Patent Publication Number: US-2023136965-A1

Title: Prohibiting inconsistent named entity recognition tag sequences

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a non-provisional application of and claims the benefit and priority under 35 U.S.C. 119(e) of U.S. Provisional Application No. 63/274,204, filed Nov. 1, 2021 entitled “PROHIBITING INCONSISTENT NAMED ENTITY RECOGNITION TAG SEQUENCES” the entire contents of which is incorporated herein by reference for all purposes. 
    
    
     FIELD 
     The present disclosure relates generally to machine learning techniques, and more particularly, to a framework for prohibiting inconsistent named entity recognition (NER) tag sequences and biasing named entity (NE) length. 
     BACKGROUND 
     Many users around the world are on instant messaging or chat platforms in order to get instant reaction. Organizations often use these instant messaging or chat platforms to engage with customers (or end users) in live conversations. However, it can be very costly for organizations to employ service people to engage in live communication with customers or end users. Chatbots or bots have begun to be developed to simulate conversations with end users, especially over the Internet. End users can communicate with bots through messaging apps that the end users have already installed and used. An intelligent bot, generally powered by artificial intelligence (AI), can communicate more intelligently and contextually in live conversations, and thus may allow for a more natural conversation between the bot and the end users for improved conversational experience. Instead of the end user learning a fixed set of keywords or commands that the bot knows how to respond to, an intelligent bot may be able to understand the end user&#39;s intention based upon user utterances in natural language and respond accordingly. 
     However, chatbots are difficult to build because these automated solutions require specific knowledge in certain fields and the application of certain techniques that may be solely within the capabilities of specialized developers. As part of building such chatbots, a developer may first understand the needs of enterprises and end users. The developer may then analyze and make decisions related to, for example, selecting data sets to be used for the analysis, preparing the input data sets for analysis (e.g., cleansing the data, extracting, formatting, and/or transforming the data prior to analysis, performing data features engineering, etc.), identifying an appropriate machine learning (ML) technique(s) or model(s) for performing the analysis, and improving the technique or model to improve results/outcomes based upon feedback. The task of identifying an appropriate model may include developing multiple models, possibly in parallel, iteratively testing and experimenting with these models, before identifying a particular model (or models) for use. Further, supervised learning-based solutions typically involve a training phase, followed by an application (i.e., inference) phase, and iterative loops between the training phase and the application phase. The developer may be responsible for carefully implementing and monitoring these phases to achieve optimal solutions. For example, to train the ML technique(s) or model(s), precise training data is required to enable the algorithms to understand and learn certain patterns or features (e.g., for chatbots—intent extraction and careful syntactic analysis, not just raw language processing) that the ML technique(s) or model(s) will use to predict the outcome desired (e.g., inference of an intent from an utterance). In order to ensure the ML technique(s) or model(s) learn these pattern and features properly, the developer may be responsible for selecting, enriching, and optimizing sets of training data and hyperparameters for the ML technique(s) or model(s). 
     BRIEF SUMMARY 
     Techniques disclosed herein relate generally to machine learning techniques. More specifically and without limitation, techniques disclosed herein relate to frameworks for prohibiting inconsistent named entity recognition tag sequences. With sufficient training data, a trained conditional random field (CRF) training procedure will learn that the inconsistent pairs do not occur in the training data. However, with small training datasets, the conditional random field training procedure can learn a model that generates inconsistent tag to tag transitions. For small training datasets, the inconsistent tag sequences can be discouraged by modifying the tag to tag transition weights. 
     In various embodiments, a computer-implemented method is provided that includes: obtaining a trained conditional random field model (CRF), where the conditional random field model comprises a set of model parameters that were learned from training data. The training data comprising strings of text annotated with tags, the set of model parameters being stored in a transition matrix, and the set of model parameters being weights associated with transitioning from one tag to another tag. Within the transition matrix, inconsistent tag sequences for the tags are identified. Inconsistent tag sequences contain a pair of tags that are inconsistent with the tag sequence logic (e.g., B-PERSON→I-LOCATION). Within the transition matrix, a cost (e.g., weight) associated with transitioning between the inconsistent pair of tags is set and the cost is equal to a predefined hyperparameter value that penalizes transitioning between the pair of tags. A string of text (e.g., sequence of confidence score lists) comprising one or more named entities is received. The string of text is input into the conditional random field model that has had the cost (e.g., weights) associated with transitioning between pairs of tags set equal to the predefined hyperparameter value. The conditional random field model classifies one or more words within the string of text as one or more named entities (e.g, returns the best sequence of labels in which each label is associated with one token piece). 
     In various embodiments, the tags are Beginning, Inside, Outside, and (BIO) tags. 
     In various embodiments, the hyperparameter is a negative number. 
     In various embodiments, the inconsistent/incorrect pair of tags can include one or more of: Outside and Inside-x, Beginning-x and Inside-y, or Inside-x and Inside-y, where x and y are different tags. 
     In various embodiments, a computer-implemented method is provided that includes: obtaining a conditional random field (CRF) algorithm comprising a set of model parameters stored in a transition matrix. The set of model parameters are costs associated with transitioning from one tag to another tag. A tag sequence that the user requests to encourage or discourage a transition between a pair of tags within the tag sequence is received. The transition matrix is initialized to include the tag sequence and set a cost associated with transitioning between the pair of tags. The cost is initialized randomly and then being updated during the conditional random field model training to penalize or reward the transitioning between the pair of tags. The conditional random field algorithm is trained on a set of training data comprising strings of text annotated with tags and named entity classifications (e.g., recognizers) to generate a conditional random field model. The training comprises performing iterative operations to learn the set of model parameters stored in the transition matrix. Each iteration involves updating the set of parameters for the conditional random field algorithm so that the value of an objective function using the set of parameters is larger or smaller than a value of the objective function using another set of parameters in a previous iteration. The objective function is constructed to measure a difference between named entities classified using the conditional random field algorithm and the named entity classifications annotated for the string of text. After the training, the conditional random field model is provided. After obtaining the transition matrix, the weight of an inconsistent pair of tags can be manually updated to a very negative predefined hyperparameter value to prevent the inconsistent/incorrect sequence of tags. 
     In various embodiments, a system is provided that includes one or more data processors and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods disclosed herein. 
     In various embodiments, a computer-program product is provided that is tangibly embodied in a non-transitory machine-readable storage medium and that includes instructions configured to cause one or more data processors to perform part or all of one or more methods disclosed herein. 
     The techniques described above and below may be implemented in a number of ways and in a number of contexts. Several example implementations and contexts are provided with reference to the following figures, as described below in more detail. However, the following implementations and contexts are but a few of many. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a simplified block diagram of a distributed environment incorporating an exemplary embodiment. 
         FIG.  2    is a simplified block diagram of a computing system implementing a master bot according to certain embodiments. 
         FIG.  3    is a simplified block diagram of a computing system implementing a skill bot according to certain embodiments. 
         FIG.  4    shows a beginning-inside-outside labeled sequence according to certain embodiments. 
         FIG.  5    shows a beginning-inside-outside labeled sequence with an inconsistent tag sequence according to certain embodiments. 
         FIG.  6    is a flowchart of a method for discouraging a conditional random field model from predicting inconsistent tag pairs according to certain embodiments. 
         FIG.  7    is a flowchart of a method for training a conditional random field algorithm to classify entities according to certain embodiments. 
         FIG.  8    depicts a simplified diagram of a distributed system for implementing various embodiments. 
         FIG.  9    is a simplified block diagram of one or more components of a system environment by which services provided by one or more components of an embodiment system may be offered as cloud services, in accordance with various embodiments. 
         FIG.  10    illustrates an example computer system that may be used to implement various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of certain embodiments. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. 
     Introduction 
     Artificial intelligence has many applications. For example, a digital assistant is an artificial intelligent driven interface that helps users accomplish a variety of tasks using natural language conversations. For each digital assistant, a customer may assemble one or more skills. Skills (also described herein as chatbots, bots, or skill bots) are individual bots that are focused on specific types of tasks, such as tracking inventory, submitting time cards, and creating expense reports. When an end user engages with the digital assistant, the digital assistant evaluates the end user input and routes the conversation to and from the appropriate chatbot. The digital assistant can be made available to end users through a variety of channels such as FACEBOOK® Messenger, SKYPE MOBILE® messenger, or a Short Message Service (SMS). Channels carry the chat back and forth from end users on various messaging platforms to the digital assistant and its various chatbots. The channels may also support user agent escalation, event-initiated conversations, and testing. 
     Training a machine learning model (e.g., a chatbot) with a training dataset, or a corpus, can allow the chatbot to understand what the user wants the chatbot to do or can otherwise improve a performance of the chatbot. For instance, chatbots can be trained to perform named-entity recognition (NER) where the chatbot can locate and classify named entities found in text. Named entity recognition can be performed by a chatbot using a machine learning model with a conditional random field (CRF) layer. Conditional random fields are a class of statistical modeling methods that are used for structured prediction. Chatbots, using a CRF-based model to perform NER, can be trained to identify multi-token named entities. However, for small training datasets, the chatbot may learn to incorrectly predict named entities, and, for example, the model may predict the B-PERSON tag for a first word and then I-LOCATION for the next word. 
     The likelihood of named entities identified with a conditional random field model is influenced by the conditional random field transition matrix that specifies the possible tag pairs in the conditional random field output. The conditional random field transition matrix includes costs (e.g., weights) that are assigned for tag-to-tag pairs and the model is more likely to predict pairs with larger weights. An inconsistent tag sequence is one that includes a pair of adjacent tags (e.g., inconsistent tag pairs) that never occur in the training data. With enough training data, the conditional random field training procedure can assign these inconsistent tag pairs a very negative weight, but a model trained on a small sample size may not sufficiently learn the negative weight and predict inconstant tag pairs. However, regardless of the training sample size, tag to tag transition weights in the conditional random field transition matrix may be altered to encourage or discourage different tag to tag pairs. For example, beginning-inside-outside (BIO) is a tagging format where words in a string can be labeled as the beginning, inside or outside of a named entity. A conditional random field model can incorrectly predict truncated named entities because the tag-to-tag transition weight for inside-beginning tag pairs in the conditional random field transition matrix is too large. Similarly, the tag-to-tag transition weight for an inside-inside tag pair may be too small. Accordingly, after training the model and obtaining the conditional random field transition matrix, the performance of a conditional random field model can be improved by manually assigning a negative constant to the inconsistent tag to tag transition weights for undesirable tag pairs. 
     Bot and Analytic Systems 
     A bot (also referred to as a skill, chatbot, chatterbot, or talkbot) is a computer program that can perform conversations with end users. The bot can generally respond to natural-language messages (e.g., questions or comments) through a messaging application that uses natural-language messages. Enterprises may use one or more bot systems to communicate with end users through a messaging application. The messaging application, which may be referred to as a channel, may be an end user preferred messaging application that the end user has already installed and familiar with. Thus, the end user does not need to download and install new applications in order to chat with the bot system. The messaging application may include, for example, over-the-top (OTT) messaging channels (such as Facebook Messenger, Facebook WhatsApp, WeChat, Line, Kik, Telegram, Talk, Skype, Slack, or SMS), virtual private assistants (such as Amazon Dot, Echo, or Show, Google Home, Apple HomePod, etc.), mobile and web app extensions that extend native or hybrid/responsive mobile apps or web applications with chat capabilities, or voice based input (such as devices or apps with interfaces that use Siri, Cortana, Google Voice, or other speech input for interaction). 
     In some examples, a bot system may be associated with a Uniform Resource Identifier (URI). The URI may identify the bot system using a string of characters. The URI may be used as a webhook for one or more messaging application systems. The URI may include, for example, a Uniform Resource Locator (URL) or a Uniform Resource Name (URN). The bot system may be designed to receive a message (e.g., a hypertext transfer protocol (HTTP) post call message) from a messaging application system. The HTTP post call message may be directed to the URI from the messaging application system. In some embodiments, the message may be different from a HTTP post call message. For example, the bot system may receive a message from a Short Message Service (SMS). While discussion herein may refer to communications that the bot system receives as a message, it should be understood that the message may be an HTTP post call message, a SMS message, or any other type of communication between two systems. 
     End users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)), just as interactions between people. In some cases, the interaction may include the end user saying “Hello” to the bot and the bot responding with a “Hi” and asking the end user how it can help. In some cases, the interaction may also be a transactional interaction with, for example, a banking bot, such as transferring money from one account to another; an informational interaction with, for example, a HR bot, such as checking for vacation balance; or an interaction with, for example, a retail bot, such as discussing returning purchased goods or seeking technical support. 
     In some embodiments, the bot system may intelligently handle end user interactions without interaction with an administrator or developer of the bot system. For example, an end user may send one or more messages to the bot system in order to achieve a desired goal. A message may include certain content, such as text, emojis, audio, image, video, or other method of conveying a message. In some embodiments, the bot system may convert the content into a standardized form (e.g., a representational state transfer (REST) call against enterprise services with the proper parameters) and generate a natural language response. The bot system may also prompt the end user for additional input parameters or request other additional information. In some embodiments, the bot system may also initiate communication with the end user, rather than passively responding to end user utterances. Described herein are various techniques for identifying an explicit invocation of a bot system and determining an input for the bot system being invoked. In certain embodiments, explicit invocation analysis is performed by a master bot based on detecting an invocation name in an utterance. In response to detection of the invocation name, the utterance may be refined for input to a skill bot associated with the invocation name. 
