Emergency and emotional state matching via artificial intelligence for automated scheduling

Abstract

In a non-limiting example of the present disclosure, systems, methods, and devices are presented for matching a user's intonation with digital assistant response types and intonation when assisting with scheduling a meeting. An electronic message can be received by a digital assistant service. The digital assistant service can detect an intent to schedule a meeting and identify a level of urgency associated with the message. The digital assistant can respond to the user making the scheduling with a message having an intonation corresponding to the identified level of urgency. The digital assistant can also perform subsequent actions for scheduling the meeting in a manner consistent with the level of urgency of the user making the scheduling. For example, if there is a higher level of urgency associated with the message, the digital assistant can attempt to schedule the meeting with a higher priority, while if there is a lower level of urgency associated with the message, the digital assistant can attempt to schedule the meeting with a lower priority.

Description

Background Art

[0001] Digital assistants have been integrated with many parts of personal and business tasks. Users have become accustomed to using digital assistants to obtain routes, check the weather, and initiate real-time communication with other people (e.g., find a contact to call, initiate a phone or video call). Since digital assistants have been given access to email functions, calendars, and contact lists, users have started to use their digital assistants to schedule meetings and appointments with other users. However, although digital assistants generally respond well to commands during those face-to-face interactions, they typically do not take into account certain context-specific user cues that human assistants commonly use when efficiently scheduling meetings.

[0002] In view of this general technological environment, aspects of the present technology disclosed herein have been considered. Additionally, although a general environment has been discussed, it should be understood that the examples described herein should not be limited to the general environment identified in the background. Summary of the Invention

[0003] This Summary of the Invention is provided to introduce a selection of design concepts that are further described below in the Detailed Description in a simplified form. This Summary of the Invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to assist in determining the scope of the claimed subject matter. Other aspects, features, and / or advantages of the examples will be set forth in part in the description that follows and, in part, will be apparent from the description or can be learned by practice of the disclosure.

[0004] Non-limiting examples of the present disclosure describe systems, methods, and devices for assisting in scheduling meetings. An electronic message can be received by a digital assistant service. When the digital assistant service receives the message, it can analyze it to identify the user intent associated with the message. If an "schedule meeting" intent is identified, the digital assistant service can apply one or more processing models to the message to determine a time component associated with the "schedule meeting" intent and an urgency component / level associated with the "schedule meeting" intent. For example, the digital assistant service can apply a sequence-to-sequence tagging model to the body of the message to determine the time range in which the meeting is to occur, and the digital assistant service can apply a binary classification model to the body of the message to determine the urgency level associated with the "schedule meeting" intent. Once the digital assistant service has identified these two components for the message, it can match the urgency level of the message with a response tone / type and perform one or more subsequent actions to schedule the corresponding meeting based at least on the identified urgency level. In some examples, the emphasis level of the user associated with the "schedule meeting" intent can also be identified and used to determine how to respond to and / or follow up on the "schedule meeting" intent. Brief Description of the Drawings

[0005] Non-limiting and non-exhaustive examples are described with reference to the following drawings.

[0006] Figure 1 is a schematic diagram showing an example distributed computing environment for assisting in scheduling meetings by using digital assistant services and artificial intelligence to utilize user urgency and emotional state matching processing techniques, where a "strong tone" is matched with the user's urgency and / or emotional state.

[0007] Figure 2 Shows another example where a digital assistant service using artificial intelligence utilizes user urgency and emotional state matching processing techniques to assist in scheduling meetings, where a "gentle tone" is matched with the user's urgency and / or emotional state.

[0008] Figure 3 is a schematic diagram for showing an example distributed computing environment for identifying a user's urgency and emotional state through user speech and utilizing those attributes to assist in scheduling meetings.

[0009] Figure 4 Shows an example response matrix that can be used by a digital assistant service to identify appropriate processing requirements and responses for responding to meeting commands based on a determined user emphasis level and meeting urgency.

[0010] Figure 5 is an exemplary method for assisting in scheduling meetings.

[0011] Figure 6 and Figure 7 are simplified diagrams of a mobile computing device that can be used to implement aspects of the present disclosure.

[0012] Figure 8 is a block diagram showing example physical components of a computing device that can be used to implement aspects of the present disclosure.

[0013] Figure 9 is a simplified block diagram of a distributed computing system in which aspects of the present disclosure can be implemented. Detailed Description of the Embodiments

[0014] Various embodiments will be described in detail with reference to the drawings, in which like reference numerals denote like parts and components throughout the several views. The reference to each embodiment does not limit the scope of the appended claims. Additionally, any examples set forth in this specification are not intended to be limiting, but merely illustrate some of the many possible embodiments of the appended claims.

[0015] Examples of the present disclosure provide systems, methods, and devices for assisting in scheduling meetings based on an analysis of user commands. User commands can be received as electronic messages, such as emails, text messages, direct digital assistant application messages, and in some examples, spoken commands. In some examples, a digital assistant service can receive an electronic message from a user based on a digital assistant referenced in the electronic message (e.g., in the body of the electronic message) and / or explicitly included in the electronic message (e.g., in the "To" field, in the "Cc" field). When the digital assistant service receives the electronic message, it can apply one or more natural language processing models to the content of the message and determine the user intent and / or command associated with the message. If the digital assistant service determines that there is a "schedule meeting" intent associated with the message, it can further analyze the text content in the message, signals associated with the message (e.g., tags, timestamps, domains of the user included in the message, etc.), and / or physical / voice characteristics of the user sending the message (e.g., body language, facial expressions, voice volume), and in cases where available, utilize one or more segments of this data to determine the urgency level associated with the "schedule meeting" intent and / or the emphasis level associated with the user. When analyzing the electronic message, the digital assistant service can apply a first processing model (e.g., a sequence-to-sequence tagging model) to determine the time occurrence / component associated with the meeting to be scheduled, and apply a second processing model (e.g., a binary classification model) to determine the urgency level / component associated with the electronic message. One or more message processing models can be trained with a pre-classified data set that has been manually classified by humans until a sufficient level of accuracy is obtained.

