Patent Publication Number: US-2023153642-A1

Title: System and method for varying verbosity of response in a group communication using artificial intelligence

Description:
RELATED APPLICATIONS 
     This application is a continuation application of U.S. patent application Ser. No. 15/390,816 filed in the United States Patent Office on Dec. 27, 2016, the entire content of which is being incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to radio communication systems and more particularly to the application of artificial intelligence in radio communication systems. 
     BACKGROUND 
     Being able to get the best search out of every search query through a virtual assistant without impeding communications is important, not only to the user of the query device but also within the management of the overall communication system. A server utilized in virtual assistant queries and responses may be managing many queries sent from different devices at one time. Efficient use of channel bandwidth, response timing, along with the ability to acquire the most accurate and up to date response are all important factors of consideration in the management of virtual assistant search queries within a communication system offering such searching capability. Systems that have had limited opportunity to take advantage of virtual assistant searching capabilities, such as public safety communication systems, would be able to consider incorporating such search capabilities if improvements were made in the ability to manage the system. Such improvements would benefit not only public safety applications, but any communication system incorporating virtual assistant query capability. 
     Accordingly, there is a need for improving the management of virtual searching capabilities within a communication system. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed invention, and explain various principles and advantages of those embodiments. 
         FIG.  1 A  is a communication system block diagram formed and operating in accordance with some embodiments. 
         FIG.  1 B  is a flowchart for managing verbosity of response in accordance with some embodiments. 
         FIG.  2    is a communication system block diagram formed and operating in accordance with some embodiments. 
         FIG.  3    is a communication exchange diagram in accordance with some embodiments. 
         FIG.  4    is a flowchart for obtaining supplemental information in accordance with some embodiments. 
         FIG.  5    is a flowchart for a timing method in accordance with some embodiments. 
     
    
    
     Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention. 
     The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. 
     DETAILED DESCRIPTION 
     Briefly, there is provided herein an artificial intelligence server providing natural language processing and query response operation incorporated into a communication system. The AI server of the various embodiments responds to queries from one or more radios within the communication system. The various methods utilized by the AI server within the various embodiments serve to minimize disruption in communication, maximize channel usage, and prioritize responses. The AI server operating in accordance with one or more of these methods is beneficial to all types of communication systems, including but not limited to broadband systems, and even extending into broadband half-duplex enabled systems, and narrowband half-duplex systems, to name a few. The benefits of extending the AI server capabilities into half-duplex communication systems, allows public safety radio communication system to advantageously provide search query capability, via virtual searching, not readily available to public safety radio users in the past. 
       FIG.  1 A  shows a communication system  100  comprising a wireless communication device  102 , an artificial intelligence (AI) server  104 , operating within a communications network  106  in accordance with some embodiments. The AI server  104  comprises language processing and response capability to operate as a virtual assistant. Virtual assistants, such as Siri provided by Apple, Inc.® and Google Now provided by Google, Inc.®, are software applications that understand natural language and complete electronic tasks in response to user inputs. The communication device  102  may be any broadband or narrowband device having a microprocessor, transceiver, and audio circuitry, such as a radio, a cell phone or the like, for receiving a verbal query user input for transmission to the AI server  104 . 
     The AI server  104  controls query and response activity in an optimized manner between the AI server  104  and the communication device  102  thereby maintaining an efficient use of channel bandwidth. In accordance with some embodiments, the AI server  104  provides prioritization of responses sent from the AI server  104  to the query, based on verbosity of the response and channel availability within the communication system  100 . The verbose response may be adjusted so as not to exceed available channel bandwidth in a variety of ways. For example, the AI server may manage a verbose response by prioritizing content within the verbose response, and then segmenting the prioritized content into shorter prioritized responses, thereby efficiently filling up available channel bandwidth without exceeding the available channel bandwidth. Content prioritization may further be based on, for example, the length and/or complexity of the verbose response. Thus, different priority factors can be used in segmenting a response. 
     In accordance with some embodiments, AI server  104  may also adjust verbosity of response by forming condensed responses. Adjusting verbosity through the use of a condensed response can be accomplished in many ways, for example by using acronyms instead of complete words, using alternative shorter words, removing extraneous words, and using predetermined codenames, to name a few. The AI server may also take prioritized responses described previously and form condensed responses. The completeness of the information in the condensed response can be adjusted based on the channel availability. For example, condensing a response by removing less important information from the response or sending less important information at a later time when channel availability has improved will enhance overall operational efficiency. Hence, by adjusting verbosity of response, the AI server  104  of the embodiments provides for improved operational efficiency of communication system  100 . 
     For additional efficient channel bandwidth management, the AI server  104  may further provide interrupt capability for responses containing higher priority content. Thus, if communication device  102  is in the midst of playing out information in response to a query pertaining to some general facet of information, the AI server can interrupt the response with a higher priority response. 
     Referring to  FIG.  1 B , there is provided a method  150  of varying information for optimized use of channel availability in a communication system in accordance with the some of the embodiments. The method  150  begins at  152  by receiving a query at an artificial intelligence (AI) server, such as AI server  104 , the AI server having natural language processing and response capability. The query to the AI server may be in the form of verbal queries or a combination of verbal with text query. 
     In accordance with the type of query sent, the AI server in this case generates a verbose response to the query at  154 . Moving to  156 , the AI server determines channel availability for the verbose response, and then adjusts verbosity of response at  158  based on channel availability, wherein channel availability is based on channel bandwidth and channel occupancy. The adjusted response to the query is sent at  160 . Hence, the communication system  100  and method  150  provide an AI server that determines channel availability for a verbose response to a communication device and manages the verbosity of response across available channels within the communication system. 
     In accordance with these method embodiments, the adjusting verbosity of the response may be accomplished in several different ways, including but not limited to: summarizing content of the response to occupy the channel bandwidth, and/or condensing content of the response so as not to exceed available channel bandwidth. The adjusting verbosity of the response may also be accomplished by prioritizing content of the verbose response; and segmenting the prioritized content into shorter segmented responses, thereby freeing up the channel bandwidth between responses. 