     A conversation with a bot may follow a specific conversation flow including multiple states. The flow may define what would happen next based on an input. In some embodiments, a state machine that includes user defined states (e.g., end user intents) and actions to take in the states or from state to state may be used to implement the bot system. A conversation may take different paths based on the end user input, which may impact the decision the bot makes for the flow. For example, at each state, based on the end user input or utterances, the bot may determine the end user&#39;s intent in order to determine the appropriate next action to take. As used herein and in the context of an utterance, the term “intent” refers to an intent of the user who provided the utterance. For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user&#39;s intent could be represented through the utterance “Order pizza.” A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user. Therefore, utterances can be phrased as questions, commands, requests, and the like, that reflect the user&#39;s intent. An intent may include a goal that the end user would like to accomplish. 
     In the context of the configuration of a chatbot, the term “intent” is used herein to refer to configuration information for mapping a user&#39;s utterance to a specific task/action or category of task/action that the chatbot can perform. In order to distinguish between the intent of an utterance (i.e., a user intent) and the intent of a chatbot, the latter is sometimes referred to herein as a “bot intent.” A bot intent may comprise a set of one or more utterances associated with the intent. For instance, an intent for ordering pizza can have various permutations of utterances that express a desire to place an order for pizza. These associated utterances can be used to train an intent classifier of the chatbot to enable the intent classifier to subsequently determine whether an input utterance from a user matches the order pizza intent. A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user. 
       FIG.  1    is a simplified block diagram of an environment  100  incorporating a chatbot system according to certain embodiments. Environment  100  comprises a digital assistant builder platform (DABP)  102  that enables users of DABP  102  to create and deploy digital assistants or chatbot systems. DABP  102  can be used to create one or more digital assistants (or DAs) or chatbot systems. For example, as shown in  FIG.  1   , user  104  representing a particular enterprise can use DABP  102  to create and deploy a digital assistant  106  for users of the particular enterprise. For example, DABP  102  can be used by a bank to create one or more digital assistants for use by the bank&#39;s customers. The same DABP  102  platform can be used by multiple enterprises to create digital assistants. As another example, an owner of a restaurant (e.g., a pizza shop) may use DABP  102  to create and deploy a digital assistant that enables customers of the restaurant to order food (e.g., order pizza). 
     For purposes of this disclosure, a “digital assistant” is an entity that helps users of the digital assistant accomplish various tasks through natural language conversations. A digital assistant can be implemented using software only (e.g., the digital assistant is a digital entity implemented using programs, code, or instructions executable by one or more processors), using hardware, or using a combination of hardware and software. A digital assistant can be embodied or implemented in various physical systems or devices, such as in a computer, a mobile phone, a watch, an appliance, a vehicle, and the like. A digital assistant is also sometimes referred to as a chatbot system. Accordingly, for purposes of this disclosure, the terms digital assistant and chatbot system are interchangeable. 
     A digital assistant, such as digital assistant  106  built using DABP  102 , can be used to perform various tasks via natural language-based conversations between the digital assistant and its users  108 . As part of a conversation, a user may provide one or more user inputs  110  to digital assistant  106  and get responses  112  back from digital assistant  106 . A conversation can include one or more of inputs  110  and responses  112 . Via these conversations, a user can request one or more tasks to be performed by the digital assistant and, in response, the digital assistant is configured to perform the user-requested tasks and respond with appropriate responses to the user. 
     User inputs  110  are generally in a natural language form and are referred to as utterances. A user utterance  110  can be in text form, such as when a user types in a sentence, a question, a text fragment, or even a single word and provides it as input to digital assistant  106 . In some embodiments, a user utterance  110  can be in audio input or speech form, such as when a user says or speaks something that is provided as input to digital assistant  106 . The utterances are typically in a language spoken by the user  108 . For example, the utterances may be in English, or some other language. When an utterance is in speech form, the speech input is converted to text form utterances in that particular language and the text utterances are then processed by digital assistant  106 . Various speech-to-text processing techniques may be used to convert a speech or audio input to a text utterance, which is then processed by digital assistant  106 . In some embodiments, the speech-to-text conversion may be done by digital assistant  106  itself. 
     An utterance, which may be a text utterance or a speech utterance, can be a fragment, a sentence, multiple sentences, one or more words, one or more questions, combinations of the aforementioned types, and the like. Digital assistant  106  is configured to apply natural language understanding (NLU) techniques to the utterance to understand the meaning of the user input. As part of the NLU processing for a utterance, digital assistant  106  is configured to perform processing to understand the meaning of the utterance, which involves identifying one or more intents and one or more entities corresponding to the utterance. Upon understanding the meaning of an utterance, digital assistant  106  may perform one or more actions or operations responsive to the understood meaning or intents. For purposes of this disclosure, it is assumed that the utterances are text utterances that have been provided directly by a user  108  of digital assistant  106  or are the results of conversion of input speech utterances to text form. This however is not intended to be limiting or restrictive in any manner. 
     For example, a user  108  input may request a pizza to be ordered by providing an utterance such as “I want to order a pizza.” Upon receiving such an utterance, digital assistant  106  is configured to understand the meaning of the utterance and take appropriate actions. The appropriate actions may involve, for example, responding to the user with questions requesting user input on the type of pizza the user desires to order, the size of the pizza, any toppings for the pizza, and the like. The responses provided by digital assistant  106  may also be in natural language form and typically in the same language as the input utterance. As part of generating these responses, digital assistant  106  may perform natural language generation (NLG). For the user ordering a pizza, via the conversation between the user and digital assistant  106 , the digital assistant may guide the user to provide all the requisite information for the pizza order, and then at the end of the conversation cause the pizza to be ordered. Digital assistant  106  may end the conversation by outputting information to the user indicating that the pizza has been ordered. 
     At a conceptual level, digital assistant  106  performs various processing in response to an utterance received from a user. In some embodiments, this processing involves a series or pipeline of processing steps including, for example, understanding the meaning of the input utterance (sometimes referred to as Natural Language Understanding (NLU)), determining an action to be performed in response to the utterance, where appropriate causing the action to be performed, generating a response to be output to the user responsive to the user utterance, outputting the response to the user, and the like. The NLU processing can include parsing the received input utterance to understand the structure and meaning of the utterance, refining and reforming the utterance to develop a better understandable form (e.g., logical form) or structure for the utterance. Generating a response may include using NLG techniques. 
     The NLU processing performed by a digital assistant, such as digital assistant  106 , can include various NLP related processing such as sentence parsing (e.g., tokenizing, lemmatizing, identifying part-of-speech tags for the sentence, identifying named entities in the sentence, generating dependency trees to represent the sentence structure, splitting a sentence into clauses, analyzing individual clauses, resolving anaphoras, performing chunking, and the like). In certain embodiments, the NLU processing or portions thereof is performed by digital assistant  106  itself. In some other embodiments, digital assistant  106  may use other resources to perform portions of the NLU processing. For example, the syntax and structure of an input utterance sentence may be identified by processing the sentence using a parser, a part-of-speech tagger, and/or a named entity recognizer. In one implementation, for the English language, a parser, a part-of-speech tagger, and a named entity recognizer such as ones provided by the Stanford Natural Language Processing (NLP) Group are used for analyzing the sentence structure and syntax. These are provided as part of the Stanford CoreNLP toolkit. 
     While the various examples provided in this disclosure show utterances in the English language, this is meant only as an example. In certain embodiments, digital assistant  106  is also capable of handling utterances in languages other than English. Digital assistant  106  may provide subsystems (e.g., components implementing NLU functionality) that are configured for performing processing for different languages. These subsystems may be implemented as pluggable units that can be called using service calls from an NLU core server. This makes the NLU processing flexible and extensible for each language, including allowing different orders of processing. A language pack may be provided for individual languages, where a language pack can register a list of subsystems that can be served from the NLU core server. 
     A digital assistant, such as digital assistant  106  depicted in  FIG.  1   , can be made available or accessible to its users  108  through a variety of different channels, such as but not limited to, via certain applications, via social media platforms, via various messaging services and applications, and other applications or channels. A single digital assistant can have several channels configured for it so that it can be run on and be accessed by different services simultaneously. 
     A digital assistant or chatbot system generally contains or is associated with one or more skills. In certain embodiments, these skills are individual chatbots (referred to as skill bots) that are configured to interact with users and fulfill specific types of tasks, such as tracking inventory, submitting timecards, creating expense reports, ordering food, checking a bank account, making reservations, buying a widget, and the like. For example, for the embodiment depicted in  FIG.  1   , digital assistant or chatbot system  106  includes skills  116 - 1 ,  116 - 2 , and so on. For purposes of this disclosure, the terms “skill” and “skills” are used synonymously with the terms “skill bot” and “skill bots,” respectively. 
     Each skill associated with a digital assistant helps a user of the digital assistant complete a task through a conversation with the user, where the conversation can include a combination of text or audio inputs provided by the user and responses provided by the skill bots. These responses may be in the form of text or audio messages to the user and/or using simple user interface elements (e.g., select lists) that are presented to the user for the user to make selections. 
     There are various ways in which a skill or skill bot can be associated or added to a digital assistant. In some instances, a skill bot can be developed by an enterprise and then added to a digital assistant using DABP  102 . In other instances, a skill bot can be developed and created using DABP  102  and then added to a digital assistant created using DABP  102 . In yet other instances, DABP  102  provides an online digital store (referred to as a “skills store”) that offers multiple skills directed to a wide range of tasks. The skills offered through the skills store may also expose various cloud services. In order to add a skill to a digital assistant being generated using DABP  102 , a user of DABP  102  can access the skills store via DABP  102 , select a desired skill, and indicate that the selected skill is to be added to the d“gital assist”nt created using DABP  102 . A skill from the skills store can be added to a digital assistant as is or in a modified form (for example, a user of DABP  102  may select and clone a particular skill bot provided by the skills store, make customizations or modifications to the selected skill bot, and then add the modified skill bot to a digital assistant created using DABP  102 ). 
     Various different architectures may be used to implement a digital assistant or chatbot system. For example, in certain embodiments, the digital assistants created and deployed using DABP  102  may be implemented using a master bot/child(or sub) bot paradigm or architecture. According to this paradigm, a digital assistant is implemented as a master bot that interacts with one or more child bots that are skill bots. For example, in the embodiment depicted in  FIG.  1   , digital assistant  106  comprises a master bot  114  and skill bots  116 - 1 ,  116 - 2 , etc. that are child bots of master bot  114 . In certain embodiments, digital assistant  106  is itself considered to act as the master bot. 
     A digital assistant implemented according to the master-child bot architecture enables users of the digital assistant to interact with multiple skills through a unified user interface, namely via the master bot. When a user engages with a digital assistant, the user input is received by the master bot. The master bot then performs processing to determine the meaning of the user input utterance. The master bot then determines whether the task requested by the user in the utterance can be handled by the master bot itself, else the master bot selects an appropriate skill bot for handling the user request and routes the conversation to the selected skill bot. This enables a user to converse with the digital assistant through a common single interface and still provide the capability to use several skill bots configured to perform specific tasks. For example, for a digital assistance developed for an enterprise, the master bot of the digital assistant may interface with skill bots with specific functionalities, such as a CRM bot for performing functions related to customer relationship management (CRM), an ERP bot for performing functions related to enterprise resource planning (ERP), an HCM bot for performing functions related to human capital management (HCM), etc. This way the end user or consumer of the digital assistant need only know how to access the digital assistant through the common master bot interface and behind the scenes multiple skill bots are provided for handling the user request. 
     In certain embodiments, in a master bot/child bots infrastructure, the master bot is configured to be aware of the available list of skill bots. The master bot may have access to metadata that identifies the various available skill bots, and for each skill bot, the capabilities of the skill bot including the tasks that can be performed by the skill bot. Upon receiving a user request in the form of an utterance, the master bot is configured to, from the multiple available skill bots, identify or predict a specific skill bot that can best serve or handle the user request. The master bot then routes the utterance (or a portion of the utterance) to that specific skill bot for further handling. Control thus flows from the master bot to the skill bots. The master bot can support multiple input and output channels. In certain embodiments, routing may be performed with the aid of processing performed by one or more available skill bots. For example, as discussed below, a skill bot can be trained to infer an intent for an utterance and to determine whether the inferred intent matches an intent with which the skill bot is configured. Thus, the routing performed by the master bot can involve the skill bot communicating to the master bot an indication of whether the skill bot has been configured with an intent suitable for handling the utterance. 
     While the embodiment in  FIG.  1    shows digital assistant  106  comprising a master bot  114  and skill bots  116 - 1 ,  116 - 2 , and  116 - 3 , this is not intended to be limiting. A digital assistant can include various other components (e.g., other systems and subsystems) that provide the functionalities of the digital assistant. These systems and subsystems may be implemented only in software (e.g., code, instructions stored on a computer-readable medium and executable by one or more processors), in hardware only, or in implementations that use a combination of software and hardware. 
     DABP  102  provides an infrastructure and various services and features that enable a user of DABP  102  to create a digital assistant including one or more skill bots associated with the digital assistant. In some instances, a skill bot can be created by cloning an existing skill bot, for example, cloning a skill bot provided by the skills store. As previously indicated, DABP  102  provides a skills store or skills catalog that offers multiple skill bots for performing various tasks. A user of DABP  102  can clone a skill bot from the skills store. As needed, modifications or customizations may be made to the cloned skill bot. In some other instances, a user of DABP  102  created a skill bot from scratch using tools and services offered by DABP  102 . As previously indicated, the skills store or skills catalog provided by DABP  102  may offer multiple skill bots for performing various tasks. 
     In certain embodiments, at a high level, creating or customizing a skill bot involves the following steps:
         (1) Configuring settings for a new skill bot   (2) Configuring one or more intents for the skill bot   (3) Configuring one or more entities for one or more intents   (4) Training the skill bot   (5) Creating a dialog flow for the skill bot   (6) Adding custom components to the skill bot as needed   (7) Testing and deploying the skill bot
 
