Intention identification in electronic documents

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving an electronic text document. Identifying a context of the document based on mapping text from the document to entries in a plurality of concept libraries. Determining an intersection between a first entry in a first of the plurality of concept libraries and a second entry in a second of the plurality of concept libraries. Identifying a relationship between at least two concepts included in the document based on the intersection between the first entry and the second entry. Determining an intent of the document based on the context and the relationship between the at least two concepts. Automatically routing the document for further processing based on the determined intent.

BACKGROUND

Various individuals may convey similar abstract concepts using different words and phrases based on each individual's pre-dispositions and manners of thinking about the concepts. Determining an intention or purpose of a document based on abstract concepts becomes difficult and imprecise using traditional methods due to the differing ways that various authors of documents think about and describe similar topics. Consequently, document processing systems categorize or route by documents based on criteria other than author intent or purpose.

SUMMARY

This specification relates to identifying an intent of electronic textual documents. More specifically, this specification relates to identifying an intent of electronic textual documents and routing the documents based on the identified intent.

In general, innovative aspects of the subject matter described in this specification can be embodied in methods that include the actions of receiving an electronic text document. Identifying a context of the document based on mapping text from the document to entries in a plurality of concept libraries. Determining an intersection between a first entry in a first of the plurality of concept libraries and a second entry in a second of the plurality of concept libraries. Identifying a relationship between at least two concepts included in the document based on the intersection between the first entry and the second entry. Determining an intent of the document based on the context and the relationship between the at least two concepts. Automatically routing the document for further processing based on the determined intent. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

In a second aspect the subject matter describe in this specification can be embodied in an electronic document routing system that includes a data store for storing data, means for storing a plurality of context entries for a plurality of lexicons, and one or more processors. The one or more processors are configured to interact with the data store and the means for storing the plurality of context entries for the plurality of lexicons. The one or more processors are configured to perform operations that include receiving an electronic text document. Identifying a context of the document using the means for storing a plurality of context entries for a plurality of lexicons. Determining an intersection between a first entry in a first concept library and a second entry in a second concept library. Identifying a relationship between at least two concepts included in the document based on the intersection between the first entry and the second entry. Determining an intent of the document based on the context and the relationship between the at least two concepts. Automatically routing the document for further processing based on the determined intent.

These and other implementations can each optionally include one or more of the following features.

In some implementations, automatically routing the document includes routing the document to a customer service representative qualified to address the intent of the document.

In some implementations, automatically routing the document includes routing the document to an interactive response system.

Some implementations include identifying a product or services that is ancillary to the intent of the document and automatically routing the document to a customer service representative qualified to offer the product or service.

In some implementations, the intersection is determined based on association mapping data that indicates an association between the first entry and the second of the plurality of concept libraries.

In some implementations, identifying the relationship includes using results of a textual analysis of the document to determine the relationship between a first concept conveyed by the first entry and a second concept conveyed by the second entry.

Some implementations include providing a notification that explains the intent of the document for display on a workstation of a customer service representative.

Some implementations include providing a notification that explains a concept conveyed by a term or phrase in the document for display on a workstation of a customer service representative.

In some implementations, mapping text from the document to entries in a plurality of concept libraries includes identifying one or more words, phrases, or patterns used within the document, the one or more words, phrases, or patterns based on a lexicon, and mapping the one or more words, phrases, or patterns to a concept intended to be conveyed by the one or more words, phrases, or patterns according to the lexicon.

In some implementations, each of the plurality of concept libraries include at least one lexicon having a plurality of context entries, where each context entry includes a set of one or more words, phrases, or patterns that are mapped to at least one concept intended to be conveyed by the set of one or more words, phrases, or patterns of the respective context entry.

In some implementations, the document is a chat message or an e-mail.

Some implementations include identifying a product or services that is ancillary to the intent of the document, and providing an advertisement related to the product or service for display on a user computing device.

Particular implementations of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. Implementations may enable efficient and consistent routing of electronic textual documents across various lexicons. Implementations may improve the accuracy with which routing systems route electronic textual documents. Implementations may enable document routing systems to make more efficient use of computing resources such as network bandwidth, processing resources, and data storage resources. Implementations may improve the accuracy with which automated customer service computer systems fulfill customer requests.

