Patent Publication Number: US-2019199668-A1

Title: Predictive responses to incoming communications

Description:
RELATED APPLICATION 
     This application is a Continuation of U.S. application Ser. No. 15/015,075, filed Feb. 3, 2016, the entire contents of which is hereby incorporated by reference. 
    
    
     BACKGROUND 
     Computing devices (e.g., mobile phones, tablet computers, etc.) may enable a user to respond to incoming communications from one or more other computing devices. In some cases, the incoming communications (e.g., an email, a text message, a meeting request, etc.) may include textual information. A computing device may enable a user of the computing device to respond to the incoming communication by allowing the user to input textual information (e.g., using an input device), and send the textual information to the one or more other computing devices as a response. 
     Some computing devices may enable a user to choose a response stored by the computing device and send the chosen stored response, such as by providing one or more options that the user may select. For example, responsive to receiving a meeting request, a computing device may enable a user of the computing device to select a response to “accept” the meeting request, “decline” the meeting request, or choose other stored options. However, the computing device may provide only a small number of stored responses, thereby reducing the likelihood that the available stored responses include a response desired by the user. Conversely, the computing device may provide a larger number of stored responses, thereby requiring the user to search through a number of responses, which may not be useful in the current context of the incoming communication. 
     SUMMARY 
     In one example, a method may include receiving, by a computing system, a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history, determining, by the computing system and based on the plurality of message histories, a plurality of message and response pairs, determining, by the computing system, a number of instances of each response for each message from the plurality of message and response pairs. The method may also include classifying, by the computing system, each response from the plurality of message and response pairs as having an information type, and storing, by the computing system, an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type 
     In another example, a computing system includes at least one communication unit, a storage device, at least one processor, and at least one module. The at least one communication unit is configured to receive a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history. The at least one module is operable by the at least one processor to determine, based on the plurality of message histories, a plurality of message and response pairs, determine a number of instances of each response for each message from the plurality of message and response pairs, classify each response from the plurality of message and response pairs as having an information type, and store, at the storage device, an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. 
     In another example, a computer-readable storage device is encoded with instructions that, when executed, cause at least one processor of a computing system to receive a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history, determine, based on the plurality of message histories, a plurality of message and response pairs, and determine a number of instances of each response for each message from the plurality of message and response pairs. The instructions may further cause the one or more processors to classify each response from the plurality of message and response pairs as having an information type, and store an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. 
     The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual diagram illustrating an example computing environment and graphical user interface (GUI) for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. 
         FIG. 2  is a block diagram illustrating one example of a response suggestion unit for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. 
         FIG. 3  is a conceptual diagram illustrating example data generated while classifying responses, in accordance with one or more aspects of the present disclosure. 
         FIG. 4  is a flow diagram illustrating an example operation for analyzing responses to incoming communications, in accordance with one or more aspects of the present disclosure. 
         FIG. 5  is a flow diagram illustrating example operations for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In general, techniques of this disclosure may enable a computing system to provide a computing device with candidate responses to incoming communications. The candidate responses may be relevant to the incoming communication and/or to a current situation (e.g., a current context) of a user of the computing device and/or of the computing device. The computing system may determine which candidate responses to send to the computing device based at least in part on the frequency with which users (e.g., the user of the computing device and/or other users) have previously selected the responses to respond to the same or similar incoming communications. For example, a computing system may receive an incoming communication, such as an email, from a computing device associated with a user. The email may include information (e.g., a sender, one or more recipients, title and body text, images, emoji, audio/video data, etc.). The computing system may determine at least one candidate response to the message by analyzing the information, identify other incoming messages that are similar to the email, determine a type of information that is typically included in a response to such an email, and determine, based on the type of information, at least one response to the email. The computing system may send the at least one candidate response to the computing device, which may output the at least one candidate response and receive an indication of a selection of one of the candidate responses, thereby enabling the user to select a particular candidate response and send the particular candidate response to one or more other computing devices (e.g., as a response to the email). 
     In this manner, techniques of the present disclosure may enable a user to send relevant responses to incoming communications without having to manually enter the response. Allowing a user to select from one or more candidate responses may be particularly helpful when it may be difficult or undesirable for the user to manually enter textual information for a response. Techniques of the present disclosure may enable a computing device to increase the user&#39;s efficiency by predicting, based on a received incoming communication, how a user is likely to respond. Accordingly, a computing device configured in accordance with techniques of this disclosure may reduce or remove the need for the user to spend time manually entering information for a response to an incoming communication. 
     In general, a computing device of a user may send information (e.g., incoming communications, location information, other contextual information, etc.) to the computing system only if the computing device receives permission from the user to send the information. For example, in situations discussed below in which the computing device may collect, transmit, or may make use of personal information about a user (e.g., incoming communications) the user may be provided with an opportunity to control whether programs or features of the computing device can collect user information (e.g., information about a user&#39;s communications, a user&#39;s social network, a user&#39;s social actions or activities, a user&#39;s profession, a user&#39;s preferences, or a user&#39;s current location), or to control whether and/or how the computing device may store and share user information. 
     In addition, certain data may be treated in one or more ways before it is stored, transmitted, or used by the computing device so that personally identifiable information is removed. For example, a user&#39;s identity may be treated so that no personally identifiable information can be determined about the user, or a user&#39;s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of the user cannot be determined. Thus, the user may have control over how information is collected about the user and stored, transmitted, and/or used by the computing device. 
       FIG. 1  is a conceptual diagram illustrating an example computing environment  1  and GUI  20  for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. As shown in the example of  FIG. 1 , computing environment  1  includes computing device  2 A, network  12 , and response suggestion system  14 . Examples of computing device  2 A may include, but are not limited to, portable, mobile, or other devices, such as mobile phones (including smartphones), laptop computers, desktop computers, tablet computers, smart television platforms, personal digital assistants (PDAs), server computers, mainframes, and the like. For instance, in the example of  FIG. 1 , computing device  2 A may be a smartphone. 
     Computing device  2 A, as shown in the example of  FIG. 1 , includes user interface (UI) device  4 . UI device  4  of computing device  2 A may be configured to function as an input device and/or an output device for computing device  2 A. UI device  4  may be implemented using various technologies. For instance, UI device  4  may be configured to receive input from a user through tactile, audio, and/or video feedback. Examples of input devices include a presence-sensitive display, a presence-sensitive or touch-sensitive input device, a mouse, a keyboard, a voice responsive system, video camera, microphone or any other type of device for detecting a command from a user. In some examples, a presence-sensitive display includes a touch-sensitive or presence-sensitive input screen, such as a resistive touchscreen, a surface acoustic wave touchscreen, a capacitive touchscreen, a projective capacitance touchscreen, a pressure sensitive screen, an acoustic pulse recognition touchscreen, or another presence-sensitive technology. That is, UI device  4  of computing device  2 A may include a presence-sensitive device that may receive tactile input from a user of computing device  2 A. UI device  4  may receive indications of the tactile input by detecting one or more gestures from the user (e.g., when the user touches or points to one or more locations of UI device  4  with a finger or a stylus pen). 
