the ranked chat snippets can inform which chat utterance and chat snippets are relevant to the task and populates them into an expandable template data structure. In some embodiments, the populating of the expandable template data structure creates the automatic chat summary data structures. 
     In some embodiments, the expandable template data structure is modified to create an adjusted template data structure. The adjusted template data structure can be modified, in some embodiments, based at least in part on the plurality of ranked chat utterances and the plurality of ranked snippets. 
     In some embodiments, digital chat records that are action task types can be ranked in one method and digital chat records that are query task types can be ranked in a second method. In some embodiments, the method of ranking digital chat records that are action task types results in a highest-ranking chat utterance which is then selected as the summary title  205  for a chat summary data structure. In some embodiments of action task type, the template scoring module  119  may be configured to execute code to perform an algorithm based at least in part on a combination of weighting factor linguistic algorithm with weights for each task keyword and elements of sets functions to rank digital chat records of the action task type. In some embodiments, the template scoring module  119  may be configured to execute code to perform the algorithm as seen in Table 2 to rank chat utterances and chat related word utterances of the action task type of digital chat records. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
             
            
               
                  Let Q be the set of keywords and R be the set of related words for the task.  
               
               
                 Q = { q 1 , q 2 , ... q n  }, where each q i  is a keyword and R = {r 1 , r 2 , ... r o },  
               
               
                 where r k  is a related word. For each q i , let P be context utterances, i.e.  
               
               
                 utterances that fall within (a fixed-size window) context of the utterance  
               
               
                 that contain q i . We score P with respect to (w.r.t.) task. P = {....}, where p j    
               
               
                 is an utterance. p j  is scored based on presence of keywords and related  
               
               
                 words in it. 
               
               
                  The score of p j  over Q is calculated as 
               
               
                   
               
            
           
           
               
               
            
               
                 
                   
                     
                       
                         
                           
                             score 
                             q 
                           
                           ( 
                           
                             p 
                             j 
                           
                           ) 
                         
                         = 
                         
                           
                             ( 
                             
                               0.2 
                               * 
                               
                                 count 
                                 ( 
                                 
                                   
                                     
                                       q 
                                       k 
                                     
                                     : 
                                     
                                       q 
                                       k 
                                     
                                   
                                   ∈ 
                                   
                                     p 
                                     j 
                                   
                                 
                                 ) 
                               
                             
                             ) 
                           
                           
                             log 
                             ⁡ 
                             ( 
                             
                               length 
                               ( 
                               
                                 p 
                                 j 
                               
                               ) 
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 (1) 
               
               
                   
               
            
           
           
               
            
               
                  The score of p j  over R is calculated as 
               
               
                   
               
            
           
           
               
               
            
               
                 
                   
                     
                       
                         
                           
                             score 
                             r 
                           
                           ( 
                           
                             p 
                             j 
                           
                           ) 
                         
                         = 
                         
                           
                             0.2 
                             * 
                             
                               count 
                               ( 
                               
                                 
                                   
                                     r 
                                     k 
                                   
                                   : 
                                   
                                     r 
                                     k 
                                   
                                 
                                 ∈ 
                                 
                                   p 
                                   j 
                                 
                               
                               ) 
                             
                           
                           
                             log 
                             ⁡ 
                             ( 
                             
                               length 
                               ( 
                               
                                 p 
                                 j 
                               
                               ) 
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 (2) 
               
               
                   
               
            
           
           
               
            
               
                  We defined a weight for each keyword, w(q i ), as the inverse document  
               
               
                 frequency (IDF) value. 
               
               
                   
               
            
           
           
               
               
            
               
                 
                   
                     
                       
                         
                           score 
                           ( 
                           
                             p 
                             j 
                           
                           ) 
                         
                         = 
                         
                           
                             ( 
                             
                               
                                 
                                   score 
                                   q 
                                 
                                 ( 
                                 
                                   p 
                                   j 
                                 
                                 ) 
                               
                               + 
                               
                                 
                                   score 
                                   r 
                                 
                                 ( 
                                 
                                   p 
                                   j 
                                 
                                 ) 
                               
                             
                             ) 
                           
                           * 
                           
                             w 
                             ⁡ 
                             ( 
                             
                               q 
                               i 
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 (3) 
               
               
                   
               
            
           
           
               
            
               
                  The final score is the accumulated sum of score(p j ) over all q i   
               
               
                   
               
            
           
           
               
               
            
               
                 
                   
                     
                       
                         
                           Score 
                           ( 
                           
                             
                               p 
                               j 
                             
                             , 
                             Q 
                           
                           ) 
                         
                         = 
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             n 
                           
                           
                             score 
                             ( 
                             
                               
                                 p 
                                 j 
                               
                               , 
                               
                                 q 
                                 i 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 (4) 
               
               
                   
               
            
           
           
               
            
               
                  Utterances in P with highest Score(p j , Q) is extracted as summary of  
               
               
                 action task chats. 
               
