Systems and methods for integrated temporary external resource allocation - Patents.com

Abstract

A website building system with temporary external resource allocation integration is provided.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 436,281, entitled "SYSTEM AND METHOD FOR INTEGRATED TEMPORAL EXTERNAL RESOURCE ALLOCATION," filed December 30, 2022, the entire contents of which are incorporated herein by reference.

[0002] Exemplary embodiments of the present disclosure relate generally to visual editing techniques, and more particularly to systems, apparatus, methods, and computer program products for integrating temporary external resource vector allocation operations, engagement transmission generation, and automated execution of pre-population operations within a website building system. [Background technology]

[0003] Various applications may provide online temporary external resource allocation capabilities. However, integrating optimized resource allocation operations into websites during the website construction process is technically complex and problematic. Through applied effort, ingenuity, and innovation, many of these identified deficiencies and problems have been resolved by developing structured solutions in accordance with embodiments of the present disclosure, many examples of which are described in detail herein. Summary of the Invention

[0004]

[0009] Embodiments herein provide a website construction system configured to provide Temporary External Resource (TER) integration within a website generated using the website construction system. In embodiments, the website construction system includes one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically generating an electronic Temporary External Resource (TER) vector allocation recommendation interface. In embodiments, the operations include obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier being selected from the plurality of website identifiers, and the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. In an embodiment, the operations further include obtaining, from the end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of the plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on one or more website construction repositories. In an embodiment, the operations further include obtaining a multidimensional Temporary External Resources (TER) matrix maintained by the website construction system and associated with the website identifiers, the multidimensional TER matrix including a plurality of TER vectors.In an embodiment, the operations further include selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to the historical editing interactions associated with the editing user identifier and the end user data. In an embodiment, the operations select a TER vector according to the TER vector. generating an electronic TER recommendation interface. In an embodiment, the operations further include transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

[0005] Embodiments herein further provide a website construction system configured to automatically generate recommended engagement transmissions. In embodiments, the website construction system comprises one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically generating recommended engagement transmissions. In embodiments, the operations include obtaining a website identifier associated with a website assembled at least in part based on the one or more website construction repositories, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. In embodiments, the operations further include obtaining end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with the plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers that access the one or more websites assembled at least in part based on the one or more website construction repositories. In embodiments, the operations further include selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to the engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and the website edit history interactions associated with the website identifier. In embodiments, the operations further include generating the engagement transmission based at least in part on the first end user identifier and the website identifier.In an embodiment, the operations further include sending the engagement transmission to a client computing device associated with the first end user identifier.

[0006] Embodiments herein further provide a website construction system configured to automatically perform an interface population operation. In embodiments, the website construction system includes one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically performing an interface population operation. In embodiments, the operations include obtaining a website identifier associated with a website assembled at least in part based on the one or more website construction repositories, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. In embodiments, the operations further include obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix. In embodiments, the operations include obtaining end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with the plurality of end-user identifiers, the end-user data being stored on a plurality of client computers associated with the plurality of end-user identifiers that access the one or more websites assembled at least in part based on the one or more website construction repositories. In an embodiment, the operations further include automatically performing an interface populating operation according to a website record of the website vector based at least in part on applying one or more trained machine learning or rule-based models to the end-user data, the website edit history interactions associated with the website identifier, and the website vector.

[0007] Embodiments herein further provide a website construction system configured to provide Temporary External Resource (TER) integration. In embodiments, the website construction system includes one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically generating an electronic Temporary External Resource (TER) vector allocation recommendation response. In embodiments, the operations include receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata. In embodiments, the operations further include extracting the allocation request metadata. In embodiments, the operations further include obtaining, based at least in part on the allocation request metadata, one or more website identifiers associated with a website to be assembled at least in part based on the one or more website construction repositories, the one or more website identifiers being selected from the plurality of website identifiers, and each of the one or more website identifiers being associated with an editing user identifier among the plurality of editing user identifiers. In an embodiment, the operations further include obtaining, from the end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of the plurality of end-user identifiers, wherein the end-user data associated with the first end-user identifier includes one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on one or more website construction repositories.In an embodiment, the operations further include selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with the editing user identifier and the end user data. In an embodiment, the operations further include generating a TER vector assignment recommendation according to the TER vector. In an embodiment, the operations further include transmitting the TER vector assignment recommendation to a requesting client computing device.

[0008] The above summary is provided solely for the purpose of summarizing some exemplary embodiments to provide a basic understanding of some aspects of the present disclosure. Accordingly, it will be understood that the above-described embodiments are merely examples and should not be construed in any way to narrow the scope or spirit of the present disclosure. It will be appreciated that the scope of the present disclosure encompasses many potential embodiments in addition to those summarized herein, some of which are further described below. Other features, aspects, and advantages of the present subject matter will become apparent from the description, drawings, and claims.

[0009] Accordingly, having described certain exemplary embodiments of the present disclosure in general terms above, non-limiting and non-exhaustive embodiments of the subject disclosure will now be described with reference to the accompanying drawings, which are not necessarily drawn to scale. Components illustrated in the accompanying drawings may or may not be present in the particular embodiments described herein. Some embodiments may include fewer (or more) components than shown in the drawings. Some embodiments may include components arranged in different ways. [Brief explanation of the drawings]

[0010] [Figure 1A] 1 illustrates example operations associated with automatically generating temporary external resource (TER) vector allocation recommendations, according to certain example embodiments described herein. [Figure 1B] 1 illustrates a signal diagram of example operations associated with automatically generating temporary external resource (TER) vector allocation recommendations, according to certain example embodiments described herein. [Figure 2A] 1 illustrates a schematic block diagram of an example architecture for automatically generating temporary external resource (TER) vector allocation recommendations, according to certain example embodiments described herein. [Figure 2B] 1 illustrates a schematic block diagram of an example architecture for automatically generating temporary external resource (TER) vector allocation recommendations, according to certain example embodiments described herein. [Figure 3A] 1 illustrates an example renderable interface configured in accordance with certain example embodiments described herein. [Figure 3B] 1 illustrates an example renderable interface configured in accordance with certain example embodiments described herein. [Figure 4A] 10 illustrates example operations associated with automatically generating an engagement transmission, according to certain example embodiments described herein. [Figure 4B] 10 illustrates a signal diagram of example operations associated with automatically generating an engagement transmission according to certain example embodiments described herein. [Figure 5A] 1 illustrates a schematic block diagram of an example architecture for automatically generating engagement transmissions, according to certain example embodiments described herein. [Figure 5B] 1 illustrates a schematic block diagram of an example architecture for automatically generating engagement transmissions, according to certain example embodiments described herein. [Figure 6A] 1 illustrates example operations associated with automatically generating interface populating operations, according to certain example embodiments described herein. [Figure 6B] 10 illustrates a signal diagram of example operations associated with automatically generating an interface populate operation, according to certain example embodiments described herein. [Figure 7A] 1 illustrates a schematic block diagram of an example architecture for automatically generating interface populating operations, according to certain example embodiments described herein. [Figure 7B] 1 illustrates a schematic block diagram of an example architecture for automatically generating interface populating operations, according to certain example embodiments described herein. [Figure 8] 1 illustrates a block diagram of an exemplary system in which an embodiment of the present disclosure may be specially configured to operate. [Figure 9] 1 illustrates a schematic block diagram of example components of an example website building system, according to certain example embodiments described herein. [Figure 10] 1 illustrates a schematic block diagram of an example repository of an example content management system of a website building system, according to certain example embodiments described herein. [Figure 11] 1 illustrates a schematic block diagram of example modules for use in an example server device, according to some example embodiments described herein. [Figure 12] 1 illustrates a schematic block diagram of example modules for use in an example client device, according to some example embodiments described herein. DETAILED DESCRIPTION OF THE INVENTION

[0011] One or more exemplary embodiments will now be described more fully below with reference to the accompanying drawings, wherein like reference numerals are used to refer to like elements throughout. In the following specification, for purposes of explanation, numerous specific details are outlined in order to provide a thorough understanding of various embodiments. However, it will be apparent that various embodiments may be practiced without these specific details (and without application to any particular network environment or standard). It should be understood that some, but not all, embodiments are shown and described herein. Indeed, embodiments may be embodied in many different forms, and thus, the disclosure should not be construed as limited to the embodiments outlined herein. Rather, these embodiments are provided so that the disclosure will satisfy applicable legal requirements. As used herein, the specification may refer to a server or a client device as an exemplary "apparatus." However, elements of the apparatus described herein may be equally applicable to the claimed systems, methods, and computer program products. Thus, use of any such terminology should not be construed to limit the spirit and scope of the embodiments of the present disclosure.

[0012] Exemplary embodiments of the present disclosure may implement Temporary External Resource (TER) vector allocation consolidation in several ways. Accordingly, various processes according to the present disclosure are described herein. Each method or process described herein may include any number of operational blocks that define a process and / or portions thereof. In some embodiments, the various processes and / or subprocesses described herein may be combined in any manner, such that it should be understood that an embodiment is configured to perform aspects of the various processes in combination, in parallel, and / or sequentially. In some embodiments, at least one additional and / or at least one alternative operation is performed in one or more of the described processes and / or at least one operation is deleted from one or more of the described processes.

[0013] Additionally, optional operations may be depicted in the processes using dotted lines (or "dashed lines." In this regard, it should be understood that the processes described herein are merely examples, that the scope of the disclosure is not limited to the exact operations depicted and described, and that the operations depicted and described should not limit the scope and spirit of the embodiments described herein and covered by the appended claims.

[0014] overview Embodiments herein relate to leveraging learnings generated based on multiple sets of unique data to inform various actions, communications, and recommendations. A website building system configured according to embodiments herein continuously receives data from different sources, which is innovatively leveraged herein to inform real-time actions, communications, and recommendations associated with a particular website. For example, a first data source available to the website building system includes historical editing interactions (e.g., website component selections) and associated metadata (e.g., timestamps associated with selections, delays associated with selections, mouse clicks, cursor positioning, etc.) for any given website identifier associated with a website assembled using the website building system by a client computing device associated with the editing user identifier. This first data source, if leveraged appropriately and efficiently (e.g., while the data remains fresh and relevant), can provide valuable insights regarding optimal electronic behavior associated with a given editing user identifier. As a further example, a second data source available to the website building system includes historical editing interactions (e.g., website component selections) and associated metadata (e.g., timestamps associated with selections, delays associated with selections, mouse clicks, cursor positioning, etc.) for any given website identifier associated with a website assembled using the website building system by a client computing device associated with the editing user identifier. The second data source includes end-user interaction data representing electronic interactions (e.g., mouse clicks, cursor position or hovering, website page traversal, website structure, location of booking widgets within the website, widget design layout, etc.) that are conducted. This second data source, if properly and efficiently leveraged (e.g., while the data remains fresh and relevant), can provide valuable insights into maximizing allocation of the Temporal External Resource (TER) vector of a multidimensional matrix or maximizing engagement associated with a website identifier when a particular end-user identifier visits an associated website.

[0015] Efficiently processing and leveraging at least the aforementioned data sources is a computationally challenging task, given the large amount of data and how frequently it is refreshed. Embodiments herein apply machine learning and / or rule-based models to the data to learn relationships between the data, which in turn informs embodiments herein about which data items are most useful when making a particular decision. Knowledge of the most useful items of data when making a particular decision allows embodiments herein to use computing resources more efficiently, as less useful items of data can be ignored.

[0016] Overview and Embodiments of Exemplary Temporary External Resource Allocation Operations Embodiments herein relate to Temporary External Resource (TER) allocation operations associated with a multidimensional TER matrix associated with a website identifier. In addition to the aforementioned improvements, maximizing TER vector allocations associated with a multidimensional TER matrix for a given website identifier reduces wasted computing resources associated with failed (e.g., negative engagement) TER vector allocations. Computing and network resources are wasted when a TER vector allocation completes but ultimately fails, or when additional electronic communication is required due to confusion surrounding a TER vector assignment. Embodiments herein use computing and network resources more efficiently by ensuring that TER vector allocations are performed at the appropriate time and with the appropriate details, not based on approximations.

[0017] 1A and 1B, exemplary operations for automatically generating electronic temporary external resource (TER) vector allocation recommendations are shown. The operations illustrated in FIG. 1A and 1B may be performed, for example, by one or more client devices 808A-N, which may include means such as memory 1202, processor 1204, input / output module 1206, communication module 1208, TER integration module 1210, and / or the like, collectively configured for TER integration. The operations may further be performed by a TER integration server 812, which may include means such as memory 1102, processor 1104, input / output module 1106, communication module 1108, TER integration module 1110, and / or the like, collectively configured for TER integration.

[0018] 1A and 1B, in step / act 102, process 100 includes obtaining a website identifier associated with a website to be assembled based at least in part on one or more website construction repositories. In an embodiment, the website identifier is selected from a plurality of website identifiers, and the website identifier is associated with an editing user identifier of the plurality of editing user identifiers. In an embodiment, the one or more website construction repositories include one or more website building tools and one or more website building tools associated with the plurality of editing user identifiers. Remember your website edit history interactions and.

[0019] In some embodiments, the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0020] 1A and 1B, in step / operation 104, process 100 includes obtaining end-user data from an end-user data corpus (e.g., repository 814), the end-user data including electronic interaction data associated with a first end-user identifier of the plurality of end-user identifiers. In some embodiments, the end-user data associated with the first end user includes one or more electronic interactions performed by a first client computing device associated with the first end-user identifier that accesses one or more websites assembled based at least in part on one or more website construction repositories.

[0021] In some embodiments shown in Figures 1A and 1B, in step / operation 106, process 100 includes obtaining (e.g., from repository 814) a multidimensional TER matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors.

[0022] 1A and 1B, in step / operation 108, process 100 includes selecting a TER vector from the multidimensional TER matrix based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with the editing user identifiers and the end user data. In some embodiments, the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled at least in part on the one or more website construction repositories.

[0023] In some embodiments, the one or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with the editing user identifier and the end user data, wherein the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the end user identifier and the website identifier and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0024] In some embodiments, the one or more rule-based models are configured to select a TER vector based at least in part on historical editing interactions associated with the editing user identifier and on end-user data, where the TER vector is identified according to the learned rules.

[0025] In some embodiments, the TER vector is calculated by maximizing the reservation vector allocation of the multidimensional TER matrix, the TER vector and the TER vector for the end user identifier. The selection is further based at least in part on the expected currency value associated with the allocation or whether the TER vector is associated with an ongoing event.

[0026] In some embodiments shown in FIGS. 1A and 1B, in step / act 110, process 100 includes generating an electronic TER vector assignment recommendation interface according to the TER vector.

[0027] In some embodiments shown in Figures 1A and 1B, in step / act 112, process 100 includes sending an electronic TER vector assignment recommendation interface to a client computing device associated with the first end user identifier or editing user identifier.

[0028] In some embodiments, the TER vector includes a plurality of TER vector records, and a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, an event time, or an event location.

[0029] In some embodiments, the one or more electronic interactions include one or more TER vector assignments associated with an end user identifier and a website identifier, and the one or more TER vector assignments are associated with an engagement indicator, and the engagement indicator is one of positive, negative, or partial.

[0030] In some embodiments, the user identifier is associated with a plurality of TER vector assignments that are associated with the website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

[0031] In some embodiments, the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier, the remote computing device being one of a point-of-sale terminal or a sensor device.

[0032] In some embodiments, a TER vector assignment operation is automatically performed according to the TER vector. In such an example, the availability records associated with the TER vector are converted to reflect the TER vector assignment, and the multidimensional TER matrix is ​​synchronized with a second multidimensional TER matrix, which can be configured to render via a website.

[0033] It will be understood that in addition to historical editing interactions, one or more machine learning models or one or more rule-based models may be applied to the look and feel or dimensions of the website, including data about the industry associated with the website, the geographic location associated with the website, applications or microservices used by the website, the services offered by the website, etc.

[0034] It will be further appreciated that multiple TER vectors may be ranked using one or more machine learning models or one or more rule-based models, such that multiple TER vectors may be presented in ranked order for selection.

[0035] 2A and 2B illustrate a schematic block diagram of an example architecture 200 for automatically generating electronic appointment recommendations, according to certain example embodiments described herein. In FIG. 2A, the exemplary architecture 200 includes a data layer 210 that maintains and aggregates data from multiple sources. In the example shown in FIG. 2A, the data sources include multidimensional TER matrix (MDTM) data 202A (provided to the data layer 210 via an MDTM feed 202B) (e.g., calendar data), TER vector allocation record (TVAR) data 204A (provided to the data layer 210 via a TVAR feed 204B) (e.g., reservation data), catalog record (CR) data 206A (provided to the data layer 210 via a CR feed 206B) (e.g., catalog data), and website property and component (WP / C) data 208A (provided to the data layer 210 via a WP / C feed). For example, the MDM data may include data associated with upcoming sessions available on each website (e.g., TER vectors), the WP / C data may include data regarding geographic locations and segments associated with each website, and the TVAR data may include data associated with participants and prices for each session (e.g., TER vector allocations). The data layer 210 sends and receives data to and from the repository 212 .

[0036] 2A , the example architecture 200 further includes a TER vector selection engine 214, which may include an ML / rule-based / pattern-based configuration or model 214A and a computing engine 214B. The TER vector selection engine 214 receives data from the repository 212 and, optionally, receives settings from an ML / rule-based / pattern-based setup user interface 216. The computing engine 214B, in conjunction with the configuration or model 214A, generates TER vector selection, assignment, or allocation recommendations and provides them to a TER assignment recommendation queue 218. In some examples, the setup user interface 216 may be configured to receive configuration settings for the TER vector selection engine 214.

[0037] Continuing with FIG. 2B, the example architecture 200 further includes a TER allocation recommendation queue 218 that includes a queue in which TER vector allocation recommendations are held and may be released to communication resources 220 according to relevance and timing.

[0038] 2B, the communications resource 220 provides a TER vector allocation operation 220A (e.g., for automated execution of a TER vector allocation operation) or a TER vector allocation recommendation transmission 220B. For example, the transmission 220B may include a widget, an email, an SMS message, and may require approval by an editing user identifier associated with the website identifier before the allocation operation is executed. In some examples, the TER vector allocation recommendation transmission 220B may include a proposal (e.g., adding a new class session at the proposed time and increasing the service price) that the editing user identifier must approve before it is sent, or an automated transmission (e.g., sending an email to an end user to sign up for an empty slot).