     UI device  4  may additionally or alternatively be configured to function as an output device by providing output to a user using tactile, audio, or video stimuli. Examples of output devices include a sound card, a video graphics adapter card, or any of one or more display devices, such as a liquid crystal display (LCD), dot matrix display, light emitting diode (LED) display, organic light-emitting diode (OLED) display, e-ink, or similar monochrome or color display capable of outputting visible information to a user of computing device  2 A. Additional examples of an output device include a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), or other device that can generate intelligible output to a user. For instance, UI device  4  may present output to a user of computing device  2 A as a graphical user interface that may be associated with functionality provided by computing device  2 A. In this way, UI device  4  may present various user interfaces of applications executing at or accessible by computing device  2 A (e.g., an electronic message application, an Internet browser application, etc.). A user of computing device  2 A may interact with a respective user interface of an application to cause computing device  2 A to perform operations relating to a function. 
     In some examples, UI device  4  of computing device  2 A may detect two-dimensional and/or three-dimensional gestures as input from a user of computing device  2 A. For instance, a sensor of UI device  4  may detect the user&#39;s movement (e.g., moving a hand, an arm, a pen, a stylus, etc.) within a threshold distance of the sensor of UI device  4 . UI device  4  may determine a two or three dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a hand-wave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, UI device  4  may, in some examples, detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which UI device  4  outputs information for display. Instead, UI device  4  may detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which UI device  4  outputs information for display. 
     In the example of  FIG. 1 , computing device  2 A includes user interface (UI) module  6 , device location module  8 , and application modules  10 A- 10 N (collectively “application modules  10 ”). Modules  6 ,  8 , and/or  10  may perform operations described herein using hardware, software, firmware, or a mixture thereof residing in and/or executing at computing device  2 A. Computing device  2 A may execute modules  6 ,  8 , and/or  10  with one processor or with multiple processors. In some examples, computing device  2 A may execute modules  6 ,  8 , and/or  10  as a virtual machine executing on underlying hardware. Modules  6 ,  8 , and/or  10  may execute as one or more services of an operating system or computing platform or may execute as one or more executable programs at an application layer of a computing platform. 
     UI module  6 , as shown in the example of  FIG. 1 , may be operable by computing device  2 A to perform one or more functions, such as receive input and send indications of such input to other components associated with computing device  2 A, such as application modules  10 . UI module  6  may also receive data from components associated with computing device  2 A such as device location module  8  or application modules  10 . Using the data received, UI module  6  may cause other components associated with computing device  2 A, such as UI device  4 , to provide output based on the data. For instance, UI module  6  may receive data from one of application modules  10  to display a GUI. 
     Application modules  10 , as shown in the example of  FIG. 1 , may include functionality to perform any variety of operations on computing device  2 A. For instance, application modules  10  may include a word processor, an email application, a web browser, a multimedia player, a calendar application, an operating system, a distributed computing application, a graphic design application, a video editing application, a web development application, or any other application. One of application modules  10  (e.g., application module  10 A) may be a text messaging or Short Message Service (SMS) application. Application module  10 A may include functionality to compose outgoing text message communications, receive incoming text message communications, respond to incoming text message communications, and other functions. Application module  10 A, in various examples, may provide data to UI module  6  causing UI device  4  to display a GUI. 
     In some examples, one or more of application modules  10  may be operable to receive incoming communications from other devices (e.g., via network  12 ). For instance, one or more of application modules  10  may receive email messages for an account associated with a user of computing device  2 A, calendar alerts or meeting requests for a user of computing device  2 A, or other incoming communications. 
     Incoming communications may include information (e.g., generated in response to input by users of the other devices). Examples of information include text (e.g., any combination of letters, words, numbers, punctuation, etc.), emoji, images, video, audio, or any other content that may be included in an incoming communication. In the example of  FIG. 1 , application module  10 A may receive an incoming communication (e.g., a text message) from another computing device (not shown). The received text message may include the information “Are you coming to the pub?” 
     In some examples, UI device  4  may detect input corresponding to a user&#39;s selection of an option to respond to an incoming communication. UI module  6  may receive an indication of the input and provide data to one or more of application modules  10  based on the indication. In some examples, responsive to receiving the data, the receiving one of applications  10  may generate a request for candidate responses to the incoming communication. For instance, in the example of  FIG. 1 , application  10 A may generate a request for candidate responses to the text message. The request may include an indication of at least part of the information included in the text message. In some examples, the request may include other information, such as location information, user information, or other information about computing device  2 A or a user of computing device  2 A. Application  10 A may only generate the request and/or obtain personal data (e.g., information included in the text message and/or other information) if the user of computing device  2 A provides explicit permission. After receiving the explicit permission, computing device  2 A may transmit the request via network  12  (e.g., to response suggestion system  14 ). 
     Network  12  represents any public or private communication network, such as a cellular network, Wi-Fi network, or other type of network for transmitting data between computing devices. In some examples, network  12  may represent one or more packet switched networks, such as the Internet. Computing device  2 A and response suggestion system  14  may send and receive data across network  12  using any suitable communication techniques. For example, computing device  2 A and response suggestion system  14  may each be operatively coupled to network  12  using respective network links. Network  12  may include network hubs, network switches, network routers, etc., that are operatively inter-coupled thereby providing for the exchange of information between computing device  2 A and response suggestion system  14 . In some examples, network links of network  12  may be Ethernet, ATM or other network connections. Such connections may include wireless and/or wired connections. Network  12  may receive the request from computing device  2 A and provide the request to response suggestion system  14 . 
     Response suggestion system  14  may represent any suitable remote computing system, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information via network  12 . In some examples, response suggestion system  14  may represent a cloud computing system that provides one or more services via network  12 . That is, in some examples, response suggestion system  14  may be a distributed computing system. One or more computing devices, such as computing device  2 A, may access the services provided by the cloud by communicating with response suggestion system  14 . While described herein as being performed at least in part by response suggestion system  14 , any or all techniques of the present disclosure may be performed by one or more other devices, such as computing device  2 A. That is, in some examples, computing device  2 A may be operable to perform one or more techniques of the present disclosure alone. 
     In the example of  FIG. 1 , response suggestion system  14  includes suggestion module  18 . Suggestion module  18  may be operable to determine at least one candidate response to an incoming communication based at least in part on information included in the incoming communication. Each candidate response may be a predicted message that a user may choose to send in response to an incoming communication. For instance, in the example of  FIG. 1 , each candidate response determined by suggestion module  18  may be a prediction of a message that the user of computing device  2 A may send in order to respond to the text message, “Are you coming to the pub?” 
