Patent Publication Number: US-10769185-B2

Title: Answer change notifications based on changes to user profile information

Description:
BACKGROUND 
     The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for providing notifications when answers to previously submitted questions may change due to changes in a user&#39;s profile information. 
     With the increased usage of computing networks, such as the Internet, humans are currently inundated and overwhelmed with the amount of information available to them from various structured and unstructured sources. However, information gaps abound as users try to piece together what they can find that they believe to be relevant during searches for information on various subjects. To assist with such searches, recent research has been directed to generating Question and Answer (QA) systems which may take an input question, analyze it, and return results indicative of the most probable answer to the input question. QA systems provide automated mechanisms for searching through large sets of sources of content, e.g., electronic documents, and analyze them with regard to an input question to determine an answer to the question and a confidence measure as to how accurate an answer is for answering the input question. 
     Examples, of QA systems are Siri® from Apple®, Cortana® from Microsoft®, and question answering pipeline of the IBM Watson™ cognitive system available from International Business Machines (IBM®) Corporation of Armonk, N.Y. The IBM Watson™ system is an application of advanced natural language processing, information retrieval, knowledge representation and reasoning, and machine learning technologies to the field of open domain question answering. The IBM Watson™ system is built on IBM&#39;s DeepQA™ technology used for hypothesis generation, massive evidence gathering, analysis, and scoring. DeepQA™ takes an input question, analyzes it, decomposes the question into constituent parts, generates one or more hypothesis based on the decomposed question and results of a primary search of answer sources, performs hypothesis and evidence scoring based on a retrieval of evidence from evidence sources, performs synthesis of the one or more hypothesis, and based on trained models, performs a final merging and ranking to output an answer to the input question along with a confidence measure. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described herein in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     In one illustrative embodiment, a method, in a data processing system comprising a processor and a memory that operate to implement an answer change notification system is provided. The method comprises receiving, by the answer change notification system, a change operation to change a portion of a user profile data structure associated with a user and identifying, by the answer change notification system, a first entry in a question and answer (QA) log data structure, corresponding to the user, having an indicator identifying a previous answer of the first entry as being dependent upon information in the user profile data structure. The method further comprises resubmitting, by the answer change notification system, a question of the first entry to a question and answer (QA) system to generate a new answer to the question in response to identifying the entry. The method also comprises receiving, by the answer change notification system, the new answer from the QA system and outputting, by the answer change notification system, to a client device associated with the user, a notification identifying the new answer to the question in response to receiving the new answer. 
     In other illustrative embodiments, a computer program product comprising a computer useable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment. 
     In yet another illustrative embodiment, a system/apparatus is provided. The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment. 
     These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The invention, as well as a preferred mode of use and further objectives and advantages thereof, will best be understood by reference to the following detailed description of illustrative embodiments when read in conjunction with the accompanying drawings, wherein: 
         FIG. 1  depicts a schematic diagram of one illustrative embodiment of a question/answer creation (QA) system in a computer network; 
         FIG. 2  is a block diagram of an example data processing system in which aspects of the illustrative embodiments are implemented; 
         FIG. 3  illustrates a cognitive system QA pipeline for processing an input question with which mechanisms for a cognitive reminder notification system are utilized in accordance with one illustrative embodiment; 
         FIG. 4A  illustrates an example scenario in which an answer to a question has been generated with a subsequent change to the answer being detected prior to the reminder notification being triggered in accordance with one illustrative embodiment; 
         FIG. 4B  illustrates an example scenario in which previous user history of activity is used to calculate a reminder notification timeframe in accordance with one illustrative embodiment; 
         FIG. 4C  illustrates an example scenario in which previous user history of activity as well as breaking down a task into sub-tasks is utilized to calculate a reminder notification timeframe in accordance with one illustrative embodiment; 
         FIG. 5  is a flowchart outlining an example operation for providing an answer reminder notification in accordance with one illustrative embodiment; 
         FIG. 6  is a flowchart outlining an example operation for checking active entries in a reminder notification database in accordance with one illustrative embodiment; 
         FIG. 7  is a flowchart outlining an example operation for calculating a timeframe for scheduling a reminder notification in accordance with one illustrative embodiment; 
         FIG. 8  is a flowchart outlining an example operation for answering an input question in which answers are logged and flagged as to whether they are dependent upon information in a user profile in accordance with one illustrative embodiment; and 
         FIG. 9  is a flowchart outlining an example operation for performing an answer change notification operation in accordance with one illustrative embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The illustrative embodiments provide mechanisms for providing answer change notifications in conjunction with a natural language processing system, such as a natural language search engine, a Question and Answer (QA) system, information retrieval system, or the like. For purposes of the following description, it will be assumed that the natural language processing system is a QA system, such as the IBM Watson™ QA system available from International Business Machines (IBM) Corporation of Armonk, N.Y. It should be appreciated that this is only an example and is not intended to state or imply any limitation as to the types of natural language processing systems with which the mechanisms of the illustrative embodiments may be utilized. To the contrary, any system that retrieves information based on a natural language query or request may make use of the mechanisms of the illustrative embodiments to generate reminders and notifications. 
     Many times, users search for information or ask questions of a QA system and receive results/answers to the search request or question that are not immediately needed or useable by the user. This is generally because the user is not aware of the results that will be returned prior to getting the results of the search request or question processing, otherwise the user would not need to submit the search request or question in the first place. For example, a user may, in August, ask the question “When should I plant my rose bushes?” and the QA system may respond with an answer of “late February to early April.” While this answer is correct, not knowing the time frame of the answer that was going to be returned, the answer is not immediately of use to the user since the time frame at which the question was asked is in August, at least 6 months prior to the time frame of the answer to the question. In such cases, the user can either try to remember the answer until such time as the answer is useable, or the user may try to remember, at a better time, to re-ask the QA system the same question. In either case, the burden is placed on the user to take the initiative of determining when the answer will be useable to the user and thus, how long the user must either remember the information or wait to re-ask the question. 
     In addition, issues may arise when a user submits a search request or question and obtains a result/answer (hereafter assumed to be an answer to a question posed to a QA system) that is not immediately useable and this answer changes before the timeframe when the answer is useable. For example, a user, in June of 2014, may ask “When does the next AIX operating system release?” and the answer may be “Oct. 27, 2015.” Between June 2014 and Oct. 27, 2015, the release date of the AIX operating system may be moved up, or moved back, and thus, the original answer to the question may no longer be correct. Thus, if the user relies on his/her own memory to either remember the answer or remember a future time to resubmit the question, the user may operate under stale information. 
     As a further consideration, search results in response to search requests, and/or answers to questions submitted to a QA system, may sometimes be dependent upon personal information about the user that submits the search request/question or is a focus of the search request/question. For example, an input natural language question may be submitted to a QA system which generates an answer to the question that is partially based on the location of the user as specified in a location field of a user&#39;s profile data structure. For example, the question “when should I fertilize my lawn?” may have a corresponding answer of “April” generated by the QA system at least partially based on a user&#39;s profile specifying in an address, location, or other similar field that the user live is Pennsylvania and other evidential information obtained from a corpus indicates that the grass growing season for Pennsylvanian is April. Should the user&#39;s location change, the answer to this question may change as well. 
     Furthermore, it is recognized that individual users have different individual reminder/notification characteristics that indicate when, what degree of difference in an answer, and how quickly a user may act upon information that the user is provided. Thus, different users may require different times from a future time point at which reminders/notifications should be provided to those users depending upon their individual characteristics. Moreover, different users may have different desired trigger conditions for receiving notifications, such as how much of a difference in answers is needed to trigger a notification and the basis for the difference in the answers that is considered significant to the user to be notified of a change. 
     In one aspect of the illustrative embodiments, the illustrative embodiments provide mechanisms for determining when an answer to a question input by a user includes a temporal characteristic that indicates that the answer is possibly not immediately useable to the user. In response to such a determination, the user may be presented with an option to schedule a future reminder notification to be sent to the user when the answer to the question is within a timeframe that the answer becomes more immediately useable by the user. Alternatively, this reminder notification may be automatically generated and stored for the user. The scheduling of the reminder notification may take a variety of forms. 
     The illustrative embodiments, when scheduling a reminder notification, comprise mechanisms for determining the best time to notify the user of the answer to their previously submitted question. In so doing, the mechanisms of the illustrative embodiments provide valuable input at the time needed. The times at which to schedule reminder notifications may be determined based on temporal characteristics of the answer, temporal characteristics of the question, and temporal characteristics associated with the user that submitted the input question. Based on a combination of these various temporal characteristics, the cognitive reminder notification mechanisms of the illustrative embodiments determine a best time to schedule the future reminder notification for output to the user. 
     The temporal characteristics of the input question, answer, and user may be determined in a variety of ways as discussed hereafter. In general, the temporal characteristics of the input question may be identified through natural language processing techniques that operate on the input question and identify time based keywords or phrases, concept relationships associated with time in the language of the input question, or the like. For example, a database of keywords and phrases may be established and used by the natural language processing mechanisms of the illustrative embodiments to determine if the input question itself specifies a time, e.g., “What day in September is labor day?” or “What month has the highest flu incidents?” Other semantic and syntactic analyses may also be performed to identify whether the question itself specifies a time. For example, lexical answer type (LAT) and focus of the question may be analyzed to identify temporal characteristics. 
     In addition to analyzing the question to determine if it specifically identifies a time characteristic, the mechanisms of the illustrative embodiments may further analyze the question to determine if, while not explicitly specifying a time characteristic, the question contains some implicit aspect of time that will be reflected in the answer, e.g., “When does it snow in NY?” or “What is the best time to go skiing in Colorado?” In these examples, the keyword “when”, while not explicitly specifying a time, such as in the case of the keywords “month” or “day” in the previous examples, is a term recognized as asking for a time answer. In addition, the keyword “time” is recognized as being related to an implicit time aspect even though a specific time is not specified by this keyword. 
     Implicit timing aspects may further be identified via known relationships between keywords or key phrases and various other keywords, key phrases, or concepts. For example, the term “skiing” in the above example may be associated with a particular type of season, e.g., the winter season, which is a time frame. Moreover, the keyword “season” may be known by calendar indication of the season start date and/or based on the location/typical season knowledge. Thus, multiple levels of chains of keywords, key phrases, and concepts may be identified within the input question that may be indicative of the input question soliciting an answer that is time based. 
     The temporal characteristics of the answer to the input may be determined in a similar manner by analyzing keywords and key phrases in the answer that is generated by the QA system and/or analyzing the answer for timing aspects. Moreover, the lexical answer type (LAT) of the answer may be analyzed to determine the type of answer provided and whether that type of answer corresponds to a temporal characteristic type, e.g., a date/time LAT. Some answers may have multiple parts, e.g., steps in a process, in which case each part may have an associated temporal characteristic or timing aspect that may be identified via analysis of the answer using the mechanisms of the illustrative embodiments. 
     Various mechanisms may be employed to assist in the analysis of the input question and the resulting answer, one example being described in commonly owned and co-pending U.S. patent application Ser. No. 14/297,335, entitled “Determining Temporal Categories for a Domain of Content for Natural Language Processing,” filed Jun. 5, 2014. While this mechanism is primarily directed to determining the correct timeframe for a particular domain of content based on natural language processing of the content in the domain, the same mechanisms may be applied to a question and/or answer to determine temporal characteristics of the question and/or answer. Moreover, the mechanisms of the co-pending application may be used to determine a temporal characteristic of the domain associated with an input question and this temporal characteristic may be utilized when determining the scheduling of a future reminder notification as described hereafter. 
     The above mechanisms are utilized to determine whether or not the question and/or answer have temporal characteristics that indicate that the user may wish to be reminded of the answer to the input question at a future time. In addition to the results of these determinations, the mechanisms of the illustrative embodiments further make note of the timeframe at which the question was input, e.g., the date/time of the submission of the input question. In some illustrative embodiments, the input question may be correlated with other similar questions and the timeframes of the submissions of the similar questions may be identified as well to assist in the scheduling of the future reminder notifications. 
     Assuming that the result of the above determinations is that the question/answer combination has some temporal characteristics, the mechanisms of the illustrative embodiments determine an appropriate time to schedule a reminder notification for a future time when the answer is likely more useable by the user. In one illustrative embodiment, the answer to the input questions is output to the user that submitted the input question, via a client machine used by the user for example, along with a request as to whether the user wishes to schedule a future reminder notification of the answer to the input question. If the user selects to not schedule a future reminder, the operation terminates with the output of the answer to the user. If the user selects to schedule a future reminder, the mechanism of the illustrative embodiments determine a best appropriate time in the future to schedule the reminder notification for output to the user and stores a reminder record for use by the cognitive reminder notification system to output a future reminder notification. 
     In one illustrative embodiment a default timeframe is selected for reminder notification scheduling. The default timeframe is a timeframe prior to the time specified in the answer, at which time the reminder notification will be output to the user. For example, a default timeframe may be one week prior to the timeframe of the answer. Thus, for example, if the answer involves the date August 4 th , then a reminder notification of the answer may be scheduled to be output one week prior to August 4 th . The default timeframe may be an arbitrarily selected timeframe, a timeframe corresponding to a temporal category of the domain associated with the question/answer as determined using the mechanisms of co-pending U.S. patent application Ser. No. 14/297,335, a default timeframe specified in a user&#39;s personal profile, a timeframe determined from timeframes associated with similarly submitted questions, or the like. 
     Thus, if the user specifies in their user personal profile data structure that they prefer to receive reminder notifications one week before the date associated with the answer of the input question, then this one week default timeframe will be used to schedule a future reminder notification. Similarly, if, through analysis of the content of a domain associated with the question/answer a particular temporal category having a corresponding temporal characteristic is determined, then the temporal characteristic may be utilized as the default timeframe for scheduling the future reminder notification. 
