Patent Publication Number: US-11024293-B1

Title: Systems and methods for personifying communications

Description:
CROSS REFERENCE 
     This application is a continuation of U.S. patent application Ser. No. 15/489,414 entitled “SYSTEMS AND METHODS FOR PERSONIFYING COMMUNICATIONS,” which was filed Apr. 17, 2017, which is a continuation of U.S. patent application Ser. No. 14/607,887 entitled “SYSTEMS AND METHODS FOR PERSONIFYING COMMUNICATIONS,” which was filed on Jan. 28, 2015 and claims priority from U.S. Provisional Patent Application No. 61/934,180 entitled “SYSTEMS AND METHODS FOR PERSONIFYING COMMUNICATIONS,” which was filed 31 Jan. 2014, and assigned to the assignee hereof. 
    
    
     BACKGROUND 
     Advancements in media delivery systems and media-related technologies continue to increase at a rapid pace. Increasing demand for media has influenced the advances made to media-related technologies. Computer systems have increasingly become an integral part of the media-related technologies. Computer systems may be used to carry out several media-related functions. The wide-spread access to media has been accelerated by the increased use of computer networks, including the Internet and cloud networking. 
     Many homes and businesses use one or more computer networks to generate, deliver, and receive data and information between the various computers connected to computer networks. Users of computer technologies continue to demand increased access to information and an increase in the efficiency of these technologies. Improving the efficiency of computer technologies is desirable to those who use and rely on computers. 
     With the wide-spread use of computers and mobile devices has come an increased presence of home automation and security products. Advancements in mobile devices allow users to monitor and/or control an aspect of a home or business automation system. Communication exchanges with the automation system can be artificial and unrefined and can, in some instances, cause confusion. 
     SUMMARY 
     According to at least one embodiment, the computer-implemented method for personifying a natural-language communication includes observing a linguistic pattern of a user. The method may also include analyzing the linguistic pattern of the user and adapting the natural-language communication based at least in part on the analyzed linguistic pattern of the user. 
     In some embodiments, observing the linguistic pattern of the user may include receiving data indicative of the linguistic pattern of the user. The data may be one of verbal data or written data. Written data may include at least one of a text message, email, social media post, or computer-readable note. Verbal data may include at least one of a recorded telephone conversation, voice command, or voice message. 
     In some embodiments, the method may further compare the written data and the verbal data and identify differences between the written data and the verbal data. In some instances, the method may also identify a geographical location of the user indicative of a geographical region and adapt the natural-language communication to use colloquialisms associated with the geographical region. 
     In further embodiments, when analyzing the linguistic pattern of the user, the method may also identify words or phrases preferred by the user and identify a preferred sentence structure of the user. In some embodiments, the words or phrases may include slang words or colloquialisms. In some instances, the method may identify a geographical region associated with the colloquialisms and adapt the natural-language communication to use colloquialisms associated with the geographical region. In further examples, the method may identify an age group associated with the slang words and colloquialisms and adapt the natural-language communication to use slang words and colloquialisms associated with the age group. 
     In some embodiments, the method may generate a message to deliver to the user. In some instances, the message may communicate an event within an automation system. 
     In some embodiments, adapting the natural-language communication may include updating an algorithm based at least in part on the analyzed linguistic pattern of the user. The algorithm may generate a message to deliver to the user. 
     According to another embodiment, an apparatus for personifying a natural-language communication is also described. The apparatus may include a processor, a memory in electronic communication with the processor and instructions stored on the memory of the processor. The processor may execute the instructions to observe a linguistic pattern of a user, analyze the linguistic pattern of the user, and adapt the natural-language communication based at least in part on the analyzed linguistic pattern of the user. 
     In some embodiments, the apparatus may be associated with an automation system. The natural-language communication may be adaptive to individual users of the automation system. In some instances, the natural-language communication may convey an event at the automation system. 
