Training for new behaviors

Abstract

The disclosed behavioral training system and method effectively teaches new target behaviors using a combination of modified video self-modeling, generalization, and applied behavior analysis techniques. According to one embodiment, the system and / or method includes receiving a selection of a target behavior, constructing a visual representation of a user environment, transmitting the constructed visual representation of the user environment to a user electronic computing device, generating a behavioral clip associated with performing the target behavior, receiving a selection of a stimulus object, and in response to receiving the selection of the stimulus object, transmitting to the user electronic computing device the generated behavioral clip and one or more rewards to encourage the user to continue engaging with the behavioral training system.

Description

[Background technology]

[0001] This application was filed as a PCT international patent application on October 14, 2022, and claims the benefit of and priority to U.S. Provisional Patent Application Serial No. 63 / 256,262, filed on October 15, 2021, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Behavior is the actions and attitudes that an individual adopts in relation to himself and his environment. Humans have the ability to learn new behaviors. Some learning may be triggered immediately by a single event; however, most learning is based on knowledge accumulated over time and repeated experience. Learning new behaviors is a complex process and often requires effort and repeated practice. Summary of the Invention

[0003] Embodiments of the present disclosure relate to a behavioral training system that uses a combination of applied behavior analysis and modified video self-modeling to enable users to achieve the performance of new behaviors.

[0004] In a first embodiment, a computer-implemented method for training a user in teaching a target behavior is disclosed. The method includes receiving a selection of the target behavior from a user electronic computing device, constructing a visual representation of a user environment, where the visual representation of the user environment includes at least one stimulus object, transmitting the constructed visual representation of the user environment to the user electronic computing device, determining a sequence of steps for the target behavior, generating a behavioral clip associated with performing the target behavior, where the behavioral clip includes visualizations of some but not all of the determined sequence of steps, receiving the selection of the stimulus object, and transmitting the generated behavioral clip to the user electronic computing device in response to receiving the selection of the stimulus object.

[0005] In a second embodiment, a system for training a user in a target behavior is disclosed, the system comprising a processor and a memory containing instructions that, when executed by the process, cause the processor to perform the following: receive a selection of the target behavior from a user electronic computing device, construct a visual representation of the user environment, the visual representation of the user environment including at least one stimulus object, transmit the constructed visual representation of the user environment to the user electronic computing device, determine a sequence of steps for the target behavior, generate a behavioral clip associated with performance of the target behavior, the behavioral clip including visualization of some but not all of the steps of the determined sequence of steps, receive a selection of the stimulus object, and transmit the generated behavioral clip to the user electronic computing device in response to receiving the selection of the stimulus object.

[0006] In a third embodiment, a system for training a user on a target behavior is disclosed, the system comprising: a display device; a processor; and a memory including instructions that, when executed by the processor, cause the processor to: display a behavioral training user interface on the display device, the behavioral training user interface including one or more user-selectable options related to the target behavior; receive one or more selections from a user related to the target behavior; transmit the one or more selections related to the target behavior to a server computing device; receive a constructed visual representation of a user environment from the server computing device, the constructed visual representation of the user environment including at least one stimulus object; display on the display device the constructed visual representation of the user environment and a prompt requesting the user to trigger the stimulus object; receive a selection of the stimulus object from the user; transmit the selection of the stimulus object to the server computing device; receive a behavioral clip related to performance of the target behavior, the behavioral clip not including content related to performance of all steps of the target behavior, and receive one or more rewards in response to transmitting the selection of the stimulus object, and display the one or more rewards on the display device to encourage the user to continue engaging with the behavioral training user interface.

[0007] The details of one or more techniques are set forth in the accompanying drawings and the description below. Other features, objects, and preferences of these techniques will become apparent from the description, drawings, and claims. [Brief description of the drawings]