     Suggestion module  18  may determine candidate responses from responses previously selected by one or more users (e.g., the user of computing device  2 A and/or other users). That is, response suggestion system  14  may receive information, such as message histories, from various sources (e.g., computing device  2 A and/or computing devices associated with other users) indicating incoming communications and the responses that users have selected as responses to the incoming communications. That is, response suggestion system  14  may receive a plurality of message histories associated with different users and that include message and response pairs. The message histories may be one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history. 
     In some examples, text analysis module  16  of response suggestion system  14  may classify each response in the message histories as having one of a set of information types. For example, the responses to “when works for you?” may include “6 pm,” “in 10 minutes,” “now,” etc. Based on those responses, text analysis module  16  may classify responses to “when works for you” as having the information type “TIME.” Similarly, text analysis module  16  may analyze the responses to “LG™,” which may include “thank you,” “great!,” “whew,” etc. and determine that the responses have the information type “TEXT.” As yet another example, the responses to “how many would you like?” may include “1,” “2,” “a few,” “several,” etc. Based on those responses, text analysis module  16  may classify responses to “how many would you like” as having the information type “NUMBER.” 
     By analyzing the incoming communications and the corresponding selected responses (e.g., chat logs), text analysis module  16  and suggestion module  18  (or other components of response suggestion system  14 ) may determine likely replies to messages. That is, text analysis module  16  and suggestion module  18  may generate a mapping of a part of a conversation to a particular response based on prior conversations. For instance, text analysis module  16  may receive a request including the information, “Are you coming?” Text analysis module  16  may analyze the request, determine a likely response includes textual information, and suggestion module  18  may determine that the most common replies to the request include “I&#39;ll be right there,” “yes,” and “no.” 
     In some examples, the information included in the incoming communication and/or the candidate responses need not be proper language. For instance, response suggestions system  14  may receive a request to determine candidate responses for the text, “&lt;3” (e.g., a heart emoticon). Text analysis module  16  may determine that such a message should have a response type of EMOTICON and, based on that, suggestion module  18  may determine that the most common responses are “&lt;3”, and “:)” (e.g., a smiley emoticon). In some examples, candidate responses may be responses that have been selected at least at a threshold frequency by the one or more users. In other examples, candidate responses may be based additionally or instead on manual review by developers or administrators of response suggestion system  14 . Manual review may be beneficial, for example, to ensure candidate responses are appropriate. 
     In some examples, suggestion module  18  may determine candidate responses based at least in part on an identity of the user of computing device  2 A and/or information about the user of computing device  2 A. Suggestion module  18  (or other components of response suggestion system  14 ) may receive a request from computing device  2 A that includes an indication of a particular incoming communication as well as an indication of the identity of the recipient of the incoming communication. Suggestion module  18  may provide personalized candidate responses based on the identity of the recipient. In another example, suggestion module  18  may receive explicit permission from a user (e.g., the user of computing device  2 A) to access communications information, social media network information, or other information pertaining to the user. For example, if the expected response type is ADDRESS, suggestion module  18  may include a home address, work address, or other address associated with the user. 
     Response suggestion system  14  may send one or more of the candidate responses to computing device  2 A. One of applications  10  may receive an indication of the determined candidate responses, and send data to UI module  6  to cause UI device  4  to display the candidate responses. For instance, in the example of  FIG. 1 , application  10 A may receive candidate responses to the text message, as determined by suggestion module  18  of response suggestion system  14 . Responsive to receiving the indication of the candidate responses, application  10 A may send data to UI module  6  to cause UI device  4  to display the candidate responses as part of GUI  20 . 
     As shown in the example of  FIG. 1 , GUI  20  includes response selection area  22 . Response selection area  22  may be displayed in response to computing device  2 A receiving input corresponding to a command to respond to the incoming communication. Response selection area  22  includes response options  24 A- 24 D (collectively “response options  24 ”). In some examples, response options  24  may represent candidate responses received from response suggestion system  14 . In other examples, response selection area  22  may represent other response options, such as responses previously entered by the user of computing device  2 A to similar incoming communications. In some examples, the candidate responses received by computing device  2 A may be modified (e.g., by a receiving one of applications  10 ) based on various types of information, and response selection area  22  may contain one or more response options representing modified candidate responses. In any case, response selection area  22  of GUI  20  may enable the user of computing device  2 A to select one of response options  24 , such as response option  24 A including the displayed text “I&#39;ll be there in 15 minutes,” response option  24 B including the displayed text “I&#39;m on my way,” or other response options. 
     Inputting and selecting a custom response or selection of one of response options  24  may cause computing device  2 A to respond to the incoming communication by sending the the selected response to one or more other computing devices. The selected response (e.g., the one of response options  24  selected by the user) may be sent to a computing device from which the incoming communication was received. In some examples, the selected response may be sent to additional or other computing devices. By enabling computing device  2 A to obtain candidate responses and display response options  24 , response suggestion system  14  may reduce the amount of time required for a user of computing device  2 A to input and send a response to an incoming communication. For instance, wearable computing devices, such as watches or other devices, may be able to display several candidate responses to a user that are based on a received incoming communication, thereby enabling the user to quickly tap or otherwise select one of the candidate responses instead of having to use voice recognition or other means to input a response. 
       FIG. 2  is a block diagram illustrating one example of response suggestion system  14 , shown in  FIG. 1 , for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. Response suggestion system  14  may include hardware, firmware, software, or any combination thereof. In the example of  FIG. 2 , response suggestion system  14  may comprise a hardware device, such as a server computer, having various hardware, firmware, and software components. However,  FIG. 2  illustrates only one particular example of response suggestion system  14 , and many other examples of response suggestion system  14  may be used in accordance with techniques of the present disclosure. In some examples, components of response suggestion system  14  may be located in a singular location. In other examples, one or more components of response suggestion system  14  may be in different locations (e.g., connected via network  12  of  FIG. 1 ). That is, in some examples response suggestion system  14  may be a conventional computing system, while in other examples, response suggestion system  14  may be a distributed or “cloud” computing system. 
     As shown in the specific example of  FIG. 2 , response suggestion system  14  includes one or more processors  40 , one or more communications units  42 , and one or more storage devices  44 . Response suggestion system  14  further includes operating system  48 , text analysis module  16 , suggestion module  18 , and response data store  50 . In other examples, response data store  50  may not be included in response suggestion system  14  and may, for example, be stored at a different computing system operatively and/or communicatively coupled to response suggestion system  14 . That is, in some examples, response data store  50  may be external to, but accessible by, response suggestion system  14 . 