     Moreover, if the input question is similar to other input questions provided by the same or other users, reminder notification scheduling temporal characteristics associated with the similar questions may be utilized to determine a timeframe for scheduling the reminder notification for the current question. For example, the mean of the reminder notification timeframes of similar questions may be utilized to determine a timeframe for scheduling the reminder notification of the current answer. 
     In the above illustrative embodiments, the reminder notification timeframe is not tied to specific characteristics of the user and the user&#39;s personal association with the answer. To the contrary, the reminder notification is referred to as a “default” timeframe because it is either independent of the user&#39;s characteristics or independent of the characteristics of the answer. That is, a default timeframe may be the same for all answers regardless of the domain, lexical answer type, or other specific characteristics of the particular answer, e.g., a default timeframe in the user&#39;s profile may specify a one-week timeframe regardless of the domain or characteristics of the answer. A default timeframe may be specified by the analysis of the content of the domain such that all questions/answers associated with a domain may have the same default timeframe specific to that domain, but independent of the particular characteristics of the user and/or answer to the specific question submitted by the user. Moreover, a default timeframe may be determined by analysis of similar questions regardless of the particular characteristics of the user that submitted the current question and/or the answer returned. 
     In addition to, or in replacement of, these default timeframes for scheduling the reminder notification, the mechanisms of the illustrative embodiments may further analyze the user&#39;s personal profile to determine the most appropriate reminder notification timeframe for that particular user. Various characteristics of the user profile, e.g., age of the user, family information, hobbies of the user, medical history of the user, etc. may be analyzed based on their correlation to the concepts in the question/answer. For example, if the user asks the question “When should I start teaching my child to do math?” and the answer may be “at 5 years old.” In looking at the question and answer, the mechanisms of the illustrative embodiments may determine that the question and answer has to do with the age of a child. By analyzing the user&#39;s profile information it may be determined from relationship or family information that the user has a three year old child whose birthday is Aug. 14, 2011. It may further be determined that for this domain, a default timeframe is approximately two months. As a result, a reminder notification may be scheduled for sending to the user when the user&#39;s child will be 4 years and 10 months old, i.e. Jun. 14, 2016. 
     Moreover, mechanisms are provided for analyzing evidence of the behavior of the user with regard to activities of the user that may be influential on the timeframe for scheduling the reminder notification. For example, tasks may be associated with an answer to the input question. These tasks, or similar tasks, may have been performed by the user in the past, or by other users in the past if the current user has not performed such tasks. Information about such tasks and the performance of such tasks may be obtained from various trusted sources about the user including electronic communications to/from the user via electronic mail, social network websites, newsgroups, instant messaging applications, or any other electronic messaging mechanism. Such information may also be obtained from electronic calendar applications associated with the user&#39;s client computing device, electronic “to-do” or notes list data structures, scheduling information and treatment information in medical records associated with the user, project management tool information associated with the user, previous reminder notification scheduling requested by the user for similar tasks, and the like. 
     The information from the various trusted sources may be analyzed to generate a concept map of the task that breaks the task into one or more sub-tasks to be completed and concepts associated with the task/sub-tasks. Each such sub-task may have an associated temporal aspect to them based on the analyzed information about the user&#39;s previous activity. The temporal aspects of the various sub-tasks may be added and/or merged together to generate a cumulative timeframe for the scheduling of the reminder notification. The reminder notification may then be set based on the cumulative timeframe. 
     It should be appreciated that regardless of which embodiment, or combination of embodiments, implemented, the result is an entry in a cognitive reminder notification database that stores information about the user, including contact information for sending the reminder notification, the question asked, the answer generated, the timeframe used to generate the scheduled reminder notification and the scheduled reminder notification time for the scheduled reminder notification which operates as a trigger for transmitting the reminder notification to the user. Thus, periodically, the cognitive reminder notification system may traverse the currently active reminder notification entries to determine if any of the entries are triggered by the current date/time. If so, the question may be resubmitted to the QA system to determine if the answer to the question has changed or not and thus, a most up-to-date answer is returned as part of the reminder notification. Alternatively, the original answer in the entry may simply be returned without checking for an update to the answer. The reminder notification message may be transmitted to the user utilizing the user&#39;s contact information which may comprise any generally known or later developed contact information including electronic mail address, client computing device IP address, instant messaging address, telephone number for text/audio communication, or the like. Once the reminder notification has been sent, the entry in the database may be marked as no longer active at which point it may be overwritten. In some cases, an option may be presented to the user to reschedule the reminder notification in which case the reminder may be postponed to a later time specified by the user, e.g., postpone for 5 days, postpone for 1 month, etc., with the entry in the database having its scheduled reminder notification time updated accordingly. 
     In some illustrative embodiments, the cognitive reminder notification system of the illustrative embodiments may periodically check all of the active entries in the cognitive reminder notification database to determine if the answers in those entries have changed or not since the original answer was generated. That is, between the time that the answer was generated and provided to the user, and when the reminder notification is sent to the user, e.g., the scheduled reminder notification time, the system may periodically check to see if the answer has changed. This check may comprise resubmitting the original question to the QA system and obtaining a new answer to the question. The new answer may be compared to the original answer to determine if there is a significant change in the answer. If there is a significant change in the answer, then a notification may be sent to the user at that time rather than waiting for the scheduled reminder notification time to be reached. The notification may specify that a change in the answer has occurred and request the user to confirm whether they wish to update the reminder notification for this answer, cancel the original reminder notification, or not make any change to the reminder notification. Based on the user input, the reminder notification timeframe and scheduled reminder notification time may be updated based on analysis of this new answer, the reminder notification as a whole may be marked as no longer active and thus, able to be overwritten, or the original reminder notification may be maintained. In any case, the new answer may be stored in replacement of the original answer and/or in conjunction with the original answer, potentially with corresponding dates/times that the original answer and the new answer were generated. In this way, when a reminder notification is sent to the user, a history of the change in the answer may be output as well indicating what the original answer to the question was, and each of the changes to the answer from the time point of the original answer to the time point of the reminder notification. 
     Thus, the illustrative embodiments provide mechanisms for automatically scheduling and sending notifications of previously obtained answers to questions based on a cognitively determined future timing of the notification. The future timing of the notification is cognitively determined based on a determined notification timeframe which is dependent upon the particular domain of the question and/or answer, a timeframe inferred from the question and/or answer, and/or other factors associated with the user, other users, similar questions/answers, and the like, as mentioned above and discussed in greater detail hereafter. Between the time that the question was originally submitted to the QA system and the answer obtained, and the time that the notification is scheduled for sending to the user, changes to the answer of the question may be monitor in a continuous, periodic, or event based manner and if a significant change to the answer is determined to have occurred, a notification can be sent to the user automatically. The mechanisms of the illustrative embodiments greatly aid users of QA systems by being able to provide automatic reminder notifications to these users at the most beneficial time for the particular user such that the answers obtained have the most usefulness for the user. 
     In a further aspect of the illustrative embodiments, the illustrative embodiments provide mechanisms for generating results/answers based on user profile information and logging these results/answers as well as the original search request/question and the fields of the user profile information that are the evidential basis for the results/answers that were generated. Thereafter, if a change in user profile information occurs, this change triggers a check of the previously logged results/answers to determine which, if any, have an evidential basis that corresponding to the fields changed in the user profile information. If the evidential basis matches one of the fields changed in the user profile, then a resubmission of the corresponding input question may be triggered to generate a new answer that takes into consideration the new status of the fields changed in the user profile information. 
     As an example, consider a situation where a user asks a question of a QA system of the type “When should I fertilize my lawn?” The QA system may utilize the user&#39;s profile information as evidence for determining a correct answer to the input question since the question is referring to the user himself/herself, i.e. when should “I” fertilize my lawn? As such, the QA system may utilize a home location field of the user profile information to indicate where the user lives as evidence of the correct answer, since the timing of the fertilization of a lawn may be dependent upon the user&#39;s home location. As a result, an answer consistent with the user&#39;s current home location may be generated, e.g., an answer of “April” based on the user&#39;s current location being Pennsylvania and other evidential passages from a corpus of information that indicates that the grass growing season starts in April for Pennsylvania. This question/answer pair, a flag indicating that the answer is based on a user profile, indications of which user profile information fields are a basis of the answer, and other evidential passage information may be logged in a question/answer log data structure corresponding to the user profile data structure. 
     If the user then moves to North Carolina and updates their home location in their user profile information, this change triggers a check of the question/answer log data structure to determine which, if any, of the entries for question/answer pairs in the question/answer log data structure are dependent upon the user profile information. From this subset of entries in the question/answer log data structure, each entry is checked to determine if the fields of the user profile information that are an evidential basis for the corresponding answer match a field that has been changed in the user profile information, e.g., the home location field which is updated in the example above. If there is a match, then the corresponding input question is resubmitted to the QA system such that a new answer, which may be the same or different from the previous answer, is generated. A notification is then transmitted to the user&#39;s computing device informing them of the update to the answer. The notification may specify the input question, the previous and new answer, and the reason for the update in the answer, e.g., a change to the home location field of the user profile information. In some embodiments, this notification may be sent only in response to a determination that the answer has changed by a significant amount. Significance of the change can be determined in a mathematical manner when the answer is numerical, a lexical difference between the previous and new answer, or any other mechanism or determining changes in alphanumeric and/or graphical, audible, or other media content associated with the user profile. 
     For example, if the user profile comprises a graphical element that is used by a QA system as part of an answer generation, and the graphical element has changed in the user profile, known or later developed graphical content change detection mechanisms may determine that a change has occurred and that change, if flagged as a basis for a previously generated answer, may initiate operations of the illustrative embodiments for generating notifications to the user of a change in the previously generated answer. For example, an image change in a user profile may be indicative of changes to the state of a user&#39;s family, e.g., a new baby being born, a previously unknown child or spouse being depicted, or the like. 
     It should be appreciated that the mechanisms for determining if an answer to a previously submitted input question should be updated based on a change to a user&#39;s profile information may be used separately or in conjunction with the other mechanisms of the illustrative embodiments described herein with regard to answers having temporal features. Thus, for example, the mechanisms of the illustrative embodiments may utilize the temporal feature based mechanisms described above to determine if the answer has a temporal aspect such that a user may need to have a reminder notification at a later time of the answer that was previously provided, such as at a time when the answer will be more useable by the user. When the answer is generated, in addition to determining if there is a temporal aspect to the answer, the illustrative embodiments may further determine if the answer is at least partially generated based on content of the user&#39;s profile information, e.g., at least one field of the user profile information is an evidential basis for the answer. In the meantime between when the answer was generated and when a reminder notification is to be output, the mechanisms of the illustrative embodiments may continue to monitor for changes to the user profile information to determine if a change to the user profile information should trigger a resubmission of the input question to the QA system for generating a new answer, as described above. Thus, some illustrative embodiments may utilize a combination of features from the above described mechanisms to achieve a more comprehensive answer reminder and change notification experience for users of QA systems. 
     Before beginning the discussion of the various aspects of the illustrative embodiments in more detail, it should first be appreciated that throughout this description the term “mechanism” will be used to refer to elements of the present invention that perform various operations, functions, and the like. A “mechanism,” as the term is used herein, may be an implementation of the functions or aspects of the illustrative embodiments in the form of an apparatus, a procedure, or a computer program product. In the case of a procedure, the procedure is implemented by one or more devices, apparatus, computers, data processing systems, or the like. In the case of a computer program product, the logic represented by computer code or instructions embodied in or on the computer program product is executed by one or more hardware devices in order to implement the functionality or perform the operations associated with the specific “mechanism.” Thus, the mechanisms described herein may be implemented as specialized hardware, software executing on general purpose hardware, software instructions stored on a medium such that the instructions are readily executable by specialized or general purpose hardware, a procedure or method for executing the functions, or a combination of any of the above. 
     The present description and claims may make use of the terms “a”, “at least one of”, and “one or more of” with regard to particular features and elements of the illustrative embodiments. It should be appreciated that these terms and phrases are intended to state that there is at least one of the particular feature or element present in the particular illustrative embodiment, but that more than one can also be present. That is, these terms/phrases are not intended to limit the description or claims to a single feature/element being present or require that a plurality of such features/elements be present. To the contrary, these terms/phrases only require at least a single feature/element with the possibility of a plurality of such features/elements being within the scope of the description and claims. 
     In addition, it should be appreciated that the following description uses a plurality of various examples for various elements of the illustrative embodiments to further illustrate example implementations of the illustrative embodiments and to aid in the understanding of the mechanisms of the illustrative embodiments. These examples intended to be non-limiting and are not exhaustive of the various possibilities for implementing the mechanisms of the illustrative embodiments. It will be apparent to those of ordinary skill in the art in view of the present description that there are many other alternative implementations for these various elements that may be utilized in addition to, or in replacement of, the examples provided herein without departing from the spirit and scope of the present invention. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The illustrative embodiments may be utilized in many different types of data processing environments. In order to provide a context for the description of the specific elements and functionality of the illustrative embodiments,  FIGS. 1-3  are provided hereafter as example environments in which aspects of the illustrative embodiments may be implemented. It should be appreciated that  FIGS. 1-3  are only examples and are not intended to assert or imply any limitation with regard to the environments in which aspects or embodiments of the present invention may be implemented. Many modifications to the depicted environments may be made without departing from the spirit and scope of the present invention. 
       FIGS. 1-3  are directed to describing an example cognitive system implementing a Question Answering (QA) pipeline (also referred to as a Question/Answer pipeline or Question and Answer pipeline), methodology, and computer program product with which the mechanisms of the illustrative embodiments are implemented. As will be discussed in greater detail hereafter, the illustrative embodiments are integrated in, augment, and extend the functionality of these QA mechanisms of the cognitive system with regard to providing automatic reminder notifications to users of answers to previously submitted questions as well as providing checks of answers to previous questions to determine if the answers have changed between the time point at which the question was answered and the time point when the answer will be most beneficial to the user. 