               
                   
               
            
           
         
       
     
     In some embodiments, the method of ranking digital chat records of inquiry task type is done using question and answer pairs since inquiry type task digital chat records have a question and answer nature. In some embodiments of inquiry type tasks, the template scoring module  119  may be configured to execute code to cluster similarities and word clusters to rank inquiry task type chats. In some embodiments of the inquiry type task, word clusters are created using morphological and semantic similarity of words. In some embodiments of the inquiry type task, morphological word-to-word distance may be measured using Levenshtein edit distance and semantic word-to-word distance that may be measured using word2vec word-vector similarity. 
     In some embodiments, the template scoring module  119  may be configured to execute code to perform the algorithm as seen in Table 3 that is configured to extract question answer pairs from a digital chat record and then rank the question answer pairs. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
             
            
               
                   
                   
                 1: procedure Question Answer Pairs 
               
            
           
           
               
               
            
               
                   
                  2: Initialize: 
               
            
           
           
               
               
               
            
               
                   
                  3: 
                 create MLC, MSC and SRC 
               
               
                   
                  4: 
                   
               
               
                   
                  5: 
                 function Score(utterances): 
               
               
                   
                  6: 
                  for utterance do 
               
               
                   
                  7: 
                   ULC = LC of utterance 
               
               
                   
                  8: 
                   CCS = |{MLC} ∩{ULC centre}| 
               
               
                   
                  9: 
                   CMS = |{MLC}∩{ULC member}| 
               
               
                   
                 10: 
                   SS = |{SRC}∩{utterance}| 
               
               
                   
                 11: 
                   normalize CCS, CMS &amp;SS 
               
               
                   
                 12: 
                  rank utterance by CCS, CMS &amp; SS 
               
               
                   
                 13: 
                  return max (utterances) 
               
            
           
           
               
               
            
               
                   
                 14: Main: 
               
            
           
           
               
               
               
            
               
                   
                 15: 
                 Find all utterances that are questions 
               
               
                   
                 16: 
                 for question do 
               
               
                   
                 17:  
                  select utterances between consecutive questions 
               
               
                   
                 18:  
                  answer = max(Score(utterances)) 
               
               
                   
                 19:  
                  score (question − answer) = 
               
            
           
           
               
               
            
               
                   
                 Score(question) + 
               
               
                   
                 return question answer with highest score 
               
               
                   
                   
               
            
           
         
       
     
     Word clusters produced with morphological distance are denoted Mean Levenshtein Cluster (MLC) and semantic distance are denoted Mean Semantic Cluster (MSC) respectively. In some embodiments, the MLC and Mean of Semantic Clusters (MSC) of table 3 is created using the corpus of all digital chat records of a subgrouping of tasks and not the extracted chat utterances of one digital chat record. 
     The term ULC of table 3, is in some embodiments, Levenshtein Cluster of ULC and is identified from words in a chat utterance of one digital chat records and not the corpus. In table 3, the number of cluster centers of ULC that match (intersect) the cluster centers of MLC are measured as Cluster Centre Similarity (CCS). Similarly, in table 3, the number of cluster members of ULC that match (intersect) the cluster centers of MLC are measured as Cluster Member Similarity (CMS). CCS and CMS are normalized by utterance length (in words). For example, normalization can be performed based on the number of words or characters of an utterance or other characteristics of the CCS and CMS. In some instances, such a normalization can categorize utterance into, for example, short utterance, medium utterance, long utterance, or other suitable category. In some other instances, normalization can be performed by identifying the number of possible boundaries in an utterance and/or number of words or characters in an utterance. 
     In Table 3, the clusters in MSC are considered relevant if task keywords and task related words of the task subgrouping are present. In table 3, the number of words of the chat utterance present in semantically relevant clusters (SRC) are normalized by utterance length (in words) and denoted as semantic similarity (SS). For example, normalization can be performed based on the number of words or characters of an utterance or other characteristics of the semantically relevant clusters (SRC). SS ranks relevance of an utterance to the task. The chat utterance that scores highest based on CCS, CMS and SS is selected. 
     In some embodiments, the chat summary system identifies chat utterances as question based on part of speech (POS) tagging algorithms. In some embodiments, the chat summary system identifies common sequence of questions by identifying all chat utterances that end with a question mark (′?′), generate the POS tag sequences and select the top k most frequently occurrences. In some embodiments, POS tag sequences that are not useful, for example, but not limited to, “Please?” and “Are you there?”, are removed. In some embodiments the POS tag sequence process is repeated. By way of example, Table 4 illustrates a list of part of speech tags used. In some embodiments, chat utterances that start with a question word such as who, what, when, and end in a question mark and whose POS tags fit a sequence for example in Table 4 are identified as questions. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Question POS Sequences 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 (ADV|VERB) + (PRON|NOUN)(VERB)(DET|ADJ)(NOUN)+(PUNC) 
               