[0016] After determining an urgency level and / or an emphasis level associated with an electronic message, a digital assistant service can match a response tone to its identified urgency level and / or emphasis level. For example, if a relatively high urgency level is detected, the digital assistant service can respond to the user with an acknowledgement at that urgency level, respond with different words selected for increased urgency compared to words that may have the same meaning but convey a different tone in a less urgent scenario, and / or adjust the pace or volume of the audible response to match the urgency level. Similarly, if a relatively low urgency level is detected, the digital assistant service can respond to the user in a normal manner without regard to the urgency level of the processed message, select milder response language and / or use a lower pace and / or volume to match the lower urgency level. In some examples, a response matrix can be utilized to determine an appropriate response type and / or tone to respond to the user. The matrix can be single-axis (e.g., for urgency or emphasis), or multi-axis (e.g., for both urgency and emphasis). In some examples, the digital assistant service can perform subsequent actions in a manner consistent with the identified urgency level and / or emphasis level. For example, if the urgency level associated with a message is high, the digital assistant service can ping potential meeting participants more frequently for necessary meeting information (if they do not provide the information immediately). In another example, if the urgency associated with a meeting is relatively low (e.g., an optional date for the meeting, an optional time for the meeting, an optional duration for the meeting, etc.), the digital assistant service can provide more options to potential meeting attendees.

[0017] The systems, methods, and devices described herein provide technical advantages for assisting with meeting scheduling and processing digital assistant commands. According to an example, when a digital assistant service identifies / determines an urgency and / or an emphasis level associated with an "Schedule Meeting" command, it can prioritize the command relative to the urgency and / or emphasis levels associated with one or more other "Schedule Meeting" commands it has received. Thus, if there is a queue of commands that the digital assistant service has received, the message with the highest urgency and / or emphasis level associated with them can be moved to the front of the queue. In this way, the computer processing costs (CPU cycles) and memory storage costs associated with processing a large number of commands or any number of commands in a low network speed environment are effectively directed to the most urgent processing commands. Additionally, by having the digital assistant service perform subsequent actions based on the urgency level, the processing costs associated with scheduling the user to utilize their client device resources when performing these actions can be reduced, in some cases shifting these loads to the server devices running the digital assistant service where these actions can be processed more efficiently.

[0018] Figure 1 is a schematic diagram showing an example distributed computing environment 100 for assisting in scheduling meetings by using a user urgency and emotional state matching processing technique through digital assistant services and artificial intelligence, where a "strong tone" is matched with the user urgency and / or emotional state. The computing environment 100 includes a meeting command sub-environment 102, a network and processing sub-environment 110, and a meeting follow-up action sub-environment 118. Each computing device described herein can communicate with each other via a wired or wireless network (such as network 112).

[0019] The meeting command sub-environment 102 includes a computing device 104 on which user 106 "Charles" drafted an email 108. The digital assistant is included in the "To" field of the email 108, and another user "Warren" is included in the "Cc" field of the email 108. The subject line of the email 108 states "Project X matter", and the email body states: " [Digital Assistant] - We need to resolve this issue in person no later than Friday." When user 106 "Charles" sends the email 108, the message can be automatically routed through the network 112 to the digital assistant service based on the email 108, which includes [Digital Assistant] in the "To" field and / or the message body. In some examples, the digital assistant service can be wholly or partly cloud-based and can be hosted on one or more server computing devices, such as the server computing device 114 in the network and processing sub-environment 110.

[0020] When the digital assistant service receives the email 108, it can extract text from the subject and body of the message, any tags associated with the email 108, and other data associated with the message (such as, sender identity, recipient identity, timestamp, attachments, etc.). The digital assistant service can apply one or more natural language processing models to the text extracted from the email 108 and determine whether there is a command associated with the message. In this example, the digital assistant service determines that there is a meeting intent / command associated with the email 108. Once the meeting intent / command has been identified, the digital assistant service can analyze the email 108 and determine whether the message contains parameters that can be used to assist in scheduling a meeting corresponding to the identified "meeting intent" of user 106.

[0021] In some examples, when identifying a specific intent associated with the email 108 and its specific parameters (e.g., meeting time, meeting location, meeting type, etc.), the digital assistant service can utilize one or more of the following models to process the natural language content in the email 108. These models include: hierarchical attention model, topic detection using clustering, hidden Markov model, maximum entropy Markov model, support vector machine model, decision tree model, deep neural network model, general sequence-to-sequence model (e.g., conditional probability recurrent neural network, transformer network), generative model, recurrent neural network for feature extractor (e.g., long short-term memory model, GRU model), deep neural network model for word-by-word classification, and latent variable graphical model. In one example, the digital assistant can utilize a sequence-to-sequence model to identify the time parameter associated with the email 108 and a binary classification model to identify the urgency level associated with the email 108. In some examples, the urgency level can be a value, a percentage of the possible maximum urgency, and / or the ratio of the identified urgency to the possible maximum urgency. In additional examples, the urgency level can be classified based on the value, percentage, and / or ratio of the urgency level (e.g., the urgency level can be classified as high, medium, low, etc.). One or more processing models applied by the digital assistant service to the email 108 can be trained on a manually classified dataset (e.g., the dataset in the training data storage unit 116). For example, a binary classification model can be trained using messages for which humans have manually classified the urgency level, and a sequence-to-sequence model can be manually trained using messages for which humans have manually classified various meeting parameters (e.g., meeting time and / or duration).

[0022] Based on its application of one or more of the various models discussed above, the digital assistant service can determine that: the email 108 has a time parameter associated with it, which is between the date and time of sending and / or receiving the email 108 (based on the timestamp associated with the email 108) and the date corresponding to Friday (the date included in the body of the email 108). This time parameter corresponds to the date / time parameter for the user 106 to have a meeting with Warren regarding Project X. Based on its application of one or more of the various models discussed above, the digital assistant service can also determine the urgency level associated with the email 108. For example, the phrase "no later than" can correspond to a relatively high urgency level. In the example, the digital assistant service can match the response type with the determined time parameter and / or urgency level. In some examples, the digital assistant service can utilize a matrix to match the response type with the determined time parameter and / or urgency level, as combined with Figure 4This will be discussed more fully. Thus, in this example, the urgency level is determined to be relatively high, and the digital assistant service responds to Charles in a "tone" that matches this urgency. Thus, as shown on computing device 120 (which may be the same or a different device from computing device 104) in the meeting follow-up action sub-environment 118, the digital assistant service has sent an email 122 to user 106 (Charles) in response to email 108, which states: "Hi, Charles, I know you urgently need to meet before Friday. I will contact the other party to schedule a meeting as soon as possible." This response is for illustrative purposes only, and other message types that match the high urgency level of email 108 may additionally or alternatively be utilized.