     Thus, it is important to first have an understanding of how question and answer creation in a cognitive system implementing a QA pipeline is implemented before describing how the mechanisms of the illustrative embodiments are integrated in and augment such QA mechanisms. It should be appreciated that the QA mechanisms described in  FIGS. 1-3  are only examples and are not intended to state or imply any limitation with regard to the type of QA mechanisms with which the illustrative embodiments are implemented. Many modifications to the example cognitive system shown in  FIGS. 1-3  may be implemented in various embodiments of the present invention without departing from the spirit and scope of the present invention. 
     As an overview, a cognitive system is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. IBM Watson™ is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like high accuracy at speeds far faster than human beings and on a larger scale. In general, such cognitive systems are able to perform the following functions:
         Navigate the complexities of human language and understanding   Ingest and process vast amounts of structured and unstructured data   Generate and evaluate hypothesis   Weigh and evaluate responses that are based only on relevant evidence   Provide situation-specific advice, insights, and guidance   Improve knowledge and learn with each iteration and interaction through machine learning processes   Enable decision making at the point of impact (contextual guidance)   Scale in proportion to the task   Extend and magnify human expertise and cognition   Identify resonating, human-like attributes and traits from natural language   Deduce various language specific or agnostic attributes from natural language   High degree of relevant recollection from data points (images, text, voice) (memorization and recall)   Predict and sense with situational awareness that mimic human cognition based on experiences   Answer questions based on natural language and specific evidence       

     In one aspect, cognitive systems provide mechanisms for answering questions posed to these cognitive systems using a Question Answering pipeline or system (QA system). The QA pipeline or system is an artificial intelligence application executing on data processing hardware that answers questions pertaining to a given subject-matter domain presented in natural language. The QA pipeline receives inputs from various sources including input over a network, a corpus of electronic documents or other data, data from a content creator, information from one or more content users, and other such inputs from other possible sources of input. Data storage devices store the corpus of data. A content creator creates content in a document for use as part of a corpus of data with the QA pipeline. The document may include any file, text, article, or source of data for use in the QA system. For example, a QA pipeline accesses a body of knowledge about the domain, or subject matter area, e.g., financial domain, medical domain, legal domain, etc., where the body of knowledge (knowledgebase) can be organized in a variety of configurations, e.g., a structured repository of domain-specific information, such as ontologies, or unstructured data related to the domain, or a collection of natural language documents about the domain. 
     Content users input questions to the cognitive system which implements the QA pipeline. The QA pipeline then answers the input questions using the content in the corpus of data by evaluating documents, sections of documents, portions of data in the corpus, or the like. When a process evaluates a given section of a document for semantic content, the process can use a variety of conventions to query such document from the QA pipeline, e.g., sending the query to the QA pipeline as a well-formed question which is then interpreted by the QA pipeline and a response is provided containing one or more answers to the question. Semantic content is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic content is content that interprets an expression, such as by using Natural Language Processing. 
     As will be described in greater detail hereafter, the QA pipeline receives an input question, parses the question to extract the major features of the question, uses the extracted features to formulate queries, and then applies those queries to the corpus of data. Based on the application of the queries to the corpus of data, the QA pipeline generates a set of hypotheses, or candidate answers to the input question, by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The QA pipeline then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. There may be hundreds or even thousands of reasoning algorithms applied, each of which performs different analysis, e.g., comparisons, natural language analysis, lexical analysis, or the like, and generates a score. For example, some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity. 
     The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model. The statistical model captures how well the reasoning algorithm performed at establishing the inference between two similar passages for a particular domain during the training period of the QA pipeline. The statistical model is used to summarize a level of confidence that the QA pipeline has regarding the evidence that the potential response, i.e. candidate answer, is inferred by the question. This process is repeated for each of the candidate answers until the QA pipeline identifies candidate answers that surface as being significantly stronger than others and thus, generates a final answer, or ranked set of answers, for the input question. The final answer, or ranked set of answers, may then be returned as the answer(s) to the input question. The returned answer(s) may be output to the original submitter of the input question as a result. In the case of a ranked set of answers, the output of the result may comprise a ranked listing with corresponding scores such that the user is informed of the relative confidence in the various answers returned. Alternatively, the output may comprise only the highest confidence (highest ranking) answer as the final answer to the input question. 
     As mentioned above, QA pipeline and mechanisms operate by accessing information from a corpus of data or information (also referred to as a corpus of content), analyzing it, and then generating answer results based on the analysis of this data. Accessing information from a corpus of data typically includes: a database query that answers questions about what is in a collection of structured records, and a search that delivers a collection of document links in response to a query against a collection of unstructured data (text, markup language, etc.). Conventional question answering systems are capable of generating answers based on the corpus of data and the input question, verifying answers to a collection of questions for the corpus of data, correcting errors in digital text using a corpus of data, and selecting answers to questions from a pool of potential answers, i.e. candidate answers. 
     Content creators, such as article authors, electronic document creators, web page authors, document database creators, and the like, determine use cases for products, solutions, and services described in such content before writing their content. Consequently, the content creators know what questions the content is intended to answer in a particular topic addressed by the content. Categorizing the questions, such as in terms of roles, type of information, tasks, or the like, associated with the question, in each document of a corpus of data allows the QA pipeline to more quickly and efficiently identify documents containing content related to a specific query. The content may also answer other questions that the content creator did not contemplate that may be useful to content users. The questions and answers may be verified by the content creator to be contained in the content for a given document. These capabilities contribute to improved accuracy, system performance, machine learning, and confidence of the QA pipeline. Content creators, automated tools, or the like, annotate or otherwise generate metadata for providing information useable by the QA pipeline to identify these question and answer attributes of the content. 
     Operating on such content, the QA pipeline generates answers for input questions using a plurality of intensive analysis mechanisms which evaluate the content to identify the most probable answers, i.e. candidate answers, for the input question. The most probable answers are output as a ranked listing of candidate answers ranked according to their relative scores or confidence measures calculated during evaluation of the candidate answers, as a single final answer having a highest ranking score or confidence measure, or which is a best match to the input question, or a combination of ranked listing and final answer. 
       FIG. 1  depicts a schematic diagram of one illustrative embodiment of a cognitive system  100  implementing a question answering (QA) pipeline  108  in a computer network  102 . The cognitive system  100  is implemented on one or more computing devices  104  (comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network  102 . The network  102  includes multiple computing devices  104  in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link comprises one or more of wires, routers, switches, transmitters, receivers, or the like. The cognitive system  100  and network  102  enables question/answer (QA) generation functionality for one or more cognitive system users via their respective computing devices  110 - 112 . Other embodiments of the cognitive system  100  may be used with components, systems, sub-systems, and/or devices other than those that are depicted herein. 
     The cognitive system  100  is configured to implement a QA pipeline  108  that receive inputs from various sources. For example, the cognitive system  100  receives input from the network  102 , a corpus of electronic documents  106 , cognitive system users, and/or other data and other possible sources of input. In one embodiment, some or all of the inputs to the cognitive system  100  are routed through the network  102 . The various computing devices  104  on the network  102  include access points for content creators and QA system users. Some of the computing devices  104  include devices for a database storing the corpus of data  106  (which is shown as a separate entity in  FIG. 1  for illustrative purposes only). Portions of the corpus of data  106  may also be provided on one or more other network attached storage devices, in one or more databases, or other computing devices not explicitly shown in  FIG. 1 . The network  102  includes local network connections and remote connections in various embodiments, such that the cognitive system  100  may operate in environments of any size, including local and global, e.g., the Internet. 
     In one embodiment, the content creator creates content in a document of the corpus of data  106  for use as part of a corpus of data with the cognitive system  100 . The document includes any file, text, article, or source of data for use in the cognitive system  100 . QA system users access the cognitive system  100  via a network connection or an Internet connection to the network  102 , and input questions to the cognitive system  100  that are answered by the content in the corpus of data  106 . In one embodiment, the questions are formed using natural language. The cognitive system  100  parses and interprets the question via a QA pipeline  108 , and provides a response to the cognitive system user, e.g., cognitive system user  110 , containing one or more answers to the question. In some embodiments, the cognitive system  100  provides a response to users in a ranked list of candidate answers while in other illustrative embodiments, the cognitive system  100  provides a single final answer or a combination of a final answer and ranked listing of other candidate answers. 
     The cognitive system  100  implements the QA pipeline  108  which comprises a plurality of stages for processing an input question and the corpus of data  106 . The QA pipeline  108  generates answers for the input question based on the processing of the input question and the corpus of data  106 . The QA pipeline  108  will be described in greater detail hereafter with regard to  FIG. 3 . 
     In some illustrative embodiments, the cognitive system  100  may be the IBM Watson™ cognitive system available from International Business Machines Corporation of Armonk, N.Y., which is augmented with the mechanisms of the illustrative embodiments described hereafter. As outlined previously, a QA pipeline of the IBM Watson™ cognitive system receives an input question which it then parses to extract the major features of the question, which in turn are then used to formulate queries that are applied to the corpus of data. Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The QA pipeline of the IBM Watson™ cognitive system then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. The scores obtained from the various reasoning algorithms are then weighted against a statistical model that summarizes a level of confidence that the QA pipeline of the IBM Watson™ cognitive system has regarding the evidence that the potential response, i.e. candidate answer, is inferred by the question. This process is be repeated for each of the candidate answers to generate ranked listing of candidate answers which may then be presented to the user that submitted the input question, or from which a final answer is selected and presented to the user. More information about the QA pipeline of the IBM Watson™ cognitive system may be obtained, for example, from the IBM Corporation website, IBM Redbooks, and the like. For example, information about the QA pipeline of the IBM Watson™ cognitive system can be found in Yuan et al., “Watson and Healthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012. 
     As shown in  FIG. 1 , the cognitive system  100  is further augmented, in accordance with the mechanisms of the illustrative embodiments, to include logic implemented in specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware, for implementing a cognitive reminder and answer change notification system  120 . The cognitive reminder and answer change notification system  120  comprises temporal characteristic identification logic  122 , reminder timeframe determination logic  124 , reminder notification and answer change output logic  126 , reminder notification database  128 , user profile database  130 , question/answer pair database  132 , and answer change determination logic  134 . 
     When a user of a client computing device  110 , for example, asks a question of the cognitive system  100 , the QA pipeline  108  processes the question and generates an answer to the question which is output to the user&#39;s client computing device  110 . In the generation of the answer to the question, the temporal characteristic identification logic  122  may operate on the question and answer to determine if the question/answer combination includes a temporal characteristics indicative of a potential need for scheduling a future reminder notification. If so, a user interface is output to the user&#39;s client computing device  110  to not only present the answer generated for the question, but an option for the user to indicate whether the user would like to have a reminder notification scheduled for a future time at which the answer will be more useful or beneficial for the user. In some illustrative embodiments, the user interface may further include a field or selectable options by which the user may specify a desired timeframe for scheduling the reminder notification. In addition, in some illustrative embodiments, whether or not a temporal characteristic of the question/answer combination is detected or not, a user interface presenting the question/answer combination may include a field or selectable options for specifying whether user wishes to obtain notifications when the answer to the question may have changed in response to changes in the user profile information of the user, as described hereafter. 
     Assuming the user has opted for scheduling a reminder notification, and the user has not specified a desired timeframe for the scheduling of the reminder notification, the reminder timeframe determination logic  124  operates on the question, the answer, information about the user and the user&#39;s previous activity, information about the domain of the question/answer, information about similar questions/answers, information about similar users and their previous activity, and/or the like to determine a most likely beneficial time for scheduling the future reminder notification for presenting this answer to the user again. The various ways in which the reminder timeframe is calculated will be described in greater detail hereafter with reference to various illustrative embodiments. Information about the user, e.g., user contact information, the question, the answer, and the determined timeframe are stored in an entry in the reminder notification database  128 . 
     Continuously, periodically, or on an event-basis, the reminder notification and answer change output logic  126  may traverse the entries in the database  128  to identify active entries in the database  128 . Active entries in the database  128  may have their answers checked to determine if there is a significant change in the answer since the time when the original answer was presented, or the last update to the answer was generated. This check may comprise resubmitting the original question to the QA pipeline  108 , obtaining a new answer to the original question, and determining if there is a significant change in the new answer when compared to the original, or previously updated, answer. If there has been a substantial change, then a notification may be sent to the user associated with the entry requesting input from the user as to how they wish to handle the change in the answer, e.g., update the reminder notification, cancel the reminder notification, or make no change. 
     Continuously, periodically, or on an event-basis, the reminder notification and answer change output logic  126  may also traverse the database  128  to identify the active entries and determine if the timeframes for the scheduled reminder notifications are met by the current date/time such that sending of the reminder notification associated with the entry is triggered. If the sending of a reminder notification for an active entry is triggered, then the reminder notification and answer change output logic  126  may generate and output a reminder notification to the user indicating the original question and the answer(s) to the original question. In the case where the answer may have changed over the time period from the time that the original answer was generated and when the reminder notification is sent, a historical listing of the changes to the answer may be output as part of this reminder notification, including the various answers and their corresponding date/time information indicating when the change was detected by the cognitive system  100 . 
     In response to the reminder notification, the user may elect to postpone the reminder notification for a specified period of time in which case the reminder notification entry in the database  128  may be updated to reflect the postponement of the reminder notification and the entry remains active. If postponement is not indicated by the user, then the entry in the database  128  is marked as no longer active such that it may be overwritten. 