     Responses can be interrupted based on priority. For example an adjusted response currently being played out at a device can be interrupted by another response, wherein that other response contains content having higher priority content. 
     Hence, the system  100  and method  150  of the embodiments provide for management of a verbose response with which to efficiently use available channel bandwidth. 
       FIG.  2    is a communication system  200  formed and operating in accordance with some embodiments. Communication system  200  comprises a plurality of communication devices  210 , comprising a plurality of half-duplex radios which may be portable handheld operated radios or mobile vehicular radios. These half-duplex radios communicate (transmit mode) using a push-to-talk (PTT) button over a communications channel to one or more of the remaining radios which listen (receive mode) and are often referred to as two-way radios or PTT radios. The AI server  240  while still providing all of the ability to vary a verbose response as described by the previous embodiments, further provides additional advantages directed to half duplex communication systems. 
     The communication system  200  may be a broadband system having PTT capability, such as enabled via broadband-over-PTT server  280 . The communication system also may be a narrowband system, such as a public safety communications system used by law enforcement, fire rescue, and the like, comprising the plurality of portable and mobile PTT radios  210 . The plurality of radios  210  each comprise microprocessor, transceiver and appropriate, RF and controller circuitry for radio communication operations. 
     In accordance with the embodiments, the artificial intelligence (AI) server  240  is incorporated into the communication system  200  for responding to queries from one or more of the half-duplex radios  210  which have been formed into a talkgroup  220 . The AI server, as previously described is implemented using, a natural language processing system and a spoken artificial query response system. Examples of such processing systems include but are not limited to Siri, OK Google, as well as others known or yet to be developed. 
     During regular radio operation the talkgroup  220  may be assigned when any user in the group wishes to converse with another user in the talkgroup. A vacant radio channel is found automatically by the system  200  and the conversation takes place on that channel. Each radio transceiver, still controlled by its&#39; respective microprocessor, can join in the formation of the talkgroup. Thus, formation of the talkgroup  220  allows a grouping of radios from within the plurality of radios  210  to listen and respond to each other&#39;s communications on a separate dedicated channel without involving the remaining system of radios. 
     In accordance with the embodiments, the AI server  240  takes advantage of the talkgroup formation to respond to queries from members of the talkgroup  220 . In accordance with the embodiments, the AI server intelligently interacts with a floor controller  250  to minimize disruption in communication, maximize channel usage, and prioritize responses amongst members of the talkgroup. The incorporation of artificial intelligence  240  into the communication system  200  advantageously allows the half-duplex radios  220  to be operated as input points to receive verbal queries from a member of the talkgroup, thereby converting the radio device operation into a virtual assistant. 
     The AI server  240  of the embodiments is able to respond to the query making efficient use of channel bandwidth. The application of AI server  240  is advantageously applicable to both narrowband and broadband communication systems having push-to-talk (PTT) capability. 
     In accordance with some embodiments, the AI server  240  intelligently interacts with a floor controller  250 , which provides a plurality of operational controls to minimize disruption of communication, maximize channel usage, and prioritize responses amongst members of the talkgroup  220 . For narrowband devices, such as land mobile radio (LMR) devices, the floor controller  250  may be entirely located within the AI server  240 , and/or embodied as a standalone floor control server. For applications extending the system  200  to broadband devices having PTT capability, such as certain PTT capable 3/4G LTE and Wi-Fi type devices, the floor controller  250  may be adapted to further operate with and/or be integrated as part of a push-to-talk on broadband server  280 . Servers and networks which support broadband PTT operation may include, but are not limited to, a WAVE™5000 server by Motorola Solutions, Inc. 
     In accordance with the following embodiments, system  200  provides the AI server  240  for time controlled query and response optimization and in further embodiments an additional query-to-query response feature which allows for supplemental information to be accessed. 
     Initially referring to the time controlled query and response optimization, in accordance some embodiments the AI server  240  intelligently interacts with the floor controller  250  to provide responses to queries sent out by one or more radios from the one or more talkgroups, and the timing of responses of at least one of the AI queries is prioritized based on context information of the communication system. 
     In accordance with the embodiments, the AI server  240  can be responsive to predetermined verbal inputs or commands indicative of an AI request by a talkgroup member to have the AI join the talkgroup. A query sent to the AI server  240  from a radio in talkgroup  220  can be optimized for timing and prioritization. For example, a first talkgroup radio  216  initiates and sends a verbal request to the floor controller  250  to add artificial intelligence (AI)  240  within the talkgroup  220 . The AI server  240  joins the talkgroup  220  via  219 . By adding the AI into the talkgroup  220 , the AI  240  and floor controller  250  are able to interoperate with the radios of the talkgroup  220  to determine response times  262 , determine a request or assign priority  264 , adjust different floor controls  266 , determine delays in delivery, delays based on confidence levels, and all other control functions related to timing and prioritization that can further enhance the management of the query response system. 