Each of the above steps is briefly described below.
       

     (1) Configuring settings for a new skill bot—Various settings may be configured for the skill bot. For example, a skill bot designer can specify one or more invocation names for the skill bot being created. These invocation names can then be used by users of a digital assistant to explicitly invoke the skill bot. For example, a user can input an invocation name in the user&#39;s utterance to explicitly invoke the corresponding skill bot. 
     (2) Configuring one or more intents and associated example utterances for the skill bot—The skill bot designer specifies one or more intents (also referred to as bot intents) for a skill bot being created. The skill bot is then trained based upon these specified intents. These intents represent categories or classes that the skill bot is trained to infer for input utterances. Upon receiving an utterance, a trained skill bot infers an intent for the utterance, where the inferred intent is selected from the predefined set of intents used to train the skill bot. The skill bot then takes an appropriate action responsive to an utterance based upon the intent inferred for that utterance. In some instances, the intents for a skill bot represent tasks that the skill bot can perform for users of the digital assistant. Each intent is given an intent identifier or intent name. For example, for a skill bot trained for a bank, the intents specified for the skill bot may include “CheckBalance,” “TransferMoney,” “DepositCheck,” and the like. 
     For each intent defined for a skill bot, the skill bot designer may also provide one or more example utterances that are representative of and illustrate the intent. These example utterances are meant to represent utterances that a user may input to the skill bot for that intent. For example, for the CheckBalance intent, example utterances may include “What&#39;s my savings account balance?”, “How much is in my checking account?”, “How much money do I have in my account,” and the like. Accordingly, various permutations of typical user utterances may be specified as example utterances for an intent. 
     The intents and the their associated example utterances are used as training data to train the skill bot. Various different training techniques may be used. As a result of this training, a predictive model is generated that is configured to take an utterance as input and output an intent inferred for the utterance by the predictive model. In some instances, input utterances are provided to an intent analysis engine, which is configured to use the trained model to predict or infer an intent for the input utterance. The skill bot may then take one or more actions based upon the inferred intent. 
     (3) Configuring entities for one or more intents of the skill bot—In some instances, additional context may be needed to enable the skill bot to properly respond to a user utterance. For example, there may be situations where a user input utterance resolves to the same intent in a skill bot. For instance, in the above example, utterances “What&#39;s my savings account balance?” and “How much is in my checking account?” both resolve to the same CheckBalance intent, but these utterances are different requests asking for different things. To clarify such requests, one or more entities are added to an intent. Using the banking skill bot example, an entity called AccountType, which defines values called “checking” and “saving” may enable the skill bot to parse the user request and respond appropriately. In the above example, while the utterances resolve to the same intent, the value associated with the AccountType entity is different for the two utterances. This enables the skill bot to perform possibly different actions for the two utterances in spite of them resolving to the same intent. One or more entities can be specified for certain intents configured for the skill bot. Entities are thus used to add context to the intent itself. Entities help describe an intent more fully and enable the skill bot to complete a user request. 
     In certain embodiments, there are two types of entities: (a) built-in entities provided by DABP  102 , and (2) custom entities that can be specified by a skill bot designer. Built-in entities are generic entities that can be used with a wide variety of bots. Examples of built-in entities include, without limitation, entities related to time, date, addresses, numbers, email addresses, duration, recurring time periods, currencies, phone numbers, URLs, and the like. Custom entities are used for more customized applications. For example, for a banking skill, an AccountType entity may be defined by the skill bot designer that enables various banking transactions by checking the user input for keywords like checking, savings, and credit cards, etc. 
     (4) Training the skill bot—A skill bot is configured to receive user input in the form of utterances parse or otherwise process the received input, and identify or select an intent that is relevant to the received user input. As indicated above, the skill bot has to be trained for this. In certain embodiments, a skill bot is trained based upon the intents configured for the skill bot and the example utterances associated with the intents (collectively, the training data), so that the skill bot can resolve user input utterances to one of its configured intents. In certain embodiments, the skill bot uses a predictive model that is trained using the training data and allows the skill bot to discern what users say (or in some cases, are trying to say). DABP  102  provides various different training techniques that can be used by a skill bot designer to train a skill bot, including various machine-learning based training techniques, rules-based training techniques, and/or combinations thereof. In certain embodiments, a portion (e.g., 80%) of the training data is used to train a skill bot model and another portion (e.g., the remaining 20%) is used to test or verify the model. Once trained, the trained model (also sometimes referred to as the trained skill bot) can then be used to handle and respond to user utterances. In certain cases, a user&#39;s utterance may be a question that requires only a single answer and no further conversation. In order to handle such situations, a Q&amp;A (question-and-answer) intent may be defined for a skill bot. This enables a skill bot to output replies to user requests without having to update the dialog definition. Q&amp;A intents are created in a similar manner as regular intents. The dialog flow for Q&amp;A intents can be different from that for regular intents. 
     (5) Creating a dialog flow for the skill bot—A dialog flow specified for a skill bot describes how the skill bot reacts as different intents for the skill bot are resolved responsive to received user input. The dialog flow defines operations or actions that a skill bot will take, e.g., how the skill bot responds to user utterances, how the skill bot prompts users for input, how the skill bot returns data. A dialog flow is like a flowchart that is followed by the skill bot. The skill bot designer specifies a dialog flow using a language, such as markdown language. In certain embodiments, a version of YAML called ObotML may be used to specify a dialog flow for a skill bot. The dialog flow definition for a skill bot acts as a model for the conversation itself, one that lets the skill bot designer choreograph the interactions between a skill bot and the users that the skill bot services. 
     In certain embodiments, the dialog flow definition for a skill bot contains three sections:
         (a) a context section   (b) a default transitions section   I a states section       