     Each of components  40 ,  42 , and  44  may be interconnected (physically, communicatively, and/or operatively) for inter-component communications. In the example of  FIG. 2 , components  40 ,  42 , and  44  may be coupled by one or more communications channels (COMM. CHANNELS)  46 . In some examples, communications channels  46  may include a system bus, network connection, inter-process communication data structure, or any other channel for communicating data. In other examples, such as where response suggestion system  14  is a distributed computing system or cloud-based computing system, communications channels  46  may include one or more network connections, such as portions of network  12  of  FIG. 1 . Modules  16 ,  50 , and  52 , as well as operating system  48 , and response data store  50  may also communicate information with one another as well as with other components in response suggestion system  14 . 
     Processors  40 , in one example, are configured to implement functionality and/or process instructions for execution within response suggestion system  14 . For example, processors  40  may be capable of processing instructions stored in storage devices  44 . Examples of processors  40  may include, any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry, including other hardware processors. 
     Response suggestion system  14 , in the example of  FIG. 2 , also includes one or more communication units  42 . Response suggestion system  14 , in one example, utilizes communication units  42  to communicate with external devices via one or more networks, such as network  12  of  FIG. 1 . Communication units  42  may include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other examples of such network interfaces may include Bluetooth, 3G and WiFi radio components as well as Universal Serial Bus (USB). In some examples, response suggestion system  14  utilizes communication units  42  to wirelessly communicate with one or more external devices such as computing device  2 A of  FIG. 1 . For instance, communication units  42  may receive a request for candidate responses from computing device  2 A, and provide the request to one or more other components of response suggestion system  14  (e.g., modules  16 ,  18 ). 
     One or more storage devices  44  may be configured to store information within response suggestion system  14  during operation. Storage devices  44 , in some examples, can be described as a computer-readable storage medium. In some examples, storage devices  44  are a temporary memory, meaning that a primary purpose of storage devices  44  is not long-term storage. Storage devices  44 , in some examples, are described as a volatile memory, meaning that storage devices  44  do not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, storage devices  44  are used to store program instructions for execution by processors  40 . Storage devices  44 , in one example, are used by software or applications running on response suggestion system  14  (e.g., modules  16 ,  50 , and  52 ) to temporarily store information during program execution. 
     Storage devices  44 , in some examples, also include one or more computer-readable storage media. Storage devices  44  may be configured to store larger amounts of information than volatile memory. Storage devices  44  may further be configured for long-term storage of information. In some examples, storage devices  44  include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable memories (EEPROM). 
     In some examples, response suggestion system  14  may contain more or fewer components. For instance, response suggestion system  14  may contain one or more input devices, such as devices configured to receive input from a user or administrator through tactical, audio, or video feedback, and/or one or more output devices, such as devices configured to provide output to a user or administrator using tactile, audio, or video stimuli. 
     In the example of  FIG. 2 , response suggestion system  14  includes operating system  48 . Operating system  48 , in some examples, controls the operation of components of response suggestion system  14 . For example, operating system  48 , in one example, facilitates the communication of modules  16  and  18  with processors  40 , communication units  42 , and storage devices  44 . Modules  16  and  18  may each include program instructions and/or data that are executable by response suggestion system  14 . As one example, text analysis module  16  and suggestion module  18  may include instructions that cause response suggestion system  14  to perform one or more of the operations and actions described in the present disclosure. 
     Response suggestion system  14  may receive one or more message histories from a plurality of different computing devices (e.g., computing devices  2  of  FIG. 1 ). The message histories may be one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history. Text analysis module  16  may analyze the message histories and generate a set of popular response types and responses based on the message histories. For example, text analysis module  16  may create a table of responses that includes information about message and response pairs. That is, the table may include information about the received message, such as the text of the received message, and information about the reply, such as the text of the reply message. In various examples, the table includes additional information about the message/response pair, such as a type of the message and a type of the response. The message/response table may be stored, in various instances, within response data store  50 . While described as a table, the message and response information may be stored within data store  50  using a range of one or more data structures, such as an array, a list, a database, a structured file (such as a file encoded using a markup language) or other data structure. That is, response data store  50  may contain data in any format capable of being created and/or accessed by one or more other components of response suggestion system  14 . 
     In some examples, response data store  50  may include information received from users and/or information provided by managers or administrators of response suggestion system  14 . For instance, response data store  50  may receive instructions from one or more modules of response suggestion system  14  (e.g., aggregation module  52 , categorization module  54 , etc.) to create or modify data within response data store  50 . In some examples, response data store  50  may require administrator approval before storing or modifying information. Requiring administrator approval before storing or modifying information may help to ensure that erroneous or inappropriate responses are not stored and thus not suggested as subsequent candidate responses. In other examples, response data store  50  may automatically store information as instructed by aggregation module  52  and/or categorization module  54 , such as when other management procedures are used. 
     Aggregation module  52  may analyze the message histories and store the message and response information within response data store  50 . For example, aggregation module  52  may process each message history from the one or more message histories and break the message history down into individual message and response pairs. Each message history may include time information, sender information, and contents information for each message included in the message history. Aggregation module  52  iterates over each message and each message history. For each message, aggregation module  52  determines a message (e.g., message N) that immediately precedes another message (e.g., message M) in the message history. In this example, message N is the message having the greatest timestamp that is less than the timestamp of message M. Aggregation module  52  analyzes the senders for message N and message M. If the senders of message N and M are different, aggregation module  52  normalizes the contents of message N and the content of message M and inserts at least the normalized contents of messages N and M into the message/response table. In normalizing the content, aggregation module  52  may change the contents of the messages N and M to be consistent cases (e.g., all lowercase, all uppercase, etc.), may remove all punctuation, correct any misspellings, etc. 
     In some examples, the message and response pairs may include more than one message and/or more than one response in instances where aggregation module  52  determines that multiple different messages are associated with a single response or multiple different responses are associated with a single message. For example, aggregation module  52  may determine that the message “what time?” immediately followed by the message “do you want to meet?” should be considered a single message for purposes of processing the message history. As another example, aggregation module  52  may determine that the response “5 pm” immediately followed by the message “tomorrow” should be considered a single response for purposes of processing the message history. Aggregation module  52  may determine that the two messages/responses should be analyzed as a single message/response based on various factors including text proximity (i.e., does message B immediately follow message A or is there an intervening message or response?) and time proximity (i.e., did the user send message B within a threshold amount of time of sending message A, such as 1 second, 3 seconds,5 seconds, etc.) Thus, even though a user may have entered two messages—“what time?” and “do you want to meet?”—and the recipient entered two responses—“5 pm” and “tomorrow’—aggregation module  52  may determine that the messages and responses should be analyzed as a single message and a single response. 
