Systems and methods for bot dialog delegation

Systems and methods for switching between natural language processing units during an interaction using a dialog delegation processor. The method includes receiving a dialog utterance from a user via a user device. The method further includes determining a general intent based on the dialog utterance using a general natural language processor. The method also includes determining at least one specific intent corresponding to the general intent using the general natural language processor. The method also includes selecting a specific natural language processor corresponding to the at least one specific intent. The method further includes determining a dialog response corresponding to the at least one of specific intent using the specific natural language processor. The method also includes generating for display the dialog response on the user device.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods for interacting with a user using natural language processing units, including systems and methods for switching between natural language processing units during an interaction.

BACKGROUND OF THE INVENTION

Generally, modern online customer services involve the use of chatbots or virtual assistants (“VAs”) as a first line of interaction for a user. Chatbots or VAs use Natural Language Processing (“NLP”) to read, decipher, and understand the interaction with the user. The capabilities and inherently supported actions of Natural Language Processing units vary depending on the context of the interactions and the sophistication of the NLP units. Most chatbots or VAs can support simple Q&A interactions but are limited when asked to perform substantive tasks.

For example, organizations with large digital presences often require multiple chatbots or VAs to assist users across it's many online services. Ideally, when a user interacts with an online service having multiple VAs, the user is unaware of which chatbot or VA they are interacting with during the interaction. Therefore, there is a need for systems and methods that allow for the interconnection of all of the chatbots or VAs within an organization, avoiding duplication of conversation flows with a single user and facilitating handoffs between chatbots or VAs.

SUMMARY OF THE INVENTION

Accordingly, an object of the invention is to provide systems and methods for switching between natural language processing units during an interaction. It is an object of the invention to provide systems and methods for switching between natural language processing units during an interaction using a dialog delegation processor. It is an object of the invention to provide systems and methods for determining user intents based on dialog utterances from a user. It is an object of the invention to provide systems and methods for selecting natural language processors corresponding to user intents. It is an object of the invention to provide systems and methods for determining a dialog response corresponding to the user intent using a natural language processor. It is an object of the invention to provide systems and methods for resolving dialog incomprehension using a dialog delegation processor.

In some aspects, a method for switching between natural language processing units during an interaction using a dialog delegation processor includes receiving, by a server computing device, intent declarations. Each intent declaration includes a set of phrases corresponding to an intent of a user. The method further includes receiving, by the server computing device, a dialog utterance from the user via a user device. The method also includes, comparing, by the server computing device, the dialog utterance with each of the intent declarations using a general natural language processor. The method also includes determining, by the server computing device, a general intent based on the comparison of the dialog utterance with each of the intent declarations using the general natural language processor. The general intent includes specific intents.

Further, the method includes determining, by the server computing device, at least one of the specific intents corresponding to the general intent based on the comparison of the dialog utterance with each of the intent declarations using the general natural language processor. The method also includes selecting, by the server computing device, a specific natural language processor corresponding to the at least one of the specific intents. Further, the method includes determining, by the server computing device, a dialog response corresponding to the at least one of the specific intents using the specific natural language processor. The method also includes generating, by the server computing device, for display the dialog response on the user device.

In some embodiments, the server computing device is further configured to determine a second specific intent corresponding to the general intent. For example, in some embodiments, the server computing device is further configured to select a second specific natural language processor corresponding to the second specific intent. In some embodiments, the server computing device is further configured to determine a second dialog response corresponding to the second specific intent using the second specific natural language processor. For example, in some embodiments, the server computing device is further configured to generate for display the second dialog response on the user device.

In some embodiments, the server computing device is further configured to determine an incomprehension based on the dialog utterance using the general natural language processor. For example, in some embodiments, the server computing device is further configured to, in response to determining the incomprehension, generate for display an error message on the user device.

In some aspects, a system for switching between natural language processing units during an interaction using a dialog delegation processor includes a server computing device communicatively coupled to a user device over a network. The server computing device is configured to receive intent declarations. Each intent declaration comprises a set of phrases corresponding to an intent of a user. The server computing device is also configured to receive a dialog utterance from the user via the user device. Further, the server computing device is configured to compare the dialog utterance with each of the intent declarations using a general language processor. The server computing device is also configured to determine a general intent based on the comparison of the dialog utterance with each of the intent declarations using the general natural language processor. The general intent includes specific intents.