DETAILED DESCRIPTION

Implementations of the present disclosure generally identify intentions conveyed in electronic text documents. More particularly, implementations of the present disclosure identify intentions conveyed in electronic text documents and route the documents according to the identified intentions. For example, natural language processing can be used in conjunction with a set of libraries to identify the intent or purpose of a document. Text in a document can be mapped to entries in context libraries to determine a general context of a document. Library entries that were triggered in determining the context can be used to determine relationships between concepts discussed in the text of the document. An author's intent of the document can be determined based on the general context in view of the identified relationships between concepts.

The document can be routed for appropriate additional processing based on the determined intent. For example, in a customer service system chat or e-mail messages can be routed to appropriate customer service representatives or interactive response systems based on the identified intentions of the messages. Routing messages based on intentions of documents may improve the accuracy with which the documents are routed. In other words, once the intention of a document is identified, a document routing system can route the document to an appropriate representative or system more accurately than, for example, routing based on other criteria such as an e-mail subject line or a webpage from which a chat message originates. Moreover, improvements in accurately routing messages may improve the efficiency of document routing systems. For example, as documents are routed more accurately, a document routing system may make more efficient use of network bandwidth (e.g., as less documents need to be re-routed), system processing resources, system data storage resources, or a combination thereof.

FIG. 1depicts an example system100that can execute implementations of the present disclosure. The example system100includes computing system102in communication with user computing device103, workstations104, interactive response system105, and concept libraries106through a network108. The computing system102can include a computing device102aand computer-readable memory provided as a persistent storage device102b, and can represent various forms of server systems including, but not limited to, a web server, an application server, a proxy server, a network server, or a server farm. User computing device103can be any type of user computing device including, but are not limited to, one or more desktop computers, laptop computers, notebook computers, tablet computers, and other appropriate devices.

A workstation104can include one or more computing devices. The computing devices can be any type of computing device with which a customer service representative (CSR)110can conduct communications with customers and access information related to customer requests (e.g., customer account information, marketing information, call technical support information, product information and/or service information). Computing devices may include, but are not limited to, one or more desktop computers, laptop computers, notebook computers, tablet computers, and other appropriate devices.

Interactive response system105can be an automatic customer support system that can interact with customers to, for example, answer questions, provide technical support, accept product orders and exchanges, or make changes to a customer's profile, products (e.g., bank accounts or insurance policies), or pending product orders. Interactive response system105can be, but is not limited to, an interactive voice response system, an interactive chat system, a web-interface, an e-mail response system. Interactive response system105can be implemented as, for example, a web server, an application server, a proxy server, a network server, or a server farm.

Network108can include a large network or combination of networks, such as a local area network (LAN), wide area network (WAN), the Internet, a cellular network, a satellite network, one or more wireless access points, or a combination thereof connecting any number of mobile clients, fixed clients, and servers.

As described in more detail below in reference toFIG. 2, each concept library106includes a group of related concepts and contextual lexicon entries related to each concept. A concept library106maps one or more different user lexicons into a common set of concepts defined within the library106. Concept libraries106may define concept groups including, but not limited to, products, product features, lines of business, business organizational units, communication channels (e.g., telephone, internet, kiosk, etc.), computer systems/devices, application software, life events, locations, vehicle types, military concepts, or other appropriate concepts of interest. In addition, concept libraries106can include association mapping data that represents associations between concepts defined in the library and one or more other libraries. For example, the association mapping data may represent common or possible associations between a concept entry in one library and concepts included in one or more other libraries. For example, a product concept such as automobile insurance may be commonly associated with concepts (e.g., vehicle types) defined in a vehicle concept library.

In some examples, one or more of the concept libraries106may be stored on the persistent storage device102bof computing system102. In some examples, the concept libraries106may be stored in one or more databases on other persistent storage devices accessible to computing system102through network108. In some implementations, the concept libraries106may be stored in one or more cloud databases on other persistent storage devices accessible to computing system102through network108.