     After aggregation module  52  has stored information for at least one message and response pair in the message and response table, aggregation module  52  may begin aggregating the responses for each message based on the normalized contents of the responses. That is, for a particular message, aggregation module  52  may identify responses having the same normalized contents (e.g., two responses having the contents “6 pm” would be identified as having the same normalized contents), counts the number of responses having the same normalized responses for a particular message, and stores the count in a row for the particular message and response pair. In other words, aggregation module  52  of response suggestion system  14  determines a number of instances of each response for each message from the plurality of message and response pairs. As stated differently, aggregation module  52  consolidates the entries in the message and response table such that there is a single row for each unique response for a particular message and the row for the unique message and response pair includes a count of the number of times the particular response was used for the particular message. 
     Categorization module  54  may categorize the information included in the messages and responses as having a particular information type. That is, categorization module  54  may apply one or more classifiers to the responses in the message and response table to classify the responses as having an information type. Example classifiers include, but are not limited to, an address classifier, a chain store classifier, an emoji classifier, a celebrity classifier, a date classifier, a day of week classifier, a time classifier, etc. In general, the classifiers may be rules configured to detect a particular type of information. The classifiers may be for any type of information and are not limited to the few examples provided. 
     Categorization module  54  may apply a time classifier to the responses to “when works for you?”. The responses may include “6 pm,” “in 10 minutes,” “now,” etc. Based on those responses and using the time classifier, categorization module  54  may classify the responses to “when works for you” as having the information type “TIME.” Similarly, categorization module  54  may analyze the responses to “LG™,” which may include “thank you,” “great!,” “whew,” etc. and determine that the responses have the information type “UNKNOWN” as none of the classifiers may be appropriate. As yet another example, the responses to “how many would you like?” may include “1,” “2,” “a few,” “several,” etc. Based on those responses, text analysis module  16  may classify responses to “how many would you like” as having the information type “QUANTITY” by, for example, applying a quantity classifier. 
     In some examples, categorization module  54  may apply an address classifier, which can detect that “123 Main St” is an address but “hello” is not. As another example, categorization module  54  may apply a chain classifier, which can detect that MegaChainStore and OtherChainStore are chain stores, but that “thank you” is not a chain store. Categorization module  54  may apply an emoji classifier, which can detect that “;)” and “:-)” are emoji but “!” is not an emoji. The celebrity classifier may detect that “Barack Obama” and “Taylor Swift” are celebrities but “Joe” is not a celebrity. Similarly, a time classifier may detect that “6 pm” and “18:00” are times but “tomorrow” is not a time, while a date classifier may detect that “tomorrow” and “July 4th” are dates but “July” is not a date. As another example, a day of week classifier may detect that “Sunday” and Monday” are days of the week but “Labor Day” is not a day of the week. Thus, in general, categorization module  54  may apply one or more classifiers to the normalized response contents to classify the responses as having one or more different information types. 
     Categorization module  54  may then invoke aggregation module  52  to further aggregation the responses based on the information type for each normalized response. For example, aggregation module  52  may, for a particular message, count a number of responses having each information type (e.g., ADDRESS, TIME, EMOJI, CELEBRITY, UNKNOWN, etc.). Aggregation module  52  stores the total number of responses for each information type within the message and response table. That is, after text analysis module  16  analyzes the message histories, text analysis module  16  of response suggestion system  14  stores an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. In some examples, text analysis module  16  may generate a list of the most common responses to each message, which may be quickly accessed when response suggestion system  14  receives a request for suggested responses to a particular incoming communication. 
     Using the message and response table, suggestion module  18  may determine one or more candidate responses for incoming communications. As shown in  FIG. 2 , suggestion module  18  includes response type prediction module  56 , response generation module  58 , and response personalization module  60 . Suggestion module  18  may receive a request for a candidate response from one or more remote computing devices, such as computing device  2 A of  FIG. 1 . The request may include at least a portion of the contents of the incoming communication. However, the request for candidate responses may, in some examples, include other information. The other information may include information pertaining to the computing device that sent the request and/or information pertaining to a user of the computing device that sent the request. For instance, a request may include a unique identifier (e.g., an ID number) identifying the user and/or the computing device that sent the request, location information indicating one or more locations of the computing device, time information indicating a time and/or time zone setting of the computing device, application information indicating an application currently being executed by the computing device and/or an activity in which the application currently being executed is engaging, communications information indicating the type of the incoming communication (e.g., email, text message, calendar alert, or other type), the type of response that the computing device will be sending, the time at which the incoming communication was received, previous communications to or from the sender, user information indicating an occupation of the user, an activity in which the user is currently engaged, contact information indicating the sender of the incoming communication, one or more recipients of the response that the computing device will be sending, or other information. That is, the request for candidate responses may include any information usable by one or more components of response suggestion system  14  to determine a current context of the computing device and tailor candidate responses to better predict a response to the incoming communication. Communications unit  42  of response suggestion system  14  may receive the request for candidate responses and send the request to one or more other components of response suggestion system  14 , such as suggestion module  18 . 
     In the example of  FIG. 2 , suggestion module  18  may be operable by processors  40  to receive a request for candidate responses, obtain information from one or more other sources, and/or determine a current context for the request. That is, in some examples, suggestion module  18  may obtain information included in a received incoming communication and other information (e.g., from the request for candidate responses or other sources) and determine a context on which to base predicted replies to the incoming communication. In some examples, suggestion module  18  may determine a context based on the information included in the request for candidate responses. In other examples, suggestion module  18  may additionally or alternatively obtain supplemental information from other sources, such as an email account associated with a user of the computing device that sent the request, a social networking account of the user, web search history associated with the user, a location history associated with the user, or other sources. 
     Suggestion module  18  may only obtain supplemental information for a user if the user provides explicit permission. That is, suggestion module  18  may only use various sources of information for predicting candidate responses if a user of the computing device that sent the request (e.g., computing device  2 ) explicitly allows response suggestion system  14  to access the various sources. For instance, computing device  2 A may prompt the user to provide a username and password for an email account, a social network service account, or other account. After receiving the information, computing device  2 A may prompt the user to allow response suggestion system  14  to access the account to obtain supplemental information. If the user explicitly agrees to allow access, computing device  2 A may send an indication of the account and access credentials (e.g., a username and password or other information) to response suggestion system  14 . If response suggestion system  14  receives explicit permission, suggestion module  18  may obtain supplemental information. 