     Context section—The skill bot designer can define variables that are used in a conversation flow in the context section. Other variables that may be named in the context section include, without limitation: variables for error handling, variables for built-in or custom entities, user variables that enable the skill bot to recognize and persist user preferences, and the like. 
     Default transitions section—Transitions for a skill bot can be defined in the dialog flow states section or in the default transitions section. The transitions defined in the default transition section act as a fallback and get triggered when there are no applicable transitions defined within a state, or the conditions required to trigger a state transition cannot be met. The default transitions section can be used to define routing that allows the skill bot to gracefully handle unexpected user actions. 
     States section—A dialog flow and its related operations are defined as a sequence of transitory states, which manage the logic within the dialog flow. Each state node within a dialog flow definition names a component that provides the functionality needed at that point in the dialog. States are thus built around the components. A state contains component-specific properties and defines the transitions to other states that get triggered after the component executes. 
     Special case scenarios may be handled using the states sections. For example, there might be times when you want to provide users the option to temporarily leave a first skill they are engaged with to do something in a second skill within the digital assistant. For example, if a user is engaged in a conversation with a shopping skill (e.g., the user has made some selections for purchase), the user may want to jump to a banking skill (e.g., the user may want to ensure that he/she has enough money for the purchase), and then return to the shopping skill to complete the user&#39;s order. To address this, an action in the first skill can be configured to initiate an interaction with the second different skill in the same digital assistant and then return to the original flow. 
     (6) Adding custom components to the skill bot—As described above, states specified in a dialog flow for a skill bot name components that provide the functionality needed corresponding to the states. Components enable a skill bot to perform functions. In certain embodiments, DABP  102  provides a set of preconfigured components for performing a wide range of functions. A skill bot designer can select one of more of these preconfigured components and associate them with states in the dialog flow for a skill bot. The skill bot designer can also create custom or new components using tools provided by DABP  102  and associate the custom components with one or more states in the dialog flow for a skill bot. 
     (7) Testing and deploying the skill bot—DABP  102  provides several features that enable the skill bot designer to test a skill bot being developed. The skill bot can then be deployed and included in a digital assistant. 
     While the description above describes how to create a skill bot, similar techniques may also be used to create a digital assistant (or the master bot). At the master bot or digital assistant level, built-in system intents may be configured for the digital assistant. These built-in system intents are used to identify general tasks that the digital assistant itself (i.e., the master bot) can handle without invoking a skill bot associated with the digital assistant. Examples of system intents defined for a master bot include: (1) Exit: applies when the user signals the desire to exit the current conversation or context in the digital assistant; (2) Help: applies when the user asks for help or orientation; and (3) UnresolvedIntent: applies to user input that doesn&#39;t match well with the exit and help intents. The digital assistant also stores information about the one or more skill bots associated with the digital assistant. This information enables the master bot to select a particular skill bot for handling an utterance. 
     At the master bot or digital assistant level, when a user inputs a phrase or utterance to the digital assistant, the digital assistant is configured to perform processing to determine how to route the utterance and the related conversation. The digital assistant determines this using a routing model, which can be rules-based, AI-based, or a combination thereof. The digital assistant uses the routing model to determine whether the conversation corresponding to the user input utterance is to be routed to a particular skill for handling, is to be handled by the digital assistant or master bot itself per a built-in system intent, or is to be handled as a different state in a current conversation flow. 
     In certain embodiments, as part of this processing, the digital assistant determines if the user input utterance explicitly identifies a skill bot using its invocation name. If an invocation name is present in the user input, then it is treated as explicit invocation of the skill bot corresponding to the invocation name. In such a scenario, the digital assistant may route the user input to the explicitly invoked skill bot for further handling. If there is no specific or explicit invocation, in certain embodiments, the digital assistant evaluates the received user input utterance and computes confidence scores for the system intents and the skill bots associated with the digital assistant. The score computed for a skill bot or system intent represents how likely the user input is representative of a task that the skill bot is configured to perform or is representative of a system intent. Any system intent or skill bot with an associated computed confidence score exceeding a threshold value (e.g., a Confidence Threshold routing parameter) is selected as a candidate for further evaluation. The digital assistant then selects, from the identified candidates, a particular system intent or a skill bot for further handling of the user input utterance. In certain embodiments, after one or more skill bots are identified as candidates, the intents associated with those candidate skills are evaluated (according to the intent model for each skill) and confidence scores are determined for each intent. In general, any intent that has a confidence score exceeding a threshold value (e.g., 70%) is treated as a candidate intent. If a particular skill bot is selected, then the user utterance is routed to that skill bot for further processing. If a system intent is selected, then one or more actions are performed by the master bot itself according to the selected system intent. 
       FIG.  2    is a simplified block diagram of a master bot (MB) system  200  according to certain embodiments. MB system  200  can be implemented in software only, hardware only, or a combination of hardware and software. MB system  200  includes a pre-processing subsystem  210 , a multiple intent subsystem (MIS)  220 , an explicit invocation subsystem (EIS)  230 , a skill bot invoker  240 , and a data store  250 . MB system  200  depicted in  FIG.  2    is merely an example of an arrangement of components in a master bot. One of ordinary skill in the art would recognize many possible variations, alternatives, and modifications. For example, in some implementations, MB system  200  may have more or fewer systems or components than those shown in  FIG.  2   , may combine two or more subsystems, or may have a different configuration or arrangement of subsystems. 
     Pre-processing subsystem  210  receives an utterance “A”  202  from a user and processes the utterance through a language detector  212  and a language parser  214 . As indicated above, an utterance can be provided in various ways including audio or text. The utterance  202  can be a sentence fragment, a complete sentence, multiple sentences, and the like. Utterance  202  can include punctuation. For example, if the utterance  202  is provided as audio, the pre-processing subsystem  210  may convert the audio to text using a speech-to-text converter (not shown) that inserts punctuation marks into the resulting text, e.g., commas, semicolons, periods, etc. 
     Language detector  212  detects the language of the utterance  202  based on the text of the utterance  202 . The manner in which the utterance  202  is handled depends on the language since each language has its own grammar and semantics. Differences between languages are taken into consideration when analyzing the syntax and structure of an utterance. 
     Language parser  214  parses the utterance  202  to extract part of speech (POS) tags for individual linguistic units (e.g., words) in the utterance  202 . POS tags include, for example, noun (NN), pronoun (PN), verb (VB), and the like. Language parser  214  may also tokenize the linguistic units of the utterance  202  (e.g., to convert each word into a separate token) and lemmatize words. A lemma is the main form of a set of words as represented in a dictionary (e.g., “run” is the lemma for run, runs, ran, running, etc.). Other types of pre-processing that the language parser  214  can perform include chunking of compound expressions, e.g., combining “credit” and “card” into a single expression “credit card.” Language parser  214  may also identify relationships between the words in the utterance  202 . For example, in some embodiments, the language parser  214  generates a dependency tree that indicates which part of the utterance (e.g. a particular noun) is a direct object, which part of the utterance is a preposition, and so on. The results of the processing performed by the language parser  214  form extracted information  205  and are provided as input to MIS  220  together with the utterance  202  itself. 
     As indicated above, the utterance  202  can include more than one sentence. For purposes of detecting multiple intents and explicit invocation, the utterance  202  can be treated as a single unit even if it includes multiple sentences. However, in certain embodiments, pre-processing can be performed, e.g., by the pre-processing subsystem  210 , to identify a single sentence among multiple sentences for multiple intents analysis and explicit invocation analysis. In general, the results produced by MIS  220  and EIS  230  are substantially the same regardless of whether the utterance  202  is processed at the level of an individual sentence or as a single unit comprising multiple sentences. 
     MIS  220  determines whether the utterance  202  represents multiple intents. Although MIS  220  can detect the presence of multiple intents in the utterance  202 , the processing performed by MIS  220  does not involve determining whether the intents of the utterance  202  match to any intents that have been configured for a bot. Instead, processing to determine whether an intent of the utterance  202  matches a bot intent can be performed by an intent classifier  242  of the MB system  200  or by an intent classifier of a skill bot (e.g., as shown in the embodiment of  FIG.  3   ). The processing performed by MIS  220  assumes that there exists a bot (e.g., a particular skill bot or the master bot itself) that can handle the utterance  202 . Therefore, the processing performed by MIS  220  does not require knowledge of what bots are in the chatbot system (e.g., the identities of skill bots registered with the master bot) or knowledge of what intents have been configured for a particular bot. 
     To determine that the utterance  202  includes multiple intents, the MIS  220  applies one or more rules from a set of rules  252  in the data store  250 . The rules applied to the utterance  202  depend on the language of the utterance  202  and may include sentence patterns that indicate the presence of multiple intents. For example, a sentence pattern may include a coordinating conjunction that joins two parts (e.g., conjuncts) of a sentence, where both parts correspond to a separate intent. If the utterance  202  matches the sentence pattern, it can be inferred that the utterance  202  represents multiple intents. It should be noted that an utterance with multiple intents does not necessarily have different intents (e.g., intents directed to different bots or to different intents within the same bot). Instead, the utterance could have separate instances of the same intent, e.g. “Place a pizza order using payment account X, then place a pizza order using payment account Y.” 
     As part of determining that the utterance  202  represents multiple intents, the MIS  220  also determines what portions of the utterance  202  are associated with each intent. MIS  220  constructs, for each intent represented in an utterance containing multiple intents, a new utterance for separate processing in place of the original utterance, e.g., an utterance “B”  206  and an utterance “C”  208 , as depicted in  FIG.  2   . Thus, the original utterance  202  can be split into two or more separate utterances that are handled one at a time. MIS  220  determines, using the extracted information  205  and/or from analysis of the utterance  202  itself, which of the two or more utterances should be handled first. For example, MIS  220  may determine that the utterance  202  contains a marker word indicating that a particular intent should be handled first. The newly formed utterance corresponding to this particular intent (e.g., one of utterance  206  or utterance  208 ) will be the first to be sent for further processing by EIS  230 . After a conversation triggered by the first utterance has ended (or has been temporarily suspended), the next highest priority utterance (e.g., the other one of utterance  206  or utterance  208 ) can then be sent to the EIS  230  for processing. 
     EIS  230  determines whether the utterance that it receives (e.g., utterance  206  or utterance  208 ) contains an invocation name of a skill bot. In certain embodiments, each skill bot in a chatbot system is assigned a unique invocation name that distinguishes the skill bot from other skill bots in the chatbot system. A list of invocation names can be maintained as part of skill bot information  254  in data store  250 . An utterance is deemed to be an explicit invocation when the utterance contains a word match to an invocation name. If a bot is not explicitly invoked, then the utterance received by the EIS  230  is deemed a non-explicitly invoking utterance  234  and is input to an intent classifier (e.g., intent classifier  242 ) of the master bot to determine which bot to use for handling the utterance. In some instances, the intent classifier  242  will determine that the master bot should handle a non-explicitly invoking utterance. In other instances, the intent classifier  242  will determine a skill bot to route the utterance to for handling. 
     The explicit invocation functionality provided by the EIS  230  has several advantages. It can reduce the amount of processing that the master bot has to perform. For example, when there is an explicit invocation, the master bot may not have to do any intent classification analysis (e.g., using the intent classifier  242 ), or may have to do reduced intent classification analysis for selecting a skill bot. Thus, explicit invocation analysis may enable selection of a particular skill bot without resorting to intent classification analysis. 
     Also, there may be situations where there is an overlap in functionalities between multiple skill bots. This may happen, for example, if the intents handled by the two skill bots overlap or are very close to each other. In such a situation, it may be difficult for the master bot to identify which of the multiple skill bots to select based upon intent classification analysis alone. In such scenarios, the explicit invocation disambiguates the particular skill bot to be used. 