     According to another embodiment, a computer-program product for personifying a natural-language communication is described. The computer-program product may include a non-transitory computer-readable medium that may store instructions executable by a processor. The instructions may observe a linguistic pattern of a user, analyze the linguistic pattern of the user, and adapt the natural-language communication based at least in part on the analyzed linguistic pattern of the user. In some embodiments, the computer-program product may be associated with an automation system and the natural-language communication may convey an event at the automation system. In further embodiments, the natural-language communication may be one of a text message, ping message, email, or voice message. 
     The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the spirit and scope of the appended claims. Features which are believed to be characteristic of the concepts disclosed herein, both as to their organization and method of operation, together with associated advantages will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purpose of illustration and description only, and not as a definition of the limits of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A further understanding of the nature and advantages of the embodiments may be realized by reference to the following drawings. In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label. 
         FIG. 1  is a block diagram of an environment in which the present systems and methods may be implemented; 
         FIG. 2  is a block diagram of another environment in which the present systems and methods may be implemented; 
         FIG. 3  is a block diagram of an example first communication module of the environments shown in  FIGS. 1 and 2 ; 
         FIG. 4  is a block diagram of an example second communication module of the environment shown in  FIG. 3 ; 
         FIG. 5  is a flow diagram illustrating a method for personifying a natural-language communication; 
         FIG. 6  is a flow diagram illustrating another method for personifying a natural-language communication; and 
         FIG. 7  is a block diagram of a computer system suitable for implementing the present systems and methods of  FIGS. 1 and 2 . 
     
    
    
     While the embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the exemplary embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the instant disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims. 
     DETAILED DESCRIPTION 
     The systems and methods described herein relate to home automation and home security, and related security systems and automation for use in commercial, residential, and business settings. More specifically, the systems and methods described herein relate to improved systems and methods of communicating with users of an automation system. The communication system may enable more personal, natural-language communication between the user and the automation system. 
       FIG. 1  is a block diagram illustrating one embodiment of an environment  100  in which the present systems and methods may be implemented. In some embodiments, the systems and methods described herein may be performed at least in part on or using a device  105 . The environment  100  may include the device  105 , a user interface  110 , and a first communication module  115 . 
     The first communication module  115  may be configured to communicate with a user of the device  105  using natural-language communication. For example, communicating with the user of the device  105  may include personifying the natural-language communication. This may involve the first communication module  115  adapting the communication to mimic a linguistic pattern of the user. Adapting to the linguistic style of the user may allow the first communication module  115  to readily convey information to the user. The information may communicate an event or interaction within an automation system. The information may further be conveyed as written or verbal message. The user interface  110  may facilitate communication between the user and the device  105 . 
     Referring now to  FIG. 2 , in some embodiments, an environment  200  may include the components of the environment  100  described above, and may further include an automation system  205 , a network  210 , a service station  215 , and a database  220 . Additionally, the environment  200  may include a device  105 - a , which may be one example of the device  105  described above with reference to  FIG. 1 . The device  105 - a  may additionally include an application  225 , a display  230 , a microphone  235 , and a speaker  240 . The device  105 - a  may include various components and functionalities that work cooperatively with the user interface  110  and the first communication module  115 , and/or may operate independently of the user interface  110  and the first communication module  115 . 
     In some embodiments, the device  105 - a  may include one or more processors, one or more memory devices, and/or a storage device. Examples of the device  105 - a  may include mobile phones, smart phones, tablets, personal digital assistants (PDAs), wearable computers, ultra-mobile PCs, etc. Further examples of the device  105 - a  may include a viewing device associated with a media content set top box, satellite set top box, cable set top box, DVRs, personal video recorders (PVRs), personal computing devices, computers, servers, etc. Additionally or alternatively, the device  105 - a  may represent an automation controller which may be one of a wall-mounted controller, a remote control, a voice activated controller, or the like. In some embodiments, the device  105 - a  may represent a single device or multiple devices. 
     In some embodiments, the device  105 - a  may connect directly to the automation system  205 . In further embodiments, the device  105 - a  may connect to the automation system  205  over the network  210 . In some embodiments, the device  105 - a  may control aspects of a monitored property as well as to receive notifications regarding monitored activity of the property. The notifications may be written notifications or verbal messages. For example, the notifications may be a text message, email, ping message, voice message, voicemail, phone call, or the like. 
