Patent Publication Number: US-10311377-B2

Title: Categorization of user interactions into predefined hierarchical categories

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 14/180,967, entitled “CATEGORIZATION OF USER INTERACTIONS INTO PREDEFINED HIERARCHICAL CATEGORIES”, filed Feb. 14, 2014, which issued on Apr. 18, 2017 as U.S. Pat. No. 9,626,629, which claims priority to U.S. Provisional Patent Application No. 61/764,962, filed Feb. 14, 2013, and which are incorporated herein in their entirety by this reference thereto. 
    
    
     BACKGROUND OF THE INVENTION 
     Technical Field 
     The embodiments herein relate to categorizing user interactions. More particularly, the invention relates to categorizing user interactions into predefined hierarchical categories. 
     Description of the Background Art 
     Presently, customer care agents and other customer service providers handle customer queries and troubleshoot customer issues on a frequent basis. Such queries vary over a wide range of topics, each topic belonging to a different domain. In this aspect, it is difficult for a customer care agent to segregate these queries to their respective domains and answer the customer within a stipulated time because the queries belong to a various domains and differ in nature. 
     It would be advantageous to classify customer queries into various classes and/or categories, for example specific queries that relate to sales or services in a business. 
     SUMMARY OF THE INVENTION 
     User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block schematic diagram showing a customer care agent environment according to the invention; 
         FIG. 2  is a flow diagram showing query categorization into predefined categories according to the invention; 
         FIGS. 3A and 3B  are block schematic diagrams showing query categorization and model execution according to the invention; 
         FIG. 4  is a block schematic diagram showing preprocessing according to the invention; 
         FIG. 5  is a flow diagram showing classification of issues in chat according to the invention; and 
         FIG. 6  is a block schematic diagram showing a machine in the example form of a computer system within which a set of instructions for causing the machine to perform one or more of the methodologies discussed herein may be executed. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries and other interactions, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into, for example, action-based or information-based interactions. 
       FIG. 1  is a block schematic diagram showing a customer care agent environment according to the invention. As shown in  FIG. 1 , a website environment  11  and a personal customer care agent node  13  are communicably interconnected via a network  12 . The personal customer care agent node is, in turn, communicably interconnected with a plurality of users  14  via the network. The customer care agent may interact with the users via various modes that comprise any one or more of online chat, surveys, forums, voice calls, and so on. 
     For purposes of the discussion herein, the term ‘network’ refers to networks that are connected to each other using the Internet Protocol (IP) and other similar protocols. Those skilled in the art will appreciate that the invention may be practiced in connection with any communications network using any communications protocol. 
     In an embodiments of the invention, the website environment comprises aggregated information from entity-based websites, social media websites, and other related websites, although the invention is not limited to this sort of website. 
     In another embodiment of the invention, the personal customer care agent is presented with user preferences in connection with obtaining information from, and presenting information to, the user, as well as for generating and publishing information based on user activity. 
     The personal customer care agent node includes personal customer care agent software. For purposes of the discussion herein, a node is any of a processor, a network element, a server, a computing device, a database, a hardware device, a physical storage, and a physical memory. 
     When a user who is connected to the network contacts a personal customer care agent, the agent receives the user&#39;s queries and either responds to the queries on his own or escalates the query to obtain a response from an appropriate entity, for example a specific department within the agent&#39;s company. The agent may have a certain window of time in which to respond to the user&#39;s queries. The agent may also have to categorize queries based on their nature. 
       FIG. 2  is a flow diagram showing query categorization into predefined categories according to the invention. In  FIG. 2 , text data is received ( 215 ), for example from a chat session, and a preprocessing step is performed ( 216 ). Features are extracted from the text ( 217 ) and a model is built ( 218 ) in a model building phase. Once the model is built, the model execution phase commences ( 219 ), in which extracted features are provided to the model. Thereafter, post processing ( 220 ), such as for example, dictionary lookups based on predicted queries, suggesting actions based on lookup queries, cross-tabulation based on another structured variable with the predicted queries, or generically building any reporting summary tables using the predicted labels, is performed 
       FIGS. 3A and 3B  are block schematic diagrams showing query categorization and model execution according to the invention. 