     In addition to the above, or as a separate mechanism from the temporal characteristic based reminder notification mechanisms, the cognitive reminder and answer change notification system  120  provides an answer change determination logic  134  that operates to determine if the answer to a previously submitted question may have changed due to a change in user profile information associated with the user that submitted the question. As noted above, when an input question is processed by the cognitive system  100  and a corresponding answer is generated, the question/answer pair may be stored in an entry in the reminder notification database  128  if it is determined that the user selects options for receiving a reminder notification and the question/answer indicate that there is a temporal characteristic to the question/answer pair. In addition, or alternatively, when a question is processed by the cognitive system  100  and an answer is generated, the question and answer pair may be logged in an entry of the question/answer pair database  132 . The entry in the question/answer pair database  132  may associate an identifier of the originator of the question (e.g., the user), the question and answer pair, and an indication of the field or fields, if any, in the originator&#39;s associated profile information, e.g., user profile information in the user profile database  130 , that is an evidential basis for the answer generated for the question. That is, when the input question is processed by the cognitive system  100 , the user that submitted the input question may be identified and a corresponding user profile from the user profile database  130  may be retrieved and used as additional evidence passages for processing by the cognitive system  100  when determining an answer to the input question. If particular fields of the user profile are actually used as an evidential basis for evaluating the answer as being a correct answer for the input question, then the identifier(s) of those field(s) are associated with the question/answer pair and logged in the corresponding entry in the question/answer pair database  132 . 
     It should be appreciated that while  FIG. 1  illustrates the question/answer pair database  132  as a separate database from that of the reminder notification database  128  for descriptive purposes, the databases  128  and  132  may in fact be integrated with one another such that an entry in the database may comprise information for both temporal characteristic based reminder notification generation and user profile based answer change notifications. Moreover, there may be separate databases or data structures for each source, e.g., user, of input questions to the cognitive system  100 . In such a case, it may not be necessary to include a user identifier in the entries of the database since separate databases are maintained for the various users or sources. 
     User profiles in the user profile database  130  are linked to corresponding question/answer pair databases  132 , or entries in a question/answer pair database  132 , such that entries for question/answer pairs associated with a user profile in the user profile database  130  may be retrieved from the question/answer pair database  132 . The answer change determination logic  134  monitors changes to user profiles in the user profile database  130 , such as a user or automated system updating information in the user profile to change the content of one or more fields of the user profile information, and initiates a determination as to whether answers to previously submitted questions may be affected by the change to the user profile. In making such a determination, the answer change determination logic  134  identifies which field or fields were changed by the change to the user profile and searches the entries in the question/answer pair database  132  corresponding to the user to identify entries flagged as being dependent upon the user profile. From this subset of entries, the answer change determination logic  134  then searches the subset of entries to identify which, if any, have field identifiers corresponding to the field or fields changed by the change to the user profile information. 
     For those entries that have matching field(s) to that of the field(s) changed by the change to the user profile, the original question is resubmitted to the QA system pipeline  108  and a new answer to the original question is obtained. Similar to the description of the operation of the reminder notification and answer change output logic  126  above, the answer change determination logic  134  may determine if there is a significant change in the new answer when compared to the original answer, or previously updated answer. Whether a change is “substantial” or not may be measured using numerical comparisons, e.g., difference calculation and comparison to a threshold, if the answer is a numerical answer, lexical distance or difference determinations if the answer is a textual answer, or any other known or later developed mechanism for evaluating differences in numerical or textual content. If there has been a substantial change, then a notification may be sent to the user associated with the entry to inform them of the question, the old answer, the new answer, and the user profile fields that were changed that instigated the change in the answer. 
     Thus, for example, assume that a user of client computing device  110  submits an original input question to the cognitive system  100  of the type “When should I fertilize my lawn?” The cognitive system  100  retrieves a user profile for the user, or associated with the client device  110 , e.g., the user may have logged onto the cognitive system  100  via the client device  110  and network  102 , and uses the user profile, along with a corpus of information associated with the cognitive system  100  (not shown), to generate an answer to the input question through the processing by the QA system pipeline  108 . As such, if a portion of the user profile is used as evidential support for the final answer returned by the QA system pipeline  108 , this information is logged in the question/answer pair database  132  in association with the question and answer pair. For example, the answer “April” may be generated at least partially based on a home location field of the user profile indicating that the user submitting the original question lives in the state of Pennsylvania and other evidential passages in the corpus of information indicating that the grass growing season starts in the month of April. In response, to the answer being at least partially dependent upon a field of the user profile, the entry in the question/answer pair database  132  corresponding to the question/answer pair is flagged as being dependent upon a user profile field, such as by setting a flag bit, and an indicator of the field or fields upon which the answer is at least partially dependent is added to the entry, e.g., an indicator of the field “home location” in the above example. 
     At a later time, the user of client device  110 , or another automated system, may update the user profile of the user to change the user&#39;s home location, e.g., changing the content of the field from “Pennsylvania” to “North Carolina”. The answer change determination logic  134  monitors changes to user profiles and determines that the user&#39;s user profile has been updated and identifies the home location as being the field changed by the update to the user profile. The entries in the question/answer pair database  132  are searched to identify which entries are flagged as being dependent upon the user profile and then this subset is further searched to determine which, if any, are dependent upon the particular field(s) changed by the update to the user profile. For those entries whose answer is dependent upon the particular field(s) changed by the update to the user profile, the question is resubmitted to the QA system pipeline  108  which again operates on both the user profile (now updated) and the corpus of information to generate a new answer. For example, a new answer of “June” may be generated based on the change to the user&#39;s home location. 
     The new answer is compared to the old answer to determine if there is a significant change in the answer. This significant change may be calculated numerically or lexically. For example, the month based answers above may be converted to numerical representations and a difference in the months may be calculated and compared to a threshold. Alternatively a lexical distance between the original answer and the new answer may be calculated and compared to a threshold. In some cases, any change in the answer may be used as a basis for instigating a notification of a change in the answer. In the example above, a threshold may be established that if a month change of 2 or more months is considered significant and thus, the change from April to June is considered significant. If the answer change is significant, then a notification may be sent to the user, via their client device  110 , indicating the change and the basis for the change, e.g., a notification of “The answer to your previous question ‘When should I fertilize my lawn?’ has changed from April to June based on your change of your home location from Pennsylvania to North Carolina.” Thus, the notification may specify the question, the old answer, the new answer, and the field(s) changes that caused the change in the answer. The question/answer pair database  132  entry may then be updated to reflect the new answer as the current or new “original” answer for possible subsequent updates of the answer should more changes to the user profile be detected. 
     It should be appreciated that while the example of the home location field in the user profile being used as a basis for answer change determination, the invention is not limited to any particular fields or the user profile being a basis for the answer change determination. To the contrary, any field or set of fields in the user profile that are changed may be a basis for initiating an answer change determination operation. For example, in a medical domain implementation of the illustrative embodiments, age of the patient, health conditions of the patient, or the like, may be recorded in the user profile and as this information is changed, may initiate the operations of the illustrative embodiments for determining answer changes and generating corresponding notifications. In such an implementation, the change to the user profile information may be automated, e.g., the user&#39;s age incrementing each year upon occurrence of the user&#39;s birthday, and may be performed by other systems which operate in conjunction with the cognitive reminder and answer change notification system  120 . For example, an electronic medical record (EMR) system, medical diagnosis system, or the like, may automatically update the user&#39;s profile in response to updates to the user&#39;s EMRs and/or in response to a diagnosis being returned by the system for the user. 
       FIG. 2  is a block diagram of an example data processing system in which aspects of the illustrative embodiments are implemented. Data processing system  200  is an example of a computer, such as server  104  or client  110  in  FIG. 1 , in which computer usable code or instructions implementing the processes for illustrative embodiments of the present invention are located. In one illustrative embodiment,  FIG. 2  represents a server computing device, such as a server  104 , which, which implements a cognitive system  100  and QA system pipeline  108  augmented to include the additional mechanisms of the illustrative embodiments described hereafter. 
     In the depicted example, data processing system  200  employs a hub architecture including north bridge and memory controller hub (NB/MCH)  202  and south bridge and input/output (I/O) controller hub (SB/ICH)  204 . Processing unit  206 , main memory  208 , and graphics processor  210  are connected to NB/MCH  202 . Graphics processor  210  is connected to NB/MCH  202  through an accelerated graphics port (AGP). 
     In the depicted example, local area network (LAN) adapter  212  connects to SB/ICH  204 . Audio adapter  216 , keyboard and mouse adapter  220 , modem  222 , read only memory (ROM)  224 , hard disk drive (HDD)  226 , CD-ROM drive  230 , universal serial bus (USB) ports and other communication ports  232 , and PCI/PCIe devices  234  connect to SB/ICH  204  through bus  238  and bus  240 . PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM  224  may be, for example, a flash basic input/output system (BIOS). 
     HDD  226  and CD-ROM drive  230  connect to SB/ICH  204  through bus  240 . HDD  226  and CD-ROM drive  230  may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. Super I/O (SIO) device  236  is connected to SB/ICH  204 . 
     An operating system runs on processing unit  206 . The operating system coordinates and provides control of various components within the data processing system  200  in  FIG. 2 . As a client, the operating system is a commercially available operating system such as Microsoft® Windows 8®. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java™ programs or applications executing on data processing system  200 . 
     As a server, data processing system  200  may be, for example, an IBM® eServer™ System P® computer system, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system  200  may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit  206 . Alternatively, a single processor system may be employed. 
     Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD  226 , and are loaded into main memory  208  for execution by processing unit  206 . The processes for illustrative embodiments of the present invention are performed by processing unit  206  using computer usable program code, which is located in a memory such as, for example, main memory  208 , ROM  224 , or in one or more peripheral devices  226  and  230 , for example. 
     A bus system, such as bus  238  or bus  240  as shown in  FIG. 2 , is comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem  222  or network adapter  212  of  FIG. 2 , includes one or more devices used to transmit and receive data. A memory may be, for example, main memory  208 , ROM  224 , or a cache such as found in NB/MCH  202  in  FIG. 2 . 
     Those of ordinary skill in the art will appreciate that the hardware depicted in  FIGS. 1 and 2  may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIGS. 1 and 2 . Also, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system, other than the SMP system mentioned previously, without departing from the spirit and scope of the present invention. 
     Moreover, the data processing system  200  may take the form of any of a number of different data processing systems including client computing devices, server computing devices, a tablet computer, laptop computer, telephone or other communication device, a personal digital assistant (PDA), or the like. In some illustrative examples, data processing system  200  may be a portable computing device that is configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data, for example. Essentially, data processing system  200  may be any known or later developed data processing system without architectural limitation. 
       FIG. 3  illustrates a cognitive system QA pipeline, of a cognitive system, for processing an input question in accordance with one illustrative embodiment. The cognitive system QA pipeline of  FIG. 3  may be implemented, for example, as QA pipeline  108  of cognitive system  100  in  FIG. 1 . It should be appreciated that the stages of the QA pipeline shown in  FIG. 3  are implemented as one or more software engines, components, or the like, which are configured with logic for implementing the functionality attributed to the particular stage. Each stage is implemented using one or more of such software engines, components or the like. The software engines, components, etc. are executed on one or more processors of one or more data processing systems or devices and utilize or operate on data stored in one or more data storage devices, memories, or the like, on one or more of the data processing systems. The QA pipeline of  FIG. 3  is augmented, for example, in one or more of the stages to implement the improved mechanism of the illustrative embodiments described hereafter, additional stages may be provided to implement the improved mechanism, or separate logic from the QA pipeline  300  may be provided for interfacing with the QA pipeline  300  and implementing the improved functionality and operations of the illustrative embodiments. 
     As shown in  FIG. 3 , the QA pipeline  300  comprises a plurality of stages  310 - 380  through which the cognitive system operates to analyze an input question and generate a final response. In an initial question input stage  310 , the QA pipeline  300  receives an input question that is presented in a natural language format. That is, a user inputs, via a user interface, an input question for which the user wishes to obtain an answer, e.g., “Who are Washington&#39;s closest advisors?” In response to receiving the input question, the next stage of the QA pipeline  300 , i.e. the question and topic analysis stage  320 , parses the input question using natural language processing (NLP) techniques to extract major features from the input question, and classify the major features according to types, e.g., names, dates, or any of a plethora of other defined topics. For example, in the example question above, the term “who” may be associated with a topic for “persons” indicating that the identity of a person is being sought, “Washington” may be identified as a proper name of a person with which the question is associated, “closest” may be identified as a word indicative of proximity or relationship, and “advisors” may be indicative of a noun or other language topic. 
     In addition, the extracted major features include key words and phrases classified into question characteristics, such as the focus of the question, the lexical answer type (LAT) of the question, and the like. As referred to herein, a lexical answer type (LAT) is a word in, or a word inferred from, the input question that indicates the type of the answer, independent of assigning semantics to that word. For example, in the question “What maneuver was invented in the 1500s to speed up the game and involves two pieces of the same color?,” the LAT is the string “maneuver.” The focus of a question is the part of the question that, if replaced by the answer, makes the question a standalone statement. For example, in the question “What drug has been shown to relieve the symptoms of ADD with relatively few side effects?,” the focus is “drug” since if this word were replaced with the answer, e.g., the answer “Adderall” can be used to replace the term “drug” to generate the sentence “Adderall has been shown to relieve the symptoms of ADD with relatively few side effects.” The focus often, but not always, contains the LAT. On the other hand, in many cases it is not possible to infer a meaningful LAT from the focus. 
     Referring again to  FIG. 3 , the identified major features are then used during the question decomposition stage  330  to decompose the question into one or more queries that are applied to the corpora of data/information  345  in order to generate one or more hypotheses. The queries are generated in any known or later developed query language, such as the Structure Query Language (SQL), or the like. The queries are applied to one or more databases storing information about the electronic texts, documents, articles, websites, and the like, that make up the corpora of data/information  345 . That is, these various sources themselves, different collections of sources, and the like, represent a different corpus  347  within the corpora  345 . There may be different corpora  347  defined for different collections of documents based on various criteria depending upon the particular implementation. For example, different corpora may be established for different topics, subject matter categories, sources of information, or the like. As one example, a first corpus may be associated with healthcare documents while a second corpus may be associated with financial documents. Alternatively, one corpus may be documents published by the U.S. Department of Energy while another corpus may be IBM Redbooks documents. Any collection of content having some similar attribute may be considered to be a corpus  347  within the corpora  345 . 