     In addition to determining that an utterance is an explicit invocation, the EIS  230  is responsible for determining whether any portion of the utterance should be used as input to the skill bot being explicitly invoked. In particular, EIS  230  can determine whether part of the utterance is not associated with the invocation. The EIS  230  can perform this determination through analysis of the utterance and/or analysis of the extracted information  205 . EIS  230  can send the part of the utterance not associated with the invocation to the invoked skill bot in lieu of sending the entire utterance that was received by the EIS  230 . In some instances, the input to the invoked skill bot is formed simply by removing any portion of the utterance associated with the invocation. For example, “I want to order pizza using Pizza Bot” can be shortened to “I want to order pizza” since “using Pizza Bot” is relevant to the invocation of the pizza bot, but irrelevant to any processing to be performed by the pizza bot. In some instances, EIS  230  may reformat the part to be sent to the invoked bot, e.g., to form a complete sentence. Thus, the EIS  230  determines not only that there is an explicit invocation, but also what to send to the skill bot when there is an explicit invocation. In some instances, there may not be any text to input to the bot being invoked. For example, if the utterance was “Pizza Bot”, then the EIS  230  could determine that the pizza bot is being invoked, but there is no text to be processed by the pizza bot. In such scenarios, the EIS  230  may indicate to the skill bot invoker  240  that there is nothing to send. 
     Skill bot invoker  240  invokes a skill bot in various ways. For instance, skill bot invoker  240  can invoke a bot in response to receiving an indication  235  that a particular skill bot has been selected as a result of an explicit invocation. The indication  235  can be sent by the EIS  230  together with the input for the explicitly invoked skill bot. In this scenario, the skill bot invoker  240  will turn control of the conversation over to the explicitly invoked skill bot. The explicitly invoked skill bot will determine an appropriate response to the input from the EIS  230  by treating the input as a stand-alone utterance. For example, the response could be to perform a specific action or to start a new conversation in a particular state, where the initial state of the new conversation depends on the input sent from the EIS  230 . 
     Another way in which skill bot invoker  240  can invoke a skill bot is through implicit invocation using the intent classifier  242 . The intent classifier  242  can be trained, using machine-learning and/or rules-based training techniques, to determine a likelihood that an utterance is representative of a task that a particular skill bot is configured to perform. The intent classifier  242  is trained on different classes, one class for each skill bot. For instance, whenever a new skill bot is registered with the master bot, a list of example utterances associated with the new skill bot can be used to train the intent classifier  242  to determine a likelihood that a particular utterance is representative of a task that the new skill bot can perform. The parameters produced as result of this training (e.g., a set of values for parameters of a machine-learning model) can be stored as part of skill bot information  254 . 
     In certain embodiments, the intent classifier  242  is implemented using a machine-learning model, as described in further detail herein. Training of the machine-learning model may involve inputting at least a subset of utterances from the example utterances associated with various skill bots to generate, as an output of the machine-learning model, inferences as to which bot is the correct bot for handling any particular training utterance. For each training utterance, an indication of the correct bot to use for the training utterance may be provided as ground truth information. The behavior of the machine-learning model can then be adapted (e.g., through back-propagation) to minimize the difference between the generated inferences and the ground truth information. 
     In certain embodiments, the intent classifier  242  determines, for each skill bot registered with the master bot, a confidence score indicating a likelihood that the skill bot can handle an utterance (e.g., the non-explicitly invoking utterance  234  received from EIS  230 ). The intent classifier  242  may also determine a confidence score for each system level intent (e.g., help, exit) that has been configured. If a particular confidence score meets one or more conditions, then the skill bot invoker  240  will invoke the bot associated with the particular confidence score. For example, a threshold confidence score value may need to be met. Thus, an output  245  of the intent classifier  242  is either an identification of a system intent or an identification of a particular skill bot. In some embodiments, in addition to meeting a threshold confidence score value, the confidence score must exceed the next highest confidence score by a certain win margin. Imposing such a condition would enable routing to a particular skill bot when the confidence scores of multiple skill bots each exceed the threshold confidence score value. 
     After identifying a bot based on evaluation of confidence scores, the skill bot invoker  240  hands over processing to the identified bot. In the case of a system intent, the identified bot is the master bot. Otherwise, the identified bot is a skill bot. Further, the skill bot invoker  240  will determine what to provide as input  247  for the identified bot. As indicated above, in the case of an explicit invocation, the input  247  can be based on a part of an utterance that is not associated with the invocation, or the input  247  can be nothing (e.g., an empty string). In the case of an implicit invocation, the input  247  can be the entire utterance. 
     Data store  250  comprises one or more computing devices that store data used by the various subsystems of the master bot system  200 . As explained above, the data store  250  includes rules  252  and skill bot information  254 . The rules  252  include, for example, rules for determining, by MIS  220 , when an utterance represents multiple intents and how to split an utterance that represents multiple intents. The rules  252  further include rules for determining, by EIS  230 , which parts of an utterance that explicitly invokes a skill bot to send to the skill bot. The skill bot information  254  includes invocation names of skill bots in the chatbot system, e.g., a list of the invocation names of all skill bots registered with a particular master bot. The skill bot information  254  can also include information used by intent classifier  242  to determine a confidence score for each skill bot in the chatbot system, e.g., parameters of a machine-learning model. 
       FIG.  3    is a simplified block diagram of a skill bot system  300  according to certain embodiments. Skill bot system  300  is a computing system that can be implemented in software only, hardware only, or a combination of hardware and software. In certain embodiments such as the embodiment depicted in  FIG.  1   , skill bot system  300  can be used to implement one or more skill bots within a digital assistant. 
     Skill bot system  300  includes an MIS  310 , an intent classifier  320 , and a conversation manager  330 . The MIS  310  is analogous to the MIS  220  in  FIG.  2    and provides similar functionality, including being operable to determine, using rules  352  in a data store  350 : (1) whether an utterance represents multiple intents and, if so, (2) how to split the utterance into a separate utterance for each intent of the multiple intents. In certain embodiments, the rules applied by MIS  310  for detecting multiple intents and for splitting an utterance are the same as those applied by MIS  220 . The MIS  310  receives an utterance  302  and extracted information  304 . The extracted information  304  is analogous to the extracted information  205  in  FIG.  1    and can be generated using the language parser  214  or a language parser local to the skill bot system  300 . 
     Intent classifier  320  can be trained in a similar manner to the intent classifier  242  discussed above in connection with the embodiment of  FIG.  2    and as described in further detail herein. For instance, in certain embodiments, the intent classifier  320  is implemented using a machine-learning model. The machine-learning model of the intent classifier  320  is trained for a particular skill bot, using at least a subset of example utterances associated with that particular skill bot as training utterances. The ground truth for each training utterance would be the particular bot intent associated with the training utterance. 
     The utterance  302  can be received directly from the user or supplied through a master bot. When the utterance  302  is supplied through a master bot, e.g., as a result of processing through MIS  220  and EIS  230  in the embodiment depicted in  FIG.  2   , the MIS  310  can be bypassed so as to avoid repeating processing already performed by MIS  220 . However, if the utterance  302  is received directly from the user, e.g., during a conversation that occurs after routing to a skill bot, then MIS  310  can process the utterance  302  to determine whether the utterance  302  represents multiple intents. If so, then MIS  310  applies one or more rules to split the utterance  302  into a separate utterance for each Intent, e.g., an utterance “D”  306  and an utterance “E”  308 . If utterance  302  does not represent multiple intents, then MIS  310  forwards the utterance  302  to intent classifier  320  for intent classification and without splitting the utterance  302 . 
     Intent classifier  320  is configured to match a received utterance (e.g., utterance  306  or  308 ) to an intent associated with skill bot system  300 . As explained above, a skill bot can be configured with one or more intents, each intent including at least one example utterance that is associated with the intent and used for training a classifier. In the embodiment of  FIG.  2   , the intent classifier  242  of the master bot system  200  is trained to determine confidence scores for individual skill bots and confidence scores for system intents. Similarly, intent classifier  320  can be trained to determine a confidence score for each intent associated with the skill bot system  300 . Whereas the classification performed by intent classifier  242  is at the bot level, the classification performed by intent classifier  320  is at the intent level and therefore finer grained. The intent classifier  320  has access to intents information  354 . The intents information  354  includes, for each intent associated with the skill bot system  300 , a list of utterances that are representative of and illustrate the meaning of the intent and are typically associated with a task performable by that intent. The intents information  354  can further include parameters produced as a result of training on this list of utterances. 
     Conversation manager  330  receives, as an output of intent classifier  320 , an indication  322  of a particular intent, identified by the intent classifier  320 , as best matching the utterance that was input to the intent classifier  320 . In some instances, the intent classifier  320  is unable to determine any match. For example, the confidence scores computed by the intent classifier  320  could fall below a threshold confidence score value if the utterance is directed to a system intent or an intent of a different skill bot. When this occurs, the skill bot system  300  may refer the utterance to the master bot for handling, e.g., to route to a different skill bot. However, if the intent classifier  320  is successful in identifying an intent within the skill bot, then the conversation manager  330  will initiate a conversation with the user. 
     The conversation initiated by the conversation manager  330  is a conversation specific to the intent identified by the intent classifier  320 . For instance, the conversation manager  330  may be implemented using a state machine configured to execute a dialog flow for the identified intent. The state machine can include a default starting state (e.g., for when the intent is invoked without any additional input) and one or more additional states, where each state has associated with it actions to be performed by the skill bot (e.g., executing a purchase transaction) and/or dialog (e.g., questions, responses) to be presented to the user. Thus, the conversation manager  330  can determine an action/dialog  335  upon receiving the indication  322  identifying the intent, and can determine additional actions or dialog in response to subsequent utterances received during the conversation. 
     Data store  350  comprises one or more computing devices that store data used by the various subsystems of the skill bot system  300 . As depicted in  FIG.  3   , the data store  350  includes the rules  352  and the intents information  354 . In certain embodiments, data store  350  can be integrated into a data store of a master bot or digital assistant, e.g., the data store  250  in  FIG.  2   . 
     Named Entity Recognition 
     Named entity recognition is a subtask of natural language processing that concerns categorizing information (e.g., entities) in unstructured text. An entity can be any word, or string of words that refers to a pre-defined category such as person names, organizations, locations, medical codes, products, expressions, quantities, monetary values, percentages, etc. 
     Beginning, inside, outside (BIO) format is a tagging format used in named entity recognition. As part of the format, an O tag can be assigned to any word that is not part of a chunk (i.e., an entity), while an I prefix before a tag can indicate that the tagged word is part and inside of a first chunk while also inside of (but beginning) a second chunk. A B prefix to a tag can indicate that the tagged word is the beginning of a chunk. For example, the sentence “Bob is in New York” can be tagged as “Bob (B-Person) is (O) in (O) New (B-Location) York (I-Location).” In some implementations, the B prefix can be used at the beginning of a chunk that immediately follows another chunk. 
     A conditional random field (CRF) transition matrix can specify the possible tag pairs in the conditional random field output, the prohibited tag pairs, and the matrix can influence the length of multi-token Named Entities. Inconsistent BIO tag sequences in conditional random field output can be avoided by modifying the trained transition weights produced by the conditional random field training procedure. An inconsistent BIO tag sequence can be one that contains a pair of adjacent tags of the form O I-X, B-X I-Y or I-XI-Y, where X≠Y. For example, any tag sequence containing O I-PERSON, B-PERSON I-LOCATION or I-LOCATION I-PERSON can be inconsistent. 
     With enough training data, the conditional random field training procedure can learn that these inconsistent tag pairs never appear in the training data, and the procedure can assign these tag-tag transitions a very negative weight. However, with small training datasets the conditional random field training procedure sometimes can incorrectly learn that a model that generates these inconsistent tag-tag transitions. 
     The conditional random field can be discouraged from finding these inconsistent tag-tag transitions by modifying the tag-tag transition weights after training by setting the weights for the inconsistent transitions to a very negative value. This can stop the conditional random field from generating these inconsistent tag-tag transitions. 
     A conditional random field model can use a matrix of transition parameters, which are tag-to-tag potentials. The transition parameters can be stored in the transition_params matrix. For example, let M be the transition_params matrix. M can be stored as M[t 0 , t 1 ], where t 0  is the preceding tag and t 1  is the following tag but it&#39;s possible that the matrix can be stored in the opposite order (i.e., M[t 1 , t 0 ]) to make broadcasting simpler. M can be stored as M[t 0 , t 1 ] and M can store logit weights, but it&#39;s possible that M stores negative logit weights. 
     The high-level overview of the algorithm can be: after training, set M[t 0 , t 1 ]=c, where c is a large negative number (e.g., c=−1e6), for every inconsistent tag pair t 0  and t 1 . In more detail, after training M can be modified along the following lines: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 c = −1e6 
               