       FIG. 3A  shows the process of model scoring or model execution, wherein a trained model  23  is applied to user interaction data to classify and label the corresponding text data. As shown in  FIG. 3A , the process of query categorization creates a system for labeling text data into predefined hierarchical categories. Labeled text data is text data that is transformed into a vector of features, such as the presence of a word, position of words, POS tags, counts, term frequencies, term frequency—inverse document frequency (tfidf), etc. This transformed vector becomes a feature vector X having labels y. The model building process identifies the unknown functional relationship f for Y=f(x) from historical chat data. The model scoring process predicts an unknown y using the learned function f for new chat data transformed to new x. 
     Query categorization proceeds when an unlabeled document  21  is provided to a relevant line extractor  22 . The relevant line extractor uses business heuristics to extract only those lines in the text of the unlabeled document that are relevant. For example, a heuristic business rule for extracting a primary issue line looks at the first line of text received from the customer that follows a greeting line by the agent but ignores the greeting line. For purposes of the discussion herein, the text may be obtained from chats, transcribed calls, forums, surveys, or any other suitable source. Relevant lines are those which are important for labeling. Extraneous and/or irrelevant lines, phrases, or tags such as greetings, welcome tones, and so on are removed from the chat by the relevant line extractor, i.e. a chat filter. While relevant line extraction is especially critical in chats, it is useful across all formats of text data, e.g. tweets, IVR, transcribed text, etc. Relevant line extraction may be implemented by any generic algorithm to extract relevant lines of text, e.g. extracting noun phrases only, extracting specific text phrases, extracting only agent lines, extracting only customer lines, extracting customer lines with sentiments, extracting question lines, etc. 
     The output of the relevant line extractor is input into an information retrieval (IR) engine  24  and feature data matrices  25  are output. For example, a feature data matrix is formed by combining the feature vectors for all historical chat text data. The feature data matrix is created from cleansed, transformed, and structured representations of the original, transcribed or typed and unstructured text interaction history. These representations can also be combined with additional structured data gathered by the platform or system, for example, interaction times, Web traversal history, etc. 
     In embodiments of the invention, the lines of text may be preprocessed (see  FIG. 4 ), which may involve several steps such as reshaping data; masking text patterns, for example different date patterns, digit patterns, phone numbers, credit card numbers, company names, URLs, etc.; converting to lower case; removing numbers; removing punctuation; tokenization; stemming; part-of-speech tagging; lemmatization; etc. 
     In embodiments of the invention, the IR engine  24  is a rule evaluator that scores every document against the model  23  which is run along with the text data. Based on rules that hit each text data point, each such data point is given a score for each of the categories. The result is a feature data matrix  25  which is produced for each level in a category tree. 
     The model/classifier  23  obtains the feature data matrices and uses different classification algorithms via a scoring data matrix  26  to label them, thus producing predicted labels  32 . The matrices comprise cleansed, transformed, and structured representations of the original, transcribed or typed and unstructured text interaction history. These representations may also be combined with additional structured data gathered by the platform or system, for example, interaction times, Web traversal history, etc. The classifier or the model may be built in a supervised or an unsupervised approach. 
     Supervised models require tagging or manual annotation, i.e. labeling of data, from which the model learns in the model building or model learning process. 
     Some examples of supervised models or classification algorithms include, decision trees, SVMs, random forests, logistic regression, etc. 
     Unsupervised models may also be used, such as, kmeans clustering, hierarchical clustering, etc. 
     Once the model is trained, i.e. during model building, by providing labeled response variables and a set of structured input features, and further validated for model lift, accuracy, etc. during a model testing or model validation phase, the model may be used for model scoring. In model scoring, for a given set of structured input features, the model can predict the response variable. 