Computing system102can use the concept libraries106to automatically and consistently identify an author's intention (e.g., purpose) conveyed in an electronic document112. Computing system102can route the document112based on the intention identified within the documents112. For example, computing system102can accurately identify intentions conveyed within the document notwithstanding specific user lexicons used within each document to describe various concepts related to the intention. Documents112may include any type of electronic text document, including but not limited to, word processor files, portable document files, e-mails, chat conversations, speech to text transcriptions, or user text entry forms of websites, for example.

For example, computing system102may receive a chat message from user computing device103. The chat message may be from a customer requesting help with their auto insurance policy. For example, the chat message may state:“I have recently pcs'd from san diego to norfolk and am trying to add a bmw to my insurance that I will be purchasing tomorrow. The website is telling me that my vin is wrong when I try to enter it.”
Computing system102may use the libraries106to identify various concepts within the chat message. Computing system102can use the concepts to identify the context of the message. For example, a military lexicon of a life event library may indicate that the term “pcs'd” indicates a move, a vehicle type library may indicate that the term “bmw” is an automobile, a location library may indicate that “san diego” and “norfolk” are locations in California and Virginia, respectively, and a product library may indicate that the pattern of terms “bmw insurance” are relevant to an automobile insurance policy. Based on the identified concepts within the chat message, computing system102can identify that a general context of the message is related to the customer's automobile insurance policy.

Computing system102can identify intersections between the concepts based on the association mapping data, and use the intersections to determine relationships between the concepts. For example, the auto insurance concept can include association data indicating that it should be associated with entries in the vehicle type library and the location library (e.g., because auto insurance policies vary by state). The move concept can include association data indicating that it should also be associated with entries in the location library. Thus, computing system102may identify an intersection between the “insurance,” the type of automobile (“BMW”), and the two locations (“San Diego” and “Norfolk”) mentioned in the chat message. Computing system102may also identify an intersection between the move (“pcs'd”) and the two locations (“San Diego” and “Norfolk”) mentioned in the chat message.

Computing system102can use textual analysis techniques (e.g., grammar parsing and/or part-of-speech tagging analysis) to determine relationships between the intersecting concepts. Textual analysis techniques can include, but are not limited to, parser algorithms (e.g., an Early parser or a Coke-Younger-Kasami algorithm), natural language processing algorithms, stemming algorithms (e.g., a Porter and Lancaster stemmer or a Lancaster stemming algorithm), a tree and dependency parser (e.g., Stanford Parser), or a combination thereof.

For example, computing system102may determine that the customer is currently in the state of Virginia because the text of the message indicates that the move (“pcs'd”) was from San Diego to Norfolk. Further, computing system102can then determine a relationship between the move concept and the two locations. Specifically, for example, computing system102can determine that the move between San Diego and Norfolk was a move from California to Virginia. In some implementations, a dynamic relationship, such as a move between two locations, can be represented as a vector indicating that the relationship between a concept (e.g., the move) changes between two or more other concepts (e.g., locations in California and Virginia). For example, the vector may indicate the direction of the change (e.g., the move) as being a change in relationship with one concept (e.g., a location in California) to a relationship with another a concept (e.g., a location in Virginia).

Computing system102can further apply the relationships between the move and the locations to the intersections of the locations with the auto insurance. For example, based on the determined relationship between the move and the two locations, computing system102can determine that because the move was “to” Virginia, the location concept of the state of Virginia is likely more relevant to the automobile insurance concept than California. In other words, the computing system102can disambiguate the intersections of the automobile insurance with the two locations to identify the more appropriate location for ultimately determining the intent of the message.

Computing system102can determine the customer's intent of the chat message based on the context (auto insurance) and the relationships between the concepts that occur in the message. For example, having determined that the general concept of the message is related to auto insurance, the computing system102can use the relationships to determine the customer's intent with respect to the auto insurance. The relationships indicate that the customer has moved from California to Virginia and the automobile insurance association mapping data indicates that automobile insurance is associated with the customers' location (e.g., the user's state). Thus, computing system102can determine that the intent of the customer's message is to related to updating their automobile insurance policy for a new state, Virginia.

