System and method for a user interface for enabling task delegation control

Abstract

To provide systems and methods used to receive and present a prediction regarding the likelihood that a member can delegate a task for completion by a task facilitation service, and to selectively enable controls for the member to delegate the task.SOLUTION: An environment 100 in which a task facilitation service 102 assigns a delegate 106 to a member 118 through which various tasks performable on behalf of the member 118 are recommended for performance by the delegate 106 and / or one or more third party services 116, wherein the task facilitation service 102 implements to reduce the cognitive load on the member and the member's family in performing various tasks in and around the member's home by identifying and delegating tasks to the delegate 106 who coordinates the performance of these tasks on behalf of these members.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims priority benefit of U.S. Provisional Patent Application No. 63 / 229,289, filed August 4, 2021, the disclosure of which is incorporated herein by reference.

[0002]

[0002] The present disclosure relates generally to task determination and delegation. In one example, systems and methods described herein can be used to receive and present predictions regarding the likelihood that a member may delegate a task for completion by a task facilitation service, and to selectively enable control for the member to delegate the task. Summary of the Invention

[0003] Disclosed embodiments may provide techniques for selectively enabling controls of a user interface and, in particular, controls for delegating completion of a task by a user of the user interface for completion by a different party, such as an assistant or proxy of a task facilitation service. In at least some embodiments, a user interface for a member of a task facilitation service includes delegation controls that can be selectively enabled by the assistant / proxy. The assistant / proxy is presented with an associated interface that provides dynamic recommendations for enabling delegation controls for the member. The assistant's user interface may include dynamic visual indicators and / or selectively enabled controls that change based on the output of the member's predictive model. For example, the model may receive details about a given task and output a predicted likelihood that the member will delegate the task.

[0004] In one aspect of the present disclosure, a computer-implemented method is provided. The method includes sending a control enablement instruction for a user's task associated with a user's computing device. The user's computing device enables activation of a task delegation control corresponding to the task in response to receiving the control enablement instruction. The task facilitation service also updates a task status of the task to indicate that the task has been at least partially delegated for completion in response to activating the task delegation control at the user's computing device.

[0005] In some embodiments, sending the control enable instruction is in response to receiving a command to enable task delegation control for the task.

[0006] In some embodiments, the method further includes receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, the likelihood indication being generated in response to determining the likelihood using a delegation likelihood model that is updated using the user's delegation activity.

[0007] In some embodiments, the method further includes receiving a likelihood indication corresponding to a likelihood that the user will delegate the task. The likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and the delegation likelihood model is updated using the user's delegation activity. The method further includes displaying a visual indicator corresponding to the likelihood in a user interface.

[0008] In some embodiments, the method further includes receiving a likelihood indication corresponding to a likelihood that the user will delegate the task. The likelihood indication is generated in response to determining the likelihood using a delegation likelihood model that is updated using the user's delegation activity. The method may further include enabling the control such that sending the control enablement indication is in response to activating the control.

[0009] In some embodiments, the method further includes receiving a likelihood indication corresponding to a likelihood that the user will delegate the task. The likelihood indication is generated in response to determining the likelihood using a delegation likelihood model that is updated using the user's delegation activity. The method may further include enabling a control at the computing device such that when the control is activated, the computing device begins transmitting the indication. In such embodiments, enabling the control may be in response to the likelihood exceeding a minimum likelihood threshold.

[0010] In some embodiments, the method further includes receiving a likelihood indication corresponding to a likelihood that the user will delegate the task. The likelihood indication is generated in response to determining the likelihood using a delegation likelihood model that is updated using the user's delegation activity. The method further includes enabling a control at the computing device such that when the control is activated, the computing device begins transmitting the indication. In such embodiments, the enabling of the control may be in response to the likelihood exceeding a minimum likelihood threshold, which may be a level below which the user is likely not to delegate the task.

[0011] In some embodiments, the method further includes sending a delegation control enablement indication corresponding to whether task delegation control is enabled, and when the delegation control enablement indication is received by a task facilitation service, the task facilitation service updates a delegability model based on the delegation control enablement indication.

[0012] In another aspect of the disclosure, a system includes one or more processors and a memory containing instructions that, when executed by the one or more processors, cause the system to perform a process described herein. In another aspect, a non-transitory computer-readable storage medium stores executable instructions thereon, the executable instructions, when executed by one or more processors of a computer system, cause the computer system to perform a process described herein.

[0013]

[0013] Various embodiments of the present disclosure are discussed in detail below. While specific implementations are discussed, this is done for purposes of illustration only. Those skilled in the art will recognize that other components and configurations can be used without departing from the spirit and scope of the present disclosure. Accordingly, the following description and drawings are illustrative and should not be construed as limiting. Numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, in some instances, well-known or conventional details are not described to avoid obscuring the description. Reference to an embodiment or embodiments in this disclosure may be a reference to the same embodiment or any embodiments, and such reference means at least one of the embodiments.

[0014] Reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. The appearances of the phrase "one embodiment" in various places in this specification do not necessarily all refer to the same embodiment, or to separate or alternative embodiments that are mutually exclusive of other embodiments. Moreover, various features are described that may be exhibited by some embodiments but not by other embodiments.

[0015]

[0015] The terms used herein generally have their ordinary meaning in the art, within the context of this disclosure and in the specific context in which each term is used. Alternative wording and synonyms may be used for any one or more of the terms described herein, and no particular importance should be placed on whether a term is recited or explained herein. In some cases, synonyms for some terms are provided. The recitation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification, including examples of any term described herein, is illustrative only and is not intended to further limit the scope and meaning of the disclosure or any exemplary term. Similarly, the disclosure is not limited to the various embodiments provided herein.

[0016]

[0016] Without intending to limit the scope of the present disclosure, examples of devices, apparatus, methods, and their related results according to embodiments of the present disclosure are provided below. Please note that titles or subtitles may be used in the examples for the convenience of the reader, and in no case should this limit the scope of the present disclosure. Unless otherwise defined, technical and scientific terms used herein have the meanings commonly understood by those skilled in the art to which the present disclosure pertains. In the case of conflict, the present disclosure, including definitions, will control.

[0017]

[0017] Additional features and advantages of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the principles disclosed herein. The features and advantages of the present disclosure may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present disclosure will become more fully apparent from the following description and the appended claims, or may be learned by practice of the principles described herein.

[0018]

[0018] Exemplary embodiments are described in detail with reference to the following figures. [Brief explanation of the drawings]

[0019] [Figure 1]

[0019] A diagram showing an illustrative example of an environment in which a task facilitation service, according to various embodiments, assigns a proxy to a member, through which various tasks that can be performed for the member can be recommended for performance by the proxy and / or one or more third-party services. [Figure 2]

[0020] FIG. 1 illustrates an illustrative example of an environment in which a proxy assignment system performs an onboarding process for members and assigns proxy to members based on member attributes and proxy attributes, according to at least one embodiment. [Figure 3]

[0021] FIG. 1 illustrates an illustrative example of an environment in which task-related data is collected and aggregated from a member area to identify one or more tasks that may be recommended to a member for delegation and execution by a proxy or third-party service, according to at least one embodiment. [Figure 4]

[0022] FIG. 1 illustrates an illustrative example of an environment in which a task recommendation system generates and ranks recommendations for tasks to be performed for members, according to at least one embodiment. [Figure 5]

[0023] FIG. 1 illustrates an illustrative example of a process for generating new tasks and task rankings that can be used to determine what tasks will be presented to members, according to at least one embodiment. [Figure 6]

[0024] FIG. 1 illustrates an illustrative example of a process for generating suggestions and monitoring member interactions with the generated suggestions, according to at least one embodiment. [Figure 7]

[0025] FIG. 1 illustrates an illustrative example of an environment in which a task facilitation service selectively enables delegated controls on a member's computing device at the discretion of a representative associated with the task facilitation service. [Figure 8]

[0026] FIG. 1 illustrates an illustrative example of an environment in which a task facilitation service selectively enables delegated control on a member's computing device without delegation. [Figure 9]

[0027] FIG. 1 illustrates an illustrative example of an environment in which delegation controls in a member's computing device are activated to delegate a task. [Figure 10]

[0028] FIG. 1 illustrates an illustrative example of an environment in which a task facilitation service selectively enables delegated controls on a member's computing device at the discretion of a representative associated with the task facilitation service. [Figure 11]

[0029] FIG. 10 shows an illustrative example of a scale for use in assessing the delegability score / value generated by the delegability model. [Figure 12]

[0030] 10A-10C illustrate illustrative examples of surrogate user interfaces associated with task facilitation services. [Figure 13A]

[0031] 13 illustrates an illustrative example of task details of the user interface of FIG. 12 including changes in response to enabling and activating delegated control on a member's computing device. [Figure 13B] 13 illustrates an illustrative example of task details of the user interface of FIG. 12 including changes in response to enabling and activating delegated control on a member's computing device. [Figure 14A]

[0032] FIG. 13 illustrates an illustrative example of task details of the user interface of FIG. 12 including a delegation enablement control that is selectively enabled based on delegation availability. [Figure 14B] FIG. 13 illustrates an illustrative example of task details of the user interface of FIG. 12 including a delegation enablement control that is selectively enabled based on delegation availability. [Figure 15]

[0033] 10 is a table illustrating different visual indicators that may be used to convey information about the likelihood that a member will delegate a task. [Figure 16]

[0034] FIG. 10 illustrates a flowchart illustrating an example method for enabling delegated control on a member's computing device. [Figure 17]

[0035] FIG. 1 illustrates a computing system architecture including various components in electrical communication with one another, according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0020]

[0036] In the accompanying figures, similar components and / or features may have the same reference label. Furthermore, various components of the same type may be distinguished by following the reference label with a dash and a second label that distinguishes between the similar components. If only a first reference label is used herein, the description is applicable to any of the similar components having the same first reference label, regardless of the second reference label.

[0021]

[0037] In the following description, for purposes of explanation, specific details are set forth in order to provide a thorough understanding of some invention embodiments. It will be apparent, however, that various embodiments may be practiced without these specific details. The figures and descriptions are not limiting. The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

[0022]

[0038] Disclosed embodiments may include a framework for identifying and recommending tasks that can be performed for a member. Through this framework, a member may be assigned a proxy that can learn about the member's preferences and behaviors over time, which can be used to recommend tasks that can be performed to reduce the member's cognitive load. Embodiments of the present disclosure may selectively enable delegation controls in a member's user interface based on past activity, demographic information, and other data collected about the member. When an enabled delegation control is activated by a member, the corresponding task may be updated or modified to indicate that the task will be delegated to a proxy or third party for completion. In particular, delegating a task reduces the need for member involvement in completing the task, reducing the member's cognitive load, among other benefits.

[0023]

[0039] 1 illustrates an illustrative example of an environment 100 in which a task facilitation service 102 assigns a surrogate 106 to a member 118, through which various tasks performable for the member 118 may be recommended for performance by the surrogate 106 and / or one or more third-party services 116, according to various embodiments. The task facilitation service 102 may be implemented to reduce the cognitive load on the member and the member's family in performing various tasks in and around the member's home by identifying and delegating tasks to a surrogate 106, who can coordinate the performance of these tasks for these members. In one embodiment, the member 118 may submit a request to the task facilitation service 102 via a computing device 120 (e.g., a laptop computer, a smartphone, etc.) to initiate an onboarding process for assignment of a surrogate 106 to the member 118 and to initiate the identification of tasks performable for the member 120. For example, the member 118 may access the task facilitation service 102 via an application provided by the task facilitation service 102 and installed on the computing device 120. Additionally or alternatively, the task facilitation service 102 may maintain a web server (not shown) that hosts one or more websites configured to present or otherwise make available an interface through which members 118 may access the task facilitation service 102 and begin the onboarding process.

[0024]

[0040] During the onboarding process, the task facilitation service 102 may collect identifying information of the member 118, which may be used by the proxy assignment system 104 to identify and assign a proxy 106 to the member 118. For example, the task facilitation service 102 may provide the member 118 with a survey or questionnaire in which the member 118 may provide identifying information that can be used by the proxy assignment system 104 to select a proxy 106 for the member 118. For example, the task facilitation service 102 may prompt the member 118 to provide detailed information regarding the member's family composition (e.g., the number of residents in the member's home, the number of children in the member's home, the number and type of pets in the member's home, etc.), the physical location of the member's home, any special wants or requirements of the member 118 (e.g., physical or emotional disabilities, etc.), etc. In some examples, the member 118 may be prompted to provide demographic information (e.g., age, ethnicity, race, written / spoken languages, etc.). The member 118 may also be prompted to indicate any personal interests or hobbies (described in further detail herein), which may be used to identify possible experiences that may be of interest to the member 118. In some examples, the task facilitation service 102 may prompt the member 118 to specify any tasks that the member 118 would like assistance with or, in some cases, would like to delegate to another entity, such as a proxy and / or third party.

[0025]

[0041] In one embodiment, the task facilitation service 102 may prompt the member 118 to indicate a level of confidence or other measure in delegating tasks to others, such as a proxy and / or a third party. For example, the task facilitation service 102 may utilize identification information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's daily life. In some cases, the task facilitation service 102 may utilize machine learning algorithms or artificial intelligence to identify categories of tasks that may be relevant to the member 118. For example, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family structure, household configuration, etc.). In some cases, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 given by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Exemplary clustering algorithms that may be trained to classify members using a sample member dataset (e.g., historical member data, hypothetical member data, etc.) to identify categories of tasks that may be relevant to the member may include a k-means clustering algorithm, a fuzzy c-means (FCM) algorithm, an expectation-maximization (EM) algorithm, a hierarchical clustering algorithm, a density-based spatial clustering of noisy applications (DBSCAN) algorithm, etc. Based on the output of the machine learning algorithm generated using the member's identification information, the task facilitation service 102 may prompt the member 118 to provide a response regarding their comfort level in delegating tasks that correspond to the task category provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's requests.

[0026]

[0042] In one embodiment, the member's identity and any information related to the member's comfort level or interest level in delegating different categories of tasks to others are provided to a proxy assignment system 104 of the task facilitation service 102 to identify a proxy 106 that can be assigned to the member 118. The proxy assignment system 104 can be implemented using a computer system or as an application or other executable code implemented on the computer system of the task facilitation service 102. The proxy assignment system 104, in one embodiment, uses the member's identity, any information related to the member's comfort level or interest in delegating tasks to others, and any other information obtained during the onboarding process as input to a classification or clustering algorithm configured to identify a proxy that may be suitable to interact and communicate with the member 118 in a productive manner. For example, the proxy 106 can be profiled based on various criteria, including (but not limited to) demographic and other identifying information, geographic location, experience in handling different categories of tasks, experience in communicating with different categories of members, etc. Using a classification or clustering algorithm, the surrogate assignment system 104 may identify a set of surrogates 106 that may be more likely to develop a positive long-term relationship with the member 118 while addressing any tasks that may need to be addressed for the member 118.

[0027]

[0043] Once the surrogate assignment system 104 identifies a set of surrogates 106 that can be assigned to the member 118 to serve as the member's 118's assistant or concierge, the surrogate assignment system 104 may evaluate data corresponding to each surrogate in the set of surrogates 106 to identify a particular surrogate that can be assigned to the member 118. For example, the surrogate assignment system 104 may rank each surrogate in the set of surrogates 106 according to the degree or vector of similarity between the member's demographic information and the surrogate's demographic information. For example, if the member and a particular surrogate share a similar background (e.g., attended college in the same city, are from the same hometown, share particular interests, etc.), the surrogate assignment system 104 may rank the particular surrogate higher compared to other surrogates who may have dissimilar backgrounds. Similarly, if the member and a particular surrogate are geographically close to each other, the surrogate assignment system 104 may rank the particular surrogate higher compared to other surrogates who may be further away from the member 118. Each factor may, in some examples, be weighted based on the factor's impact on building a positive long-term relationship between the member and the surrogate. For example, based on historical data corresponding to member interactions with surrogates, the surrogate assignment system 104 may identify correlations between different factors and the polarity (e.g., positive, negative, etc.) of these interactions. Based on these correlations (or lack thereof), the surrogate assignment system 104 may apply a weight to each factor.

[0028]

[0044] In some examples, each surrogate in the set of identified surrogates 106 may be assigned a score corresponding to various factors that correspond to the degree or vector of similarity between the member's demographic information and the surrogate's demographic information. For example, each factor may have a possible range of scores corresponding to the weight assigned to the factor. As an illustrative example, the various factors used to derive the surrogate score may each have a possible score between 1 and 10. However, based on the weight assigned to each factor, the possible scores may be multiplied by a weighting factor such that factors with greater weights may be multiplied by a higher weighting factor compared to factors with lesser weights. The result is a set of different scoring ranges that correspond to the importance or relevance of the factors in determining a match between the member 118 and the surrogate. The scores determined for the various factors may be aggregated to obtain a composite score for each surrogate in the set of surrogates 106. These composite scores may be used to create a ranking of the set of surrogates 106.

[0029]

[0045] In one embodiment, the proxy assignment system 104 uses the rankings of the set of proxy 106 to select a proxy that can be assigned to the member 118. For example, the proxy assignment system 104 may select the highest ranked proxy and determine the proxy's availability to engage the member 118 in identifying and recommending tasks, coordinating the resolution of the tasks, and possibly communicating with the member 118 to ensure that the member's 118 requests are addressed. If the selected proxy is unavailable (e.g., the proxy is already engaged with one or more other members), the proxy assignment system 104 may select another proxy according to the aforementioned ranking and determine the proxy's availability to engage the member 118. This process may be repeated until an available proxy is identified from the set of proxy 106 to engage the member 118. In some examples, proxy availability may be used as a factor used to obtain the aforementioned proxy score, whereby a proxy that is unavailable or that potentially does not have sufficient bandwidth to accommodate a new member 118 may be assigned a lower proxy score. Thus, unavailable substitutes may be ranked lower than other substitutes that may be available for allocation to members 118 .

[0030]

[0046] In one embodiment, the surrogate assignment system 104 may select a surrogate from the set of surrogates 106 based on information corresponding to each surrogate's availability. For example, the surrogate assignment system 104 may automatically select the first available surrogate from the set of surrogates 106. In some examples, the surrogate assignment system 104 may automatically select the first available surrogate that meets one or more criteria corresponding to the member's identification information (e.g., the surrogate whose profile most closely matches the member profile, etc.). For example, the surrogate assignment system 104 may automatically select an available surrogate that is within geographic proximity of the member 118, shares a similar background to that of the member 118, etc.

[0031]

[0047] In one embodiment, the surrogate 106 may be an automated process, such as a bot, that may be configured to automatically engage and interact with the member 118. For example, the surrogate assignment system 104 may utilize responses provided by the member 118 during the onboarding process as input to a machine learning algorithm or artificial intelligence to generate a member profile and a bot that may serve as the surrogate 106 for the member 118. The bot may be configured to autonomously chat with the member 118 to generate tasks and suggestions, perform tasks on behalf of the member 118 according to any accepted suggestions, and the like, as described herein. The bot may be configured according to parameters or characteristics of the member 118 defined in the member profile. As the bot communicates with the member 118 over time, the bot may be updated to improve the bot's interaction with the member 118.

[0032]

[0048] Data associated with the member 118 collected during the onboarding process, as well as any data corresponding to the selected representative, may be stored in the user data store 108. The user data store 108 may include an entry corresponding to each member 118 of the task facilitation service 102. The entry may include identifying information for the corresponding member 118 and an identifier or other information corresponding to the representative assigned to the member 118. As described in more detail herein, the entries in the user data store 108 may further include historical data corresponding to communications between the member 118 and the assigned representative over time. For example, when the member 118 interacts with the representative 106 via a chat session or stream, messages exchanged via the chat session or stream may be recorded in the user data store 108.

[0033]

[0049] In one embodiment, data associated with the member 118 is used by the task facilitation service 102 to create a member profile corresponding to the member 118. As described above, the task facilitation service 102 may provide the member 118 with a survey or questionnaire in which the member 118 may provide identifying information associated with the member 118. Responses provided by the member 118 to this survey or questionnaire may be used by the task facilitation service 102 to generate an initial member profile corresponding to the member 118. In one embodiment, once the proxy assignment system 104 assigns a proxy to the member 118, the task facilitation service 102 may prompt the member 118 to create a new member profile corresponding to the member 118. For example, the task facilitation service 102 may provide the member 118 with a survey or questionnaire that includes a set of questions that can be used to supplement information previously provided during the onboarding process described above. For example, through the survey or questionnaire, the task facilitation service 102 may prompt the member 118 to provide additional information about their family, important dates (e.g., birthdays, etc.), dietary restrictions, etc. Based on the response provided by the member 118 , the task facilitation service 102 may update the member profile corresponding to the member 118 .

[0034]

[0050] In some examples, the member profile may be accessible to the member 118, such as through an application or web portal provided by the task facilitation service 102. Through the application or web portal, the member 118 may add, remove, or edit any information in the member profile. The member profile, in some examples, may be divided into various sections corresponding to the member, the member's family, the member's home, etc. Each of these sections may be supplemented based on data related to the member 118 collected during the onboarding process and any responses to surveys or questionnaires given to the member 118 after assignment of a representative to the member 118. Additionally, each section may include additional questions or prompts that the member 118 may use to provide additional information that may be used to expand the member profile. For example, through the member profile, the member 118 may be prompted to provide any authentication information that may be used to access any external accounts (e.g., credit card accounts, retailer accounts, etc.) to facilitate task completion.

[0035]

[0051] In one embodiment, certain information in a member profile may be hidden from the member 118 or a representative. For example, as a representative develops a relationship with the member 118 through the completion of various tasks, the representative may modify the member profile to provide notes about the member 118 (e.g., the member's idiosyncrasies, any feedback about the member, etc.). Thus, when the member 118 accesses the member's member profile, these notes may be hidden so that the member 118 cannot review these notes or, in some cases, cannot access any sections of the member profile designated as unavailable to the member by the representative 118 or task facilitation service 102.

[0036]

[0052] As described in further detail herein, a proxy assigned to a member 118 may add or possibly modify information in the member profile based on information shared with the proxy and / or the proxy's own observations about the member 118. Additionally, the task facilitation service 102 may automatically surface relevant portions of the member profile when creating or performing a task on behalf of the member 118. For example, if a proxy is generating a task related to meal planning for the member 118, the task facilitation service 102 may automatically identify portions of the member profile that may be contextually relevant to meal planning and surface these portions of the member profile (e.g., dietary preferences, dietary restrictions, etc.) to the proxy. In some examples, if the proxy needs additional information to create or perform a task on behalf of the member 118, the proxy may invite the member 118 to update certain portions of the member profile instead of having the member 118 share the additional information through a chat session or other communication session between the member 118 and the assigned proxy.

[0037]

[0053] In one embodiment, once the proxy assignment system 104 assigns a particular proxy to a member 118, the proxy assignment system 104 notifies the member 118 and the particular proxy of the pairing. Additionally, the proxy assignment system 104 may establish a chat session or other communication session between the member 118 and the assigned proxy to facilitate communication between the member 118 and the proxy. For example, the member 118 may exchange messages with the assigned proxy via the chat session or other communication session through an application provided by the task facilitation service 102 and installed on the computing device 120, or through a web portal provided by the task facilitation service 102. Similarly, the proxy may be provided with an interface through which the proxy may exchange messages with the member 118.

[0038]

[0054] In some examples, the member 118 may initiate, or possibly resume, a chat session with the assigned representative. For example, through an application or web portal provided by the task facilitation service 102, the member may send a message to the representative via a chat session or other communication session to communicate with the representative. The member 118 may submit a message to the representative indicating that the member 118 would like assistance with a particular task. As an illustrative example, the member 118 may submit a message to the representative indicating that the member 118 would like the representative's assistance with an upcoming move to Denver next month. The representative may be presented with the submitted message via an interface provided by the task facilitation service 102. The representative may thus evaluate the message and generate a corresponding task to be performed to assist the member 118. For example, the representative may access a task creation form via an interface provided by the task facilitation service 102, through which the representative may provide information regarding the task. The information may include information related to the member 118 (e.g., member name, member address, etc.) and various parameters of the task itself (e.g., allocated budget, time frame for completion of the task, etc.). The parameters of the task may further include any member preferences (eg, preferred brands, preferred third-party services 116, etc.).

[0039]

[0055] In one embodiment, the proxy can provide information obtained from the member 118 about the task specified in one or more messages exchanged between the member 118 and the proxy to a task recommendation system 112 of the task facilitation service 102 to dynamically and in real time identify any additional task parameters that may be needed to generate one or more suggestions for completing the task. The task recommendation system 112 can be implemented using a computer system or as an application or other executable code implemented on the computer system of the task facilitation service 102. The task recommendation system 112, in one embodiment, provides the proxy with an interface through which the proxy can generate a task that can be presented to the member through a chat session (e.g., via an application utilized by the member 118, etc.) and completed for the member 118 by the proxy and / or one or more third-party services 116. For example, the proxy may provide the name of the task, any known parameters of the task provided by the member (e.g., budget, time frame, task operations to be performed, etc.), etc. As an illustrative example, if member 118 sends the message "Hey Russell, can you help with our move to Denver in 2 months," the representative may evaluate the message and generate a task entitled "Move to Denver." For this task, the representative may indicate that the time frame for completion of the task is 2 months, as indicated by member 118. Additionally, the representative may add additional information that the representative knows about the member. For example, the representative may indicate any preferred moving companies, any budget constraints, etc.

[0040]

[0056] In one embodiment, the task recommendation system 112 provides the surrogate with any relevant information from the member profile corresponding to the member 118 that can be used to generate a task. For example, if the surrogate generates a new task titled "Moving to Denver," the task recommendation system 112 may determine that the new task corresponds to moving to a new city or other location. Accordingly, the task recommendation system 112 may process the member profile to identify portions of the member profile that may be relevant to the task (e.g., the physical location of the member's home, the number of residents in the member's home, the square footage and number of rooms in the member's home, etc.). The task recommendation system 112 may automatically surface these portions of the member profile to the surrogate to enable the surrogate to use this information to generate a new task. Alternatively, the task recommendation system 112 may automatically use this information to populate one or more fields in a task template for the creation of a new task.