[0008] The following drawings illustrate certain embodiments of the present disclosure and therefore do not limit the scope of the disclosure. The drawings are not to scale and are intended for use in conjunction with the descriptions in the following detailed description. Embodiments of the present disclosure will now be described in conjunction with the accompanying drawings, in which like numerals refer to like elements. [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a behavior learning system according to the present disclosure. [Diagram 2] FIG. 2 is a diagram showing an example of the configuration of a behavioral training engine of the system of FIG. [Diagram 3] FIG. 3 is a diagram showing an example of the configuration of the behavior construction module of the behavior training engine of FIG. [Figure 4] FIG. 4 illustrates an example method for learning new behaviors according to the present disclosure that can be performed using the system of FIG. [Diagram 5] FIG. 5 illustrates an example visual representation of the behavioral training user interface of FIG. 1 displaying a built environment. [Figure 6] FIG. 6 illustrates an example visual representation of the behavioral training user interface of FIG. 1 displaying a built environment that integrates elements related to a selected target behavior. [Figure 7] FIG. 7 illustrates an example visual representation of the behavioral training user interface of FIG. 1 displaying snippets of behavioral clips. [Figure 8] FIG. 8 illustrates an example visual representation of the behavioral training user interface of FIG. 1 displaying rewards. [Figure 9] FIG. 9 illustrates an example visual representation of the behavioral training user interface of FIG. 1 displaying modifications to elements of the integrated built environment. [Figure 10] FIG. 10 illustrates another example visual representation of the behavioral training user interface of FIG. 1 displaying another modification to an element of the integrated built environment. [Figure 11] FIG. 11 is a diagram illustrating example physical components of the computing device of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Various embodiments will now be described in detail with reference to the drawings, in which like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments is not intended to limit the scope of the claims appended hereto. Moreover, the illustrative examples herein are not intended to be limiting, but merely to illustrate some of the many possible embodiments for the appended claims.

[0010] In general, the subject matter of the present disclosure relates to a platform for learning new behaviors that uses a combination of applied behavior analysis principles, generalization, and modified video self-modeling principles to enable users to achieve real-life execution of new behaviors.

[0011] Individuals often experience difficulties when learning new behaviors. In some cases, this may be due to cognitive rigidity, an inability to mentally adapt to new demands or information. In other examples, the disclosed behavioral training system can be used to assist users with other types of disabilities, such as autism, attention-deficit / hyperactivity disorder, and intellectual disabilities. Additionally, even users who do not experience cognitive or behavioral rigidity or disabilities may experience difficulties in learning new behaviors because different users learn differently based on their characteristics, diagnosis, functioning, and age. For example, difficulties may simply be due to the complex nature of the behavior, the inability to imitate it, cognitive understanding of the reasons and consequences, and social understanding of the behavior.

[0012] Difficulties in learning new behaviors may or may not be associated with a disability. Often, environmental, social, medical, and / or developmental reasons require that a particular behavior be learned. There may be other reasons for learning a behavior. A system that helps users train new behaviors in this way would assist individuals who have difficulty learning new behaviors.

[0013] In one example, a user with a medical condition may need to learn new behaviors related to wearing equipment for the treatment or rehabilitation of that condition. For example, a user may need to wear a helmet for rehabilitation. However, due to sensory issues or lack of ability to understand or visualize the benefits of wearing a helmet, they may not necessarily wear the helmet to protect themselves in case of a head hit. In another example, a user may need to unlearn a particular learned behavior by learning a new alternative behavior. A user may need to learn how to get from one place to another, transition to a new activity, give and receive toys or playthings, use a toilet, be food selective, not engage in violence, or self-harm. Other examples related to learning new behaviors are also possible.

[0014] In other instances, younger children or those who exhibit strong cognitive rigidity or other behavioral characteristics may have difficulty entering new spaces, toilet training, dealing with changes in the environment, wearing new clothing, sharing objects with others, etc. Client characteristics that generally make it difficult to learn new behaviors may be improved in clinical settings with therapeutic techniques such as video self-modeling and chaining.

[0015] Video self-modeling is a therapeutic technique used to teach a client new behaviors or skills and involves using a video recording as a model. A new behavior or skill that the client has not yet mastered or has not mastered, but that the client or the client's caregiver would like the client to master, may be referred to as a "target behavior." In video self-modeling, for example, the user may be shown a video recording of themselves successfully performing all of the steps associated with the target behavior in order to successfully complete the target behavior or skill at a later date without the benefit of the video recording.

[0016] For example, if the target behavior was to drink from a cup, the user could be shown footage of the user moving their hand to the cup, grasping the handle of the cup, picking up the cup, bringing the cup to their mouth, and sipping from the cup to their mouth. Footage of a user performing a target behavior is typically created by first obtaining footage of the user performing the target behavior with the assistance of a caregiver or third party, and then using video editing software to remove the caregiver or third party from the footage so that the resulting final footage appears to show the user performing the target behavior independently. Video self-modeling is used to help users learn the steps to successfully perform a target behavior. However, showing a series of steps to achieve a target behavior may take a long time before the user is able to successfully complete the target behavior or skill. For example, individuals on the autism spectrum may have difficulty concentrating on a video uninterrupted and / or for extended periods of time, may become confused when a behavior involves several steps, and may fixate on or get stuck on minor and / or non-essential details in the video recording.