Computing system102can then route the chat message to an appropriate CSR110or an appropriate interactive response system105based on the determined intent. For example, the computing system102can route the chat message to a workstation104of a CSR110who has an automobile insurance sales license in Virginia. In some implementations, computing system102can route the message to multiple CSRs110. For example, computing system102also identified that the customer has used military lexicon to indicate a move (e.g. “pcs'd”). Therefore, the computing system102may route the customer's chat message to a CSR110who is trained to address other issues related with a military move, so that CSR110can offer additional assistance to the customer once the first CSR110addresses the customer's automobile insurance questions.

In some implementations, computing system102can route the chat message to an interactive response system105. For example, after determining the intent of the chat message, the computing system102can determine whether or not the intent is something that an interactive response system105can perform. If so, then computing system102can route the chat message to an appropriate interactive response system105. If not, then computing system102can route the chat message to the workstation104of an appropriate CSR110.

In some implementations, computing system102can distinguish between multiple parties and/or contexts within a single document and different intentions expressed within the document with respect to the different parties/contexts. For example, a customer's chat message may refer to a first intention with respect to himself and another intention with respect to a family member. For instance, an e-mail message from a customer may state “I am PCS'ing to Fort Carson but my family is moving to Texas and need to update our automobile insurance policies.” Computing system102can identify the first party as the customer and the second party as the customer's family. Furthermore, using the processes described herein, computing system102can determine the customer's intention with respect to each party. For example, computing system102may determine that the customer intends to update an insurance policy on one of the customer's vehicles to a Colorado policy (e.g., the location of Fort Carson) and an insurance policy on another one of the customer's vehicles to a Texas policy for the customer's family.

FIG. 2depicts a graphical representation of an example concept library200. As noted above, concept libraries200may be used (e.g., by computing system102) to map concepts or groups of concepts to combinations of words, phrases and/or patterns of words and phrases used within different lexicons to describe the similar or the same concepts. For example, a library200may be generated to map words, phrases, or patterns of words and phrases used by customers (e.g., a lexicon) to describe various products offered by a business to the specific products (e.g., the concepts). Further, the various words, phrases, or patterns of words and phrases used by members of a legal department to describe the same set of products may be different from those used by the customers. Thus, the library200also may map words, phrases, or patterns of words and phrases used by legal staff (e.g., another lexicon) to describe the same products to the specific products. In some examples, a library200may be generated to map multiple different lexicons to any particular group of concepts including, but not limited to, products, product features, lines of business, business organizational units, communication channels, computer systems/devices, application software, life events, locations, vehicle types, military concepts, or other appropriate concepts.

More specifically, a concept library200includes one or more lexicons202, each of which includes multiple context entries204. Each context entry204may include a set of one or more words (W), phrases (PH), patterns (PS), or any combination of the three that may be used to describe a particular concept206in the related lexicon202(e.g., a customer lexicon). In other words, the lexicons202map various sets of words, phrases, and patterns used by speakers of the particular lexicon to concepts206that those words phrases, or patterns are intended, by users of the lexicon, to convey. Furthermore, each library may map the various lexicons202to a specific group of concepts206(e.g., products, product features, lines of business, etc.).

For example, lexicons202may be defined to map the various words, phrases, and patterns that are used by different groups of people (e.g., groups of people in different departments within an organization such as a business) to convey similar concepts206(e.g., products offered by the business). Lexicons202may include, but are not limited to, a customer lexicon (Lcust), a customer service representative (CSR) lexicon (LCSR), an engineering department lexicon (Leng), and a legal department (Llegal).

For example,FIG. 2depicts an exemplary concept library200for a line of products (LLprod), such as, for example, financial products. Products (concepts206) defined in the product library (LLprod) may include, for example, consumer loan, credit card, auto insurance policy, and home insurance policy. Taking the credit card as an example, when discussing a credit card, customers may tend to refer a business's credit card products as a “charge card,” “credit card,” “creditcard,” or “payment card,” for example. Referring to the same credit card product, customer service representatives may refer to the credit card products as “credit card,” “mastercard,” “master card,” “visa,” “amex,” or “American Express.” Referring again to the same credit card products, other groups (e.g., legal staff, engineers, and marketing staff) may use yet other words, phrases, or patterns to describe the products. Therefore, the product library (LLprod) includes each set of words, phrases, and patterns in corresponding context entries204(e.g., context entry (W, PH, Ps)1for each respective lexicon202(e.g., Lcust-LN), where the first context entry (W, PH, Ps)1for each lexicon describes a first concept206(e.g., credit cards or products), and the nth context entry (W, PH, Ps)nfor each lexicon describes an nth concept206(e.g., productn).