     The queries are applied to one or more databases storing information about the electronic texts, documents, articles, websites, and the like, that make up the corpus of data/information, e.g., the corpus of data  106  in  FIG. 1 . The queries are applied to the corpus of data/information at the hypothesis generation stage  340  to generate results identifying potential hypotheses for answering the input question, which can then be evaluated. That is, the application of the queries results in the extraction of portions of the corpus of data/information matching the criteria of the particular query. These portions of the corpus are then analyzed and used, during the hypothesis generation stage  340 , to generate hypotheses for answering the input question. These hypotheses are also referred to herein as “candidate answers” for the input question. For any input question, at this stage  340 , there may be hundreds of hypotheses or candidate answers generated that may need to be evaluated. 
     The QA pipeline  300 , in stage  350 , then performs a deep analysis and comparison of the language of the input question and the language of each hypothesis or “candidate answer,” as well as performs evidence scoring to evaluate the likelihood that the particular hypothesis is a correct answer for the input question. As mentioned above, this involves using a plurality of reasoning algorithms, each performing a separate type of analysis of the language of the input question and/or content of the corpus that provides evidence in support of, or not in support of, the hypothesis. Each reasoning algorithm generates a score based on the analysis it performs which indicates a measure of relevance of the individual portions of the corpus of data/information extracted by application of the queries as well as a measure of the correctness of the corresponding hypothesis, i.e. a measure of confidence in the hypothesis. There are various ways of generating such scores depending upon the particular analysis being performed. In generally, however, these algorithms look for particular terms, phrases, or patterns of text that are indicative of terms, phrases, or patterns of interest and determine a degree of matching with higher degrees of matching being given relatively higher scores than lower degrees of matching. 
     Thus, for example, an algorithm may be configured to look for the exact term from an input question or synonyms to that term in the input question, e.g., the exact term or synonyms for the term “movie,” and generate a score based on a frequency of use of these exact terms or synonyms. In such a case, exact matches will be given the highest scores, while synonyms may be given lower scores based on a relative ranking of the synonyms as may be specified by a subject matter expert (person with knowledge of the particular domain and terminology used) or automatically determined from frequency of use of the synonym in the corpus corresponding to the domain. Thus, for example, an exact match of the term “movie” in content of the corpus (also referred to as evidence, or evidence passages) is given a highest score. A synonym of movie, such as “motion picture” may be given a lower score but still higher than a synonym of the type “film” or “moving picture show.” Instances of the exact matches and synonyms for each evidence passage may be compiled and used in a quantitative function to generate a score for the degree of matching of the evidence passage to the input question. 
     Thus, for example, a hypothesis or candidate answer to the input question of “What was the first movie?” is “The Horse in Motion.” If the evidence passage contains the statements “The first motion picture ever made was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was a movie of a horse running,” and the algorithm is looking for exact matches or synonyms to the focus of the input question, i.e. “movie,” then an exact match of “movie” is found in the second sentence of the evidence passage and a highly scored synonym to “movie,” i.e. “motion picture,” is found in the first sentence of the evidence passage. This may be combined with further analysis of the evidence passage to identify that the text of the candidate answer is present in the evidence passage as well, i.e. “The Horse in Motion.” These factors may be combined to give this evidence passage a relatively high score as supporting evidence for the candidate answer “The Horse in Motion” being a correct answer. 
     It should be appreciated that this is just one simple example of how scoring can be performed. Many other algorithms of various complexity may be used to generate scores for candidate answers and evidence without departing from the spirit and scope of the present invention. 
     In the synthesis stage  360 , the large number of scores generated by the various reasoning algorithms are synthesized into confidence scores or confidence measures for the various hypotheses. This process involves applying weights to the various scores, where the weights have been determined through training of the statistical model employed by the QA pipeline  300  and/or dynamically updated. For example, the weights for scores generated by algorithms that identify exactly matching terms and synonym may be set relatively higher than other algorithms that are evaluating publication dates for evidence passages. The weights themselves may be specified by subject matter experts or learned through machine learning processes that evaluate the significance of characteristics evidence passages and their relative importance to overall candidate answer generation. 
     The weighted scores are processed in accordance with a statistical model generated through training of the QA pipeline  300  that identifies a manner by which these scores may be combined to generate a confidence score or measure for the individual hypotheses or candidate answers. This confidence score or measure summarizes the level of confidence that the QA pipeline  300  has about the evidence that the candidate answer is inferred by the input question, i.e. that the candidate answer is the correct answer for the input question. 
     The resulting confidence scores or measures are processed by a final confidence merging and ranking stage  370  which compares the confidence scores and measures to each other, compares them against predetermined thresholds, or performs any other analysis on the confidence scores to determine which hypotheses/candidate answers are the most likely to be the correct answer to the input question. The hypotheses/candidate answers are ranked according to these comparisons to generate a ranked listing of hypotheses/candidate answers (hereafter simply referred to as “candidate answers”). From the ranked listing of candidate answers, at stage  380 , a final answer and confidence score, or final set of candidate answers and confidence scores, are generated and output to the submitter of the original input question via a graphical user interface or other mechanism for outputting information. 
     As shown in  FIG. 3 , in accordance with one illustrative embodiment, the QA pipeline  300  is associated with a cognitive reminder and answer change notification system  390 , which may be the cognitive reminder and answer change notification system  120  in  FIG. 1 , for example, that comprises a temporal characteristic identification engine  391 , reminder timeframe determination engine  392 , reminder/answer change notification output engine  393 , reminder notification database  394 , and temporal characteristics recognition data structures  395 . In addition, the cognitive reminder and answer change notification system  390  may implement the question/answer (QA) pair database  132  and answer change determination logic  134  described above for determining if a change in a user profile, such as in user profile database  396 , causes a change in an answer to a previously submitted question logged in the QA pair database  132 . 
     The elements of the cognitive reminder and answer change notification system  390  may operate in conjunction with a user profile database  396  which may be part of the cognitive reminder notification system  390  or otherwise provided as part of the cognitive system in which the QA pipeline  300  is provided (as shown). The user profile database  396  may store personal information about registered users of the cognitive system and QA pipeline  300  including various demographic information, family/friend information, contact information, and the like, as may be obtained through a registration process, for example. Alternatively, the user profile database  396  may be compiled from various other systems, such as one or more social networking websites, one or more instant messaging systems, online forums and/or newsgroups, or any other source that may have registered user information that may be accessed by the cognitive system. Moreover, in accordance with the mechanisms of some illustrative embodiments that implement an answer change notification operation, the user profile, as noted above, may provide user profile information for use along with the corpus or corpora  345 ,  347  when answering input questions  310 , and upon which an answer may be dependent. 
     The cognitive reminder notification system  390  monitors the processing of questions by the QA pipeline  300  to determine if the question submitted to the QA pipeline  300 , the answer generated by the QA pipeline for an input question, or both have temporal characteristics indicative of a possible need to schedule a future reminder notification. Natural language processing of the question may be performed to determine if the question itself mentions a temporal characteristic, e.g., through keyword, key phrase, concept identification, or any other known or later developed natural language processing technique for extracting features from the input question. 
     For example, the temporal characteristic identification engine  391  of the cognitive reminder notification system  390  may interface with the question and topic analysis stage  320  of the QA pipeline  300  which may be augmented to include logic that identifies and extracts major features associated with temporal characteristics explicitly specified in the input question, if not already configured to do so. For example, the question may specifically indicate that the answer sought is a “day” or a “month”, or a “time of day”, or a “year”, e.g., “What day in September is Labor Day?” or “What month has the highest flu incidents?” or “What time of day should I take my Prednisone pill?” In such examples, keywords such as “day”, “month”, and key phrases such as “time of day” are indicative of an explicitly specified temporal characteristic in the question itself. Thus, the processing of the input question to extract major features as already performed by the QA pipeline  300  may be leveraged to assist in determining whether to schedule a reminder notification for the answer generated by the QA pipeline  300 . 
     In addition, the extraction of major features during stage  320  may also be augmented with logic to extract major features of the input question indicative of an aspect of time that is not an explicit specification of a particular time characteristic. An aspect of time differs from an explicitly specified temporal characteristic in that it is not clear from the question itself what type of date/time answer is being sought although it is clear that an answer having a temporal characteristic is being sought. For example, keywords such as “when” and “time” are keywords that are indicative of an aspect of time being sought in the answer to the input question, but without specifying a particular type of time aspect (temporal characteristic), e.g., day, month, year, time of day, etc. For example, questions such as “When does it snow in NY?” and “What is the best time to go skiing in Colorado?” are questions that reference aspects of time but without explicitly stating the type of temporal characteristic being sought since the answer may be a date, a day, a month, a time of the day, a range of dates, a combination of two or more of these, or any other temporal characteristic. 
     Aspects of time may further be dependent upon particular associations of keywords and phrases with temporal concepts, such as seasons, times of day, months, years, etc. For example, the keyword “skiing” may be associated with “winter” which in turn may be associated with particular months of the year, e.g., November to March. The keyword “skiing” may also be associated with a season, such as a ski season, similar to the way “deer hunting” may be associated with a “hunting season.” Moreover, location information in the question may be informative as to the temporal characteristic in the answer, e.g., winter in Colorado may comprise different months of the year than winter in Toledo, Spain. 
     The logic of stage  320  and/or the logic of the temporal characteristic identification engine  391  may be configured to identify these temporal characteristics and aspects in the input question. The keywords, key phrases, and concepts that specifically identify a temporal characteristic in the input question may be specified in one or more of the temporal characteristics recognition data structures  395 . The temporal characteristics recognition data structures  395  may further store associations of keywords, key phrases, concepts, and the like to one another and to specific temporal characteristics such that they may be used to identify aspects of time in the input question and map those aspects of time to particular types of temporal characteristics. 
     Thus, with the mechanisms of the illustrative embodiments, the temporal characteristic identification engine  391  analyzes the keywords, key phrases, concepts, and other major features extracted from the input question by stage  320  of the QA pipeline  300  to thereby identify temporal characteristics of the input question. The identification of the temporal characteristics is performed using natural language processing techniques that operate on the input question to identify time based keywords or phrases, relationships associated with time in the language of the input question, or the like. In addition to analyzing the question to determine if it specifically identifies a temporal characteristic, the mechanisms of the illustrative embodiments may further analyze the question to determine if, while not explicitly specifying a temporal characteristic, the question contains some aspect of time that will be reflected in the answer. These aspects of time may further be identified via known relationships between keywords or key phrases and various other keywords, key phrases, or concepts. In either case, the specific temporal characteristics in the input question or the aspects of time are used to generate an association of temporal characteristics with the input question which may be used to identify the possibility of a need to schedule a future reminder notification. 
     In addition to analyzing the input question to identify such temporal characteristics of the input question, the temporal characteristic identification engine  391  may also analyze the resulting answer to determine if the resulting answer has any temporal characteristics present. That is, there may be cases where the input question does not itself have temporal characteristics identified as being associated with the input question yet the answer to the input question may have one or more temporal characteristics associated with it. Thus, it may be beneficial to analyze both the input question and the answer to determine if temporal characteristics are present. In addition, it is necessary to analyze the answer to the input question to identify the temporal characteristics present so as to determine when to schedule a future reminder notification if needed, as discussed hereafter. 
     The temporal characteristics of the answer to the input question may be determined in a similar manner by analyzing keywords, key phrases, and concepts in the answer that is generated by the QA pipeline  300  and/or analyzing the answer for timing aspects. Moreover, the lexical answer type (LAT) of the answer may be analyzed to determine the type of answer provided and whether that type of answer corresponds to a timeframe type, e.g., a LAT indicative of a year, date, time, season, or the like. The temporal characteristics identification engine  391  may utilize similar logic as discussed above, and similar keywords, key phrases, concepts, associations of these, and the like, as specified in the temporal characteristics recognition data structures  395 , to identify such temporal characteristics in the answer to the input question. Thus, through the analysis of the input question and the answer to the input question generated by the QA pipeline  300 , the temporal characteristics identification engine  391  identifies whether or not the input question and/or answer comprise one or more temporal characteristics indicative of a possible need for scheduling a further reminder notification of the answer to the input question. 
     As mentioned above, various mechanisms may be employed to assist in the analysis of the input question and the resulting answer, one example of which is described in commonly owned and co-pending U.S. patent application Ser. No. 14/297,335, entitled “Determining Temporal Categories for a Domain of Content for Natural Language Processing,” filed Jun. 5, 2014. The mechanisms described in this co-pending patent application may be applied to a question and/or answer to determine temporal characteristics of the question and/or answer. Moreover, the mechanisms of the co-pending application may be used to determine a temporal characteristic of the domain associated with an input question and this temporal characteristic may be utilized when determining the scheduling of a future reminder notification as described hereafter. Of course, other known or later developed mechanisms for identifying temporal characteristics may be used as well without departing from the spirit and scope of the illustrative embodiments. 
     In addition to determining whether or not the input question and/or answer have associated temporal characteristics that may be indicative of a need to schedule a future reminder notification, the mechanisms of the illustrative embodiments further make note of the timeframe at which the question was input, e.g., the date/time of the submission of the input question. In some illustrative embodiments, the input question may be correlated with other similar questions and the timeframes of the submissions of the similar questions may be identified as well to assist in the scheduling of the future reminder notifications. That is, similar questions submitted at approximately a similar timeframe as the current question may have had reminder notifications scheduled for these similar questions and thus, may be representative of the particular timespan that should be used for scheduling a reminder notification for the current question. Alternatively, similar questions that were submitted at different timeframes from the current question may still be utilized with the timespans for the reminder notifications of these similar questions being determined based on the timeframes of their submission and subsequent scheduling of the reminder notification for those questions. Moreover, the timeframe of the submission of the input question is a base timeframe from which the reminder notification is scheduled in the manner described hereafter. 