     In some embodiments, the automation system  205  may include a sensor  245 . The sensor  245  shown in  FIG. 2  may represent one or more separate sensors or a combination of two or more sensors in a single sensor device. For example, the sensor  245  may represent one or more camera sensors and one or more motion sensors connected to environment  200 . Additionally, or alternatively, the sensor  245  may represent a combination sensor such as both a camera sensor and a motion sensor integrated in the same sensor device. Although the sensor  245  is depicted as connecting directly to automation system  205 , in some embodiments, the sensor  245  may connect to the automation system  205  over the network  210 . Additionally, or alternatively, the sensor  245  may be integrated with a home appliance or fixture such as a light bulb fixture. The sensor  245  may include an accelerometer to enable the sensor  245  to detect movement. The sensor  245  may include a wireless communication device enabling the sensor  245  to send and receive data and/or information to and from one or more devices in the environment  200 . Additionally, or alternatively, the sensor  245  may include a GPS sensor to enable the sensor  245  to track a location of the sensor  245 . The sensor  245  may include a proximity sensor to enable the sensor  245  to detect proximity of a person relative to a predetermined distance from a dwelling (e.g., geo-fencing). The sensor  245  may include one or more security detection sensors such as, for example, a glass break sensor, a motion detection sensor, or both. Additionally, or alternatively, the sensor  245  may include a smoke detection sensor, a carbon monoxide sensor, or both. 
     In some embodiments, the application  225  may allow a user to control an aspect of the monitored property, including security, energy management, locking or unlocking a door, checking the status of a door, locating a person or an item, controlling lighting, thermostats, cameras, receiving notification regarding a current status or an anomaly associated with a home, an office, a place of business, and the like. In some configurations, the application  225  may enable the automation system  205  to interface with the device  105 - a  and enable the user interface  110  to communicate automation, security, and/or user management content via the display  230  or the speaker  240 . 
     In some embodiments, the device  105 - a  may communicate with the automation system  205  and the service station  215  via the network  210 . Examples of the network  210  include cloud networks, local area networks (LAN), wide area networks (WAN), virtual private networks (VPN), wireless networks (using 802.11, for example), and/or cellular networks (using 3G and/or LTE, for example), etc. The network  210  may be a single network, or may include multiple interconnected, overlapping, or coincidental networks. For example, in some embodiments, the network  210  may include multiple networks interconnected to facilitate communication or may include redundant networks. For example, the network  210  may represent a first network (e.g., the Internet) and a second network (e.g., cellular networks). 
     The service station  215  shown in  FIG. 2  may represent one or more separate service stations or a combination of service stations. The service station  215  may be a network operations center, a monitoring center, a service station or any similar station in association with a provider of the automation system. In some embodiments, the service station  215  may include a second communication module  250 . The second communication module  250  may communicate events, occurrences, or the like associated with the automation system  205 . 
     In some embodiments, the service station  215  may be coupled to the database  220 . The database  220  may include, for example, linguistic patterns for individual users associated with each automation system  205  monitored by the service station  215 . For example, the database  220  may include a linguistic pattern module  255  which may store the linguistic patterns of users associated with each automation system  205 . The database  220  may include other information including, for example, historical information about the automation system  205  and other aspects of the environment  200 , contact information for various response personnel, and the like. 
       FIG. 3  is a block diagram illustrating one example of a first communication module  115 - a . The first communication module  115 - a  may be one example of the first communication module  115  depicted in  FIGS. 1 and/or 2 . As depicted, the first communication module  115 - a  may include a data collection module  300 , a data analysis module  305 , a linguistic algorithm module  310 , and a message module  315 . The first communication module  115 - a  may include additional modules and capabilities in other embodiments. Similarly, the first communication module  115 - a  may include a fewer number of modules and functionalities than that which is described with reference to  FIG. 3 . The first communication module  115 - a  may personify natural-language communications. For example, the first communication module  115 - a  may adapt communications sent to a user to contain a natural-language style. The first communication module may further adapt the natural-language message based at least on a linguistic pattern of the user. 