[0041]

[0057] In one embodiment, a delegate can access a resource library maintained by the task facilitation service 102 to obtain task templates that can be used to generate new tasks that can be performed on behalf of the member 118. The resource library can serve as a repository for different task templates corresponding to different task categories (e.g., vehicle maintenance tasks, home maintenance tasks, family-related event tasks, caregiving tasks, experience-related tasks, etc.). A task template can include multiple task definition fields that can be used to define a task that can be performed for the member 118. For example, a task definition field corresponding to a vehicle maintenance task can be used to define the make and model of the member's vehicle, the age of the vehicle, information corresponding to the last time the vehicle was maintained, any reported accidents related to the vehicle, a description of any problems related to the vehicle, etc. Thus, each task template maintained in the resource library can include fields specific to the task category associated with the task template. In some cases, a delegate may further define custom fields for a task template, through which the delegate can supply additional information that can be useful in defining and completing a task. These custom fields can be added to the task template so that if the delegate retrieves the task template in the future and creates a similar task, these custom fields will be available to the delegate.

[0042]

[0058] In some cases, when a surrogate selects a particular task template from the resource library, the task recommendation system 112 may automatically identify the relevant portions of the member profile that correspond to the member 118. For example, each template may be associated with a particular task category, as described above. Additionally, different portions of the member profile may similarly be associated with different task categories, such that the task recommendation system 112 may identify the relevant portions of the member profile in response to the surrogate's selection of a task template. From these relevant portions of the member profile, the task recommendation system 112 may automatically retrieve information that can be used to populate one or more fields of the selected task template. For example, if the member 118 indicates in their member profile that they drive a 2020 Subaru Outback, and this information is indicated in the portion of their member profile that corresponds to the member's vehicle, the task recommendation system 112 may automatically retrieve this information from the member profile to populate fields in the task template that correspond to the make, model, and year of the member's vehicle (e.g., "Make=Subaru," "Model=Outback," "Year=2020," etc.). This may reduce the amount of data entry that a delegate needs to perform to populate a task template for a new task.

[0043]

[0059] In one embodiment, based on the task template selected by the delegate, the task recommendation system 112 automatically determines which portions of the member profile the delegate may access for task creation. For example, if the delegate selects a task template from a resource library that corresponds to a vehicle maintenance task (e.g., the template's task category is specified as "vehicle maintenance"), the task recommendation system 112 may process the member profile to identify one or more portions of the member profile that may be relevant to the vehicle maintenance task (e.g., information corresponding to the make and model of the member's vehicle, the age of the vehicle, when the vehicle was last maintained, etc.). The task recommendation system 112 may present these relevant portions of the member profile to the delegate while hiding any other portions of the member profile that may not be relevant to the task category selected by the delegate. This may prevent the delegate from accessing any information from the member profile without a specific need for the information, thereby reducing the member's information exposure.

[0044]

[0060] In one embodiment, the representative may provide the generated task to the task recommendation system 112 to determine whether additional member input is needed to create a proposal that can be presented to the member for completion of the task. The task recommendation system 112 may process the generated task and information corresponding to the member 118 from the user data store 108 using a machine learning algorithm or artificial intelligence to, for example, automatically identify additional parameters for the task and any additional information that may be requested from the member 118 for the generation of the proposal. For example, the task recommendation system 112 may use the generated task, information corresponding to the member 118 (e.g., a member profile), and historical data corresponding to tasks performed for other similarly situated members as inputs to the machine learning algorithm or artificial intelligence to identify any additional parameters that can be automatically completed for the task and any additional information that may be requested from the member 118 to define the task. For example, if the task relates to an upcoming trip to another city, the task recommendation system 112 may utilize a machine learning algorithm or artificial intelligence to identify similarly situated members (e.g., members within the same geographic area as the member 118, members with similar task delegation susceptibility, members who have performed similar tasks, etc.). Based on the task generated for the member 118, the member's 118 characteristics from the member profile stored in the user data store 108, and data corresponding to these similarly situated members, the task recommendation system 112 may provide additional parameters for the task. As an illustrative example, for the task "Moving to Denver" described above, the task recommendation system 112 may provide a recommended budget for the task, one or more moving companies that the member 118 may approve (used by other similarly situated members with positive feedback), etc. The representative may consider these additional parameters and select one or more of these parameters for inclusion in the task.

[0045]

[0061] If the task recommendation system 112 determines that additional member input is required for the task, the task recommendation system 112 may provide the surrogate with recommendations for questions that can be presented to the member 118 regarding the task. Returning to the “Moving to Denver” task example, if the task recommendation system 112 determines that understanding one or more parameters of the member's home (e.g., square footage, number of rooms, etc.) is important for the task, the task recommendation system 112 may provide the surrogate with recommendations prompting the member 118 to provide these one or more parameters. The surrogate may review the recommendations provided by the task recommendation system 112 and prompt the member 118 via the chat session to provide additional task parameters. This process may reduce the number of prompts provided to the member 118 to define a particular task, thereby reducing the cognitive load on the member 118. In some cases, rather than providing the surrogate with recommendations for questions that can be presented to the member 118 regarding the task, the task recommendation system 112 may automatically present these questions to the member 118 via the chat session. For example, if the task recommendation system 112 determines that a task requires a question regarding the square footage of the member's home, the task recommendation system 112 may automatically prompt the member 118 via a chat session to provide the square footage of the member's home. In one embodiment, the information provided by the member 118 in response to these questions may be used to automatically supplement the member profile for future tasks, so that this information may be readily available to a proxy and / or the task recommendation system 112 to define new tasks.

[0046]

[0062] In one embodiment, the task facilitation service 102 automatically generates a specific chat or other communication session corresponding to the task. This specific chat or other communication session corresponding to the task may be separate from a previously established chat session between the member 118 and the representative. Through this task-specific chat or other communication session, the member 118 and the representative may exchange messages related to the specific task. For example, through this task-specific chat or other communication session, the representative may prompt the member 118 for information that may be needed to determine one or more parameters of the task. Similarly, if the member 118 has questions related to the specific task, the member 118 may pose these questions through the task-specific chat or other communication session. Implementing a task-specific chat or other communication session may reduce the number of messages exchanged through other chat or communication sessions while ensuring that communications within these task-specific chat or other communication sessions are relevant to the corresponding task.

[0047]

[0063] In one embodiment, once the surrogate obtains the necessary task-related information from the member 118 and / or through the task recommendation system 112 (e.g., task parameters accumulated through ratings of tasks performed for similarly situated members, etc.), the surrogate may utilize the task coordination system 114 of the task facilitation service 102 to generate one or more proposals for solving the task. The task coordination system 114 may be implemented using a computer system or as an application or other executable code implemented on the computer system of the task facilitation service 102. In some examples, the surrogate may utilize a resource library maintained by the task coordination system 114 to identify one or more third-party services 116 and / or resources (e.g., retailers, restaurants, websites, brands, types of products, specific products, etc.) that may be used for the performance of the task for the member 118 according to one or more task parameters identified by the surrogate and the task recommendation system 112, as described above. The proposals may specify a time frame for completion of the task, identification of any third-party services 116 (if any) to be engaged for the completion of the task, a budget estimate for the completion of the task, resources or types of resources to be used for the completion of the task, etc. The delegate may present a proposal to the member 118 via a chat session, seeking a response from the member 118 to advance the proposal or provide an alternative proposal for completing the task.

[0048]

[0064] In one embodiment, the task recommendation system 112 can provide the surrogate with a recommendation regarding whether the surrogate should provide suggestions to the member 118 and provide the member with the option to defer to the surrogate for completion of the defined task. For example, in addition to providing member- and task-related information to the task recommendation system 112 to identify additional parameters of the task, the surrogate can present the member 118 with one or more suggestions for completing the task and indicate its recommendation to the task recommendation system 112 to present or omit the option to defer to a surrogate for completion of the task. The task recommendation system 112 can utilize machine learning algorithms or artificial intelligence to generate the above-described recommendations. The task recommendation system 112 can utilize the information provided by the surrogate, data about similarly situated members from the user data store 108, and task data corresponding to similar tasks from the task data store 110 (e.g., tasks having similar parameters to the submitted task, tasks performed for similarly situated members, etc.) to determine whether to recommend presenting one or more suggestions for completing the task and whether to present the member 118 with the option to defer to a surrogate for completion of the task.

[0049]

[0065] If the surrogate determines that the member should be presented with the option to defer to the surrogate for completion of the task, the surrogate may present this option to the member via a chat session. The option may be presented in the form of a button or other graphical user interface (GUI) element that the member may select to indicate its acceptance of the option. For example, the member may be presented with a “Run With It” button to give the member the option to defer all decisions related to the performance of the task to the surrogate. If the member 118 selects that option, the surrogate may present a proposal selected by the surrogate for completion of the task on behalf of the member 118 and may proceed to coordinate with one or more third-party services 116 for performance and completion of the task according to the proposal. Thus, rather than allowing the member 118 to select a specific proposal for completion of the task, the surrogate may instead select a specific proposal on behalf of the member 118. The proposal may still be presented to the member 118 for the member 118 to verify how the task should be completed. Any actions taken by the surrogate on behalf of the member 118 for completion of the task may be recorded in the entry corresponding to the task in the task data store 110. Alternatively, if the member 118 declines the option and instead indicates that the representative should provide one or more suggestions for completing the task, the representative may generate one or more suggestions as described above.

[0050]

[0066] In one embodiment, the task recommendation system 112 records the member's response to being presented with the option to follow the surrogate for completing the task for use in training the machine learning algorithm or artificial intelligence used to make recommendations to the surrogate for presenting the options. For example, if the surrogate chooses to present the option to the member 118, the task recommendation system 112 may record whether the member 118 selected the option or declined the offer and requested to be presented with one or more suggestions related to the task. Similarly, if the surrogate chooses to be presented with one or more suggestions without being presented with the option to follow the surrogate, the task recommendation system 112 may record whether the member 118 was satisfied with the presentation of these one or more suggestions or whether the surrogate requested to select a suggestion on the member's behalf, and therefore may follow the surrogate for completing the task. These member responses, along with data corresponding to the task, the surrogate's actions (e.g., presenting options, presenting suggestions, etc.), and the recommendations given by the task recommendation system 112, may be stored in the task data store 110 for use by the task recommendation system 112 in training and / or enhancing the machine learning algorithm or artificial intelligence.

[0051]

[0067] In one embodiment, the surrogate may suggest one or more tasks based on member characteristics, task history, and other factors. For example, when the member 118 communicates with the surrogate via a chat session, the surrogate may evaluate any messages from the member 118 to identify any tasks that can be performed to reduce the member's cognitive load. As an illustrative example, if the member 118 indicates through the chat session that the member's spouse's birthday is approaching, the surrogate may utilize that knowledge of the member 118 to develop one or more tasks that can be recommended to the member 118 in anticipation of the member's spouse's birthday. The surrogate may recommend tasks such as purchasing a cake, ordering flowers, or setting up a unique travel experience for the member 118. In some embodiments, the surrogate may generate task suggestions without member input. For example, as part of the onboarding process, the member 118 may give the task facilitation service 102 access to one or more member resources, such as the member's calendar, the member's personal fitness device (e.g., a fitness tracker, exercise equipment with communication capabilities, etc.), the member's vehicle data, etc. The data collected from these member resources may be monitored by a surrogate, which may parse the data to generate task suggestions for the member 118 .

[0052]

[0068] In one embodiment, data collected from the member 118 over a chat session with a representative may be evaluated by the task recommendation system 112 to identify one or more tasks that can be presented to the member 118 for completion. For example, the task recommendation system 112 may utilize natural language processing (NLP) or other artificial intelligence to evaluate messages or other communications received from the member 118 to identify intent. The intent may correspond to a problem the member 118 wishes to have solved. Examples of intent may include (for example) topic, sentiment, complexity, and urgency. Topics may include, but are not limited to, subjects, products, services, technical issues, usage questions, complaints, purchase requests, etc. Intent may be determined, for example, based on semantic analysis of the message (e.g., by identifying keywords, sentence structure, repeated words, punctuation characters, and / or non-article words), user input (e.g., selecting one or more categories), and / or message-related statistics (e.g., typing speed and / or response latency). The intent may be used by the NLP algorithm or other artificial intelligence to identify possible tasks that can be recommended to the member 118. For example, based on the identified intent, the task recommendation system 112 may process any incoming messages from the member 118 using NLP or other artificial intelligence to detect new tasks or other problems that the member 118 would like to solve. In some cases, the task recommendation system 112 may utilize historical task data and corresponding messages from the task data store 110 to train the NLP or other artificial intelligence to identify possible tasks. If the task recommendation system 112 identifies one or more possible tasks that it can recommend to the member 118, the task recommendation system 112 may present these possible tasks to a representative, who may select a task that can be shared with the member 118 via a chat session.

[0053]

[0069] In one embodiment, the task recommendation system 112 may generate a list of possible tasks that can be presented to the member 118 for completion to reduce the member's cognitive load. For example, based on an evaluation of data collected from different member sources (e.g., personal fitness or biometric devices, video and audio recordings, etc.), the task recommendation system 112 may identify an initial set of tasks that can be completed for the member 118. Additionally, the task recommendation system 112 may identify additional and / or alternative tasks based on external factors. For example, the task recommendation system 112 may identify seasonal tasks (e.g., collecting leaves, cleaning rain barrels, etc.) based on the member's geographic location. As another example, the task recommendation system 112 may identify tasks that have been performed for other members within the member's geographic area and / or tasks that are potentially similarly situated (e.g., sharing one or more characteristics with the member 118). For example, if various members within the member's neighborhood have their rain barrels cleaned or their driveways closed for the winter, the task recommendation system 112 may determine that these tasks can be performed for the member 118 and appeal to the member 118 for completion.

[0054]

[0070] In one embodiment, the task recommendation system 112 can use the initial set of tasks, member-specific data from the user data store 108 (e.g., characteristics, demographics, location, past responses to recommendations and suggestions, etc.), data corresponding to similarly situated members from the user data store 108, and historical data corresponding to tasks previously performed for the member 118 and other similarly situated members from the task data store 110 as inputs to a machine learning algorithm or artificial intelligence to identify a set of tasks that can be recommended to the member 118 for performance. For example, the initial set of tasks may include a task related to cleaning a rain barrel, but based on the member's preferences, the member 118 may prefer to perform this task themselves. Thus, the output of the machine learning algorithm or artificial intelligence (e.g., a set of tasks that can be recommended to the member 118) may omit this task. Furthermore, in addition to the set of tasks that can be recommended to the member 118, the output of the machine learning algorithm or artificial intelligence may specify, for each identified task, a recommendation for the presentation of a button or other GUI element that the member 118 can select to indicate that they wish to follow a proxy for performance of the task, as described above.

[0055]

[0071] A list of the set of tasks that may be recommended to the member 118 may be provided to the representative for final determination as to which tasks may be presented to the member 118 through a task-specific interface (e.g., a communication session specific to these tasks, etc.). In one embodiment, the task recommendation system 112 may rank the list of the set of tasks based on the likelihood that the member 118 will select a task to delegate to a representative for performance by and / or coordination with the third-party service 116. Alternatively, the task recommendation system 112 may rank the list of the set of tasks based on the level of urgency of completion of each task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to the member's own prioritization of particular tasks or categories of tasks) and / or the potential risk to the member 118 if the task is not performed. For example, a task corresponding to replacing or installing a carbon monoxide detector in the member's home may be ranked higher than a task corresponding to replacing a refrigerator water heater filter, as the carbon monoxide filter may be more important to the member's safety. As another illustrative example, if a member 118 places great importance on maintaining the member's vehicle, the task recommendation system 112 may rank tasks related to vehicle maintenance higher than tasks related to other types of maintenance. As yet another illustrative example, the task recommendation system 112 may rank tasks related to an upcoming birthday higher than tasks that may be completed after the upcoming birthday.

[0056]

[0072] The surrogate may review the set of tasks recommended by the task recommendation system 112 and select one or more of these tasks for presentation to the member 118 via task-specific interfaces corresponding to those tasks. Additionally, as described above, the surrogate may determine whether a task should be presented with an option to delegate task performance to the surrogate (e.g., via a button or other GUI element indicating the member's preference for delegating task performance to the surrogate). In some examples, one or more tasks may be presented to the member 118 according to a ranking generated by the task recommendation system 112. Alternatively, one or more tasks may be presented according to the surrogate's understanding of the member's own preferences for task prioritization. Through an interface provided by the task facilitation service 102, the member 118 may access any of the task-specific interfaces associated with these tasks to select one or more tasks that can be performed with the surrogate's assistance. Alternatively, the member 118 may reject any presented tasks that the member 118 would rather perform personally or that the member 118 does not wish to perform, as the case may be.

[0057]

[0073] In one embodiment, the task recommendation system 112 may automatically select one or more of the tasks for presentation to the member 118 via a task-specific interface without proxy interaction. For example, the task recommendation system 112 may utilize a machine learning algorithm or artificial intelligence to select which tasks from a list of a set of tasks previously ranked by the task recommendation system 112 may be presented to the member 118 through the task-specific interface. As an illustrative example, the task recommendation system 112 may use a member profile corresponding to the member 118 from the user data store 108 (which may include historical data corresponding to member proxy communications, proxy performance and member feedback corresponding to presented tasks / suggestions, etc.), tasks currently in progress for the member 118, and a list of a set of tasks as input to the machine learning algorithm or artificial intelligence. Output generated by the machine learning algorithm or artificial intelligence may indicate which tasks from the list of a set of tasks should be automatically presented to the member 118 via the task-specific interface corresponding to those tasks. As member 118 interacts with these newly presented tasks, task recommendation system 112 may record and use these interactions to further train machine learning algorithms or artificial intelligence to better determine which tasks to present member 118 and other similarly situated members.

[0058]

[0074] In one embodiment, the task recommendation system 112 may monitor chat sessions between the member 118 and the proxy, as well as the member's interactions with task-specific interfaces associated with different tasks provided by the task facilitation service 102 and that may be performed on behalf of the member 118, to collect data regarding the member's selection of tasks to delegate to a proxy for performance. For example, the task recommendation system 112 may process messages corresponding to tasks presented to the member 118 by the proxy over the chat session and any interactions with the task-specific interfaces corresponding to these tasks (e.g., any task-specific communication sessions, member-generated discussions related to particular tasks, etc.) to determine the polarity or sentiment corresponding to each task. For example, if the member 118 indicates in a message to the proxy that they would prefer not to receive any task recommendations corresponding to vehicle maintenance, the task recommendation system 112 may attribute a negative polarity or sentiment to the task corresponding to vehicle maintenance. Alternatively, if the member 118 selects a task related to cleaning a rain barrel for delegation to a proxy and / or indicates in a message to the proxy that recommending this task was a great idea, the task recommendation system 112 may attribute a positive polarity or sentiment to this task. In one embodiment, the task recommendation system 112 may use these responses to tasks recommended to the member 118 to further train or enhance the machine learning algorithms or artificial intelligence utilized to generate task recommendations that may be presented to the member 118 and other similarly situated members of the task facilitation service 102 .

[0059]

[0075] In one embodiment, in addition to recommending tasks that can be performed for the member 118, the surrogate may recommend to the member 118 one or more curated experiences that may appeal to taking the member's mind off urgent matters and spending more time with themselves and their family. As described above, during the onboarding process, the member 118 may be prompted to indicate any of their interests or hobbies that the member 118 finds enjoyable. Furthermore, as the surrogate continues to interact with the member 118 over a chat session, the surrogate may prompt the member 118 to provide additional information about their interests in a natural way. For example, the surrogate may ask the member 118, "What are you doing this weekend?" Based on the member's response, the surrogate may update the member profile to indicate the member's preferences. Thus, over time, the surrogate and task facilitation service 102 may develop a deeper understanding of the member's interests and hobbies.

[0060]

[0076] In one embodiment, the task facilitation service 102 generates a set of experiences that may be available to members in each geographic market in which the task facilitation service 102 operates. For example, the task facilitation service 102 may partner with various organizations within each geographic market to identify unique and / or time-limited experience opportunities that may be of interest to members of the task facilitation service. Additionally, for experiences that do not require curation (e.g., hikes, walks, etc.), the task facilitation service 102 may identify popular experiences within each geographic market that may appeal to its members. Information collected by the task facilitation service 102 may be stored in a resource library or other repository accessible to the task recommendation system 112 and various proxies 106.

[0061]

[0077] In one embodiment, for each available experience, the task facilitation service 102 can generate a template that includes both the information needed from the member 118 to plan the experience on their behalf and a skeleton of what the experience recommendation proposal will look like when presented to the member 118. This may make it easier for the delegate to complete the definition of the experience and associated task. In some examples, the template may incorporate data from various sources that provide high-quality recommendations, such as travel guides, food and restaurant guides, reputable publications, etc. In one embodiment, when a delegate selects a particular template for the creation of an experience and associated task, the task recommendation system 112 can automatically identify portions of the member profile that can be used to populate the template. For example, if the delegate selects a template corresponding to a restaurant evening, the task recommendation system 112 may automatically process the member profile to identify any information corresponding to the member's dining preferences and restrictions, which can be used to populate one or more fields in the task template selected by the delegate.

[0062]

[0078] In one embodiment, the task recommendation system 112 periodically (e.g., monthly, bimonthly, etc.) or in response to a trigger event (e.g., a set number of tasks completed, a member request, etc.) selects a set of experiences that can be recommended to the member 118. For example, similar to identifying tasks that can be recommended to the member 118, the task recommendation system 112 may use at least the set of available experiences from the user data store 108 and the member's preferences as input to a machine learning algorithm or artificial intelligence to obtain, as output, a set of experiences that can be recommended to the member 118. The task recommendation system 112 may present this set of experiences to the member 118, possibly via a chat session on behalf of a representative or through a task-specific interface corresponding to each of the set of experiences. Each experience recommendation may specify a description of the experience and any associated costs that may be incurred by the member 118. Additionally, for each presented experience recommendation, the task recommendation system 112 may provide a button or other GUI element that may be selectable by the member 118 to request curation of an experience for the member 118.

[0063]

[0079] If the member 118 selects a particular experience recommendation corresponding to an experience they would like to curate on their behalf, the task recommendation service 112 or a surrogate may generate one or more new tasks related to the curation of the selected experience recommendation. For example, if the member 118 selects an experience recommendation related to a weekend picnic, the task recommendation service 112 or a surrogate may add a new task to the member's task list so that the member 118 may assess their progress in completing the task. Additionally, the surrogate may ask the member 118 detailed questions related to the selected experience to assist the surrogate in determining suggestions for completing tasks related to the selected experience. For example, if the member 118 selects an experience recommendation related to curating a weekend picnic, the surrogate may ask the member 118 regarding the number of adults and children participating, as this information may guide the surrogate to curate a weekend picnic for all parties and identify appropriate third-party services 116 and possible venues for the weekend picnic. Responses provided by the member 118 may be used to update the member profile for similar experiences and related tasks, so that these responses may be used to automatically retrieve information that can be used for experience curation.

[0064]

[0080] Similar to the process described above for completing a task for the member 118, the surrogate can generate one or more suggestions for curating the selected experience. For example, the surrogate may generate a suggestion that provides, among other things, a list of days / times for the experience, a list of possible venues for the experience (e.g., parks, movie theaters, hiking trails, etc.), a list of possible dining options and corresponding prices, options for meal delivery or pickup, etc. The various options in the suggestion may be presented to the member 118 via an experience-specific chat or communication session (e.g., a task-specific interface corresponding to the particular experience), and also via an application or web portal provided by the task facilitation service 102. Based on the member's responses to the various options presented in the suggestion, the surrogate may indicate that it is beginning the curation process for the experience. Additionally, the surrogate may provide information related to the experience that may be relevant to the member 118. For example, if the member 118 selects the option to pick up food from a selected restaurant for a weekend picnic, the surrogate may provide detailed driving directions from the member's home to the restaurant to pick up the food (which would not be provided if the member 118 selected a delivery option), detailed driving directions from the restaurant to the selected venue, parking information, a list of food to be ordered, and a total price for the food order. The member 118 may review this suggestion and may decide whether to accept the suggestion. If the member 118 accepts the suggestion, the surrogate may proceed to perform various tasks to curate the selected experience.

[0065]

[0081] When a member 118 selects a particular suggestion for a particular task or selects a button or other GUI element associated with a particular task to indicate a desire to have a representative perform the task, if the task is to be completed using a third-party service 116, the representative may coordinate with one or more third-party services 116 for the completion of the task for the member's 118's benefit. For example, the representative may utilize the task coordination system 114 of the task facilitation service 102 to identify and contact one or more third-party services 116 for the performance of the task. As described above, the task coordination system 114 may include a resource library containing detailed information related to third-party services 116 that may be available to perform the task on behalf of a member of the task facilitation service 102. For example, an entry for a third-party service in the resource library may include contact information for the third-party service, any available price sheets for services or products offered by the third-party service, a list of products and / or services offered by the third-party service, business hours, ratings or scores by different categories of members, etc. The representative may query the resource library to identify one or more third-party services that will perform the task and to determine the estimated cost of performing the task. In some examples, the representative may contact one or more third-party services 116 to obtain estimates for completion of the task and to coordinate the performance of the task for the member 118 .

[0066]

[0082] In some examples, the resource library may further include detailed information corresponding to other services and other entities that may be associated with or affiliated with the task facilitation service 102 and that are contracted to perform various tasks on behalf of members of the task facilitation service 102. These other services and other entities may provide services or goods at rates agreed upon with the task facilitation service 102. Thus, when a representative selects one of these other services or other entities from the resource library, the representative may be able to determine certain parameters for the completion of the task (e.g., price, availability, time required, etc.).

[0067]

[0083] In one embodiment, for a given task, the delegate (e.g., through a web portal or application provided by the task facilitation service) can query a resource library to identify one or more third-party services and other services / entities affiliated with the task facilitation service 102 and ask the resource library for a quote for completing the task. For example, for a newly created task, the delegate may send a work offer to these one or more third-party services and other services / entities. The work offer may indicate various characteristics of the task to be completed (e.g., the scope of the task, the approximate geographic location of the member 118 or where the task should be completed, the desired budget, etc.). Through the application or web portal provided by the task facilitation service 102, the third-party service or other service / entity can review the work offer and decide whether to submit a quote for completing the task or decline the work offer. If the third-party service or other service / entity chooses to decline the work offer, the delegate may receive a notification indicating that the third-party service or other service / entity declined the work offer. Alternatively, if a third-party service or other service / entity chooses to bid to perform a task (e.g., accepts a work offer), the third-party service or other service / entity may submit an estimate for completion of the task, which may indicate an estimated cost for completion of the task, the time required to complete the task, an estimated date that the third-party service or other service / entity will be available to begin performing the task, etc.