     In accordance with a further query embodiment, talkgroup assignment can also be modified into sub talkgroups based on the query sent from the originating radio. For example, in response to a radio  218  sending a query to which the response is only to be played to two designated radios,  216  and  218 , the AI server  240  responds by assigning the response to sub-talk-group (SUB-T/G)  222 , so that the response is only heard by members  216 ,  218  of the sub-talkgroup  222 . 
     Accordingly, the system  200  allows for specifying, to the AI server  240 , by the radio sending the query, such as radio  216 , a subgroup  222  of talkgroup members  216 ,  218  from talkgroup  220 , to hear the response, and sending the response, by the AI server, to the subgroup  222  of talkgroup members. 
     The AI server  240  responds to other queries from members of the talkgroup  220  within the radio communication system  200 . The AI server intelligently interacts with a floor controller  240  to minimize disruption in communication, maximize channel usage, and prioritize responses amongst members of the talkgroup. The incorporation of artificial intelligence into a public safety radio communication system advantageously provides half-duplex radios with additional verbal and/or text query and response while maintaining regular talkgroup operation. The application of AI is applicable to both narrowband and broadband communication systems having push-to-talk (PTT) capability. 
     Additionally, in a public safety environment, it is important that the AI server  240  provide useful information in response to user queries so as not to inhibit time-critical public safety services (e.g., responding to a distress call, responding to an emergency at a correct location, and the like). Therefore, in some embodiments it may be advantageous to have the AI server  240  alternatively be automatically assigned to every talkgroup upon formation of such Talkgroups within the communication system  200 . 
     In some embodiments, the AI server  240  may insert itself into the talkgroup based on certain keyword triggers. This self-insertion request is particularly advantageous for announcements, events, and the like. For example, if a user of a portable device asks to another member of the talkgroup “When does Main Street close for the holiday parade?” if no response is provided within a certain amount of time by a member of the Talkgroup, then the AI server will self insert into the talkgroup and respond to the query. For example, “Main Street will close between 10 am and noon for the holiday parade.” Additional information may even further advantageously be provided by the AI server  240 , of which an individual user might not be aware, such as: “Detour provided at Second Street.” 
     In accordance with further embodiments, if the AI server  240  is unable to determine a response to a search query after a predetermined time, the AI server can automatically remove itself from the talkgroup, and even redirect the query to another source, if available, such as the dispatcher  230 , multi media  270  or another talkgroup member. The multimedia resource  270  may provide, for example, a streaming video response in response to a redirected query. The dispatcher  240  may be a narrowband dispatcher or a broadband dispatcher. This self-removal of the AI advantageously maintains efficiency of the system  200  by redirecting queries  249 ,  269  to other resources, freeing up the AI server to attend to other queries to which it can provide a response. 
     For embodiments in which the AI  240  redirects the query to another resource to obtain supplemental information, these queries may be redirected, for example, to dispatcher  230  in text format inquiring if the dispatcher has resources to answer the query. The decision to send the query to the dispatcher  230  may be based on contexts factors associated with that dispatcher and knowledge of the working environment of the various radio users of the plurality of radios  210 , and assigned members working in talkgroups within the communication system  200 . Floor control is automatically provided to dispatcher  230  upon confirmation that the dispatcher  230  does indeed have information with which to respond to the query. The dispatcher  230  then sends a verbal response over dispatch radio to the query radio of the talkgroup  220 . 
     In other embodiments to obtain supplemental information, the AI  240  may verbally inquire to other radios of the talkgroup  220  as to whether they have information with which to respond to the query. If a radio member confirms such knowledge, then the AI server  240  can redirect the query to that radio member within the talkgroup  220  along with providing floor control to respond to the query. A response can then be played out from the initiating radio. Depending on the type of query sent, the response may be played out to all or some members of a talkgroup. If the query included a command or instruction to restrict the response to certain talkgroup members (radios  216 ,  218  of the sub-group  222 ), then the response can be limited to those members as previously described. Thus, although it may be important for a response to get to one or more members, and that the group be aware of that situation, it may not be necessary for the entire talkgroup to listen to the response. It can be sufficient for certain members to simply be made aware that a response did occur. 
     In another supplemental information embodiment, the redirected query may be a text query sent to the multimedia resource  270  which may, as a result of the query, generate a streaming video response for transmission back to the initiating query radio  216 . The streaming video may be played out at all radios of sub-talkgroup  222 , which in this example is formed of radios  216  and  218 . If no sub-talkgroup was formed, and no other restrictions were placed, the streaming video can be played out through the radios members of talkgroup  220 . 