     Assuming that at least one temporal characteristic is identified, in one or more of the input question or the answer to the input question, the temporal characteristic(s) may be analyzed to determine if the answer is associated with a future timeframe, e.g., date/time/season/year, from the current timeframe. For example, the temporal characteristics of the answer may be compared to the timeframe of the submission of the input question to determine whether the temporal characteristics of the answer are at a timeframe in the future compared to the timeframe of the submission of the input question. For example, if the question “When should I plant my rose bushes?” is submitted in August and the answer is returned by the QA system may be “late February to early April.” Thus, the answer is for a timeframe that is at least 6 months in the future. Therefore, it may be determined that there is a potential need for the scheduling of a reminder notification for the answer to the input question. 
     In response to a determination that the answer comprises a temporal characteristic directed to a future timeframe compared to the timeframe of the submission of the input question, the user may be presented with a user interface that outputs the answer to the input question and an option to schedule a future reminder notification to be sent to the user when the answer to the question is within a timeframe that the answer becomes more immediately useable by the user. Alternatively, this reminder notification may be automatically generated and stored for the user. The scheduling of the reminder notification may take a variety of forms. 
     In response to a user input requesting the scheduling of a reminder notification, or in response to initiating an automatic operation for generating a reminder notification, the reminder timeframe determination engine  392  determines the best time to notify the user of the answer to their previously submitted question. The reminder timeframe determination engine  392  determines a date/time to schedule the reminder notification based on temporal characteristics of the answer, temporal characteristics of the question, and temporal characteristics associated with the user that submitted the input question. Based on a combination of these various temporal characteristics, the cognitive reminder notification mechanisms of the illustrative embodiments determine a best time to schedule the future reminder notification for output to the user. 
     In one illustrative embodiment, the reminder timeframe determination engine  392  selects a default timeframe, or timespan, for reminder notification scheduling. The default timeframe is a timeframe prior to the time specified in the answer, at which time the reminder notification will be output to the user. A time span is a time from the submission time of the input question to a time at which the reminder notification is scheduled for output to the user. For example, a default timeframe may be one week prior to the timeframe of the answer. Thus, for example, if the answer involves the date August 4 th , then a reminder notification of the answer may be scheduled to be output one week prior to August 4 th . The default timeframe may be an arbitrarily selected user independent timeframe that the reminder timeframe determination engine  392  is pre-configured with, a timeframe associated with the user as specified, for example, in a user profile data structure of the user profile database  396 , a timeframe corresponding to a temporal category of the domain associated with the question/answer as determined using the mechanisms of co-pending U.S. patent application Ser. No. 14/297,335, a timeframe determined from timeframes associated with similarly submitted questions, or the like. 
     Thus, for example, the reminder timeframe determination engine  392  may comprise a configuration data structure that defines for the reminder timeframe determination engine one or more configuration parameters that govern the operation of the reminder timeframe determination engine  392 . One of these configuration parameters may comprise a default timeframe for scheduling reminder notifications, where this default timeframe is independent of the particular users, the domain of the question, and/or the concepts in the question/answer. In some cases, multiple default timeframes may be pre-configured in the reminder timeframe determination engine  392 , e.g., one default timeframe for each domain that the cognitive system or QA pipeline  300  operates on. This default timeframe may be subtracted from the temporal characteristic of the answer, or another function of the default timeframe may be utilized without departing from the spirit and scope of the present invention, such that a date/time for sending the reminder notification is generated. 
     Alternatively, or in addition, if the user specifies in their user personal profile data structure in the database  396  a default timeframe, e.g., a default that they prefer to receive reminder notifications one week before the date associated with the answer of the input question, then this one week default timeframe will be used to schedule a future reminder notification, regardless of the temporal characteristic type of the answer. Similarly, if, through analysis of the content of a domain associated with the question/answer, such as via the mechanisms of the co-pending U.S. patent application Ser. No. 14/297,335 or other domain analysis mechanism, a particular temporal category having a corresponding default timeframe is determined, then the default timeframe of the domain may be utilized as the default timeframe for scheduling the future reminder notification. 
     Moreover, if the input question is similar to other input questions provided by the same or other users, reminder notification scheduling timeframes associated with the similar questions may be utilized to determine a default timeframe for scheduling the reminder notification for the current question. For example, the mean of the reminder notification timeframes of similar questions may be utilized to determine a default timeframe for scheduling the reminder notification of the current answer. Many mechanisms exist for identifying similar questions including mechanisms that operate based on keyword matching, clustering of extracted features of questions, and the like. Any known or later developed mechanism for identifying similar questions may be utilized and, having identified one or more similar questions, the mechanisms of the illustrative embodiments may lookup in the reminder notification database  394  the corresponding timeframes used to generate the reminder notifications for these similar questions, e.g., a difference between a timestamp associated with the original input question and the date/time of the reminder notification or a difference between the temporal characteristic in the answer and a date/time of the reminder notification, for example. The average, mean, or any other statistical or mathematical function of the reminder notification timeframes may be utilized to generate a default timeframe for the reminder notification of the current question/answer. 
     It should be appreciated that any combination of the above methods of defining a default timeframe for the reminder notification of the current question/answer may be utilized without departing from the spirit and scope of the illustrative embodiments. For example, a prioritization of methods for defining a default timeframe for the reminder notification may be utilized or a function combining two or more of these methods may also be utilized. For example, a user specified timeframe may have a higher priority that a pre-configured user independent default timeframe. A timeframe calculated form similar questions may be given a higher priority than a pre-configured user independent default timeframe but may have a lower priority than a priority of a user specified timeframe, or in some illustrative embodiments may have a relatively higher priority than the user specified timeframe. A timeframe calculated based on analysis of the domain may also be prioritized relative to the other methods of calculating a default timeframe in a similar manner. Alternatively, various weights may be associated with the different sources of timeframes and a weighted function of a combination of these timeframes may be used to calculate a timeframe for scheduling the reminder notification for the current question/answer. Any combination of these methods, or prioritization of these methods, may be used without departing from the spirit and scope of the illustrative embodiments. 
     In addition to, or in replacement of, these default timeframes for scheduling the reminder notification, the reminder timeframe determination engine  392  may further analyze the user&#39;s personal profile data structure, in the database  396  for example, to determine the most appropriate reminder notification timeframe for that particular user. Various characteristics of the user profile, e.g., age of the user, family information, hobbies of the user, medical history of the user, etc. may be analyzed based on their correlation to the concepts in the question/answer. For example, if the user asks the question “When should I start teaching my child to do math?” and the answer may be “at 5 years old.” In looking at the question and answer, the mechanisms of the illustrative embodiments may determine that the question and answer has to do with the age of a child. By analyzing the user&#39;s profile information it may be determined from relationship or family information that the user has a three year old child whose birthday is Aug. 14, 2011. It may further be determined that for this domain, a default timeframe is approximately two months. As a result, a reminder notification may be scheduled for sending to the user when the user&#39;s child will be 4 years and 10 months old, i.e. Jun. 14, 2016. 
     As another example, the user may ask a question about vacationing in Washington D.C. and specifically about traveling to Washington D.C. by automobile. If a reminder notification is to be set for the user, the user&#39;s current home location may be compared to the geographic location of Washington D.C. to calculate a geographical/road distance that the user would have to travel in order to travel from their home location to Washington D.C. and an estimate of travel time may be generated based on a predetermined expected travel speed, or posted speeds along a specified route (e.g., most direct route) from the home location to the Washington D.C., and the calculated geographical/road distance. From the results of this travel time calculation, and an answer specifying a temporal characteristic as to when the user is likely to want to arrive in Washington D.C., a reminder notification may be scheduled for a date/time prior to the desired arrival at Washington D.C. taking into account the travel time, e.g., if the travel time is 4 days, and the user wishes to be reminded 3 weeks before the time that the vacation should commence, then the reminder notification may be set for 3 weeks and 4 days prior to the date on which the user wishes to arrive in Washington, D.C. 
     It can be appreciated from the examples above, that there are many different types of evaluations that may be made based on the information stored in the user profile data structure of the database  396  depending upon the particular concepts in the question/answer. The reminder timeframe determination engine  392  is configured with logic to identify various relationships between concepts in the question/answer and information in the user profile data structures so as to make determinations as to the best time to schedule a reminder notification. Again, this may take into account priorities and specific requests by the user for particular timeframes for the reminder notifications as well, as previously discussed above. 
     Moreover, the reminder timeframe determination engine  392  comprises logic for analyzing evidence of the behavior of the user with regard to activities of the user that may be influential on the timeframe for scheduling the reminder notification. For example, tasks may be associated with an answer to the input question. These tasks, or similar tasks, may have been performed by the user in the past, or by other users in the past if the current user has not performed such tasks. Information about such tasks and the performance of such tasks may be obtained from various trusted sources  398 - 399  associated with the user. There may be a predetermined set of trusted sources  398 - 399  with which the mechanisms of the illustrative embodiments have established interfaces  397  for accessing information about user activities and personal profile information. The information regarding the user&#39;s previous activities, actions, performance of tasks, and the like, may be obtained from various sources including analysis of electronic communications to/from the user via electronic mail, postings to social network websites, postings to newsgroups, instant messages passed via an instant messaging application, analysis of medical records, criminal records, activity logs, or any other electronic messaging mechanism or source of electronic content representative of user activity. Such information may also be obtained from electronic calendar applications associated with the user&#39;s client computing device, electronic to-do or notes list data structures, scheduling information and treatment information in medical records associated with the user, project management tool information associated with the user, previous reminder notification schedules requested by the user for similar tasks, and the like. 
     The reminder timeframe determination engine  392  comprises logic and one or more interfaces  397  for accessing these trusted sources  398 - 399  of information about the user&#39;s previous activities, and/or other users&#39; previous activities with regard to similar tasks, so as to obtain this information from the various trusted sources for analysis. For example, the user may ask a question regarding when it is a good time to have the user&#39;s child administered the chicken pox vaccine. The answer returned may be of the type “when the child is 12 to 15 months old.” Looking at the user&#39;s personal information in the user&#39;s personal profile, it may be determined that the user is the parent of a 6 month old baby. Thus, the answer to the original question is not of great use to the user for another 6 to 9 months away and therefore, a reminder notification may need to be scheduled for the user. Assuming the user requests such a reminder notification, or the system automatically initiates the generation of such a reminder notification, the scheduled timing for the reminder notification is determined by the reminder timeframe determination engine  392 . The reminder notification may initially be scheduled based on a default timeframe determined from one or more of a user and answer independent default timeframe, a default timeframe specified by the user, and/or a default timeframe determined based on analysis of the domain and/or prior scheduling of reminder notifications for similar questions, as discussed above. 
     Moreover, the user&#39;s personal information may be analyzed to determine their geographical home location and how close it may be to a hospital or clinic so as to determine how long it will take for the user to travel to the particular location that is most likely to provide the vaccine. Furthermore, the reminder timeframe determination engine  392  may access the user&#39;s medical records to analyze a historical timeline of treatment recommendations, appointments, treatments administered, and the like, to determine approximately how long it takes the user to obtain a recommended treatment after the treatment has been recommended. For example, if the user&#39;s medical records indicate the user is often recommended to obtain treatments but then waits at least 2 weeks to schedule the appointment to obtain the treatment, then this is indicative of the user&#39;s need for at least 2 weeks to schedule an appointment to obtain treatment. Moreover, information about the user&#39;s doctor availability may be obtained from the medical records to determine how far out the doctor is scheduling appointments, i.e. how “booked” the doctor is. These factors may be combined to determine that, for example, a 3 week time period is needed prior to the date that the vaccine is to be administered, for the reminder notification to be scheduled. This timeframe may be compared to the default timeframe and the timeframe providing the most or least, depending upon the desired implementation, time separation from the desired time of the administering of the vaccine (at 12 months old for example) may be selected. Thus, for example, if the default timeframe is 2 weeks, and the implementation selects a timeframe providing the most time separation from the answer&#39;s temporal characteristic, then a 3 week time period will be selected and used to schedule a reminder notification 3 weeks prior to the user&#39;s child becoming 12 months old. 
     As mentioned above, the answer to a question may be associated with a task, which may in turn be broken down into sub-tasks. The association of the concepts in the answer may be correlated to such tasks and sub-tasks using various sources that indicate the tasks/sub-tasks associated with particular concepts. Each task and/or sub-task may be evaluated based on the user&#39;s prior history of activity to determine for each task/sub-task how much time it takes the user to complete the task/sub-task, such that the cumulative amount of time is indicative of an amount of time prior to an answer&#39;s temporal characteristic the user requires to achieve the task associated with the answer. That is, the answer to the input question may be analyzed to generate a concept map of the task that breaks the task into one or more sub-tasks to be completed. Each such sub-task may have an associated temporal aspect based on the analyzed information about the user&#39;s previous activity. The temporal aspects of the various sub-tasks may be added/merged together to generate a cumulative timeframe for the scheduling of the reminder notification. The reminder notification may then be set based on the cumulative timeframe. 
     Thus, for example, if the answer is about traveling to Walt Disney World™ in Orlando, Fla. for vacation, there may be multiple sub-tasks required to achieve this task associated with the answer to the question, including purchasing airplane tickets, obtaining hotel reservations, obtaining amusement park tickets, traveling to the location, etc. A mapping of these tasks/sub-tasks may be obtained by analyzing the concepts in the answer and associating with each concept a corresponding task/sub-task. For each such task/sub-task, a corresponding timeframe may be calculated as discussed above and the timeframes added to obtain the total amount of time that it is expected that the user will require to achieve the overall task associated with the answer to the question. This timeframe may then be used as a basis for scheduling a reminder notification prior to the expected initiation time of the task, i.e. the temporal characteristic of the answer to the question. 