[0068]

[0084] The representative may use any given estimates from third-party services and / or other services / entities to generate different proposals for completing the task. These different proposals may be presented to the member 118 through a task-specific interface corresponding to the particular task to be completed. If the member 118 selects a particular proposal from the set of proposals presented through the task-specific interface, the representative may send a notification to the third-party service or other service / entity that submitted the estimate associated with the selected proposal to indicate that it has been selected for completion of the task. Thus, the representative may utilize the task coordination system 114 to coordinate with the third-party service or other service / entity for the completion of the task, as described in more detail herein.

[0069]

[0085] In some cases, when a task is completed by a surrogate 106, the surrogate 106 may utilize the task coordination system 114 of the task facilitation service 102 to identify any resources that can be utilized by the surrogate 106 to perform the task. The resource library may contain detailed information regarding different resources available for performing the task. As an illustrative example, if the surrogate 106 is tasked with purchasing a set of filters for a member's home, the surrogate 106 may query the resource library to identify retailers that can sell filters acceptable to the member 118 and of a quality and / or price corresponding to the proposal accepted by the member 118. Additionally, the surrogate 106 may retrieve the member's 118's available payment information from the user data store 108, which can be used to pay for any resources needed by the surrogate 106 to complete the task. Using the example above, the surrogate 106 may retrieve the member's 118's payment information from the user data store 108 to complete a purchase with a retailer of a set of filters to be used in the member's home.

[0070]

[0086] In one embodiment, the task coordination system 114 uses a machine learning algorithm or artificial intelligence to select one or more third-party services 116 and / or resources on behalf of the surrogate for performance of the task. For example, the task coordination system 114 may utilize selected suggestions or parameters associated with the task (e.g., if the member 118 followed the surrogate to determine how the task should be performed) and historical task data from the task data store 110 corresponding to similar tasks as input to the machine learning algorithm or artificial intelligence. The machine learning algorithm or artificial intelligence may generate as output a list of one or more third-party services 116 that can perform the task with a high probability of satisfaction for the member 118. If the task is performed by the surrogate 106, the machine learning algorithm or artificial intelligence may generate as output a list of resources (e.g., retailers, restaurants, brands, etc.) that can be used by the surrogate 106 to perform the task with a high probability of satisfaction for the member 118. As described above, the resource library may include, for each third-party service 116, a rating or score associated with the satisfaction of the third-party service 116 as determined by members of the task facilitation service 102. Additionally, the resource library may include a rating or score associated with the satisfaction of each resource (e.g., retailer, restaurant, brand, product, material, etc.) as determined by a member of the task facilitation service 102. For example, upon completion of a task, the surrogate may prompt the member 118 to provide a rating or score regarding the performance of a third-party service in completing the task for the member 118. As another example, if a task is performed by a surrogate 106, the surrogate may prompt the member 118 to provide a rating or score regarding the performance of the surrogate and the resources utilized by the surrogate for the completion of the task.Each rating or score is associated with the member who provided the rating or score such that the task coordination system 114 may use machine learning algorithms or artificial intelligence to determine the likelihood of satisfaction for performing the task based on the satisfaction of resources utilized by or on behalf of third-party services for similar tasks for similarly situated members. The task coordination system 114 may generate a list of recommended third-party services 116 and / or resources for performing the task, whereby the list may be ranked according to the likelihood of satisfaction (e.g., score or other metric) assigned to each identified third-party service and / or resource.

[0071]

[0087] In some examples, if a task cannot be completed by a third-party service or other service / entity according to the estimates provided in the selected proposal, the member 118 may be given the option to cancel the particular task or, in some cases, make changes to the task. For example, if the new estimated cost for performing the task exceeds a maximum amount specified in the selected proposal, the member 118 may ask a delegate to find an alternative third-party service or other service / entity for performing the task within the budget specified in the proposal. Similarly, if the time frame for completion of the task is not within the time frame indicated in the proposal, the member 118 may ask a delegate to find an alternative third-party service or other service / entity for performing the task within the original time frame. The member's intervention may be recorded by the task recommendation system 112 and the task coordination system 114 to retrain the corresponding machine learning algorithms or artificial intelligence to better identify third-party services 116 and / or other services / entities that can perform the task within the defined proposal parameters.

[0072]

[0088] In one embodiment, once a delegate contracts with one or more third-party services 116 or other services / entities to perform a task, the task coordination system 114 may monitor the performance of the task by these third-party services 116 or other services / entities. For example, the task coordination system 114 may record any information provided by the third-party services 116 or other services / entities regarding the time frame for performance of the task, the costs associated with performing the task, any status updates regarding the performance of the task, etc. The task coordination system 114 may associate this information with the data record in the task data store 110 corresponding to the task being performed. Status updates provided by the third-party services 116 or other services / entities may be automatically provided to the member 118 and the delegate via an application or web portal provided by the task facilitation service 102.

[0073]

[0089] In one embodiment, when a task is performed by a surrogate 106, the task coordination system 114 may monitor the performance of the task by the surrogate 106. For example, the task coordination system 114 may monitor any communications in real time between the surrogate 106 and the member 118 regarding the surrogate's performance of the task. These communications may include messages from the surrogate 106 indicating any status updates regarding the performance of the task, any purchases or expenses incurred by the surrogate 106 in performing the task, the time frame for completion of the task, etc. The task coordination system 114 may associate these messages from the surrogate 106 with the data record in the task data store 110 corresponding to the task being performed.

[0074]

[0090] In some cases, the surrogate may automatically pay for services and / or goods rendered by one or more third-party services 116 on behalf of the member 118, or for purchases made by the surrogate to complete a task. For example, during the onboarding process, the member 118 may provide payment information (e.g., credit card number and related information, debit card number and related information, banking information, etc.) that can be used by the surrogate to make payments to the third-party service 116 or for purchases made by the surrogate 106 for the member 118. Thus, the member 118 may not be required to provide any payment information to enable the surrogate 106 and / or third-party service 116 to begin performing a task for the member 118. This may further reduce the cognitive load on the member 118 to manage the performance of a task.

[0075]

[0091] As described above, once a task is completed, the member 118 may be prompted to provide feedback regarding the completion of the task. For example, the member 118 may be prompted to provide feedback regarding the performance and expertise of the selected third-party service 116 in performing the task. Additionally, the member 118 may be prompted to provide feedback regarding the quality of the suggestions provided by the surrogate and regarding whether the performance of the task addressed underlying issues related to the task. Using the responses provided by the member 118, the task facilitation service 102 may train or potentially update the machine learning algorithms or artificial intelligence utilized by the task recommendation system 112 and the task coordination system 114 to better identify tasks, make suggestions, identify third-party services 116 and / or other services / entities to complete the task for the member 118 and other similarly situated members, identify resources that can be provided to the surrogate 106 to perform the task for the member 118, etc.

[0076]

[0092] With respect to the processes described herein, it should be noted that various operations performed by the surrogate 106 may additionally or alternatively be performed using one or more machine learning algorithms or artificial intelligence. For example, as the surrogate 106 performs, or possibly adjusts the performance of, tasks on behalf of the member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback associated with the performance of these tasks by the surrogate 106 and / or third-party service 116. In one embodiment, the task recommendation system 112 may utilize machine learning algorithms or artificial intelligence to automatically and dynamically generate new tasks based on various attributes of the member's profile (e.g., historical data corresponding to member surrogate communications, member feedback corresponding to surrogate performances and presented tasks / suggestions, etc.), with or without surrogate interaction, after the member's profile has been updated over a period of time (e.g., six months, one year, etc.) or for a set of tasks (e.g., 20 tasks, 30 tasks, etc.). The task recommendation system 112 may automatically communicate with the members 118 to obtain any additional information needed for new tasks and automatically generate suggestions that can be presented to the members 118 for the implementation of these tasks. The surrogate 106 may monitor communications between the task recommendation system 112 and the members 118 to ensure that the conversation maintains a positive polarity (e.g., the members 118 are satisfied with their interactions with the task recommendation system 112 or other bots, etc.). If the surrogate 106 determines that the conversation has a negative polarity (e.g., the members 118 are expressing frustration, the task recommendation system 112 or bots are unable to process the members' responses or questions, etc.), the surrogate 106 may intervene in the conversation. This may enable the surrogate 106 to address any member concerns and perform any tasks on behalf of the members 118.

[0077]

[0093] Thus, unlike automated customer service systems and environments where these systems and environments may have little knowledge of the user interacting with an agent or other automated system, the task recommendation system 112 can continuously update the member profile to provide up-to-date historical information about the member 118 based on the member's automated interactions with the system or surrogate 106 and based on tasks performed on behalf of the member 118 over time. This historical information, which may be automatically and dynamically updated as the member 118 or system interacts with the surrogate 106 and as tasks are devised, suggested, and performed for the member 118 over time, can be used by the task recommendation system 112 to predict, identify, and present appropriate or intelligent responses to the member's 118's queries, requests, and / or goals.

[0078]

[0094] 2 illustrates an illustrative example of an environment 200 in which a proxy assignment system 104 performs an onboarding process for a member 118 and assigns a proxy 106 to the member 118 based on member and proxy attributes, according to at least one embodiment. In environment 200, in response to a request from a member 118 to initiate an onboarding process to create an account with a task facilitation service, the task facilitation service's proxy assignment system 104 may send one or more onboarding prompts to the member 118 to gather information about the member 118 that can be used to create a member profile and identify possible tasks that can be presented to the member 118 based on the member profile. For example, as shown in FIG. 2, the member 118 may submit their request to a member onboarding subsystem 202 of the proxy assignment system 104. The member onboarding subsystem 202 may be implemented using a computer system or as an application or other executable code implemented on the computer system of the proxy assignment system 104.

[0079]

[0095] In one embodiment, the member onboarding subsystem 202 of the proxy assignment system 104 selects one or more questions that may be administered to the member 118 to assemble initial information about the member 118 that may be used to generate a member profile for the member 118. For example, the member onboarding subsystem 202 may initially prompt the member 118 to provide basic demographic information about the member 118. As an illustrative example, the member onboarding subsystem 202 may prompt the member 118 to provide their physical address, age, information about other members of their household (e.g., spouse, children, other dependents, etc.), information about any interests or hobbies, languages ​​spoken in the household, etc. Additionally, the member onboarding subsystem 202 may prompt the member 118 to indicate their comfort level with delegating certain categories of tasks (e.g., cleaning tasks, repair tasks, maintenance tasks, etc.). In some examples, the member onboarding subsystem 202 may prompt the member 118 to indicate what initial tasks the member 118 is interested in delegating to other members to reduce cognitive load on the member 118.

[0080]

[0096] The member onboarding subsystem 202 may provide responses to these initial prompts to the member modeling subsystem 204 to begin the process of generating a member profile for the member 118. The member modeling subsystem 204 may be implemented using a computer system or as an application or other executable code implemented on the computer system of the surrogate assignment system 104. In one embodiment, the member modeling subsystem 204 may implement a machine learning algorithm or artificial intelligence trained to identify additional prompts that can be submitted to the member 118 to obtain additional information that can be used to generate a member profile for the member 118. Furthermore, the machine learning algorithm or artificial intelligence may be configured to use the responses given by the member 118 in response to the various prompts submitted to the member 118, as well as other member data from the user data store 108, to identify surrogates that may be best suited to interact with the member 118 and generate a member profile for the member 118 that can be used to perform various tasks for the member 118 according to the member's preferences and behaviors.

[0081]

[0097] As an illustrative example, when member 118 provides basic information about member 118 in response to initial prompts from member onboarding subsystem 202, member modeling subsystem 204 may process the provided information using a classification or clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family structure, household configuration, etc.). In some cases, a dataset of input member characteristics corresponding to responses to prompts provided by member onboarding subsystem 292 provided by a sample member (e.g., tester, etc.) may be analyzed using a clustering algorithm to identify different types of members who may interact with the task facilitation service. Furthermore, once an actual member completes the onboarding process, member modeling subsystem 204 may retrain the clustering algorithm and / or adjust various clusters corresponding to different member types to more accurately predict the member type of onboarding members, such as member 118.

[0082]

[0098] In one embodiment, based on the initial classification of the member 118 based on the initial responses provided by the member 118 during the onboarding process, the member modeling subsystem 204 may identify additional questions or prompts that can be given to the member 118 to obtain additional information that can be used to better classify the member 118 as belonging to a particular member type or classification. As an illustrative example, if the member modeling subsystem 204 determines that the member 118 may belong to a particular class of members that share similar baseline characteristics with the member 118, the member modeling subsystem 204 may evaluate member profiles corresponding to members within the particular class of members to identify additional questions or prompts that can be used to determine whether the member 118 shares more with these members. For example, if a significant number of members within a particular class have a particular type of vehicle in which tasks are performed, the member modeling subsystem 204 may determine that questions related to the member's vehicle may be highly relevant in identifying possible tasks for the member 118. As another illustrative example, if a particular class of members is known to prefer handling their own landscaping, the member modeling subsystem 204 may determine that questions related to the member's landscaping preferences may be highly relevant when determining whether the member 118 should be recommended to delegate landscaping tasks to others and how frequently such recommendations may be given. This tailored approach to member onboarding may alleviate the burden on the member 118 from engaging in a cumbersome process of responding to countless questions, which may include irrelevant or unnecessary questions.

[0083]

[0099] Based on responses provided by the member 118 to the member onboarding subsystem 202, the member modeling subsystem 204 may generate a member profile or model of the member 118 that can be used to identify and recommend tasks and suggestions to the member 118 over time. The member profile or model may define a set of attributes of the member 118 that can be used by a representative to determine how best to approach the member 118 in a conversation, when recommending tasks and suggestions to the member 118, and when performing tasks for the member 118. These attributes may include measures of the member's behavior or preferences in delegating certain categories of tasks to others or performing certain categories of tasks themselves. For example, the member attributes determined by the member modeling subsystem 204 may provide a score or other metric corresponding to the likelihood that the member 118 will delegate different categories of tasks to others. As another example, the member attributes may provide an indication of the member's preferences that is presented with a suggestion for completing a task (if delegated), or simply allows another person to decide on the member 118. Other member attributes may indicate whether the member 118 is interested in budget, brand recognition, reviews (e.g., restaurant reviews, product reviews, etc.), punctuality, response speed, etc. Member attributes may also include basic information about the member 118 provided during the onboarding process described above.

[0084]

[0100] In one embodiment, the member modeling subsystem 204 allows the member 118 to access their member profile to provide additional information that can be used to complement the member profile and / or to modify any previously added information. For example, through an application or web portal provided by the task facilitation service, the member 118 may be provided with a link or other interactive element that the member 118 can use to access their member profile. Within the member profile, the member 118 may add, remove, or edit any information within the member profile. As described above, the member profile may be divided into various sections corresponding to different member characteristics, such as personal demographics, family composition, household configuration, payment information, etc. The member modeling subsystem 204 may automatically populate elements of these various sections based on the member's previously provided responses to prompts provided by the member modeling subsystem 204 during the onboarding process and any responses provided by the member 118 to surveys or questionnaires provided to the member 118 during the onboarding process. Each section of the member profile may further include additional questions or prompts that the member 118 can use to provide additional information that can be used to expand the member profile.

[0085]

[0101] In some examples, a member 118 may designate one or more sections or subsections of a member profile as private, such that these one or more sections or subsections are not visible to agents or any other entities other than the member 118. For example, a member 118 may indicate that payment information associated with one or more payment methods should be hidden, such that agents assigned to the member 118 cannot view the payment information. However, the payment information may be utilized by the task facilitation service for payment processing (e.g., for payments to third-party services, etc.) without the payment information being disclosed to agents.

[0086]

[0102] As described above, certain information within a member profile may be hidden from the member 118. For example, as the relationship between the member 118 and the assigned representative develops, the assigned representative may add personal notes about the member 118. These personal notes may not be relevant to the member 118 and therefore may be hidden from the member 118. Thus, when the member 118 accesses the member profile, any section or subsection designated as accessible only by the representative may be automatically hidden from the member 118.

[0087]

[0103] In one embodiment, the member modeling subsystem 204 provides the identified member attributes to a member-surrogate pairing subsystem 206 to identify a surrogate that can be assigned to the member 118. The member-surrogate pairing subsystem 206 can be implemented using a computer system or as an application or other executable code implemented on the computer system of the surrogate assignment system 104. The member-surrogate pairing subsystem 206 can use the provided member attributes to select a surrogate from a set of surrogates 106 that can be assigned to the member 118, to assist the member 118 in identifying and performing tasks for the member 118, and in some cases, to reduce the cognitive load in the member 118's daily life.

[0088]

[0104] In one embodiment, the member-surrogate pairing subsystem 206 implements a machine learning algorithm or artificial intelligence that utilizes given member attributes as input to identify a surrogate or set of surrogates that can be assigned to the member 118 that would provide a high likelihood of a positive relationship between the member 118 and the identified surrogate. The machine learning algorithm or artificial intelligence may be trained using unsupervised training techniques. For example, a dataset of input member attributes and surrogate attributes may be analyzed using a clustering algorithm to identify correlations between different types of members and surrogates. Conversely, a dataset of input member attributes and surrogate attributes may also be analyzed using a clustering algorithm to identify types of members and types of surrogates that are less well suited to each other. Exemplary clustering algorithms that may be trained using sample member attributes and surrogate attributes (e.g., historical data, hypothetical data, etc.) to identify potential pairings may include a k-means clustering algorithm, a fuzzy c-means (FCM) algorithm, an expectation-maximization (EM) algorithm, a hierarchical clustering algorithm, a density-based spatial clustering for noisy applications (DBSCAN) algorithm, etc. Based on the output of the machine learning algorithm generated using member attributes and data from the proxy data store 208 as input, the member-proxy pairing subsystem 206 may identify one or more proxy from the group of proxy 106 that may be assigned to the member 118.

[0089]

[0105] The proxy data store 208 may include an entry for each proxy in the group 106 of proxy associated with the task facilitation service. The entry corresponding to a proxy may specify various characteristics of the proxy. These characteristics may be similar to those collected by the member onboarding subsystem 202 during the onboarding of the member 118. For example, characteristics about a proxy may include the proxy's physical address, age, information about other members of the household (e.g., spouse, children, other dependents, etc.), information about any interests or hobbies, languages ​​spoken in the household, etc. Additionally, an entry in the proxy data store 208 corresponding to a particular proxy may indicate the proxy's performance with respect to other members of the task facilitation service. As described in more detail herein, the task facilitation service may monitor the proxy's performance and solicit member feedback regarding the member's relationship with their assigned proxy. Based on the given feedback and an evaluation of the proxy's performance, the task facilitation service may determine the proxy's performance with respect to the member's relationship and assistance. One or more metrics related to the proxy's performance may be added to the proxy's entry in the proxy data store 208. For example, an entry may specify a performance score for each member-proxy pairing for the particular proxy associated with the entry. As an illustrative example, if the surrogate had a positive relationship with a particular member and helped reduce the member's cognitive load, the pairing may be assigned a high performance score. Alternatively, if the surrogate had a neutral or negative relationship with a particular member, the pairing may be assigned a lower score. These performance scores and surrogate characteristics from the surrogate data store 208 may be used by the member-surrogate pairing subsystem 206 as inputs with member attributes to identify one or more surrogates that can be assigned to the member 118.

[0090]

[0106] Once the member-surrogate pairing subsystem 206 identifies a set of surrogates that can be assigned to the member 118, the member-surrogate pairing subsystem 206 may select a surrogate from one or more surrogates for assignment to the member 118. For example, the member-surrogate pairing subsystem 206 may rank the set of surrogates according to a probability or other metric corresponding to the likely compatibility between the member 118 and each surrogate in the set of surrogates. Based on the ranking of the set of surrogates, the member-surrogate pairing subsystem 206 may select the highest-ranked surrogate from the set of surrogates and determine whether the surrogate is available for assignment. For example, from the surrogate data store 208, the member-surrogate pairing subsystem 206 may determine whether the surrogate is currently assigned to a threshold number of other members or, in some cases, is unavailable for assignment (e.g., on vacation, etc.). If the selected surrogate is unavailable, the member-surrogate pairing subsystem 206 may select an alternative surrogate from the identified set of surrogates and identify the availability of the alternative surrogate. Once a proxy is selected, the member-proxy pairing subsystem 206 may assign the proxy to the member 118 and update the proxy's corresponding entry in the proxy data store 208 to indicate the assignment.

[0091]

[0107] In one embodiment, rather than using a machine learning algorithm or artificial intelligence to identify an initial set of surrogates from which a surrogate may be selected for assignment to a member 118, the member-surrogate pairing subsystem 206 may select an available surrogate from the group of surrogates 106. For example, the member-surrogate pairing subsystem 206 may identify a surrogate from the group of surrogates 106 available for assignment to a member 118 and assign the surrogate to the member 118. Similar to the process described above, once the member-surrogate pairing subsystem 206 selects a surrogate, the member-surrogate pairing subsystem 206 may update an entry corresponding to the selected surrogate in the surrogate data store 208 to record the assignment.

[0092]

[0108] In some cases, rather than using a machine learning algorithm or artificial intelligence to identify an initial set of surrogates from which a surrogate may be selected, the member-surrogate pairing subsystem 206 may automatically select a first available surrogate from a group of surrogates 106. In some cases, the member-surrogate pairing subsystem 206 may automatically narrow down the group of surrogates 106 based on one or more criteria corresponding to the member's identity. For example, if the member 118 is located in Seattle, Washington, the member-surrogate pairing subsystem 206 may automatically narrow down the group of surrogates 106 so that the pool of surrogates that can be assigned to the member 118 includes surrogates located within a geographic proximity of Seattle, Washington (e.g., within 100 miles of Seattle, within 200 miles of Seattle, etc.). As another example, if the member 118 has children, the member-surrogate pairing subsystem 206 may narrow down the group of surrogates 106 so that the pool of surrogates includes surrogates who also have children. From the identified pool, the member-surrogate pairing subsystem 206 may automatically select a first available surrogate for assignment to the member 118.

[0093]

[0109] In one embodiment, during the onboarding process, the member 118 can provide the member onboarding subsystem 202 with information regarding one or more tasks that the member 118 would like to delegate to a proxy. The member onboarding subsystem 202 can provide this information to the member modeling subsystem 204, which can use this information, in addition to the member attributes described above, to identify parameters related to the tasks that the member 118 would like to delegate to a proxy for performance of the tasks. For example, the parameters related to these tasks can specify the nature of these tasks (e.g., cleaning a rain barrel, installing a carbon monoxide detector, planning a party, etc.), the level of urgency for completion of these tasks (e.g., timing requirements, deadlines, dates corresponding to upcoming events, etc.), any member preferences for completion of these tasks, etc. These parameters, in addition to the member attributes identified by the member modeling subsystem 204, can be used as input to a machine learning algorithm or artificial intelligence to identify an initial set of proxy from which a proxy may be selected for assignment to the member 118. Alternatively, the member-surrogate pairing subsystem 206 may query the surrogate data store 208 to identify one or more surrogates that may be associated with these particular task parameters (e.g., surrogates skilled in handling such tasks, surrogates that have previously performed similar tasks with positive member feedback, etc.). The member-surrogate pairing subsystem 206 may select an available surrogate from the identified one or more surrogates to assign to the member 118.

[0094]

[0110] Once a representative is assigned to the member 118, the member-proxy pairing subsystem 206 may provide the representative with the member 118's contact information (e.g., phone number, email address, etc.) and instruct the representative to initiate contact with the member 118 to complete the onboarding process. For example, through an application or web portal provided to the representative by the task facilitation service, the representative may receive information corresponding to the member 118 (e.g., name, demographic information, family information, home information, etc.) and instructions to initiate a communication session with the member 118. This may allow the selected representative to begin a relationship with the member 118 and identify tasks that can be delegated to the representative to perform on behalf of the member 118. In some cases, the member-proxy pairing subsystem 206 may establish a communication session between the representative and the member 118. For example, the member-proxy pairing subsystem 206 may initiate a chat session between the representative and the member 118, whereby the member 118 may communicate with the selected representative via the application or web portal provided by the task facilitation service. Additionally, the representative may communicate with the member 118 via chat sessions using an application or web portal provided by the task facilitation service.

[0095]

[0111] In one embodiment, the surrogate assignment system 104 may further monitor the relationship between the member 118 and the assigned surrogate to determine whether the member 118 should be reassigned to another surrogate in the set of surrogates 106. For example, the member 118 may be prompted by the member-surrogate pairing subsystem 206 (periodically and / or in response to a trigger event) to provide feedback regarding its relationship with the assigned surrogate. As an illustrative example, when the surrogate completes a particular task for the member 118, the member-surrogate pairing subsystem 206 may prompt the member 118 to provide feedback regarding the surrogate's performance related to the completed task. As another example, the member-surrogate pairing subsystem 206 may prompt the member 118 at specific time intervals (e.g., monthly, bimonthly, etc.) to provide feedback regarding the member's relationship with the assigned surrogate. In some cases, the member 118 may provide feedback regarding the member's relationship with the assigned surrogate at any time without being prompted by the member-surrogate pairing subsystem 206. For example, through an application provided by the task facilitation service, the member 118 may manually generate a feedback form that may be provided to the member proxy pairing subsystem 206 for evaluation.

[0096]

[0112] In one embodiment, the member-surrogate pairing subsystem 206 may utilize feedback provided by the member 118 to determine whether to assign a new surrogate to the member 118. For example, the member-surrogate pairing subsystem 206 may process the obtained feedback using a machine learning algorithm or artificial intelligence to determine a relationship score for the relationship between the member 118 and the assigned surrogate. The machine learning algorithm or artificial intelligence may be trained using supervised training techniques. For example, a dataset of input feedback, known member and surrogate attributes, and resulting relationship scores may be selected for training the machine learning model. The machine learning model may be evaluated to determine whether it is generating accurate relationship scores based on sample inputs fed to the machine learning model. Based on this evaluation, the machine learning model may be modified to increase the likelihood that the machine learning model will produce the desired results. The machine learning model may further be dynamically trained by soliciting feedback from surrogates and administrators of the task facilitation service regarding the ratings and relationship scores provided by the machine learning algorithm or artificial intelligence for surrogate reassignment. For example, if the member-surrogate pairing subsystem 206 determines that a member should be assigned a new surrogate based on the relationship score for a particular member-surrogate pairing (e.g., the relationship score is below a threshold), the member-surrogate pairing subsystem 206 may select a new surrogate that may be assigned to the member. Additionally, the member-surrogate pairing subsystem 206 may obtain new feedback from the member corresponding to the new relationship. A machine learning algorithm or artificial intelligence may use this feedback to determine a new relationship score for the pairing and determine whether this new relationship score represents an improvement over the previous relationship score that resulted in the surrogate reassignment.This determination may be used to further train a machine learning algorithm or artificial intelligence to provide a more accurate relationship score that may be used to determine whether to assign a new representative to a member.