In some implementations, a concept library200may include only one lexicon202. In such an implementation the various words, phrases, and patterns used by each different lexicon to describe a particular concept may be grouped together in a single context entry204and mapped to the particular concept. In other words, the context entries204for each different lexicon may be combined into a single entry as opposed to being divided into separate contextual entries, one for each different lexicon, as described above.

In some implementations, libraries200may be updated as the words, phrases, and patterns used within one or more lexicons to describe particular concepts change. For example, one or more computing systems may monitor changes in each lexicon and update corresponding libraries as appropriate. In some implementations, a library200may be updated to modify, add, or remove various concepts defined within the library. For example, as products change, new products are offered, and old products are discontinued.

In some implementation, one or more lexicons may be unique to a particular organization, for example, a business. The lexicon may represent words or phrases used internally by the business to describe products, product features, lines of business, and other business affairs in a manner that maintains confidentiality of the businesses internal processes.

FIG. 3Adepicts graphical representations of example association mappings300and304between concept libraries. Mapping300illustrates example libraries200that may be associated with an automobile insurance concept entry of a products library (LLProd). For instance, as illustrated in the example chat message discussed above, the concept of automobile insurance is expected to be associated with a vehicle. Accordingly, the concept entry for automobile insurance in the products library may include data indicating that words or phrases in a text document are expected to be associated with other words or phrases related to an automobile which can be found in the vehicle library (LLVehicle). Similarly, the concept of automobile insurance is expected to be associated with a location because automobile policy requirements vary by state. Accordingly, the concept entry for automobile insurance in the products library also may include data indicating that words or phrases in a text document are expected to be associated with other words or phrases related to locations which can be found in the vehicle library (LLLocation).

Mapping302illustrates example libraries200that may be associated with a move concept entry of a life event library (LLEvent). For instance, as illustrated in the example chat message discussed above, the concept of a move life event is expected to be associated with a one or more locations. Accordingly, the concept entry for move life event in the life event library also may include data indicating that words or phrases in a text document are expected to be associated with other words or phrases related to locations which can be found in the location library (LLLocation). Similarly, the concept of a move life event also may be expected to be associated with one or more products. For example, during a move a customer may commonly update insurance products such as automobile insurance, renter's insurance, homeowner's insurance, addresses for bank accounts, or a combination thereof. Accordingly, the concept entry for a move life event in the life event library may include data indicating that words or phrases in a text document are expected to be associated with other words or phrases related to products which can be found in the products library (LLprod). Furthermore, the concept of a move life event also may be expected to be associated with a business group. For example, a customer service center may have a business group that specializes in helping customers manage updating documents and accounts when a customer moves. Accordingly, the concept entry for a move life event in the life event library may include data indicating that the move concept is associated with a particular business group which can be found in the business organization library (LLOrg).

FIG. 3Bdepicts a graphical representation of an example concept library350that includes library association mapping data (LA).FIG. 3Bdepicts an exemplary concept library350for a line of products (LLprod), similar to the concept library200discussed above in reference toFIG. 2. Concept library350includes one or more lexicons352, each of which includes multiple context entries354. Each context entry354may include a set of one or more words (W), phrases (PH), patterns (PS), or any combination thereof that may be used to describe a particular concept356in the related lexicon352(e.g., a customer lexicon). In some examples, the context entries354can include association data (LA) in addition to the words (W), phrases (PH), or patterns (PS). The association data (LA) represents associations between a particular concept entry of a library and one or more other libraries. For example, the association mapping data may represent common or possible associations between a concept entry of one library and concepts included in one or more other libraries. In some implementations, the association data (LA) can be associated with a specific context entry354in a specific lexicon as represented by LAappearing in the context entry354. In some implementations, the association data (e.g., LA1and LAn) can be associated with the concept356represented by the context entries354of multiple lexicons352. For example, a product concept such as automobile insurance is commonly associated with concepts (e.g., vehicle types) defined in a vehicle concept library. Accordingly, association data LAlinking an automobile insurance concept356to a vehicle library can be included in context entries354related to or included with an automobile insurance concept356in a product library (LLprod).