     Suggestion module  18  analyzes the request for candidate responses and extracts the included contents of the incoming communication to which computing device  2 A is requesting candidate responses. Response type prediction module  56  may determine which types of responses are popular for responding to a message with the contents of the incoming communication. For example, response type prediction module  56  may perform a lookup in response data store  50  based on at least a portion of the contents of the incoming communication to retrieve information on one or more popular types of responses. As one example, if the incoming communication includes the contents “how are you doing?”, a popular response type may be “EMOJI” or “UNKNOWN.” A type of response may be a popular type of response if, for example, a percentage of all replies to incoming communications having the same contents exceeds a threshold popularity value (e.g., greater than fifteen percent, twenty-five percent, fifty percent, etc.). The lookup performed by response type prediction module  56  on response data store  50  returns each type of response that satisfies the threshold popularity value. In some examples, none of the response types may be popular enough to satisfy the threshold popularity value. In such instances, response type prediction module  56  may retrieve a certain number of the most popular response types, such as three, five, etc. 
     Response generation module  58  may generate a plurality of possible responses to send to computing device  2 A by, for example, retrieving popular responses having one or more of the types determined by response type prediction module  56 . For example, if the incoming communication includes the contents “how are you doing?”, the popular responses of response type EMOJI may include a thumbs up emoji, a thumbs down emoji, a happy face emoji, or other emoji. 
     For certain response types, response personalization module  60  may customize the possible candidate responses. As one example, if the incoming communication includes the content “where do you live?”, response type prediction module  56  may determine that the predicted response type is ADDRESS and response generation module  58  may determine that the content of the incoming communication indicates that the response should include an address of the user to whom the incoming communication is directed. Response personalization module  60  may determine the home address of the user and populate a candidate response with the home address. As another example, if the incoming communication includes the contents “what time can I call you?”, the predicted response type may be TIME and response generation module  58  may determine a list of the most common times included in responses. However, the most popular times may not be times at which the user is available. Instead of merely providing the most popular times, response personalization module  60  may analyze a calendar of the user, messaging/phone usage habits of the user, sleeping habits of the user, etc. and determine one or more at which the user is predicted to be available and include those times as possible candidate responses. 
     In some examples, response personalization module  60  may also customize the suggested candidate responses based on other factors, such as the type of the incoming communication (e.g., email, SMS, asynchronous, synchronous, long form, short form, etc.), the type of device that received the incoming communication (e.g., smartphone, watch, portable computer, etc.), etc. Personalization module  60  may also maintain a history of candidate responses selected by the user (i.e., candidate responses used by the user to reply to incoming communications) and may personalize the candidate responses based on the responses selected by the user. 
     Suggestion module  18  sends the list of candidate responses to computing device  2 A via communication units  42 . Computing device  2 A may display the list of suggested responses and detect a user input selecting one or more of the candidate responses or inputting a different response to the incoming communication. Computing device  2 A may provide an indication of the user input to response suggestion system  14  for inclusion in the candidate response history for the user and for analysis by text analysis module  16  and inclusion in response data store  50 . In this way, the candidate response types and suggested candidate responses may be continually updated and improved based how various users actually respond to incoming communications. 
     By determining the various types of responses actually used by various users and further determining popular responses of the particular types, response suggestion system  14  may decrease the time and/or effort required to respond to incoming communications while increasing the likelihood that the candidate responses will actually be of interest to the user. Furthermore, by receiving an indication of a selected response and updating a database of stored responses and associated context information, response suggestion system  14  may continually improve the suggested candidate responses, which may result in candidate responses that are more likely to be relevant to a user&#39;s current situation. Consequently, in accordance with one or more techniques of the present disclosure, the user may be presented with at least one response option that he or she may select to send as a response to an incoming communication thereby reducing the likelihood that the user will need to manually enter a response. 
       FIG. 3  is a conceptual diagram illustrating example data generated while classifying responses, in accordance with one or more aspects of the present disclosure. The example of  FIG. 3  is described below within the context of  FIG. 2 . 
     Text analysis module  16  may analyze the message histories received by response suggestion system  14  and generate a set of popular response types and responses based on the message histories. For example, text analysis module  16  may create a table of responses that includes information about message and response pairs. That is, the table may include information about the received message, such as the text of the received message, and information about the reply, such as the text of the reply message. Tables  70 - 76  of  FIG. 4  illustrate various tables that may be stored within response data store  50  and that may be generated and/or modified by text analysis module  16  while generating the set of popular response types and responses. 
     In various examples, tables  70 - 76  may include additional information about the message/response pair, such as a type of the message and a type of the response. The message/response table may be stored, in various instances, within response data store  50 . While described as a table, the message and response information may be stored within data store  50  using a range of one or more data structures, such as an array, a list, a database, a structured file (such as a file encoded using a markup language) or other data structure. That is, response data store  50  may contain data in any format capable of being created and/or accessed by one or more other components of response suggestion system  14 . 
     Aggregation module  52  may analyze the message histories and store the message and response information within table  70 . For example, aggregation module  52  may process each message history from the one or more message histories and break the message history down into individual message and response pairs, each of which may be stored in a separate row of table  70 . For example, the first row of table  70  stores the message and response pair “are you coming” and “on my way”. Aggregation module  52  iterates over each message and each message history, normalizes the messages and responses, and inserts them into response data store  50 . 
     After aggregation module  52  has stored information for at least one message and response pair in the message and response table (e.g., table  70 ), aggregation module  52  may begin aggregating the responses for each message based on the normalized contents of the responses and store the aggregated responses in table  72 . That is, for a particular message, aggregation module  52  may identify responses having the same normalized contents (e.g., two responses having the contents “6 pm” would be identified as having the same normalized contents), counts the number of responses having the same normalized responses for a particular message, and stores the count in a row for the particular message and response pair, as shown in table  72 . As stated differently, aggregation module  52  consolidates the entries in the message and response table such that there is a single row for each unique response for a particular message and the row for the unique message and response pair includes a count of the number of times the particular response was used for the particular message. 
     Categorization module  54  may categorize the information included in the messages and responses as having a particular information type and store the message and response type in addition to the number of response having that particular response type within table  74 . That is, categorization module  54  may apply one or more classifiers to the responses in the message and response table to classify the responses as having an information type. Example classifiers include, but are not limited to, an address classifier, a chain store classifier, an emoji classifier, a celebrity classifier, a date classifier, a day of week classifier, a time classifier, etc. In general, the classifiers may be rules configured to detect a particular type of information. The classifiers may be for any type of information and are not limited to the few examples provided. 
     Categorization module  54  may then invoke aggregation module  52  to further aggregation the responses based on the information type for each normalized response. For example, aggregation module  52  may, for a particular message, count a number of responses having each information type (e.g., ADDRESS, TIME, EMOJI, CELEBRITY, UNKNOWN, etc.). Aggregation module  52  stores the total number of responses for each information type within message and response table  76 . That is, after text analysis module  16  analyzes the message histories, text analysis module  16  of response suggestion system  14  stores an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. In some examples, text analysis module  16  may generate a list of the most common responses to each message, which may be quickly accessed when response suggestion system  14  receives a request for suggested responses to a particular incoming communication. 