[0097]

[0113] In one embodiment, the proxy assignment system 104 may process messages exchanged between the member 118 and the assigned proxy in real time to better understand the relationship between the member 118 and the assigned proxy and to better identify techniques that may be performed by the assigned proxy to improve that relationship with the member 118. For example, the proxy assignment system 104 may process messages exchanged between the member 118 and the assigned proxy using a machine learning algorithm or artificial intelligence to determine various attributes or idiosyncrasies of the member 118. As an illustrative example, if the member 118 indicates to the proxy that he or she prefers to handle any automotive tasks (e.g., scheduling maintenance appointments, purchasing oil and filters, etc.) personally, the machine learning algorithm or artificial intelligence may update the member profile to indicate that the proxy 106 should not recommend delegating automotive tasks to the proxy 106 and / or third-party services. In some cases, based on messages exchanged between the member 118 and the assigned representative, the machine learning algorithm or artificial intelligence may generate a behavioral profile of the member 118 that may indicate any personality attributes of the member 118 and any temperaments or quirks of the member 118 that may be useful to the representative 106 in approaching the member 118 in a conversation. In some cases, the machine learning algorithm or artificial intelligence may generate one or more recommendations based on the member's behavioral profile for approaching and communicating with the member 118.

[0098]

[0114] In one embodiment, the surrogate assignment system 104 may further process messages exchanged between the member 118 and the assigned surrogate in real time to obtain any additional information that may be used to supplement the member profile. For example, if the member 118, during a conversation with the surrogate via a communication channel, indicates that a new family member has moved into the member's home, the surrogate assignment system 104 may automatically process this message in real time to determine that the member profile can be updated and information corresponding to this new family member added. Thus, the surrogate assignment system 104 may use the information provided by the member 118 to automatically update the appropriate sections of the member profile (e.g., sections related to the member's family).

[0099]

[0115] In some examples, the surrogate assignment system 104 may determine whether additional information may be requested from the member 118 based on information added to the member profile. Returning to the example above related to the introduction of a new family member to the member's home, the surrogate assignment system 104 may determine whether to recommend questions or prompts that may be submitted to the member 118 to obtain additional information about the new family member. For example, if the member 118 has not provided names and other identifying information corresponding to this new family member, the surrogate assignment system 104 may recommend questions or prompts that may be used to obtain the new family member's names and other identifying information (e.g., "What are the new family member's names?", "How old are the new family member?", "Do the new family member have any dietary restrictions?", etc.). These recommendations may be provided to the surrogate, who may communicate these questions or prompts to the member 118 via the communication session.

[0100]

[0116] 3 shows an illustrative example of an environment 300 in which task-related data is collected and aggregated from a member area 302 to identify one or more tasks that may be recommended to a member for performance by a surrogate 106 and / or a third-party service 116, according to at least one embodiment. In the environment 300, a member may send task-related data via a computing device 120 (e.g., a laptop computer, a smartphone, etc.) to a surrogate 106 assigned to the member to identify one or more tasks that may be performed for the member. For example, in one embodiment, a member may manually enter one or more tasks that the member wants to delegate to a surrogate 106 for performance. The task facilitation service 102 may provide the member, via an application or web portal provided by the task facilitation service 102, the option for manual entry 304 of tasks that may be delegated to a surrogate 106 or added to the member's list of tasks.

[0101]

[0117] If the member selects the option for manual task entry 304, the task facilitation service 102 may provide a task template in which the member, via an application or web portal interface, may enter various details related to the task. The task template may include various fields in which the member may provide a name for the task, a description of the task (e.g., "I need to have the gutters cleaned before the next storm," "I want the painter to touch up the bathroom," etc.), a time frame for the performance of the task (e.g., a specific due date, date range, level of urgency, etc.), a budget for the performance of the task (e.g., an unlimited budget, a specific maximum amount, etc.), etc.

[0102]

[0118] In some examples, if the member selects the option for manual task entry 304, the task facilitation service 102 may provide the member with different task templates that can be used to create a new task. As described above, the task facilitation service may maintain a resource library that serves as a repository for different task templates corresponding to different task categories (e.g., vehicle maintenance tasks, home maintenance tasks, family-related event tasks, caregiving tasks, experience-related tasks, etc.). A task template may include multiple task definition fields that can be used to define a task that can be performed for a member. For example, a task definition field corresponding to a vehicle maintenance task may be used to define the make and model of the member's vehicle, the age of the vehicle, information corresponding to the last time the vehicle was maintained, any reported accidents related to the vehicle, a description of any problems related to the vehicle, etc. Thus, each task template maintained in the resource library may include fields that are specific to the task category associated with the task template.

[0103]

[0119] Through the resource library, a member may evaluate each of the available task templates to select a particular task template that may be closely associated with the new task the member wishes to create. Once the member selects a particular task template, the member may populate one or more task definition fields that may be used to define the task that may be performed for the member. These fields may be specific to the task category associated with the task template. In some examples, based on the selected task template, the task facilitation service 102 may automatically populate one or more task definition fields based on information specified in the member profile, as described above.

[0104]

[0120] In one embodiment, a task template given to a member may be specifically tailored according to the member's characteristics identified by the task facilitation service 102. As described above, the task facilitation service 102 may generate a member profile or model for the member during the member's onboarding process, which may be used to identify and recommend tasks and suggestions to the member over time. The member profile or model may define a set of member attributes that may be used by the proxy 106 to determine how best to approach the member when conversing with them, recommending tasks and suggestions to them, and performing tasks for them. These attributes may include measures of the member's behavior or preferences when delegating certain categories of tasks to others or performing certain categories of tasks themselves. These member attributes may indicate whether the member is budget-conscious, brand-aware, reviews (e.g., restaurant reviews, product reviews, etc.), punctuality, response speed, etc. Based on these member attributes, the task facilitation service 102 may omit certain fields from the task template. For example, if the member attributes specify that the member is not concerned with a budget for completing the task, the task facilitation service 102 may omit a field from the task template that corresponds to the member's budget for the task. As another illustrative example, if the task facilitation service 102 determines that the member prefers high-end or prestigious brands for performance of its task, the task facilitation service 102 may omit one or more fields that correspond to the selection or identification of a brand for performance of the task, since the task facilitation service 102 may utilize a resource library to identify high-end or prestigious brands for performance of the task.

[0105]

[0121] When a member submits, via computing device 120 or through an interface provided by task facilitation service 102, a completed task template corresponding to a task to be performed for the member's benefit, a surrogate 106 assigned to the member may retrieve the completed task template and begin evaluating the task to determine how to best perform the task for the member. For example, the surrogate 106 may evaluate the completed task template and generate a new task for the member that corresponds to the task-related details provided by the member in the completed task template. Additionally, based on the surrogate's knowledge of the member (e.g., from interactions with the member, from the member profile, etc.), the surrogate 106 may determine whether to prompt the member for additional information that can be used to determine how to best perform the task for the member. For example, if the member indicated that they would like to have their gutters cleaned but did not indicate when the gutters should be cleaned, the surrogate 106 may communicate with the member via an active chat session associated with the newly created task to inquire about a time frame for cleaning the member's gutters. As another example, if a member submits a task without a specific budget for performance of the task, and the surrogate 106 knows (e.g., based on the member profile, personal knowledge of the member, etc.) that the member adheres to a budget, the surrogate 106 may communicate with the member to determine what the budget should be for performance of the task. As noted above, any information obtained in response to these communications may be used to supplement the member profile, so that for future tasks, this newly obtained information may be automatically retrieved from the member profile without requiring additional prompting of the member.

[0106]

[0122] In one embodiment, a member can submit a request to the surrogate 106 to generate a project, which may include one or more tasks that need to be completed for the project or project, where one or more tasks may be determined by the surrogate 106 and / or by the task recommendation system 112. For example, via a chat session established between the member and the assigned surrogate 106, the member may indicate a desire to initiate a project. As an illustrative example, the member may send a message to the surrogate 106 requesting help in planning the member's move to Denver in August. In response to this message, the surrogate 106 may identify one or more tasks that may be involved in this project (e.g., moving to Denver) and generate these one or more tasks for presentation to the member. For example, the surrogate 106 may generate tasks including, but not limited to, defining a moving budget, finding a moving company, disposing of any unwanted possessions, coordinating utility services at the current location and the new location, etc. These tasks may be presented to the member via a project-specific interface to enable the member to evaluate each of these tasks related to the project and coordinate with the surrogate 106 to determine how each of these tasks may be performed (e.g., the member performs some tasks themselves, the member delegates some tasks to a surrogate, the member defines parameters for the performance of the tasks, etc.).

[0107]

[0123] As described above, when a member requests the creation of a project that includes one or more tasks to be performed as part of the project, a project-specific interface may be created. The project interface may include links or other graphical user interface (GUI) elements corresponding to each of the tasks associated with the project. Selection of a particular link or other GUI element corresponding to a particular task associated with the project may cause the task facilitation service 102 to present an interface specific to the particular task. Through this interface, the member may communicate with the representative 106 to exchange messages related to the particular task, review suggestions related to the particular task, monitor the performance of the particular task, etc.

[0108]

[0124] In one embodiment, messages exchanged between the member and the surrogate 106 may be processed by the task recommendation system 112 to identify potential projects and / or tasks that can be recommended to the surrogate 106 for presentation to the member. As described above, the task recommendation system 112 may utilize NLP or other artificial intelligence to evaluate exchanged messages or other communications from the member to identify possible tasks that can be recommended to the member. For example, the task recommendation system 112 may process any incoming messages from the member using NLP or other artificial intelligence to detect new projects, new tasks, or other problems that the member may want to solve. In some examples, the task recommendation system 112 may utilize historical task data and corresponding messages from a task data store to train NLP or other artificial intelligence to identify possible tasks. If the task recommendation system 112 identifies one or more possible projects and / or tasks that can be recommended to the member, the task recommendation system 112 may present these possible tasks to the surrogate 106, who may select projects and / or tasks that can be shared with the member via a chat session.

[0109]

[0125] In one embodiment, when the task recommendation system 112 identifies a project that may be suggested to a member based on messages exchanged between the member and the surrogate 106, the task recommendation system 112 may utilize a resource library maintained by the task facilitation service 102 to identify one or more tasks related to the project that may be recommended to the surrogate 106. For example, if the task recommendation system 112 identifies a project related to a member's indication that they are preparing to move to Denver, the task recommendation system 112 may query the resource library to identify any tasks related to moving to the new location. In some examples, the query to the resource library may include member attributes from the member profile, which may enable the task recommendation system 112 to identify any tasks that may have been performed on a similar project or suggested to similarly situated members (e.g., members in a similar geographic location, members with attributes similar to those of the current member, etc.).

[0110]

[0126] In one embodiment, the task recommendation system 112 uses a machine learning algorithm or other artificial intelligence to identify tasks that can be recommended to the surrogate 106 for the identified project. For example, the task recommendation system 112 may identify any tasks that can be associated with the identified project from the resource library described above. The task recommendation system 112 may process the identified tasks and the member profile using a machine learning algorithm or other artificial intelligence to determine which of the identified tasks can be recommended to the surrogate 106 for presentation to the member. Additionally, the task recommendation system 112 may provide the surrogate 106 with any tasks that may need to be performed for the member with the option to refer the task to the surrogate 106 for completion. For example, if the task recommendation system 112 determines, based on the member profile, that the member may fully delegate the task to the surrogate 106 without having to review or provide any other input, the task recommendation system 112 may provide the surrogate 106 with a task with a recommendation that presents the member with the option to refer the task to the surrogate 106 (e.g., through a “Run With It” button).

[0111]

[0127] In some examples, the task recommendation system 112 may provide a list of a set of tasks that can be recommended to the member to the surrogate 106 for final determination of which tasks can be presented to the member. As described above, the task recommendation system 112 may rank the list of a set of tasks based on the likelihood that the member will select the task for delegation to a surrogate for performance and coordination with a third-party service 116 or other service / entity affiliated with the task facilitation service 102. Alternatively, the task recommendation system 112 may rank the list of a set of tasks based on the level of urgency for completion of each task. For example, if the task recommendation system 112 determines that a task corresponding to hiring a moving company is of greater urgency than a task corresponding to coordinating utility services, the task recommendation system 112 may rank the former task higher than the latter task.

[0112]

[0128] In one embodiment, the task recommendation system 112 identifies a project that can be created based on messages exchanged between the member and the surrogate 106. If the task recommendation system 112 identifies one or more tasks associated with the identified project, the task recommendation system 112, via the surrogate 106, may provide the member with a definition of the project and the tasks associated with the identified project to obtain the member's approval to proceed with the project. For example, via an application or web portal provided by the task facilitation service 102 accessed using the computing device 120, the member may review the proposed project and the associated tasks to determine whether to proceed with the proposed project. The member may communicate with the surrogate 106 through a project-specific communication session to further define the project and / or any tasks associated with the project, including defining the project's scope and any tasks proposed for completion of the project. As an illustrative example, if the surrogate 106 proposes a project corresponding to the member's upcoming move to Denver and any tasks associated with the proposed project, the member may communicate with the surrogate 106 to review the proposed project and the associated tasks (e.g., inquire about the timeline, inquire about the budget, etc.). Based on the member's communication with the representative 106, the representative 106 and / or task recommendation system 112 may identify any questions that can be posed to the member to further define the scope of the project and any associated tasks. For example, the representative 106 may prompt the member to indicate the amount of square footage of the member's existing home that may be useful in determining the scope of moving services that may be needed for the project corresponding to an upcoming move to Denver. Information obtained through the member's responses to these prompts may be used to supplement the member profile, as described above.

[0113]

[0129] In one embodiment, once a member approves a particular project to be performed for them, the task recommendation system 112 assigns a priority to the project and associated tasks based on input from the member (e.g., deadline, desired priority, etc.). For example, if a member indicates that a project related to an upcoming move to Denver is more urgent than a project related to vehicle maintenance, the task recommendation system 112 may prioritize the project related to the upcoming move to Denver over other projects related to vehicle maintenance. This may cause an application or web portal accessed by the member via the computing device 120 to display the project related to the upcoming move to Denver more prominently than these other projects. In some examples, the priority assigned to a particular project may be further assigned to tasks associated with the project. For example, the task recommendation system 112 may use the priority of each of the projects created for the member as another factor in ranking the various tasks identified by the representative 106 and / or the task recommendation system 112.

[0114]

[0130] Tasks related to the project may be added to an active queue that may be used by the task recommendation system 112 to determine which tasks the surrogate 106 will work on for the member. For example, the surrogate 106 may be presented with a limited set of tasks based on a prioritization or ranking of the tasks performed by the task recommendation system 112. Selecting a limited set of tasks may limit the number of tasks that can be worked on by the surrogate 106 at a given time, which may reduce the risk that the surrogate 106 will become overwhelmed with working on the member's task list.

[0115]

[0131] In one embodiment, the task facilitation service 102 may present a member with a task list corresponding to the member's current and upcoming tasks via an application implemented on the member's computing device 120 or an application accessed via a web portal provided by the task facilitation service 102. The task facilitation service 102 may provide the status of each task (e.g., created, in progress, scheduled, completed, etc.) via the task list. In some examples, the task facilitation service 102 may allow the member to filter tasks as needed, thus allowing the member to customize and determine which tasks will be presented to the member via the application or web portal.

[0116]

[0132] In addition to presenting a task list corresponding to a member's current and upcoming tasks, the task facilitation service 102 may signal which of these tasks are assigned to the member or to a surrogate 106. For example, the task facilitation service 102 may display an assignment tag on each task presented to the member via an application or web portal. The assignment tag may explicitly indicate whether the corresponding task is assigned to the member or to a surrogate 106. Additionally or alternatively, tasks may be presented to the member via an application or web portal using color coding, where the color used for the task may further indicate whether the task is assigned to the member or to a surrogate 106. As an illustrative example, if a task is assigned to a surrogate 106, the task may be presented with a “surrogate” attribute tag and presented within the task bubble using an orange shade to further indicate that the task has been assigned to the surrogate 106. Alternatively, if a task is assigned to a member, the task may be presented with a “member” attribute tag and presented within the task bubble using a green shade to further indicate that the task has been assigned to the member. It should be noted that although attribute tags and color indicators are used throughout this disclosure for illustrative purposes, other assignment indicators may be utilized to distinguish between tasks assigned to members and tasks assigned to delegates 106.

[0117]

[0133] In one embodiment, the task facilitation service 102 may provide a member, via an application or web portal, with the option to obtain more information about a particular task from a task list. For example, each task presented via a task list may include an option to obtain more information related to the task. In one embodiment, if a member selects the option to obtain more information for a particular task, the task facilitation service 102 may evaluate the member profile to determine how much information the member should be provided with without increasing the likelihood of cognitive overload for the member. For example, if a member has a tendency to delegate tasks to a proxy 106 and generally delegates all aspects of the task to a proxy 106, the task facilitation service 102 may provide basic information related to the task (e.g., a short task description, an estimated completion time for the task, etc.). However, if the member is more detail-oriented and highly involved in completing the task, the task facilitation service 102 may provide more information related to the task (e.g., a detailed task description, the steps to be performed to complete the task, any budget information for the task, etc.). In one embodiment, the task facilitation service 102 may utilize machine learning algorithms or artificial intelligence to determine how much information related to a task should be presented to the member 102. For example, the task facilitation service 102 may use the member profile and data corresponding to the task as input to the machine learning algorithm or artificial intelligence. The resulting output may provide a recommendation as to what information related to the task should be presented to the member. In some examples, the recommendation may be provided to a surrogate 106, which may evaluate the recommendation and determine what information about the selected task should be presented to the member. When information for a task is provided to the member, the task facilitation service 102 may monitor the member's interaction with the surrogate 106 to identify the member's response to the presentation of information.The responses may be used to further train the machine learning algorithm or artificial intelligence to provide better recommendations regarding task information that may be presented to members of the task facilitation service 102 .

[0118]

[0134] In one embodiment, a member may submit, via computing device 120, one or more user records 306 that may be used to identify tasks that may be performed for the member. For example, a member may upload to task facilitation service 102 one or more digital images of the member area 302 that may show a problem in the member area 302 for which a task may be created. As an illustrative example, a member may capture an image of a broken baseboard that needs repair. As another illustrative example, a member may capture an image of a clogged gutter. A surrogate 106 may retrieve these digital images and manually identify one or more tasks that may be performed to resolve the problem represented in the uploaded digital image. For example, if a surrogate 106 receives a digital image showing a broken baseboard, the surrogate 106 may generate a new task corresponding to repairing the broken baseboard. Similarly, if a surrogate 106 receives a digital image showing a clogged gutter, the surrogate 106 may generate a task corresponding to cleaning the member's gutter.

[0119]

[0135] The user's recordings 306 may further include audio and / or video recordings within the member area 302 corresponding to possible problems for which tasks may be generated. For example, a member may utilize their smartphone or other recording device to generate audio and / or video recordings of different portions of the member area 302 to highlight problems that may be used to generate one or more tasks that may be performed to resolve the problems. As an illustrative example, during a chat session with a representative 106, a member may walk through the member area 302 with their smartphone and record a video highlighting the problems they would like resolved by the task facilitation service 102. While walking through the member area 302, the member may indicate (e.g., by speaking into their smartphone, pointing out the problems, etc.) what these problems are and possible instructions or other parameters (e.g., timeframe, budget, level of urgency, etc.) for resolving these problems. Using the broken baseboard example described above, a member may record a video highlighting the broken baseboard while indicating, "We are preparing to sell our house, so I would like this baseboard repaired immediately." Thus, the video may highlight the problem associated with the broken baseboard and the level of urgency for the member to have the baseboard repaired within a short time frame in order to sell the member's home.

[0120]

[0136] A member may provide a user record 306, via computing device 120, to a surrogate 106, who may review the user record 306 to identify any tasks that may be recommended to the member to resolve any of the problems indicated by the member in the user record 306. For example, the surrogate 106 may analyze the provided user record 306 and identify tasks that may be performed to resolve any problems identified by the member in the user record 306 and / or detected by the surrogate 106 based on the surrogate's analysis of the user record 306. As an illustrative example, if a member provides a user record 306 in which the member indicates that there are broken baseboards that the member would like repaired, the surrogate 106 may further determine, based on the user record 306, that the member's home may have a termite problem (e.g., the presence of termites or termite damage in the broken baseboards). Accordingly, the surrogate 106 may communicate with the member via a chat session to indicate additional problems and recommend tasks to resolve the additional problems.

[0121]

[0137] In some examples, the surrogate 106 may prompt the member to generate one or more user records 306 that can be used to assist the surrogate 106 in defining one or more tasks that can be performed for the member. For example, if the member indicates via a chat session that they are preparing to move to Denver, the surrogate 106 may request that the member generate one or more user records 306 related to the member area 302 (e.g., home, apartment, etc.) so that the surrogate 106 can identify tasks that can be associated with this project. For example, using the user records 306 provided by the member, the surrogate 106 may determine the square footage of the member area 302, identify any special moving requirements for completion of the project (e.g., special moving instructions for fragile items, insurance, etc.), identify any repair or maintenance items that may need to be resolved for the project, etc. In some examples, the surrogate 106 may use the user records 306 to identify one or more task parameters that can be used in defining the tasks to be performed for the member. For example, if a member manually enters a new task related to repairing the member's broken baseboard, the representative 106 may use any user records 306 associated with the broken baseboard to identify the type of baseboard to be repaired, the extent of the repair, the time frame for the repair, etc.

[0122]

[0138] In one embodiment, the surrogate 106 can generate one or more suggestions for completion of a given task that are presented to the member via an application or web portal provided by the task facilitation service 102. The suggestions may include one or more options presented to the member that may be created and / or collected by the surrogate 106 while researching the given task. In some examples, the surrogate 106 may be provided with one or more templates that can be used to generate these one or more suggestions. For example, the task facilitation service 102 may maintain suggestion templates for different task types, whereby a suggestion template for a particular task type may include various data fields related to the task type. As an illustrative example, for a task related to planning a birthday party, the surrogate 106 may utilize a suggestion template corresponding to event planning. The suggestion template corresponding to event planning may include data fields corresponding to venue options, catering options, entertainment options, etc.

[0123]

[0139] In one embodiment, data fields within a proposal template may be toggled on or off to give the surrogate 106 the ability to determine what information is presented to the member in the proposal. For example, for a task related to renting a ballroom jump house for a party, the corresponding proposal template may include data fields corresponding to the rental company's location / address, the rental company's business hours and availability, the estimated cost, the rental company's ratings / reviews, etc. The surrogate 106 may toggle any of these data fields on or off based on the surrogate's knowledge of the member's preferences. For example, if the surrogate 106 has established a relationship with the member and thereby knows with a high degree of confidence that the member will trust the surrogate 106 to select a reputable company for the member's task, the surrogate 106 may toggle off a data field corresponding to a rating / review for the corresponding company from the proposal template. Similarly, if the surrogate 106 knows that the member is not interested in the rental company's location / address for the purpose of the proposal, the surrogate 106 may toggle off a data field corresponding to the location / address for the corresponding company from the proposal template. Although some data fields may be toggled off within the proposal template, the representative 106 may complete these data fields to provide additional information that can be used by the task facilitation service 102 to supplement the proposal's resource library, as described in more detail herein.

[0124]

[0140] In one embodiment, the task facilitation service 102 utilizes a machine learning algorithm or artificial intelligence to generate recommendations for the surrogate 106 regarding data fields that may be presented to the member in a proposal. For example, the task facilitation service 102 may use a member profile or model associated with the member, historical task data for the member (e.g., previously completed tasks, the task for which the proposal is given, etc.), and information corresponding to the task for which the proposal is being generated (e.g., the type or category of the task, etc.) as input to the machine learning algorithm or artificial intelligence. The output of the machine learning algorithm or artificial intelligence may define which data fields in the proposal template should be toggled on or off. For example, if the task facilitation service 102 determines, based on the member profile or model, the historical task data for the member, and an evaluation of the information corresponding to the task for which the proposal is being generated, that the member may not be interested in viewing information related to company ratings / reviews or information related to company locations / addresses, the task facilitation service 102 may automatically toggle these data fields off from the proposal template. The task facilitation service 102 may, in some instances, maintain an option to toggle these data fields on to give the surrogate 106 the ability to present these data fields to the member in the proposal. For example, if the task facilitation service 102 automatically toggled off a data field corresponding to the estimated cost for renting a balloon jump house from a particular company, but the member expressed interest in the possible costs involved, the surrogate 106 may toggle on the data field corresponding to the estimated cost.

[0125]

[0141] In some examples, when suggestions are presented to members, the task facilitation service 102 may monitor the surrogate 106 and the member's interaction with the suggestions to obtain data that can be used to further train the machine learning algorithm or artificial intelligence. For example, if the surrogate 106 presents suggestions based on recommendations generated by a machine learning algorithm or artificial intelligence without any ratings / reviews for a particular company, and the member indicates that they are interested in ratings / reviews for the particular company (e.g., through a message to the surrogate 106, through selecting an option in the suggestion to view ratings / reviews for the particular company, etc.), the task facilitation service may use this feedback to further train the machine learning algorithm or artificial intelligence to increase the likelihood of recommending the presentation of ratings / reviews for the selected company for similar tasks or task types.

[0126]

[0142] In one embodiment, the task facilitation service 102, via the task coordination system 114, maintains a resource library that can be used to automatically populate one or more data fields of a particular proposal template. The resource library may include entries corresponding to companies and / or products previously used by a representative for proposals related to or otherwise associated with a particular task or task type. For example, when a representative 106 generates a proposal for a task related to repairing a roof near Lynnwood, Washington, the task coordination system 114 may retrieve information related to a roofing contractor selected by the representative 106 for the task. The task coordination system 114 may generate an entry in the resource library corresponding to the roofing contractor and associate this entry with "roof repair" and "Lynnwood, Washington." Thus, if another representative receives a task corresponding to repairing a roof for a member located near Lynnwood, Washington (e.g., Everett, Washington), the other representative may query the resource library for roofing contractors near Lynnwood, Washington. In response to the query, the resource library may return an entry corresponding to the roofing contractor previously selected by the representative 106. If another representative selects this roofing contractor, the task coordination system 114 may automatically populate the data fields of the proposal template with information available for the roofing contractor from the resource library.