       FIG. 3B  depicts the process of model building from trained data samples, wherein text interactions are labeled, annotated, or tagged during a tagging process from which a model is built. As shown in  FIG. 3B , the model  23  ( FIG. 3A ) comprises machine learning models, such as, decision trees, SVMs, random forests, rules, etc., that are based on predicted tags  32 . In embodiments of the invention, the model can also be modified and/or edited based on user rules or requirements. For example, additional rules may be added by the user. For example, an additional rule for purchase query category may be added, e.g. “if chat text contains purchase or buy, category is purchase query.” In this way, a model built on interaction data from one client may become applicable to a different client in the same or similar domain because the feature vectors that are used for model building or the rules of the model may be applicable to it 
     During model execution  30 , the model and unlabeled text data are obtained and classified into user interactions that are either action-based or information-based chats (see  FIG. 3A ). In  FIG. 3B , unlabeled text data is input to a relevant line extractor  22 , preprocessed  31 , and thereafter provided to model execution  30 . A model and the preprocessing module  31  are used to parse the chat sessions and extract relevant or important features. The features may be, for example, most frequent n-grams; most discriminatory n-grams; rules, e.g. AND, OR, proximity, or any other more complex rules; any structured data, such as, handle times, delays in response, etc. In an embodiment, the rule extraction system uses a natural language model to extract the part of speech (POS) tags that can be used as features. The feature selection may be performed based on mutual information, binormal separation, tf-idf, pca, or any other machine learning algorithm. 
     The output of the rule extraction system is provided to the model execution module, which is responsible for building machine learning models based on the predicted tags  32 . During a model execution stage, the classifier or model  23  is applied to the unlabeled text data  21  to classify the chats into either an action-based chat session or an information-based chat session. The chats may also be further categorized into sub-categories of an information-based or action-based type of chat. Examples of sub-categories can include, ‘mail sent,’ ‘agent provided customer the steps to resolve the issue,’ ‘agent provided the customer with information about the current air fares,’ ‘agent changed the status of a customer,’ and so on. 
     The rule extraction system extracts different rules, i.e. features, that qualify the text and then checks the distribution of the extracted features across different categories hierarchically. Based on the relevance of the categories, the rule extraction system provides scores. The top rules for each category, based on their scores, are written in a model file  23  which can be read and edited manually. 
     In embodiments of the invention, the source of text is a chat session transcript in the form of text chat that is transcribed text or that is obtained through social media, forums, and the like. The process of classifying resolutions in a chat session comprises three phases, including a training phase, a testing phase, and an application phase. Examples of a resolution of one or more queries include an information-based query where an agent provides price information for a product, or provides detailed information regarding product insurance, in response to corresponding customer queries. Examples of an action request-based query include an agent canceling services on request of a customer, or the agent updating account information on behalf of the customer. The chats are labelled for the resolution type, and the model is trained, validated, and then used for scoring, as depicted in  FIGS. 3A and 3B , in a similar fashion as described for query categorization process above 
       FIG. 4  is a block schematic diagram showing preprocessing according to the invention. In  FIG. 4 , spaces are stripped  40  from the lines of text, punctuation is removed  41 , tokenization is performed  42 , POS tagging is performed  43 , stop words are removed  44 , lemmatization is performed  49 , stemming is performed  45 , chunking and chinking is performed  46 , and the text is indexed  47 . Thereafter, rule extraction is performed  48 , as described above. 
       FIG. 5  is a flow diagram showing classification of customer queries or issues in chat according to the invention. There are typically two different types of issues that a user wants to address during an interaction session. The user may either seek information from an agent or the user may put forth an action oriented request to the agent. 
     Initially, a text filter filters the relevant sections in the interaction session. The lines of text may be preprocessed, as described above, which involves several steps such as reshaping data; masking text patterns, for example different date patterns, digit patterns, phone numbers, credit card numbers, company names, URLs, etc.; converting to lower case; removing numbers; removing punctuation; tokenization; stemming; part-of-speech tagging; lemmatization; etc. The feature extractor extracts important features from the interaction session, from the preprocessed or the original chat text. 
     To categorize the different kinds of issues in a session, a category tree is initially built ( 301 ) to segregate issues into different categories. These categories generally cover all of the issue categories that relate to the business or domain in question. For example, in embodiments of the invention the categories cover a range of billing related issues, such as late payment, miscellaneous charges, and so on; or payment related issues. 