FIG. 4depicts example processes400and500that can be executed in accordance with implementations of the present disclosure. In some examples, the example processes400and500can be provided as one or more computer-executable programs executed using one or more computing devices (e.g., computing system102, as shown inFIG. 1and described above). In some examples, the process400is executed to identified intentions conveyed within an electronic textual document and route the document accordingly based on the identified intentions. In some examples, the process500is executed to extract concepts conveyed within one or more electronic textual documents using one or more different lexicons.

A computing system receives an electronic text document (410). Electronic documents may include any type of electronic text document, including but not limited to, word processor files, portable document files, e-mails, chat conversations, speech to text transcriptions, or user text entry forms of web sites, for example. For example, the electronic document may be a chat message or an e-mail from a customer to a customer service center.

The computing system identifies a context of the document (420). For example, the computing system can identify a context of the document by mapping text from the document to concept entries in a plurality of concept libraries. The computing system can determine concepts described in the document by identifying one or more words, phrases, or patterns that are used within the electronic document and that are based on a lexicon (510). For example, a particular word, phrase, or pattern of words may be used within lexicon A to describe concept X, while a different word, phrase or pattern of words may be used within lexicon B to describe the same concept X. For example, the lexicon may be one of a customer lexicon, a military lexicon, a customer service representative lexicon, or a technical staff lexicon. In some examples, known sets of one or more words, phrases, or patterns corresponding to one or more known lexicons may be identified within the electronic document.

The computing system can use a concept library to map the one or more words, phrases, or patterns to a concept intended to be conveyed by the one or more words, phrases, or patterns in the lexicon (520). For example, the concept library may include at least one lexicon having a plurality of context entries. Each context entry of the concept library may include a set of one or more words, phrases, or patterns that are mapped to at least one concept which is intended to be conveyed, according to the respective lexicon, by the set of one or more words, phrases, or patterns that are included in the respective context entry. In some examples, the concept library may be one of a product library, a product feature library, a location library, a vehicle library, a line of business library, or a life events library. In other words, the concept library may map words, phrases, or patterns of words using in one or more lexicons to concept sets that include, but are not limited to, products, product features, geographic locations, vehicle types, lines of business, or life events.

Referring back to step (420), the computing system can identify a general context of the document based on the identified concepts. For example, if the computing system identifies an insurance related concept and a vehicle related concept the general context of the document is likely related to automobile insurance. As another example, if the computing system identifies a fraud related concept and a credit card related concept, the general context of the document is likely related to potential fraudulent use of a credit card.

The computing system determines an intersection between entries in a first library and a second library (430). For example, one or more of library concept entries can include association mapping data that represents associations between the concept entry in the library and one or more other libraries. For example, the association mapping data may represent common or possible associations between a concept entry in one library and concepts included in one or more other libraries. For example, an automobiles insurance concept entry in a product library may include association mapping data indicating that the concept automobile insurance should be associated with a concept entry from a vehicle library such as automobile generally or a specific type, make, or model of automobile.

The computing system identifies a relationship between two concepts included in the document based on the intersection (440). For example, the computing system can use textual analysis techniques to determine relationships between the intersecting concepts. For example, results of the textual analysis can indicate how intersecting concepts are related, changes in relationships between multiple intersecting concepts, types of relationships between concepts, or combinations thereof. For example, as discussed above in reference toFIG. 1, textual analyses results can be used to determine the direction of a move concept between two different location concepts. In some implementations, a dynamic relationship, such as a move between two locations, can be represented as a vector indicating that the relationship between one concept (e.g., a move) changes between two or more other concepts (e.g., locations). For example, a relationship vector may indicate the direction of the change in relationship (e.g., the move) as being a change in relationship with one concept (e.g., a first location) to a relationship with another a concept (e.g., a second location). Accordingly, the relationship vector may indicate a move “from” the first location and a corresponding move “to” the second location. In some implementations, the relationship vector can be used to infer or track a future or potential change between the concepts (e.g. a return from deployment typically follows any deployment) even though it is not been expressly stated.