[0127]

[0143] In one embodiment, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to automatically process the member profile associated with the member 118, the selected proposal template, and the resource library to dynamically identify any resources that may be involved in preparing the proposal. The machine learning algorithm or artificial intelligence can be trained using supervised training techniques. For example, a dataset of sample member profiles, proposal templates and / or tasks, available resources (e.g., entries corresponding to third-party services, other services / entities, retailers, products, etc.), and completed proposals can be selected for training the machine learning model. The machine learning model can be evaluated to determine whether, based on the sample inputs provided to the machine learning model, it identifies appropriate resources that can be used to automatically complete the proposal template for presentation of the proposal. Based on this evaluation, the machine learning model can be modified to increase the likelihood that the machine learning model will produce the desired results. The machine learning model can be further dynamically trained by soliciting feedback from task facilitation service representatives and members regarding the identification of resources from the resource library and the proposals automatically generated by the task facilitation service 102 using these resources. For example, if the task facilitation service 102 generates a suggestion that is not appealing to the member 118 based on the member profile associated with the member 118 and selected resources from the resource library (e.g., the suggestion is not related to the task, the suggestion corresponds to resources that are not available to the member 118, the suggestion includes resources that the member 118 does not approve of, etc.), the task facilitation service 102 may update its machine learning algorithms or artificial intelligence based on this feedback to reduce the likelihood that similar resources and suggestions are generated for similarly situated members.

[0128]

[0144] The surrogate 106 may generate, via the proposal template, additional suggested options for companies and / or products that can be used to complete the task. For example, for a particular proposal, the surrogate 106 may generate a recommended option that may correspond to the company or product that the surrogate 106 is recommending for completing the task. Furthermore, to provide the member with additional options or choices, the surrogate 106 may generate additional options that correspond to other companies or products that can complete the task. In some examples, if the surrogate 106 knows that the member has delegated decision-making regarding the completion of the task to the surrogate 106, the surrogate 106 may refrain from generating additional suggested options outside of the recommended options. However, the surrogate 106 may still present the member with the selected suggested options for completing the task to keep the member informed about the status of the task.

[0129]

[0145] In one embodiment, once the surrogate 106 completes defining the proposal through the use of a proposal template, the task facilitation service 102 may present the proposal to the member through an application or web portal provided by the task facilitation service 102. In some examples, the surrogate 106 may send a notification to the member to indicate that a proposal has been prepared for a particular task and that the proposal is ready for review via the application or web portal provided by the task facilitation service 102. The proposal presented to the member may indicate the task for which the proposal was prepared, as well as an indication of one or more options provided to the member. For example, the proposal may include links to a recommended proposal option and other options (if any) prepared by the surrogate 106 for the particular task. These links may enable the member to navigate between one or more options prepared by the surrogate 106 via the application or web portal.

[0130]

[0146] For each proposal option, the member may be presented with information corresponding to the company (e.g., a third-party service or other service / entity associated with the task facilitation service 102) or product selected by the surrogate 106 and information corresponding to the data fields selected for presentation by the surrogate 106 via the proposal template. For example, for a task related to a roof inspection of the member's home, the surrogate 106 may present, for a particular roofing contractor (e.g., a proposal option), one or more reviews or testimonials for the roofing contractor, the roofing contractor's rates and availability (if any) subject to the member's task completion timeframe, the roofing contractor's website, the roofing contractor's contact information, any estimated costs, and an indication of the surrogate's 106's next steps if the member should select this particular roofing contractor for the task. In some examples, the member may select what details or data fields related to a particular proposal are presented via the application or web portal. For example, if the member is presented with an estimated total cost for each proposal option and the member is not interested in reviewing the estimated total cost for each proposal option, the member may toggle off this particular data field from the proposal via the application or web portal. Alternatively, if a member is interested in reviewing further details about each suggestion option (e.g., additional reviews, additional company or product information, etc.), the member may request that this further details be presented via the suggestion.

[0131]

[0147] In one embodiment, based on the member's interaction with a given suggestion, the task facilitation service 102 can further train a machine learning algorithm or artificial intelligence used to determine or recommend what information should be presented to the member and what information should be presented to similarly situated members for similar tasks or task types. As described above, the task facilitation service 102 may use a machine learning algorithm or artificial intelligence to generate recommendations for the surrogate 106 regarding data fields that may be presented to the member in the suggestion. The task facilitation service 102 may monitor or track the member's interaction with the suggestion to determine the member's preferences regarding the information presented in the suggestion for a particular task. Additionally, the task facilitation service 102 may monitor or track any messages exchanged between the member and the surrogate 106 related to the suggestion to further identify the member's preferences. For example, if the member sends a message to the surrogate 106 indicating that the member wishes to view more information about services offered by each of the companies specified in the suggestion, the task facilitation service 102 may determine that the member may wish to view additional information about services offered by companies related to a particular task or task type. In some examples, the task facilitation service 102 may solicit feedback from the member regarding the suggestions given by the surrogate 106 to identify the member's preferences. This feedback and information obtained through the member's interaction with the surrogate 106 regarding the suggestions or the suggestions themselves may be used to retrain the machine learning algorithm or artificial intelligence to provide more accurate or improved recommendations to the member and for information that should be presented to similarly situated members in the suggestions for similar tasks or task types.

[0132]

[0148] In some examples, each proposal presented to a member may specify any costs associated with each proposal option. These costs may be presented in different formats based on the requirements of the associated task or project. For example, if a task or project corresponds to purchasing airline tickets, each proposal option for the corresponding proposal may present a fixed price for the airline tickets. As another illustrative example, for each proposal option, the agent 106 may provide a budget for the completion of the task according to the selected option (e.g., "I intend to spend up to $150 on Halloween decorations for the party"). As yet another illustrative example, for tasks or projects that may involve payment schedules, the proposal options for proposals related to the task or project may specify a payment schedule for each of these proposal options (e.g., "$100 for the initial consultation and $300 for subsequent services," "$1,500 down payment to reserve the venue and $1,500 usage fee after the event," etc.).

[0133]

[0149] If a member accepts a particular proposed option for a task or project, the surrogate 106 may communicate with the member to ensure that the member agrees to pay the proposed cost for the particular proposed option and any associated taxes and fees. In some examples, if a proposed option is selected with a static payment amount (e.g., a fixed price, a "max X dollars," a tiered payment schedule with static amounts, etc.), the member may be notified by the surrogate 106 if the actual payment amount required to fulfill the proposed option exceeds a threshold percentage or amount beyond the initially proposed static payment amount. For example, if the surrogate 106 determines that the member may need to spend more than 120% of the cost specified in the selected proposed option, the surrogate 106 may send a notification to the member to reconfirm the payment amount before proceeding with the proposed option.

[0134]

[0150] In one embodiment, if the member accepts a suggested option from a presented proposal, the task facilitation service 102 moves the task associated with the presented proposal to an execution state, and the surrogate 106 may proceed to execute the proposal according to the selected suggested option. For example, the surrogate 106 may contact one or more third-party services 116 to coordinate the performance of the task according to parameters defined in the proposal accepted by the member.

[0135]

[0151] In one embodiment, the surrogate 106 utilizes the task coordination system 114 to assist in coordinating the performance of the task according to the parameters defined in the proposal accepted by the member. For example, if coordination with the third-party service 116 can be performed automatically (e.g., the third-party service 116 provides an automated system for ordering, scheduling, and payment), the task coordination system 114 may interact directly with the third-party service 116 to coordinate the performance of the task according to the selected proposal options. The task coordination system 114 may provide any information (e.g., confirmation, order status, reservation status, etc.) to the surrogate 106. The surrogate 106 may then provide this information to the member via an application or web portal utilized by the member to access the task facilitation service 102. Alternatively, the surrogate 106 may send information to the member via other communication methods (e.g., email message, text message, etc.) to indicate that the third-party service 116 has begun performance of the task according to the selected proposal options. When a surrogate 106 is performing a task for a member 118 , the surrogate 106 may provide the member 118 with status updates regarding the representative's performance of the task via an application or web portal provided by the task facilitation service 102 .

[0136]

[0152] In one embodiment, the task coordination system 114 can monitor the performance of tasks on behalf of the member by the surrogate 106, the third-party service 116, and / or other services / entities associated with the task facilitation service 102. For example, the task coordination system 114 may record any information provided by the third-party service 116 regarding the time frame for performance of the task, the costs associated with performing the task, any status updates regarding the performance of the task, etc. The task coordination system 114 may associate this information with a data record corresponding to the task being performed. The status updates provided by the third-party service 116 may be automatically provided to the member and to the surrogate 106 via an application or web portal provided by the task facilitation service 102. Alternatively, the status updates may be provided to the surrogate 106, who may provide these status updates to the member via a chat session established between the member and the surrogate 106 for a particular task / project or through other communication methods. In some examples, if a task is to be performed by the surrogate 106, the task coordination system 114 may monitor the performance of the task by the surrogate 106 and record any updates provided by the surrogate 106 to the member via an application or web portal.

[0137]

[0153] Upon completion of the task, the member may provide feedback regarding the performance of the surrogate 106, third-party service 116, and / or other service / entity associated with the task facilitation service 102 that performed the task according to the suggested option selected by the member. For example, the member may exchange one or more messages with the surrogate 106 via a chat session corresponding to the particular task / project being completed to indicate the member's feedback regarding the completion of the task. For example, the member may indicate that the member is pleased with how the task was completed. The member may additionally or alternatively provide feedback indicating areas of improvement regarding the performance of the task. For example, if the member is dissatisfied with the final cost for the performance of the task and / or has any input regarding the quality of the performance (e.g., timeliness, quality of the deliverables, professionalism of the third-party service 116, etc.), the member may so indicate in one or more messages to the surrogate 106. In one embodiment, the task facilitation service uses machine learning algorithms or artificial intelligence to process the feedback provided by the member to improve the recommendations provided by the task facilitation service 102 for the suggested options, the third-party service 116 or other service / entity, and / or processes that may be implemented for the completion of similar tasks. For example, if the task facilitation service 102 detects that a member is dissatisfied with the results provided by a third-party service 116 or other service / entity for a particular task, the task facilitation service 102 may use this feedback to further train the machine learning algorithm or artificial intelligence to reduce the likelihood that the third-party service 116 or other service / entity will be recommended for similar tasks and to members in similar situations.As another example, if the task facilitation service 102 detects that a member is pleased with the results provided by the surrogate 106 for a particular task, the task facilitation service 102 may use this feedback to further train machine learning algorithms or artificial intelligence to reinforce actions performed by the surrogate for similar tasks and / or for members in similar situations.

[0138]

[0154] 4 shows an illustrative example of an environment 400 in which the task recommendation system 112 generates and ranks recommendations for tasks to be performed for the member 118, according to at least one embodiment. In the environment 400, the member 118 and / or the representative 106 interact with a task creation subsystem 402 of the task recommendation system 112 to generate new tasks or projects that can be performed for the member 118. The task creation subsystem 402 may be implemented using a computer system or as an application or other executable code implemented on the computer system of the task recommendation system 112.

[0139]

[0155] In one embodiment, a member 118 can access the task creation subsystem 402 to request the creation of one or more tasks as part of an onboarding process implemented by a task facilitation service. For example, during the onboarding process, the member 118 can provide information related to one or more tasks that the member 118 would like to potentially delegate to a surrogate 106. The task creation subsystem 402 can use this information to identify parameters related to the tasks that the member 118 would like to delegate to a surrogate 106 for performance of the tasks. For example, the parameters related to these tasks can specify the nature of these tasks (e.g., cleaning gutters, installing carbon monoxide detectors, planning a party, etc.), the level of urgency for completion of these tasks (e.g., timing requirements, deadlines, dates corresponding to upcoming events, etc.), any member preferences for completion of these tasks, etc. The task creation subsystem 402 can use these parameters to automatically create tasks that can be presented to the surrogate 106 when assigned to the member 118 during the onboarding process.

[0140]

[0156] The member 118 may further access the task creation subsystem 402 to create a new task or project at any time after the onboarding process is complete. For example, the task facilitation service may provide a widget or other user interface element through which the member 118 may manually create a new task or project via the task facilitation service's application or web portal. In one embodiment, the task creation subsystem 402 provides various task templates that can be used by the member 118 to create a new task or project. The task creation subsystem 402 may maintain task templates for different task types or categories in the task data store 110. Each task template may include different data fields for defining a task, whereby the different task fields may correspond to the task type or category for the task being defined. The member 118 may provide task information via these different task fields to define a task that can be submitted to the task creation subsystem 402 or a delegate 106 for processing. The task data store 110, in some examples, may be associated with a resource library. This resource library may maintain various task templates for the creation of new tasks.

[0141]

[0157] As described above, each task template may be associated with a particular task category. Accordingly, multiple task definition fields within a particular task template may be associated with the task category assigned to the task template. For example, task definition fields corresponding to a vehicle maintenance task may be used to define the make and model of the member's vehicle, the age of the vehicle, information corresponding to the last time the vehicle was maintained, any reported accidents related to the vehicle, a description of any problems related to the vehicle, etc. In some examples, a member accessing a particular task template may further define custom fields for the task template that may provide additional information that may be useful to the member in defining and completing the task. These custom fields may be added to the task template, so that they may be available to the member and / or delegate when they retrieve the task template in the future to create similar tasks.

[0142]

[0158] In one embodiment, data fields presented in a task template used by a member 118 to manually define a new task may be selected based on decisions generated using an artificial intelligence machine learning algorithm. For example, the task creation subsystem 402 may use the member profile from the user data store 108 and the selected task template from the task data store 110 as inputs to the machine learning algorithm or artificial intelligence to identify which data fields may be omitted from the task template when presented to the member 118 for definition of a new task or project. For example, if the member 118 is known to delegate maintenance tasks to a delegate 106 and is indifferent to budget considerations, the task creation subsystem 402 may present the member 118 with a task template that specifically omits any budget-related and other data fields that may define instructions for the completion of the task. In some examples, the task creation subsystem 402 may enable the member 118 to add, remove, and / or modify data fields for a task template. For example, if the task creation subsystem 402 removes a data field corresponding to a budget for a task based on an evaluation of the member profile, the member 118 may request that a data field be added to the task template to allow the member 118 to define a budget for the task. The task creation subsystem 402 may, in some examples, utilize this member change to the task template to retrain a machine learning algorithm or artificial intelligence to improve the likelihood of providing the member 118 with a task template without the member 118 having to make any modifications to the task template to define a new task.

[0143]

[0159] In some examples, when a member selects a particular task template for the creation of a task related to an experience, the task creation subsystem 402 can automatically identify portions of the member profile that can be used to populate the selected task template. For example, if a member selects a task template that corresponds to an evening out at a restaurant, the task creation subsystem 402 can automatically process the member profile to identify any information corresponding to the member's dining preferences and restrictions that can be used to populate one or more fields in the task template selected by the member. The member can review these automatically populated data fields to ensure that they are accurately populated. If the member makes any changes to the information in the automatically populated data fields, the task creation subsystem 402 can use these changes to automatically update the member profile to incorporate these changes.

[0144]

[0160] In one embodiment, the task creation subsystem 402 further enables the surrogate 106 to create new tasks or projects on behalf of the member 118. The surrogate 106 may request from the task creation subsystem 402 a task template that corresponds to the task type or category for the task being defined. The surrogate 106 may define various parameters associated with the new task or project, including the assignment of the task (e.g., to the surrogate 106, the member 118, etc.), via the task template. In some examples, the task creation subsystem 402 may use a machine learning algorithm or artificial intelligence to identify which data fields will be presented in the task template to the surrogate 106 for creation of the new task or project. For example, similar to the process described above related to member creation of a task or project, the task creation subsystem 402 may use the member profile from the user data store 108 and the selected task template from the task data store 110 as inputs to the machine learning algorithm or artificial intelligence. However, rather than identifying which data fields may be omitted from the task template, the task creation subsystem 402 may indicate which data fields may be omitted from the task when presented to the member 118 via an application or web portal provided by the task facilitation service. Thus, the representative 106 may need to provide all necessary information for a new task or project regardless of whether all information is presented to the member 118.

[0145]

[0161] Similar to the process described above with respect to selecting a member for a particular task template, the task creation subsystem 402 may automatically identify portions of the member profile that can be used to populate the selected task template. The surrogate 106 may review these automatically populated data fields to ensure that these data fields are accurately populated. If the surrogate 106 makes any changes to the information in the automatically populated data fields (e.g., based on the surrogate's personal knowledge of the member 118), the task creation subsystem 402 may use these changes to automatically update the member profile to incorporate these changes. In some examples, if changes made to the task template by the surrogate 106 result in changes being made to the member profile, the task creation subsystem 402 may prompt the member 118 to verify that the proposed changes to the member profile are accurate. If the member 118 indicates that the proposed changes are inaccurate or if the member 118 provides alternative changes, the task creation subsystem 402 may automatically update the corresponding data fields in the task template and the member profile to reflect the accurate information indicated by the member 118.

[0146]

[0162] In one embodiment, the task creation subsystem 402 can automatically monitor, in real time, messages exchanged between the member 118 and the surrogate 106 to identify tasks that can be recommended to the member 118. For example, the task creation subsystem 402 can utilize natural language processing (NLP) or other artificial intelligence to evaluate received messages or other communications from the member 118 to identify possible tasks that can be recommended to the member 118. For example, the task creation subsystem 402 can process every incoming message from the member 118 using NLP or other artificial intelligence to detect new tasks or other problems that the member 118 would like to solve. In some examples, the task creation subsystem 402 can utilize historical task data from the task data store 110 and corresponding messages from the task data store 110 to train the NLP or other artificial intelligence to identify possible tasks. If the task creation subsystem 402 identifies one or more possible tasks that can be recommended to the member 118, the task creation subsystem 402 can present these possible tasks to the surrogate 106, who can select a task that can be shared with the member 118 via a chat session.

[0147]

[0163] The task recommendation system 112 may further include a task ranking subsystem 406 that may be configured to rank a set of tasks for the member 118, including tasks that may be recommended to the member 118 for completion by the member 118 or delegate 106. The task ranking subsystem 406 may be implemented using a computer system or as an application or other executable code implemented on the computer system of the task recommendation system 112. In one embodiment, the task ranking subsystem 406 may rank the list of the set of tasks based on the likelihood that the member 118 will select the task for delegation to a delegate for performance and coordination with third-party services and / or other services / entities associated with the task facilitation service. Alternatively, the task ranking subsystem 406 may rank the list of the set of tasks based on the level of urgency for completion of each task. The level of urgency may be determined based on member characteristics from the user data store 108 (e.g., data corresponding to the member's own prioritization of certain tasks or categories of tasks) and / or the potential risk to the member 118 if the task is not performed.

[0148]

[0164] In one embodiment, the task ranking subsystem 406 provides the task selection subsystem 404 with a ranked list of a set of tasks that can be recommended to the member 118. The task selection subsystem 404 can be implemented using a computer system or as an application or other executable code implemented on the computer system of the task recommendation system 112. The task selection subsystem 404 can be configured to select which tasks from the ranked list of the set of tasks can be recommended to the member 118 by the representative 106. For example, if an application or web portal provided by a task facilitation service is configured to present the member 118 with a limited number of task recommendations from the ranked list of the set of tasks, the task selection subsystem 404 can process the ranked list and the member's profile from the user data store 108 to determine which task recommendations should be presented to the member 118. In some examples, the selection made by the task selection subsystem 404 can correspond to the ranking of the set of tasks in the list. Alternatively, the task selection subsystem 404 may process the ranked list of the set of tasks as well as the member profile and the member's existing tasks (e.g., tasks in progress, tasks accepted by the member 118, etc.) to determine which tasks may be recommended to the member 118. For example, if the ranked list of the set of tasks includes a task corresponding to gutter cleaning, but the member 118 already has an ongoing task corresponding to gutter repair due to a recent storm, the task selection subsystem 404 may refrain from selecting the task corresponding to gutter cleaning, since this may be performed in conjunction with the gutter repair. Thus, the task selection subsystem 404 may provide another layer to further refine the ranked list of the set of tasks for presentation to the member 118.

[0149]

[0165] The task selection subsystem 404 may provide the surrogate 106 with a new list of tasks that can be recommended to the member 118. The surrogate 106 may review this new list of tasks to determine which tasks can be presented to the member 118 via an application or web portal provided by the task facilitation service. For example, the surrogate 106 may review the set of tasks recommended by the task selection subsystem 404 and select one or more of these tasks for presentation to the member 118 via individual interfaces corresponding to those one or more tasks. Additionally, as described above, the surrogate 106 may determine whether a task will be presented with an option to refer the task to the surrogate 106 for performance (e.g., using a button or other GUI element to indicate the member's preference to refer the task to the surrogate 106 for performance). In some examples, one or more tasks may be presented to the member 118 according to a ranking generated by the task ranking subsystem 406 and refined by the task selection subsystem 404. Alternatively, one or more tasks may be presented according to the surrogate's understanding of the member's own preferences for prioritizing tasks. Through an interface corresponding to one or more tasks recommended to the member 118, the member 118 may select one or more tasks that may be performed with the assistance of the representative 106. The member 118 may alternatively reject any presented tasks that the member 118 would like to personally perform or that the member 118 does not wish to perform.

[0150]

[0166] In one embodiment, the task selection subsystem 404 monitors different interfaces corresponding to recommended tasks, including any corresponding chat or other communication sessions between the member 118 and the surrogate 106, to collect data regarding the member's selection of tasks for delegation to the surrogate 106 for implementation. For example, the task selection subsystem 404 may process messages corresponding to tasks presented to the member 118 by the surrogate 106 through different interfaces corresponding to recommended tasks to determine a polarity or sentiment corresponding to each task. For example, if the member 118 indicates in a message to the surrogate 106 sent through a communication session related to a particular task that he or she prefers not to receive any task recommendations corresponding to vehicle maintenance, the task selection subsystem 404 may attribute a negative polarity or sentiment to the task corresponding to vehicle maintenance. Alternatively, if the member 118 selects a task related to gutter cleaning for delegation to the surrogate 106 (e.g., through a communication session related to the gutter cleaning task presented to the member 118) and / or indicates in a message to the surrogate 106 that recommending this task was a good idea, the task selection subsystem 404 may attribute a positive polarity or sentiment to this task. In one embodiment, the task selection subsystem 404 can use these responses to tasks recommended to the member 118 to further train or augment the machine learning algorithms or artificial intelligence utilized by the task ranking subsystem 406 to generate task recommendations that can be presented to the member 118 and other similarly situated members of the task facilitation service. Additionally, the task selection subsystem 404 can update the member's profile or model to update the member's preferences and known behavioral characteristics based on the member's selection of tasks from those recommended by the surrogate 106 and / or their feelings regarding the tasks recommended by the surrogate 106.

[0151]

[0167] FIG. 5 shows an illustrative example of a process 500 for generating new tasks and task rankings that can be used to determine what tasks will be presented to a member, according to at least one embodiment. Process 500 may be implemented by a task recommendation system of a task facilitation service. In step 502, the task recommendation system may receive task-related data. As described above, a member of the task facilitation service may manually provide task-related data via a task template corresponding to a particular task category or type. The task template may include various fields in which the member may provide a name for the task, a task description, a time frame for performing the task, a budget for performing the task, etc. The task template provided to the member may be custom tailored according to the member's characteristics identified by the task facilitation service and the characteristics corresponding to the particular task category or type associated with the selected task template. The member may provide a completed task template to the task recommendation system for generation of a new task.

[0152]

[0168] In some examples, a representative assigned to a member may provide task-related data to the task recommendation system. For example, a representative assigned to a member may obtain a task template from the member and initiate an evaluation of the task to determine how to best perform the task for the member. For example, the representative may evaluate the task template and send a request to the task recommendation system to generate a new task for the member that corresponds to the task-related details provided by the member in the task template.

[0153]

[0169] In step 504, the task recommendation system may generate one or more new tasks based on task-related data provided by the member and / or the member's assigned delegate. For example, the task recommendation system may generate a new entry in the task data store corresponding to the new task. Additionally, the task recommendation system may assign a unique identifier to the newly generated task, which may facilitate tracking of specific tasks associated with the member in the task facilitation service.

[0154]

[0170] In step 506, the task recommendation system may determine whether additional task information is needed for the newly created task. For example, the task recommendation system may evaluate a member profile or model to determine whether to recommend to a surrogate that they obtain additional information that can be used to determine how best to perform the task for the member. For example, if a member indicated that they would like their gutters cleaned but did not indicate via the task template when the gutters should be cleaned, the task recommendation system may prompt the surrogate to obtain this information from the member. As another example, if a member submits a task without a specific budget and the task recommendation system determines that the member is budget-conscious, the task recommendation system may prompt the surrogate to communicate with the member to determine what the budget should be for the performance of the task. In some embodiments, the decision regarding whether additional task information is needed may be made by the surrogate based on the surrogate's knowledge of the member. Any information obtained in response to these communications may be used to supplement the member profile, so that for future tasks, this newly obtained information may be automatically retrieved from the member profile without requiring additional prompting from the member.

[0155]

[0171] If the task recommendation system determines that additional task information is needed for the new task, the task recommendation system may obtain the additional task information from the member or a representative in step 508 and modify the new task to incorporate this additional information in step 510. For example, the representative may prompt the member to provide this additional information based on a determination by the task recommendation system. Alternatively, the task recommendation system may communicate directly with the member to obtain the additional task information.

[0156]

[0172] In step 512, the task recommendation system determines whether there are any other existing tasks related to the member that have not yet been performed (e.g., are not in progress). As described above, the task recommendation system can rank the list of the set of tasks based on the likelihood that the member will select the task for delegation to a representative for performance and coordination with third-party services. Alternatively, the task recommendation system may rank the list of the set of tasks based on the level of urgency for completion of each task. Thus, if there are currently other existing tasks for the member, the task recommendation system may revise the existing ranking of the tasks in step 514 to incorporate the new task into the ranking. For example, if the new task has a higher level of urgency compared to pending tasks in the existing ranking of tasks, the task recommendation system may revise the ranking so that the new task is given a higher ranking or priority for future performance.