               
                 (ADV|VERB) + (PRON|NOUN)VERB(PRON|NOUN|DET)(ADJ)?(NOUN)?(PUNC) 
               
               
                 (NOUN)(VERB)(DET)?(ADJ)*(NOUN) + (ADJ)?(PUNC) 
               
               
                 (ADV|VERB) + (PRON|NOUN)(VERB)(PUNC) 
               
               
                 (ADV|VERB) + (DET|ADJ)(NOUN)(VERB)+(PUNC) 
               
               
                   
               
            
           
         
       
     
     In some embodiments of chat summary system  100 , after finding the question chat utterances in a digital chat record, chat utterances that occur between two consecutive question chat utterances can be ranked by their relevance-score our other suitable metric. In some embodiments, relevance score of chat utterances can be calculated using the function Score(utterances) shown in TABLE 3. In some instances, the top ranked chat utterance between two consecutive question chat utterances are chosen as the answer to the preceding question. In some embodiments, the template scoring module  119  may be configured to execute code so one question answer pair is generated for every question. In some embodiments, when there are no chat utterances between two consecutive questions, the two consecutive questions are treated as one question. In some embodiments, the question-answer pairs are ranked by sum of scores of question and answer in that pair, see for example Table 3. In some embodiments, the top ranked question-answer pair can be the summary title  205  of the chat summary data structure of an inquiry type digital chat record. 
     Method  400  may include modifying  413  an expandable template data structure  FIG.  5    associated with a subgroup of the tasks. In some embodiments, the expandable template data structure is modified based on the ranked chat utterances and the ranked chat snippets. 
     Method  400  may include populating  415  an expandable template data structure, with a subset of the ranked chat utterances and a subset of the ranked chat snippets based in part on ranking scores of the chat utterances and chat snippets. In some embodiments, an adjusted template data structure (modified expandable template data structure), is populated with a subset of the ranked chat utterances and a subset of the ranked chat snippets based in part on ranking scores of the chat utterances and chat snippets. In some embodiments, the highest-ranking chat utterance or question answer pair can be used as a summary title  205  of chat summary data structure. In some embodiments of the chat summary system, the populated expandable template data structure forms the chat summary data structure. In some embodiments, the chat summary data structure is created in a human readable chat summary data structure, a SQL compatible format summary or both. Method  400  may include storing  417  the chat summary data structure or structures in memory  125 . 
     In some embodiments, software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device. 
     Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same embodiment(s), though it may. Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the present disclosure. 
     As used herein, the term “user” shall refer to at least one user. In some embodiments, the term “user” or “administrator” should be understood to refer to a provider of an application or applications as described herein and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the terms “user” or “developer” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data. 
     In addition, the term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     In some embodiments, programmed computing systems with associated devices can be configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols. 
     Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, Application Specific Integrated Circuits (ASIC), Programmable Logic Devices (PLD), Digital Signal Processors (DSP), Field Programmable Gate Array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth. 
     Computer systems, and systems, as used herein, can include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, Application Programming Interfaces (API), computer code, data, data variables, or any combination thereof that can be processed by a computing device as computer-executable instructions. 
     As used herein, term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud servers can be, for example, a collection of servers serving one or more functions of a single server. 
     In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and/or output any digital object and/or data unit (e.g., from inside and/or outside of an application) that can be in any suitable form such as, without limitation, a file, a contact, a task, an email, a message, a map, an entire application (e.g., a calculator), data points, and other suitable data. In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may be implemented across one or more of various computer platforms such as, but not limited to: (1) Linux, (2) Microsoft Windows, (3) OS X (Mac OS), (4) Solaris, (5) UNIX (6) VMWare, (7) Android, (8) Java Platforms, (9) Open Web Platform, (10) Kubernetes or other suitable computer platforms. 
     In some embodiments, computer-based systems of the present disclosure may be configured to utilize hardwired circuitry and/or hardware components that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry or hardware components and/or software 
     In some embodiments, computer-based systems of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000. As used herein, the term “user” shall have a meaning of at least one user. 
     In some embodiments, the machine learning model or plurality of machine learning models of the present disclosure may be configured to utilize one or more AI/machine learning techniques chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, a neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. 
     In some embodiments and, optionally, in combination of any embodiment described above or below, a trained neural network model may specify a neural network by at least a neural network topology, a series of activation functions, and connection weights. 
     The aforementioned examples are, of course, illustrative and not restrictive. At least some aspects of the present disclosure will now be described with reference to the following numbered clauses. 
     1. A method comprising; 
     receiving, by a processor, a corpus of digital chats records between a plurality of customers and a plurality of customer service representatives of an enterprise,
         wherein each digital chat record of the corpus of digital chat records is a chat data structure comprising chat content data and chat metadata of a digital chat record, and   wherein each chat metadata comprises a task identifier corresponding to a task of a plurality of tasks that a customer interacted about with a customer service representative during the digital chat of the corpus of digital chat records;       