[0023] According to some examples, the digital assistant service may also perform follow-up actions associated with assisting in scheduling a meeting in various ways based on the determined urgency of the analyzed message. For example, if the message associated with it has a high urgency, the digital assistant service may send echo information to the participants more frequently and / or more times for the information required to schedule the meeting compared to operations for messages with a relatively low urgency associated with them.

[0024] The digital assistant service may match a processing priority level with the determined urgency and / or time parameters identified from email 108. For example, since there may be a queue of messages to be processed by the digital assistant service, the digital assistant service may prioritize the processing of those messages based on the urgency levels and / or time parameters associated with those messages. Thus, in some examples, the higher the determined urgency associated with a particular message, the higher the priority that message may have in the digital assistant processing it, resulting in a response and / or execution of additional follow-up actions. Similarly, the lower the determined urgency associated with a particular message, the lower the priority that message may have in the digital assistant processing it, resulting in a response and / or execution of additional follow-up operations. In additional examples, the time parameter may additionally or alternatively be a factor considered by the digital assistant service when prioritizing messages to be processed. For example, if it is determined that the user intends to schedule a meeting within a relatively long time frame (e.g., three weeks, three months, etc.), the digital assistant service may prioritize other messages where the user intends to schedule a meeting within a relatively short time frame (e.g., one week, "no later than Friday", etc.). In this way, the digital assistant service can effectively manage the processing costs associated with handling a large number of messages / commands.

[0025] Figure 2Another example computing environment 200 is shown, where a digital assistant service utilizing artificial intelligence uses user urgency and mood state matching processing techniques to assist in scheduling a meeting, where a "gentle tone" is matched to the user urgency and / or mood state. The computing environment 200 includes a meeting command sub-environment 202 and a meeting follow-up action sub-environment 218. The digital assistant service may reside, in whole or in part, on one or more server computing devices, which are not shown together with the communication network for ease of illustration.

[0026] In this example, user 206 "Charles" has composed an email 208 on computing device 204. Email 208 includes [Digital Assistant] in the "To" field, another user "Warren" in the "CC" field, and the subject line "Project X Matter". Thus, in most cases, email 208 is similar to Figure 1 email 108 in. However, email 208 states that " [Digital Assistant] - I hope to get together before Friday so that we can discuss this." When user 206 Charles sends email 208, based on including [Digital Assistant] in the "CC" field and / or including the @[Digital Assistant] tag in the body of the email, the email can be automatically directed to the digital assistant service. In any case, when the digital assistant service receives email 208, it will extract the text, tags, and metadata associated with email 208, which will be used to determine the user intent / command associated with email 208.

[0027] Once the digital assistant service determines that email 208 includes the intent / command of user 206 to schedule a meeting, the digital assistant service can apply one or more natural language processing models to the text extracted from email 208 to determine one or more parameters associated with the meeting and to determine the level of urgency associated with email 208. For example, based on applying a sequence-to-sequence tagging model to the extracted text, the digital assistant service can determine that the time parameter for scheduling the meeting is "before Friday", and based on applying a binary classification model to the extracted text, the digital assistant service can determine the level of urgency associated with email 208. In some examples, one or both of the sequence-to-sequence tagging model and the binary classification model can be trained on a manually classified dataset, and the digital assistant service can thus be able to determine the meeting parameters and the level of urgency to a degree of certainty corresponding to that training.

[0028] In this example, in addition to determining that the time parameter for scheduling the meeting is "before Friday", the digital assistant service also determines that there is a low urgency level associated with email 208. Thus, the digital assistant matches its response to the low urgency level, as shown by email 222 in the subsequent action sub-environment 218, which is displayed on a computing device 220 (which can be the same or a different computing device from computing device 204). Specifically, email 222 from the [digital assistant] to user 206 (Charles) states: Hi, Charles, I know you want to meet before Friday. I will contact the other party and schedule the meeting. Thus, unlike Figure 1 the response of the digital assistant in Figure 1 , email 222 has a lower degree of urgency associated with it (e.g., it does not explicitly confirm the urgency associated with the message and it does not include urgency terms such as "I will contact the other party as soon as possible to schedule the meeting").

[0029] Figure 3 FIG. shows a schematic diagram for illustrating an example distributed computing environment 300 that is operative to identify a user's urgency and emotional state from a user utterance and to utilize those attributes to assist in scheduling a meeting. The computing environment 300 includes a user utterance sub-environment 302, a network and processing sub-environment 312, a response sub-environment 316, a first subsequent action sub-environment 322, and a second subsequent action sub-environment 332.

[0030] In the user utterance sub-environment 302, user 304A (Pamela) says, while speaking within the audible range of digital assistant device 306A: "[Digital assistant] - we have to have a team meeting on project X within the next two weeks. Please schedule", as shown by utterance 308. The digital assistant device 306A is capable of detecting various voice attributes associated with utterance 308, such as frequency and amplitude, which can be used to determine the urgency level associated with utterance 308 and / or the perceptual state of user 304A. Thus, when Pamela emphasizes the words "have to have" in utterance 308, the digital assistant device 306A is capable of detecting that feature and / or sending information corresponding to that feature to the digital assistant service for processing.

[0031] In some examples, based on user 304A including the term [digital assistant] in utterance 308, the digital assistant device 306A can receive utterance 306A via network 314 and send it to the digital assistant service, which can reside on one or more server computing devices (such as server computing device 313). Other mechanisms for "waking up" the digital assistant device 306A and / or having an utterance sent to the digital assistant service are contemplated herein, such as interacting with a button, utilizing other voice commands and keywords, etc.