[0039] In other embodiments, the website building system may be configured to provide Temporary External Resource (TER) integration to request entities that have assembled electronic products outside of the website building system (e.g., via an API). For example, the system may receive a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata. The system may extract the allocation request metadata and, based at least in part on the allocation request metadata, obtain one or more website identifiers associated with the website to be assembled based at least in part on one or more website building repositories. Further based on the allocation request metadata, the system may retrieve end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with a first end-user identifier of the plurality of end-user identifiers. The system may then apply one or more trained machine learning models or one or more rule-based models to the historical editing interactions associated with the editing user identifier and the end user data to select a TER vector. The system may then generate a TER vector assignment recommendation according to the TER vector and send the TER vector assignment recommendation to the requesting client computing device.

[0040] In some of these embodiments, the one or more website identifiers are obtained based on a determined similarity between data associated with the one or more website identifiers and one or more items of assignment request metadata (e.g., an similarity mapping). The first end user identifier may also be selected based on a determined similarity between data associated with the first end user identifier and one or more items of assignment request metadata.

[0041] 3A illustrates an example renderable interface 300 configured in accordance with certain example embodiments described herein. In FIG. 3A, the renderable interface 300 depicts an example multi-dimensional TER matrix associated with a website identifier (not shown). The multi-dimensional TER matrix includes a plurality of TER vectors 302A, 302B.

[0042] 3B illustrates an example renderable interface 310 configured in accordance with certain example embodiments described herein. In FIG. 3B, the renderable interface 310 depicts an example multi-dimensional TER matrix associated with a website identifier (not shown). The multi-dimensional TER matrix includes a plurality of TER vectors 312A, 312B.

[0043] Exemplary Engagement Transmission Generation Methods and Embodiments Embodiments herein further relate to predicting an engagement level associated with a given end user identifier for a given website identifier, and generating an engagement transmission tailored to the given end user identifier after receiving an engagement transmission by the given end user identifier to improve or increase engagement with the website associated with the website identifier.

[0044] 4A and 4B, exemplary operations are shown automatically generating recommended engagement transmissions. The operations illustrated in FIG. 4A and 4B may be performed, for example, by one or more client devices 808A-N, which may include means such as memory 1202, processor 1204, input / output module 1206, communication module 1208, TER integration module 1210, and / or the like, collectively configured for TER integration. The operations may further be performed by TER integration server 812, which may include means such as memory 1102, processor 1104, input / output module 1106, communication module 1108, TER integration module 1110, and / or the like, collectively configured for TER integration.

[0045] 4A and 4B, in step / act 402, process 400 includes obtaining a website identifier associated with a website to be assembled based at least in part on one or more website construction repositories. The website identifier is selected from a plurality of website identifiers, and the website identifier is associated with an editing user identifier among the plurality of editing user identifiers. In embodiments, the one or more website construction repositories include one or more website building tools and one or more website identifiers associated with the plurality of editing user identifiers. and storing edit history interactions. In embodiments, the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0046] 4A and 4B, in step / act 402, process 404 includes obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more of the electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on one or more website construction repositories.

[0047] In some embodiments shown in Figures 4A and 4B, in step / operation 404, process 400 includes selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to an engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and website edit history interactions associated with the website identifier.

[0048] In some embodiments, the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0049] In some embodiments, the end user data is further associated with a plurality of TER vector assignments, and all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator. In some embodiments, the engagement indicator is positive or negative. In some embodiments, selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

[0050] In some embodiments, the one or more trained machine learning models are configured to generate an engagement score for the first end user identifier based at least in part on historical edit interactions associated with the edit user identifier and the end user data, where the engagement score represents a programmatically generated likelihood that a client computing device associated with the first end user identifier will engage with the website after receiving the engagement transmission. In some embodiments, selecting the first end user identifier is based at least in part on an engagement score that exceeds an engagement score threshold.

[0051] In some embodiments, one or more rule-based models are configured to select a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to the learned rules.

[0052] 4A and 4B, in step / operation 406, process 400 includes generating an engagement transmission based on the first end user identifier and the website identifier. In some embodiments, the engagement transmission includes an electronic communication. In some embodiments, the electronic communication includes an electronic incentive, a change in event type, or a change in event time.

[0053] In some embodiments shown in FIGS. 4A and 4B, in step / act 408, process 400 includes sending an engagement transmission to a client computing device associated with the first end user identifier.

[0054] Optionally, in some embodiments, the TER vector assignment operation is performed automatically based at least in part on the engagement transmission.

[0055] 5A and 5B illustrate schematic block diagrams of an example architecture 500 for automatically generating recommended engagement transmissions, according to certain example embodiments described herein. In FIG. 5A, the example architecture 500 includes a data layer 510 that maintains and aggregates data from multiple sources. In the example shown in FIG. 5A, the data sources include multidimensional TER matrix (MDTM) data 502A (provided to the data layer 510 via an MDTM feed 502B) (e.g., calendar data), TER vector allocation record (TVAR) data 504A (provided to the data layer 510 via a TVAR feed 504B) (e.g., reservation data), and client property (CP) data 506A (provided to the data layer 510 via a CP feed 506B) (e.g., client information such as geographic location, city, language, services, etc.). The data layer 510 receives and transmits data from a repository 512.

[0056] 5A, the exemplary architecture 500 further includes an engagement transmission generation engine 514, which may include an ML / rule-based / pattern-based configuration or model 514A and a computing engine 514B. The engagement transmission generation engine 514 receives data from the repository 512 and, optionally, receives settings from an ML / rule-based / pattern-based setup user interface 516. The engagement transmission generation engine 514 may include one or more trained machine learning models, including, for example, a first model configured to determine an engagement score associated with an end-user identifier (e.g., predict a client's likelihood to churn, accepting client characteristics (last class attendance, number of classes attended, number of classes not attended, how the client initially joined) as inputs and outputting the likelihood to churn at the current moment). The engagement transmission generation engine 514 may further include a second model (e.g., a recommender system model trained based on previous client behavior and related to client stay and return). For example, clients who purchase a membership are more likely to be retained, and clients who are offered a new type of class of coupons are more likely to stay longer. The second model receives as input the characteristics of each client and company, matches the client with either similar clients or companies with similar companies, and attempts to provide optimization recommendations (e.g., "Suggest this client purchase a membership with a discount," "Contact this client").

[0057] A computing engine 514B in conjunction with the configuration or model 514A generates engagement transmissions and provides them to an engagement transmission queue 518.

[0058] Continuing with Figure 5B, the example architecture 500 further includes an engagement transmission queue 518 that includes a queue in which engagement transmissions may be held and released, according to relevance and timing, to communication resources 520. Also in Figure 5B, communication resources 520 provide engagement transmission 520A.

[0059] Exemplary Interface Population Operation Methods and Embodiments Embodiments herein further relate to providing interface population operations, whereby templates for inclusion in a website may be pre-populated according to machine learning-based recommendations during the website construction process within a website construction system. For example, certain interface elements (e.g., pricing, content attributes, etc.) may be pre-populated for an edit user identifier based at least in part on applying a machine learning model trained on the corpus of data described herein. Pre-populating such interface elements reduces wasted computing resources dedicated to populating interface elements in a failed or less meaningful manner, and the need to regenerate one or more interfaces for inclusion on the website. In some embodiments, one or more interfaces that may be pre-populated are associated with a TER vector assignment and a multidimensional matrix associated with a given website identifier. For the same reasons outlined above with respect to maximizing and optimizing a TER vector assignment associated with a multidimensional matrix for a given website identifier, embodiments herein enable more efficient use of computing and network resources by providing informed suggestions in a shorter timeframe while the data remains fresh and relevant.

[0060] 6A and 6B, exemplary operations are shown automatically generating a pre-populated website building tool. The operations illustrated in FIG. 6A and 6B may be performed, for example, by one or more client devices 808A-N, which may include means such as memory 1202, processor 1204, input / output module 1206, communications module 1208, TER integration module 1210, and / or the like, collectively configured for TER integration. The operations may further be performed by TER integration server 812, which may include means such as memory 1102, processor 1104, input / output module 1106, communications module 1108, TER integration module 1110, and / or the like, collectively configured for TER integration.

[0061] 6A and 6B , in step / operation 602, process 600 includes obtaining a website identifier associated with a website to be assembled based at least in part on one or more website construction repositories. The website identifier is selected from a plurality of website identifiers, and the website identifier is associated with an editing user identifier among the plurality of editing user identifiers. In embodiments, the one or more website construction repositories store one or more website building tools and one or more website editing history interactions associated with the plurality of editing user identifiers. In embodiments, the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0062] 6A and 6B, in step / act 604, process 600 includes obtaining end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with a plurality of end-user identifiers. The end-user data is stored in one or more website construction repositories, at least and one or more of electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers accessing one or more websites assembled based in part on the plurality of end user identifiers.

[0063] In some embodiments shown in Figures 6A and 6B, in step / operation 606, process 600 includes obtaining a website vector associated with the website identifier, where the website vector includes a plurality of website records and a resource matrix.

[0064] In some embodiments shown in FIGS. 6A and 6B, in step / operation 608, process 600 includes automatically performing an interface populating operation according to the website record of the website vector based at least in part on applying one or more trained machine learning models or rule-based models to the end user data, the website edit history interactions associated with the website identifier, and the website vector.

[0065] In some embodiments, one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled at least in part on the one or more website construction repositories. The one or more trained machine learning models may be configured to identify interface populating actions based at least in part on the historical editing interactions associated with the editing user identifiers, the end user data, and the website vectors. In some examples, the interface populating actions may include pre-populating prices, images, or reservation-related elements, or modifying a current user interface to present the same information.

[0066] As a further example, one or more trained machine learning models may be configured to suggest templates of services with properties that encapsulate insights drawn from data the system has (e.g., TVAR data) for a large number of businesses using big data and machine learning techniques. The machine learning models learn patterns associated with successful businesses (e.g., number of bookings, gross paid value (GPV), repeat customers) to find common characteristics of the services offered, as well as other business capabilities and apps available and the types of impact these may have on the businesses.

[0067] The one or more rule-based models may be configured to identify interface populating actions according to learned rules based at least in part on historical editing interactions associated with the editing user identifier, end user data, and website vectors.

[0068] In embodiments, the process may optionally include presenting output of the interface population operation for inclusion in a website. The process may optionally include the output in the website upon receiving electronic approval from the client computing device. In embodiments, the interface population operation includes generating a new template, updating an existing template, or pre-populating one or more interface elements of a TER vector assignment template for the website identifier.

[0069] The interface element includes one or more of an image, a currency element, or a TER allocation element. The TER allocation element may include a selectable icon for completing a TER allocation operation according to the TER vector, or a menu icon for completing a TER allocation operation according to the TER vector.

[0070] In an embodiment, the resource matrix includes a plurality of TER vectors.

[0071] In some embodiments, the one or more machine learning models are configured as a clustering model, which may cluster website identifiers according to website records of website vectors associated with the website identifiers.

[0072] In embodiments, the interface populating operation allows a business to integrate a pricing engine to ensure that it is not offering over- or under-priced services or otherwise recommend prices based on the gathered knowledge. The interface populating operation may further enable a business to understand the impact on its business by providing specific business tools and applications based on the impact they have had on similar businesses (e.g., same business segment, same geographic location). The interface populating operation may further enable a business to provide recommendations regarding highlighting the impact that content (text / images) may have on sales. The interface populating operation may further enable cross-selling of additional services or products.

[0073] 7A and 7B illustrate schematic block diagrams of an example architecture 700 for automatically generating pre-populated website building tools, according to certain example embodiments described herein. In FIG. 7A, the example architecture 700 includes a data layer 710 that maintains and aggregates data from multiple sources. In the example shown in FIG. 7A, the data sources include multidimensional TER matrix (MDTM) data 702A (provided to data layer 710 via MDM feed 702B) (e.g., calendar data), TER vector allocation record (TVAR) data 704A (provided to data layer 710 via TVAR feed 704B) (e.g., reservation data), resource (R) data 706A (provided to data layer 710 via R feed 706B) (e.g., data on the number of staff at a company may enable offering similar-sized companies with flexible pricing), and additional data available in the website building system (WBS data 708A) (e.g., geographic data to create a common ground for service-oriented companies that are primarily local businesses for some segments (e.g., fitness, beauty, and wellness)) (e.g., data that may be derived by accessing a catalog associated with the website, which may include, for example, resource inventory, orders, goods / services for sale, and payments). Data layer 710 receives and sends data to repository 712.

[0074] 7A , the example architecture 700 further includes a template pre-population engine 714, which may include an ML / rule-based / pattern-based configuration or model 714A and an analytics engine 714B. The template pre-population engine 714 receives data from the repository 712, as well as optionally additional WBS data 716. The analytics engine 714B may include components that maintain metrics / KPIs that define successful companies per company vertical and other dimensions, components that analyze the transformed input data for ML processing, and / or components that match data insights from the ML engine and successful company properties (e.g., known metrics associated with successful companies or offerings) and create specific service template recommendations for each company.

[0075] Continuing with FIG. 7B, the example architecture 700 further includes a template queue 718 from which a template gallery 720A, a TER vector editing interface 720B, and an application dashboard 720C can be accessed.

[0076] 1A, 1B, 4A, 4B, 6A, and 6B illustrate flowcharts and signal diagrams describing the operation of apparatuses, methods, systems, and computer program products according to example embodiments contemplated herein. It will be understood that each flowchart block, and combinations of flowchart blocks, may be implemented by various means, such as hardware, firmware, processors, modules, circuits, and / or other devices associated with the execution of software including one or more computer program instructions. For example, one or more of the above-described operations may be implemented by an apparatus executing computer program instructions. In this regard, the computer program instructions may be stored by the memory 1102 of the TER integration server 812 and executed by the processor 1104 of the TER integration server 812.

[0077] As will be understood, any disclosed computer program instructions can be loaded into a computer or other programmable device (e.g., hardware) to create a machine such that the computer or other programmable device implements the functions specified in the flowchart blocks. These computer program instructions can also be stored in a computer-readable memory that can instruct the computer or other programmable device to function in a particular manner, such that the instructions stored in the computer-readable memory, when executed, implement the functions specified in the flowchart blocks and produce an article of manufacture. The computer program instructions can also be loaded into a computer or other programmable device to cause a series of operations performed by the computer or other programmable device to create a computer-implemented process, such that the instructions executing on the computer or other programmable device provide operations for implementing the functions specified in the flowchart blocks.

[0078] The flowchart blocks support a combination of means for performing a specified function and a combination of operations for performing a specified function. It will be understood that one or more blocks of the flowcharts, and combinations of blocks of the flowcharts, may be implemented by a special-purpose hardware-based computer system that performs the specified function, or a combination of special-purpose hardware and computer instructions.

[0079] definition The term "temporary external resource" or "TER" refers to an external resource (e.g., external to the website building system) that is made available for allocation by a website building system through a website assembled using the website building system. A temporary external resource (TER) may be associated with a TER start time and a TER end time such that the TER is available for allocation for a period of time beginning at the TER start time and ending at the TER end time. In accordance with embodiments herein, assigning a TER to an end user identifier reserves the temporary external resource for use by the end user identifier from the TER start time to the TER end time. A TER may be geographic (e.g., a geographic TER) or non-geographic (e.g., a non-geographic TER). A geographic TER is associated with a relative location or physical component, while a non-geographic TER is not associated with a relative location or physical component. In some examples, a TER may be associated with an event (e.g., a class, a program, a tutorial, a presentation, a speech, an outing, an excursion, a service, a table, a concert, a sporting event, etc.). The reservation may be a reservation or reservation of a vehicle (e.g., a parking space), a reservation of an electric vehicle charging station, a reservation of a bicycle rental or bicycle rental docking station, a reservation of a computing resource or portion of a computing resource, and / or the like.

[0080] The term "website building tools" refers to structural objects or electronic building blocks used to assemble a website in accordance with the website building system described herein. By way of example, website building tools may include pages, subpages, containers, components, atomic components, content elements, layout elements, templates, layouts, layout rules, add-on applications, third-party applications, procedural code, application programming interfaces, etc.

[0081] The terms “website editing history interaction,” “editing history interaction,” and “history editing interaction” refer to electronic interactions performed by a client computing device associated with editing a user identifier in the course of assembling a website in accordance with the website building system described herein. For example, such interactions may include editing or selection of content, logic, layout, template, elements, attributes, and / or temporal aspects of the interaction, including timing between edits or selections. As a further example, such interactions may include electronic interactions with a website building tool (e.g., mouse clicks, touchscreen selections, cursor hovers, cursor selections, etc.) and / or temporal aspects of the interaction, including timing between electronic interactions.

[0082] The term "editor user identifier" refers to one or more data items by which an editor user (e.g., a user who builds or edits a website using a website building system according to an embodiment of the present specification) can be uniquely identified. For example, the editor user identifier may include one or more of ASCII text, a cryptographic key, an identification certificate, a pointer, an IP address, a URL, a MAC address, a memory address, an object signature, a hash value, or other unique identifier, or a combination thereof.

[0083] The terms "electronic TER recommendation interface" or "electronic TER vector assignment recommendation interface" or "electronic TER assignment recommendation interface" refer to a computing environment configured to display one or more interface elements representing data associated with a TER recommendation or TER vector assignment operation.

[0084] The term "website identifier" refers to one or more data items by which a website can be uniquely identified. For example, a website identifier may include one or more of ASCII text, a cryptographic key, an identification certificate, a pointer, an IP address, a URL, a MAC address, a memory address, an object signature, a hash value, or other unique identifier, or a combination thereof.

[0085] The term "end-user data" refers to electronic interaction data associated with a plurality of end-user identifiers who access a plurality of websites assembled in accordance with the website building system as defined herein.

[0086] The term "end user identifier" refers to one or more data items by which an end user (e.g., a user who accesses or interacts with a website assembled using a website building system according to embodiments herein) can be uniquely identified. For example, the end user identifier may include one or more of ASCII text, a cryptographic key, an identification certificate, a pointer, an IP address, a URL, a MAC address, a memory address, an object signature, a hash value, or other unique identifier, or a combination thereof.

[0087] The term "electronic interaction data" refers to electronic interactions conducted by a client device having an electronic interface (e.g., a website). Electronic interaction data may include interactions with a touchscreen, mouse clicks, cursor position, cursor hovering, etc. Electronic interaction data may be further associated with metadata such as a timestamp at which the electronic interaction occurred, such that the electronic interaction data includes a temporal aspect.

[0088] The terms "multidimensional matrix" and "multidimensional TER matrix" refer to a data structure having multiple dimensions (e.g., columns and rows, where the intersection of a column and a row may represent a TER vector available for assignment to an end user identifier). In some examples, a multidimensional matrix includes more than two dimensions. In some examples, a multidimensional matrix may represent a calendar of availability associated with a website identifier, and a TER vector may represent a scheduling slot associated with the calendar of availability. A multidimensional matrix may be implemented in a wide variety of manners, such as a linked list, a matrix, a hash table, a binary and non-binary tree, etc., and / or any combination of the foregoing. In some embodiments, a multidimensional matrix may include a multidimensional reservation matrix.