     grouping, by the processor, the corpus of digital chat records into subgroups of tasks of the plurality of tasks,
         wherein each subgroup corresponds to one task of the plurality of tasks;       

     generating, by the processor via a keyword chat module, for each subgroup of digital chat records, a plurality of task keywords and a plurality of task related words from the chat content data of each digital chat record based at least in part on a tokenization analysis of the corpus of digital chat records; 
     generating, by the processor, for each subgroup of digital chat records, an expandable template data structure based at least in part on the plurality of task keywords and the plurality of task related words; 
     receiving, by the processor, a digital chat record; 
     determining, by the processor, from the metadata of the digital chat record the task identifier corresponding to the task of the plurality of tasks; 
     extracting, by the processor, a plurality of chat utterances from the chat content data from the digital chat record based at least in part on the plurality of task keywords associated with the task and the plurality of task related words associated with the task; 
     extracting, by the processor, a plurality of chat snippets from the plurality of chat utterances based at least in part on a data proximity window in relation to a task keyword; 
     ranking, by the processor via a template scoring module, the plurality of chat utterances and the plurality of chat snippets based at least in part on the expandable template data structure associated with the task identifier to:
         i) assign a ranking score to each chat utterance and each chat snippet, and   ii) form a plurality of ranked chat utterances and a plurality of ranked chat snippets;       