Further, the server computing device is configured to determine at least one of the specific intents corresponding to the general intent based on the comparison of the dialog utterance with each of the intent declarations using the general natural language processor. The server computing device is further configured to select a specific natural language processor corresponding to the at least one of the specific intents. The server computing device is also configured to determine a dialog response corresponding to the at least one of the specific intents using the specific natural language processor. Further, the server computing device is configured to generate for display the dialog response on the user device.

In some embodiments, the server computing device is further configured to determine a second specific intent corresponding to the general intent. For example, in some embodiments, the server computing device is further configured to select a second specific natural language processor corresponding to the second specific intent. In some embodiments, the server computing device is further configured to determine a second dialog response corresponding to the second specific intent using the second specific natural language processor. For example, in some embodiments, the server computing device is further configured to generate for display the second dialog response on the user device.

In some embodiments, the server computing device is further configured to determine an incomprehension based on the dialog utterance using the general natural language processor. For example, in some embodiments, the server computing device is further configured to, in response to determining the incomprehension, generate for display an error message on the user device.

In some aspects, a method for resolving dialog incomprehension using a dialog delegation processor includes receiving, by a server computing device, a dialog utterance from a user via a user device. The method further includes determining, by the server computing device, an incomprehension based on the dialog utterance using a general natural language processor. The method also includes, in response to determining the incomprehension, incrementing, by the server computing device, an incomprehension count. The method also includes, in response to determining that the incomprehension count exceeds one, initiating, by the server computing device, a search language processor. Further, the method includes, in response to determining that the incomprehension count exceeds two, generating, by the server computing device, for display a query message corresponding to a user option. The method also includes, in response to determining that the incomprehension count exceeds three, generating, by the server computing device, for display an error message.

In some embodiments, the search language processor is configured to determine an intent based on the dialog utterance. For example, in some embodiments, the server computing device is further configured to select a natural language processor corresponding to the determined intent. In some embodiments, the server computing device is further configured to determine a dialog response corresponding to the determined intent using the natural language processor.

In other embodiments, the user option includes speaking with a human. In some embodiments, the server computing device is further configured to receive a rephrased dialog utterance from the user via the user device.

Other aspects and advantages of the invention can become apparent from the following drawings and description, all of which illustrate the principles of the invention, by way of example only.

DETAILED DESCRIPTION OF THE INVENTION

In some aspects, the systems and methods described herein can include one or more mechanisms or methods for switching between natural language processing units during an interaction. The system and methods can include mechanisms or methods for switching between natural language processing units during an interaction using a dialog delegation processor. The systems and methods described herein can provide systems and methods for determining user intents based on dialog utterances from a user. The systems and methods described herein can include one or more mechanisms or methods for selecting natural language processors corresponding to user intents. The systems and methods described herein can facilitate users with systems and methods for determining a dialog response corresponding to the user intent using a natural language processor. The systems and methods described herein can facilitate users with systems and methods for resolving dialog incomprehension using a dialog delegation processor.

The systems and methods described herein can be implemented using a data communications network, server computing devices, and mobile devices. For example, referring toFIGS.1and2, an exemplary communications system100includes data communications network150, exemplary server computing devices200, and exemplary user devices250. In some embodiments, the system100includes one or more server computing devices200and one or more user devices250. Each server computing device200can include a processor202, memory204, storage206, and communication circuitry208. Each user device250can include a processor252, memory254, storage256, and communication circuitry258. In some embodiments, communication circuitry208of the server computing devices200is communicatively coupled to the communication circuitry258of the user devices250via data communications network150. Communication circuitry208and communication circuitry258can use Bluetooth, Wi-Fi, or any comparable data transfer connection. The user devices250can include personal workstations, laptops, tablets, mobile devices, or any other comparable device.

FIG.3illustrates an exemplary dialog processing system300using user devices250and dialog delegation processor310. A dialog delegation processor310can reuse a conversational flow regardless of the origin of the interaction and the knowledge base where the conversational flow is defined. When dialog delegation is activated, an intent known to the system via a dialog delegate configuration, can be initiated transparently for the user on a different NLP350or VA that triggers the flow, and then returns back to the NLP350the user started interacting with. This means different online service teams can create different conversational flows, and these can all be reused.