     Thus, in addition to generating a default timeframe and analyzing the user&#39;s profile information to obtain a user specific timeframe, a further user specific timeframe may be generated based on analysis of the user&#39;s prior activities regarding similar tasks associated with the answer to the question. For example, if the user has performed the activity previously, then the user&#39;s past calendar entries, “to-do” lists and other data structures may be utilized to generate estimates of the time required to accomplish the corresponding task/sub-task. Moreover, information from such data structures of other similar users may be utilized to obtain data for estimating times required to accomplish tasks/sub-tasks, such as via social networking service or the like. 
     It should be appreciated that regardless of which embodiment, or combination of embodiments, implemented, the result is an entry in the reminder notification database  394  that stores information about the user, including contact information for sending the reminder notification, the question asked (including the original question, a timestamp associated with the submission of the question, and the like), the answer generated (including the actual answer generated, the confidence measure associated with the answer, and/or additional information about the answer), and the timeframe for the scheduled reminder notification which operates as a trigger for transmitting the reminder notification to the user, e.g. the applicable timeframe and the date/time at which the reminder notification is to be sent to the user based on this applicable timeframe. Additional information about the question/answer and reminder notification that may be deemed useful for performing processing of reminder notifications may also be stored in the entry of the database  394  depending upon the particular implementation chosen. For example, as mentioned above and discussed hereafter, the entry may store a history of changes to the answer based on periodic checking for updates to the answer during the time period between the timestamp of the original submission of the question and the subsequent sending of a reminder notification. Moreover information about any postponement of the reminder notification may also be stored in the entry. Additionally, a bit may be associated with the entry for indicating whether the entry is currently active or inactive such that it may be overwritten. 
     The reminder/answer change notification output engine  393  operates to traverse the entries in the reminder notification database  394  to identify active entries, determine which, if any, of the active entries have had their reminder notification triggered, and determine for those active entries that have not had their reminder notification trigger, whether the current answer in the entry has been updated or not. The reminder/answer change notification output engine  393  may operate continuously, periodically, or in response to the occurrence of an event. Continuous operation means that the reminder/answer change notification output engine  393  continuously traverses the reminder notification database  394  checking active entries for triggering of their reminder notifications, checking for answer changes, and inactivating entries that have had their reminder notifications activated and which have not received a response from the user to postpone the reminder notification. Periodic operation involves a schedule by which the reminder/answer change notification output engine  393  periodically initiates its operation to check the entries of the reminder notification database, check to see if reminder notifications have been triggered, check for updated answers, and inactivate entries. Event based operation involves certain predefined events, such as an administrator initiating the operation of the reminder/answer change notification output engine  393 , a change in a corpus used to generate the answers to input questions, or the like, being the trigger for initiating the operations of the reminder/answer change notification output engine  393 . 
     As mentioned above, the reminder output engine  393  has two primary operations that it performs. The first primary operation is to check the entries of the reminder notification database  394 , identify active entries in the database  394 , and determine if the active entries have had their reminder notifications triggered or not. A reminder notification is triggered if the current date/time is equal to or past the reminder notification date/time. If the current date/time is equal to or past the reminder notification date/time, and thus, the reminder notification is triggered, the reminder/answer change notification output engine  393  generates and outputs a reminder notification to the user specified in the entry, via the user contact information. The reminder notification preferably includes an indication of the original question submitted by the user, the answer to the question, and optionally a user selectable option to postpone the reminder notification to a later time. In some illustrative embodiments, if the answer has been updated since the question was originally submitted and answered, a history of changes to the answer may be provided in the reminder notification along with timestamp information associated with the original answer and each subsequent change to the answer. 
     In some illustrative embodiments, prior to sending the response notification to the user, the original question may be resubmitted to the QA pipeline  300  to determine if the answer to the question has changed or not and thus, a most up-to-date answer is returned as part of the reminder notification. The reminder notification message may be transmitted to the user utilizing the user&#39;s contact information which may comprise any generally known or later developed contact information including electronic mail address, client computing device IP address, instant messaging address, telephone number for text/audio communication, or the like. Once the reminder notification has been sent, the entry in the database  394  may be marked as no longer active, such as by setting the active bit to a non-active state value, at which point it may be overwritten. 
     As mentioned above, one aspect of the reminder notification may be an option to postpone the reminder notification to a later time. The option may specify predetermined postponement time periods or allow the user to specify an amount of time to postpone the reminder notification, e.g., postpone for 5 days, postpone for 1 month, etc., with the entry in the database having its reminder notification timeframe updated accordingly. In such a situation, the postponement of the reminder notification nullifies the operation to set the entry to an inactive state and thus, the entry remains active. A timeout period may be observed for determining whether the user responds with a postponement request by selecting the option in the reminder notification such that if a postponement request is not received within the timeout period, then the entry will be set to an inactive state. 
     As a second primary operation, the reminder output engine  393  continuously, periodically, or in response to the occurrence of an event, checks each of the active entries in the cognitive reminder notification database  394  to determine whether or not the most recent answer corresponding to the entry has changed. In some cases this may be a check to determine if the original answer has changed, assuming no other changes have occurred prior to the current check. In other cases, if there has already been a change to the answer since the original answer was generated by the QA pipeline  300 , then the check is to see whether or not the most recent prior answer has changed since the last check of the answer. 
     That is, between the time that the original answer was generated and provided to the user, and when the reminder notification is sent to the user according to the scheduled reminder notification timeframe, the reminder output engine  393  may perform checks to see if the answer has changed. This check may comprise resubmitting the original question to the QA pipeline  300  and obtaining a new answer to the question. The new answer may be compared to the original answer, or a most recently obtained answer for the same question from a prior submission of the question to the QA pipeline  300 , to determine if there is a significant change in the answer. A determination as to whether a change is significant or not may be performed in various ways including, but not limited to, performing a character string comparison of the answers to determine if an amount of difference in the character strings is greater than a threshold, comparing numerical values in the answer to determine if a difference in numerical values is greater than a threshold, comparing concepts in the answers to determine if different concepts are being referenced in the answer, or the like. One example mechanism that may be utilized in the illustrative embodiments to determine whether an answer to a question has changed is described in commonly owned and co-pending U.S. patent application Ser. No. 14/446,113 entitled “Changed Answer Notification in a Question and Answer System,” filed Jul. 29, 2014. Of course other known or later developed mechanisms for detecting a change in an answer to a question may be used without departing from the spirit and scope of the illustrative embodiments. 
     If there is a significant change in the answer to the question, then a notification may be sent to the user at that time rather than waiting for the reminder notification timeframe to be reached. The notification may specify that a change in the answer has occurred and request the user to confirm whether they wish to update the reminder notification for this answer, cancel the original reminder notification, or not make any change to the reminder notification. Based on the user input, the reminder notification timeframe may be updated based on analysis of this new answer, such as may be performed by the reminder timeframe determination engine  392  in the manner previously described above, the reminder notification as a whole may be marked as no longer active and thus, able to be overwritten, or the original reminder notification may be maintained. In any case, the new answer may be stored in replacement of the original answer and/or in conjunction with the original answer, potentially with corresponding dates/times that the original answer and the new answer were generated, in the entry for the reminder notification in the database  394 . In this way, when a reminder notification is sent to the user, a history of the change in the answer may be output by the reminder output engine  393  as part of the reminder notification, thereby indicating what the original answer to the question was and each of the changes to the answer from the time point of the original answer to the time point of the reminder notification. 
     Furthermore, in some illustrative embodiments, in addition to, or alternative to, the temporal characteristic based mechanisms for generating notifications of answers to input questions, mechanisms are provided for identifying changes to user profiles that instigate a change in an answer to a previously submitted question. To perform such identification, the answer change determination logic  134  monitors changes to user profiles in the user profile database  396 , correlates such changes with entries in the QA pair database  132  associated with the user profile, determines which entries are dependent upon user profile information fields, matches the changed fields of the user profile information to the fields that are an evidential basis for an answer to a question in an entry, resubmits the corresponding question back to the pipeline  300  as a new input question  310 , evaluates any difference between the newly generated answer and the previous answer, and generates notifications that are output to the user in response to the evaluation indicating a substantial change in the answer to the question, with subsequent updating of the entry in the QA pair database  132 . These operations have been described above and may be implemented in a similar fashion in the cognitive reminder and answer change notification system  390  in  FIG. 3 . 
     As noted previously, the answer change notification mechanisms, in an embodiment that combines such mechanisms with the temporal characteristic based notification mechanisms described above, may monitor for changes to user profiles in the user profile database  396  in the period of time between when an answer was last generated or updated, and when a subsequent temporal characteristic based notification is transmitted. Such monitoring may continue on after such time period as well after the temporal characteristic based notification and/or answer change notifications are sent. Thus, at any time when a user profile is updated or changed in any way, the mechanisms of the answer change notification elements of the illustrative embodiments may operate either in conjunction with the other mechanisms described above or as a separate implementation. 
       FIGS. 4A-4C  illustrate example scenarios of questions and answers with corresponding answer reminder notifications in accordance with various illustrative embodiments.  FIG. 4A  illustrates an example scenario in which an answer to a question has been generated with a subsequent change to the answer being detected prior to the reminder notification being triggered in accordance with one illustrative embodiment. As shown in  FIG. 4A , a user first submits a question of the type “How should I teach my children to write?” to a cognitive system, such as the IBM Watson™ Question and Answer (QA) system, for example. Through processing of the question by the QA pipeline of the cognitive system, an answer of the type “Kids should start writing upper-case letters at age 5” is generated. Based on operation of the mechanisms of the illustrative embodiments, the question and answer are analyzed to identify temporal characteristics in one or both of the question and answer. In this case, the question does not specifically identify a temporal characteristic and there may not be any correlation of keywords, key phrases, or concepts in the question to particular temporal characteristics. However, the answer does have a temporal characteristic in that the answer mentions the “age” of the child. Thus, the mechanisms of the illustrative embodiments will determine that there is a possibility of a need to schedule a reminder notification. 
     As a result of the answer generation and the analysis of the question/answer to determine if there is a temporal characteristic potentially indicative of a need for scheduling a reminder notification, the cognitive system returns a response to the user indicating the answer, i.e. “Kids should start writing upper-case letters at age 5” along with a request, or follow-on question, asking the user if the user would like to be notified or receive a reminder of the answer at a best appropriate time. The user may then respond with “Yes” or “No” in the example scenario, although in other embodiments, the user may be presented with options for setting the reminder notification timeframe according to a user preference, e.g., by way of predefined timeframe values, free-form fields for the user to enter the timeframe, or the like. 
     At this point, it should be noted that in the example scenarios shown in  FIGS. 4A-4C , the entry of the question as well as the receipt of the answer and requests/follow-on questions are provided in the form of a textual messaging session between the user and the cognitive system. However, this is intended as just one example of the way in which the user may interface with the cognitive system and any other known or later developed manner for exchanging information with a user and presenting results of the operations of the cognitive system may be used without departing from the spirit and scope of the illustrative embodiments including, but not limited to, graphical user interfaces, audible messaging with voice recognition, electronic mail correspondence, or the like. 
     In response to a “Yes” response from the user indicating that the user would like to have a reminder notification scheduled, the mechanisms of the illustrative embodiments may schedule the reminder notification based on a default timeframe that is user and answer independent, a user specified timeframe in a user profile or specified in the response to the request/follow-on question, a default timeframe determined from analysis of a domain, analysis of the user profile and information in the user profile with regard to its relevance to concepts in the question/answer, analysis of the user&#39;s previous actions with regard to tasks or subtasks associated with the answer to the question, a prioritized combination of the above, a weighted function of a combination of the above, or the like. 
     In the example shown in  FIG. 4A , the user&#39;s profile is evaluated which includes information about the user&#39;s children including their birthdays and/or ages so that it can be determined that the ages of the user&#39;s children are 5 and 2. Based on this information, and a timeframe determined by the mechanisms of the illustrative embodiments, a reminder notification is scheduled for 3 weeks prior to the younger child&#39;s 5 th  birthday (the older child has already met the timeframe of 5 years old and thus, is not used as a basis for scheduling the reminder notification). This reminder notification is stored as an entry in the reminder notification database which is then continuously, periodically, or in response to an event, checked to determine if the answer to the question has changed, as well as check to see if the reminder notification has been triggered. 
     In this example scenario, the entry in the database is checked periodically between the time the question was originally submitted by the user and the scheduled time that the reminder notification is to be sent to the user. For example, it is assumed that a year from the date of the original submission of the question to the cognitive system, the answer changes to an answer of “Kids should start writing lower-case letters at age 4.” Comparing this new answer to the old answer, the mechanisms of the illustrative embodiments determine that there is a significant change in the answer since the answer now mentions “lower-case” letters and the numerical value of the age has changed from 5 to 4. As a result, a notification is sent to the user of the change in the answer along with a request as to whether the user wishes to update the reminder notification status, cancel the reminder notification, or leave the reminder notification unchanged. If the user selects to cancel the reminder notification, then the entry for the reminder notification is placed into an inactive state and may be overwritten. If the user selects to leave the reminder notification unchanged, then the reminder notification timeframe is not changed, however the entry may be updated to reflect the new answer as the current answer for the question such that repeated notifications are not sent to the user when the user has already been informed of the update to the answer. Only additional changes to the answer will cause subsequent change notifications. 
     If the user selects to update the reminder notification, the mechanisms of the illustrative embodiments will again be executed to calculate the best timeframe for scheduling the reminder notification based on the new answer. In this example scenario, by looking at the user&#39;s profile it is determined that the user&#39;s children are now age 6 and 3. As a result, the cognitive system updates the reminder notification entry with a new reminder notification timeframe and/or scheduled reminder notification time to be approximately 3 weeks prior to the younger child&#39;s 4 th  birthday. Thus, as shown in  FIG. 4A , reminder notifications may be set for answers to previously asked questions and the answers to these reminder notifications may be checked for updates with subsequent updating of the reminder notifications when appropriate. 