     modifying, by the processor, the expandable template data structure to form an adjusted template data structure based at least in part on the plurality of ranked chat utterances and the plurality of ranked chat snippets; 
     populating, by the processor, the adjusted template data structure with a subset of the ranked chat utterances from the plurality of ranked chat utterances and a subset of the ranked chat snippets from the plurality of the ranked chat snippets based at least in part on the ranking score to form a chat summary data structure for the digital chat record; and 
     storing, by the processor, the chat summary data structure for the digital chat record in a database. 
     2. The method of clause 1, wherein the digital chat record is received as a new digital chat record from a chat platform associated with the enterprise. 
     3. The method of clause 1, wherein the digital chat record is a digital chat record from the corpus of digital chats records. 
     4. The method of clauses 1, 2 or 3, wherein the data proximity window is a context window having a size of 2. 
     5. The method of any of the preceding clauses, further comprising: 
     parsing, by the processor, the digital chat record to remove a stop word before the extracting the plurality of chat utterances and the plurality of chat snippets. 
     6. The method of any of the preceding clauses, wherein the chat content data further comprises chat lines; and wherein the method further comprises: 
     parsing, by the processor, the digital chat record to remove a one-word chat line from the digital chat record before the extracting the plurality of chat utterances and the plurality of chat snippets. 
     7. The method any of the preceding clauses, further comprising: 
     differentiating, by the processor, similar chat content data before extracting the plurality of chat utterances and the plurality of chat snippets. 
     8. The method of any of the preceding clauses. further comprising; 
     parsing, by the processor, the digital chat record to remove a stop word before the extracting chat utterances and chat snippets. 
     9. The method of clauses 6, 7 or 8, further comprising 
     separating, by the processor, the chat lines of the customer from the chat lines of the customer service representative of the enterprise. 
     10. The method of any of the preceding clauses, further comprising 
     determining, by the processor, a task type for a subgroup of tasks. 
     11. The method of claim  10 , wherein the task type for a subgroup of tasks is either an action task type or an inquiry task type. 
     12. The method of any of the preceding clauses, wherein the chat summary data structure comprises a human readable chat summary data structure. 
     13. The method any of the preceding clauses, wherein the chat summary data structure comprises a structured query language compatible format summary. 
     14. The method of claim  11 , wherein the rank of chat utterances and chat snippets of the action task type is based on at least in part, a weighting factor algorithm, a weight identified for each task keyword and elements of sets. 
     15. The method of claim  11 , wherein ranking the query task type further comprises; 
     identifying, by the processor, question-answer pairs in chat utterances and chat snippets; and 
     ranking, by the processor via a scoring module, the question-answer pairs based on at least in part word clustering and unions of sets. 
     16. A system, comprising: 
     a processor; and 
     a memory storing instructions which, when executed by the processor, causes the processors to:
         receive, a corpus of digital chats records between a plurality of customers and a plurality of customer service representatives of an enterprise,
           wherein each digital chat record of the corpus of digital chat records is a chat data structure comprising chat content data and chat metadata of a digital chat record, and   wherein each chat metadata comprises a task identifier corresponding to a task of a plurality of tasks that a customer interacted about with a customer service representative during the digital chat of the corpus of digital chat records;   
           group the corpus of digital chat records into subgroups of tasks of the plurality of tasks,
           wherein each subgroup corresponds to one task of the plurality of tasks;   
           generate via a keyword chat module, for each subgroup of digital chat records, a plurality of task keywords and a plurality of task related words from the chat content data of each digital chat record based at least in part on a tokenization analysis of the corpus of digital chat records;   generate for each subgroup of digital chat records, an expandable template data structure based at least in part on the plurality of task keywords and the plurality of task related words;   receive a digital chat record;   determine from the metadata of the digital chat record the task identifier corresponding to the task of the plurality of tasks;   extract a plurality of chat utterances from the chat content data from the digital chat record based at least in part on the plurality of task keywords associated with the task and the plurality of task related words associated with the task;   extract a plurality of chat snippets from the plurality of chat utterances based at least in part on a data proximity window in relation to a task keyword;   rank via a template scoring module, the plurality of chat utterances and the plurality of chat snippets based at least in part on the expandable template data structure associated with the task identifier to:
           i) assign a ranking score to each chat utterance and each chat snippet, and   ii) form a plurality of ranked chat utterances and a plurality of ranked chat snippets;   
           modify the expandable template data structure to form an adjusted template data structure based at least in part on the plurality of ranked chat utterances and the plurality of ranked chat snippets;   populate the adjusted template data structure with a subset of the ranked chat utterances from the plurality of ranked chat utterances and a subset of the ranked chat snippets from the plurality of the ranked chat snippets based at least in part on the ranking score to form a chat summary data structure for the digital chat record; and   store the chat summary data structure for the digital chat record in a database.
 
17. The system of claim  16 , wherein the instructions further cause the processor to:
       

     determine a task type for a subgroup of tasks. 
     18. The system of claim  17 , wherein the task type for a subgroup of tasks is either an action task type or an inquiry task type. 
     19. The system of clauses 16, 17 or 18, wherein the chat summary data structure comprises a human readable chat summary data structure. 
     20. The system of clauses 16, 17, or 18 wherein the chat summary data structure comprises a structured query language compatible format summary. 
     While one or more embodiments of the present disclosure have been described, it is understood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that various embodiments of the methodologies, the systems, and the devices described herein can be utilized in any combination with each other. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and/or any desired steps may be eliminated).