               
                   
                 for x in tags: 
               
               
                   
                  M[tag_id[‘O’], tag_id[‘I−’+x]] = c 
               
               
                   
                 for x in tags: 
               
               
                   
                  for y in tags: 
               
               
                   
                   if x != y: 
               
               
                   
                    M[tag_id[‘B−’+x], tag_id[‘I−’+y]] = c 
               
               
                   
                    M[tag_id[‘I−’+x], tag_id[‘I−’+y]] = c 
               
               
                   
                   
               
            
           
         
       
     
     A NER system can mis-recognize a long Named Entity as two adjacent shorter Named Entities. To address this issue, we can lower the value of M [tag_id [‘I-’+x], tag_id [‘B-’+x]] to discourage starting a new Named Entity immediately after a previous Named Entity. The simplest approach may be to set it to a constant negative number b (b&gt;&gt;c) from the transition matrix M after training. That is:
         M[tag_id[‘I-’+x], tag_id[‘B-’+x]]=b
 
Here b can be a negative hyper-parameter.
       

     We can discourage adjacent same-type named entities by setting b very negative (but still larger than c), say b=−1e2. We need to check if there are any adjacent same-type named entities in the training data? If not, then perhaps setting b very negative is reasonable. We can encourage longer named entities by increasing M[tag_id [‘I-’+x], tag_id[‘I-’+x]]. 
       FIG.  4    shows a beginning-inside-outside labeled sequence  400  according to some embodiments. The sequence is a string of words forming the sentence Jane Doe Lives in San Francisco, Calif. The sequence contains three named entities: a person Jane Emily Doe  402   a - c , a location San Francisco and a location California  404   a - c . The sequence also contains outside words  406   a - b  that do not belong to a named entity. 
     The words in the sequence are assigned tags, and outside words can be  406  tagged with O  408 . The first word Jane  402   a  in the first named entity Jane Emily Doe  402   a - c  can be labeled with the tag B-Person  410 , where the prefix B can designate that the tagged word as the beginning of a named entity and the tag Person indicates that the named entity is a person. The second word in the first named entity Jane Emily Doe  402   a - c  is Emily  402   b  and Emily  402   b  can be tagged with I-Person  412 . The third word, Doe  402   b , in Jane Emily Doe  402   a - c  can also tagged with I-Person  412  where the tag person can show that the word is part of a named entity and that the named entity is a person. The prefix I can indicate that the tagged word is not the first named entity&#39;s first word. 
     The second named entity San Francisco Calif.  404   a - c  begins with a first word San  404   a  that can be tagged with B-Location  414 . Location can indicate that San  404   a  refers to a location and the prefix B can show that San  404   a  is the first word in a named entity. The second word Francisco  404   b  and the third word California  404   c  can be tagged with I-Location  416 . Location can indicate that the tagged words are part of a named entity that refers to a location and the prefix I can indicate that the tagged words are not at the beginning of a named entity. 
       FIG.  5    shows a beginning-inside-outside (BIO) labeled sequence  500  with inconsistent tag pairs according to some embodiments. Inconsistent tag pairs can be pairs of tags that do not appear in the training data and inconstant tag pairs can include: O I-X, B-X I-Y, I-X I-Y where X and Y are different named entity types. O can stand for outside, B can stand for beginning, and I can stand for inside. For example, X can be person and Y can be location. With enough training data the conditional random field training procedure can learn that inconsistent tag pairs are unlikely to occur and the tag-to-tag transitions for the inconsistent tag pairs can be assigned a very negative weight (i.e., learned) in the transition matrix. 
     The labeled sequence  500  contains the sentence “Jane Emily Doe lives in San Francisco Calif.” from  FIG.  4   . The first named entity Jane Emily Doe  502   a - c  is labeled with an inconsistent tag pair I-Person  510  I-Location  512   a  of the form I-X I-Y where X and Y are separate tag categories. In some implementations of BIO labeling, named entities begin with a label with a B prefix and a named entity has the one type (e.g., Jane Emily Doe should refer to both a person and a location simultaneously). While Jane  502   a  is appropriately labeled with B-Person  508  and Emily  502   b  is also properly labeled with I-Person  510 , Doe  502   c  is incorrectly labeled as I-Location  512   a . While it is possible that a location can follow a name, and Jane Emily can be a valid name, a new named entity would begin with a tag containing the prefix B. Accordingly, I-Person  510  I-Location  512   a  is an inconsistent tag pair. 
     The labeled sequence  500  also contains outside words  506   a - b  that precede a second named entity  504   a - c . Outside words  506   a - b  can be labeled with O  514  and the first word in the named entity San  504   a  can be labeled as I-Location  512   b . The transition from outside word Francisco  504   b  to San  504   b  can be an inconsistent tag pair of the form O I-X. As discussed above, in some implementations, entities tagged with O  514  are not part of a named entity and the beginning of a named entity should receive a tag with a B prefix. Accordingly, a tag following an O  514  can be an O  514  or a tag with a B prefix and a O I-X tag pair is inconsistent. 
     The second named entity San Francisco Calif.  504   a - c  contains an inconsistent tag pair taking the form B-X I-Y. The second word in the named entity Francisco  504   b  is tagged as B-Person  516  and the third word California  504   c  is tagged with I-Location  512   c . While in this case the named entity starts with an incorrectly tagged word, San  504   a , a tag pair of I-Location  512   b  for San  504   a  and B-Person  516  for Francisco  504   b  can be consistent because a location can precede a person. However, Francisco  504   b  and California  504   c  form an inconsistent tag pair. The tag B-Person  508  for Francisco  504   b  can be followed by an I-Person  510  tag, an O  514  tag, or a B-X tag. The tags B-Person  516  I-Location  512   c  can be an inconsistent tag pair because a named entity can have one category (e.g., Francisco California can be a name or a place, but it is likely is not both at the same time) and a new named entity, with a new category, should begin with a tag containing a B prefix. 
     Discouraging Inconsistent Tag Pairs 
     Inconsistent tag pairs can be discouraged in a trained conditional random field (CRF) model. A cost for transitioning between inconsistent tag pairs in the model&#39;s transition matrix can be set to a negative value so that the conditional random field model is unlikely to predict an inconsistent pair. 
       FIG.  6    is a flowchart of a method for discouraging a conditional random field model from predicting inconsistent tag pairs according to certain embodiments. 
     At block  610 , a conditional random field (CRF) model is obtained. The conditional random field model includes model parameters that are stored in a transition matrix. The model parameters can be learned from training data that can include strings of text annotated with tags. The model parameters can be tag to tag potentials where the weight assigned to each potential indicates the likelihood of a transition from one tag to another. The weight of the tag-to-tag potentials can be adjusted to encourage or discourage the conditional random field model from predicting a particular tag pair. 
     At block  620 , inconsistent tag sequences, containing a pair of tags, are identified for the tags in the transition matrix. The inconsistent tag sequence can contain a pair of tags of a form that is inconsistent with tag sequence logic. Inconsistent tag pairs can be any tag pair of tags that does not appear in the training data and the tags can be beginning-inside-outside tags. In some implementations, the tags can be beginning inside outside (BIO) tags. The inconsistent tag pairs can be a pair of adjacent tags of the form O I-X, B-X I-Y, or I-X I-Y where X and Y are different tag categories. Inconsistent tag pairs can be any pair of tags that appears with a frequency below a frequency cutoff value (e.g., the tag-to-tag pair appears less than once for each 100,000 tag pairs). 
     At block  630 , a cost associated with transitioning between the pair of tags is set. The cost can be equal to a predefined hyperparameter value that is set to penalize transitioning between the pair of tags. The cost can be intended to discourage the pair of tags and the predetermined hyperparameter can be a negative value. The cost can be set at or below a threshold after training and, for example, the threshold can be −100. 
     At block  640 , a string of text with one or more named entities is received. A named entity can be any word, or string of words in the string of text that refers to a pre-defined category such as person names, organizations, locations, medical codes, products, expressions, quantities, monetary values, percentages, etc. 
     At block  650 , the string of text is input into the conditional random field model. The cost associated with transitioning between a pair of tags in the conditional random field model&#39;s transition matrix can be set equal to the predetermined hyperparameter value. 
     At block  660 , The conditional random field model can identify and tag words in the string. The tags can indicate whether the word is part of a named entity, the named entity&#39;s category, and whether the tagged word is at the beginning or inside a named entity. 
     Encouraging Longer Named Entities 
     A conditional random field (CRF) algorithm can be trained to produce a model that predicts the second tag in a tag pair. A user may want to encourage or discourage the conditional random field model from predicting certain tag to tag transitions. For instance, the user may want to encourage longer named entities by discouraging tag to tag transitions from one named entity to a new named entity (e.g., I-X B-X). The user can encourage or discourage certain tag to tag transitions through the selection of initial cost values in the conditional random field algorithm&#39;s transition matrix prior to training. 
       FIG.  7    is a flowchart of a method for training a conditional random field algorithm to classify entities according to certain embodiments. 
     At block  710 , a conditional random field algorithm is obtained. The conditional random field algorithm includes model parameters stored in a transition matrix and the transition parameters are costs associated with transitioning from tag to tag. In some implementations, the tags can be beginning inside outside (BIO) tags. 
     At block  720 , a tag sequence is received. The tag sequence contains a transition between a pair of tags that the user wants to encourage or discourage. In some circumstances, the user may want to encourage longer named entities by encouraging a transition between a pair of tags (e.g., tag to tag transitions) taking the form I-X I-X where X is a single tag category. In some circumstances, the user may want to encourage shorter tags by encouraging a transition between tags taking the form I-X O, I-X B-X, I-X B-Y, B-X B-X, B-X B-Y, or B-X, O, where X and Y are separate tag categories. 
     At block  730 , a transition matrix is initialized to include the tag sequence. A cost associated with transitioning between the pair of tags is set equal to a predefined hyperparameter. In some implementations, the hyperparameter can be a negative number. For example, the hyperparameter can be −1e2. In some implementations, the hyperparameter can be a positive number. For example, the hyperparameter can be 1e2. 
     At block  740 , the conditional random field algorithm is trained on a set of training data to generate a conditional random field model. The training data including strings of text annotated with tags and named entity classifications. The conditional random field algorithm has the cost associated with transitioning between pairs of tags in the transition matrix set equal to the predefined hyperparameter value. 
     Training the conditional random field algorithm to produce a conditional random field model includes performing iterative operations to learn the set of model parameters stored in the transition matrix. Each iteration in the training involves finding the set of parameter values for the conditional random field algorithm so that a value of an objective function using the set of parameters is larger or smaller than a value of tee objective function using another set of parameters in a previous iteration. 
     The objective function is constructed to measure a difference between named entities classified using the conditional random field algorithm and the named entity classifications annotated for the strings of text. 
     At block  750 , the conditional random field model is provided. 
     Illustrative Systems 
       FIG.  8    depicts a simplified diagram of a distributed system  800 . In the illustrated example, distributed system  800  includes one or more client computing devices  802 ,  804 ,  806 , and  808 , coupled to a server  812  via one or more communication networks  810 . Clients computing devices  802 ,  804 ,  806 , and  808  may be configured to execute one or more applications. 
     In various examples, server  812  may be adapted to run one or more services or software applications that enable one or more embodiments described in this disclosure. In certain examples, server  812  may also provide other services or software applications that may include non-virtual and virtual environments. In some examples, these services may be offered as web-based or cloud services, such as under a Software as a Service (SaaS) model to the users of client computing devices  802 ,  804 ,  806 , and/or  808 . Users operating client computing devices  802 ,  804 ,  806 , and/or  808  may in turn utilize one or more client applications to interact with server  812  to utilize the services provided by these components. 
     In the configuration depicted in  FIG.  8   , server  812  may include one or more components  818 ,  820  and  822  that implement the functions performed by server  812 . These components may include software components that may be executed by one or more processors, hardware components, or combinations thereof. It should be appreciated that various different system configurations are possible, which may be different from distributed system  800 . The example shown in  FIG.  8    is thus one example of a distributed system for implementing an example system and is not intended to be limiting. 
     Users may use client computing devices  802 ,  804 ,  806 , and/or  808  to execute one or more applications, models or chatbots, which may generate one or more events or models that may then be implemented or serviced in accordance with the teachings of this disclosure. A client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via this interface. Although  FIG.  8    depicts only four client computing devices, any number of client computing devices may be supported. 
     The client devices may include various types of computing systems such as portable handheld devices, general purpose computers such as personal computers and laptops, workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computing devices may run various types and versions of software applications and operating systems (e.g., Microsoft Windows®, Apple Macintosh®, UNIX® or UNIX-like operating systems, Linux or Linux-like operating systems such as Google Chrome™ OS) including various mobile operating systems (e.g., Microsoft Windows Mobile®, iOS®, Windows Phone®, Android™, BlackBerry®, Palm OS®). Portable handheld devices may include cellular phones, smartphones, (e.g., an iPhone®), tablets (e.g., iPad®), personal digital assistants (PDAs), and the like. Wearable devices may include Google Glass® head mounted display, and other devices. Gaming systems may include various handheld gaming devices, Internet-enabled gaming devices (e.g., a Microsoft Xbox® gaming console with or without a Kinect® gesture input device, Sony PlayStation® system, various gaming systems provided by Nintendo®, and others), and the like. The client devices may be capable of executing various different applications such as various Internet-related apps, communication applications (e.g., E-mail applications, short message service (SMS) applications) and may use various communication protocols. 