[0032] When the digital assistant service receives utterance 308, it can utilize one or more natural language processing models to determine the user intent associated with utterance 308, determine one or more parameters associated with the determined intent, and determine the urgency and / or emphasis level associated with utterance 308. In this example, the digital assistant service determines that there is a meeting intent / command associated with utterance 308. Once the meeting intent / command has been identified, the digital assistant service can determine whether utterance 308 contains parameters (e.g., meeting time, meeting location, meeting type, etc.) that it can use to assist in scheduling a meeting corresponding to the meeting intent of user 304A. When determining whether utterance 308 includes parameters that the digital assistant can use to assist in scheduling a meeting, the digital assistant service can utilize one or more of the following models to process utterance 308, and these models include: hierarchical attention model, topic detection using clustering, hidden Markov model, maximum entropy Markov model, support vector machine model, decision tree model, deep neural network model, general sequence-to-sequence model (e.g., conditional probability recurrent neural network, transformer network), generative model, recurrent neural network for feature extractor (e.g., long short-term memory model, GRU model), deep neural network model for word-by-word classification, and latent variable graphical model.

[0033] In a specific example, the digital assistant service can utilize a sequence-to-sequence model to identify the time parameter associated with utterance 308 and utilize a binary classification model to identify the urgency level associated with utterance 308. The digital assistant service can additionally or alternatively apply one or more prosody feature processing models to the speech input to determine the urgency level and / or emphasis level associated with utterance 308. The determined urgency level and / or emphasis level can be a value, a percentage of the possible maximum value, and / or a ratio of the identified urgency and / or emphasis to the possible maximum urgency and / or emphasis. In additional examples, it can be classified based on the value, percentage, and / or ratio of the urgency level and / or emphasis level (e.g., classified as high, medium, low, etc.). One or more processing models applied by the digital assistant service to utterance 308 can be trained on a manually classified dataset. For example, a binary classification model can be trained using utterances for which humans have manually classified the urgency level and / or emphasis level, and a sequence-to-sequence model can be manually trained using messages for which humans have manually classified various meeting parameters (e.g., meeting time and / or duration).

[0034] In this example, based on the application of one or more of the above models, the digital assistant service determines that there is a high urgency level and / or emphasis level associated with utterance 308. Accordingly, the digital assistant service identifies a tone and response type that match the urgency level and emphasis level, and responds via digital assistant device 306B with utterance 318, which states: "No problem, Pamela! I've started looking for a time for the meeting and I'll get back to you as soon as possible!" Digital assistant devices 306A and 306B can be the same or different devices, where digital assistant device 306A receives utterance 308 from first user 304A, and digital assistant device 306B generates utterance 318 for user 304B. In some examples, based on the high urgency associated with utterance 308, the digital assistant service can prioritize the processing of the utterance according to that level. For example, if there is a queue of electronic messages and / or utterances already received by the digital assistant service to be processed, the digital assistant service can prioritize the processing of electronic messages and / or utterances associated with a relatively higher priority over those associated with a relatively lower priority. In this way, the digital assistant service can ensure a quick response to high-priority / urgency messages in the presence of limited available processing resources, and then move on to lower-priority messages when more processing resources are freed up and / or high-priority / urgency messages are removed from the queue. In additional examples, the digital assistant service can assign a higher processing priority to electronic messages and / or utterances for meetings with an arrangement window closer to the current date and time compared to those for meetings with an arrangement window further from the current date or of longer duration. In additional examples, the digital assistant service can prioritize the processing of electronic messages and / or utterances based on a combination of the urgency level and / or when the meeting needs to be scheduled. In additional examples, for a given utterance or electronic message, the weight of the urgency level in the prioritization can be less than, equal to, or greater than the weight of the meeting date / window.

[0035] In some examples, a digital assistant service can access auxiliary information associated with one or more of the accounts / profiles of user 304A. For example, user 304A may have explicitly provided the digital assistant service with access to their calendar, email, and / or contact information. Thus, when the digital assistant service processes utterance 308, it can identify "Project X" from a previous meeting invitation and identify one or more potential attendees who were invited to the previous meeting for the new meeting. In this example, the attendee is Warren 328A / 328B. The digital assistant service with access to user 304A's email and / or contact information can identify the contact information of the second user 328A / 328B "Warren" and send an electronic message to one of Warren's accounts and / or devices. In this example, as shown in the first follow-up action sub-environment 322, the digital assistant sends 320 electronic message 326 to Warren displayed on computing device 324A. Electronic message 326 states: "Warren - Are you available to meet with Pamela on any of the following dates / times? - [Date / Time 1]. - [Date / Time 2]. - [Date / Time 3]. - [Date / Time 3]. - [Date / Time 4]."

[0036] According to an example, based on the urgency level associated with the utterance, the emphasis level associated with the utterance, and / or the date / time of the meeting to be scheduled, for the information required to schedule a meeting for the digital assistant service, the digital assistant service may send echo information back to the user more or less. For example, if the digital assistant service determines that the urgency level associated with the utterance and / or the electronic message is high, or a meeting needs to be scheduled in the near future, compared to an utterance and / or an electronic message with a lower urgency associated therewith, or for a meeting that can be scheduled further out, the digital assistant service may send echo information to the invitee more frequently for the information it requires. Additionally, for utterances and / or electronic messages with a high urgency level, emphasis level, and / or meetings that need to be scheduled as soon as possible, the digital assistant service may reduce the number of choices it presents to the user when determining the information it needs to schedule the corresponding meeting. Thus, in this example, in the case where there is a high urgency level associated with utterance 308 and the second users 328A / 328B do not respond to the electronic message 326 within a threshold duration (which may be short due to the urgent nature of the utterance / meeting), the digital assistant service sends an echo message to the users 328A / 328B by sending electronic message 334 on computing device 324B in the second follow-up action sub-environment 332. The electronic message states: "Warren - Pamela needs to meet with the team as soon as possible. Can you meet at [date / time 1]?" Thus, the electronic message not only has fewer choices presented to the users 328A / 328B, but also includes the urgency level associated therewith corresponding to the urgency of utterance 308.

[0037] In some examples, if the digital assistant service determines that there are no available meeting options that precisely meet the meeting request parameters, the digital assistant service may modify one of the request attributes with a lower determined priority (e.g., the duration of the meeting, day of the week, etc.). For example, if the user initially requests a one-hour meeting (or if the user's default preference is a one-hour meeting), the digital assistant service may determine that due to the urgency and availability (or lack thereof) of the meeting attendees, the meeting should be scheduled in a forty-five-minute time slot because that is the only duration that is mutually available to the attendees.