       FIG. 4  is a flow diagram illustrating an example operation for analyzing responses to incoming communications, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, the example operations of  FIG. 5  are described below within the context of  FIGS. 1 and 2 . 
     Response suggestion system  14  receives one or more message histories form one or more computing devices  2  ( 100 ). Each message history may include one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history. 
     Response suggestion system  14  determines, based on the message histories, a plurality of message and response pairs ( 102 ). For example, for each message, aggregation module  52  may determine a message (e.g., message N) that immediately precedes another message (e.g., message M) in the message history. Aggregation module  52  may analyze the senders for message N and message M. If the senders of message N and M are different, aggregation module  52  determines that messages N and M for a message and response pair ( 102 ). 
     Aggregation module  52  may further normalize each message from the plurality of message and response pairs ( 104 ). In normalizing the content, aggregation module  52  may change the contents of the messages N and M to be consistent cases (e.g., all lowercase, all uppercase, etc.), may remove all punctuation, correct any misspellings, etc. 
     Aggregation module  52  may aggregate the responses for each message based on the contents of the responses ( 106 ). As one example, for a particular message, aggregation module  52  may identify responses having the same normalized contents (e.g., two responses having the contents “6 pm” would be identified as having the same normalized contents). That is, aggregation module  52  may aggregate, based on a respective information type for each response from the one or more aggregated message and response pairs, the one or more aggregated message and response pairs into one or more consolidated message and response pairs. 
     In some examples, while aggregating the responses, aggregation module  52  may count the number of responses having the same normalized responses for a particular message ( 108 ). In other words, aggregation module  52  of response suggestion system  14  may determine a number of instances of each response for each message from the plurality of message and response. 
     Categorization module  54  of response suggestion system  14  may classify each response from the plurality of message and response pairs as having an information type ( 110 ). That is, categorization module  54  may apply one or more classifiers to the responses in the message and response table to classify the responses as having an information type. Example classifiers include, but are not limited to, an address classifier, a chain store classifier, an emoji classifier, a celebrity classifier, a date classifier, a day of week classifier, a time classifier, etc. Categorization module  54  may then invoke aggregation module  52  to further aggregation the responses based on the information type for each normalized response. In various instances, aggregation module  52  may also determine, based on the number of instances of each response, a respective consolidated number of instances of the respective information type for each of the one or more consolidated message and response pairs. 
     Aggregation module  52  may store an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type within response data store  50  ( 112 ). In some examples, aggregation module  52  further stores a respective indication of each of the one or more consolidated message and response pairs, a respective information type, and a respective consolidated number of instances of the respective information type. 
       FIG. 5  is a flow diagram illustrating example operations for providing candidate responses to an incoming communication, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, the example operations of  FIG. 5  are described below within the context of  FIGS. 1 and 2 . 
     Computing device  2 A may receive an incoming communication from another computing device (e.g., computing device  2 N) ( 200 ). The incoming communication includes information, is associated with a sender and a recipient. Responsive to receiving the incoming communication, computing device  2 A may send a request for candidate responses to response suggestion system  14  ( 202 ). 
     Suggestion module  18  of response suggestion system  14  may be operable by processors  40  to receive a request for candidate responses, obtain information from one or more other sources, and/or determine a current context for the request. Suggestion module  18  analyzes the request for candidate responses and determines one or more popular response types based on the contents of the incoming communication ( 204 ). Response type prediction module  56  of suggestion module  18  may determine which types of responses are popular for responding to a message with the contents of the incoming communication. For example, response type prediction module  56  may perform a lookup in response data store  50  based on at least a portion of the contents of the incoming communication to retrieve information on one or more popular types of responses. The lookup performed by response type prediction module  56  on response data store  50  returns each type of response that satisfies a threshold popularity value. 
     Response generation module  58  may generate a plurality of possible responses to send to computing device  2 A by, for example, retrieving popular responses having one or more of the types determined by response type prediction module  56  ( 206 ). For example, if the incoming communication includes the contents “what time do you want to meet?”, the popular responses of response type TIME may include 8 am, 5 pm, and 7 pm. In other words, response generation module may determine, based at least in part on the plurality of popular responses and the type of information expected to be included in the response, one or more candidate responses. In some examples, the one or more candidate responses are determined based on the identity of the sender of the incoming communication and the identity of the recipient of the incoming communication. In some examples, the one or more candidate responses are determined based on a communication type of the incoming communication (e.g., email, simple messaging service, synchronous communication etc.). 
     Response suggestion system  14  sends an indication of the one or more candidate responses to computing device  2 A ( 208 ). Computing device  2 A receives the indication of the one or more candidate responses and displays information about at least one of the one or more candidate responses ( 210 ). Computing device  2 A may be configured to display the candidate responses differently based on the type of information included in the candidate response. For example, if the candidate response includes information of type TIME, computing device  2 A may display the most popular candidate response in a time picker user interface. As another example, if the candidate response includes information of type EMOJI, computing device  2 A may display the candidate responses in an emoji picker user interface. 
     EXAMPLE 1 
     A method comprising: receiving, by a computing system, a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history; determining, by the computing system and based on the plurality of message histories, a plurality of message and response pairs; determining, by the computing system, a number of instances of each response for each message from the plurality of message and response pairs; classifying, by the computing system, each response from the plurality of message and response pairs as having an information type; and storing, by the computing system, an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. 
     EXAMPLE 2 
     The method of example 1, further comprising: receiving, by the computing system and from a computing device, a request for a suggested response to an incoming communication, the request including an indication of the incoming communication; determining, by the computing system and based on the request, a type of information expected to be included in a response to the incoming communication; determining, by the computing system, and based on the request and the type of information, one or more candidate responses; and sending, by the computing system and to the computing device, an indication of the one or more candidate responses. 
     EXAMPLE 3 
     The method of example 2, wherein the request for the suggested response to the incoming communication includes an identity of a sender of the incoming communication and an identity of the recipient of the incoming communication, and where the one or more candidate responses are determined based on the identity of the sender and the identity of the recipient. 
     EXAMPLE 4 
     The method of any of examples 2 and 3, wherein the request for the suggested response to the incoming communication includes a communication type of the incoming communication, and wherein the one or more candidate responses are determined based on the communication type of the incoming communication. 
     EXAMPLE 5 
     The method of any of examples 2-4, wherein the request for the suggested response to the incoming communication includes a device type of the computing device, and wherein the one or more candidate responses are determined based on the device type of the computing device. 