[0089] The term "TER vector" refers to a data structure representing a vector of a multidimensional matrix, where a TER vector includes multiple TER vector records. In some examples, a TER vector may include data representing a calendar slot associated with a website identifier. As a further example, a TER vector record may include data associated with the TER vector including a TER identifier, a start time, a stop time, and optionally, a location component. A TER vector record may include data representing an instructor associated with the TER vector, a price associated with the TER vector, etc. In some embodiments, a TER vector is a reservation vector.

[0090] The term "trained machine learning model" refers to a machine learning task. Machine learning is a method used to devise complex models and algorithms suitable for prediction. Machine learning models are computer-implemented algorithms that can learn from data, whether or not they rely on rule-based programming. These models enable reliable and repeatable decisions and outcomes, revealing hidden insights through machine-based learning from historical relationships and trends in data. In some embodiments, the machine learning model is a clustering model.

[0091] A machine learning model is first fitted or trained on a training dataset (e.g., a set of examples used to fit the model's parameters). The model may be trained on the training dataset using supervised or unsupervised learning. The model is run on the training dataset and then generates a result for each input vector in the training dataset that is compared to a target. Based on the results of the comparison and the particular learning algorithm used, the model's parameters are adjusted. Model fitting may include both variable selection and parameter estimation. The fitted model is subsequently used to predict responses to observations in a second dataset called a validation dataset. The validation dataset is used to tune the model's hyperparameters (e.g., the number of hidden units in a neural network). In some embodiments, the model can be and / or is trained in real time while in use (e.g., online training).

[0092] The terms "rule-based model" and "rule-based model" refer to a data construct configured to programmatically make decisions based on rules associated with it. The rules may be learned or curated over time based on a data corpus according to embodiments herein.

[0093] A trained machine learning model and / or rule-based model according to embodiments herein may be configured to optimize a TER vector assignment associated with a multi-dimensional TER matrix according to whether an end user is likely to agree and / or approve the TER vector assignment. A trained machine learning model and / or rule-based model according to embodiments herein may be configured to optimize a TER vector assignment associated with a multi-dimensional TER matrix according to which TER vector assignment is most beneficial to provide. A trained machine learning model and / or rule-based model according to embodiments herein may be configured to optimize a TER vector assignment associated with a multi-dimensional TER matrix according to load balancing across other external resources (e.g., spreading load evenly across days, staff, available equipment, available resources, etc.).

[0094] The term "TER vector assignment" refers to assigning a TER vector to a particular end user identifier such that the TER vector is no longer available for assignment to another end user identifier. Examples of a TER vector assignment, in some embodiments, may include scheduling a calendar slot for a particular end user identifier, designating an electric vehicle charging location for an end user identifier associated with an electric vehicle, designating a docking station for an end user identifier associated with a bicycle rental, or designating a computing resource for a particular computing task. In some embodiments, the TER vector assignment is a reservation vector assignment.

[0095] The terms "TER assignment operation" and "TER vector assignment operation" refer to the electronic conversion of at least one record (e.g., availability record or flag) of a TER vector in a repository based on the fact that the TER vector has been assigned to a particular end user identifier and is no longer available for assignment to another end user identifier. In accordance with embodiments herein, the TER assignment operation or TER vector assignment operation may be performed automatically, substantially instantaneously, after a TER vector assignment is proposed or recommended. Alternatively, the TER assignment operation or TER vector assignment operation may be performed in response to receiving an approval selection associated with the TER vector assignment from a client device associated with the end user identifier. In some embodiments, the TER assignment operation is a reservation vector assignment operation.

[0096] The term "engagement indicator" refers to one or more data items that indicate whether there has been positive, negative, or partial engagement with a TER Vector associated with an end user identifier. For example, a positive engagement indicator may be associated with an end user identifier that completes a transaction or arrives during a start time associated with a TER Vector. A negative engagement indicator may be associated with an end user identifier that does not complete a transaction or arrives during a time frame associated with a TER Vector. A partial engagement indicator may be associated with an end user identifier that partially completes a transaction or arrives late during a time frame associated with a TER vector. In some examples, the engagement indicator indicates whether an event was attended by a user (e.g., on time or late) or whether the user did not show up.

[0097] The term "event identifier" refers to one or more data items by which an event can be uniquely identified. For example, an event identifier may include one or more of ASCII text, a cryptographic key, an identification certificate, a pointer, an IP address, a URL, a MAC address, a memory address, an object signature, a hash value, or other unique identifier, or a combination thereof.

[0098] The term "event type identifier" refers to one or more data items by which an event type can be uniquely identified. For example, an event type identifier may include one or more of ASCII text, a cryptographic key, an identification certificate, a pointer, an IP address, a URL, a MAC address, a memory address, an object signature, a hash value, or other unique identifier, or a combination thereof. In some examples, an event type identifier may indicate that the event is a class, a program, a tutorial, a presentation, a speech, an outing, an excursion, a charging slot, or a computing resource.

[0099] The term "TER start time" refers to a network timestamp associated with a TER vector that represents the intended start time associated with the TER vector. In some examples, the TER start time indicates when an event will begin or when an external resource becomes available.

[0100] The term "TER end time" refers to a network timestamp associated with a TER vector that represents the intended end time associated with the TER vector. In some examples, the TER end time indicates when an event ends or when external resources are no longer available.

[0101] The term "TER location" refers to a geographic location (eg, represented using GPS coordinates) associated with a TER vector.

[0102] The term "availability record" refers to a record of a TER vector that indicates whether the TER is available for allocation. For example, if the availability record is empty, the TER vector may be available for allocation. If the availability record is not empty (e.g., the flag is set), the TER vector may not be available for allocation.

[0103] The term "engagement transmission" refers to an electronic communication configured to convey an engagement recommendation. In some embodiments, the engagement transmission includes an interface, including a computing environment, configured to display one or more interface elements representing data associated with the engagement transmission recommendation. The suggested engagement, in some embodiments, may include a TER vector assignment, a promotion, outreach (e.g., phone call, email, message), motivational or financial incentives, a new event time or location, a new event instructor, a new event type, a new media experience at the event, and / or the like. The suggested or recommended engagement for an end user identifier (e.g., a human) may be based at least in part on motivational psychology, and the suggested or recommended engagement for a non-human assignment recipient (e.g., an autonomous vehicle, a computing process, etc.) may be based at least in part on motivational psychology. The engagement may be based at least in part on trust or computing constraints. The engagement may be selected based at least in part on sensor data received from the IoT device or other sensors associated with the end user identifier or other entity.

[0104] The term "engagement score" refers to a programmatically generated likelihood that a client computing device associated with an end user identifier will engage with a website after receiving an engagement transmission. Whether an end user identifier will engage with a website after receiving an engagement transmission may, in some embodiments, be based at least in part on poor timing of booked events, poor timing of payments, missed events, negative feedback, poor performance metrics for attended events, and / or the like.

[0105] The term "website vector" refers to a data structure (eg, also data structures) having multiple website records that represent websites and store data associated with the websites.

[0106] The term "resource matrix" refers to a data structure that represents resources and services provided by or available to a website. In some examples, the services or resources provided by or available to a website may include services provided by a service provider.

[0107] The term "interface population operations" refers to a series of one or more instructions that, when executed, populate one or more interface elements of an interface component (e.g., a template) for inclusion in a website within a website-building system without input or action from an editing user. In some examples, the interface population operations include pre-populating a template for inclusion in a website being assembled using the website-building system such that the interface elements are pre-populated according to suggestions or recommendations provided for the editing user assembling the website.

[0108] The term "TER vector assignment recommendation request" refers to an electronic request including one or more data items received from an external computing device or entity for a TER vector assignment recommendation. In some embodiments, the TER vector assignment recommendation request is an API request.

[0109] The term "allocation request metadata" refers to one or more data items that accompany a TER vector allocation recommendation request. In some examples, the allocation request metadata may include a website identifier, a user identifier, data associated with the user identifier, multidimensional matrix information (e.g., available calendar slots or services), or other data associated with the requesting entity for preparing an allocation recommendation.

[0110] The term "TER vector assignment recommendation response" refers to an electronic response that includes one or more data items configured for transmission to an external computing device or entity (e.g., a requesting client computing device), where the one or more data items may represent electronic data with recommendations for one or more TER vector assignments. In some embodiments, the TER vector assignment recommendation response is an API response.

[0111] Terms such as "client device," "computing device," "user device," etc. may be used interchangeably to refer to computer hardware that is configured (physically or by executing software) to access one or more applications, services, or repositories made available by a server, and that is configured to send and receive data directly or indirectly, among various other functions. The server is often (but not always) on a separate computer system, in which case the client device accesses the service over a network.

[0112] Exemplary client devices include, but are not limited to, smartphones, tablet computers, laptop computers, wearable devices (e.g., integrated within a watch or smartwatch, eyeglasses, helmets, hats, clothing, earphones with wireless connectivity, etc.), personal computers, desktop computers, enterprise computers, etc., and any other computing device known to one of skill in the art in light of this disclosure. In some embodiments, the client device is associated with a user.

[0113] Terms such as “data,” “content,” “digital content,” “digital content object,” “signal,” “information,” and similar terms may be used interchangeably to refer to data that may be transmitted, received, and / or stored in accordance with embodiments of the present disclosure. Accordingly, the use of any such terms should not be construed as limiting the spirit and scope of embodiments of the present disclosure. Furthermore, where a computing device is described herein as receiving data from another computing device, it will be understood that the data may be received directly from the other computing device or indirectly through one or more intermediate computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like, sometimes referred to herein as a “network.” Similarly, where a computing device is described herein as transmitting data to another computing device, it will be understood that the data may be transmitted directly to the other computing device or indirectly through one or more intermediate computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like.

[0114] The term "computer-readable storage medium" refers to a non-transitory, physical, or tangible storage medium (e.g., volatile or non-volatile memory), which may be distinguished from a "computer-readable transmission medium," which refers to an electromagnetic signal. Such media can take many forms, including, but not limited to, non-transitory computer-readable storage media (e.g., non-volatile media, volatile media) and transmission media. Transmission media include, for example, coaxial cables, copper wire, fiber optic cables, and carrier waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio waves, light waves, or infrared waves. Signals include artificially or naturally occurring transient variations in amplitude, frequency, phase, polarization, or other physical characteristics that are transmitted in a transmission medium.

[0115] Examples of non-transitory computer-readable media include magnetic computer-readable media (e.g., floppy disks, hard disks, magnetic tapes, any other magnetic media), optical computer-readable media (e.g., compact disk-read-only memory (CD-ROM), digital versatile disk (DVD), or Blu-ray disk), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), FLASH-EPROM, or any other memory medium that a computer can read. The term computer-readable storage medium is used herein to refer to any computer-readable medium excluding transmission media. However, while an embodiment is described as using a computer-readable storage medium, it will be understood that other types of computer-readable media may be used in place of or in addition to the computer-readable storage medium in alternative embodiments.

[0116] The terms “application,” “software application,” “app,” “product,” “service,” or similar terms refer to a computer program or group of computer programs designed to perform a coordinated function, task, or activity for the benefit of a user or group of users. A software application may run on a server or group of servers (e.g., physical or virtual servers in a cloud-based computing environment). In particular embodiments, an application is designed to be used by and interact with one or more local, networked, or remote computing devices, such as, but not limited to, client devices. Non-limiting examples of applications include website editing services, document editing services, word processors, spreadsheet applications, accounting applications, web browsers, email clients, media players, file viewers, collaborative document management services, video games, audio-video conferencing, and photo / video editors.

[0117] In some embodiments, the application is a cloud product. When associated with a client device, such as a mobile device, communication with hardware and software modules executing outside the application is typically provided through application programming interfaces (APIs) provided by the mobile device operating system.

[0118] The term "comprising" means including, but not limited to, and should be interpreted in the manner typically used in patent contexts. Use of broader terms such as "comprises," "includes," and "having" should be understood to support narrower terms such as "consisting of," "consisting essentially of," and "comprised substantially of."

[0119] The words "illustrative," "example," "exemplary," and the like are used herein to mean "serving as an example, instance, or illustration" without denoting a qualitative rating or level of quality. Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0120] Phrases such as "in one embodiment," "according to one embodiment," and the like generally mean that the particular feature, structure, or characteristic that follows the phrase may be included in at least one embodiment of the present disclosure, and may be included in multiple embodiments of the present disclosure (importantly, such phrases do not necessarily refer to the same embodiment).

[0121] The terms "about," "approximately," and the like, when used in connection with numbers, can refer to the particular number or, alternatively, to a range surrounding the particular number, as would be understood by one of ordinary skill in the art.

[0122] When the specification states that a component or feature "may include," "can include," or "includes," When a statement is made that a component or function is a feature, it does not necessarily mean that that component or function is not included or is not required to be included. Such components or functions may be optionally included or excluded in some embodiments.

[0123] The term "plurality" refers to two or more of an item.

[0124] The term "set" refers to a collection of one or more items.

[0125] The term "or" is used herein in both its alternative and conjunctive sense, unless otherwise indicated.

[0126] After explaining a set of definitions that are called out throughout this application, an example system architecture and example devices are described below for implementing example embodiments and features of the present disclosure.

[0127] Exemplary Computing Systems, Methods, and Apparatus of the Present Disclosure The methods, apparatus, systems, and computer program products of the present disclosure may be embodied by any of a variety of computing devices. For example, the methods, apparatus, systems, and computer program products of the exemplary embodiments may be embodied by a network device, such as a server or other network entity, configured to communicate with one or more devices, such as one or more client devices. Additionally or alternatively, the computing device may include a fixed computing device, such as a personal computer or computer workstation. Furthermore, the exemplary embodiments may be embodied by any of a variety of mobile devices, such as a portable digital assistant (PDA), a mobile phone, a smartphone, a laptop computer, a tablet computer, a wearable, or any combination of the aforementioned devices.

[0128] 8 illustrates a block diagram of an exemplary system that may be specially configured within which embodiments of the present disclosure may operate. In this regard, FIG. 8 illustrates an overview of a computing system 800 that may include one or more devices and subsystems configured to perform some or all of the various operations and processes described herein. In some examples, such system 800 implements TER integration within a WBS via a TER integration system 810, in accordance with some embodiments described herein.

[0129] The computing system 800 is illustrated by a TER integrated system 810 communicatively connected to one or more client devices 808A-808B (referred to as "client devices 808"; FIG. 8's depiction of "B" client devices is for illustrative purposes only) via a network 802. Stated another way, a user may access the TER integrated system 810 via at least one communications network 802 using one or more client devices 808. In some embodiments, each of the client devices 808A-B is embodied by one or more user-facing computing devices embodied in hardware, software, firmware, and / or combinations thereof configured to perform some or all of the TER integrated system functionality described herein. That is, the client devices 808A-B may be implemented as circuits, modules, networked processors, suitable network servers, and / or other types of processing devices (e.g., the Controllers or computing devices). For example, in some embodiments, client devices 808A-B are embodied by personal computers, desktop computers, laptop computers, computing terminals, smartphones, netbooks, tablet computers, personal digital assistants, wearable devices, smart home devices, and / or other networked devices, which may be used for any suitable purpose in addition to performing some or all of the TER integrated system functionality described herein. In some example contexts, client devices 808A-B are configured to execute one or more computing programs to perform various functions described herein. For example, client devices 808A-B may execute web-based applications or applets (e.g., accessible via a website), software applications (e.g., “apps”) installed on client devices 808A-B, or other computer-coded instructions accessible to client devices 808.

[0130] In some embodiments, client devices 808A-B may include various hardware, software, firmware, etc. for interfacing with TER integrated system 810. Stated another way, client devices 808A-B may be configured to access TER integrated system 810 and / or render information provided by TER integrated system 810 (e.g., via software applications executing on client devices 808). According to some embodiments, client devices 808A-B include displays for rendering various interfaces. For example, in some embodiments, client devices 808A-B are configured to display such interface(s) on the displays of client devices 808A-B for viewing, editing, and / or otherwise interacting with at least selected components that may be provided by TER integrated system 810.

[0131] In some embodiments, the TER integration system 810 includes one or more servers, such as the TER integration server 812. For example, the TER integration system 810 may implement some of its functionality on a server or set of servers (e.g., the TER integration server 812) and some of its functionality on client elements (e.g., the client devices 808A-B). In some embodiments, the TER integration system 810 dynamically determines whether to implement some functions on a server or a client platform. In some embodiments, the TER integration system 810 includes other servers and components, as described below with respect to the exemplary depicted embodiment of the website building system 910 of FIG. 9.

[0132] The TER integration server 812 may be any suitable network server and / or other type of processing device. In this regard, the TER integration server 812 may be embodied by any of a variety of devices, for example, the TER integration server 812 may be embodied as a computer or multiple computers. For example, the TER integration server 812 may be

[0133] The device may be configured to receive / transmit data and may include any of a variety of fixed terminals, such as a server, desktop, or kiosk, or may include any of a variety of mobile terminals, such as a portable digital assistant (PDA), a mobile phone, a smartphone, a laptop computer, a tablet computer, or in some embodiments, a peripheral device that connects to one or more fixed or mobile terminals. The exemplary embodiments contemplated herein may have a variety of form factors and designs, including, but not limited to, 11 with respect to server device 1100. TER integrated server 812 may, in some embodiments, comprise several servers or computing devices that perform interconnected and / or distributed functions. Notwithstanding the many arrangements contemplated herein, TER integrated server 812 is shown and described herein as a single computing device to avoid unnecessarily complicating the present disclosure.

[0134] In some embodiments, the TER integration server 812 is configured to access the communications network 802 for communicating with one or more client devices 808 via one or more software modules, hardware modules, or a combination thereof. Additionally or alternatively, the TER integration server 812 is configured to execute any of a myriad of processes associated with implementing TER integration via software, hardware, or a combination thereof. Stated differently, the TER integration server 812 may include circuits, modules, networked processors, or the like configured to perform some or all of the TER integration functionality as described herein. In this regard, for example, in some embodiments, the TER integration server 812 receives and processes data. For example, the client devices 808A-B and / or applications may communicate with the TER integration system 810 (e.g., the TER integration server 812) via one or more application programming interfaces (APIs), web interfaces, web services, or the like.

[0135] In some embodiments, TER integrated system 810 includes at least one repository, such as repository 814. Such repository(ies) may be hosted by TER integrated server 812 or may otherwise be hosted by a device in communication with TER integrated server 812. As depicted, in some embodiments, TER integrated server 812 is communicatively coupled to repository 814. In some embodiments, TER integrated server 812 may be located remotely from repository 814. In this regard, in some embodiments, TER integrated server 812 is directly coupled to repository 814 within TER integrated system 810.