The dialog delegate functionality makes use of the existing mechanism which handles fetching bot and dialog definitions320, and moving through the scripted dialogs until a response is available to the user. This process can be implemented in strategic places in order to programmatically or declaratively indicate when a delegation to a separate dialog is needed. The configuration object consists of two basic fields: dialogld—a unique identifier of a dialog defined in the system's content store which holds conversation/intent flow declarations, triggerUtterance—the utterance to send to the NLP350so it can start the targeted dialog, which based on the conditions or scripted flow, needs to be commenced at this point, and isInitializing/isActive runtime flags. The conversation state330and conversation active transcript340are stored in the system's content store or database.

Current configurations hold an array of declared dialogs (which may come from multiple bots, but same NLPs350or different NLPs350to be used in order. The dialog processing system300will loop through these dialogs until an intent is matched or return either an incomprehension or an escalation if the declared NLPs350are not able to match the user utterance. Using the dialog delegation processor310, the dialog processing system300does not have to loop through all of the declared dialogs because the dialog delegation processor310forces an interaction with a specific dialog which is known to the conversation flow designers to be able to handle the user intended action and remain on that dialog until the action is fulfilled.

As illustrated byFIG.4, during an interaction400, the user is unaware of any switching between bots or NLPs350; the user presents an utterance410and they are presented with the dialog responses420as they are available. Conversation flows are aware of the content available to the dialog processing system300, and can use the content to facilitate the user requests. Once the intent has been fulfilled, the dialog delegation processor310clears its status and the process can commence once again. The dialog delegation processor310also allows for multiple delegations during an interaction.

In some embodiments, the dialog processing system300delegates intent definitions into multiple hots. For example, there is a starting NLP350that classifies the customer's main intent, and then routes the request, via dialog delegation, to the NLP350ready to handle the fulfillment. Depending on the channel, the NLP350may use different NLPs350. Part of this solution addresses strengths and weakness of different NLPs350, for example a particular NLP350may classify the intent because it classifies intents better, and have another NLP350with the full-blown definition handle fulfillment. In other embodiments, different organizations maintain their own domain expertise content. In this scenario there is a main NLP350that is in charge of routing the request to the hot with the correct domain content. At runtime, the implementation of dialog delegation has not had to be enhanced, but rather this is a pattern that relies on bot configurations and conversation flow design. This embodiment takes the effort off the system and places in the hands of those who maintain the different training models.

As illustrated inFIG.5, in some embodiments, the dialog delegation processor310is the part of the dialog processing system that resolves the response template to present to the user. NLPs350within the system have a many-to-many relationship with dialog definitions. In prior art systems, the only way to specify the dialogs a NLP350could interact with, was by adding them in order of precedence to the NLP350's configuration dialogs array. The dialog processing system350, however, will loop through these dialogs while the user's utterance410hasn't been classified, and then on a match and dialog state changes.

As illustrated inFIG.6, the method steps for a dialog delegation processor310include fetching the NLP350's definition320which holds the dialogs array, looping through the array, while the state of the conversation330is “Incomprehension,” calling the NLP350associated with the dialog definition passing of the values it expects from the conversation state330and other available data. Once a response420has been classified, the dialog delegation processor310selects the appropriate response definition, sometimes called dialog script. The system deduces the correct one based on the state of the conversation and intent definition primarily. However, in some embodiments, if the NLP350has the ability of adding dialog delegation to the state and the runtime conditions are met, the executing function detects this and turns the dialog delegation mode on. NLP350adds the dialog delegate configuration to the state under the dialogDelegate object and it sets flags to activate the execution of the dialog delegate code in the current function. Recursion is utilized for this, given that the current executing function holds the responsibility of classifying an intent, ensuring that the NLP350updates its dialog state and then resolves the proper response definition for the interaction. This is one of the points in time when delegation can happen.

Once the dialog state changes out of incomprehension, the final response template is selected based on runtime conditions if any is given, or defaults are chosen. In some embodiments, responses can also provide a definition for dialog delegation. In other embodiments, the only necessary condition that needs to exist at this point to decide if delegation needs to be activated, is to have that definition present in the response template. This will also add dialog delegate configuration to the state under the dialogDelegate object and it sets flags to activate the execution of the dialog delegate code in the current function. This is a different function, and is the one looping over the different dialogs, but with the associated runtime flags, looping is skipped, and the dialog definition is always forced to be that of the dialog delegate object.