     In some embodiments, the data collection module  300  may collect various linguistic data. For example, the data collection module  300  may collect verbal data or written data associated with a user. The data may be indicative of a linguistic pattern of the user. The verbal data may include at least one of a recorded telephone conversation, voice command, voice message, or the like. For example, the user may be using a device (e.g. device  105 ) with a user interface (e.g., user interface  110 ). For example, the user interface may allow the data collection module  300  to gather recorded voice data. In some instances, the device may include a microphone (e.g., microphone  235 ) capable of collecting the user&#39;s speech. The data collection module  300  may use the microphone to record the user&#39;s speech. 
     In other embodiments, the data collection module  300  may collect written data indicative of a linguistic pattern associated with the user. Written data may include at least one of a text message, email, social media post, computer-readable note, or the like. In some embodiments, the data collection module  300  may use a device (e.g. device  105 ) associated with the user to gather information. For example, the device may have a user interface associated with a keyboard for entering text (i.e. written data). The data collection module  300  may gather the written data. 
     In some embodiments, the data analysis module  305  may analyze the data collected by the data collection module  300 . For example, the data analysis module  305  may identify the verbal and written linguistics patterns of the user. The linguistic patterns may include the form, meaning, and context of language. In some embodiments, linguistic patterns may include sentence structure, colloquialisms, phrases, speech patterns, writing patterns, writing style, grammar habits, a user&#39;s lexicon, discourse, dialect, or the like. 
     In some embodiments, the data analysis module  305  may differentiate and identify the written and verbal linguistic patterns of a particular user. For example, the data analysis module  305  may determine a particular linguistic written pattern of the user. This may include identifying the relationship between the words used to form a sentence and determine written habits of the user. For example, a user may prefer to use passive writing; another user may prefer use conjunctions. In further examples, a user may prefer to use shorthand. The data analysis module  305  may identify these types of linguistic patterns. 
     In further embodiments, the data analysis module  305  may analyze the verbal linguistic patterns of a user. For example, the data analysis module  305  may determine the spoken sentence structure patterns of a user. For example, the user may use proper English or Gregarian language. A user may use more colloquialisms when speaking than when writing. The data analysis module  305  may identify all these patterns. 
     In some embodiments, the data analysis module  305  may identify colloquialisms or slang words used in the verbal and written data. In some instances, the colloquialisms or slang words may be generic. In other instances, the colloquialisms and slang words may reveal information about the user. For example, the data analysis module  305  may determine if the slang words and colloquialisms identify an age group or geographical region associated with the user. For example, some colloquialisms are inherent in a general geographical region (e.g., sneakers versus tennis shoes, bubbler versus drinking fountain versus water fountain). The data analysis module  305  may collect the various colloquialisms and use them to map a geographic region associated with the user. In other embodiments, slang words may reveal information about the user. For example, the data analysis module  305  may identify slang words associated with a particular age group (e.g., lol, wassup, ducky, and how, peeps, etc.). 
     In some embodiments, the first communication module  115 - a  may contain geographical information relating to the location of a device or an automation system. Therefore, the first communication module  115 - a  may use linguistic patterns associated with that geographical region. However, in some instances, a user may be from a different geographical region. In those instances, the data analysis module  305  may identify a geographical region where the user is from and adapt to the local jargon. For example, a user may be from the Northeast but may have moved to the Mountain West and installed an automation system at a home there. The first communication module  115 - a  may initially use Mountain West jargon. However, the first communication module  115 - a  may adapt to use jargon of the Northeast to conform with the linguistic patterns of the user based at least in part on the analysis performed by the data analysis module  305 . 
     The linguistic algorithm module  310  may use the analysis generated by the data analysis module  305  to create, adapt, update, or correct a linguistic algorithm. For example, the linguistic algorithm module  310  may contain an algorithm for generating natural-language communications. The algorithm may be a generic algorithm that generates a generic natural-language communication. In other embodiments, the algorithm may be unique to the user. For example, the algorithm may adapt to the linguistic pattern of the user. In some instances, the algorithm may include colloquialisms preferred by the user. The linguistic algorithm module  310  may adapt to an age group associated with the user. For example, the data analysis module  305  may identify an age group associated with the user, the linguistic algorithm module  310  may then incorporate other slang words and colloquialisms associated with that age group. 