[0136] Alternatively or additionally, in some embodiments, the TER integrated server 812 is wirelessly coupled to the repository 814. In still other embodiments, the repository 814 is embodied as a subsystem or subsystems of the TER integrated server 812. That is, the TER integrated server 812 may comprise the repository 814. Alternatively or additionally, in some embodiments, the repository 814 is embodied as a virtual repository executing on the TER integrated server 812.

[0137] Repository 814 may be embodied by hardware, software, or a combination thereof to store, generate, and / or retrieve data and information utilized by TER integrated system 810 to perform the operations described herein. For example, repository 814 may be stored by any suitable storage device configured to store some or all of the information described herein (e.g., memory 1102 of TER integrated server 812, or a memory system separate from TER integrated server 812, such as one or more database systems, back-end data servers, network databases, cloud storage devices, or network-attached storage (NAS) device(s), or the like provided by another device (e.g., an online application or third-party provider), such as separate database server(s). Repository 814 may be accessed from TER integrated server 812 (e.g., via memory 1102 and / or processor(s) 1104) and / or The repository 814 may include data received from the client device 808, and therefore, a corresponding storage device may store this data. The repository 814 may store various data in any of a myriad of manners, formats, tables, computing devices, and / or the like. For example, in some embodiments, the repository 814 includes one or more sub-repositories configured to store particular data processed by the TER integrated system 810. The repository 814 includes information accessed and stored by the TER integrated server 812 to facilitate the operation of the TER integrated system 810.

[0138] The TER integrated system 810 (e.g., the TER integrated server 812) may communicate with one or more client devices 808A-B via a communications network 802. The communications network 802 may include any one or more wired and / or wireless communications networks, including, for example, a wired or wireless local area network (LAN), personal area network (PAN), metropolitan area network (MAN), or wide area network (WAN), or the like, or a combination thereof, as well as any hardware, software, and / or firmware necessary to implement one or more networks (e.g., network routers, switches, hubs, etc.). For example, the communications network 802 may include cellular, mobile broadband, Long Term Evolution (LTE), GSM / EDGE, UMTS / HSPA, IEEE 802.11, IEEE 802.16, IEEE 802.20, Wi-Fi, dial-up, and / or WiMAX networks.

[0139] Additionally, the communications network 802 may include a public network such as the Internet, a private network such as an intranet, or a combination thereof, and may utilize a variety of currently available or later developed networking protocols, including, but not limited to, Transmission Control Protocol / Internet Protocol (TCP / IP)-based networking protocols. For example, the networking protocol may be customized to suit the needs of the TER integration system 810, such as JavaScript Object Notation (JSON) objects transmitted over a WebSocket channel. In some embodiments, the protocol is JSON over RPC, JSON over REST / HTTP, the like, or a combination thereof.

[0140] In some embodiments, TER integrated system 810 is a standalone system. In other embodiments, TER integrated system 810 is embedded within a larger editing system. For example, in certain embodiments, TER integrated system 810 is associated with a visual design system, and even further, in some embodiments, the visual design system is one or more of a document building system, a website building system, or an application building system.

[0141] An example of a TER integrated system (e.g., TER integrated system 810 as depicted in FIG. 8) is depicted in FIG. 9. In particular, FIG. 9 depicts a computing system 900 including a website building system (“WBS”) 910 as an exemplary TER integrated system for, e.g., creating and / or updating a hierarchical website.

[0142] The WBS 910 can be online (e.g., an application is edited and stored on a server or set of servers), offline, or partially online (a website is edited locally but uploaded to a central server for publication). The WBS 910 is comprised of end users, designers, subscribers, and users who design the website and are the "users of users" who access the created website. The WBS 910 may be accessed by various users via network 902, including drivers, subscriber users or site editors, and code editors. End users may typically access the WBS 910 in a read-only mode, but the WBS (and website) may allow end users to make changes to the website, such as, for example, adding or editing data records, adding talkbacks to news articles, adding blog entries to blogs, and / or the like.

[0143] In some embodiments, WBS 910 may allow multiple levels of users, and different permissions and capabilities may be associated with and / or assigned to each level. For example, users may register with WBS 910 (e.g., via a WBS server that manages users, websites, and end-user access parameters).

[0144] 9 , in addition to TER integration services 912 and repository 914, WBS 910 may comprise WBS site manager 905, object marketplace 915, RT (runtime) server 920, WBS editor 930, site creation system 940, and WBS content management system 1000. WBS 910 is depicted in communication with embodiments of client devices 808A-B, which are depicted as being operated by WBS vendor staff 908A, WBS site designers 908B (e.g., users), site visitors 908N (e.g., users of users), and external systems 970. For example, WBS vendor staff 908A may be an employee of an associated website building system vendor and may create and maintain various WBS elements, such as templates, content / layout elements, and / or the like. In some embodiments, site designer 908B may use WBS 910 to build his or her site for use by site visitors 908N.

[0145] Additionally or alternatively, site designer 908B may be an external site designer or consultant, while a website building system vendor may use site designer 908B, for example, to create a template site for inclusion in WBS 910. In some embodiments, site viewers 908N may only view the system. Additionally or alternatively, in some embodiments, site viewers 908N may be permitted some form of site input or editing (e.g., sending talkbacks or posting blog posts). In still further embodiments, WBS 910 includes a limited site creation system 940 configured to enable viewers 908N to build (e.g., user pages) within a social networking site. It is contemplated by the present disclosure that site viewers 908N may include site designer 908B.

[0146] In some embodiments, WBS Site Manager 905 is used by site designers 908B to manage their created sites (e.g., to process payments for site hosting or to set permissions for site access). In some embodiments, WBS RT (runtime) server 920 handles runtime access by one or more (e.g., potentially many) site visitors 908N. In some embodiments, such access is read-only, but in certain embodiments, such access involves interactions that may affect back-end data or front-end displays (e.g., purchasing a product or posting a comment on a blog). In some embodiments, WBS RT server 920 provides pages to site designers 908B (e.g., when previewing a site or as a front-end to WBS editor 930).

[0147] In some embodiments, the object marketplace 915 facilitates the trading of objects (e.g., between object vendors and site designers 908B) through the WBS 910. For example, as add-on applications, templates, and element types. In some embodiments, the WBS editor 930 allows the site designer 908B to edit site pages (e.g., manual or automatically generated), such as editing content, logic, layout, attributes, etc. For example, in some embodiments, the WBS editor 930 allows the site designer 908B to adapt a particular template and its elements according to their company or industry.

[0148] In some embodiments, the site generation system 940 creates the actual site based on the integration and analysis of information entered by the site designer 908B (e.g., via a questionnaire) and pre-specified and stored in the CMS 1000, along with information from external systems 970 and internal information maintained within the content management system 1000 that may be gathered from use of the WBS 910 by other designers. Additionally or alternatively, the CMS 1000 is maintained in centralized storage or locally by the site designer 908B. An exemplary repository of the CMS 1000 is described below with respect to FIG. 10.

[0149] 10 , an exemplary CMS 1000 is illustrated. WBS 910 may utilize CMS 1000, which includes a series of repositories stored across one or more servers or server farms, to support the creation of various websites. For example, CMS 1000 may include one or more of a user information / profile repository 1012, a WBS component repository 1016, a WBS site repository 1009, an enterprise intelligence (BI) repository 1010, and an edit history repository 1011. Additionally or alternatively, CMS 1000 may include one or more of survey type repository 1001, content element type repository 1002, LE (layout element) type repository 1003, design kit repository 1004, completed survey repository 1005, CER (content element repository) 1006, LER (layout element repository) 1007, layout selection store 1008, rules repository 1013, business / industry repository 1014, and ML / AI (machine learning / artificial intelligence) repository 1015. CMS 1000 may also include a CMS coordinator 1017 for coordinating and controlling access to such one or more repositories.

[0150] The WBS 910 may be used to create and / or update hierarchical websites based on visual editing or automated generation based on collected enterprise knowledge, where collected enterprise knowledge refers to the collection of relevant content for the website being created, which may be collected, for example, from external systems 670 or other sources. Further details regarding collected enterprise knowledge are found in U.S. Patent Application No. 15 / 607,586, filed May 29, 2017, entitled "SYSTEM AND METHOD FOR THE CREATION AND UPDATE OF HIERARCHICAL WEBSITES BASED ON No. 10,073,923, entitled "Collected Business Knowledge," which application is incorporated herein by reference in its entirety.

[0151] In some embodiments, WBS910 uses an internal data architecture to store WBS-based sites. For example, this architecture may organize the internal data and elements of a processed site within WBS910. This architecture may differ from the external view of the site (e.g., as seen by an end user) and may also differ from the way the corresponding HTML page sent to a browser is organized. For example, in some embodiments, the internal data architecture includes additional properties for each element in a page (e.g., author, creation time, access permissions, links to templates, SEO-related information, and / or the like) that are relevant to editing and maintaining the site within WBS910 but are not visible externally to end users (or even to some editing users). An internal version of the site may be stored in a site repository, as described in further detail below.

[0152] In some embodiments, the WBS 910 is used with applications. For example, a visual application is a website that includes pages, containers, and components. Each page is displayed separately and includes one or more components. In some embodiments, components include containers as well as atomic components. In some embodiments, the WBS 910 supports hierarchical arrangement of components using various types of container components, including atomic components (e.g., text, image, shape, video, and / or the like) and other components (e.g., regular containers, single-page containers, multi-page containers, gallery containers, and / or the like). Subpages contained within a container component are called minipages, each of which may include multiple components. Some container components may display only one of the minipages at a time, while other container components may display multiple minipages simultaneously.

[0153] In some examples, a page may use a template, i.e., a general page template or a component template. In an exemplary embodiment, an application master page containing components duplicated on all other regular pages is a template. In another exemplary embodiment, an application header / footer that is repeated on all pages is a template. In some embodiments, a template may be used for an entire page or page section. WBS910 may provide inheritance between templates, pages, or components, possibly including multi-level inheritance, multiple inheritance, and diamond inheritance (e.g., A inherits from B and C, and both B and C inherit from D). In some embodiments, WBS910 supports site templates.

[0154] In some embodiments, the visual arrangement of components within a page is a layout. In some embodiments, the WBS 910 supports dynamic layout processing, whereby editing a given component (or other changes that affect it, such as externally driven content changes) can affect other components. Further details regarding dynamic layout processing are described in commonly owned U.S. Patent Application No. 10,185,703, filed February 20, 2013 as U.S. Patent Application No. 13 / 771,119, entitled "WEB SITE DESIGN SYSTEM INTEGRATING DYNAMIC LAYOUT AND DYNAMIC CONTENT," which is incorporated herein by reference in its entirety.

[0155] In some embodiments, the WBS 910 is extended using add-on applications, such as third-party applications and components, list applications, and WBS configurable applications. In particular embodiments, such add-on applications may be added and integrated into the designed website. Such add-on applications may be purchased (or otherwise obtained) through several distribution mechanisms, such as those pre-included in the WBS design environment, from an application store (e.g., the WBS Object Marketplace 915 or integrated externally), or directly from a third-party vendor. Such third-party applications may be hosted on the WBS vendor's servers, the third-party application vendor's servers, and / or a fourth-party server infrastructure.

[0156] In some embodiments, the WBS 910 adds procedural code to some or all of the entities (e.g., applications, pages, elements, components, etc.). Such code can be written in a standard language (such as JavaScript), an extended version of a standard language, or a language proprietary to a particular WBS910. The executed code may reference APIs provided by the WBS910 itself or by external providers. The code may also reference internal structures and objects of the WBS910, such as pages, components, and their attributes.

[0157] In some embodiments, procedural code elements may be activated via event triggers, which may be associated with user activity (e.g., mouse movements or clicks, page transitions, and / or the like), activity associated with other users (e.g., an underlying database or a particular database record being updated by another user, and / or the like), system events, or other types of conditions. The activated code may execute within a client element of the WBS (e.g., client device 808), a server platform, a combination of the two, or a dynamically determined execution platform. Further details regarding activation of customized back-end functionality are described in commonly owned U.S. Patent Application No. 10,209,966, filed July 24, 2018 as U.S. Patent Application No. 16 / 044,461, entitled "CUSTOM BACK-END FUNCTIONALITY IN AN ONLINE WEBSITE BUILDING ENVIRONMENT," which is incorporated herein by reference in its entirety.

[0158] 11 illustrates a block diagram of an exemplary device that may be specially configured in accordance with an exemplary embodiment of the present disclosure. In some embodiments, the TER integration system 810 and / or the TER integration server 812 are embodied by one or more computing systems, such as device 1100 as shown and described in FIG.

[0159] 11 shows a schematic block diagram of example modules or circuits, some or all of which may be included in server device 1100. As illustrated in FIG. 11 , according to some example embodiments, server device 1100 may include various means, such as memory 1102, processor 1104, input / output module 1106, communication module 1108, and / or TER integration module 1110. Server device 1100 may be configured to use one or more of modules 1102-1110 to perform operations related to implementing TER integration functionality with respect to FIGS. 1-10. In other words, systems, methods, apparatuses, and / or computer program products as described herein are configured to convert or otherwise manipulate general-purpose computer(s) so that the general-purpose computer functions as a special-purpose computer to provide TER integration as described herein.

[0160] The use of the terms “module” and “circuit” as used herein with respect to components 1102-1110 is sometimes described using functional language, but it should be understood that a particular implementation necessarily involves the use of specific hardware configured to perform the functions associated with each module or circuit as described herein. It should also be understood that certain components among these components 802-814 may include similar or common hardware. For example, two or more modules may both utilize the same processor, network interface, storage medium, etc. to perform their associated functions such that duplicated hardware is not required for each module. In this regard, some of the components or modules described in connection with TER integration server 812 may be housed within this device, for example, while other components or modules may be housed within another of these devices, or yet another device not explicitly illustrated in FIG. 8 . It will be understood that such systems may be accommodated by the various components of the system, such as a network, a cloud computing system, a network interface, a network controller ...

[0161] While the terms “module” and “circuit” should be broadly understood to include hardware, in some embodiments, the terms “module” and “circuit” also include software for configuring hardware. That is, in some embodiments, each of modules 1102-1110 may be embodied in hardware, software, or a combination thereof to perform the operations described herein. In some embodiments, some of modules 1102-1110 may be embodied entirely in hardware or entirely in software, while other modules may be embodied in a combination of hardware and software. For example, in some embodiments, the terms “module” and “circuit” may include processing circuitry, storage media, network interfaces, input / output devices, etc. In some embodiments, other elements of server device 1100 may provide or complement the functionality of a particular module or circuit. For example, processor 1104 may provide processing functionality, memory 1102 may provide storage functionality, communication module 1108 may provide network interface functionality, etc.

[0162] In some embodiments, one or more of the modules 1102-1110 may share hardware to eliminate duplicate hardware requirements. Additionally or alternatively, in some embodiments, one or more of the modules 1102-1110 may be combined, such that a single module includes means configured to perform the operations of two or more of the modules 1102-1110. Additionally or alternatively, one or more of the modules 1102-1110 may be embodied by two or more sub-modules.

[0163] In some embodiments, the processor 1104 (and / or a coprocessor or any other processing circuitry assisting or otherwise associated with the processor) may communicate with the memory 1102, for example, via a bus for passing information between components of the TER integrated server 812. The memory 1102 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories, or some combination thereof. In other words, for example, the memory 1102 may be an electronic storage device (e.g., a non-transitory computer-readable storage medium). The memory 1102 may be configured to store information, data, content, applications, instructions, or the like to enable the server apparatus 1100 (e.g., the TER integrated server 812) to perform various functions in accordance with an exemplary embodiment of the present disclosure.

[0164] Although illustrated in FIG. 11 as a single memory, memory 1102 may comprise multiple memory components. The multiple memory components may be embodied on a single computing device or distributed across multiple computing devices. In various embodiments, memory 1102 may include, for example, a hard disk, random access memory, cache memory, flash memory, a compact disc read-only memory (CD-ROM), a digital versatile disc read-only memory (DVD-ROM), an optical disk, a circuit configured to store information, or some combination thereof. Memory 1102 may be configured to store information, data, applications, instructions, or the like to enable server device 1100 to perform various functions in accordance with the example embodiments discussed herein. For example, in at least some embodiments, memory 1102 may store data for processing by processor 1104. The memory 1102 is configured to buffer the data stored in the memory 1102. Additionally or alternatively, in at least some embodiments, the memory 1102 is configured to store program instructions for execution by the processor 1104. The memory 1102 may store information in the form of static and / or dynamic information. This stored information may be stored and / or used by the server device 1100 (e.g., the TER integration server 812) in the course of performing its functions.

[0165] The processor 1104 may be embodied in several different ways, e.g., it may include one or more processing devices configured to perform independently. Additionally or alternatively, the processor 1104 may include one or more processors configured in tandem via a bus to enable independent execution of instructions, pipelining, and / or multithreading. The processor 1104 may be embodied as a variety of means, including, e.g., one or more microprocessors with associated digital signal processor(s), one or more processors without associated digital signal processors, one or more coprocessors, one or more multi-core processors, one or more controllers, processing circuitry, one or more computers, various other processing elements including integrated circuits such as ASICs (application-specific integrated circuits) or FPGAs (field-programmable gate arrays), or some combination thereof. Use of the term “processing circuitry” may be understood to include a single core processor, a multi-core processor, multiple processors internal to a device, and / or a remote or “cloud” processor. Thus, although illustrated in FIG. 11 as a single processor, in some embodiments, the processor 1104 comprises multiple processors. The multiple processors may be embodied on a single computing device or may be distributed across multiple such devices collectively configured to function as the TER integrated server 812. The multiple processors may be in operable communication with each other and collectively configured to perform one or more functions of the TER integrated server 812 as described herein.

[0166] In an exemplary embodiment, processor 1104 is configured to execute instructions stored in memory 1102 or otherwise accessible to processor 1104. Alternatively or additionally, processor 1104 may be configured to execute hard-coded functions. Thus, whether configured by hardware or software methods, or a combination thereof, processor 1104 may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to embodiments of the present disclosure while configured accordingly. Alternatively, as another example, when processor 1104 is embodied as an executor of software instructions, the instructions, when executed, may specifically configure processor 1104 to perform one or more algorithms and / or operations described herein. For example, these instructions, when executed by processor 1104, may cause server device 1100 (e.g., TER integration server 812) to perform one or more functions of system 800 as described herein.