       FIG. 4B  illustrates an example scenario in which previous user history of activity is used to calculate a reminder notification timeframe in accordance with one illustrative embodiment. As shown in  FIG. 4B , the scenario again starts with the user submitting an original question of the type “When should I get a flu shot?” with the QA pipeline of the cognitive system generating the answer “September” and the request/follow-on question asking if the user wishes a reminder notification to be set (based on the fact that the question and answer both have temporal characteristics). In response to the user responding with a “Yes” answer, the cognitive system looks to the user&#39;s mobile phone records  420  including a call log and an electronic calendar application to determine how long it takes the user to have recommended medical treatment administered. From the call log, the mechanisms of the illustrative embodiment determine that the user called the user&#39;s doctor on a particular date, and from the electronic calendar application, the mechanisms of the illustrative embodiment determine that the user&#39;s subsequent appointment with the doctor was scheduled 4 weeks later than the call date. Thus, the mechanisms of the illustrative embodiment determine that the user requires 4 weeks to schedule an appointment to have a medical treatment. As a result, the reminder notification for the user to obtain the flu shot is scheduled 4 weeks prior to next September. This information, along with the question, answer, and other reminder notification database entry information as discussed above, is stored in an entry in the reminder notification database. Thus, the illustrative embodiments are able to set reminder notifications for answers to previously submitted questions based on a timeframe determined from the analysis of prior actions of the user. 
       FIG. 4C  illustrates an example scenario in which previous user history of activity as well as breaking down a task into sub-tasks is utilized to calculate a reminder notification timeframe in accordance with one illustrative embodiment. As with the previous scenarios, the scenario starts with the user submitting an original question, e.g., “When is the best time to have an outdoor party?”, with a subsequent answer being generated by the QA pipeline of the cognitive system, e.g., “May”. It should be noted that this answer may have been generated based on many factors including the use&#39;s geographic location, historical weather conditions, or the like. As with the previous scenarios, since the question and the answer both include temporal characteristics, the mechanisms of the illustrative embodiments request or provide a follow-on question as to whether the user would like to have a reminder notification scheduled for the answer to the question. 
     In response to the user responding with a “Yes” answer to the request or follow-on question, the mechanisms of the illustrative embodiments may look into various applications associated with the user that store information about previous activities of the user. One such application may be a to-do application, notes application, calendar application, or the like, which stores information about previous events that the user may have coordinated. For example, the user&#39;s to-do application may store an entry for a previous birthday party hosted by the user along with a listing of sub-tasks that the user needed to perform to host the birthday party, such as creating invitations, sending invitations, getting paper supplies, buying food, etc. Each of these sub-tasks may have associated entries that specify a time/date when they were “checked-off” or otherwise indicated as having been completed. For example, it may be determined that the user created the invitations four days before mailing the invitations which was completed 8 weeks (2 months) prior to the scheduled birthday party. In addition, it may be determined that the user bought the food for the party 3 days prior to the party and obtained the paper supplies 1 day prior to the party. Based on the aggregate of these timeframes associated with the sub-tasks, the mechanisms of the illustrative embodiments may determine that a reminder notification for the best time to have an outdoor party should be scheduled for approximately 2 months and 4 days prior to next May. A corresponding entry may then be created and stored in the reminder notification database. 
     Thus, the illustrative embodiments provide mechanisms for generating reminder notifications for answers to input questions. The reminder notifications may be scheduled according to various criteria including default timeframes, domain related time intervals, user personal information in user profiles as it relates to concepts in the answer to the question, analysis of similar questions and their associated schedules of reminder notifications, activity histories of the user, activity histories of other users, and the like. The illustrative embodiments further provide mechanisms for checking for updates to answers to previous questions and notifying users of changes to these answers. The mechanisms also provide logic and functionality for updating reminder notifications based on changes to the answer to the question and/or postponement of the reminder notification. Overall, these mechanisms provide for an enhancement to cognitive systems by making answers to questions available when they are of most use to the user based on the temporal aspects of the question and/or the answer. 
       FIG. 5  is a flowchart outlining an example operation for providing an answer reminder notification in accordance with one illustrative embodiment. As shown in  FIG. 5 , the operation starts with receiving an input question (step  510 ). The input question is processed by the QA pipeline to generate an answer (step  520 ). The question and answer are analyzed to determine if they include any temporal characteristics (step  530 ). As discussed above, this may involve identifying explicitly specified temporal characteristics in the question/answer, concepts related to temporal characteristics, or the like. These determinations as to whether temporal characteristics are present may be based on identification of keywords, key phrases, concepts, or the like as discussed above. 
     If the question/answer does not contain any temporal characteristics, the answer is output to the user client device that submitted the input question (step  545 ) and the operation terminates. If the question/answer does contain at least one temporal characteristic, then the answer is output to the user client device along with an option to generate a reminder notification for the answer (step  550 ). A determination is made as to whether the user requests that such an answer reminder notification be generated (step  560 ). If not, the operation terminates. If the user does request an answer reminder notification, the timeframe for the reminder notification is calculated (step  570 ) and the reminder notification is scheduled based on the timeframe relative to the temporal characteristic of the answer (step  580 ). A corresponding entry is then stored in the reminder notification database for use is generating a reminder notification at a later time closer to the time at which the answer will be of most use to the user (step  590 ). The operation then terminates. 
       FIG. 6  is a flowchart outlining an example operation for checking active entries in a reminder notification database in accordance with one illustrative embodiment. As shown in  FIG. 6 , the operation starts with the initiation of a check of active reminder notification entries in the reminder notification database (step  610 ). As mentioned above, this may be done on a continuous basis, periodically, or in response to an event, such as an administrator requesting the check to be performed, an update to a corpus of documents used to generate answers to questions by the QA system, or any other suitable event. 
     In response to initiating the check of the active reminder notification entries, the next entry in the reminder notification database is selected (step  620 ) and a determination is made as to whether this entry is active or not (step  630 ). If the entry is not active, e.g., its active bit is set to a non-active state value, then the operation jumps to step  696  where it is determined if the entry is the last entry (step  696 ). If it is not the last entry, the operation returns to step  620  where the next entry is selected. If the entry is the last entry, the operation terminates. 
     If the entry is active, the question associated with the entry is resubmitted to the QA pipeline to check for a significant change in the answer (step  640 ). A determination is made as to whether the answer has significantly changed since either the original answer was generated or the most current version of the answer was generated, assuming a prior check was made resulting in a change to the answer (step  650 ). If there is a significant change in the answer to the question, as may be determined in a variety of ways by comparing the current answer to the previous answer as noted above, then an answer change notification is sent to the user with an option to update the reminder notification, cancel the reminder notification, or not make any change to the reminder notification (step  660 ). Thereafter, whichever option that is selected by the user is then performed by the cognitive system to either update, cancel, or do nothing to the reminder notification entry in the reminder notification database (step  670 ). The operation then proceeds to step  696  to check to see if it is the last entry or not and either terminate or return to step  620  to select the next entry. 
     If there has not been a significant change in the answer to the input question, a determination is made as to whether the reminder notification has been triggered (step  680 ). A reminder notification is triggered if the current date/time (timeframe) equals or is later than the scheduled timeframe of the reminder notification. If not, the operation again branches or jumps to step  696 . If the reminder notification has been triggered, the answer reminder notification is generated and output (step  690 ). As mentioned above, this answer reminder notification may comprise the question, the answer, a history of changes to the answer, and an option to postpone the reminder notification. The answer reminder notification may be output based on contact information for the user stored in the entry corresponding to the reminder notification in the reminder notification database. 
     A determination is made as to whether the user has responded with a request to postpone the reminder notification within a predetermined time period (step  692 ). If the user has not requested postponement, or the option to postpone has timed out, i.e. the predetermined time period has expired, then the entry is marked as inactive and the operation again branches/jumps to step  696 . If the user has requested postponement, then the reminder notification entry in the database is updated to reflect the postponement time period and the entry remains active. The operation then continues to step  696  as discussed above. 
       FIG. 7  is a flowchart outlining an example operation for calculating a timeframe for scheduling a reminder notification in accordance with one illustrative embodiment. The operation outlined in  FIG. 7  assumes that an answer to a natural language question has already been generated by a QA system, the user has been notified of the answer, and the user has selected to have a reminder notification of the answer scheduled, or alternatively, the reminder notification is automatically being set. As such, the process in  FIG. 7  is to determine the timeframe to use in determining when to schedule the reminder notification. 
     As shown in  FIG. 7 , the operation starts by identifying concepts in the answer (step  710 ) and mapping these concepts to tasks/sub-tasks (if any) (step  720 ). A user profile for the user submitting the original natural language question is retrieved (step  730 ) and user profile information, e.g., demographic information, contact information, family information, associations, and the like, is correlated with the concepts in the answer (step  740 ). User activity history information is retrieved, such as from “to-do” lists, calendar entries, posts to various social network, newsgroups, or the like, electronic communications, such as electronic mail messages, instant messages, Tweets on Twitter™, or any other type of electronic communication (step  750 ). The user activity history information is correlated with the concepts and tasks/sub-tasks based on the concept mapping (step  760 ). In addition, reminder notification information for similar queries from other users is accessed to identify timeframes used to schedule reminder notifications for these similar queries (step  770 ). 
     It should be appreciated that each of these sources of information, e.g., user profile information, user activity information, and reminder notification information for similar queries, have associated temporal aspects associated with them that is mapped to a task/sub-task via the concept mapping. These temporal aspects may be associated with these various tasks/sub-tasks to generate a representation of how much time it is likely to take to perform the particular task/sub-task. A combination of the temporal aspects associated with the concepts, tasks, and/or sub-tasks, a timeframe for the reminder notification is calculated (step  780 ). As mentioned above, a weighted function for calculating this timeframe based on these various components of the timeframe calculation may be utilized to accomplish this. Of course, if the operations of steps  730 - 780  results in an inability to correlate user profile information, user activity history information, and notification information for similar queries sufficiently, a default timeframe may be selected for use in scheduling the reminder notification (step  790 ). This default timeframe may be specified in a user profile, an arbitrarily selected timeframe for all users, a default timeframe for the domain of the natural language question, or the like. Alternatively, the user may set a setting in the user profile information stating that a default timeframe should be used and in such a case, the operations of steps  740 - 780  may be bypassed. Based on the determined timeframe, the reminder notification is scheduled relative to the temporal characteristic of the answer (step  795 ) and the operation terminates. 
       FIG. 8  is a flowchart outlining an example operation for answering an input question in which answers are logged and flagged as to whether they are dependent upon information in a user profile in accordance with one illustrative embodiment. The operation outlined in  FIG. 8  assumes that a user profile for a user has been established which includes fields of user specific information, such as demographic information, medical information, contact information, residence information, and the like. Any information that is personal to the user may be a basis for the generating of answers to input questions and thus, may be a potential trigger for resubmitting a previously submitted question to obtain an updated answer. 
     As shown in  FIG. 8 , the operation starts by receiving an input question (step  810 ) and a corresponding user profile for the user that submitted the question is retrieved (step  820 ). The input question is processed by the QA system pipeline in the manner previously described above, using the user&#39;s profile as part of the evidence passages evaluated when generating the answer to the input question (step  830 ). A determination is made as to whether the resulting answer is in fact dependent upon information in the user profile, e.g., one or more fields of information in the user profile (step  840 ). This determination may be based on whether or not the supporting evidence passages of the answer includes a portion of the user profile information. 
     If the answer is not dependent on the user profile, the answer is output in a normal fashion and optionally, the question and answer pair may be logged without setting a dependency flag, i.e. a flag bit or other indicator that indicates the answer is at least partially dependent upon the user profile information (step  850 ). If the answer is dependent upon information in the user profile, the question and answer pair is logged and the dependency flag is set to indicate that it is dependent upon the user profile information. Moreover, the identifiers of the portions of user profile information that are the basis for the answer are stored in the entry in the log data structure (step  860 ). The answer is then output (step  870 ) and the operation terminates. 
       FIG. 9  is a flowchart outlining an example operation for performing an answer change notification operation in accordance with one illustrative embodiment. It is assumed that the QA system is configured to retrieve a user profile for a user that submits an input question and utilize the user profile as further evidence passages for evaluation when generating an answer to the input question, such as described in  FIG. 8  above. 
     As shown in  FIG. 9 , the operation starts by receiving a change to a user profile (step  910 ). Question and answer pair entries for the user corresponding to the changed user profile are retrieved (step  920 ). The question and answer pair entries are searched to identify a subset of entries that have their flags set indicating that they are dependent upon user profile information (step  930 ). The entries in the subset are further searched to identify entries that have user profile information identifiers, e.g., field identifiers, that match the portions of the user profile changed by the received change to the user profile (step  940 ). For those entries having matching user profile information identifiers, the corresponding question is resubmitted to the QA system for generation of new answers to the question (step  950 ). It should be noted that the QA system operates using the updated user profile as evidence passage information along with the corpus when generating answers to input questions, such as in the manner outlined in  FIG. 8  above. 
     The new answers to the questions are received and compared to the questions&#39; previous answers (step  960 ). A determination is made as to whether the answer change is significant (step  970 ) and for those answers that have changed by a significant amount, a notification of the answer change is generated and output to the user that submitted the question (step  980 ). The question and answer pair entry is also updated to reflect the new answer (step  990 ). The operation then terminates. 
     As noted above, it should be appreciated that the illustrative embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In one example embodiment, the mechanisms of the illustrative embodiments are implemented in software or program code, which includes but is not limited to firmware, resident software, microcode, etc. 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
     Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters. 
     The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.