[0157]

[0173] If the task recommendation system determines that there are no other existing tasks, the task recommendation system may generate a ranking of newly created tasks for performance of these tasks in step 516. The task recommendation system may rank the list of sets of tasks based on the likelihood that the member will select the tasks for delegation to a delegate for performance and coordination with third-party services and / or other services / entities associated with the task facilitation service that may be assigned to perform the tasks. Alternatively, the task recommendation system may rank the list of sets of tasks based on the level of urgency for completion of each task. In step 518, the task recommendation system may present the ranking of the sets of tasks to the delegate. In one embodiment, the task recommendation system presents to the delegate a ranked list of sets of tasks that may be recommended to the member 118 in step 518. The delegate may select which tasks may be recommended to the member from the ranked list of sets of tasks.

[0158]

[0174] FIG. 6 shows an illustrative example of a process 600 for generating suggestions and monitoring member interaction with the generated suggestions, according to at least one embodiment. Process 600 may be performed by a task coordination system of a task facilitation service. At step 602, the task coordination system may receive a request to generate suggestions for a particular task. The request may be submitted by a representative, who may have received authorization from the member to perform the task for the member. For example, once the representative obtains the necessary task-related information from the member and / or through a task recommendation system (e.g., task parameters obtained via ratings of tasks performed for similarly situated members), the representative can utilize the task coordination system to generate one or more suggestions for solving the task.

[0159]

[0175] In step 604, the task coordination system provides the representative with a proposal template corresponding to the task type. The proposal template may be provided via a user interface provided to the representative by the task facilitation service. As described above, the proposal may include one or more options presented to the member that may be created and / or collected by the representative while researching a given task. In some examples, the representative may access one or more templates via the task coordination system that may be used to generate these one or more proposals. For example, the task coordination system may maintain proposal templates for different task types, whereby a proposal template for a particular task type may include various data fields related to the task type.

[0160]

[0176] In step 606, the task coordination system may record the suggestions generated by the representative for a particular task so that the suggestions can be presented to the member for the particular task. For example, the task coordination system may add the suggestions to a task data store so that the member's interactions with the suggestions can be recorded for further training of the machine learning algorithms or artificial intelligence described above that are used to generate and maintain member profiles and define personalized suggestion templates for different task types and for different members. Additionally, the task coordination system may store the suggestions in the user data store in association with the member entry in the user data store, as described above.

[0161]

[0177] In step 608, the task coordination system may monitor the member's interaction with the proposal to identify possible future proposal template modifications. As described above, when a proposal is presented to a member, the task coordination system may monitor the member's interaction with the surrogate and with the proposal to obtain data that can be used to further train the machine learning algorithm or artificial intelligence utilized to define the proposal template for the particular member. For example, if the surrogate submits a proposal without any ratings / reviews for a particular company based on a recommendation generated by the task coordination system, and the member indicates (e.g., through a message to the surrogate, through selecting an option in the proposal to view ratings / reviews for the particular company, etc.) that the member is interested in ratings / reviews for the particular company, the task coordination system may use this feedback to further train the machine learning algorithm or artificial intelligence to increase the likelihood of recommending the submission of ratings / reviews for the selected company for similar tasks or task types.

[0162]

[0178] As noted above, at least some embodiments of the present disclosure may include a button or similar feature that allows a member to delegate or pass on a task to a proxy for completion. More generally, embodiments of the present disclosure may include a delegation control presented to or available to a member (e.g., through a user interface) that, when activated, automatically delegates a task for completion by a task facilitation service. For example, in some embodiments, the delegation control may be an interactive control element (e.g., a button, a checkbox, a selectable icon, etc.) visually associated with task information presented on a graphical user interface (GUI) executing on a computing device associated with the member. In response to activation of the delegation control (e.g., by clicking or otherwise manipulating an interactive element associated with the delegation control), the computing device associated with the member may generate, update, send, etc., instructions receivable by the task facilitation service that communicate to the task facilitation service that the task will be delegated to the task facilitation service for completion. The task facilitation service may then proceed to complete the task without or with only minimal interaction from the member. In other words, activation of delegation control may grant the task facilitation service permission to identify potential options for completing the task, select an option for completing the task, complete the task according to the selected option, or complete any aspect of the task without or with limited additional interaction with the member.

[0163]

[0179] When a member delegates a task, the cognitive load associated with that task should be reduced due to the member's reduced role in completing the task. However, delegating a task generally involves relinquishing some control over the task and, therefore, can be a source of stress, anxiety, and / or additional cognitive load for the member. This is particularly true when the member delegates a task that may result in delegating beyond the member's comfort level. To address this issue, among other things, some embodiments of the present disclosure may include delegation controls on the member's computing device that are dynamically enabled by the task facilitation service. In such embodiments, the task facilitation service allows the member to delegate only some tasks and does so by selectively enabling and disabling corresponding delegation features on the member's computing device. For example, the task facilitation service may only allow the delegation of, and enable delegation controls for, tasks that meet certain criteria. Such criteria may include, but are not limited to, the type of task involved, what or how much additional information may be required by the task facilitation service to complete the task, the likelihood that the member will actually delegate the task, and the member's past history of delegation. In at least some embodiments, the task facilitation service may progressively expand the range of tasks that can be delegated, thereby increasing a member's comfort level with delegating some tasks and reducing the stress and cognitive load that may be associated with delegation.

[0164]

[0180] In some embodiments, a delegate associated with a task facilitation service and assigned to a member may selectively enable delegation control for the member. For example, in one embodiment, the task facilitation service's model may use data related to the member, the task, etc. to provide a delegate with a recommendation as to whether delegation control for the task should be enabled. The delegate may then enable or disable delegation for the task based on the recommendation and the delegate's experience with the member. In other embodiments, the task facilitation service may automatically enable or disable delegation control independent of the intervening delegate.

[0165]

[0181] Enabling delegation control for a member may be facilitated, at least in part, by one or more models, algorithms, etc. that determine whether the member is likely to delegate a given task. For example, a task facilitation service may include a profile / model associated with the member that reflects and / or predicts the member's behavior and preferences. In particular, the member profile / model may be based on information provided by the member (e.g., during onboarding), information provided by a representative working with the member, the member's tracked activities, data obtained from external sources (e.g., social media accounts, productivity software, calendar software, etc.), data associated with other members (including, but not limited to, other members with similar demographics as the member), and any other similar data sources. The member profile / model may also assess the member's likelihood to delegate a task. For example, the model may rely on the member profile (and / or other profiles of similar members) and information about the task (and / or similar tasks) to generate a metric indicative of the member's likelihood to delegate a task. In embodiments in which the delegate enables the delegate control, a metric (or a secondary value or recommendation based on the metric) may be provided to the delegate to inform the delegate's decision regarding enabling the delegate control. In other embodiments, if the metric meets a certain threshold, the task facilitation service may automatically enable the delegate control.

[0166]

[0182] In at least some embodiments, a member's interaction with delegation controls may be used to provide feedback to the task facilitation service for use in updating various models, algorithms, etc. maintained by the task facilitation service. For example, when a member activates delegation controls, the task facilitation service may use that activation as positive feedback that the associated task is one that the member is likely to delegate. Conversely, if the member does not activate delegation controls, the task facilitation service may use such deactivation as negative feedback. In either case, the task facilitation service may use the member's actions to update, train, or otherwise improve models, algorithms, etc., including, but not limited to, the member profile associated with the member and the delegation control model used to determine whether the member is likely to delegate a task.

[0167]

[0183] As described above, the task facilitation service may be configured to gradually train / coach members to delegate a wider range of tasks. For example, the task facilitation service may generally enable delegation control (or strongly recommend that a proxy enable delegation control) for tasks that have a high likelihood of delegation by the member (e.g., 90% or higher). However, the task facilitation service may also be configured to occasionally enable delegation control (or recommend that a proxy enable delegation control) for tasks when there is a lower confidence (e.g., 70-90%) that the member will delegate the task. By doing so, the task facilitation service may gradually expand the boundaries of what the member is willing to delegate and therefore reduce the member's overall cognitive load around delegating tasks.

[0168]

[0184] In the context of this disclosure, delegation of a task by a member generally refers to a process in which part or all of a task is identified for completion by a task facilitation service with no or relatively little involvement by the member after delegation. Task delegation may include delegating any or all parts of a task to a task facilitation service. For example, delegating a task may include delegating any of the following: defining / exploring the task; generating options for completing the task; selecting options for completing the task; coordinating the completion of the task; overseeing the completion of included tasks; and coordinating payment related to the completion of the task.

[0169]

[0185] In a first example, a member may have a task related to booking a birthday dinner that the member delegates to a task facilitation service. The member may provide a few details (e.g., who the dinner is for, list of attendees, date or date range, etc.) but may optionally delegate the remainder of the birthday reservation task to the task facilitation service. For example, the task facilitation service may select the type of cuisine, restaurant / location, and time of dinner, and may coordinate transportation to and from the dinner. The task facilitation service may also contact the restaurant to make the reservation, generate and send invitations to guests, and perform other similar tasks generally related to planning a dinner.

[0170]

[0186] In another example, a member may identify and delegate a home maintenance task, such as gutter cleaning. The member may not provide any specific details, so the task facilitation service may research, identify, and contact reputable gutter cleaning companies in the member's area, arrange a date and time for the gutter cleaning, and process payment for the cleaning once completed.

[0171]

[0187] In yet another example, a member may work with a task facilitation service (e.g., a delegate associated with the task facilitation service) to elaborate on a task in detail and then, once the task is defined, delegate to the task facilitation service the selection of options for completing the task and for general execution of the task. For example, a member may provide the task facilitation service with a range of vacation dates, location, budget, and a list of interests, and then delegate to the task facilitation service the booking of transportation, lodging, and activities, and other similar arrangements, consistent with the details provided by the member.

[0172]

[0188] In some embodiments, a delegated task may involve at least some interaction between the member and the task facilitation service. For example, the task facilitation service may review and select options to complete the task, but may still present the options to the member for approval. In some embodiments, there may be conditions (e.g., conditions specific to the task facilitation service's members or general conditions / rules) that specify instances in which member feedback or approval is required. For example, if the option selected by the task facilitation service exceeds a certain cost, the task facilitation service may require approval by the member to proceed with the option. Similarly, if the option selected by the task facilitation service is for goods and services that exceed a certain time frame or are subject to some legal restrictions, the task facilitation service may also require approval by the member to proceed with the selected option. Thus, in some examples, the task facilitation service may still interact with the member despite the task being delegated by the member.

[0173]

[0189] When a task is delegated, the task facilitation service may generally attempt to complete the delegated task according to information about the member that is accessible to the task facilitation service. Such information may include a member profile associated with the member (e.g., a member profile created during onboarding and subsequently updated based on the member's activities), historical interactions between the member and the task facilitation service, information about previous tasks completed by the member and the task facilitation service, information about other members who share demographics with the member, and external information accessible by the task facilitation service (e.g., weather forecasts, traffic information, news, community calendars, etc.). Thus, while a member may delegate a task for completion by the task facilitation service, the task facilitation service may nevertheless complete the delegated task based on an informed prediction of how the member prefers the task to be completed.

[0174]

[0190] Further aspects of task delegation controls and their enablement and use will now be provided with reference to the figures.

[0175]

[0191] 1 , embodiments of the present disclosure may include dynamically enabled task delegation controls on the computing device 120 of a member 118. Each delegation control may generally be associated with a corresponding task such that, when the delegation control is activated, the task is delegated to the task facilitation service 102 (including a representative 106 of the task facilitation service 102 or a third party associated with the task facilitation service 102) for completion. Such delegation generally allows the task facilitation service 102 to complete the task without or with only limited additional interaction with the member 118. Thus, the delegation control enables the member 118 to quickly and efficiently delegate tasks for completion by the task facilitation service 102, thereby reducing or eliminating the cognitive load on the member 118 associated with the task.

[0176]

[0192] Enabling delegated controls on the computing device 120 of the member 118 may be controlled by the task facilitation service 102 and may be based at least in part on a member model associated with the member 118. In some embodiments, the member model may be, or may be part of, a member profile created during onboarding of the member 118 or a separate model updated and maintained by the task facilitation service 102. Generally, however, the terms “member model” and “user model” are used herein to refer to models specifically related to members that model the characteristics of members to predict their behavior, preferences, and other aspects.

[0177]

[0193] In some embodiments, the task facilitation service 102 may determine, based on the member model, whether the member 118 is likely to delegate a given task. If so, the task facilitation service 102 may enable delegation control for the task at the computing device 120. Alternatively, the enablement of task delegation control at the computing device 120 may be at the discretion of the surrogate 106 of the task facilitation service 102. In such embodiments, the task facilitation service 102 may provide a metric, value, recommendation, or similar data corresponding to the likelihood that the member 118 will delegate the task to the surrogate 106. The surrogate 106 may then make an informed decision regarding whether to enable delegation control for the task at the computing device 120.

[0178]

[0194] The member model associated with a member 118 may be based on past activity and interactions between the member 118 and the task facilitation service 102, particularly based on the member's 118's past delegation activity. Thus, whether the task facilitation service 102 enables delegation control for a task may also be based on the member's 118's past delegation activity. In other words, whether the member 118 activates delegation control may be used as feedback for the member's model. Doing so updates the member model to reflect the member's 118's evolving tendencies and preferences regarding task delegation. Thus, the decision by the task facilitation service 102 to enable delegation control for a member 118 is similarly based on the member's 118's evolving tendencies and preferences.

[0179]

[0195] The task facilitation service 102 may also determine whether to enable delegation control for a task based on task data associated with the task. Task data generally refers to any information related to a task and generally includes information related to the nature and scope of the task, as well as data for similar tasks, including data for the member 118 and other members associated with the task facilitation service 102. In one example, the task facilitation service 102 may only recommend delegation of tasks for which the task facilitation service 102 has sufficient task data or for which the task facilitation service 102 may be able to predict sufficient task data that may be missing. For example, the task facilitation service 102 may generally recommend tasks related to purchasing gifts for a member, but the task facilitation service 102 may do so only if information about the recipient has been provided by the member 118 or is available to the task facilitation service 102 (e.g., included in the member 118's profile). As another example, the member 118 may have a task to book a date night with their spouse. If the task facilitation service 102 is able to independently gather or predict sufficient information to complete the task (e.g., available dates based on the member's 118 calendar accessible by the task facilitation service 102, the member's 118 dining and budget preferences according to the member's profile, etc.), the task facilitation service 102 may enable delegation control for the date night task.

[0180]

[0196] Certain task data may prevent a task from being delegated, and therefore, may prevent corresponding delegation controls from being enabled by the task facilitation service 102. For example, in some embodiments, if the budget for a task is unknown or exceeds a predefined threshold, the task may not be delegated and delegation controls may not be enabled. In such cases, delegation may not be available and delegation controls may not be enabled until a member 118 provides a budget or certifies a budget that meets or exceeds the predefined threshold. In other embodiments, delegation controls for a task may not be enabled if the budget for the task exceeds a predefined threshold, regardless of whether a member 118 certifies spending above the predefined threshold. Thus, if task data for a task indicates that the task has an unknown or high budget, the task facilitation service 102 may not enable delegation controls for the task.

[0181]

[0197] As another example, delegation control may not be available for tasks that are relatively simple and / or do not require payment. For example, a member 118 may have a research-type task that involves determining an answer to a question or gathering information on a topic. In such a case, the task facilitation service 102 may simply complete the task (e.g., by researching and providing an answer to the member's question) without requiring the member 118 to formally delegate the task to the task facilitation service 102. Thus, if the task data indicates that the task is a simple task or does not require payment, the task facilitation service 102 may not enable delegation control for the task.

[0182]

[0198] In still other embodiments, delegation may not be available for tasks that meet certain criteria related to task performance, such as the time required to complete the task or the general complexity of the task. For example, the task of planning a long car trip may be particularly complex (e.g., may include multiple subtasks related to booking accommodations, transportation, activities, etc.) and may take a significant amount of time for the task facilitation service 102 (e.g., a surrogate 106) to complete. In such cases, review by the member 118 may be required one or more times during the task's completion by the task facilitation service 102 to ensure that the task is completed according to the member's expectations. In particular, doing so reduces the likelihood that the task facilitation service 102 will waste resources by performing task completion in a manner that is unsatisfactory to the member 118 and improves the likelihood that the task will be completed in a timely manner by avoiding having to repeat aspects of the task. Thus, if task data for a task indicates that the task may be particularly time-consuming or complex, the task facilitation service 102 may not enable delegation control for the task.

[0183]

[0199] In other embodiments, task delegation may be limited by policies and legal requirements regarding third-party purchases. For example, alcohol purchases, purchases over a certain dollar amount (e.g., $1,000), or other purchases that may be subject to the task facilitation service's 102 legal requirements and / or general policies may not be performed by the task facilitation service 102 or may require explicit authorization from the member 118 to be completed by the task facilitation service 102. Thus, if task data indicates that a task may be subject to restrictions or policies regarding third-party purchases, the task facilitation service 102 may enable delegation control for the task.

[0184]

[0200] In yet other embodiments, task delegation may be limited based on the feasibility of the task. For example, the task facilitation service 102 may not allow task delegation if the task has an unrealistic deadline (e.g., planning a month-long car trip by tomorrow), is impossible to complete (e.g., buying tickets to a canceled or sold-out event), or is outside the scope of tasks that can be completed by the task facilitation service 102. Thus, if the task data indicates that the task is not feasible or is outside the scope of the work of the task facilitation service 102, the task facilitation service 102 may not enable delegation control for the task.

[0185]

[0201] In another embodiment, task delegation may be limited based on the history of the member 118. For example, in some embodiments, the task facilitation service 102 may not allow new members to delegate tasks. In such cases, a member may be considered new if the member has been using the task facilitation service 102 for less than a threshold amount of time (e.g., less than one month), if the member has completed fewer than a threshold number of tasks (e.g., fewer than five tasks) using the task facilitation service 102, if the member has completed fewer than a threshold number of tasks using the task facilitation service 102 with a rating (e.g., fewer than five tasks with a four-star or five-star rating (out of five) by the member after completing the tasks), or other similar metrics. Similarly, task delegation may be limited based on the member's history, including the member's history of delegated tasks. For example, the task facilitation service 102 may limit the number of tasks that can be delegated at any one time based on when the delegation became available to the member 118, how many tasks the member 118 has delegated in the past, how the member 118 rated the completion of previously delegated tasks, etc.

[0186]

[0202] In yet other embodiments, task delegation may be limited based on preferences or settings provided by the member 118 to the task facilitation service 102. For example, the member 118 may provide preferences or configure settings on the computing device 120 regarding whether and what types of tasks can be delegated. In one such case, the member 118 may simply disable delegation for all tasks. As a result of such settings, the task facilitation service 102 may not enable delegation control on the computing device 120. In another case, the member 118 may provide criteria (e.g., budget, time, type of task, etc.) that can be used to identify when delegation can be enabled for a task. If the task meets the criteria provided by the member 118 in addition to being recommended by the task facilitation service 102's various models and processes related to enabling delegation control, the task facilitation service 102 may then enable delegation control.

[0187]

[0203] As described above, activation or deactivation of an enabled delegate control may be used to update the member model of the member 118. For example, in response to activation of delegate control by the member 118, the task facilitation service 102 may initiate the task delegation process and may update the member model with data corresponding to the delegated task. In response to the member 118 delegating a task, the member model may be updated so that the task facilitation service 102 is more likely to enable delegate control for similar tasks. In embodiments where enabling delegate control is at the discretion of the surrogate 106, updating the member model may cause a stronger recommendation to be given to the surrogate 106 for similar tasks. Conversely, if the member 118 chooses not to activate delegate control for a given task, the member model may be updated so that the likelihood of enabling delegate control or the strength of recommendations given to the surrogate 106 for similar tasks may be reduced.

[0188]

[0204] In at least some embodiments, the task facilitation service 102 may be configured to gradually encourage the member 118 to delegate tasks to the task facilitation service 102 more frequently over time. Thus, the task facilitation service 102 may use delegation control to train, coach, or encourage the member 118 to delegate tasks, thereby reducing the cognitive load of the member 118. For example, in some implementations, the task facilitation service 102 may be configured to enable delegation control for the member 118 or be biased toward providing a positive recommendation to the delegate 106 in favor of enabling delegation control. In other embodiments, the task facilitation service 102 may reward the member 118 in response to the member 118 delegating a task. Such rewards may include, but are not limited to, monetary rewards (e.g., awards, discounts, coupons, gift cards, etc.), congratulations, gamified rewards (e.g., badges, medals, levels), etc. Thus, the task facilitation service 102 may not only reduce the cognitive load of the member 118 with respect to the current task, but may also assist the member 118 in expanding the range of tasks that the member 118 is willing to delegate over time, thereby further reducing the cognitive load of the member 118.

[0189]

[0205] For a variety of notable reasons, delegation controls and related processes disclosed herein are distinct from traditional controls, such as controls directed at facilitating a customer's purchase of a product or service. For example, traditional controls for facilitating a purchase are generally enabled solely based on the availability of a customer's shipping and purchase information. Thus, the activation of such traditional controls does not rely on customer modeling, particularly customer modeling based on the customer's past behavior. In contrast, the activation of delegate controls disclosed herein is customized based on one or more models that reflect a member's behavior, preferences, and the like. Thus, delegate controls reflect a member's dynamic behavior and preferences and, in some embodiments, may be used to encourage a member toward certain behaviors. For example, by customizing the activation of delegate controls specifically for a particular member, the member may be encouraged to delegate more tasks to task facilitation services over time, ultimately reducing the member's overall cognitive load.

[0190]

[0206] Another difference from traditional purchase facilitation controls is that by relying on a member model, activation of delegated controls in embodiments of the present disclosure may be related to the likelihood that the member will actually activate the delegated control. For example, a delegated control may be activated for a task similar to one or more tasks previously delegated by the member, assuming that the member is likely to activate the delegated control for the task in light of the member's past behavior. In contrast, traditional purchase facilitation controls may not be activated based on the likelihood that the customer will actually use the control. Rather, if the customer provides the required purchase and shipping information, the control is activated regardless of whether the customer is likely to actually activate the control. This additional difference enhances the ability of the systems and methods disclosed herein to be tailored to specific members and facilitates the use of delegated controls to direct and encourage member behavior.

[0191]

[0207] In addition to being distinct from traditional purchasing controls, the techniques for control activation provided by implementations of the present disclosure are distinguishable from traditional user interfaces, providing improved dynamism and user-specific tailoring of the interface. For example, many traditional user interfaces always enable all controls and features, which can result in cluttered interfaces, steep learning curves, and poor user experiences, especially when the interface presents controls in a non-intuitive manner or without consideration of preferences and needs. In contrast, implementations of the present disclosure allow activation of specific user interface controls (e.g., delegation controls for tasks) for specific user interface items based on and in response to changes in user-specific data. In other words, in contrast to traditional, substantially static user interfaces, implementations of the present disclosure include user interfaces that include controls that can be specifically enabled and disabled to suit a user's preferences in a manner that can change over time or evolve with the user without direct user intervention.

[0192]

[0208] Although given in the context of task delegation for task facilitation services, the systems and methods included in this disclosure more generally provide techniques for selectively enabling user interface features based on user preferences, past user activity, etc. The systems and methods included in this disclosure also provide techniques for dynamically enabling user interface features on a fine-grained (e.g., task-by-task) basis. While these results are beneficial separately, when considered in combination, they provide substantial improvements to the user experience and substantial savings in computing resources.

[0193]

[0209] In particular, implementations of the present disclosure improve the user experience and conserve computing resources by providing a streamlined user interface and by reducing the likelihood that a user / member will unintentionally delegate a task (including subsequently reversing the user / member's decision to delegate a task). With regard to streamlining the user interface, for example, at least some implementations include dynamic controls and corresponding visual indicators that clearly show whether a task can and should be delegated. For example, the user interface may include dynamic icons or visual control elements that delegate tasks and are presented based on past user activity and preferences. Thus, a user / member can clearly determine whether a task can and should be delegated without having to drill down into the task or access details about the task. Doing so not only improves the overall effectiveness and ease of navigation of the user interface, but also conserves computing resources that would otherwise be required to access and present tasks.

[0194]

[0210] Another way implementations of the present disclosure improve user experience and conserve computing resources is by reducing the likelihood that a user / member will unintentionally delegate a task. As described in more detail below, delegating a task initiates various resource-intensive processes, including generating suggestions, updating user-specific data, updating task data, etc. When a user / member undelegates a task, similar resource intensive processing may be required to undo, reset, delete, or reinstate the task. For example, in addition to deleting or resuming a task, undelegation may require deleting user records or system data. Furthermore, in cases where the system relies on delegation data for other reasons, such as training machine learning models, a user undelegating a task may impair the predictive ability or accuracy of those models, and in some instances, may require retraining of the models. For at least these reasons, increasing the likelihood that tasks delegated by a user / member will remain delegated can substantially improve the overall performance of the task delegation system in addition to conserving computing resources and improving users' experience with the system. Thus, by incorporating dynamic task delegation controls that are selectively enabled based on user-specific preferences, past user activity, and other similar data, implementations of the present disclosure provide technical solutions for improving overall performance, efficiency, and accuracy.

[0195]

[0211] The above are merely examples of technical improvements and benefits provided by implementations of the present disclosure. Other improvements provided by implementations of the present disclosure, relating to computing resource savings, model training and accuracy, ease of navigating the user interface, etc., will be apparent to those skilled in the art having the benefit of this disclosure.

[0196]

[0212] 7 illustrates an illustrative example of an environment 700 including the task facilitation service 102 described in the context of FIG. 1 and illustrates a first exemplary approach for dynamically enabling delegated control at the computing devices 120 of members 118. Accordingly, for clarity only, some elements of the task facilitation service 102 included in FIG. 1 have been omitted from FIG. 7.

[0197]

[0213] As previously described, the task facilitation service 102 generally assists members 118 in identifying, delegating, and completing tasks. To that end, the task facilitation service 102 collects and stores member data, for example, in a user data store 108, and task data, for example, in a task data store 110. As shown in FIG. 1 , the task facilitation service 102 may further include one or more surrogates 106 with which members 118 may interact and communicate. In the embodiment of FIG. 7 , the surrogates 106 are shown as surrogate users 722 and corresponding surrogate computing devices 724, although in other embodiments, the surrogates 106 may instead be virtual entities. Furthermore, while FIG. 7 includes only one surrogate 106, the task facilitation service 102 may include multiple surrogates, with each member 118 assigned to or able to interact with one or more of the multiple surrogates. Similarly, a given surrogate may be responsible for communicating and interacting with multiple members.