[0038] Figure 4An example response matrix 400 is shown, which can be used by a digital assistant service to identify appropriate handling requirements and responses for responding to a meeting command based on a determined user emphasis level and meeting urgency. The response matrix 400 includes the user's urgency level on the X-axis and the user's emphasis level on the Y-axis. The response matrix 400 includes four quadrants, each quadrant associated with at least one response tone and / or type and at least one handling requirement for processing the user's "schedule meeting" command based on the detected emphasis level and urgency level of the user associated with the "schedule meeting" command.

[0039] In response to plotting the user's "schedule meeting" command in the first quadrant 402, which corresponds to a high emphasis level but a low urgency level of the user associated with the "schedule meeting" command, the digital assistant service can respond to the user with a relatively mild response tone / type. Additionally, in response to plotting the user's "schedule meeting" command in the first quadrant 402, the digital assistant service can assign a relatively low priority to process the command relative to other user commands the digital assistant service has received. In some examples, the relatively low processing priority assigned to the command in the first quadrant 402 can be relatively high compared to the priority of the command plotted in the third quadrant 406, but relatively low compared to the priority of the commands plotted in the second quadrant 404 and the fourth quadrant 408.

[0040] In response to plotting the user's "schedule meeting" command in the second quadrant 404, which corresponds to a high emphasis level and a high urgency level of the user associated with the "schedule meeting" command, the digital assistant service can respond to the user with a relatively strong response tone / type. Additionally, in response to plotting the user's "schedule meeting" command in the second quadrant 404, the digital assistant service can assign the highest priority to process the command relative to other user commands the digital assistant service has received. The highest processing priority assigned to the command in the second quadrant 404 can be relatively higher than the priority of the commands plotted in each of the first quadrant 402, the third quadrant 406, and the fourth quadrant 408.

[0041] In response to the user's "Schedule Meeting" command being plotted in the third quadrant 406, which corresponds to a low emphasis level and a low urgency level of the user associated with the "Schedule Meeting" command, the digital assistant service can respond to the user with the least intense response tone / type. Additionally, in response to the user's "Schedule Meeting" command being plotted in the third quadrant 406, the digital assistant service can assign the lowest priority to process the command relative to other commands that the digital assistant service has received. The lowest processing priority assigned to the command in the third quadrant 406 can be relatively lower than the priority of the commands plotted in each of the first quadrant 402, the second quadrant 404, and the fourth quadrant 408.

[0042] In response to the user's "Schedule Meeting" command being plotted in the fourth quadrant 408, which corresponds to a low emphasis level but a high urgency level of the user associated with the "Schedule Meeting" command, the digital assistant service can respond to the user with a relatively mild response tone / type. Additionally, in response to the user's "Schedule Meeting" command being plotted in the fourth quadrant 408, the digital assistant service can assign a relatively medium priority to process the command relative to other commands that the digital assistant service has received. The relatively medium processing priority assigned to the command in the fourth quadrant 408 can be relatively lower than the priority of the commands in the second quadrant 404, but relatively higher than the priority of the commands in each of the first quadrant 402 and the third quadrant 406.

[0043] Figure 5 is an exemplary method 500 for assisting in scheduling a meeting. Method 500 begins with a start operation and the process continues to operation 502.

[0044] At operation 502, an electronic message is received from the user making the scheduling. The message is received by the digital assistant service. In some examples, the message can be received via email or other electronic message input, an input field in an operating system, etc. In some examples, the message includes only natural language. In other examples, the message can include natural language, such as the language in the body of an email, as well as additional information. The additional information can include one or more tags or information entered into one or more fields (such as the "To" field, "Cc" field, location tag, meeting type tag, etc.) and attachments. In some examples, the additional information can additionally or alternatively include metadata, such as a timestamp associated with the creation of the message, a timestamp associated with editing the message, etc.

[0045] From operation 502, the process proceeds to operation 504, where an intention to schedule a meeting is detected from the electronic message. In an example, the intention to schedule a meeting can be detected by analyzing one or more tags associated with the message (e.g., a schedule meeting tag, a meeting location tag). In other examples, the intention to schedule a meeting can be detected based on the message being addressed to the digital assistant and / or the digital assistant being included in the message body (i.e., the primary use of the digital assistant can be to schedule meetings). In still other examples, the intention to schedule a meeting can be detected based on applying one or more natural language processing models to the message and / or a combination of the above.

[0046] In addition to detecting the intention to schedule a meeting, the digital assistant service can also identify one or more meeting parameters associated with the meeting. For example, the digital assistant can identify the location where the meeting is to occur, the time when the meeting is to occur, the date when the meeting is to occur, the time window when the meeting is to occur, the participants who are to attend the meeting, the type of the meeting, etc. by applying one or more processing models. In an example, the digital assistant service can apply one or more of the following models when identifying one or more meeting parameters associated with the electronic message: a hierarchical attention model, topic detection using clustering, a hidden Markov model, a maximum entropy Markov model, a support vector machine model, a decision tree model, a deep neural network model, a general sequence-to-sequence model (e.g., a conditional probability recurrent neural network, a transformer network), a generative model, a recurrent neural network for a feature extractor (e.g., a long short-term memory model, a GRU model), a deep neural network model for word-by-word classification, and a latent variable graphical model.

[0047] From operation 504, the process proceeds to operation 506, where the urgency level associated with the electronic message is identified. When determining the urgency level associated with the message, one or more of the above models can be applied to the message. In some examples, the user can manually tag the message with the urgency level, and the digital assistant can utilize the tag to automatically characterize the message. In a specific example, the digital assistant service can utilize a binary classification model to identify the urgency level associated with the message. In some examples, the emotional state and / or emphasis level of the user sending the message can be determined by the digital assistant service based on the application of one or more sentiment analysis models applied to the electronic message. The urgency level associated with the message can be additionally or alternatively determined to identify the emotional state and / or emphasis level of the user.

[0048] From operation 506, the process proceeds to operation 508, in which a response to the electronic message is sent to the scheduling user in a tone corresponding to the level of urgency identified from the electronic message. In some examples, the tone of the response can be matched to the identified level of urgency and / or emphasis level based on the application of one or more matrices, as described above for Figure 4 described. In an example where only a single one of the emphasis level or urgency level is identified by the digital assistant service, a linear matrix (e.g., a single axis) can be applied. For example, if only the urgency level associated with the message is identified, a response with a tone along a single urgency axis can be matched to the identified urgency level. Similarly, if only the emphasis level associated with the user is identified, a response with a tone along a single emphasis axis can be matched to the identified emphasis level of the user. Or, if both the emphasis level and the urgency level are identified by the digital assistant service, a multi-axis matrix can be used to identify an appropriate response by the digital assistant service. Other mechanisms for matching the response tone to the urgency level and / or emphasis level are contemplated (applying score values to the urgency level and / or emphasis level, matching the score values of the urgency level and / or emphasis level to the response type / tone, weighting the urgency level and / or emphasis level, etc.).