     In another supplemental information embodiment, the redirected query may be a text query sent to the dispatcher  230  operating in broadband. The dispatcher  230  can generate a video streaming response for transmission back to the initiating query radio  216 . Hence, a video streaming response to a verbal query that originally initiated at a half duplex radio  216  has been provided, by using the AI server&#39;s ability to redirect queries. 
     In accordance with further embodiments, the control of the timing and the control of the supplemental information are optimized through various floor control operations of communication system  200 . Floor controller  250  of AI server  230  provides floor control operations based on a variety of floor control factors  260 , of which only a few are shown. Floor control operations may be based on the length of an expected response. For example, the AI server  240  can request and/or lock the floor for a predetermined required time to complete a response  262 . 
     Priority of an AI floor request can be dependent on a radio requestor priority. Some radio users, identified by radio user ID, and/or some types of verbal requests may have a higher priority and be responded to prior to others  264 . 
     If a channel is heavily used, the AI server  240  can adjust content and/or depth of a response to fill available floor time  266 . For example, a radio channel which is heavily used may have a response adjusted into a summary response so as not to impact channel usage. 
     In some embodiments, it may be desirable to have an order of response content prioritized and segmented into smaller responses, thereby freeing up channel availability between responses. 
     Priority (and thus capability to interrupt AI) of other talkgroup members can be adjusted based on the applicability of the content to them. For instance, if a member of the talkgroup is listening to a response and determines that the response is no longer relevant to the talkgroups current conditions or needs further detail, that higher priority user can interrupt the response by pressing PTT and verbalizing a new query. For example, if rookie police office sent a query asking for an entire map to be streamed  219  over multimedia  270  of the entire holiday parade route, then a senior office could interrupt that response with a query “does the holiday parade cross Main and Second Street?” 
     Accordingly, by incorporating floor control operations with artificial intelligence, communication systems, such as a public safety communication system, and half duplex radios operating within the system can now advantageously provide talkgroup operation with additional query search capability. 
       FIG.  3    shows an example of a communication exchange diagram in which artificial intelligence is added as a member of the talkgroup in accordance with some of the embodiments. At  310 , a first sub-talkgroup radio  301  sends a verbal request to add artificial intelligence (AI) within the talkgroup. This request is received by group manager  350  (equivalent to floor controller of  FIG.  2   ) at  310 . The request  310  triggers a response from the group manager  350  to add artificial intelligence  340  (AI) to the talkgroup at  312 . By adding the artificial intelligence  340  into the group, the AI  340  and floor manager  350  are able to interoperate with the radios of the talkgroup to determine response times, an increase or decrease in delivery time, determine priority, assign priority, and all other control functions that can further enhance the management of the query response system. 
     A query is sent at  314  to the AI  340  from a second sub-talkgroup radio  304 . A word analysis is performed by the AI  340  to determine a confidence level that the query is intended for the AI. Depending on the confidence level, a request for a delay is sent to the floor  350 , as indicated at  316  where the AI  340  sends a request to the group manager  350  requesting floor time at a particular time, T, and for a predetermined duration time, Td. The group manager  350  then proceeds to grant the floor to the AI  340  at  318 . The AI  340  has already completed the search and is ready to generate and send a response to radios  302  and  301  at  320 , after which time the floor can be removed at  322 . 
     The response from the AI is sent to the query radio  302 , and automatically also sent to the initiator radio  301 , unless otherwise specified by the requestor. These radios are all operating on the same sub-talkgroup channel, so unless there is an instruction to assign or direct the response to a different free channel, then all the radios within the talkgroup will hear the response. 
     Thus, the timeline  300  illustrates has demonstrated some of the timing factors that can be taken into consideration in accordance with the embodiments. 
     Referring to  FIG.  4   , there is shown a method  400  in accordance with a query-to-query embodiment. The query-to-query embodiment allows for supplemental information to be acquired beyond that normally available from the AI server  240  of  FIG.  2   . Beginning at  402  a talkgroup is formed from a plurality of radios followed by initiating a query at one of the radios via the push-to-talk (PTT) button at  404 . 
     The method can further comprise triggering the AI server via an input of the radio prior to initiating the query, thereby allowing the AI server to join the talkgroup. The input to the radio (the trigger) can be for example, a PTT verbal pre-command, and/or a non-PTT out of band text message, depending on the type of system. The search query can follow the pre-command. For example, the pre-command may be the spoken word “Einstein”, followed by the search query “How many registered firearms are located at this location?” 
     The method  400  proceeds by transmitting the search query at  406  to the AI artificial intelligence server, the AI server being the AI server  240  of  FIG.  2    having natural language processing and response capability as previously described. 