     EXAMPLE 6 
     The method of any of examples 1-5, further comprising: normalizing, by the computing system, each message from the plurality of message and response pairs; aggregating, by the computing system and based on the normalized messages, the plurality of message and response pairs into one or more aggregated message and response pairs; and determining, by the computing system, a number of instances of each response for respective normalized messages from the aggregated message and response pairs, wherein classifying each response from the plurality of message and response pairs comprises classifying, by the computing system, each response from the one or more aggregated message and response pairs as having the information type. 
     EXAMPLE 7 
     The method of example 6, further comprising: aggregating, by the computing system and based on a respective information type for each response from the one or more aggregated message and response pairs, the one or more aggregated message and response pairs into one or more consolidated message and response pairs; and determining, by the computing system and based on the number of instances of each response, a respective consolidated number of instances of the respective information type for each of the one or more consolidated message and response pairs, wherein storing the indication of each unique message from the plurality of message and response pairs includes storing, by the computing system, a respective indication of each of the one or more consolidated message and response pairs, a respective information type, and a respective consolidated number of instances of the respective information type. 
     EXAMPLE 8 
     A computing system, comprising: at least one communication unit configured to receive a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history; a storage device; at least one processor; and at least one module, operable by the at least one processor to: determine, based on the plurality of message histories, a plurality of message and response pairs; determine a number of instances of each response for each message from the plurality of message and response pairs; classify each response from the plurality of message and response pairs as having an information type; and store, at the storage device, an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. 
     EXAMPLE 9 
     The computing system of example 8, wherein the at least one module is further operable by the at least one processor to: receive, via the at least one communication unit and from a computing device, a request for a suggested response to an incoming communication, the request including an indication of the incoming communication; determine, based on the request, a type of information expected to be included in a response to the incoming communication; determine, based on the request and the type of information, one or more candidate responses; and send, via the at least one communication unit and to the computing device, an indication of the one or more candidate responses. 
     EXAMPLE 10 
     The computing system of example 9, wherein the request for the suggested response to the incoming communication includes an identity of a sender of the incoming communication and an identity of the recipient of the incoming communication, and wherein the one or more candidate responses are determined based on the identity of the sender and the identity of the recipient. 
     EXAMPLE 11 
     The computing system of any of examples 9 and 10, wherein the request for the suggested response to the incoming communication includes a communication type of the incoming communication, and wherein the one or more candidate responses are determined based on the communication type of the incoming communication. 
     EXAMPLE 12 
     The computing system of any of examples 9-11, wherein the request for the suggested response to the incoming communication includes a device type of the computing device, and wherein the one or more candidate responses are determined based on the device type of the computing device. 
     EXAMPLE 13 
     The computing system of any of examples 8-12, wherein the at least one module is further operable by the at least one processor to: normalize each message from the plurality of message and response pairs; aggregate, based on the normalized messages, the plurality of message and response pairs into one or more aggregated message and response pairs; determine a number of instances of each response for respective normalized messages from the aggregated message and response pairs; and classify each response from the plurality of message and response pairs by at least classifying each response from the one or more aggregated message and response pairs as having the information type. 
     EXAMPLE 14 
     The computing system of example 13, wherein the at least one module is further operable by the at least one processor to: aggregate, based on a respective information type for each response from the one or more aggregated message and response pairs, the one or more aggregated message and response pairs into one or more consolidated message and response pairs; determine, based on the number of instances of each response, a respective consolidated number of instances of the respective information type for each of the one or more consolidated message and response pairs; and store the indication of each unique message from the plurality of message and response pairs by at least storing a respective indication of each of the one or more consolidated message and response pairs, a respective information type, and a respective consolidated number of instances of the respective information type. 
     EXAMPLE 15 
     A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause at least one processor of a computing system to: receive a plurality of message histories, wherein each message history from the plurality of message histories includes one or more of a simple messaging service message history, multimedia messaging service message history, an instant messaging message history, or an email message history; determine, based on the plurality of message histories, a plurality of message and response pairs; determine a number of instances of each response for each message from the plurality of message and response pairs; classify each response from the plurality of message and response pairs as having an information type; and store an indication of each unique message from the plurality of message and response pairs, an indication of the information type associated with the respective response associated with the unique message, and a number of responses from the plurality of message and response pairs having the information type. 
     EXAMPLE 16 
     The non-transitory computer-readable storage medium of example 15, wherein the instructions further cause the one or more processors to: receive, from a computing device, a request for a suggested response to an incoming communication, the request including an indication of the incoming communication; determine, based on the request, a type of information expected to be included in a response to the incoming communication; determine, based on the request and the type of information, one or more candidate responses; and send, to the computing device, an indication of the one or more candidate responses. 
     EXAMPLE 17 
     The non-transitory computer-readable storage medium of example 16, wherein the request for the suggested response to the incoming communication includes an identity of a sender of the incoming communication and an identity of the recipient of the incoming communication, and wherein the one or more candidate responses are determined based on the identity of the sender and the identity of the recipient. 
     EXAMPLE 18 
     The non-transitory computer-readable storage medium of any of examples 16 and 17 wherein the request for the suggested response to the incoming communication includes a communication type of the incoming communication, and wherein the one or more candidate responses are determined based on the communication type of the incoming communication. 
     EXAMPLE 19 
     The non-transitory computer-readable storage medium of any of examples 15-18, wherein the instructions further cause the one or more processors to: normalize each message from the plurality of message and response pairs; aggregate, based on the normalized messages, the plurality of message and response pairs into one or more aggregated message and response pairs; determine a number of instances of each response for respective normalized messages from the aggregated message and response pairs; and classify each response from the plurality of message and response pairs by at least classifying each response from the one or more aggregated message and response pairs as having the information type. 
     EXAMPLE 20 
     The non-transitory computer-readable storage medium of example 19, wherein the instructions further cause the one or more processors to: aggregate, based on a respective information type for each response from the one or more aggregated message and response pairs, the one or more aggregated message and response pairs into one or more consolidated message and response pairs; determine, based on the number of instances of each response, a respective consolidated number of instances of the respective information type for each of the one or more consolidated message and response pairs; and store the indication of each unique message from the plurality of message and response pairs by at least storing a respective indication of each of the one or more consolidated message and response pairs, a respective information type, and a respective consolidated number of instances of the respective information type. 
     EXAMPLE 21 
     A computing system comprising means for performing the method recited by any of examples 1-8. 
     EXAMPLE 22 
     A computer-readable storage medium comprising instructions that, when executed, cause one or more processors of a computing device to perform the method recited by any of examples 1-8. 
     In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof If implemented in software, the functions may be stored on or transmitted over, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media, which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium. 
     By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are instead directed to non-transient, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements. 
     The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware. 
     Various examples have been described. These and other examples are within the scope of the following claims.