[0198]

[0214] Members 118 may interact and communicate with the task facilitation service 102 (including surrogates 106) using computing devices 120. In at least some embodiments, the task facilitation service 102 may host an account for a member 118 that is accessible by the member 118 from multiple computing devices (e.g., laptop, tablet, smartphone, desktop) associated with the member 118. For simplicity and clarity, a group of computing devices available to a member 118 will be referred to herein as a singular computing device 120, although it should be understood that any operation or functionality described herein with respect to a computing device 120 may be distributed or replicated across any number of computing devices associated with the member 118. Thus, for example, as described in further detail below, enabling delegated control by a surrogate 106 will nevertheless be referred to herein as enabling delegated control at a computing device 120, even though the enablement may be on any or all of multiple computing devices associated with the member 118.

[0199]

[0215] Embodiments of the present disclosure are generally directed to systems and processes for enabling delegation controls on a computing device 120 associated with a member 118. While specific examples and further details regarding delegation controls are provided later in this disclosure, the term “delegation control” refers to functionality on a computing device 120 that enables a member 118 to delegate a task associated with the member 118 for completion by the task facilitation service 102 (including completion by a delegate 106). Thus, enabling a given delegation control for a given task on a computing device 120 generally refers to making the delegation control accessible to the member 118 or allowing the delegation control to be activated by the member 118. In contrast, activating a delegation control generally refers to the member providing suitable input to the enabled delegation control to communicate that the member 118 wishes to delegate the corresponding task. Thus, a delegation control is referred to herein as being “activated” when the corresponding task is in the process of being delegated or has been delegated for completion by the task facilitation service 102.

[0200]

[0216] In some embodiments, the delegate control may include a visual interactive control element of a user interface presented to the member 118 by the member's computing device 120. Examples of visual interactive control elements include, but are not necessarily limited to, buttons, radio buttons, check boxes, icons, etc. In such embodiments, the member 118 may activate the delegate control by clicking, tapping, or interacting with the visual interactive control element. In other embodiments, the delegate control may encompass other input modalities. In general, any input modality available at the computing device 120 may provide the basis for the delegate control. For example, without limitation, delegated control according to the present disclosure may be activated using voice / audio input (e.g., by the member 118 uttering "Delegated task: Buy Mom a birthday present"), gesture (e.g., swiping in a direction or pattern on a touchscreen), movement (e.g., shaking or tapping a device that includes an accelerometer or similar motion-based sensor in a prescribed manner), physical input (e.g., a button), manipulating visual elements of an interface (e.g., dragging and dropping an item from one location on the screen to another), or any other suitable input modality. Regardless of the input modality that forms the basis of the delegated control, when the member provides the requisite input associated with the delegated control, the delegated control is activated and begins delegating the corresponding task. If the delegated control is disabled, the computing device 120 may take no action when the member 118 attempts to activate the delegated control. Alternatively, the computing device 120 may provide feedback to the member 118 (e.g., in the form of an error or similar message) communicating that task delegation is not currently available and / or that the member 118 must contact the representative 106 if the member 118 wishes to delegate the task.

[0201]

[0217] Generally, the process for enabling delegated control on a computing device 120 includes the task facilitation service 102 identifying a task associated with the member 118. The task facilitation service 102 then determines whether delegated control should be enabled for the task on the computing device 120 associated with the member 118. If the task facilitation service 102 determines that delegated control should be enabled, the task facilitation service 102 generates or updates corresponding instructions that, when received by the computing device 120, enable delegated control on the computing device 120.

[0202]

[0218] This disclosure uses the term “instructions” to refer to mechanisms that facilitate communication between computing devices, software applications, etc. Generally, instructions may be generated, updated, transmitted, etc. in response to operation of a first computing device and may later be received, read, accessed, etc. by a second computing device. For example, instructions may be a message, data packet, or similar object generated or populated by a first computing device and sent to a second computing device. As another example, instructions may be based on the creation or modification of a stored value. In such cases, the stored value may be created or updated by the first computing device and subsequently accessed by the second computing device. The stored value may be stored on the first computing device, on the second computing device, or in a location mutually accessible (directly or indirectly) by both the first computing device and the second computing device (e.g., a database or similar data store). Accordingly, to the extent this disclosure refers to receiving instructions, such references to receiving include receiving transmitted data (e.g., receiving at the second computing device data transmitted from a first computing device to a second computing device), but more generally further encompass accessing or obtaining the data, for example, by reading the data from a data source. Similarly, sending instructions includes sending data from a computing device, but may further include generating or updating a value. Accordingly, to the extent this disclosure refers to sending and receiving instructions, such references should be interpreted broadly to include any suitable mechanism for providing data between computing devices, and not limited to implementations in which data is provided directly between computing devices via a communications link established between the computing devices.

[0203]

[0219] Examples of the above processes related to enabling delegated control are shown in each of Figures 7 and 8. Referring first to Figure 7, a process for enabling delegated control on a computing device 120 is shown that relies on a decision by a delegate 106 to enable delegated control on the computing device 120.

[0204]

[0220] In FIG. 7 , a member model 709 corresponding to a member 118 is updated using member data stored in the user data store 108. The member model 709 is associated with the member 118 and captures various aspects of the member 118, including, but not limited to, the member's 118's behavior, preferences, personality, or similar aspects, including, but not limited to, the member's 118's behavior, preferences, tendencies, etc., regarding task delegation. In particular, the user data store 108 stores data related to the member's 118's previous task-related activities, and more particularly, details related to the member's 118's past delegation activities. For example, the user data store 108 may include details about different tasks, whether delegation control was enabled for those tasks, whether the member 118 activated delegation control for the tasks, and any feedback received from the member 118 regarding completion of delegated tasks. The member model 709 may be updated with delegation-related activities such that the member model 709 may be used to predict the likelihood that the member 118 will delegate a particular task. In some embodiments, member model 709 may be a member profile generated during onboarding, while in other embodiments, member model 709 may instead be a separate model, algorithm, etc. for use in predicting delegation activity. In such cases, member model 709 may be updated and trained separately from the member profile, or may be linked to or notified by the member profile such that member model 709 is dynamically updated as the member profile changes.

[0205]

[0221] Delegation control model 750 may rely on member model 709 and task data for the task associated with member 118 to determine the likelihood that member 118 will delegate the task to task facilitation service 102. In the embodiment shown in Figure 7, delegation control model 750 outputs a recommendation to surrogate 106. Surrogate 106 may then determine whether to enable delegation control for the task at computing device 120 based on the recommendation provided by delegation control model 750.

[0206]

[0222] In at least one exemplary embodiment, the recommendation may be presented to a surrogate user 722 via a representative computing device 724. The surrogate user 722 may then decide to generate or update instructions to enable delegated control for the task on a computing device 120. A computing device 120 associated with a member 118 is generally configured to receive or access instructions for delegated control and to selectively enable delegated control in response to the instructions.

[0207]

[0223] Figure 8 shows an alternative illustrative example of an environment 800 including the task facilitation service 102 described in the context of Figure 1. In contrast to the environment 700 of Figure 7, the environment 800 omits the surrogate 106 at least to the extent that the surrogate 106 is involved in enabling delegated control. Thus, in the embodiment of Figure 8, instructions for enabling delegated control at the computing device 120 of the member 118 may be based on the output of the delegated control model 750 and may not be subject to the discretion of any intermediary (e.g., the surrogate 106).

[0208]

[0224] 7 , delegation control model 750 may rely on member model 709 (stored in user data store 108 and updated using member data including data related to the member's 118's previous delegation activity) and the task data store in task data store 110 for the task to determine the likelihood that the member will delegate the task for completion by the task facilitation service 102. If the output of delegation control model 750 meets the applicable criteria, the task facilitation service 102 may then generate or update instructions regarding activation of delegation control for the task on the member's computing device 120. For example, if the output of delegation control model 750 indicates that the member 118 will likely activate delegation control, the task facilitation service 102 may update or generate instructions that, when received by the member computing device 120, cause the member's computing device 120 to activate delegation control. Thus, instead of simply providing a recommendation as to whether delegated control should be enabled on computing device 120 (as in the embodiment of FIG. 7), the output of delegated control model 750 in the embodiment of FIG. 8 is used directly by task facilitation service 102 to selectively enable delegated control.

[0209]

[0225] 7 and 8 may also be performed to disable delegation control at computing device 120. Following enabling delegation control for a task, task facilitation service 102 may determine that the task should no longer be delegated by member 118. For example, task facilitation service 102 may determine that the task is unlikely to be delegated by a member (e.g., by determining that it falls below a delegation likelihood threshold) based on changes to user data store 108, task data store 110, member model 709, or other data and models of task facilitation service 102. In response to this determination, task facilitation service 102 may update instructions associated with the delegation control to disable delegation control at computing device 120. As with enabling delegation control, disabling delegation control may be performed automatically by task facilitation service 102 or based on a decision by an intermediary, such as delegate 106. In embodiments involving a surrogate 106, the task facilitation service 102 may provide an alert, message, or other communication to the surrogate 106 if the task facilitation service 102 determines that delegated control must be overridden to help inform the surrogate's 106 decision.

[0210]

[0226] FIG. 9 depicts another illustrative example of an environment 900 including aspects of the task facilitation service 102 described in the context of FIG. 1 , illustrating the activation of delegation control by a member 118 at the member's computing device 120. Generally, the activation of delegation control for a given task by a member 118 causes the computing device 120 to generate or update an indication that delegation control has been activated and an indication that the corresponding task should be delegated. In response to the indication, the task facilitation service 102 updates the task data for the task to indicate that the task has been delegated. The task facilitation service 102 may also store data related to the member's interaction with the delegation control. As previously described, such interaction data may be used by the task facilitation service 102 to update the member model 709 corresponding to the member 118, the delegation control model 750, and other delegation-related models of the task facilitation service 102 (shown in FIGS. 7 and 8, respectively). Thus, activation of delegation control by a member 118 is used to further refine the model of the task facilitation service 102, thereby improving the model regarding delegation and the overall predictive ability of the task facilitation service 102.

[0211]

[0227] In addition to updating the model in response to a member's activation of a delegation control, the task facilitation service 102 may also be configured to update the model in response to deactivation of an enabled delegation control. For example, in some embodiments, a member 118 may be able to explicitly decline to delegate a task by, for example, clicking a button or other user interface element indicating that the member does not want to delegate the task. As another example, deactivation of a delegation control may be determined based on the member 118 beginning to complete a task without activating an enabled delegation control for the task. In yet another example, a delegation control may be subject to a “timeout” whereby the enabled delegation control is considered deactivated if it is not activated within a certain amount of time. In any of the above cases, deactivation of a delegation control may result in the task facilitation service 102 determining that the member 118 is generally unwilling or uninterested in delegating the task and may use such a determination as negative feedback for training the task facilitation service's 102 model. In some embodiments, deactivation of a delegation control may also result in disabling the delegation control on the computing device 120.

[0212]

[0228] In yet other embodiments, the model of the task facilitation service 102 may be updated in response to feedback provided by the surrogate 106. For example, the member 118 may directly request the surrogate 106 to delegate a task and may make such a request without enabling delegation control for the task or activating enabled delegation control for the task. In such a case, the surrogate 106 may modify the task data for the task to indicate that the task is to be delegated without enabling delegation control and / or without the member 118 activating delegation control. As another example, the member 118 may directly instruct the surrogate 106 that the task is not to be delegated. In such a case, the surrogate 106 may similarly modify the task data for the task to indicate that the task is not to be delegated or, if the task is already delegated, to un-delegate the task. Again, this may be done without enabling delegation control on the computing device 120 of the member 118. Regardless of how such delegation-related instructions are provided to the surrogate 106, subsequent modifications to the task data to indicate the delegation status of the task made by the surrogate 106 may also be used by the task facilitation service 102 to inform and update various delegation-related models of the task facilitation service 102.

[0213]

[0229] As shown in the particular example of FIG. 9 , after activation of delegated control at computing device 120, an indication is generated, updated, provided, or made available to task facilitation service 102 that delegated control has been activated by member 118 using computing device 120. In the particular embodiment of FIG. 9 , the indication is received by surrogate 106 and presented to surrogate user 722 via surrogate's computing device 724. In response to surrogate user 722 confirming the task delegation, a corresponding indication is provided to task coordination system 114, which updates each of user data store 108 and task data store 110 to reflect the activation of delegated control by member 118 and the delegation of the task, respectively. In an alternative embodiment, surrogate 106 and confirmation by surrogate 106 may be omitted from the process, such that receipt of the indication by task facilitation service 102 causes the task to be delegated and the various data stores to be updated without additional approval or confirmation by surrogate 106.

[0214]

[0230] Some embodiments may support a process similar to that shown in FIG. 9 for undelegating a previously delegated task. In at least some embodiments, after activating a delegation control for a task, the member 118 may toggle the delegation control, or by toggling the delegation control, activate a second control, or provide a second input that activates a second control in the undelegation process. In response to toggling the delegation control or activating the second control, the computing device 120 may generate, update, etc., instructions corresponding to the task to indicate that the task should be undelegated. In response to receiving the instructions, the task facilitation service 102 may undelegate the task by, for example, updating task data associated with the task. In some embodiments, undelegation of a task may be facilitated by the surrogate 106. For example, in response to receiving an instruction from the computing device 120, the task facilitation service 102 may alert the surrogate 106, which may then initiate communication with the member 118 to gather additional information about the task and the member's request to undelegate the task. The surrogate 106 may then confirm that the task is to be de-delegated, and the task facilitation service 102 may update relevant data. De-delegating the task may also cause the task facilitation service 102 to update delegation-related models, such as, but not limited to, the member model 709 and the delegation control model 750 (shown in FIGS. 7 and 8).

[0215]

[0231] 10 is a schematic diagram of an environment 1000 according to the present disclosure. As described above, implementations of the present disclosure may include surrogates 106 that may be assigned and may serve as a point of contact for members 118. Generally, the surrogates 106 communicate with the members 118 and facilitate the completion of tasks on behalf of and with the members 118. While the surrogates 106 may be computer-based entities, in at least some embodiments, the surrogates 106 may include human users. Thus, the surrogates 106 may include each of the surrogate users 722 and a surrogate computing device 724.

[0216]

[0232] Among the various tasks handled by the surrogate 106, the surrogate 106 may act as a coach or advisor, assisting the member 118 to reduce the member's cognitive load. In at least some embodiments of the present disclosure, the surrogate 106 facilitates reducing cognitive load by helping the member 118 delegate tasks for completion by the task facilitation service 102. To do so, the surrogate 106 may determine whether delegation controls for a task should be enabled on the computing device 120 associated with the member 118. As previously explained, delegation controls refer to functionality of the computing device 120 that facilitates delegation of a task for completion by the task facilitation service 102. Such functionality may be in the form of a user interface element (e.g., a button, a slider, etc.), a command (e.g., a voice command), or similar feature that, when activated by the member 118, delegates the task associated with the delegation control for completion by the task facilitation service 102. In contrast to non-delegated tasks, which the member 118 may work with a surrogate 106 to complete, delegated tasks are generally completed by the task facilitation service 102 with minimal additional effort required by the member 118.

[0217]

[0233] Task delegation is a tool by which the member 118 can reduce the member's task list and corresponding cognitive load. To facilitate task delegation, activation of delegation controls can be at the discretion of the surrogate 106. For example, with reference to FIG. 10 , the surrogate computing device 724 can present a user interface to the surrogate user 722 in which information about the member 118 and the member's 118's tasks can be provided. The user interface of the surrogate computing device 724 can further present information regarding the likelihood that a particular task will be delegated by the member 118. The user interface of the surrogate computing device 724 can further include various controls by which the surrogate user 722 can activate delegation controls at the computing device 120 for subsequent activation by the member 118. For example, in response to activating an activation control at the surrogate computing device 724 for a task, the surrogate computing device 724 or the task facilitation service 102 can send a delegation control activation instruction to the computing device 120 that activates a corresponding delegation control at the computing device 120. Thus, after sending the delegated control enablement instruction, the member 118 may activate the enabled delegated control to delegate the corresponding task.

[0218]

[0234] In at least some embodiments, the proxy computing device 724 may present the likelihood that the member 118 will delegate the task. The likelihood may be presented, for example, as a value, a graphic, or a similar indicator, in a user interface presented by the proxy computing device 724 to the proxy user 722. The likelihood information presented by the proxy computing device 724 may be based at least in part on the output of a delegation feasibility model, such as the delegation feasibility model 750 shown and described above in the context of FIGS. 7 and 8. For example, the proxy computing device 724 may receive a delegation feasibility indication generated by the delegation feasibility model 750 or the task facilitation service 102 for a task and may update the user interface to display a corresponding indicator to the proxy user 722. The proxy user 722 may then rely on the indicator, and thus the prediction of the delegation feasibility model 750, to determine whether to enable delegation control at the computing device 120.

[0219]

[0235] While enabling delegated control at the computing device 120 may be fully automated, involving the surrogate user 722 and relying on the discretion of the surrogate user 722 may provide some benefits. For example, through interactions between the surrogate user 722 and the member 118, the surrogate user 722 may have some insight into the member 118's preferences, tendencies, mood, etc. that may not be immediately apparent based on data collected by the task facilitation service 102 regarding the member 118. By relying on such additional information, the surrogate user 722 may more accurately determine when delegation may be appropriate to facilitate a successful and satisfying delegation experience for the member 118, as well as identify when additional coaching and interaction with the member 118 may be needed.

[0220]

[0236] As described above, the delegability information presented to the proxy user 722 may be provided in part by the delegability model 750. Generally, the task facilitation service 102 uses the delegability model 750 to predict whether the member 118 is likely to delegate a particular task. The results generated by the delegability model 750 may then be provided to the proxy computing device 724 and presented to the proxy user 722 for consideration when deciding to enable delegation control at the computing device 120 of the member 118.

[0221]

[0237] Delegability model 750 may rely on a wide range of data to determine whether member 118 is likely to delegate a task. For purposes of this disclosure, delegability model 750 may be considered to rely on data if the data is used to train delegability model 750, used as input to delegability model 750 when evaluating whether member 118 is likely to delegate a task, or used to inform the output of delegability model 750.

[0222]

[0238] In at least some implementations, the delegability model 750 may rely on user-specific data when determining whether the member 118 is likely to delegate a task. User-specific data generally refers to any information or data collected about the member 118. The user-specific data may include, among other things, the member 118's previous delegation activity. Previous delegation activity may include, for example, but is not limited to, information about various tasks for which delegation was previously enabled and the member 118's response (e.g., whether the member 118 delegated the task, refused to delegate the task, or took no action when presented with the option to delegate the task). The delegability model 750 may additionally or alternatively rely on user data for users of the task facilitation service 102 other than the member 118, task data for the task being assessed by the delegability model 750, task data for previous tasks, and any other suitable data that may facilitate determining the member 118's likelihood to delegate a task.

[0223]

[0239] As noted above, in some implementations of the present disclosure, the delegability model 750 generates a delegability that indicates the likelihood that the member 118 will delegate a particular task. While the delegability generated by the delegability model 750 may take any suitable form, FIG. 11 shows an example scale 1100 in which delegability ranges from 0 (indicating substantial certainty that the member 118 will not delegate the task) to 100 (indicating substantial certainty that the member 118 will delegate the task). Thus, when presented with a task, the delegability model 750 may output a score between 0 and 100 that indicates the likelihood that the member 118 will delegate the task.

[0224]

[0240] The task facilitation service 102 or the proxy computing device 724 may then compare the score / value generated by the delegation likelihood model 750 to a scale, threshold, or similar distribution for interpretation and to provide a corresponding recommendation regarding the presentation of delegated controls to the member 118. For example, in some implementations, if the delegation likelihood model 750 generates a particularly low score, the task facilitation service 102 may provide a recommendation that the delegated controls corresponding to the proxy user 722 should not be presented to the member 118. As the likelihood increases, the task facilitation service 102 may provide a recommendation to the proxy user 722 that delegated controls may be enabled or are generally recommended, but with additional interaction and coaching of the member 118 by the proxy user 722. If the likelihood of delegation is relatively high, such as when the member 118 has regularly delegated similar tasks in the past, the task facilitation service 102 may recommend enabling delegated controls for the task without any additional interaction between the proxy user 722 and the member 118.

[0225]

[0241] In at least some implementations, delegation may be unavailable until the task facilitation service 102 completes a predetermined number, type, complexity, etc. of tasks on behalf of the member 118. In such implementations, the task facilitation service 102 may further use the delegation possibility model 750 to predict whether the member 118 is likely to delegate a task, but the task facilitation service 102 may not provide a corresponding recommendation, and any prediction by the delegation possibility model 750 may be used primarily to gather additional data about the member 118 for later prediction by the delegation possibility model 750 when delegation becomes available.

[0226]

[0242] As noted above, in some implementations, the delegation likelihood model 750 may generate a value from 0 to 100 (or a similar continuum), which may be cross-referenced with a corresponding scale. As shown in FIG. 11 , the scale 1100 may be divided into various ranges or sections corresponding to various levels of certainty that the member 118 will delegate a given task. For example, the scale 1100 includes a first range 1102 indicating a range of scores / values ​​where the member 118 is least likely to delegate the task. The scale 1100 further includes a second range 1104 indicating a range of scores / values ​​where the member 118 is least likely to delegate the task, a third range 1106 indicating a range of scores / values ​​where the member 118 is likely to delegate the task, and a fourth range 1108 indicating a range of scores / values ​​where the member 118 is most likely to delegate the task. Based on this distribution, the scale 1100 generally includes a threshold 1110 (e.g., a value of 50) below which the delegability model 750 predicts that the member 118 is generally unlikely to delegate the task, and above which the delegability model 750 predicts that the member 118 is likely to delegate the task. In some implementations, the scale 1100 shown in FIG. 11 may be specific to the member 118 and may be included as part of the member model 709 or stored with respect to the member 118. In other implementations, the scale 1100 may instead be shared among multiple members, including being a common scale used for all members associated with the task facilitation service 102.

[0227]

[0243] In view of the above, when presented with a task for a member 118, the delegation feasibility model 750 may generate a score or value based on the information available to the delegation feasibility model 750. The task facilitation service 102 may then use the score or value generated by the delegation feasibility model 750 to generate a likelihood indication. The likelihood indication may include, among other things, a value indicating the likelihood that the member 118 will delegate the task and / or a recommendation regarding enabling delegation control at the computing device 120 for the task. As shown in FIG. 7 , the delegation indication may be provided to the surrogate 106 and used to update the user interface of the surrogate's computing device 724 so that the surrogate user 722 can make an informed decision regarding enabling delegation control at the computing device 120. Alternatively, as shown in FIG. 8 , the likelihood indication may instead be used by the task facilitation service 102 to determine whether to enable delegation control at the computing device 120 independently of the surrogate 106.

[0228]

[0244] By way of example, in some implementations, a delegation feasibility score that falls within first range 1102 may cause delegation feasibility model 750 to generate a feasibility indication, when received by surrogate 106, that causes the surrogate computing device 724 to present a user interface element (e.g., a graphic, a control, etc.) communicating that enabling delegated control for a task at computing device 120 is generally discouraged. In some implementations, receiving such a feasibility indication may prohibit enabling delegated control at computing device 120, for example, by disabling or removing a corresponding control in the user interface presented by surrogate computing device 724. Similarly, a delegation feasibility score that falls within first range 1102 may cause delegation feasibility model 750 to generate a feasibility indication, when received by surrogate 106, that causes the surrogate computing device 724 to present a user interface element (e.g., a graphic, a control, etc.) communicating that enabling delegated control for a task at computing device 120 is highly recommended. In such a case, surrogate computing device 724 may present a user interface control or similar element to enable delegated control at computing device 120. The proxy computing device 724 may further present additional notifications, messages, alerts, visual elements, etc. indicating that delegation for the task is highly likely and that activation of delegated control on the computing device 120 is strongly recommended.

[0229]

[0245] The delegation feasibility model 750 or the computing device 120 may similarly generate a likelihood indication in response to the delegation feasibility model 750 determining that the task falls within one of the second range 1104 and the third range 1106. In such a case, the likelihood indication may cause the proxy computing device 724 to update and present information regarding the likelihood that the member 118 will delegate the task. Receiving the likelihood indication may cause the proxy computing device 724 to further enable control at the proxy computing device 724 to enable delegation control at the computing device 120 for the task. However, the likelihood indication may cause the proxy computing device 724 to indicate that further review by the proxy user 722 is recommended before or in conjunction with enabling delegation control. For example, in response to receiving a possibility indication corresponding to the second range 1104 or the third range 1106, the proxy user 722 may access the member 118's account and user information to determine whether delegated control for the task should be enabled on the computing device 120, access previous chat logs or similar communications with the member 118, or simply rely on their own personal experience with the member 118.

[0230]

[0246] In some implementations, receiving a feasibility indication for a task that falls within the second range 1104 or the third range 1106 may further prompt the proxy user 722 to defer to the member 118 regarding delegation of the task. For example, following enablement of delegation control on the computing device 120, the proxy user 722 may send a message to or initiate communication with the member 118 encouraging the member 118 to activate delegation control and further explaining doing so (e.g., delegating the task to the task facilitation service 102 for completion with minimal future interaction with the member 118). By doing so, the proxy user 722 may actively coach or encourage the member 118 to delegate tasks that the member 118 might normally be concerned about delegating. In other words, for tasks that fall within the third range 1106, the surrogate user 722 may encourage or reinforce the member 118's tendency to delegate tasks, and for tasks that fall within the second range 1104, the surrogate user 722 may encourage or reinforce the member 118's trustworthiness to delegate tasks that the member 118 might normally be concerned about delegating. In either case, further interaction with the surrogate user 722 may drive the member 118 to delegate more tasks to the task facilitation service 102 with the goal of lowering the member 118's cognitive load. In other words, in at least some cases, even if the delegation possibility model 750 determines that the member 118 is unlikely to delegate a task (e.g., the value / score generated by the delegation possibility model 750 is below the threshold 1110 shown in FIG. 11 ), the task facilitation service 102 may enable or recommend the surrogate computing device 724 to enable delegation control for the task.