     The linguistic algorithm module  310  may also contain different algorithms based on the linguistic patterns of a user&#39;s written or verbal communications. For example, the data analysis module  305  may identify different patterns in a user&#39;s written and verbal communications. The differences may require different algorithms to adapt communications to their delivery method. For example, a written linguistic algorithm may generate written communications and a verbal linguistic algorithm may generate verbal communications. 
     In further embodiments, as discussed below, the linguistic algorithm module  310  may communicate with a linguistic algorithm module  410  associated with a second communication module  250 - a  (See  FIG. 4 ). 
     In some embodiments, the first communication module  115 - a  may further include the message module  315 . The message module  315  may use the algorithm in the linguistic algorithm module  310  to generate a message. The message may convey information to a user of an automation system. For example, the message may alert the user to an unauthorized entry to an automation system, that an automation system is armed/disarmed, that a door was left unlocked, that another user accessed the system, or the like. The message may be a text message, ping message, email, voice message, phone call, or the like. 
     In some embodiments, the message may be a natural-language communication. For example, the natural-language communication may appear to be generated by a person rather than a computer. In some instances, the user may easily interpret the natural-language communication and its meaning. In some instances, the natural-language communication may ease the anxiety a user experiences when interfacing with an automation system. As the linguistic algorithm module  310  advances, the messages generated by the message module  315  may be more refined. Therefore, the user may eventually feel as though they are talking to a friend rather than a computer. For example, the natural-language communication may adapt to linguistic patterns of the user. 
       FIG. 4  is a block diagram illustrating one example of the second communication module  250 - a . The second communication module  250 - a  may be one example of the second communication module  250  depicted in  FIG. 2 . As depicted, the second communication module  250 - a  may include a data collection module  400 , a data analysis module  305 - a , a linguistic algorithm module  410 , and a message module  315 - a . The second communication module  250 - a  may include additional modules and capabilities in other embodiments. Similarly, the second communication module  250 - a  may include a few number of modules and functionalities than that which is described with reference to  FIG. 4 . 
     The data collection module  400  may operate similar to data collection module  300  (See  FIG. 3 ). For example, data collection module  400  may collect various written and verbal data associated with a user. However, since the data collection module  400  is remote from the user, a device (e.g. device  105 ) associated with a user may collect and transfer the data to the data collection module  400 . In some embodiments, the device may automatically send data to the data collection module  400 . In other embodiments, the data collection module  400  may request information from the device. The device may be any device associated with the user and the data collection module  400  may communicate with a variety of devices. In some embodiments, the device and the data collection module  400  may communicate over a network (e.g., network  210 ). 
     The data analysis module  305 - a  may be one example of a data analysis module  305  depicted in  FIG. 3 . Therefore, data analysis module  305 - a  may perform similar functions as data analysis module  305 . 
     The linguistic algorithm module  410  may operate similar to linguistic algorithm module  310  (See  FIG. 3 ). For example, the linguistic algorithm module  410  may perform the same functions as the linguistic algorithm module  310 . In some embodiments, the linguistic algorithm module  410  and the linguistic algorithm module  310  may communicate. For example, the linguistic algorithm module  410  may use the data analyzed by the data analysis module  305 - a  to adapt to a linguistic pattern of the user. The linguistic algorithm module  410  may send adapted algorithm to the linguistic algorithm module  310  to ensure both modules  310 ,  410  are using substantially the same algorithms. Similarly, updates made by the linguistic algorithm module  310  may be sent to the linguistic algorithm module  410 . In some embodiments, the linguistic algorithm module  410  may ping the linguistic algorithm module  310  for updates and vice versa. Therefore, in some embodiments, the natural-language communication may appear the same regardless of whether the first or second communication module  115 - a ,  250 - b  generated it. 