     At  408 , in accordance with the query-to-query embodiment, in response to receiving the query, the AI server determines that an alternate response resource is capable of responding to the query or providing a better response. The AI server  240  then requests that the floor controller  250  give the talkgroup floor to the alternate response resource at  410 , such as dispatcher  230  or another radio of the talkgroup of  FIG.  2   . The verbal query is redirected from the AI server  240  via the floor controller to alternate resource at  412 . In some embodiments it may be useful for the AI server to convert the verbal query to a text query prior to redirecting the query to the alternate resource. For example, verbal queries converted to text format can be redirected to the dispatcher  230  from the AI server  240 . Text format is preferred so as not to disrupt dispatch radio audio communications which may be on-going with other users. An advantage of redirecting a query request to a dispatcher  230  is that this dispatch resource can research the text version of the query and return, via dispatch transmit, a verbal researched response to the initiating query radio. The response is thus provided straight from the dispatcher  230  to the initiating query radio  216  without having to go back through the AI server  240 , further enhancing efficiency of operation. 
     If the AI server does not know the answer to a query or determines that an alternate source would have a better response, the AI server can also redirect the query to that source. For example another member of the talkgroup or a multimedia source  270 . 
     The floor controller  250  automatically provides the floor to the alternative resource when the AI server  240  seeks additional information to the query or redirects the query. Utilizing alternate resources further provides improved use of channel bandwidth and efficiency in managing the query response portion of the communication system. 
     Moving to  FIG.  5   , a method  500  is shown which summarizes the timing embodiments that have been described in  FIG.  2   . Method  500  begins with the formation of a talkgroup at  502 , followed by sending a query from a PTT radio device at  504 . In some embodiments, AI server may automatically insert and later remove itself from the talkgroup upon sending the response. In some embodiments, the AI server  240  and a floor controller  250  may automatically be assigned upon formation of one or more talkgroups within a communication system. In yet still other embodiments, a radio member of the talkgroup sends an AI request, as was described in  FIG.  3   , for the AI server to join the talkgroup. 
     In accordance with some embodiments, controlling timing and prioritization of the response sent to the PTT device by the AI server will greatly improve the overall management of the query response system. For example, the AI server  240  of  FIG.  2    waiting to generate and send the response, until after a predetermined response wait time has expired, provides members of the talkgroup the opportunity to provide relevant information and make other members aware of it. Using the expiration wait time provides the assurance that a response to the query, from the AI server, can still be received when no member of the talkgroup is able to respond. The wait time can be determined based on analyzing the query to provide a confidence level that the query was intended for the AI  240 , radio channel bandwidth and the AI server&#39;s floor control availability, or any combination of, thereby providing efficient control of channel usage. Analyzing the query to determine the confidence level may include name searching. For example, if the AI is named Einstein, and the query includes the name “Einstein” the confidence will be very high that this is a query directed to the AI  240 . When the confidence is high, the AI  240  can respond immediately and hence have a zero wait time. However, if the query starts with the name of another user in the talkgroup, it is likely that the query was not directed to the AI server. In this case, the AI waits the maximum wait time before responding. If during the wait time, the AI determines that the requestee has responded, then the AI  240  will not respond unless it has additional information that would be useful. 
     The method  500  can be further enhanced if desired by applying priority. For example, by determining a priority for sending a response from the AI server  240 , and sending the response based on the priority. Prioritizing of a radio query, and its&#39; subsequent response, may be based on context factors, such as radio user identity, rank of requestor, rank of other members of the talkgroup, number of members in the talkgroup. For example, a Fire Chief, a Police Officer, a Detective, to name a few. Prioritizing of the radio query may also be based on context factors associated with an incident scene of the query radio, such as incident scene type and public safety information pertaining to the incident scene. For example, traffic accident, airplane explosion, train derailment, robbery, home invasion, are just a few examples. The prioritizing of the radio query may be based on verbal query words having predetermined keyword priority rankings, such as “FIRE”, “TOXIC”, “EMERGENCY”, “POISON” and “EXPLOSION” and the like. 
     In accordance with some embodiments method  500  may further adjust a response wait time, by the AI server based on a confidence level that the verbal query was intended for the AI server or for members of the talkgroup. 
     In accordance with some embodiments method  500  may further determine an anticipated response time. Depending on the type of priority, it may further be desired to lock the floor control for the anticipated response time. For example, in mission critical events, such as fire rescue, the generation and transmission of a high priority response is critical when other members of a talkgroup have not been able to answer a query and the confidence level is fairly high that the intended recipient of the query is now the AI server  240 . 
     Method  500  can be further enhanced by re-prioritizing automated verbal responses to the query, from the AI server  240 , in response to changes in incident scene context determined by the AI server, wherein the incident scene context is monitored as part of the query. 
     The following Table provides a few examples Confidence levels, Channel Occupancy, AI priority, wait time, and response time needed. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                   
               