     Network(s)  810  may be any type of network familiar to those skilled in the art that may support data communications using any of a variety of available protocols, including without limitation TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), AppleTalk®, and the like. Merely by way of example, network(s)  810  may be a local area network (LAN), networks based on Ethernet, Token-Ring, a wide-area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infra-red network, a wireless network (e.g., a network operating under any of the Institute of Electrical and Electronics (IEEE) 1002.11 suite of protocols, Bluetooth®, and/or any other wireless protocol), and/or any combination of these and/or other networks. 
     Server  812  may be composed of one or more general purpose computers, specialized server computers (including, by way of example, PC (personal computer) servers, UNIX® servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination. Server  812  may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization such as one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices for the server. In various examples, server  812  may be adapted to run one or more services or software applications that provide the functionality described in the foregoing disclosure. 
     The computing systems in server  812  may run one or more operating systems including any of those discussed above, as well as any commercially available server operating system. Server  812  may also run any of a variety of additional server applications and/or mid-tier applications, including HTTP (hypertext transport protocol) servers, FTP (file transfer protocol) servers, CGI (common gateway interface) servers, JAVA® servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle®, Microsoft®, Sybase®, IBM® (International Business Machines), and the like. 
     In some implementations, server  812  may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices  802 ,  804 ,  806 , and  808 . As an example, data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. Server  812  may also include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices  802 ,  804 ,  806 , and  808 . 
     Distributed system  800  may also include one or more data repositories  814 ,  816 . These data repositories may be used to store data and other information in certain examples. For example, one or more of the data repositories  814 ,  816  may be used to store information such as information related to chatbot performance or generated models for use by chatbots used by server  812  when performing various functions in accordance with various embodiments. Data repositories  814 ,  816  may reside in a variety of locations. For example, a data repository used by server  812  may be local to server  812  or may be remote from server  812  and in communication with server  812  via a network-based or dedicated connection. Data repositories  814 ,  816  may be of different types. In certain examples, a data repository used by server  812  may be a database, for example, a relational database, such as databases provided by Oracle Corporation® and other vendors. One or more of these databases may be adapted to enable storage, update, and retrieval of data to and from the database in response to SQL-formatted commands. 
     In certain examples, one or more of data repositories  814 ,  816  may also be used by applications to store application data. The data repositories used by applications may be of different types such as, for example, a key-value store repository, an object store repository, or a general storage repository supported by a file system. 
     In certain examples, the functionalities described in this disclosure may be offered as services via a cloud environment.  FIG.  9    is a simplified block diagram of a cloud-based system environment in which various services may be offered as cloud services in accordance with certain examples. In the example depicted in  FIG.  9   , cloud infrastructure system  902  may provide one or more cloud services that may be requested by users using one or more client computing devices  904 ,  906 , and  908 . Cloud infrastructure system  902  may comprise one or more computers and/or servers that may include those described above for server  812 . The computers in cloud infrastructure system  902  may be organized as general purpose computers, specialized server computers, server farms, server clusters, or any other appropriate arrangement and/or combination. 
     Network(s)  910  may facilitate communication and exchange of data between clients  904 ,  906 , and  908  and cloud infrastructure system  902 . Network(s)  910  may include one or more networks. The networks may be of the same or different types. Network(s)  910  may support one or more communication protocols, including wired and/or wireless protocols, for facilitating the communications. 
     The example depicted in  FIG.  9    is only one example of a cloud infrastructure system and is not intended to be limiting. It should be appreciated that, in some other examples, cloud infrastructure system  902  may have more or fewer components than those depicted in  FIG.  9   , may combine two or more components, or may have a different configuration or arrangement of components. For example, although  FIG.  9    depicts three client computing devices, any number of client computing devices may be supported in alternative examples. 
     The term cloud service is generally used to refer to a service that is made available to users on demand and via a communication network such as the Internet by systems (e.g., cloud infrastructure system  902 ) of a service provider. Typically, in a public cloud environment, servers and systems that make up the cloud service provider&#39;s system are different from the customer&#39;s own on-premise servers and systems. The cloud service provider&#39;s systems are managed by the cloud service provider. Customers may thus avail themselves of cloud services provided by a cloud service provider without having to purchase separate licenses, support, or hardware and software resources for the services. For example, a cloud service provider&#39;s system may host an application, and a user may, via the Internet, on demand, order and use the application without the user having to buy infrastructure resources for executing the application. Cloud services are designed to provide easy, scalable access to applications, resources and services. Several providers offer cloud services. For example, several cloud services are offered by Oracle Corporation® of Redwood Shores, Calif., such as middleware services, database services, Java cloud services, and others. 
     In certain examples, cloud infrastructure system  902  may provide one or more cloud services using different models such as under a Software as a Service (SaaS) model, a Platform as a Service (PaaS) model, an Infrastructure as a Service (IaaS) model, and others, including hybrid service models. Cloud infrastructure system  902  may include a suite of applications, middleware, databases, and other resources that enable provision of the various cloud services. 
     A SaaS model enables an application or software to be delivered to a customer over a communication network like the Internet, as a service, without the customer having to buy the hardware or software for the underlying application. For example, a SaaS model may be used to provide customers access to on-demand applications that are hosted by cloud infrastructure system  902 . Examples of SaaS services provided by Oracle Corporation® include, without limitation, various services for human resources/capital management, customer relationship management (CRM), enterprise resource planning (ERP), supply chain management (SCM), enterprise performance management (EPM), analytics services, social applications, and others. 
     An IaaS model is generally used to provide infrastructure resources (e.g., servers, storage, hardware and networking resources) to a customer as a cloud service to provide elastic compute and storage capabilities. Various IaaS services are provided by Oracle Corporation®. 
     A PaaS model is generally used to provide, as a service, platform and environment resources that enable customers to develop, run, and manage applications and services without the customer having to procure, build, or maintain such resources. Examples of PaaS services provided by Oracle Corporation® include, without limitation, Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS), data management cloud service, various application development solutions services, and others. 
     Cloud services are generally provided on an on-demand self-service basis, subscription-based, elastically scalable, reliable, highly available, and secure manner. For example, a customer, via a subscription order, may order one or more services provided by cloud infrastructure system  902 . Cloud infrastructure system  902  then performs processing to provide the services requested in the customer&#39;s subscription order. For example, a user may use utterances to request the cloud infrastructure system to take a certain action (e.g., an intent), as described above, and/or provide services for a chatbot system as described herein. Cloud infrastructure system  902  may be configured to provide one or even multiple cloud services. 
     Cloud infrastructure system  902  may provide the cloud services via different deployment models. In a public cloud model, cloud infrastructure system  902  may be owned by a third party cloud services provider and the cloud services are offered to any general public customer, where the customer may be an individual or an enterprise. In certain other examples, under a private cloud model, cloud infrastructure system  902  may be operated within an organization (e.g., within an enterprise organization) and services provided to customers that are within the organization. For example, the customers may be various departments of an enterprise such as the Human Resources department, the Payroll department, etc. or even individuals within the enterprise. In certain other examples, under a community cloud model, the cloud infrastructure system  902  and the services provided may be shared by several organizations in a related community. Various other models such as hybrids of the above mentioned models may also be used. 
     Client computing devices  904 ,  906 , and  908  may be of different types (such as client computing devices  802 ,  804 ,  806 , and  808  depicted in  FIG.  8   ) and may be capable of operating one or more client applications. A user may use a client device to interact with cloud infrastructure system  902 , such as to request a service provided by cloud infrastructure system  902 . For example, a user may use a client device to request information or action from a chatbot as described in this disclosure. 
     In some examples, the processing performed by cloud infrastructure system  902  for providing services may involve model training and deployment. This analysis may involve using, analyzing, and manipulating data sets to train and deploy one or more models. This analysis may be performed by one or more processors, possibly processing the data in parallel, performing simulations using the data, and the like. For example, big data analysis may be performed by cloud infrastructure system  902  for generating and training one or more models for a chatbot system. The data used for this analysis may include structured data (e.g., data stored in a database or structured according to a structured model) and/or unstructured data (e.g., data blobs (binary large objects)). 
     As depicted in the example in  FIG.  9   , cloud infrastructure system  902  may include infrastructure resources  930  that are utilized for facilitating the provision of various cloud services offered by cloud infrastructure system  902 . Infrastructure resources  930  may include, for example, processing resources, storage or memory resources, networking resources, and the like. In certain examples, the storage virtual machines that are available for servicing storage requested from applications may be part of cloud infrastructure system  902 . In other examples, the storage virtual machines may be part of different systems. 
     In certain examples, to facilitate efficient provisioning of these resources for supporting the various cloud services provided by cloud infrastructure system  902  for different customers, the resources may be bundled into sets of resources or resource modules (also referred to as “pods”). Each resource module or pod may comprise a pre-integrated and optimized combination of resources of one or more types. In certain examples, different pods may be pre-provisioned for different types of cloud services. For example, a first set of pods may be provisioned for a database service, a second set of pods, which may include a different combination of resources than a pod in the first set of pods, may be provisioned for Java service, and the like. For some services, the resources allocated for provisioning the services may be shared between the services. 
     Cloud infrastructure system  902  may itself internally use services  932  that are shared by different components of cloud infrastructure system  902  and which facilitate the provisioning of services by cloud infrastructure system  902 . These internal shared services may include, without limitation, a security and identity service, an integration service, an enterprise repository service, an enterprise manager service, a virus scanning and white list service, a high availability, backup and recovery service, service for enabling cloud support, an email service, a notification service, a file transfer service, and the like. 
     Cloud infrastructure system  902  may comprise multiple subsystems. These subsystems may be implemented in software, or hardware, or combinations thereof. As depicted in  FIG.  9   , the subsystems may include a user interface subsystem  912  that enables users or customers of cloud infrastructure system  902  to interact with cloud infrastructure system  902 . User interface subsystem  912  may include various different interfaces such as a web interface  914 , an online store interface  916  where cloud services provided by cloud infrastructure system  902  are advertised and are purchasable by a consumer, and other interfaces  918 . For example, a customer may, using a client device, request (service request  934 ) one or more services provided by cloud infrastructure system  902  using one or more of interfaces  914 ,  916 , and  918 . For example, a customer may access the online store, browse cloud services offered by cloud infrastructure system  902 , and place a subscription order for one or more services offered by cloud infrastructure system  902  that the customer wishes to subscribe to. The service request may include information identifying the customer and one or more services that the customer desires to subscribe to. For example, a customer may place a subscription order for a service offered by cloud infrastructure system  902 . As part of the order, the customer may provide information identifying a chatbot system for which the service is to be provided and optionally one or more credentials for the chatbot system. 
     In certain examples, such as the example depicted in  FIG.  9   , cloud infrastructure system  902  may comprise an order management subsystem (OMS)  920  that is configured to process the new order. As part of this processing, OMS  920  may be configured to: create an account for the customer, if not done already; receive billing and/or accounting information from the customer that is to be used for billing the customer for providing the requested service to the customer; verify the customer information; upon verification, book the order for the customer; and orchestrate various workflows to prepare the order for provisioning. 
     Once properly validated, OMS  920  may then invoke the order provisioning subsystem (OPS)  924  that is configured to provision resources for the order including processing, memory, and networking resources. The provisioning may include allocating resources for the order and configuring the resources to facilitate the service requested by the customer order. The manner in which resources are provisioned for an order and the type of the provisioned resources may depend upon the type of cloud service that has been ordered by the customer. For example, according to one workflow, OPS  924  may be configured to determine the particular cloud service being requested and identify a number of pods that may have been pre-configured for that particular cloud service. The number of pods that are allocated for an order may depend upon the size/amount/level/scope of the requested service. For example, the number of pods to be allocated may be determined based upon the number of users to be supported by the service, the duration of time for which the service is being requested, and the like. The allocated pods may then be customized for the particular requesting customer for providing the requested service. 
     In certain examples, setup phase processing, as described above, may be performed by cloud infrastructure system  902  as part of the provisioning process. Cloud infrastructure system  902  may generate an application ID and select a storage virtual machine for an application from among storage virtual machines provided by cloud infrastructure system  902  itself or from storage virtual machines provided by other systems other than cloud infrastructure system  902 . 