Moreover, textual analysis results can be used in combination with an established relationship determine another relationship between two or more of the same or different concepts. For example, an established relationship between a first and a second concept can be used to disambiguate or prioritize relationships between a third concept and two or more other concepts. For example, as discussed in the example above in reference toFIG. 1, an established relationship between a move concept and two different location concepts can be used to disambiguate which of the locations is more relevant to another concept such as automobile insurance. That is, after establishing the relationship between the move as being a move from a first location to a second location, the “new” or “to” location of a customer is likely more relevant to an automobile insurance policy.

The computing system determines an intent of the document (450). For example, the computing system can determine an intent of the document based on the context and relationships between concepts. For example, having determined a general concept of a document, the computing system can use the relationships to determine a intent of the user within the context. For example, within a context related to automobile insurance, the computing system can use relationships between a move concept and location concepts to determine that the intent of the document is likely to update an automobile insurance policy due to a customer's move.

The computing system can route the document based on the determined intent (460). For example, the computing system can automatically route the document to a customer service representative or an interactive response system for further processing based on the determined intent. For example, the computing system can route the document to a workstation of CSR who is qualified to address the intentions of the customer who authored the document.

In some implementations, the computing system can determine whether or not the intent of the document is something that an interactive response system can address. If so, then computing system can route the chat message to an appropriate interactive response system. If not, then computing system can route the chat message to the workstation of an appropriate customer service representative. For example, if the computing system determine that the intent of a particular chat messages is to drop an automobile from an auto insurance policy, the computing system can respond to the chat message to confirm the intention. For example, the computing system can send a tailored response such as “It looks like you want to drop the 2014 BMW 320i from your policy, is that correct?”

In some implementations, the computing system can identify products or services that are ancillary to the intent and route the document to a CSR or interactive response system to offer the additional products or services to the customer who authored the document. For example, ancillary services to updating automobile insurance due to a move may include additional services related to a move including, for example, updating a customer's address, scheduling movers, updating renter's or homeowner's insurance policies, finding a real estate agent, or obtaining a mortgage. Therefore, the computing system can route the document to a second CSR or interactive response system to offer such ancillary services to a customer after the customer's intention is addressed (e.g., the automobile insurance policy is updated).

In some implementations, the computing system can provide an advertisement to the customer for the ancillary products or services while the user is waiting to be connected with the first CSR or interactive response system. For example, the advertisement can be an advertisement in a chat window, an advertisement in an e-mail response to the document, or advertisements on a webpage through which the customer sent the document. In some implementations.

In some implementations, the computing system can provide notifications to a CSR who is working with the customer to address the intent of the document so that the CSR can offer the ancillary products or services. For example, the computing system can provide a notification on the CSR's workstation to ask whether the customer would like assistance in updating the customer's address, scheduling movers, updating renter's or homeowner's insurance policies, finding a real estate agent, or obtaining a mortgage.

In some implementations, the computing system can provide notifications to a CSR based on the intent or concepts within the document. That is, the computing system can provide a notification for display on a CSR's workstation. For example, a notification can explain the intent of a chat message to the CSR. A notification can explain a term used to convey a concept to the CSR. For example, the computing system identifies that the document includes terms from a particular or un-common lexicon, the computing system can provide a notification explaining the concept conveyed by the terms. For example, if a customer is a military member a CSR may not familiar with military specific terms. Thus, if the document contains a term such as “pcs” or “pcs,” the computing system can provide a notification that the term “pcs” means “permanent change of station” and indicates that the customer has moved or will be moving. As another example, a military member may state that they are “onboard LHD-6.” The computing system can provide a notification to a CSR explaining the term “LHD-6” and implications of the customer being “onboard LHD-6.” For example, the notification may explain that the customer is likely at sea on an amphibious landing ship that is similar to but smaller than an aircraft carrier and that the customer is, therefore, unreachable by telephone.

In some implementations, the computing system can store data indicating document intent and message context in association with the documents. For example, the stored information can be presented to CSRs if the document (e.g., e-mail or chat message) is referenced at a later time or by other CSRs if the customer's request (e.g., the document) is transferred to different CSRs.