[0167] In some embodiments, server apparatus 1100 further includes an input / output module 1106 that, in turn, communicates with processor 1104 to provide audible, visual, mechanical, or other output, and / or that, in some embodiments, may receive input indications from a user, client device 808, or another source. In this regard, input / output module 1106 may include means for performing analog-to-digital and / or digital-to-analog data conversion. Input / output module 1106 may include, for example, a display, a touchscreen, a keyboard, buttons, a click wheel, a mouse, a joystick, an imaging device (e.g., a camera), a motion sensor (e.g., an accelerometer and / or gyroscope), a microphone, an audio recorder, a speaker, a biometric scanner, and the like. and / or support for other input / output mechanisms. The input / output module 1106 may include a user interface, such as a web user interface, a mobile application, a client device, or a kiosk. The processor 1104 and / or user interface circuitry comprising the processor 1104 may be configured to control one or more functions of a display or one or more user interface elements through computer program instructions (e.g., software and / or firmware) stored in memory accessible to the processor 1104 (e.g., memory 1102 and / or the like). In some embodiments, aspects of the input / output module 1106 may be reduced compared to embodiments in which the server device 1100 may be implemented as an end-user machine or other type of device designed for complex user interaction. In some embodiments (as with other components discussed herein), the input / output module 1106 may be eliminated from the server device 1100. The input / output module 1106 may communicate with the memory 1102, the communications module 1108, and / or any other component(s), such as via a bus. More than one input / output module 1106 and / or other components may be included in server device 1100, although only one is shown in FIG. 11 to avoid overcomplicating the disclosure (e.g., as well as other components discussed herein).

[0168] In some embodiments, communications module 1108 includes any means, such as devices or circuits, embodied in either hardware, software, firmware, or a combination of hardware, software, and / or firmware, configured to receive and / or transmit data from / to a network and / or other devices, circuits, or modules in communication with server apparatus 1100. In this regard, communications module 1108 may include, for example, a network interface for enabling communication with a wired or wireless communications network. For example, in some embodiments, communications module 1108 is configured to receive and / or transmit any data that may be stored by memory 1102 using any protocol that may be used for communication between computing devices. For example, communications module 1108 may include one or more network interface cards, antennas, transmitters, receivers, buses, switches, routers, modems, and supporting hardware and / or software, and / or firmware / software, or any other devices suitable for enabling communication over a network. Additionally or alternatively, in some embodiments, the communications module 1108 includes circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to process reception of signals received via the antenna(s). These signals may be transmitted in accordance with Bluetooth® v1.0 to v3.0, Bluetooth Low Energy, Bluetooth 5.0, Bluetooth 5.1, Bluetooth 5.2, Bluetooth 5.3, Bluetooth 5.4, Bluetooth 5.5, Bluetooth 5.6, Bluetooth 5.7, Bluetooth 5.8, Bluetooth 5.9, Bluetooth 5.1 ...1, Bluetooth 5.2, Bluetooth 5.2, Bluetooth 5.3, Bluetooth 5.4, Bluetooth 5.4, Bluetooth 5.5, Bluetooth 5.5, Bluetooth 5.5, Bluetooth 5.6, Bluetooth 5.7, Bluetooth 5.8, Bluetooth 5.1, Bluetooth 5.1, Bluetooth 5.2, Bluetooth 5.3, Bluetooth 5.4, Bluetooth 5.4, Bluetooth 5.5, Bluetooth 5.5, Bluetooth 5.5, Bluetooth 5.5, Bluetooth 5.6, Bluetooth 5.7, Bluetooth 5.8, Bluetooth 5.1, Bluetooth 5.1, Bluetooth 5.2, Bluetooth The signals may be transmitted by the TER integrated server 812 using any of a number of wireless personal area network (PAN) technologies, such as Bluetooth Low Energy (BLE), infrared radio (e.g., IrDA), ultra-wideband (UWB), or inductive radio transmission. Additionally, it should be understood that these signals may be transmitted using Wi-Fi, near field communication (NFC), Worldwide Interoperability for Microwave Access (WiMAX), or other proximity-based communication protocols. The communications module 1108 may additionally or alternatively communicate with the memory 1102, the input / output module 1106, and / or any other components of the server device 1100, such as via a bus.

[0169] In some embodiments, the TER integration module 1110 is included in the server device 1100 and configured to perform the functions discussed herein in connection with TER integration. In some embodiments, the TER integration module 1110 includes hardware, software, firmware, and / or a combination of such components, The TER integration module 1110 is configured to support various aspects of such TER integration related functions, features, and / or services as described herein.

[0170] It should be understood that in some embodiments, the TER-integrated module 1110 performs one or more of such exemplary actions in combination with another module of the server device 1100, such as one or more of the memory 1102, the processor 1104, the input / output module 1106, and the communications module 1108. For example, in some embodiments, the TER-integrated module 1110 utilizes processing circuitry, such as the processor 1104 and / or the like, to perform one or more of its corresponding operations. In a further example, some or all of the functions of the TER-integrated module 1110 may, in some embodiments, be performed by the processor 1104. In this regard, some or all of the exemplary TER-integrated processes and algorithms discussed herein may be performed by at least one processor 1104 and / or the TER-integrated module 1110. It should also be understood that in some embodiments, the TER-integrated module 1110 may include a separate processor, a specially configured field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC) for performing its corresponding functions.

[0171] Additionally or alternatively, in some embodiments, the TER integration module 1110 utilizes the memory 1102 to store the collected information. For example, in some implementations, the TER integration module 1110 includes hardware, software, firmware, and / or a combination thereof that interacts with the repository 814 and / or the memory 1102 (as illustrated in FIG. 8 ) to transmit, retrieve, update, and / or store data values ​​embodied by and / or associated with the TER integration module 1110.

[0172] FIG. 12 illustrates a block diagram of an exemplary client device that may be specially configured in accordance with an exemplary embodiment of the present disclosure. In some embodiments, client devices 808A-B are embodied by one or more computing systems, such as client device 1200 as depicted and described in FIG. 12 . Client device 1200 includes memory 1202, processor 1204, input / output module 1206, communication module 1208, and / or TER integration module 1210. Client device 1200 may be configured using one or more of a set of circuits to perform the operations described herein. Modules 1202-1210 may function similarly or identically to similarly named modules depicted and described with respect to server device 1100. For the sake of brevity, repeated disclosure regarding the functionality of such similarly named sets of circuits will be omitted herein.

[0173] In some embodiments, the TER integration module 1210 is included in the client apparatus 1200 (e.g., the client device 808) and is configured to perform, among other things, the functions discussed herein in connection with TER integration. In some embodiments, the TER integration module 1210 includes hardware, software, firmware, and / or a combination of such components configured to support various aspects of such TER integration-related functions, features, and / or services of the TER integration module 1210.

[0174] In some embodiments, one or more of the modules 1202-1210 may be combined. Alternatively or additionally, in some embodiments, one or more of the modules may perform some or all of the described functionality associated with another component. For example, in some embodiments, one or more of modules 1202-1210 are combined into a single module embodied in hardware, software, firmware, and / or combinations thereof. Similarly, in some embodiments, one or more of the modules, e.g., TER integration module 1210, are combined with processor 1204 such that processor 1204 performs one or more of the operations described above with respect to each of these modules.

[0175] Thus, particular embodiments of the present subject matter have been described. While the specification contains many specific implementation details, these should not be construed as limiting the scope of any invention or what may be claimed, but rather as a description of features specific to particular embodiments of a particular invention. Other embodiments are within the scope of the following claims. Certain features described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, while features may be described above as working in a particular combination, and even initially claimed as such, one or more features from a claimed combination may, in some cases, be deleted from that combination, and the claimed combination may be directed to a subcombination or variations of the subcombination.

[0176] Similarly, although operations are depicted in a particular order in the figures, this should not be understood as requiring such operations to be performed in the particular order shown or in a sequential order, or that all illustrated operations be performed, to achieve desirable results, unless otherwise specified. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged in multiple software products. Any operational steps indicated by dashed lines in one or more of the flow diagrams illustrated herein are optional for purposes of the depicted embodiments.

[0177] In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results, unless otherwise specified. In certain implementations, multitasking and parallel processing may be advantageous.

[0178] Thus, a non-transitory computer-readable storage medium may be configured to store firmware, one or more application programs, and / or other software, including instructions and / or other computer-readable program code portions that may be executed to control processors of components of server device 1100 and / or client device 1200 to perform various operations, including examples shown herein. Thus, a series of computer-readable program code portions may be embodied in one or more computer program products and used in conjunction with devices, databases, and / or other programmable devices to generate the machine-implemented processes discussed herein. Also, note that all or a portion of the information discussed herein may be based on data received, generated, and / or maintained by one or more components of TER integration server 812 and / or client device 808. In some embodiments, one or more external systems (such as remote cloud computing and / or data storage systems) may also be utilized to provide at least a portion of the functionality discussed herein.

[0179] As discussed above and understood based on the present disclosure, embodiments of the present disclosure may be configured as a system, a method, an apparatus, a computing device, a personal computer, a server, a mobile device, a back-end network device, etc. Accordingly, embodiments may comprise a variety of means, including entirely hardware or any combination of software and hardware. Furthermore, embodiments may take the form of a computer program product on at least one non-transitory computer-readable storage medium having computer-readable program instructions embodied therein (e.g., computer software stored on a hardware device). Any suitable computer-readable storage medium may be utilized, including a non-transitory hard disk, a CD-ROM, a flash memory, an optical storage device, or a magnetic storage device.

[0180] As will be appreciated, any such computer program instructions and / or other types of code may be loaded into the circuitry of a computer, processor, or other programmable apparatus to produce a machine, such that the computer, processor, or other programmable circuitry executing the code on the machine creates means for implementing various functions, including those described herein in connection with the components of the TER integration server 812 and client device 808.

[0181] The computing systems described herein may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server sends information / data (e.g., HTML pages) to a client device (e.g., for the purpose of displaying the information / data to and receiving user input from a user interacting with the client device or an administrative user interacting with the administrative device). Information / data generated at a client device may be received from the client device by the server.

[0182] The following exemplary embodiments are provided, the numbering of which should not be construed as designating a level of importance or relevance.

[0183] Example 1. A website construction system is configured to provide Temporary External Resource (TER) integration within websites generated using the website construction system. The website construction system comprises one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers. The website construction system further comprises one or more processors configured to execute software instructions to perform operations for automatically generating an electronic Temporary External Resource (TER) vector allocation recommendation interface. The operations include obtaining a website identifier associated with a website assembled at least in part based on the one or more website construction repositories, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The operations include obtaining end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with a first end-user identifier among the plurality of end-user identifiers, the end-user data associated with the first end-user identifier representing one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled at least in part based on the one or more website construction repositories. The operations further include obtaining a multidimensional Temporary External Resources (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors. The operations further include selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with the editing user identifier and the end user data. The operations further include generating an electronic TER recommendation interface according to the TER vectors and transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

[0184] Example 2. The system of Example 1, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0185] Example 3. The system of Example 1 or 2, wherein one or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and website identifier, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0186] Example 4. The system of any of the preceding examples, wherein one or more rule-based models are configured to select TER vectors based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vectors are identified according to learned rules.

[0187] Example 5. The system of any of the preceding examples, wherein the TER vectors are further selected based at least in part on maximizing a TER vector assignment to a multidimensional TER matrix.

[0188] Example 6. The system of any of the preceding examples, wherein the TER vector is further selected based at least in part on one of an expected currency value and a first end user identifier associated with a TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

[0189] Example 7. The system of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0190] Example 8. The system of any of the preceding examples, wherein the TER vector includes multiple TER vector records.

[0191] Example 9. The system of any of the preceding examples, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0192] Example 10. The system of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and the website identifier.

[0193] Example 11. The system of any of the preceding examples, wherein one or more TER vector assignments are associated with an engagement indicator.

[0194] Example 12. The system of any of the preceding examples, wherein the engagement indicator is one of positive, negative, or partial.

[0195] Example 13. The system of any of the preceding examples, wherein a first end user identifier is associated with a plurality of TER vector assignments that are associated with a website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

[0196] Example 14. The system of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0197] Example 15. The system of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0198] Example 16. The system of any of the preceding examples, wherein the website identifier is associated with a transaction category type.

[0199] Example 17. The system of any of the preceding examples, wherein the transaction category type is one of electronic or non-electronic.

[0200] Example 18. The system of any of the preceding examples, wherein the electronic TER recommendation interface includes one or more selectable interface elements.

[0201] Example 19. The system of any of the preceding examples, wherein the operations further include automatically performing a TER vector assignment operation according to the TER vector.

[0202] Example 20. The system of any of the preceding examples, wherein the operations further include, based at least in part on performing the TER vector assignment operation, converting availability records associated with the TER vector to reflect the TER vector assignment operation, and synchronizing the multidimensional TER matrix with a second multidimensional TER matrix.

[0203] Example 21. The system of any of the preceding examples, wherein the operations further include configuring the second multidimensional TER matrix for display via a website.

[0204] Example 22. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a device, cause the device to automatically generate an electronic temporary external resource (TER) vector allocation recommendation interface within a website building system. A non-transitory computer-readable storage medium having operations for dynamically generating a website, the operations including: acquiring a website identifier associated with a website assembled at least in part based on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers; and acquiring, from an end-user data corpus, end-user data including electronic interaction data associated with a first end user identifier among the plurality of end user identifiers, the end-user data associated with the first end user identifier including one or more electronic interactions performed by a first client computing device associated with the first end user identifier accessing the one or more websites assembled at least in part based on the one or more website construction repositories. The operations further include obtaining a multidimensional Temporary External Resource (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors. The operations further include selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with the editing user identifier and the end user data. The operations further include generating an electronic TER recommendation interface according to the TER vectors and transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or editing user identifier.

[0205] Example 23. The storage medium of Example 22, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0206] Example 24. The storage medium of any of the preceding examples, wherein one or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and website identifier, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0207] Example 25. The storage medium of any of the preceding examples, wherein one or more rule-based models are configured to select TER vectors based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vectors are identified according to learned rules.

[0208] Example 26. The storage medium of any of the preceding examples, wherein the TER vectors are further selected based at least in part on maximizing a TER vector assignment to a multidimensional TER matrix.

[0209] Example 27. The storage medium of any of the preceding examples, wherein the TER vector is further selected based at least in part on one of an expected currency value and a first end user identifier associated with the TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

[0210] Example 28. The storage medium of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0211] Example 29. The storage medium of any of the preceding examples, wherein the TER vector includes a plurality of TER vector records.

[0212] Example 30. The storage medium of any of the preceding examples, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0213] Example 31. The storage medium of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and a website identifier.

[0214] Example 32. The storage medium of any of the preceding examples, wherein one or more TER vector assignments are associated with an engagement indicator.

[0215] Example 33. The storage medium of any of the preceding examples, wherein the engagement indicator is one of positive, negative, or partial.

[0216] Example 34. The storage medium of any of the preceding examples, wherein a first end user identifier is associated with a plurality of TER vector assignments that are associated with a website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

[0217] Example 35. The storage medium of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0218] Example 36. The storage medium of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0219] Example 37. The storage medium of any of the preceding examples, wherein the website identifier is associated with a transaction category type.

[0220] Example 38. The storage medium of any of the preceding examples, wherein the transaction category type is one of electronic or non-electronic.

[0221] Example 39. The storage medium of any of the preceding examples, wherein the electronic TER recommendation interface includes one or more selectable interface elements.

[0222] Example 40. The operation automatically performs the TER vector allocation operation according to the TER vector. 3. The storage medium of any of the preceding examples, further comprising:

[0223] Example 41. The storage medium of any of the preceding examples, wherein the operations further include, based at least in part on performing a TER vector allocation operation, converting availability records associated with the TER vector to reflect the TER vector allocation operation, and synchronizing the multidimensional TER matrix with a second multidimensional TER matrix.

[0224] Example 42. The storage medium of any of the preceding examples, wherein the operations further include configuring the second multidimensional TER matrix for display via a website.

[0225] Example 43. A computer-implemented method for automatically generating an electronic Temporary External Resource (TER) vector allocation recommendation interface within a website construction system. The method includes: acquiring website identifiers associated with websites assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The method further includes acquiring end user data from an end user data corpus, the end user data including electronic interaction data associated with a first end user identifier among the plurality of end user identifiers, the end user data associated with the first end user identifier including one or more electronic interactions performed by a first client computing device associated with the first end user identifier accessing the one or more websites assembled based at least in part on the one or more website construction repositories. The method further includes obtaining a multidimensional Temporary External Resource (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors. The method further includes selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with the editing user identifier and the end user data. The method further includes generating an electronic TER recommendation interface according to the TER vectors and transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or editing user identifier.

[0226] Example 44. The method of Example 43, in which the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on one or more website construction repositories.

[0227] Example 45. One or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is used to perform a TER assignment operation according to the TER vector of a first end user identifier and a website identifier. A method as described in any of the preceding examples, wherein a TER assignment action is selected according to a first programmatically generated likelihood to be executed and a second programmatically generated likelihood to be associated with a positive engagement indicator.

[0228] Example 46. The method of any of the preceding examples, wherein one or more rule-based models are configured to select TER vectors based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vectors are identified according to learned rules.

[0229] Example 47. The method of any of the preceding examples, wherein the TER vectors are further selected based at least in part on maximizing the TER vector assignment to the multidimensional TER matrix.

[0230] Example 48. The method of any of the preceding examples, wherein the TER vector is further selected based at least in part on one of an expected currency value and a first end user identifier associated with a TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

[0231] Example 49. The method of any of the preceding examples, wherein the one or more website building tools include one or more of a page, subpage, container, component, atomic component, content element, layout element, template, or layout rule.

[0232] Example 50. The method of any of the preceding examples, wherein the TER vector includes multiple TER vector records.

[0233] Example 51. The method of any of the preceding examples, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0234] Example 52. The method of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and a website identifier.

[0235] Example 53. The method of any of the preceding examples, wherein one or more TER vector assignments are associated with an engagement indicator.

[0236] Example 54. The method of any of the preceding examples, wherein the engagement indicator is one of positive, negative, or partial.

[0237] Example 55. The method of any of the preceding examples, wherein a first end user identifier is associated with a plurality of TER vector assignments that are associated with a website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

[0238] Example 56. The method of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0239] Example 57. The remote computing device is a POS terminal or a sensor device. The method according to any of the preceding examples, wherein the method is one of:

[0240] Example 58. The method of any of the preceding examples, wherein the website identifier is associated with a transaction category type.

[0241] Example 59. The method of any of the preceding examples, wherein the transaction category type is one of electronic or non-electronic.

[0242] Example 60. The method of any of the preceding examples, wherein the electronic TER recommendation interface includes one or more selectable interface elements.

[0243] Example 61. The method of any of the preceding examples, further comprising automatically performing a TER vector allocation operation according to the TER vector.

[0244] Example 62. The method of any of the preceding examples, further including, based at least in part on performing the TER vector assignment operation, converting availability records associated with the TER vector to reflect the TER vector assignment operation, and synchronizing the multidimensional TER matrix with a second multidimensional TER matrix.

[0245] Example 63. The method of any of the preceding examples, further comprising configuring a second multidimensional TER matrix for display via a website.