     The features created in the training phase can include rules, such as features based on POS tags, word counts, position of words, presence of unigrams, bigrams, trigrams, or higher order ngrams, presence of a first word AND a second word (AND rule), presence of a first word OR a second word (OR rule), proximity rules such as NEAR, directional proximity rules such as ADJACENT, proximity rules operating within a limited scope or window of words or characters, negation rules such as NOT, etc. The rules help to extract relevant features during the session. 
     Once the features are created, a training phase is implemented ( 303 ) and the relevant lines or phrases are extracted. 
     During a pre-processing session, the text may also be generalized by replacing specific words with standard words. For example, the feature extractor recognizes a word such as ‘obtain’ instead of ‘take’ or ‘get.’ 
     A scoring matrix is generated ( 304 ) based on the scores for each category for each chat transcript in the entire corpus of chat texts, and the matrix is then given a score based on the relevance of the matrix generated. The score obtained is based on a comparison between the rules created and the category tree. 
     Based on the predicted categories ( 305 ) of customer queries above, the process of classifying issues in a chat helps the agent to respond to customer queries quickly and to transfer or direct the customers to a concerned department appropriately. The issues may be pre-defined into hierarchical categories. 
     The model may be tested by comparing the predicted tags with manual tagging ( 306 ) and model performance can be determined based upon the comparison ( 307 ). 
     The various actions in the exemplary method shown on  FIG. 5  may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments of the invention, some actions listed in  FIG. 5  may be omitted. 
     Computer Implementation 
       FIG. 6  is a block diagram of a computer system that may be used to implement certain features of some of the embodiments of the invention. The computer system may be a server computer, a client computer, a personal computer (PC), a user device, a tablet PC, a laptop computer, a personal digital assistant (PDA), a cellular telephone, an iPhone, an iPad, a Blackberry, a processor, a telephone, a web appliance, a network router, switch or bridge, a console, a hand-held console, a (hand-held) gaming device, a music player, any portable, mobile, hand-held device, wearable device, or any machine capable of executing a set of instructions, sequential or otherwise, that specify actions to be taken by that machine. 
     The computing system  190  may include one or more central processing units (“processors”)  195 , memory  191 , input/output devices  194 , e.g. keyboard and pointing devices, touch devices, display devices, storage devices  192 , e.g. disk drives, and network adapters  193 , e.g. network interfaces, that are connected to an interconnect  196 . 
     In  FIG. 6 , the interconnect is illustrated as an abstraction that represents any one or more separate physical buses, point-to-point connections, or both connected by appropriate bridges, adapters, or controllers. The interconnect, therefore, may include, for example a system bus, a peripheral component interconnect (PCI) bus or PCI-Express bus, a HyperTransport or industry standard architecture (ISA) bus, a small computer system interface (SCSI) bus, a universal serial bus (USB), IIC (12C) bus, or an Institute of Electrical and Electronics Engineers (IEEE) standard 1394 bus, also referred to as Firewire. 
     The memory  191  and storage devices  192  are computer-readable storage media that may store instructions that implement at least portions of the various embodiments of the invention. In addition, the data structures and message structures may be stored or transmitted via a data transmission medium, e.g. a signal on a communications link. Various communications links may be used, e.g. the Internet, a local area network, a wide area network, or a point-to-point dial-up connection. Thus, computer readable media can include computer-readable storage media, e.g. non-transitory media, and computer-readable transmission media. 
     The instructions stored in memory  191  can be implemented as software and/or firmware to program one or more processors to carry out the actions described above. In some embodiments of the invention, such software or firmware may be initially provided to the processing system  190  by downloading it from a remote system through the computing system, e.g. via the network adapter  193 . 
     The various embodiments of the invention introduced herein can be implemented by, for example, programmable circuitry, e.g. one or more microprocessors, programmed with software and/or firmware, entirely in special-purpose hardwired, i.e. non-programmable, circuitry, or in a combination of such forms. Special-purpose hardwired circuitry may be in the form of, for example, one or more ASICs, PLDs, FPGAs, etc. 
     Although the invention is described herein with reference to the preferred embodiment, one skilled in the art will readily appreciate that other applications may be substituted for those set forth herein without departing from the spirit and scope of the present invention. Accordingly, the invention should only be limited by the claims included below.