     The message module  315 - a  may be one example of the message module  315  depicted in  FIG. 3 . Therefore, the message module  315 - a  may perform similar functions as the message module  315 . 
       FIG. 5  is a flow diagram illustrating one embodiment of a method  500  for personifying a natural-language communication. In some configurations, the method  500  may be implemented in whole or in part by the first communication module  115  shown in  FIGS. 1, 2 and/or 3 . In further configurations, the method  500  may be implemented in whole or in part by the second communication module  250  shown in  FIGS. 2 and/or 4 . In still further embodiments, the method  500  may be performed generally by the device  105  shown in  FIGS. 1 and/or 2 , or even more generally by the environments  100 ,  200  shown in  FIGS. 1 and/or 2 . 
     At block  505 , the linguistic pattern of a user may be observed. This may include collecting verbal and written data indicative of a linguistic pattern of the user. The data may be collected via a device (e.g. device  105 ). In some embodiments, the device may collect and store the data. In other embodiments, the device may transfer the data to a service station which may additionally or alternatively collect and store the data. 
     In further embodiments, the data may be collected via a survey. For example, at block  505 , the user may be given a survey containing different questions to assess the linguistic characteristics of the user. The survey may be a written and/or verbal survey. It may include multiple choice question, yes/no questions, open-ended questions, or some combination of different types of questions. In some embodiments, the survey may set a baseline for adapting the natural-language communications to a linguistic pattern of the user. 
     At block  510 , the linguistic pattern of the user may be analyzed. For example, the different characteristics and individualisms of the linguistic pattern may be observed and recorded. In some embodiments, the verbal and written data may be differentiated and analyzed separately. In other embodiments, the written data and verbal data may be analyzed concurrently. 
     At block  515 , a natural-language communication may be adapted based at least in part on the analyzed linguistic pattern of the user. This may include adapting the natural-language communication to use words or phrases favored by the user. The natural-language communication may also adapt to a preferred sentence structure or syntax of the user. In some embodiments, the natural-language communication may adapt to use slang words or colloquialisms associated with an age group and/or a geographical region associated with the user. The natural-language communication may differ for verbal and written data, or in some instances, may be the same for both verbal and written communications. 
       FIG. 6  is a flow diagram illustrating one embodiment of a method  600  to personify a natural-language communication. In some configurations, the method  600  may be implemented in whole or in part by the first communication module  115  depicted in  FIGS. 1, 2 , and/or  3 . In other embodiments, the method  600  may be implemented in whole or in part by the second communication module  250  depicted in  FIGS. 2 and/or 4 . In some configurations, the method  600  may be implemented in whole or in part with the method  500  depicted in  FIG. 5 . 
     At block  605 , data may be gathered relating to a linguistic pattern of the user. The data may be gathered by a device (e.g. device  105 ) or conversely may be gathered by a service station (e.g. service station  215 ). At block  610 , it may be determined whether the data is written data or verbal data. 
     If the data is written data, at block  615 , a preferred sentence structure may be identified. For example, the sentence structure of the user may be analyzed to determine the type of sentence structure the user prefers. For instance, a user may prefer to use one of a simple sentence, complex sentence, compound sentence, special compound sentence, or the like. In some instances, a user may prefer to use a combination of sentence structures. 
     At block  620 , preferred words or phrases may be identified. For example, a series of colloquialisms or slang words preferred by the user may be identified. In some instances, the relationship between the words or phrases and structure of the words or phrases may also be identified. For example, a user may prefer to write-out numbers (e.g. 5 versus five). A user may also use emoticons or internet slang (e.g. :o) or lol, rofl). In other embodiments, a user may prefer conjunctions. Other types of linguistic patterns may also be identified. In some embodiments, at block  620 , the preferred words or phrases may categorize the individual user. For example, the preferred words or phrases may identify a geographical region, dialect, or age group associated with the user. 