               
                   
                   
                 Conf. 
                   
                   
                   
                 Required 
                   
               
               
                   
                   
                 Level that 
                   
                   
                   
                 Response 
                 Effect 
               
               
                   
                   
                 Query is 
                 CH 
                 AI 
                   
                 Length 
                 Timeline/Verbosity/ 
               
               
                 Req. 
                   
                 for AI 
                 AVL 
                 Priority 
                 Wait 
                 Time 
                 Query-to-Query 
               
               
                 Type 
                 Query 
                 (H/M/L) 
                 (H/M/L) 
                 (H/M/L) 
                 Time 
                 (seconds) 
                 Information 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Fire 
                 Einstein, 
                 H 
                 L 
                 H 
                 0 
                 30 
                 Timeline 
               
               
                 Chief 
                 What 
                   
                   
                   
                   
                   
                 AI requests CH. availability 
               
               
                   
                 flammable 
                   
                   
                   
                   
                   
                 for 30 seconds with high 
               
               
                   
                 materials are 
                   
                   
                   
                   
                   
                 priority to send response 
               
               
                   
                 in this 
                   
                   
                   
                   
                   
                 “Chemicals, x, y, z” 
               
               
                   
                 building? 
                   
                   
                   
                   
                   
                   
               
               
                 Police 
                 Who are 
                 M 
                 L 
                 M 
                 10 
                 10 
                 Timeline 
               
               
                 Officer 
                 members 
                   
                   
                   
                   
                   
                 AI waits 10 seconds to see if 
               
               
                   
                 ABC Gang? 
                   
                   
                   
                   
                   
                 anyone responds, if not, then 
               
               
                   
                   
                   
                   
                   
                   
                   
                 requests floor for 5 seconds 
               
               
                   
                   
                   
                   
                   
                   
                   
                 to send response “member 
               
               
                   
                   
                   
                   
                   
                   
                   
                 names Lee, Barb” 
               
               
                 Detective 
                 What plate 
                 L 
                 L 
                 M 
                 10 
                 10 
                 Query-Query info. &amp; 
               
               
                   
                 numbers on 
                   
                   
                   
                   
                   
                 Timeline 
               
               
                   
                 red corvette? 
                   
                   
                   
                   
                   
                 AI does not have answer, but 
               
               
                   
                   
                   
                   
                   
                   
                   
                 determines that Officer 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Smith should know answer, 
               
               
                   
                   
                   
                   
                   
                   
                   
                 AI waits for officer to 
               
               
                   
                   
                   
                   
                   
                   
                   
                 answer, if no response then 
               
               
                   
                   
                   
                   
                   
                   
                   
                 requests floor. “Officer 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Smith, please provide license 
               
               
                   
                   
                   
                   
                   
                   
                   
                 plate #s from your position.” 
               
               
                   
                   
                   
                   
                   
                   
                   
                 AI transfers floor control to 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Officer Smith 
               
               
                   
               
            
           
         
       
     
     The Table is meant to provide non-limiting, examples of just a few scenarios in which the various embodiments be applied. 
     Accordingly, there has been provided a communication system incorporating artificial intelligence and methods for controlling an AI server within the system. A method for varying verbosity of response, a method for determining timing of a response, and a method for obtaining supplemental information for a response have all been provided. The system and methods have provided for optimized usage of channel bandwidth, improved timing, and redirection of query/response for more accurate information acquisition. Improved management of virtual assistant search queries and responses can now be obtained using an artificial intelligence (AI) server managed in accordance with the described methods provided by the various embodiments. 
     In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings. 
     The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of any or all the claims. The invention is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued. 
     Moreover in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially”, “essentially”, “approximately”, “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed. 
     It will be appreciated that some embodiments may be comprised of one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein. Alternatively, some or all functions could be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches could be used. 
     Moreover, an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation. 
     The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.