[0246] Example 64. A website building system configured to automatically generate recommended engagement transmissions. The website building system comprises one or more website building repositories that store one or more website building components and one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically generating recommended engagement transmissions. The operations include obtaining a website identifier associated with a website assembled at least in part based on the one or more website building repositories, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The operations further include obtaining end user data from an end user data corpus, the end user data including electronic interaction data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access the one or more websites assembled at least in part based on the one or more website building repositories. The operations further include selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to the engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and the website edit history interactions associated with the website identifier. The operations further include generating an engagement transmission based at least in part on the first end user identifier and the website identifier, and sending the engagement transmission to a client computing device associated with the first end user identifier.

[0247] Example 65. A method for assembling a website using one or more trained machine learning models based at least in part on one or more website building repositories, comprising: a method for assembling a website using one or more trained machine learning models based at least in part on one or more website building repositories; and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers accessing one or more websites assembled at least in part based on the bird.

[0248] Example 66. The system of any of the preceding examples, wherein the end user data is further associated with a plurality of TER vector assignments, and wherein all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator.

[0249] Example 67. The system of any of the preceding examples, wherein the engagement indicator is positive or negative.

[0250] Example 68. The system of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

[0251] Example 69. The system of any of the preceding examples, wherein the one or more trained machine learning models are configured to generate an engagement score for a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data, the engagement score representing a programmatically generated likelihood that a client computing device associated with the first end user identifier will engage with the website after receiving an engagement transmission.

[0252] Example 70. The system of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on an engagement score exceeding an engagement score threshold.

[0253] Example 71. The system of any of the preceding examples, wherein the one or more rule-based models are configured to select a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to the learned rules.

[0254] Example 72. The system of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0255] Example 73. The system of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0256] Example 74. The system of any of the preceding examples, wherein the engagement transmission includes electronic communication.

[0257] Example 75. The system of any of the preceding examples, wherein the engagement transmission includes an electronic incentive communication.

[0258] Example 76. The system of any of the preceding examples, wherein the engagement transmission includes a change in event type.

[0259] Example 77. The system of any of the preceding examples, wherein the engagement transmission includes a change in the event time.

[0260] Example 78. The system of any of the preceding examples, wherein the operations further include automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

[0261] Example 79. The system of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0262] Example 80. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically generating recommended engagement transmissions. The operations include obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The operations further include obtaining end-user data from an end-user data corpus, the end-user data including electronic interaction data associated with the plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing the one or more websites assembled based at least in part on the one or more website construction repositories. The operations further include selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to the engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and the website edit history interactions associated with the website identifier. The operations further include generating an engagement transmission based at least in part on the first end user identifier and the website identifier, and sending the engagement transmission to a client computing device associated with the first end user identifier.

[0263] Example 81. The storage medium of Example 80, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0264] Example 82. The storage medium of any of the preceding examples, wherein the end user data is further associated with a plurality of TER vector assignments, and all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator.

[0265] Example 83. The storage medium of any of the preceding examples, wherein the engagement indicator is positive or negative.

[0266] Example 84. The storage medium of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

[0267] Example 85. The storage medium of any of the preceding examples, wherein the one or more trained machine learning models are configured to generate an engagement score for a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data, the engagement score representing a programmatically generated likelihood that a client computing device associated with the first end user identifier will engage with the website after receiving an engagement transmission.

[0268] Example 86. The storage medium of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on an engagement score exceeding an engagement score threshold.

[0269] Example 87. The storage medium of any of the preceding examples, wherein one or more rule-based models are configured to select a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to the learned rules.

[0270] Example 88. The storage medium of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0271] Example 89. The storage medium of any of the preceding examples, wherein the remote computing device is one of a POS terminal or a sensor device.

[0272] Example 90. The storage medium of any of the preceding examples, wherein the engagement transmission comprises an electronic communication.

[0273] Example 91. The storage medium of any of the preceding examples, wherein the engagement transmission includes an electronic incentive communication.

[0274] Example 92. The storage medium of any of the preceding examples, wherein the engagement transmission includes a change in event type.

[0275] Example 93. The storage medium of any of the preceding examples, wherein the engagement transmission includes a change in the event time.

[0276] Example 94. The storage medium of any of the preceding examples, wherein the operations further include automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

[0277] Example 95. One or more website builders can create pages, subpages, containers, and components. a storage medium according to any of the preceding examples, comprising one or more of a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0278] Example 96. A computer-implemented method for automatically generating recommended engagement transmissions. The method includes: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The method further includes obtaining end user data from an end user data corpus, the end user data including electronic interaction data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers accessing the one or more websites assembled based at least in part on the one or more website construction repositories. The method further includes selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to the engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and the website edit history interactions associated with the website identifier. The method further includes generating an engagement transmission based at least in part on the first end user identifier and the website identifier, and sending the engagement transmission to a client computing device associated with the first end user identifier.

[0279] Example 97. The method of Example 96, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0280] Example 98. The method of any of the preceding examples, wherein the end user data is further associated with a plurality of TER vector assignments, and all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator.

[0281] Example 99. The method of any of the preceding examples, wherein the engagement indicator is positive or negative.

[0282] Example 100. The method of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

[0283] Example 101. One or more trained machine learning models are configured to generate an engagement score for a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and the end user data. and wherein the engagement score represents a programmatically generated likelihood that a client computing device associated with the first end user identifier will engage with the website after receiving the engagement transmission.

[0284] Example 102. The method of any of the preceding examples, wherein selecting the first end user identifier is based at least in part on an engagement score exceeding an engagement score threshold.

[0285] Example 103. The method of any of the preceding examples, wherein one or more rule-based models are configured to select a first end user identifier based at least in part on historical editing interactions associated with the editing user identifier and on end user data, and the first end user identifier is identified according to the learned rules.

[0286] Example 104. The method of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0287] Example 105. The method of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0288] Example 106. The method of any of the preceding examples, wherein the engagement transmission includes electronic communication.

[0289] Example 107. The method of any of the preceding examples, wherein the engagement transmission includes an electronic incentive communication.

[0290] Example 108. The method of any of the preceding examples, wherein the engagement transmission includes a change in event type.

[0291] Example 109. The method of any of the preceding examples, wherein the engagement transmission includes a change in the event time.

[0292] Example 110. The method of any of the preceding examples, further including automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

[0293] Example 111. The method of any of the preceding examples, wherein the one or more website building tools include one or more of a page, subpage, container, component, atomic component, content element, layout element, template, or layout rule.

[0294] Example 112. A website construction system configured to automatically perform an interface populating operation. The website construction system includes one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, and one or more processors configured to execute software instructions to perform operations for automatically performing an interface populating operation. The operations include obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier being associated with a plurality of editing user identifiers. The method includes: obtaining a website vector associated with the website identifier, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers; the operations further include obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix; the operations further include obtaining end user data from an end user data corpus, the end user data including electronic interaction data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers accessing one or more websites assembled at least in part based on one or more website construction repositories; the operations further include automatically performing interface populating operations according to the website records of the website vector based at least in part on applying one or more trained machine learning or rule-based models to the end user data, the website editing history interactions associated with the website identifiers, and the website vector.

[0295] Example 113. The system of Example 112, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites that are assembled based at least in part on the one or more website construction repositories.

[0296] Example 114. The system of any of the preceding examples, wherein the one or more trained machine learning models are configured to identify interface throwing actions based at least in part on historical editing interactions associated with an editing user identifier, end user data, and website vectors.

[0297] Example 115. The system of any of the preceding examples, wherein the one or more rule-based models are configured to identify interface populating actions according to learned rules based at least in part on historical editing interactions associated with the editing user identifier, end user data, and website vectors.

[0298] Example 116. The system of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0299] Example 117. The system of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0300] Example 118. The system of any of the preceding examples, wherein the operation further includes presenting output of the interface populating operation for inclusion on a website.

[0301] Example 119. The system of any of the preceding examples, wherein the interface populating operation includes generating a new template.

[0302] Example 120. The system of any of the preceding examples, wherein the interface populating operation includes updating an existing template.

[0303] Example 121. The system of any of the preceding examples, wherein the interface population operation includes pre-populating one or more interface elements of a temporary external resource (TER) vector allocation template for a website identifier.

[0304] Example 122. The system of any of the preceding examples, wherein the interface element includes one or more of an image, a currency element, or a TER assignment element.

[0305] Example 123. The system of any of the preceding examples, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to the TER vector.

[0306] Example 124. The system of any of the preceding examples, wherein the TER assignment element includes a menu icon for completing a TER assignment operation according to the TER vector.

[0307] Example 125. The system of any of the preceding examples, wherein the resource matrix includes a plurality of TER vectors.

[0308] Example 126. The system of any of the preceding examples, wherein the operations further include, upon receiving electronic approval from the client computing device, including the output on a website.

[0309] Example 127. The system of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0310] Example 128. A non-transitory computer-readable storage medium containing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically performing an interface populating operation. The operations include obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The operations further include obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix. The operations further include obtaining end user data from an end user data corpus, the end user data including electronic interaction data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers accessing the one or more websites assembled based at least in part on the one or more website construction repositories. The operations further include automatically performing interface populating operations according to a website record of the website vector based at least in part on applying one or more trained machine learning models or rule-based models to the end user data, the website edit history interactions associated with the website identifier, and the website vector.

[0311] Example 129. The storage medium of Example 128, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0312] Example 130. The storage medium of any of the preceding examples, wherein the one or more trained machine learning models are configured to identify interface throwing actions based at least in part on historical editing interactions associated with an editing user identifier, end user data, and website vectors.

[0313] Example 131. The storage medium of any of the preceding examples, wherein the one or more rule-based models are configured to identify interface populating actions according to learned rules based at least in part on historical editing interactions associated with an editing user identifier, end user data, and website vectors.

[0314] Example 132. The storage medium of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0315] Example 133. The storage medium of any of the preceding examples, wherein the remote computing device is one of a POS terminal or a sensor device.

[0316] Example 134. The storage medium of any of the preceding examples, wherein the operation further includes presenting output of the interface populating operation for inclusion on a website.

[0317] Example 135. The storage medium of any of the preceding examples, wherein the interface populating operation includes generating a new template.

[0318] Example 136. The storage medium of any of the preceding examples, wherein the interface populating operation includes updating an existing template.

[0319] Example 137. The storage medium of any of the preceding examples, wherein the interface population operation includes pre-populating one or more interface elements of a temporary external resource (TER) vector allocation template for a website identifier.

[0320] Example 138. The storage medium of any of the preceding examples, wherein the interface element includes one or more of an image, a currency element, or a TER assignment element.

[0321] Example 139. The storage medium of any of the preceding examples, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to the TER vector.

[0322] Example 140. The storage medium of any of the preceding examples, wherein the TER assignment element includes a menu icon for completing a TER assignment operation according to the TER vector.

[0323] Example 141. The storage medium of any of the preceding examples, wherein the resource matrix includes a plurality of TER vectors.

[0324] Example 142. The storage medium of any of the preceding examples, wherein the operations further include, upon receiving electronic approval from the client computing device, including the output on a website.

[0325] Example 143. The storage medium of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0326] Example 144. A computer-implemented method for automatically performing interface population operations. The method includes obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from the plurality of website identifiers, the website identifier being associated with an editing user identifier among the plurality of editing user identifiers. The method further includes obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix. The method further includes obtaining end user data from an end user data corpus, the end user data including electronic interaction data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers accessing the one or more websites assembled based at least in part on the one or more website construction repositories. The method further includes automatically performing interface populating operations according to website records of the website vector based at least in part on applying one or more trained machine learning models or rule-based models to the end-user data, the website edit history interactions associated with the website identifier, and the website vector.

[0327] Example 145. The method of Example 144, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0328] Example 146. The method of any of the preceding examples, wherein the one or more trained machine learning models are configured to identify interface throwing actions based at least in part on historical editing interactions associated with an editing user identifier, end user data, and website vectors.

[0329] Example 147. The method of any of the preceding examples, wherein the one or more rule-based models are configured to identify interface populating actions according to learned rules based at least in part on historical editing interactions associated with the editing user identifier, end user data, and website vectors.

[0330] Example 148. The method of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

[0331] Example 149. The method of any of the preceding examples, wherein the remote computing device is one of a POS terminal or a sensor device.

[0332] Example 150. The method of any of the preceding examples, wherein the operation further includes presenting output of the interface populating operation for inclusion on a website.

[0333] Example 151. The method of any of the preceding examples, wherein the interface populating operation includes generating a new template.

[0334] Example 152. The method of any of the preceding examples, wherein the interface populating operation includes updating an existing template.

[0335] Example 153. The method of any of the preceding examples, wherein the interface population operation includes pre-populating one or more interface elements of a temporary external resource (TER) vector allocation template for the website identifier.

[0336] Example 154. The method of any of the preceding examples, wherein the interface element includes one or more of an image, a currency element, or a TER assignment element.

[0337] Example 155. The method of any of the preceding examples, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to the TER vector.

[0338] Example 156. The method of any of the preceding examples, wherein the TER assignment element includes a menu icon for completing a TER assignment operation according to the TER vector.

[0339] Example 157. The method of any of the preceding examples, wherein the resource matrix includes a plurality of TER vectors.

[0340] Example 158. The method of any of the preceding examples, further comprising including the output on a website upon receiving electronic approval from the client computing device.

[0341] Example 159. The method of any of the preceding examples, wherein the one or more website building tools include one or more of a page, subpage, container, component, atomic component, content element, layout element, template, or layout rule.

[0342] Example 160. A website construction system configured to provide Temporary External Resource (TER) integration. The website construction system comprises: one or more website construction repositories that store one or more website editing history interactions associated with a plurality of editing user identifiers; and one or more processors configured to execute software instructions to perform operations for automatically generating an electronic Temporary External Resource (TER) vector allocation recommendation response. The operations include receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata. The operations further include extracting the allocation request metadata. The operations include extracting the allocation request metadata from the allocation request metadata. The method includes: obtaining, based at least in part on the end-user data, one or more website identifiers associated with websites assembled at least in part based on one or more website construction repositories, where the one or more website identifiers are selected from a plurality of website identifiers, and the one or more website identifiers are each associated with an editing user identifier among the plurality of editing user identifiers; and obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier among the plurality of end-user identifiers, where the end-user data associated with the first end-user identifier includes one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing the one or more websites assembled at least in part based on the one or more website construction repositories. The operations further include selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to the historical editing interactions associated with the editing user identifier and the end-user data. The operations further include generating a TER vector assignment recommendation according to the TER vector and transmitting the TER vector assignment recommendation to the requesting client computing device.

[0343] Example 161. The system of Example 160, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites that are assembled based at least in part on the one or more website construction repositories.

[0344] Example 162. The system of any of the preceding examples, wherein the one or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0345] Example 163. The system of any of the preceding examples, wherein the one or more rule-based models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is identified according to learned rules.

[0346] Example 164. The system of any of the preceding examples, wherein the TER vector is further selected based at least in part on maximizing the TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

[0347] Example 165. The system of any of the preceding examples, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of assignment request metadata.

[0348] Example 166. The system of any of the preceding examples, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between the end user data and one or more items of allocation request metadata.

[0349] Example 167. The system of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0350] Example 168. The system of any of the preceding examples, wherein the TER vector includes multiple TER vector records.

[0351] Example 169. The system of any of the preceding examples, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0352] Example 170. The system of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and one or more website identifiers.

[0353] Example 171. The system of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with one or more website identifiers.

[0354] Example 172. The system of any of the preceding examples, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

[0355] Example 173. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of the device, cause the device to perform operations for automatically generating an electronic temporary external resource (TER) vector allocation recommendation response. The operations include receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata. The operations further include extracting the allocation request metadata. The operations further include: acquiring, based at least in part on the assignment request metadata, one or more website identifiers associated with websites to be assembled at least in part based on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing historical interactions associated with a plurality of editing user identifiers, the one or more website identifiers being selected from the plurality of website identifiers, the one or more website identifiers each being associated with an editing user identifier among the plurality of editing user identifiers; and acquiring, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier among the plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing the one or more websites to be assembled at least in part based on the one or more website construction repositories. and selecting a TER vector based at least in part on applying the TER vector to the requesting client computing device and the end-user data. The operations further include generating a TER vector assignment recommendation according to the TER vector and transmitting the TER vector assignment recommendation to the requesting client computing device.

[0356] Example 174. The storage medium of Example 173, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites that are assembled based at least in part on the one or more website construction repositories.

[0357] Example 175. The storage medium of any of the preceding examples, wherein the one or more trained machine learning models are configured to select a TER vector based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0358] Example 176. The storage medium of any of the preceding examples, wherein one or more rule-based models are configured to select TER vectors based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vectors are identified according to learned rules.

[0359] Example 177. The storage medium of any of the preceding examples, wherein the TER vector is further selected based at least in part on maximizing the TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

[0360] Example 178. The storage medium of any of the preceding examples, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of assignment request metadata.

[0361] Example 179. The storage medium of any of the preceding examples, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between the end user data and one or more items of allocation request metadata.

[0362] Example 180. The storage medium of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0363] Example 181. The storage medium of any of the preceding examples, wherein the TER vector includes a plurality of TER vector records.

[0364] Example 182. A TER vector record among multiple TER vector records is The storage medium of any of the preceding examples includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0365] Example 183. The storage medium of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and one or more website identifiers.

[0366] Example 184. The storage medium of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with one or more website identifiers.

[0367] Example 185. The storage medium of any of the preceding examples, wherein the remote computing device is one of a POS terminal or a sensor device.

[0368] Example 186. A computer-implemented method for automatically generating an electronic temporary external resource (TER) vector allocation recommendation response. The method includes receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata. The method further includes extracting the allocation request metadata. The method further includes: acquiring, based at least in part on the assignment request metadata, one or more website identifiers associated with websites to be assembled at least in part based on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing historical interactions associated with a plurality of editing user identifiers, the one or more website identifiers being selected from the plurality of website identifiers, each of the one or more website identifiers being associated with an editing user identifier among the plurality of editing user identifiers; and acquiring, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier among the plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing the one or more websites to be assembled at least in part based on the one or more website construction repositories. The method further includes selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to the historical editing interactions associated with the editing user identifiers and the end-user data. The method further includes generating a TER vector allocation recommendation according to the TER vector, and transmitting the TER vector allocation recommendation to a requesting client computing device.

[0369] Example 187. The method of Example 186, in which the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on one or more website construction repositories, and end user data associated with a plurality of end user identifiers, the end user data including one or more electronic interactions conducted by a plurality of client computing devices associated with a plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

[0370] Example 188. One or more trained machine learning models generate a user-defined attribute based, at least in part, on historical editing interactions associated with an editing user identifier and end user data. and configured to select a TER vector, wherein the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

[0371] Example 189. The method of any of the preceding examples, wherein one or more rule-based models are configured to select TER vectors based at least in part on historical editing interactions associated with an editing user identifier and end user data, and the TER vectors are identified according to learned rules.