     At block  615 , the written data analysis may be compared to a verbal data analysis (if available). For example, at block  615 , the sentence structures of the verbal and written data, as well as preferred words and phrases, may be compared. At block  630 , it may be determined if the data shows similar patterns. If the comparison does not shown similar patterns, at block  640 , a written linguistic algorithm may be updated. If the data is similar, the method  600  may continue to block  635 . At block  635 , an overall linguistic algorithm may be updated. The overall linguistic algorithm may generate both written and verbal communications. 
     If, at block  610 , it is determined the data is verbal data, the method  600  may continue to block  645 . At block  645 , a preferred sentence structure may be identified. Similar to block  615 , the sentence structure of the user may be analyzed to determine the type of sentence structure the user prefers. For instance, a user may prefer to use one of a simple sentence, complex sentence, compound sentence, special compound sentence, or the like. In some instances, a user may prefer to use a combination of sentence structures. 
     At block  650 , preferred words or phrases may be identified. Similar to block  620 , a series of colloquialisms or slang words preferred by the user may be identified. In some instances, the relationship between the words or phrases and structure of the words or phrases may also be identified. For example, a user may use slang or may speak in internet slang (e.g. don&#39;t know versus dunno, y&#39;all, or saying LOL, ROFL, etc.). In some embodiments, at block  620 , the preferred words or phrases may categorize the individual user. For example, the preferred words or phrases may identify a geographical region, dialect, or age group associated with the user. 
     At block  655 , the verbal data analysis may be compared to the written data analysis (if the written data analysis is available). At block  660 , it may be determined if the verbal data and written data have similar patterns. If similar patterns exist, at block  635 , an overall linguistic algorithm may be updated. If similar patterns do not exist, at block  665 , a verbal linguistic algorithm may be updated. The verbal linguistic algorithm may generate verbal messages. 
     In some embodiments, the verbal, written, or overall linguistic algorithms (referred to generally in this paragraph as “algorithm”) may be updated at various time intervals. For example, the algorithm may be updated at set time intervals. For example, it may be updated approximately every half-hour, hourly, bi-daily, daily, weekly, monthly, etc. In some embodiments, the algorithm may be updated more frequently for a new user and less frequently for an older user. For example, for a new user, the algorithm may be updated daily, whereas an older user&#39;s algorithm may be updated monthly. As the new user&#39;s algorithm becomes more personalized, the algorithm may reduce the frequency of updates. For example, the algorithm may first update every bi-daily, then daily, then bi-weekly, then weekly, etc. As the algorithm is refined, it may update less often. 
     In other embodiments, the algorithm may not be updated on a set time table, but rather based upon the amount of information observed and analyzed. For example, the method  500  may have a predetermined amount of data to collect before updating the algorithm to ensure a proper sampling size. In some instances, this may include approximately five different pieces of data (e.g., two emails, one text message, two voice commands, and one voice message). In other embodiments, it may include more or less. As with a set time table updating system, using a data set, the algorithm may update more frequently at first then expand out to update after a larger sampling size is collected. 
       FIG. 7  depicts a block diagram of a controller  700  suitable for implementing the present systems and methods. The controller  700  may be an example of the device  105  illustrated in  FIGS. 1 and/or 2 . In one configuration, the controller  700  may include a bus  705  which interconnects major subsystems of controller  700 , such as a central processor  710 , a system memory  715  (typically RAM, but which may also include ROM, flash RAM, or the like), an input/output controller  720 , an external audio device, such as a speaker system  725  via an audio output interface  730 , an external device, such as a display screen  735  via display adapter  740 , an input device  745  (e.g., remote control device interfaced with an input controller  750 ), multiple USB devices  765  (interfaced with a USB controller  770 ), one or more cellular radios  790 , and a storage interface  780 . Also included are at least one sensor  755  connected to bus  705  through a sensor controller  760  and a network interface  785  (coupled directly to bus  705 ). 