     Cloud infrastructure system  902  may send a response or notification  944  to the requesting customer to indicate when the requested service is now ready for use. In some instances, information (e.g., a link) may be sent to the customer that enables the customer to start using and availing the benefits of the requested services. In certain examples, for a customer requesting the service, the response may include a chatbot system ID generated by cloud infrastructure system  902  and information identifying a chatbot system selected by cloud infrastructure system  902  for the chatbot system corresponding to the chatbot system ID. 
     Cloud infrastructure system  902  may provide services to multiple customers. For each customer, cloud infrastructure system  902  is responsible for managing information related to one or more subscription orders received from the customer, maintaining customer data related to the orders, and providing the requested services to the customer. Cloud infrastructure system  902  may also collect usage statistics regarding a customer&#39;s use of subscribed services. For example, statistics may be collected for the amount of storage used, the amount of data transferred, the number of users, and the amount of system up time and system down time, and the like. This usage information may be used to bill the customer. Billing may be done, for example, on a monthly cycle. 
     Cloud infrastructure system  902  may provide services to multiple customers in parallel. Cloud infrastructure system  902  may store information for these customers, including possibly proprietary information. In certain examples, cloud infrastructure system  902  comprises an identity management subsystem (IMS)  928  that is configured to manage customer information and provide the separation of the managed information such that information related to one customer is not accessible by another customer. IMS  928  may be configured to provide various security-related services such as identity services, such as information access management, authentication and authorization services, services for managing customer identities and roles and related capabilities, and the like. 
       FIG.  10    illustrates an example of computer system  1000 . In some examples, computer system  1000  may be used to implement any of the digital assistant or chatbot systems within a distributed environment, and various servers and computer systems described above. As shown in  FIG.  10   , computer system  1000  includes various subsystems including a processing subsystem  1004  that communicates with a number of other subsystems via a bus subsystem  1002 . These other subsystems may include a processing acceleration unit  1006 , an I/O subsystem  1008 , a storage subsystem  1018 , and a communications subsystem  1024 . Storage subsystem  1018  may include non-transitory computer-readable storage media including storage media  1022  and a system memory  1010 . 
     Bus subsystem  1002  provides a mechanism for letting the various components and subsystems of computer system  1000  communicate with each other as intended. Although bus subsystem  1002  is shown schematically as a single bus, alternative examples of the bus subsystem may utilize multiple buses. Bus subsystem  1002  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, a local bus using any of a variety of bus architectures, and the like. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which may be implemented as a Mezzanine bus manufactured to the IEEE P1386.1 standard, and the like. 
     Processing subsystem  1004  controls the operation of computer system  1000  and may comprise one or more processors, application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). The processors may include be single core or multicore processors. The processing resources of computer system  1000  may be organized into one or more processing units  1032 ,  1034 , etc. A processing unit may include one or more processors, one or more cores from the same or different processors, a combination of cores and processors, or other combinations of cores and processors. In some examples, processing subsystem  1004  may include one or more special purpose co-processors such as graphics processors, digital signal processors (DSPs), or the like. In some examples, some or all of the processing units of processing subsystem  1004  may be implemented using customized circuits, such as application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). 
     In some examples, the processing units in processing subsystem  1004  may execute instructions stored in system memory  1010  or on computer readable storage media  1022 . In various examples, the processing units may execute a variety of programs or code instructions and may maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed may be resident in system memory  1010  and/or on computer-readable storage media  1022  including potentially on one or more storage devices. Through suitable programming, processing subsystem  1004  may provide various functionalities described above. In instances where computer system  1000  is executing one or more virtual machines, one or more processing units may be allocated to each virtual machine. 
     In certain examples, a processing acceleration unit  1006  may optionally be provided for performing customized processing or for off-loading some of the processing performed by processing subsystem  1004  so as to accelerate the overall processing performed by computer system  1000 . 
     I/O subsystem  1008  may include devices and mechanisms for inputting information to computer system  1000  and/or for outputting information from or via computer system  1000 . In general, use of the term input device is intended to include all possible types of devices and mechanisms for inputting information to computer system  1000 . User interface input devices may include, for example, a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may also include motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, the Microsoft Xbox® 360 game controller, devices that provide an interface for receiving input using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., “blinking” while taking pictures and/or making a menu selection) from users and transforms the eye gestures as inputs to an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator) through voice commands. 
     Other examples of user interface input devices include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, and medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like. 
     In general, use of the term output device is intended to include all possible types of devices and mechanisms for outputting information from computer system  1000  to a user or other computer. User interface output devices may include a display subsystem, indicator lights, or non-visual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like. For example, user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems. 
     Storage subsystem  1018  provides a repository or data store for storing information and data that is used by computer system  1000 . Storage subsystem  1018  provides a tangible non-transitory computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some examples. Storage subsystem  1018  may store software (e.g., programs, code modules, instructions) that when executed by processing subsystem  1004  provides the functionality described above. The software may be executed by one or more processing units of processing subsystem  1004 . Storage subsystem  1018  may also provide authentication in accordance with the teachings of this disclosure. 
     Storage subsystem  1018  may include one or more non-transitory memory devices, including volatile and non-volatile memory devices. As shown in  FIG.  10   , storage subsystem  1018  includes a system memory  1010  and a computer-readable storage media  1022 . System memory  1010  may include a number of memories including a volatile main random access memory (RAM) for storage of instructions and data during program execution and a non-volatile read only memory (ROM) or flash memory in which fixed instructions are stored. In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system  1000 , such as during start-up, may typically be stored in the ROM. The RAM typically contains data and/or program modules that are presently being operated and executed by processing subsystem  1004 . In some implementations, system memory  1010  may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), and the like. 
     By way of example, and not limitation, as depicted in  FIG.  10   , system memory  1010  may load application programs  1012  that are being executed, which may include various applications such as Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data  1014 , and an operating system  1016 . By way of example, operating system  1016  may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® OS, Palm® OS operating systems, and others. 
     Computer-readable storage media  1022  may store programming and data constructs that provide the functionality of some examples. Computer-readable media  1022  may provide storage of computer-readable instructions, data structures, program modules, and other data for computer system  1000 . Software (programs, code modules, instructions) that, when executed by processing subsystem  1004  provides the functionality described above, may be stored in storage subsystem  1018 . By way of example, computer-readable storage media  1022  may include non-volatile memory such as a hard disk drive, a magnetic disk drive, an optical disk drive such as a CD ROM, DVD, a Blu-Ray® disk, or other optical media. Computer-readable storage media  1022  may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media  1022  may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. 
     In certain examples, storage subsystem  1018  may also include a computer-readable storage media reader  1020  that may further be connected to computer-readable storage media  1022 . Reader  1020  may receive and be configured to read data from a memory device such as a disk, a flash drive, etc. 
     In certain examples, computer system  1000  may support virtualization technologies, including but not limited to virtualization of processing and memory resources. For example, computer system  1000  may provide support for executing one or more virtual machines. In certain examples, computer system  1000  may execute a program such as a hypervisor that facilitated the configuring and managing of the virtual machines. Each virtual machine may be allocated memory, compute (e.g., processors, cores), I/O, and networking resources. Each virtual machine generally runs independently of the other virtual machines. A virtual machine typically runs its own operating system, which may be the same as or different from the operating systems executed by other virtual machines executed by computer system  1000 . Accordingly, multiple operating systems may potentially be run concurrently by computer system  1000 . 
     Communications subsystem  1024  provides an interface to other computer systems and networks. Communications subsystem  1024  serves as an interface for receiving data from and transmitting data to other systems from computer system  1000 . For example, communications subsystem  1024  may enable computer system  1000  to establish a communication channel to one or more client devices via the Internet for receiving and sending information from and to the client devices. For example, when computer system  1000  is used to implement bot system  1000  depicted in  FIG.  1   , the communication subsystem may be used to communicate with a chatbot system selected for an application. 
     Communication subsystem  1024  may support both wired and/or wireless communication protocols. In certain examples, communications subsystem  1024  may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution)), WiFi (IEEE 1002.XX family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some examples, communications subsystem  1024  may provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface. 
     Communication subsystem  1024  may receive and transmit data in various forms. In some examples, in addition to other forms, communications subsystem  1024  may receive input communications in the form of structured and/or unstructured data feeds  1026 , event streams  1028 , event updates  1030 , and the like. For example, communications subsystem  1024  may be configured to receive (or send) data feeds  1026  in real-time from users of social media networks and/or other communication services such as Twitter® feeds, Facebookx updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources. 
     In certain examples, communications subsystem  1024  may be configured to receive data in the form of continuous data streams, which may include event streams  1028  of real-time events and/or event updates  1030 , that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. 
     Communications subsystem  1024  may also be configured to communicate data from computer system  1000  to other computer systems or networks. The data may be communicated in various different forms such as structured and/or unstructured data feeds  1026 , event streams  1028 , event updates  1030 , and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system  1000 . 
     Computer system  1000  may be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a personal computer, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system. Due to the ever-changing nature of computers and networks, the description of computer system  1000  depicted in  FIG.  10    is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in  FIG.  9    are possible. Based on the disclosure and teachings provided herein, it should be appreciate there are other ways and/or methods to implement the various examples. 
     Although specific examples have been described, various modifications, alterations, alternative constructions, and equivalents are possible. Examples are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although certain examples have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that this is not intended to be limiting. Although some flowcharts describe operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may have additional steps not included in the figure. Various features and aspects of the above-described examples may be used individually or jointly. 
     Further, while certain examples have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also possible. Certain examples may be implemented only in hardware, or only in software, or using combinations thereof. The various processes described herein may be implemented on the same processor or different processors in any combination. 
     Where devices, systems, components or modules are described as being configured to perform certain operations or functions, such configuration may be accomplished, for example, by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation such as by executing computer instructions or code, or processors or cores programmed to execute code or instructions stored on a non-transitory memory medium, or any combination thereof. Processes may communicate using a variety of techniques including but not limited to conventional techniques for inter-process communications, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times. 
     Specific details are given in this disclosure to provide a thorough understanding of the examples. However, examples may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the examples. This description provides example examples only, and is not intended to limit the scope, applicability, or configuration of other examples. Rather, the preceding description of the examples will provide those skilled in the art with an enabling description for implementing various examples. Various changes may be made in the function and arrangement of elements. 
     The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that additions, subtractions, deletions, and other modifications and changes may be made thereunto without departing from the broader spirit and scope as set forth in the claims. Thus, although specific examples have been described, these are not intended to be limiting. Various modifications and equivalents are within the scope of the following claims. 
     In the foregoing specification, aspects of the disclosure are described with reference to specific examples thereof, but those skilled in the art will recognize that the disclosure is not limited thereto. Various features and aspects of the above-described disclosure may be used individually or jointly. Further, examples may be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. 
     In the foregoing description, for the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate examples, the methods may be performed in a different order than that described. It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software. 
     Where components are described as being configured to perform certain operations, such configuration may be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof. 
     While illustrative examples of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art.