[0372] Example 190. The method of any of the preceding examples, wherein the TER vector is further selected based at least in part on maximizing the TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

[0373] Example 191. The method of any of the preceding examples, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of assignment request metadata.

[0374] Example 192. The method of any of the preceding examples, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between the end user data and one or more items of allocation request metadata.

[0375] Example 193. The method of any of the preceding examples, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

[0376] Example 194. The method of any of the preceding examples, wherein the TER vector includes multiple TER vector records.

[0377] Example 195. The method of any of the preceding examples, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

[0378] Example 196. The method of any of the preceding examples, wherein the one or more electronic interactions include one or more TER vector assignments associated with a first end user identifier and one or more website identifiers.

[0379] Example 197. The method of any of the preceding examples, wherein the end-user data further includes electronic interaction data received from a remote computing device associated with one or more website identifiers.

[0380] Example 198. The method of any of the preceding examples, wherein the remote computing device is one of a POS terminal or a sensor device.

[0381] conclusion Many modifications and other embodiments of the disclosure described herein are within the scope of the foregoing description and related teachings. Those skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the accompanying drawings will understand. Accordingly, it is to be understood that the disclosure is not limited to the particular embodiments disclosed, and that modifications and other embodiments are intended to be included within the scope of the appended claims. Furthermore, while the foregoing specification and associated drawings describe exemplary embodiments in the context of particular example combinations of elements and / or functions, it should be understood that different combinations of elements and / or functions may be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, it is contemplated that combinations of elements and / or functions other than those expressly described above may also be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

1. 1. A website building system configured to provide temporary external resource (TER) integration within a website generated using the website building system, the website building system comprising: one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; 1. One or more processors configured to execute software instructions for performing operations for automatically generating an electronic temporary external resource (TER) vector allocation recommendation interface, the operations comprising: obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier selected from a plurality of website identifiers, the website identifier associated with an editor user identifier of the plurality of editor user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; Obtaining a multidimensional temporary external resource (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic TER recommendation interface according to the TER vector; and one or more processors, the one or more processors including transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

2. 2. The website construction system of claim 1, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

3. the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the website identifier, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

4. 2. The website building system of claim 1, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

5. The website building system of claim 3 , wherein the TER vectors are further selected based at least in part on maximizing a TER vector assignment to the multidimensional TER matrix.

6. 4. The website building system of claim 3, wherein the TER vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

7. The website building system of claim 1 , wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

8. The website building system of claim 1 , wherein the TER vector includes a plurality of TER vector records.

9. 9. The website building system of claim 8, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

10. The website building system of claim 1 , wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the website identifier.

11. The website building system of claim 10 , wherein the one or more TER vector assignments are associated with an engagement indicator.

12. The website building system of claim 11 , wherein the engagement indicator is one of positive, negative, or partial.

13. 12. The website building system of claim 11, wherein the first end user identifier is associated with a plurality of TER vector assignments associated with the website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

14. The website building system of claim 1 , wherein the end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

15. 15. The website building system of claim 14, wherein the remote computing device is one of a POS terminal or a sensor device.

16. The website building system of claim 1 , wherein the website identifier is associated with a transaction category type.

17. 17. The website building system of claim 16, wherein the transaction category type is one of electronic or non-electronic.

18. The website building system of claim 1 , wherein the electronic TER recommendation interface includes one or more selectable interface elements.

19. The operation is The website construction system of claim 1 , further comprising: automatically performing a TER vector allocation operation according to the TER vector.

20. The operation is Based at least in part on performing the TER vector assignment operation, converting an availability record associated with the TER vector to reflect the TER vector allocation operation; 20. The website building system of claim 19, further comprising: synchronizing the multi-dimensional TER matrix with a second multi-dimensional TER matrix.

21. The operation is 21. The website building system of claim 20, further comprising configuring the second multidimensional TER matrix for display via the website.

22. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically generating an electronic temporary external resource (TER) vector allocation recommendation interface within a website building system, the operations comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; Obtaining a multidimensional temporary external resource (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic TER recommendation interface according to the TER vector; and transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

23. 23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

24. 23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and wherein the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the website identifier and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

25. 23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

26. 25. The non-transitory computer-readable storage medium of claim 24, wherein the TER vectors are further selected based at least in part on maximizing a TER vector assignment to the multidimensional TER matrix.

27. 25. The non-transitory computer-readable storage medium of claim 24, wherein the TER vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

28. 23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

29. 23. The non-transitory method of claim 22, wherein the TER vector comprises a plurality of TER vector records. A temporary computer-readable storage medium.

30. 30. The non-transitory computer-readable storage medium of claim 29, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

31. 23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the website identifier.

32. 32. The non-transitory computer-readable storage medium of claim 31 , wherein the one or more TER vector assignments are associated with an engagement indicator.

33. 33. The non-transitory computer-readable storage medium of claim 32, wherein the engagement indicator is one of positive, negative, or partial.

34. 33. The non-transitory computer-readable storage medium of claim 32, wherein the first end user identifier is associated with a plurality of TER vector assignments associated with the website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

35. 23. The non-transitory computer-readable storage medium of claim 22, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

36. 36. The non-transitory computer-readable storage medium of claim 35, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

37. 23. The non-transitory computer-readable storage medium of claim 22, wherein the website identifier is associated with a transaction category type.

38. 38. The non-transitory computer-readable storage medium of claim 37, wherein the transaction category type is one of electronic or non-electronic.

39. 23. The non-transitory computer-readable storage medium of claim 22, wherein the electronic TER recommendation interface includes one or more selectable interface elements.

40. The operation is 23. The non-transitory computer-readable storage medium of claim 22, further comprising automatically performing a TER vector allocation operation according to the TER vector.

41. The operation is Based at least in part on performing the TER vector assignment operation, converting an availability record associated with the TER vector to reflect the TER vector allocation operation; 41. The non-transitory computer-readable storage medium of claim 40, further comprising: synchronizing the multi-dimensional TER matrix with a second multi-dimensional TER matrix.

42. The operation is constructing said second multidimensional TER matrix for display via said website.

42. The non-transitory computer-readable storage medium of claim 41, further comprising:

43. 1. A computer-implemented method for automatically generating an electronic temporary external resource (TER) vector allocation recommendation interface in a website building system, the method comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; Obtaining a multidimensional temporary external resource (TER) matrix maintained by the website building system and associated with the website identifier, the multidimensional TER matrix including a plurality of TER vectors; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic TER recommendation interface according to the TER vector; transmitting the electronic TER recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

44. 44. The method of claim 43, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

45. 44. The method of claim 43, wherein the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical edit interactions associated with the editing user identifier and the end user data, and wherein the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the website identifier, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

46. 44. The method of claim 43, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

47. 46. ​​The method of claim 45, wherein the TER vectors are further selected based at least in part on maximizing a TER vector assignment to the multidimensional TER matrix.

48. 46. ​​The method of claim 45, wherein the TER vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a TER vector assignment for the TER vector, or whether the TER vector is associated with an ongoing event.

49. 44. The method of claim 43, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

50. 44. The method of claim 43, wherein the TER vector comprises a plurality of TER vector records.

51. 51. The method of claim 50, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

52. 44. The method of claim 43, wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the website identifier.

53. 53. The method of claim 52, wherein the one or more TER vector assignments are associated with an engagement indicator.

54. 54. The method of claim 53, wherein the engagement indicator is one of positive, negative, or partial.

55. 54. The method of claim 53, wherein the first end user identifier is associated with a plurality of TER vector assignments associated with the website identifier, and all TER vector assignments of the plurality of TER vector assignments are associated with a positive engagement indicator.

56. 44. The method of claim 43, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

57. 57. The method of claim 56, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

58. 44. The method of claim 43, wherein the website identifier is associated with a transaction category type.

59. 60. The method of claim 58, wherein the transaction category type is one of electronic or non-electronic.

60. 44. The method of claim 43, wherein the electronic TER recommendation interface includes one or more selectable interface elements.

61. 44. The method of claim 43, further comprising automatically performing a TER vector assignment operation according to the TER vector.

62. Based at least in part on performing the TER vector assignment operation, converting an availability record associated with the TER vector to reflect the TER vector allocation operation; 62. The method of claim 61, further comprising: synchronizing the multidimensional TER matrix with a second multidimensional TER matrix.

63. 63. The method of claim 62, further comprising configuring the second multidimensional TER matrix for display via the website.

64. 1. A website building system configured to automatically generate recommended engagement transmissions, the website building system comprising: one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; One or more processors configured to execute software instructions for performing operations for automatically generating recommended engagement transmissions, the operations comprising: obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier selected from a plurality of website identifiers, the website identifier associated with an editor user identifier of the plurality of editor user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to an engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and website edit history interactions associated with the website identifier; generating an engagement transmission based at least in part on the first end user identifier and the website identifier; and sending the engagement transmission to a client computing device associated with the first end user identifier.

65. the one or more trained machine learning models assemble a website based at least in part on the one or more website construction repositories; and and end-user data associated with said plurality of end-user identifiers, said end-user data including one or more electronic interactions performed by said plurality of client computing devices associated with said plurality of end-user identifiers accessing one or more websites assembled based at least in part on a website construction repository.

66. 66. The website building system of claim 65, wherein the end user data is further associated with a plurality of TER vector assignments, and all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator.

67. 67. The website building system of claim 66, wherein the engagement indicator is positive or negative.

68. 67. The website building system of claim 66, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

69. 65. The website building system of claim 64, wherein the one or more trained machine learning models are configured to generate the engagement score for the first end user identifier based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, the engagement score representing a programmatically generated likelihood that the client computing device associated with the first end user identifier will engage with the website after receiving the engagement transmission.

70. 70. The website building system of claim 69, wherein selecting the first end user identifier is based at least in part on the engagement score exceeding an engagement score threshold.

71. 65. The website building system of claim 64, wherein the one or more rule-based models are configured to select the first end user identifier based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to learned rules.

72. 65. The website building system of claim 64, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

73. 73. The website building system of claim 72, wherein the remote computing device is one of a POS terminal or a sensor device.

74. 65. The website building system of claim 64, wherein the engagement transmission comprises an electronic communication.

75. 65. The website building system of claim 64, wherein the engagement transmission comprises an electronic incentive communication.

76. 65. The website building system of claim 64, wherein the engagement transmission comprises a change in event type.

77. 65. The website building system of claim 64, wherein the engagement transmission includes a change in an event time.

78. The operation is 65. The website building system of claim 64, further comprising automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

79. 65. The website building system of claim 64, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

80. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically generating recommended engagement transmissions, the operations comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to an engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and website edit history interactions associated with the website identifier; generating an engagement transmission based at least in part on the first end user identifier and the website identifier; and sending the engagement transmission to a client computing device associated with the first end user identifier.

81. 81. The non-transitory computer-readable storage medium of claim 80, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by the plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

82. 82. The non-transitory computer-readable storage medium of claim 81 , wherein the end user data is further associated with a plurality of TER vector assignments, and wherein every TER vector assignment of the plurality of TER vector assignments is associated with an engagement indicator.

83. 83. The non-transitory computer-readable storage medium of claim 82, wherein the engagement indicator is positive or negative.

84. 84. The non-transitory computer-readable storage medium of claim 83, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

85. 81. The non-transitory computer-readable storage medium of claim 80, wherein the one or more trained machine learning models are configured to generate the engagement score for the first end user identifier based at least in part on the historical edit interactions associated with the editing user identifier and the end user data, the engagement score representing a programmatically generated likelihood that the client computing device associated with the first end user identifier will engage with the website after receiving the engagement transmission.

86. 86. The non-transitory computer-readable storage medium of claim 85, wherein selecting the first end user identifier is based at least in part on the engagement score exceeding an engagement score threshold.

87. 81. The non-transitory computer-readable storage medium of claim 80, wherein the one or more rule-based models are configured to select the first end user identifier based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to learned rules.

88. 81. The non-transitory computer-readable storage medium of claim 80, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

89. 90. The non-transitory computer-readable storage medium of claim 88, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

90. 81. The non-transitory computer-readable storage medium of claim 80, wherein the engagement transmission comprises an electronic communication.

91. 81. The non-transitory computer-readable storage medium of claim 80, wherein the engagement transmission comprises an electronic incentive communication.

92. 81. The non-transitory computer-readable storage medium of claim 80, wherein the engagement transmission comprises a change in an event type.

93. 81. The non-transitory computer-readable storage medium of claim 80, wherein the engagement transmission includes a change in an event time.

94. The operation is 81. The non-transitory computer-readable storage medium of claim 80, further comprising automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

95. 81. The non-transitory computer-readable storage medium of claim 80, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

96. 1. A computer-implemented method for automatically generating recommended engagement transmissions, the method comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a first end user identifier of the plurality of end user identifiers as an engagement transmission candidate according to an engagement score based at least in part on applying one or more trained machine learning or rule-based models to the end user data and website edit history interactions associated with the website identifier; generating an engagement transmission based at least in part on the first end user identifier and the website identifier; sending the engagement transmission to a client computing device associated with the first end user identifier.

97. 97. The method of claim 96, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by the plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

98. 98. The method of claim 97, wherein the end user data is further associated with a plurality of TER vector assignments, and wherein all TER vector assignments of the plurality of TER vector assignments are associated with an engagement indicator.

99. 99. The method of claim 98, wherein the engagement indicator is positive or negative.

100. 100. The method of claim 99, wherein selecting the first end user identifier is based at least in part on a count of positive engagement indicators.

101. 97. The method of claim 96, wherein the one or more trained machine learning models are configured to generate the engagement score for the first end user identifier based at least in part on the historical edit interactions associated with the editing user identifier and the end user data, the engagement score representing a programmatically generated likelihood that the client computing device associated with the first end user identifier will engage with the website after receiving the engagement transmission.

102. 102. The method of claim 101, wherein selecting the first end user identifier is based at least in part on the engagement score exceeding an engagement score threshold.

103. 97. The method of claim 96, wherein the one or more rule-based models are configured to select the first end user identifier based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the first end user identifier is identified according to learned rules.

104. 97. The method of claim 96, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

105. 105. The method of claim 104, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

106. 97. The method of claim 96, wherein the engagement transmission comprises an electronic communication.

107. 97. The method of claim 96, wherein the engagement transmission comprises an electronic incentive communication.

108. 97. The method of claim 96, wherein the engagement transmission comprises a change in event type.

109. 97. The method of claim 96, wherein the engagement transmission comprises a change in an event time.

110. 97. The method of claim 96, further comprising automatically performing a TER vector assignment operation based at least in part on the engagement transmission.

111. 97. The method of claim 96, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

112. 1. A website construction system configured to automatically perform an interface populating operation, the website construction system comprising: one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; One or more processors configured to execute software instructions to perform operations for automatically performing an interface populating operation, said operations comprising: obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier selected from a plurality of website identifiers, the website identifier associated with an editor user identifier of the plurality of editor user identifiers; obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; and automatically performing interface populating operations according to website records of the website vector based at least in part on applying one or more trained machine learning or rule-based models to the end user data, website edit history interactions associated with the website identifier, and the website vector.

113. 113. The website construction system of claim 112, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by the plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

114. 113. The website building system of claim 112, wherein the one or more trained machine learning models are configured to identify the interface injection actions based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vector.

115. 113. The website building system of claim 112, wherein the one or more rule-based models are configured to identify the interface populating actions according to learned rules based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vectors.

116. 113. The website building system of claim 112, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

117. 117. The website building system of claim 116, wherein the remote computing device is one of a POS terminal or a sensor device.

118. The operation is 113. The website building system of claim 112, further comprising presenting an output of the interface populating operation for inclusion on the website.

119. 113. The website building system of claim 112, wherein the interface populating operation includes creating a new template.

120. 113. The website building system of claim 112, wherein the interface populating operation includes updating an existing template.

121. 113. The website building system of claim 112, wherein the interface population operation includes pre-populating one or more interface elements of a temporary external resource (TER) vector allocation template for the website identifier.

122. 122. The website building system of claim 121, wherein the interface elements include one or more of an image, a currency element, or a TER allocation element.

123. 123. The website building system of claim 122, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to a TER vector.

124. 123. The website building system of claim 122, wherein the TER allocation element includes a menu icon for completing a TER allocation operation according to a TER vector.

125. 113. The website building system of claim 112, wherein the resource matrix includes a plurality of TER vectors.

126. The operation is 119. The website building system of claim 118, further comprising including the output on the website upon receiving electronic approval from a client computing device.

127. 113. The website building system of claim 112, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

128. 1. A non-transitory computer-readable storage medium containing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically performing an interface populating operation, the operations comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix; From the End User Data Corpus, associated with multiple End User Identifiers acquiring end-user data including electronic interaction data, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; and automatically performing interface populating operations according to website records of the website vector based at least in part on applying one or more trained machine learning or rule-based models to the end user data, website edit history interactions associated with the website identifier, and the website vector.

129. 129. The non-transitory computer-readable storage medium of claim 128, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by the plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

130. 129. The non-transitory computer-readable storage medium of claim 128, wherein the one or more trained machine learning models are configured to identify the interface populating actions based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vector.

131. 129. The non-transitory computer-readable storage medium of claim 128, wherein the one or more rule-based models are configured to identify the interface populating actions according to learned rules based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vectors.

132. 129. The non-transitory computer-readable storage medium of claim 128, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

133. 133. The non-transitory computer-readable storage medium of claim 132, wherein the remote computing device is one of a point-of-sale terminal or a sensor device.

134. The operation is 129. The non-transitory computer-readable storage medium of claim 128, further comprising presenting an output of the interface populating operation for inclusion on the website.

135. 129. The non-transitory computer-readable storage medium of claim 128, wherein the interface populating operation includes generating a new template.

136. 129. The non-transitory computer-readable storage medium of claim 128, wherein the interface populating operation comprises updating an existing template.

137. The interface populating operation pre-populates one or more interface elements of a temporary external resource (TER) vector allocation template for the website identifier.

129. The non-transitory computer-readable storage medium of claim 128, comprising:

138. 138. The non-transitory computer-readable storage medium of claim 137, wherein the interface element includes one or more of an image, a currency element, or a TER allocation element.

139. 139. The non-transitory computer-readable storage medium of claim 138, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to a TER vector.

140. 139. The non-transitory computer-readable storage medium of claim 138, wherein the TER assignment element includes a menu icon for completing a TER assignment operation according to a TER vector.

141. 129. The non-transitory computer-readable storage medium of claim 128, wherein the resource matrix comprises a plurality of TER vectors.