[0231]

[0247] In at least some implementations, the task facilitation service 102 may encourage members 118 to delegate tasks. In such cases, the amount and type of incentive may be based on how much encouragement the member 118 may need before they are likely to delegate a task. Examples of incentives may include virtual rewards (e.g., badges, medals, points, etc.), monetary rewards (e.g., in-app currency, coupons, discounts, etc.), etc. The amount of the incentive may vary based, for example, on where the task is placed on the scale 1100 by the delegability model 750. For example, delegation incentives for tasks in the fourth range 1108 or the upper range of the third range 1106 may generally be less encouraging than tasks that fall in the lower range of the third range 1106 or the second range 1104. Thus, such incentives may be used to encourage members 118 in place of or in addition to any direct encouragement received from the task facilitation service 102, e.g., from a delegate 106. In at least some implementations, whether incentives are provided may also be at the discretion of the delegate 106. Thus, in some instances where a member 118 may need an additional boost or motivation to delegate a task, the delegate may provide an incentive to encourage the member 118 to delegate the task.

[0232]

[0248] 12 , an example of a user interface 1200 that may be presented on a representative computing device 724 is provided. User interface 1200 is intended as a non-limiting example of a user interface in accordance with the present disclosure and provides a context for the various concepts described herein. The present disclosure contemplates that the various concepts described in the context of FIG. 12 and subsequent figures may be readily adapted by those skilled in the art to a variety of other user interface designs. Accordingly, the present disclosure is deemed to encompass any user interface that includes the features and functionality described herein, regardless of any other design choices that may be made.

[0233]

[0249] 12 , user interface 1200 may include a member page 1202 for presenting information about a member 118. Without limitation, member page 1202 may include a member information section 1204 for presenting general information about the member 118, an image 1206 of the member 118, and controls 1208 for contacting and communicating with the member 118. Member page 1202 may further include a current task list 1210 that includes a list of current tasks associated with the member 118. As shown, in at least some embodiments, a surrogate user 722 may select a task included in current task list 1210 and, in response, be presented with task details 1212 for the selected task. In the context of FIG. 12 , task details 1212 corresponds to task 1232 for "Schedule gutter cleaning."

[0234]

[0250] The task details 1212 may include general task information 1214 (e.g., due date, general notes / instructions, etc.). The task details 1212 may further include a delegation information section 1216 for the task. In particular, the delegation information section 1216 may include a likelihood indicator 1218. The likelihood indicator 1218 may generally present information regarding the likelihood that the member 118 will delegate the selected task. For example, in the embodiment shown in FIG. 12 , the likelihood indicator 1218 includes a likelihood value 1220 and a graphic 1222 that indicate the likelihood that the member 118 will delegate the selected task for completion by the task facilitation service 102. The state of the likelihood indicator 1218 is generally determined by the delegation likelihood predicted by a delegation likelihood model (e.g., delegation likelihood model 750) of the task facilitation service 102 and thus reflects a prediction that the member 118 will delegate the selected task based on information available to the task facilitation service 102. 12, the state of the likelihood indication 1218 is represented by the number displayed in the likelihood value 1220 and the relative completeness of the circle in the graphic 1222. Thus, as shown, the delegation likelihood model 750 and the task facilitation service 102 predict that there is generally an 85% chance that the member 118 will delegate the selected task.

[0235]

[0251] Delegation information section 1216 may further include a delegation enable control 1224 that, when activated by proxy user 722, generates instructions associated with member 118 that, when received by computing device 120, cause computing device 120 to enable delegation control for the selected task. While shown as a button, delegation enable control 1224 may be any suitable control of user interface 1200, including non-visual controls such as voice commands, gestures, etc.

[0236]

[0252] Referring to the current task list 1210, the delegation status of the tasks listed in the current task list 1210 may be reflected by an icon or similar element associated with each task. For example, in FIG. 12 , task 1225 to buy a "ticket to Vancouver" is associated with icon 1226 in the form of a lightning bolt with no fill. In the context of user interface 1200, the lightning bolt with no fill represents that delegation control is enabled but not activated by the member 118. Similarly, task 1228 to buy a "gift for Kate" is associated with icon 1230 in the form of a lightning bolt with no fill. In the context of user interface 1200, the lightning bolt with fill represents that delegation control is enabled and activated by the member 118, i.e., the task 1228 has been delegated for completion by the task facilitation service 102. Delegation control for the remaining tasks included in the current task list 1210 may not yet be enabled and / or available.

[0237]

[0253] 13A and 13B are partial views of the user interface 1200 focusing on the task 1232. Referring initially to FIG. 13A , the task 1232 and task details 1212 are shown following activation of the delegation enable control 1224 by the proxy computing device 724. For example, the task details 1212 shown in FIG. 12 may be updated as shown in FIG. 13A in response to the user 722 clicking or otherwise activating the proxy delegation enable control 1224. As previously described, activating the delegation enable control 1224 generates an instruction, when received by the computing device 120, that causes the computing device 120 to activate the delegation control corresponding to the task 1232, which may then be activated by the member 118. Thus, in at least some implementations, the delegation enable control 1224 or other aspects of the delegation information section 1216 may be updated in response to the proxy computing device 724 activating the delegation enable control 1224. 13A , the text on the delegation enable control 1224 has been updated from “Unlock delegation?” to “Unlock delegation.” As further shown, activation of the delegation enable control 1224 also resulted in the task 1232 being updated with an icon 1234 and the delegation information section 1216 being updated to include an icon 1236. Like the icon 1226 of FIG. 12 , the icons 1234 and 1236 are in the form of lightning bolts with no fill, which, in the context of the exemplary user interface 1200, indicate that delegation control has been enabled for the task 1232 but has not yet been activated by the member 118.

[0238]

[0254] 13B similarly shows task 1232 and task details 1212, but after activation of enabled delegation controls on computing device 120 associated with member 118. In particular, the text of delegation enablement control 1224 has been updated to reflect that member 118 has delegated the task (e.g., by reading "Delegated!"). Icons 1234 and 1236 have similarly been updated to solid lightning bolts, which, in the context of user interface 1200, indicate that member 118 has delegated task 1232 by activating enabled delegation controls on computing device 120.

[0239]

[0255] In some embodiments of the present disclosure, the proxy computing device 724 may be prevented from enabling delegation controls on the computing device 120 of the member 118 in some circumstances. For example, FIG. 14A shows a task 1232 and task details 1212. As shown, in contrast to the previous example in which the delegation possibility model 750 and the task facilitation service 102 determined that the member 118 had an 85% chance of delegating the task 1232, FIG. 14A shows an example in which the delegation possibility model 750 and the task facilitation service 102 determined that the member 118 had only a 30% chance that they would delegate the task 1232. In such a case, the delegation enablement control 1224 may be omitted from the delegation information section 1216 such that the proxy user 722 is prevented from enabling delegation controls on the computing device 120. In other words, when the member 118 is particularly unlikely to delegate a task, the proxy computing device 724 may prevent the delegation control from being enabled. By doing so, the proxy computing device 724 may avoid delegations being presented to the member 118 before the member 118 is ready or comfortable considering delegating the task.

[0240]

[0256] 14A , the proxy computing device 724 may generally be configured to enable the delegation enable control 1224 only if the likelihood of delegation by the member 118 exceeds some minimum likelihood threshold. For example, in the example of FIG. 14 , such threshold may be 40%, such that if the likelihood of delegation is below 40%, the proxy computing device 724 may disable the delegation enable control 1224, but if the likelihood of delegation is 40% or greater, the proxy computing device 724 may enable the delegation enable control 1224. Notably, in at least some implementations, the minimum likelihood threshold for enabling the delegation enable control 1224 may be below 50% or a similar level. In other words, in at least some implementations, when the delegation likelihood model 750 and the task facilitation service 102 determine that the member 118 is unlikely to delegate the task, the proxy user 722 may further be presented with the delegation enable control 1224. By doing so, the proxy user 722 can be informed of edge cases where additional coaching, guidance, or explanation may motivate the member 118 to delegate the task.

[0241]

[0257] In other implementations, the delegation enablement control 1224 may still be presented by the proxy computing device 724 to the proxy user 722 when the member 118 is unlikely to delegate a task. In such cases, the proxy computing device 724 may be updated with text, graphics, etc. indicating possible concerns about the member 118 delegating the task, thereby signaling to the proxy user 722 that additional coaching or explanation of the member 118 regarding the delegation process to the member 118 may be required before enabling the corresponding delegation control at the computing device 120. In some implementations, the proxy computing device 724 may also prompt or request confirmation from the proxy user 722 (e.g., using a pop-up or similar confirmation window) before sending an instruction to enable the delegation control.

[0242]

[0258] In particular, the determination by the delegation likelihood model 750 and the task facilitation service 102 regarding the likelihood that the member 118 will delegate a given task is generally dynamic. In other words, the delegation likelihood model 750 may initially predict that the member 118 is unlikely to delegate the task, but the delegation likelihood model 750 may later predict differently. FIG. 14B , for example, illustrates task 1232 and task details 1212 and the subsequent general improvement in the likelihood that the member 118 will delegate task 1232. Such improvement may be the result of successful delegation and completion of other tasks, coaching of the member 118 by the proxy user 722, obtaining additional information about the member 118's characteristics and preferences, and other additional information and / or interactions between the member 118 and the task facilitation service 102. In the particular example of FIG. 14B , such additional information and interaction history may cause the delegation likelihood model 750 to revise the original 30% likelihood of delegation by the member 118 to 66%. Thus, when the proxy user 722 accesses the task details 1212 for the task 1232, the delegation information section 1216 may reflect the improved delegation possibility. For example, the possibility value 1220 in FIG. 14B shows 66%, the graphic 1222 is shown as a fuller circle, and the delegation enable control 1224 is presented and available for activation by the proxy user 722.

[0243]

[0259] As described above, the delegation information section 1216 may include elements for conveying to the proxy user 722 the likelihood that the member 118 will delegate a given task. In the particular implementation shown in FIGS. 12-14B , the task details 1212 include the delegation information section 1216 that further includes a likelihood value 1220 in the form of a percentage and a graphic 1222 in the form of a ring or circle, each of which varies based on the underlying likelihood that the member 118 will delegate the selected task as determined, for example, by the delegation likelihood model 750 and the task facilitation service 102.

[0244]

[0260] In some implementations, the surrogate user 722 may select or drill down into the delegation information section 1216 to obtain further details and information regarding the likelihood prediction in the delegation information section 1216. For example, the surrogate user 722 may select the delegation information section 1216 to obtain additional information regarding the underlying values / recommendations of the factors presented by the delegation information section 1216. For example, drilling down into the delegation information section 1216 may indicate to the surrogate user 722 that the low likelihood of delegation is based primarily on the cost associated with completing the task, unfamiliarity with the delegation process, or similar factors. The surrogate user 722 may then use this additional information to further inform whether to activate the delegation enablement control 1224 and / or whether to remind or coach the member 118 regarding the delegation of the corresponding task.

[0245]

[0261] 12-14B , the delegation enablement control 1224 may include or be accompanied by a corresponding control for refusing to enable the delegation control on the computing device 120. When activated, such a control may prevent the enablement of the delegation control for the task (e.g., permanently, temporarily, pending a possible change in delegation, etc.). In some implementations, enabling the delegation control may also be denied due to inactivity on the part of the proxy user 722. For example, the delegation enablement control 1224 may be subject to a timeout or similar feature that disables the delegation enablement control 1224 after a certain amount of time or after a reminder / inquiry regarding enabling the delegation control is given to the proxy user 722.

[0246]

[0262] In addition to enabling delegation control at the computing device 120, activating the delegation enable control 1224 may send an indication that the proxy user 722 has enabled delegation control at the computing device 120. In particular, such an indication may be used to update the delegation possibility model 750. For example, if the proxy user 722 enables delegation control despite predicting that the member 118 will not delegate the task, the proxy computing device 724 may send an indication that causes the delegation possibility model 750 to be updated to prefer to recommend enabling delegation control at a later time for a similar task or in a similar situation. Similarly, if the proxy user 722 does not enable delegation control despite the delegation possibility model 750 predicting that the member 118 will delegate the task, the indication may modify the delegation possibility model 750 to prefer not to recommend enabling delegation control at a later time for a similar task or in a similar situation.

[0247]

[0263] FIG. 15 is a table 1500 providing additional examples of visual elements that may be used to communicate the likelihood that a member 118 will delegate a task. Row 1502 of table 1500, for example, illustrates the use of percentages to communicate the likelihood of delegation. In such an implementation, 0% may correspond to a substantial certainty that the member 118 will not delegate the task, while 100% may correspond to a substantial certainty that the member 118 will delegate the task. Thus, values ​​between 0% and 100% correspond to relative degrees of certainty regarding the likelihood that the member 118 will or will not delegate the task. More specifically, values ​​below 50% may correspond to a general prediction that the member 118 is unlikely to delegate the task, with lower percentages corresponding to a higher certainty of not delegating. Similarly, values ​​above 50% may correspond to a general prediction that the member 118 is likely to delegate the task, with higher percentages corresponding to a higher certainty of delegation. The percentages in row 1502 may be replaced with any numerical values ​​and ranges. For example, instead of a range of 0 to 100%, implementations of the present disclosure may rely on a scale of 0 to 1, 0 to 10, 1 to 5, or any other suitable range. The numerical values ​​representing delegation potential may also be rounded or otherwise modified based on the scale used.

[0248]

[0264] Row 1504 is a second example of a visual element that may be used to communicate the likelihood that member 118 will delegate a particular task. More particularly, row 1504 illustrates the use of a grade metaphor. Consistent with traditional American grading, which ranges from F to A and may include modifiers (e.g., a "+" indicating a grade slightly above a given letter grade or a "-" indicating a grade slightly below a given letter grade), a grade of "F" may generally correspond to a high certainty that member 118 will not delegate the task, a grade of "A" may generally correspond to a high certainty that member 118 will delegate the task, and intermediate grades correspond to various degrees of certainty regarding delegation and non-delegation.

[0249]

[0265] Rows 1506-1514 provide additional non-limiting examples of elements that may be used in a user interface to convey the relative likelihood that a member 118 will delegate a task. Row 1506 illustrates the use of different colors, row 1508 illustrates a changing graphic (e.g., a partially completed circle or wheel), row 1510 illustrates the use of text, row 1512 illustrates the use of a status bar, and row 1514 illustrates the use of conventional symbols or graphics to convey the relative certainty that a member 118 may delegate or refuse to delegate a given task. As noted above, the above examples are non-limiting, and any suitable representation of the likelihood that a member 118 will delegate a given task, and optionally, the certainty of such prediction, may be used. Additionally, while visual elements are provided in FIG. 15 , non-visual elements (e.g., audio) may be used in some embodiments to convey likelihood information.

[0250]

[0266] 16 is a flowchart of an example method 1600 for enabling delegated control according to the present disclosure. While reference is made to previous figures and their elements in the following description, implementation of method 1600 is not necessarily limited to the specific embodiments described herein.

[0251]

[0267] As previously described herein, the proxy computing device 724 associated with the proxy user 722 may generally present a user interface to the proxy user 722. In particular, the user interface may enable the proxy user 722 to access task information and details for tasks associated with the member 118. The user interface may further enable the proxy user 722 to selectively enable delegation controls for the member 118's tasks on the computing device 120 associated with the member 118. When activated by the member 118, the enabled delegation controls send a delegation indication that the corresponding task is to be delegated, which, when received by the task facilitation service 102, initiates the task delegation process for the task at the task facilitation service 102.

[0252]

[0268] With the above in mind, method 1600 will now be described in further detail. At operation 1602, a proxy computing device 724 receives a delegability indication for a task. For example, in some implementations, the proxy computing device 724 may be included in or in communication with the task facilitation service 102. The task facilitation service 102 may then include a delegability model 750 that is trained based on, and / or receives input from, data collected by the task facilitation service 102 to predict the likelihood that a member 118 associated with the task will delegate the task for completion by the task facilitation service 102. Data relied upon by the delegability model 750 may include, but is not limited to, data about the member 118 (including, but not limited to, past delegation activity), data about the task, data about other members of the task facilitation service 102, data about other tasks, etc. Generally, the delegation possibility indication corresponds to the likelihood that the member 118 will delegate the task, and therefore whether the proxy user 722 must enable delegation control on the computing device 120 associated with the member 118 for the task.

[0253]

[0269] At act 1604, in response to receiving the delegation availability indication at act 1602, the proxy computing device 724 presents delegation availability information to the proxy user 722 based on the delegation availability indication. As illustrated and described above in the context of Figures 12-15, for example, the delegation availability information may include textual, numerical, graphical, or similar elements to convey the likelihood that the member 118 will delegate the task. However, the delegation availability information may more generally be presented to the proxy user 722 in any suitable manner, such that the proxy user 722 may use the delegation information to inform the proxy user's 722 decision to enable delegated control at the computing device 120.

[0254]

[0270] In at least some embodiments, presenting the delegation availability information to the proxy user 722 may further include presenting or enabling a control that, when activated by the proxy user 722, enables delegation control on the computing device 120. For example, as shown in FIG. 12 , presenting the delegation information to the proxy user 722 may include displaying or enabling a delegation enablement control 1224 that, when activated by the proxy user 722, enables delegation control on the computing device 120.

[0255]

[0271] In operation 1606, the proxy computing device 724 receives an indication that the delegate control is to be enabled at the computing device 120. For example, in an implementation when the delegate enablement control 1224 is in the form of a button or similar interactive control, the proxy computing device 724 receives an indication that the proxy user 722 has activated the control.

[0256]

[0272] In operation 1608, in response to receiving an indication that the delegated control is to be enabled, the proxy computing device 724 transmits a delegated control enablement instruction. When received by the computing device 120, the delegated control enablement instruction causes the computing device 120 to enable the delegated control corresponding to the task for optional activation by the member 118.

[0257]

[0273] Finally, at operation 1610, the proxy computing device 724 may optionally transmit an indication that the proxy user 722 has enabled delegation control on the computing device 120. In particular, such an indication may be used to provide feedback for refining and updating the delegation possibility model 750. For example, in some instances, the proxy user 722 may enable delegation control despite the delegation possibility model 750 predicting a low likelihood of delegation by the member 118. In such cases, based on the personal relationship between the proxy user 722 and the member 118, the proxy user 722 may have a more sophisticated understanding of the member 118 than is represented by the data available to the delegation possibility model 750. Thus, the delegation possibility model 750 may later be modified or updated to prefer to recommend enabling delegation control for similar tasks or in similar situations. Similarly, the proxy user 722 may not enable delegation control despite the delegation possibility model 750 predicting that the member 118 will delegate a task. In such cases, the delegability model 750 may later be modified or updated to prefer not to recommend enabling delegation control for similar tasks or in similar situations.

[0258]

[0274] 17 illustrates a computing system architecture 1700 including various components in electronic communication with each other, according to various embodiments. The exemplary computing system architecture 1700 illustrated in FIG. 17 includes a computing device 1702 having various components in electronic communication with each other using connections 1706, such as a bus, according to some implementations. The exemplary computing system architecture 1700 includes a processor 1704 in electronic communication with the various system components using connections 1706 and including system memory 1714. In some embodiments, the system memory 1714 includes read-only memory (ROM), random access memory (RAM), and other such memory technologies, including, but not limited to, those described herein. In some embodiments, the exemplary computing system architecture 1700 includes a cache 1708 of high-speed memory directly connected to the processor 1704, in close proximity to the processor 1704, or integrated as part of the processor 1704. The system architecture 1700 can copy data from the memory 1714 and / or the storage device 1710 to the cache 1708 for quick access by the processor 1704. In this manner, the cache 1708 can provide performance improvements that reduce or eliminate processor delays in the processor 1704 due to waiting for data. Using modules, methods, and services such as those described herein, the processor 1704 can be configured to perform various acts. In some embodiments, the cache 1708 can include multiple types of cache, including, for example, a level 1 (L1) cache and a level 2 (L2) cache. The memory 1714 is sometimes referred to herein as system memory or computer system memory. The memory 1714 can, at various times, include elements of an operating system, one or more applications, data associated with the operating system or one or more applications, or other such data associated with the computing device 1702.

[0259]

[0275] Other system memory 1714 may also be available for use. Memory 1714 may include multiple different types of memory with different performance characteristics. Processor 1704 may include any general-purpose processor and one or more hardware or software services, such as services 1712 stored in storage device 1710, configured to control processor 1704 as well as special-purpose processors, where software instructions are incorporated into the actual processor design. Processor 1704 may be a complete self-contained computing system including multiple cores or processors, connectors (e.g., buses), memory, memory controllers, caches, etc. In some embodiments, such self-contained computing systems with multiple cores are symmetric. In some embodiments, such self-contained computing systems with multiple cores are asymmetric. In some embodiments, processor 1704 may be a microprocessor, a microcontroller, a digital signal processor (“DSP”), or a combination of these and / or other types of processors. In some embodiments, the processor 1704 may include multiple elements, such as a core, one or more registers, and one or more processing units, such as an arithmetic logic unit (ALU), a floating point unit (FPU), a graphics processing unit (GPU), a physical processing unit (PPU), a digital systems processing (DSP) unit, or a combination of these and / or other such processing units.

[0260]

[0276] To enable user interaction with computing system architecture 1700, input device(s) 1716 may represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, a pen, and other such input devices. Output device(s) 1718 may also be one or more of several output mechanisms known to those skilled in the art, including, but not limited to, a monitor, speakers, a printer, a tactile device, and other such output devices. In some instances, a multimodal system may enable a user to provide multiple types of input for communication with computing system architecture 1700. In some embodiments, input device(s) 1716 and / or output device(s) 1718 may be coupled to computing device 1702 using a remote connection device, such as, for example, a communications interface, such as network interface 1720 described herein. In such embodiments, the communications interface may govern and manage input and output received from attached input device(s) 1716 and / or output device(s) 1718. As contemplate...

Claims

1. sending a control enablement instruction for a user's task associated with a user's computing device, wherein the user's computing device enables activation of a task delegation control corresponding to the task in response to receiving the control enablement instruction, and wherein a task facilitation service updates a task status of the task to reflect that the task has been at least partially delegated for completion in response to activation of the task delegation control at the user's computing device; 1. A computer-implemented method comprising:

2. The computer-implemented method of claim 1 , wherein sending the control enable instruction is in response to receiving a command to enable the task delegation control for the task.

3. 2. The computer-implemented method of claim 1, further comprising receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities.

4. receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; displaying a visual indicator in a user interface corresponding to said possibility; and The computer-implemented method of claim 1 further comprising:

5. receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein sending the control enable instruction is in response to the activation of the control. The computer-implemented method of claim 1 further comprising:

6. receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control on the computing device, wherein when the control is activated, the computing device initiates transmission of the instructions, and wherein enabling the control is in response to the likelihood exceeding a minimum likelihood threshold. The computer-implemented method of claim 1 further comprising:

7. receiving a likelihood indication corresponding to a likelihood that the user will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control on the computing device, wherein when the control is activated, the computing device initiates the transmission of the instructions, wherein the enabling of the control is responsive to the likelihood exceeding a minimum likelihood threshold, and wherein the minimum likelihood threshold is below a level below which the user is likely not to delegate the task. The computer-implemented method of claim 1 further comprising:

8. 2. The computer-implemented method of claim 1, further comprising: sending a delegation control enablement indication corresponding to whether the task delegation control is enabled; wherein when the delegation control enablement indication is received by a task facilitation service, the task facilitation service updates a delegability model based on the delegation control enablement indication.

9. 1. A computing device, comprising: one or more processors; A memory that stores instructions the instructions, when executed by the one or more processors, cause the computing device to: sending instructions, which when received by a user's computing device cause the user's computing device to activate a task delegation control associated with a task, wherein when the task delegation control is activated, the user's computing device sends a delegation instruction for the task, and wherein, when received by a task facilitation service, the delegation instruction causes the task facilitation service to delegate the task. A computing device that causes

10. 10. The computing device of claim 9, wherein the instructions further cause the computing device to receive a control enabling instruction, wherein transmitting the instruction is in response to receiving the control enabling instruction.

11. 10. The computing device of claim 9, wherein the instructions further cause the computing device to receive a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities.

12. The instructions may cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; displaying a visual indicator of said possibility in a user interface; and The computing device of claim 9 , further comprising:

13. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to transmit the instruction; The computing device of claim 9 , further comprising:

14. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to transmit the instruction, and wherein enabling the control is responsive to the likelihood exceeding a minimum likelihood threshold. The computing device of claim 9 , further comprising:

15. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to send the instruction, wherein enabling the control is responsive to the likelihood exceeding a minimum likelihood threshold, and wherein the minimum likelihood threshold is below a level below which the user is likely not to delegate the task. The computing device of claim 9 , further comprising:

16. 10. The computing device of claim 9, wherein the instructions further cause the computing device to send a delegated control enablement instruction, wherein when the delegated control enablement instruction is received by a task facilitation service, the task facilitation service updates a delegability model based on the delegated control enablement instruction.

17. A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors of a computing device, cause the computing device to: sending instructions, which when received by a user's computing device cause the user's computing device to activate a task delegation control associated with a task, wherein when the task delegation control is activated, the user's computing device sends a delegation instruction for the task, and wherein, when received by a task facilitation service, the delegation instruction causes the task facilitation service to delegate the task. A non-transitory computer-readable storage medium that causes

18. 20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions further cause the computing device to receive a control enabling instruction, wherein transmitting the instruction is in response to receiving the control enabling instruction.

19. 20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions further cause the computing device to receive a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation feasibility model, and wherein the delegation feasibility model is updated using the user's delegation activities.

20. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; displaying a visual indicator of said possibility in a user interface; and 20. The non-transitory computer-readable storage medium of claim 17, further comprising:

21. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to transmit the instruction; 20. The non-transitory computer-readable storage medium of claim 17, further comprising:

22. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to transmit the instruction, and wherein enabling the control is responsive to the likelihood exceeding a minimum likelihood threshold.

20. The non-transitory computer-readable storage medium of claim 17, further comprising:

23. The instructions cause the computing device to: receiving a likelihood indication corresponding to a likelihood that a user of the user's computing device will delegate the task, wherein the likelihood indication is generated in response to determining the likelihood using a delegation likelihood model, and wherein the delegation likelihood model is updated using the user's delegation activities; enabling a control, wherein when activated, the control causes the computing device to send the instruction, wherein enabling the control is responsive to the likelihood exceeding a minimum likelihood threshold, and wherein the minimum likelihood threshold is below a level below which the user is likely not to delegate the task.

20. The non-transitory computer-readable storage medium of claim 17, further comprising:

24. 20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions further cause the computing device to send a delegated control enablement instruction, wherein when the delegated control enablement instruction is received by a task facilitation service, the task facilitation service updates a delegability model based on the delegated control enablement instruction.

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