[0049] From operation 508, the process proceeds to operation 510, in which one or more follow-up actions for scheduling the meeting are performed by the digital assistant service. In some examples, the type and / or mechanism of the follow-up action can be indicated by the level of urgency and / or emphasis level associated with the message and / or the user. For example, the digital assistant service can send a message to one or more users who are to attend the meeting being scheduled, and based on the increased identified level of urgency and / or emphasis level, the digital assistant service can send echo information to those users more frequently (if they do not respond) for the necessary information. In another example, when there is a relatively low level of urgency and / or emphasis level associated with the "schedule meeting" message, the digital assistant service can send more time and / or date options to potential meeting attendees, and when there is a relatively high level of urgency and / or emphasis level associated with the "schedule meeting" message, the digital assistant service can send fewer options to potential meeting attendees.

[0050] The process proceeds from operation 510 to the end operation, and method 500 ends.

[0051] Figure 6 and Figure 7Shown is a mobile computing device 600 that can be used to implement embodiments of the present disclosure, such as, for example, a mobile phone, a smartphone, a wearable computer (such as smart glasses), a tablet computer, an e-reader, a laptop computer, or other AR-compatible computing devices. Referring to Figure 6 , an aspect of the mobile computing device 600 for implementing these aspects is shown. In a basic configuration, the mobile computing device 600 is a handheld computer having both an input unit and an output unit. The mobile computing device 600 typically includes a display 605 and one or more input buttons 610 that allow a user to input information into the mobile computing device 600. The display 605 of the mobile computing device 600 can also be used as an input device (e.g., a touchscreen display). If included, the optional side input unit 615 allows for further user input. The side input unit 615 can be a rotary switch, a button, or any other type of manual input unit. In alternative aspects, the mobile computing device 600 can incorporate more or fewer input units. For example, in some embodiments, the display 605 may not be a touchscreen. In yet another alternative embodiment, the mobile computing device 600 is a portable telephone system, such as a cellular phone. The mobile computing device 600 can also include an optional keyboard 635. The optional keyboard 635 can be a physical keyboard or a "soft" keyboard generated on a touchscreen display. In various embodiments, the output unit includes: a display 605 for presenting a graphical user interface (GUI), a visual indicator 620 (e.g., a light-emitting diode), and / or an audio transducer 625 (e.g., a speaker). In some aspects, the mobile computing device 600 incorporates a vibration sensor for providing haptic feedback to the user. In yet another aspect, the mobile computing device 600 incorporates input and / or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., an HDMI port) for sending signals to or receiving signals from external devices.

[0052] Figure 7 is a block diagram showing an aspect of the architecture of a mobile computing device. That is, the mobile computing device 700 can incorporate a system (i.e., an architecture) 702 to implement some aspects. In one embodiment, the system 702 is implemented as a "smartphone" capable of running one or more applications (e.g., a browser, an email client, a calendar, a contact manager, a messaging client, a game, and a media client / player). In some aspects, the system 702 is integrated as a computing device, such as an integrated personal digital assistant (PDA) and a wireless phone.

[0053] One or more applications 766 may be loaded into the memory 762 and run on or in association with the operating system 864. Examples of applications include: a telephone dialer program, an e-mail program, a personal information management (PIM) program, a word processing program, a spreadsheet program, an Internet browser program, a messaging program, and the like. The system 702 also includes a non-volatile storage area 768 within the memory 762. The non-volatile storage area 768 can be used to store persistent information that should not be lost if the system 702 loses power. The applications 766 may use and store information in the non-volatile storage area 768, such as e-mail or other messages used by an e-mail application. A synchronization application (not shown) is also located on the system 702 and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area 768 synchronized with the corresponding information stored at the host computer. It should be appreciated that other applications may be loaded into the memory 762 and run on the mobile computing device 700, including instructions for providing and operating a digital assistant computing platform.

[0054] The system 702 has a power supply 770, which may be implemented as one or more batteries. The power supply 770 may also include an external power source, such as an AC adapter or a docking station that supplies power to supplement or recharge the battery.

[0055] The system 702 may also include a radio interface layer 772 that performs the functions of sending and receiving radio frequency communications. The radio interface layer 772 facilitates a wireless connection between the system 702 and the "outside world" via a communication carrier or service provider. Transmissions to and from the radio interface layer 772 are under the control of the operating system 764. In other words, communications received by the radio interface layer 772 may be propagated to the applications 766 via the operating system 764, and vice versa.

[0056] The visual indicator 620 can be used to provide visual notifications, and / or the audio interface 774 can be used to generate audible notifications via the audio converter 625. In the illustrated embodiment, the visual indicator 620 is a light-emitting diode (LED), and the audio converter 625 is a speaker. These devices can be directly coupled to the power supply 770 so that when activated, they remain operational for a period of time indicated by the notification mechanism, even if the processor 760 and other components may be turned off to conserve battery power. The LED can be programmed to be always on until the user takes an action to indicate the power-on state of the device. The audio interface 774 is used to provide audible signals to the user and to receive audible signals from the user. For example, in addition to being coupled to the audio converter 625, the audio interface 774 can also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. According to an embodiment of the present disclosure, as will be described below, the microphone can also be used as an audio sensor to facilitate control of the notifications. The system 702 can also include a video interface 776 that enables operation of the on-board camera 630 to record still images, video streams, and the like.

[0057] The mobile computing device 700 implementing the system 702 can have additional features or functionality. For example, the mobile computing device 700 can also include additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or magnetic tapes. These additional storage units are illustrated by the non-volatile storage area 768 in Figure 7 the figure.