142. The operation is 135. The non-transitory computer-readable storage medium of claim 134, further comprising including the output on the website upon receiving electronic approval from a client computing device.

143. 129. The non-transitory computer-readable storage medium of claim 128, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

144. 1. A computer-implemented method for automatically performing an interface populating operation, the method comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining a website vector associated with the website identifier, the website vector including a plurality of website records and a resource matrix; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a plurality of end-user identifiers, the end-user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end-user identifiers accessing one or more websites assembled based at least in part on the one or more website construction repositories; and automatically performing interface populating operations according to website records of the website vector based at least in part on applying one or more trained machine learning or rule-based models to the end user data, website edit history interactions associated with the website identifier, and the website vector.

145. 145. The method of claim 144, wherein the one or more trained machine learning models are trained using historical editing interactions associated with the plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by the plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

146. 145. The method of claim 144, wherein the one or more trained machine learning models are configured to identify the interface injection action based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vector.

147. 145. The method of claim 144, wherein the one or more rule-based models are configured to identify the interface populating actions according to learned rules based at least in part on the historical editing interactions associated with the editing user identifier, the end user data, and the website vectors.

148. 145. The method of claim 144, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

149. 149. The method of claim 148, wherein the remote computing device is one of a POS terminal or a sensor device.

150. 145. The method of claim 144, further comprising presenting an output of the interface populating operation for inclusion on the website.

151. 145. The method of claim 144, wherein the interface populating operation includes creating a new template.

152. 145. The method of claim 144, wherein the interface populating operation includes updating an existing template.

153. 145. The method of claim 144, wherein the interface populating operation comprises pre-populating one or more interface elements of a temporary external resource (TER) vector allocation template for the website identifier.

154. 154. The method of claim 153, wherein the interface element includes one or more of an image, a currency element, or a TER allocation element.

155. 155. The method of claim 154, wherein the TER assignment element includes a selectable icon for completing a TER assignment operation according to a TER vector.

156. 155. The method of claim 154, wherein the TER assignment element includes a menu icon for completing a TER assignment operation according to a TER vector.

157. 145. The method of claim 144, wherein the resource matrix comprises a plurality of TER vectors.

158. 151. The method of claim 150, further comprising including the output on the website upon receiving electronic approval from a client computing device.

159. 145. The method of claim 144, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

160. 1. A website building system configured to provide temporary external resource (TER) integration, the website building system comprising: one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; One or more processors configured to execute software instructions for performing operations for automatically generating an electronic temporary external resource (TER) vector allocation recommendation response, the operations comprising: receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata; extracting the allocation request metadata; Based at least in part on the allocation request metadata, obtaining one or more website identifiers associated with websites to be assembled based at least in part on the one or more website construction repositories, the one or more website identifiers being selected from a plurality of website identifiers, each of the one or more website identifiers being associated with an editor user identifier of the plurality of editor user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating a TER vector allocation recommendation according to the TER vector; and transmitting the TER vector allocation recommendation to a requesting client computing device.

161. 161. The website construction system of claim 160, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

162. 161. The website building system of claim 160, wherein the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

163. 161. The website building system of claim 160, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

164. 163. The website building system of claim 162, wherein the TER vector is further selected based at least in part on maximizing a TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

165. 161. The website building system of claim 160, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of the assignment request metadata.

166. 161. The website building system of claim 160, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between end user data and one or more items of the assignment request metadata.

167. 161. The website building system of claim 160, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

168. 161. The website building system of claim 160, wherein the TER vector comprises a plurality of TER vector records.

169. 169. The website building system of claim 168, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

170. 161. The website building system of claim 160, wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the one or more website identifiers.

171. 161. The website building system of claim 160, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the one or more website identifiers.

172. The remote computing device is a POS terminal or a sensor device.

172. The website building system of claim 171, wherein the website building system is one of the above.

173. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of an apparatus, cause the apparatus to perform operations for automatically generating an electronic temporary external resource (TER) vector allocation recommendation response, the operations comprising: receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata; extracting the allocation request metadata; Based at least in part on the allocation request metadata, obtaining one or more website identifiers associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the one or more website identifiers being selected from a plurality of website identifiers, each of the one or more website identifiers being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating a TER vector allocation recommendation according to the TER vector; and transmitting the TER vector allocation recommendation to a requesting client computing device.

174. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

175. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

176. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

177. 176. The non-transitory computer-readable storage medium of claim 175, wherein the TER vector is further selected based at least in part on maximizing a TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

178. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of the assignment request metadata.

179. 174. The non-transitory computer-readable storage medium of claim 173, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between end user data and one or more items of the assignment request metadata.

180. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

181. 174. The non-transitory computer-readable storage medium of claim 173, wherein the TER vector comprises a plurality of TER vector records.

182. 182. The non-transitory computer-readable storage medium of claim 181, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

183. 174. The non-transitory computer-readable storage medium of claim 173, wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the one or more website identifiers.

184. 174. The non-transitory computer-readable storage medium of claim 173, wherein the end-user data further comprises electronic interaction data received from a remote computing device associated with the one or more website identifiers.

185. 185. The non-transitory computer-readable storage medium of claim 184, wherein the remote computing device is one of a POS terminal or a sensor device.

186. 1. A computer-implemented method for automatically generating an electronic temporary external resource (TER) vector allocation recommendation response, the method comprising: receiving a TER vector allocation recommendation request, the TER vector allocation recommendation request including allocation request metadata; extracting the allocation request metadata; Based at least in part on the allocation request metadata, obtaining one or more website identifiers associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the one or more website identifiers being selected from a plurality of website identifiers, each of the one or more website identifiers being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; selecting a TER vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating a TER vector allocation recommendation according to the TER vector; and sending the TER vector allocation recommendation to a requesting client computing device.

187. 187. The method of claim 186, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

188. 187. The method of claim 186, wherein the one or more trained machine learning models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is selected according to a first programmatically generated likelihood that a TER assignment operation will be performed according to the TER vector of the first end user identifier and the one or more website identifiers, and a second programmatically generated likelihood that the TER assignment operation will be associated with a positive engagement indicator.

189. 187. The method of claim 186, wherein the one or more rule-based models are configured to select the TER vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the TER vector is identified according to learned rules.

190. 189. The method of claim 188, wherein the TER vector is further selected based at least in part on maximizing a TER vector allocation for a multidimensional matrix associated with the allocation request metadata.

191. 187. The method of claim 186, wherein the one or more website identifiers are obtained based at least in part on a determined similarity between data associated with the one or more website identifiers and one or more items of the assignment request metadata.

192. 187. The method of claim 186, wherein the first end user identifier is selected based at least in part on interactions associated with the first end user identifier and a determined similarity between end user data and one or more items of the assignment request metadata.

193. 187. The method of claim 186, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

194. 187. The method of claim 186, wherein the TER vector comprises a plurality of TER vector records.

195. 200. The method of claim 194, wherein a TER vector record of the plurality of TER vector records includes one or more of an event identifier, an event type identifier, a TER start time, a TER end time, or a TER location.

196. 187. The method of claim 186, wherein the one or more electronic interactions include one or more TER vector assignments associated with the first end user identifier and the one or more website identifiers.

197. 187. The method of claim 186, wherein the end-user data further comprises electronic interaction data received from a remote computing device associated with the one or more website identifiers.

198. 200. The method of claim 197, wherein the remote computing device is one of a POS terminal or a sensor device.

199. 1. A website building system configured to provide reservation integration within a website created using said website building system, said website building system comprising: one or more website construction repositories that store one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers; One or more processors configured to execute software instructions for performing operations for automatically generating an electronic booking recommendation interface, the operations comprising: obtaining a website identifier associated with a website to be assembled based at least in part on the one or more website construction repositories, the website identifier selected from a plurality of website identifiers, the website identifier associated with an editor user identifier of the plurality of editor user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories, wherein the end-user data associated with the first end-user identifier obtaining, including one or more electronic interactions being performed; Obtaining a multi-dimensional reservation matrix maintained by the website building system and associated with the website identifier, the multi-dimensional reservation matrix including a plurality of reservation vectors; selecting a reservation vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic reservation recommendation interface according to the reservation vector; and one or more processors, wherein the electronic booking recommendation interface is sent to a client computing device associated with the first end user identifier or the editing user identifier.

200. 200. The website construction system of claim 199, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

201. 200. The website building system of claim 199, wherein the one or more trained machine learning models are configured to select the reservation vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reservation vector is selected according to a first programmatically generated likelihood that a reservation assignment operation will be performed in accordance with the reservation vector of the first end user identifier and the website identifier, and a second programmatically generated likelihood that the reservation assignment operation will be associated with a positive engagement indicator.

202. 200. The website building system of claim 199, wherein the one or more rule-based models are configured to select the reserved vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reserved vector is identified according to learned rules.

203. 202. The website building system of claim 201, wherein the reservation vector is further selected based at least in part on maximizing reservation vector allocation for the multidimensional reservation matrix.

204. 202. The website building system of claim 201, wherein the reservation vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a reservation vector assignment for the reservation vector, or whether the reservation vector is associated with an ongoing event.

205. 200. The website building system of claim 199, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

206. 200. The website building system of claim 199, wherein the reservation vector includes a plurality of reservation vector records.

207. 207. The website building system of claim 206, wherein a reservation vector record of the plurality of reservation vector records includes one or more of an event identifier, an event type identifier, an event time, or an event location.

208. 200. The website construction system of claim 199, wherein the one or more electronic interactions include one or more reserved vector assignments associated with the first end user identifier and the website identifier.

209. 209. The website building system of claim 208, wherein the one or more reserved vector assignments are associated with an engagement indicator.

210. 210. The website building system of claim 209, wherein the engagement indicator is one of positive, negative, or partial.

211. 209. The website building system of claim 209, wherein the first end user identifier is associated with a plurality of reserved vector assignments associated with the website identifier, and all reserved vector assignments of the plurality of reserved vector assignments are associated with a positive engagement indicator.

212. 200. The website building system of claim 199, wherein end-user data further includes electronic interaction data received from a remote computing device associated with the website identifier.

213. 213. The website building system of claim 212, wherein the remote computing device is one of a POS terminal or a sensor device.

214. 200. The website building system of claim 199, wherein the website identifier is associated with a transaction category type.

215. 215. The website building system of claim 214, wherein the transaction category type is one of electronic or non-electronic.

216. 200. The website building system of claim 199, wherein the electronic booking recommendation interface includes one or more selectable interface elements.

217. The operation is 200. The website construction system of claim 199, further comprising automatically performing a reserved vector allocation operation according to the reserved vector.

218. The operation is based at least in part on performing the reservation vector allocation operation; converting an availability record associated with the reservation vector to reflect the reservation vector allocation operation; 218. The website building system of claim 217, further comprising: synchronizing the multidimensional booking matrix with a second multidimensional booking matrix.

219. The operation is 219. The website building system of claim 218, further comprising configuring the second multidimensional reservation matrix for display via the website.

220. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a device, cause the device to perform operations for automatically generating an electronic appointment recommendation interface, the operations comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; Obtaining a multi-dimensional reservation matrix maintained by a website building system and associated with the website identifier, the multi-dimensional reservation matrix including a plurality of reservation vectors; selecting a reservation vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic reservation recommendation interface according to the reservation vector; and transmitting the electronic appointment recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

221. 221. The non-transitory computer-readable storage medium of claim 220, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

222. 221. The non-transitory computer-readable storage medium of claim 220, wherein the one or more trained machine learning models are configured to select the reservation vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reservation vector is selected according to a first programmatically generated likelihood that a reservation assignment operation will be performed in accordance with the reservation vector of the first end user identifier and the website identifier, and a second programmatically generated likelihood that the reservation assignment operation will be associated with a positive engagement indicator.

223. 221. The non-transitory computer-readable storage medium of claim 220, wherein the one or more rule-based models are configured to select the reserved vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reserved vector is identified according to learned rules.

224. 223. The non-transitory computer-readable storage medium of claim 222, wherein the reservation vector is further selected based at least in part on maximizing a reservation vector assignment for the multidimensional reservation matrix.

225. 223. The non-transitory computer-readable storage medium of claim 222, wherein the reservation vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a reservation vector assignment for the reservation vector, or whether the reservation vector is associated with an ongoing event.

226. 221. The non-transitory computer-readable storage medium of claim 220, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

227. 221. The non-transitory computer-readable storage medium of claim 220, wherein the reservation vector comprises a plurality of reservation vector records.

228. 228. The non-transitory computer-readable storage medium of claim 227, wherein a reservation vector record of the plurality of reservation vector records includes one or more of an event identifier, an event type identifier, an event time, or an event location.

229. 221. The non-transitory computer-readable storage medium of claim 220, wherein the one or more electronic interactions include one or more reserved vector assignments associated with the first end user identifier and the website identifier.

230. 230. The non-transitory computer-readable storage medium of claim 229, wherein the one or more reserved vector assignments are associated with an engagement indicator.

231. 231. The non-transitory computer-readable storage medium of claim 230, wherein the engagement indicator is one of positive, negative, or partial.

232. 231. The non-transitory computer-readable storage medium of claim 230, wherein the first end user identifier is associated with a plurality of reserved vector assignments associated with the website identifier, and all reserved vector assignments of the plurality of reserved vector assignments are associated with a positive engagement indicator.

233. 221. The non-transitory computer-readable storage medium of claim 220, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

234. 234. The non-transitory computer-readable storage medium of claim 233, wherein the remote computing device is one of a POS terminal or a sensor device.

235. the website identifier is associated with a transaction category type; 221. The non-transitory computer-readable storage medium of claim 220.

236. 236. The non-transitory computer-readable storage medium of claim 235, wherein the transaction category type is one of electronic or non-electronic.

237. 221. The non-transitory computer-readable storage medium of claim 220, wherein the electronic appointment recommendation interface includes one or more selectable interface elements.

238. The operation is 221. The non-transitory computer-readable storage medium of claim 220, further comprising automatically performing a reservation vector allocation operation according to the reservation vector.

239. The operation is based at least in part on performing the reservation vector allocation operation; converting an availability record associated with the reservation vector to reflect the reservation vector allocation operation; 221. The non-transitory computer-readable storage medium of claim 220, further comprising: synchronizing the multi-dimensional reservation matrix with a second multi-dimensional reservation matrix.

240. The operation is 240. The non-transitory computer-readable storage medium of claim 239, further comprising configuring the second multi-dimensional reservation matrix for display via the website.

241. 1. A computer-implemented method for automatically generating an electronic appointment recommendation interface, the method comprising: obtaining a website identifier associated with a website assembled based at least in part on one or more website construction repositories, the one or more website construction repositories storing one or more website construction components and one or more website editing history interactions associated with a plurality of editing user identifiers, the website identifier being selected from a plurality of website identifiers, the website identifier being associated with an editing user identifier of the plurality of editing user identifiers; obtaining, from an end-user data corpus, end-user data including electronic interaction data associated with a first end-user identifier of a plurality of end-user identifiers, the end-user data associated with the first end-user identifier including one or more electronic interactions performed by a first client computing device associated with the first end-user identifier accessing one or more websites assembled based at least in part on the one or more website construction repositories; Obtaining a multi-dimensional reservation matrix maintained by a website building system and associated with the website identifier, the multi-dimensional reservation matrix including a plurality of reservation vectors; selecting a reservation vector based at least in part on applying one or more trained machine learning models or one or more rule-based models to historical editing interactions associated with said editing user identifier and to said end user data; generating an electronic reservation recommendation interface according to the reservation vector; transmitting the electronic booking recommendation interface to a client computing device associated with the first end user identifier or the editing user identifier.

242. 242. The method of claim 241, wherein the one or more trained machine learning models are trained using historical editing interactions associated with a plurality of editing user identifiers that assemble websites based at least in part on the one or more website construction repositories, and end user data associated with the plurality of end user identifiers, the end user data including one or more electronic interactions performed by a plurality of client computing devices associated with the plurality of end user identifiers that access one or more websites assembled based at least in part on the one or more website construction repositories.

243. 242. The method of claim 241, wherein the one or more trained machine learning models are configured to select the reservation vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reservation vector is selected according to a first programmatically generated likelihood that a reservation assignment operation will be performed according to the reservation vector of the first end user identifier and the website identifier, and a second programmatically generated likelihood that the reservation assignment operation will be associated with a positive engagement indicator.

244. 242. The method of claim 241, wherein the one or more rule-based models are configured to select the reserved vector based at least in part on the historical editing interactions associated with the editing user identifier and the end user data, and the reserved vector is identified according to learned rules.

245. 244. The method of claim 243, wherein the reservation vector is further selected based at least in part on maximizing reservation vector assignment for the multidimensional reservation matrix.

246. 244. The method of claim 243, wherein the reservation vector is further selected based at least in part on one of an expected currency value and the first end user identifier associated with a reservation vector assignment for the reservation vector, or whether the reservation vector is associated with an ongoing event.

247. 242. The method of claim 241, wherein the one or more website building tools include one or more of a page, a subpage, a container, a component, an atomic component, a content element, a layout element, a template, or a layout rule.

248. 242. The method of claim 241, wherein the reservation vector comprises a plurality of reservation vector records.

249. 249. The method of claim 248, wherein a reservation vector record of the plurality of reservation vector records includes one or more of an event identifier, an event type identifier, an event time, or an event location.

250. 242. The method of claim 241, wherein the one or more electronic interactions include one or more reserved vector assignments associated with the first end user identifier and the website identifier.

251. 251. The method of claim 250, wherein the one or more reserved vector assignments are associated with an engagement indicator.

252. 252. The method of claim 251, wherein the engagement indicator is one of positive, negative, or partial.

253. 252. The method of claim 251, wherein the first end user identifier is associated with a plurality of reserved vector assignments associated with the website identifier, and all reserved vector assignments of the plurality of reserved vector assignments are associated with a positive engagement indicator.

254. 242. The method of claim 241, wherein end-user data further comprises electronic interaction data received from a remote computing device associated with the website identifier.

255. 255. The method of claim 254, wherein the remote computing device is one of a POS terminal or a sensor device.

256. 242. The method of claim 241, wherein the website identifier is associated with a transaction category type.

257. 257. The method of claim 256, wherein the transaction category type is one of electronic or non-electronic.

258. 242. The method of claim 241, wherein the electronic appointment recommendation interface includes one or more selectable interface elements.

259. 242. The method of claim 241, further comprising automatically performing a reservation vector allocation operation according to the reservation vector.

260. based at least in part on performing the reservation vector allocation operation; converting an availability record associated with the reservation vector to reflect the reservation vector allocation operation; 242. The method of claim 241, further comprising: synchronizing the multi-dimensional reservation matrix with a second multi-dimensional reservation matrix.

261. 261. The method of claim 260, further comprising configuring the second multidimensional reservation matrix for display via the website.

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