In some embodiments, once the intent that was invoked to be completed by delegation is fulfilled, the dialogDelegated related values runtime are reset and the dialog delegation mode is turned off. In some embodiments, if the delegated dialog returns an incomprehension, the state will clear out of the dialog delegation and return control of the interactions to the default array of dialogs.

Referring toFIG.7, there are situations when a single intent requires the collection of a lot of information. It is easier to break them down into multiple smaller intents that focus on collecting specific data. In some embodiments, dialog processor310connects the fulfillment of one intent to the initiation of the next until all the data needed for fulfillment has been collected. This makes complex intent definitions easier to manage and error handling is more focused. That is, as the sections or the intent are broken out, it is easier to add error handling blocks without polluting the conversation design configuration.

An exemplary process800for switching between natural language processing units350during an interaction using a dialog delegation processor310is illustrated inFIG.8. The process800begins by receiving, by a server computing device200, intent declarations in step802. Each intent declaration includes a set of phrases corresponding to an intent of a user. Process800continues by receiving, by the server computing device200, a dialog utterance410from the user via a user device250in step804. Process800continues by comparing, by the server computing device200, the dialog utterance410with each of the intent declarations using a general natural language processor350in step806.

Process800continues by determining, by the server computing device200, a general intent based on the comparison of the dialog utterance410with each of the intent declarations using the general natural language processor350in step808. The general intent includes specific intents. Process800continues by determining, by the server computing device200, at least one of the specific intents corresponding to the general intent based on the comparison of the dialog utterance410with each of the intent declarations using the general natural language processor350in step810.

Process800continues by selecting, by the server computing device200, a specific natural language processor350corresponding to the at least one of the specific intents in step812. Process800continues by determining, by the server computing device200, a dialog response420corresponding to the at least one of the specific intents using the specific natural language processor350in step814. Process800finishes by generating, by the server computing device200, for display the dialog response420on the user device250in step816.

In some embodiments, the server computing device200is further configured to determine a second specific intent corresponding to the general intent. For example, in some embodiments, the server computing device200is further configured to select a second specific natural language processor350corresponding to the second specific intent. In some embodiments, the server computing device200is further configured to determine a second dialog response corresponding to the second specific intent using the second specific natural language processor350. For example, in some embodiments, the server computing device200is further configured to generate for display the second dialog response on the user device.

In some embodiments, the server computing device200is further configured to determine an incomprehension based on the dialog utterance410using the general natural language processor350. For example, in some embodiments, the server computing device200is further configured to, in response to determining the incomprehension, generate for display an error message on the user device250.

In some aspects, process800can be implemented on a system for switching between natural language processing units350during an interaction using a dialog delegation processor310. The system includes a server computing device200communicatively coupled to a user device250over a network150. The server computing device200is configured to receive intent declarations. Each intent declaration includes a set of phrases corresponding to an intent of a user. The server computing device200is also configured to receive a dialog utterance410from the user via the user device250. Further, the server computing device200is configured to compare the dialog utterance410with each of the intent declarations using a general language processor350. The server computing device200is also configured to determine a general intent based on the comparison of the dialog utterance with each of the intent declarations using the general natural language processor350. The general intent includes specific intents.

Further, the server computing device200is configured to determine at least one of the specific intents corresponding to the general intent based on the comparison of the dialog utterance410with each of the intent declarations using the general natural language processor350. The server computing device200is further configured to select a specific natural language processor350corresponding to the at least one of the specific intents. The server computing device200is also configured to determine a dialog response420corresponding to the at least one of the specific intents using the specific natural language processor350. Further, the server computing device200is configured to generate for display the dialog response on the user device250.

In some embodiments, the server computing device200is further configured to determine a second specific intent corresponding to the general intent. For example, in some embodiments, the server computing device200is further configured to select a second specific natural language processor350corresponding to the second specific intent. In some embodiments, the server computing device200is further configured to determine a second dialog response corresponding to the second specific intent using the second specific natural language processor350. For example, in some embodiments, the server computing device200is further configured to generate for display the second dialog response on the user device.

In some embodiments, the server computing device200is further configured to determine an incomprehension based on the dialog utterance410using the general natural language processor350. For example, in some embodiments, the server computing device200is further configured to, in response to determining the incomprehension, generate for display an error message on the user device250.