[0058] Data / information generated or captured by the mobile computing device 700 and stored via the system 702 can be stored locally on the mobile computing device 700 as described above, or the data can be stored on any number of storage media that can be accessed by the device via the radio interface layer 772 or via a wired connection between the mobile computing device 700 and a separate computing device associated with the mobile computing device 700 (e.g., a server computer in a distributed computing network such as the Internet). It should be understood that such data / information can be accessed via the radio interface layer 772 or via a distributed computing network, via the mobile computing device 700. Similarly, such data / information can be easily transmitted between computing devices for storage and use according to well-known data / information transmission and storage means, including email and collaborative data / information sharing systems.

[0059] FIG. 8 is a block diagram illustrating physical components (e.g., hardware) of a computing device 800 that can be used to implement aspects of the present disclosure. The computing device components described below can have computer-executable instructions for assisting in scheduling a meeting. In a basic configuration, computing device 800 can include at least one processing unit 802 and system memory 804. Depending on the configuration and type of the computing device, system memory 804 can include, but is not limited to: volatile storage units (e.g., random access memory), non-volatile storage units (e.g., read-only memory), flash memory, or any combination of such memories. System memory 804 can include an operating system 805 suitable for running one or more digital assistant programs. For example, operating system 805 can be suitable for controlling the operation of computing device 800. Additionally, embodiments of the present disclosure can be implemented in conjunction with a graphics library, other operating systems, or any other application programs, and are not limited to any particular application or system. This basic configuration is shown by those components within dashed line 808 in Figure 8 FIG. 8. Computing device 800 can have additional features or functionality. For example, computing device 800 can also include additional data storage devices (removable and / or non-removable), such as, for example, magnetic disks, optical disks, or magnetic tapes. Such additional storage units are shown by removable storage device 809 and non-removable storage device 810 in Figure 8 FIG. 8.

[0060] As described above, a number of program modules and data files can be stored in system memory 804. When executed on processing unit 802, program modules 806 (e.g., digital assistant meeting application 820) can perform processes including, but not limited to, aspects as described herein. According to an example, an urgency detection engine 811 can perform one or more operations associated with applying one or more language processing models to natural language in a message and determining an urgency level associated with meeting scheduling. An emphasis detection engine 813 can perform one or more operations associated with determining an emphasis level associated with a user who has sent an "schedule meeting" command to a digital assistant service. The emphasis detection engine 813 can analyze lexical and / or prosodic features of the command when determining the emphasis level. The emphasis detection engine 813 can additionally or alternatively analyze a user's facial expression, body demeanor, heart rate, etc. when determining the user's emphasis level. A message queue processing engine 815 can perform one or more operations associated with assigning relative priorities when processing commands received by a digital assistant service, at least based on the urgency level associated with one or more of the received commands. An intonation matching engine 817 can perform one or more operations associated with matching a response intonation and / or response type to a received meeting command, based on the identified urgency level and / or emphasis level associated with the meeting command and / or the user sending the meeting command.

[0061] In addition, embodiments of the present disclosure may be implemented in a circuit including the following: discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit using a microprocessor, or on a single chip containing electronic components or a microprocessor. For example, embodiments of the present disclosure may be implemented via a system-on-chip (SoC), where Figure 8 each or many of the components shown may be integrated onto a single integrated circuit. Such an SoC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all of which may be integrated (or "burned") onto a chip substrate as a single integrated circuit. When operating via an SoC, the functions described herein for the capabilities of the client switching protocol may be operated via an application-specific logic unit integrated with other components of the computing device 800 on a single integrated circuit (chip). Embodiments of the present disclosure may also be implemented using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to: mechanical, optical, fluidic, and quantum technologies. Additionally, embodiments of the present disclosure may be implemented within a general-purpose computer or in any other circuit or system.

[0062] The computing device 800 may also have one or more input devices 812, such as a keyboard, mouse, pen, voice or speech input device, touch or swipe input device, etc. Output devices 814 such as a display, speaker, printer, etc. may also be included. The foregoing devices are examples, and other devices may be used. The computing device 800 may include one or more communication connections 816 that allow communication with other computing devices 850. Examples of suitable communication connections 816 include but are not limited to: radio frequency (RF) transmitter, receiver, and / or transceiver circuitry, universal serial bus (USB), parallel and / or serial ports.

[0063] As used herein, the term computer-readable medium may include computer storage media. Computer storage media can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, or program modules. System memory 804, removable storage device 809, and non-removable storage device 810 are all examples of computer storage media (e.g., memory storage units). Computer storage media can include: RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical storage units, magnetic tape cartridges, tapes, magnetic disk storage units or other magnetic storage devices, or any other article that can be used to store information and can be accessed by computing device 800. Any such computer storage media can be part of computing device 800. Computer storage media does not include carrier waves or other propagated or modulated data signals.

[0064] Communication media can embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism and include any information delivery medium. The term "modulated data signal" can describe a signal having one or more sets of characteristics or a signal that has been altered in such a way as to encode information in the signal. By way of example and not limitation, communication media can include wired media such as a wired network or direct line connection, and wireless media such as sound, radio frequency (RF), infrared, and other wireless media.

[0065] Figure 9 An aspect of an architecture of a system for processing data received at a computing system from a remote source (e.g., personal / general-purpose computer 904, tablet computing device 906, or mobile computing device 908 as described above) is shown. Content displayed at server device 902 can be stored in different communication channels or other storage types. For example, a directory service 922, a portal 924, a mailbox service 926, an instant messaging storage unit 928, or a social networking site 930 can be used to store various documents. Program module 806 can be used by a client communicating with server device 902, and / or program module 806 can be used by server device 902. Server device 902 can provide data to client computing devices such as personal / general-purpose computer 904, tablet computing device 906, and / or mobile computing device 908 (e.g., smart phone) or provide data from these client computing devices via network 915. By way of example, as described above for Figures 6 - 8The described computer system may be embodied in a personal / general purpose computer 904, a tablet computing device 906, and / or a mobile computing device 908 (e.g., a smart phone). In addition to receiving graphical data that may be used for preprocessing at a graphics initiation system or postprocessing at a receiving computing system, any of these embodiments of the computing device may obtain content from a storage unit 916.

[0066] For example, aspects of the present disclosure have been described above with reference to block diagrams and / or operational descriptions of methods, systems, and computer program products according to aspects of the present disclosure. The functions / actions noted in these blocks may not occur in any order shown in any flowchart. For example, two blocks shown as sequential may in fact be executed substantially in parallel, or the blocks may sometimes be executed in the reverse order, depending upon the functionality / action involved.