     Bus  705  may allow data communication between central processor  710  and system memory  715 , which may include read-only memory (ROM) or flash memory (neither shown), and random access memory (RAM) (not shown), as previously noted. The RAM is generally the main memory into which the operating system and application programs are loaded. The ROM or flash memory can contain, among other code, the Basic Input-Output system (BIOS) which controls basic hardware operation such as the interaction with peripheral components or devices. For example, first communication module  115 - b  to implement the present systems and methods may be stored within the system memory  715 . The first communication module  115 - b  may be an example of the first communication module  115  illustrated in  FIGS. 1, 2 , and/or  3 . Applications (e.g., application  225 ) resident with controller  700  are generally stored on and accessed via a non-transitory computer readable medium, such as a hard disk drive (e.g., fixed disk  775 ) or other storage medium. Additionally, applications can be in the form of electronic signals modulated in accordance with the application and data communication technology when accessed via network interface  785 . 
     Storage interface  780 , as with the other storage interfaces of controller  700 , can connect to a standard computer readable medium for storage and/or retrieval of information, such as a fixed disk drive  775 . Fixed disk drive  775  may be a part of controller  700  or may be separate and accessed through other interface systems. Network interface  785  may provide a direct connection to a remote server via a direct network link to the Internet via a POP (point of presence). Network interface  785  may provide such connection using wireless techniques, including digital cellular telephone connection, Cellular Digital Packet Data (CDPD) connection, digital satellite data connection, or the like. In some embodiments, one or more sensors (e.g., motion sensor, smoke sensor, glass break sensor, door sensor, window sensor, carbon monoxide sensor, and the like) connect to controller  700  wirelessly via network interface  785 . In one configuration, the cellular radio  790  may include a receiver and transmitter to wirelessly receive and transmit communications via, for example, a cellular network. The cellular radio  790  may be used to transmit information to the service station  215  via the network  210 . 
     Many other devices or subsystems (not shown) may be connected in a similar manner (e.g., entertainment system, computing device, remote cameras, wireless key fob, wall mounted user interface device, cell radio module, battery, alarm siren, door lock, lighting system, thermostat, home appliance monitor, utility equipment monitor, and so on). Conversely, all of the devices shown in  FIG. 7  need not be present to practice the present systems and methods. The devices and subsystems can be interconnected in different ways from that shown in  FIG. 7 . The aspect of some operations of a system such as that shown in  FIG. 7  are readily known in the art and are not discussed in detail in this application. Code to implement the present disclosure can be stored in a non-transitory computer-readable medium such as one or more of system memory  715  or fixed disk  775 . The operating system provided on controller  700  may be iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. 
     Moreover, regarding the signals described herein, those skilled in the art will recognize that a signal can be directly transmitted from a first block to a second block, or a signal can be modified (e.g., amplified, attenuated, delayed, latched, buffered, inverted, filtered, or otherwise modified) between the blocks. Although the signals of the above described embodiment are characterized as transmitted from one block to the next, other embodiments of the present systems and methods may include modified signals in place of such directly transmitted signals as long as the informational and/or functional aspect of the signal is transmitted between blocks. To some extent, a signal input at a second block can be conceptualized as a second signal derived from a first signal output from a first block due to physical limitations of the circuitry involved (e.g., there will inevitably be some attenuation and delay). Therefore, as used herein, a second signal derived from a first signal includes the first signal or any modifications to the first signal, whether due to circuit limitations or due to passage through other circuit elements which do not change the informational and/or final functional aspect of the first signal. 
     While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered exemplary in nature since many other architectures can be implemented to achieve the same functionality. 
     The process parameters and sequence of steps described and/or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and/or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed. The various exemplary methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein or include additional steps in addition to those disclosed. 
     Furthermore, while various embodiments have been described and/or illustrated herein in the context of fully functional computing systems, one or more of these exemplary embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these software modules may configure a computing system to perform one or more of the exemplary embodiments disclosed herein. 
     The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the present systems and methods and their practical applications, to thereby enable others skilled in the art to best utilize the present systems and methods and various embodiments with various modifications as may be suited to the particular use contemplated. 
     Unless otherwise noted, the terms “a” or “an,” as used in the specification and claims, are to be construed as meaning “at least one of” In addition, for ease of use, the words “including” and “having,” as used in the specification and claims, are interchangeable with and have the same meaning as the word “comprising.” In addition, the term “based on” as used in the specification and the claims is to be construed as meaning “based at least upon.”