Referring toFIG.9, an exemplary dialog delegation processor310is illustrated for incomprehension delegation. Incomprehensions occur when an NLP350is not able to resolve the user utterance410. This can happen for many reasons, for example, badly formed sentences, poor grammar that the NLP350is not trained to handle, or simply not having the intent in the knowledge base. In some embodiments, after an N number of incomprehensions, the dialog processor310looks at the configurations and decides which NLP350to delegate to. The system has an “escalation” configuration in place that holds information specific to each team, for example, phone number to call in, message, links or other organization specific values. At runtime, the conversation state stores an incomprehension counter910, the dialog processor310then checks runtime values, the incomprehension counter910, and other pertinent context, and decides if it should execute incomprehension delegation. Based on the incomprehension count value, the dialog processor310selects the correct configuration information. For example, in some embodiments, the dialog processor310executes a search language processor after two incomprehensions, and after a third incomprehension the dialog processor310asks the user if they want to speak to a representative (human).

An exemplary process1000for resolving dialog incomprehension using a dialog delegation processor310is illustrated inFIG.10. The process1000begins by receiving, by a server computing device200, a dialog utterance410from a user via a user device250in step1002. Process1000continues by determining, by the server computing device200, an incomprehension based on the dialog utterance410using a general natural language processor350in step1004. In response to determining the incomprehension, process1000continues by incrementing, by the server computing device200, an incomprehension count910in step1006.

Process1000continues by determining whether the incomprehension count910exceeds three in step1008. In response to determining that the incomprehension count910exceeds three, process1000continues by generating, by the server computing device200, for display an error message in step1010. Otherwise, process1000continues by determining whether the incomprehension count910exceeds two in step1012. In response to determining that the incomprehension count910exceeds two, process1000continues by generating, by the server computing device200, for display a query message corresponding to a user option in step1014. Otherwise, process1000continues by determining whether the incomprehension count910exceeds one in step1016. In response to determining that the incomprehension count910exceeds one, process1000continues by initiating, by the server computing device200, a search language processor in step1018.

In some embodiments, the search language processor is configured to determine an intent based on the dialog utterance410. For example, in some embodiments, the server computing device200is further configured to select a natural language processor350corresponding to the determined intent. In some embodiments, the server computing device200is further configured to determine a dialog response420corresponding to the determined intent using the natural language processor350. In other embodiments, the user option includes speaking with a human. In some embodiments, the server computing device200is further configured to receive a rephrased dialog utterance from the user via the user device250.

In some aspects, process1000can be implemented on a system for resolving dialog incomprehension using a dialog delegation processor310. The system includes a server computing device200communicatively coupled to a user device250over a network150. The server computing device200is configured to receive a dialog utterance410from a user via the user device250. Further, the server computing device200is configured to determine an incomprehension based on the dialog utterance410using a general natural language processor350. In response to determining the incomprehension, the server computing device is also configured to increment an incomprehension count910.

Further, the server computing device200is configured to determine whether the incomprehension count910exceeds three. In response to determining that the incomprehension count910exceeds three, the server computing device200is configured to generate for display an error message. Otherwise, the server computing device200is configured to determine whether the incomprehension count910exceeds two. In response to determining that the incomprehension count910exceeds two, the server computing device200is configured to generate for display a query message corresponding to a user option. Otherwise, the server computing device200is configured to determine whether the incomprehension count910exceeds one. In response to determining that the incomprehension count910exceeds one, the server computing device is configured to initiate a search language processor.

In some embodiments, the search language processor is configured to determine an intent based on the dialog utterance410. For example, in some embodiments, the server computing device200is further configured to select a natural language processor350corresponding to the determined intent. In some embodiments, the server computing device200is further configured to determine a dialog response420corresponding to the determined intent using the natural language processor350. In other embodiments, the user option includes speaking with a human. In some embodiments, the server computing device200is further configured to receive a rephrased dialog utterance from the user via the user device250.

To provide for interaction with a user, the above described techniques can be implemented on a computing device in communication with a display device, e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display) monitor, a mobile device display or screen, a holographic device and/or projector, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse, a trackball, a touchpad, or a motion sensor, by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, and/or tactile input.

The above-described techniques can be implemented using supervised learning and/or machine learning algorithms. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. Each example is a pair consisting of an input object and a desired output value. A supervised learning algorithm or machine learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.