[0067] The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the claimed disclosure in any way. It is believed that the aspects, examples, and details provided in this application are sufficient to convey possession and enable others to make and use the best mode of the claimed disclosure. The claimed disclosure should not be construed as limited to any aspect, example, or detail provided in this application. Each feature (structural and methodological), whether shown and described in combination or separately, is intended to be selectively included or omitted to produce embodiments having a particular set of features. The description and illustration of the present disclosure have been provided, and those skilled in the art may envision variations, modifications, and alternative aspects that fall within the spirit of the broader aspects of the general inventive concept embodied in this application without departing from the broader scope of the claimed disclosure.

[0068] The various embodiments described above are provided by way of illustration only and should not be construed as limiting the appended claims. Those skilled in the art will readily recognize that various modifications and changes can be made without following the example embodiments and applications shown and described herein and without departing from the true spirit and scope of the appended claims.

Claims

1. A method for assisting in meeting scheduling, the method comprises: receiving an electronic message by a digital assistant service, the electronic message being sent from a user making the scheduling; detecting an intention to schedule a meeting from the electronic message; identifying an urgency level of the electronic message for the user making the scheduling; identifying an emotional level of the user making the scheduling; determining a response tone corresponding to the identified urgency level and the identified emotional level for responding to the user making the scheduling based on analyzing a response matrix, wherein the response matrix includes the urgency level on the x-axis and the emotional level on the y-axis; responding to the user making the scheduling with an electronic message having the response tone corresponding to the identified urgency level and the identified emotional level; and automatically performing subsequent actions for scheduling the meeting by the digital assistant service based on the analysis of the response matrix.

2. The method according to claim 1, further comprises: prioritizing the electronic message for processing in a queue including a plurality of other electronic messages based on the identified urgency level associated with the electronic message of the user making the scheduling and the identified urgency level of at least one of the other electronic messages.

3. The method according to claim 1, wherein, the electronic message includes a time cue corresponding to a deadline by which the user making the scheduling wishes to schedule the meeting.

4. The method according to claim 3, wherein, performing subsequent actions for scheduling the meeting includes: when the identified urgency level is relatively high, scheduling the meeting closer to the current date; and when the identified urgency level is relatively low, scheduling the meeting closer to the deadline.

5. The method according to claim 3, wherein, the urgency level associated with the electronic message of the user making the scheduling is identified at least in part by applying a binary classification model to at least one sentence in the electronic message.

6. The method according to claim 4, wherein, the time cue corresponding to the deadline is identified at least in part by applying a sequence-to-sequence model to at least one sentence in the electronic message.

7. The method according to claim 5, wherein, the urgency level associated with the electronic message of the user making the scheduling is identified at least in part by analyzing facial cues of the user making the scheduling.

8. The method according to claim 1, wherein, the urgency level associated with the electronic message of the user making the scheduling is identified at least in part by applying one or more emotion analysis models to the electronic message.

9. A system for assisting in meeting scheduling, comprises: a memory for storing executable program code; and one or more processors functionally coupled to the memory, the one or more processors responding to and operating on computer-executable instructions included in the program code to: Receive utterances from a user making an arrangement by a digital assistant service; Detect the intention to arrange a meeting from the utterances; Identify the urgency level of the utterances for the user making the arrangement; Identify the emotional level of the user making the arrangement; Determine a response intonation corresponding to the identified urgency level and the identified emotional level based on analyzing a response matrix for responding to the user making the arrangement, wherein the response matrix includes the urgency level on the x-axis and the emotional level on the y-axis; Respond to the user making the arrangement with the response intonation corresponding to the identified urgency level and the identified emotional level; and Automatically perform subsequent actions for arranging the meeting by the digital assistant service based on the analysis of the response matrix.

10. The system according to claim 9, wherein, the one or more processors also respond to and operate on computer-executable instructions included in the program code to: Prioritize the utterances for processing in a queue including a plurality of other utterances based on the identified urgency level associated with the utterance of the user making the arrangement and the identified urgency level of at least one of the other utterances.

11. The system according to claim 9, wherein, Performing subsequent actions for arranging the meeting includes: Querying at least one potential meeting participant for available dates and times to attend the meeting; Not receiving a response from the potential meeting participant within a threshold duration; and Sending an additional follow-up message to the potential meeting participant.

12. A computer-readable storage device comprising executable instructions that, when executed by one or more processors, assist in arranging a meeting, the computer-readable storage device including instructions executable by the one or more processors for the following operations: Receive an electronic message by a digital assistant service, the electronic message being sent from a user making an arrangement; Detect the intention to arrange a meeting from the electronic message; Identify the urgency level of the electronic message for the user making the arrangement; Identify the emotional level of the user making the arrangement; Determine a response intonation corresponding to the identified urgency level and the identified emotional level based on analyzing a response matrix for responding to the user making the arrangement, wherein the response matrix includes the urgency level on the x-axis and the emotional level on the y-axis; Respond to the user making the arrangement with an electronic message having the response intonation corresponding to the identified urgency level and the identified emotional level; and Automatically perform subsequent actions for arranging the meeting by the digital assistant service based on the analysis of the response matrix.

13. The computer-readable storage device according to claim 12, wherein, the instructions are also executable by the one or more processors for: In a queue comprising a plurality of other electronic messages, prioritize the electronic message for processing based on the identified urgency level associated with the electronic message of the user making the arrangement and the identified urgency level of at least one of the other electronic messages.

14. The computer-readable storage device according to claim 12, wherein, when performing the subsequent action for arranging the meeting, the instructions are further executable by the one or more processors for: query at least one potential meeting attendee for available dates and times to attend the meeting; if a response from the potential meeting attendee is not received within a threshold duration, send an additional follow-up message to the potential meeting attendee, wherein the threshold duration is based on the identified urgency level associated with the electronic message of the user making the arrangement.

15. The computer-readable storage device according to claim 12, wherein, the electronic message includes a time prompt corresponding to the deadline by which the user making the arrangement wishes to arrange the meeting, and wherein, when performing the subsequent action for arranging the meeting, the instructions are further executable by the one or more processors for: when the identified urgency level is relatively high, schedule the meeting closer to the current date; and when the identified urgency level is relatively low, schedule the meeting closer to the deadline.

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