[0017] Generalization is another technique for teaching users new behaviors. Generalization is the ability of a learner to perform a skill or new behavior under different conditions (stimulus generalization), to apply a skill in different ways (response generalization), and to continue to demonstrate the skill over time (retention). Generalization is a way to teach a new behavior so that the user learns the behavior itself outside of the components of the environment or stimulus. For example, to continue teaching users how to use the toilet, the user is shown pictures and videos of the main bathroom in the client's home, which has yellow tile floors, blue wallpaper, a sink to the left of the toilet, and a door to the toilet to the left of the sink.

[0018] Generalization provides a way for a user to learn, for example, the behavior of going to the toilet without needing the environmental cues associated with a single toilet environment. In other words, generalization provides a way for a user to learn the behavior of going to the toilet no matter what environment the toilet is in.

[0019] One way to apply generalization is to vary the environment and stimuli associated with the target behavior. For example, when training a user for a new behavior, the environmental cues and stimuli can be changed periodically or at a constant pace based on the user's progress in learning the target behavior, allowing the user to learn the behavior without relying on environmental cues to initiate or complete the behavior.

[0020] Chaining is another technique based on task analysis to teach users new behaviors. Chaining breaks down a task into small steps and teaches each step individually. For example, a person learning to wash their hands might start by turning on the tap. Once this first skill is mastered, the next skill might be to put their hands under water. Chaining may be effective in helping with everyday tasks. However, because chaining focuses on learning each step involved in a behavior in order, it may take a long time to master the target behavior.

[0021] In some embodiments, the disclosed behavioral training system uses a combination of modified video self-modeling techniques, generalization and applied behavior analysis techniques to effectively teach new behaviors. For example, the disclosed behavioral training system may be a computer-implemented platform accessible to a user using an electronic computing device. The disclosed behavioral training system may build a visual representation of the user's real-life environment using photographs or digital representations of the user's environment and the user and, optionally, photographs or digital representations of the user's therapist(s), caregiver(s), parents, friends, teachers, or other individuals who may be part of or helpful in the process of the user learning the target behavior.

[0022] Additionally, the disclosed behavioral learning platform can teach a user new behaviors by reinforcing the new behavior on the platform, rather than reinforcing the behavior when it is performed in reality. For example, the disclosed behavioral learning platform can use a novel approach to the principle of differential reinforcement of alternative behaviors by reinforcing a different behavior, such as a user's engagement with a behavioral learning platform that shows a digital likeness of the user accomplishing the behavior, rather than the user's behavior in real life, to help the user achieve real-life change.

[0023] For example, the behavioral training system may use a photograph or digital representation of the user and the user's real-world environment to construct a visual representation of the user in a built environment. The behavioral training system may then display a video of the visual representation of the user beginning to perform a goal behavior. However, instead of showing all of the steps required to complete the goal behavior, as in video self-modeling, the behavioral training system may, for example, skip one or more intermediate steps, or simply show a visual representation of the user performing the last step of the goal behavior or a visual representation of the user completing the goal behavior.

[0024] The behavioral training system encourages the user to interact with the built environment as a first step toward mastering a target behavior. In some examples, the behavioral training system can use personalized rewards to reinforce the user's progress toward successfully mastering the target behavior.

[0025] For example, for the goal behavior of drinking from a cup, the behavioral education platform may display an image of the user's environment along with an image of the user in the displayed environment. Upon receiving a trigger, the behavioral education platform may display an image of the user walking toward a cup on a table in the built environment. Prompts may be used to help the user understand what stimuli will trigger the behavior. In the case of the goal behavior of drinking from a cup, the prompts may include the user touching, clicking, or otherwise selecting an image of the cup on a display screen displaying the built environment.

[0026] After the image of the user reaches the cup, the behavioral education platform can transition directly to an image or short video click of the user already drinking from the cup - not showing the intermediate steps in this example of the user grasping the cup handle, picking up the cup from the table, and bringing it up to the user's mouth.

[0027] In some examples, the behavioral education platform may use visual or audio cues to prompt the user to trigger the prompt. For example, for a target behavior of drinking from a cup, the size of the cup may be increased so that it appears disproportionately large relative to the built environment. Increasing the size of an object may encourage the user to intuitively select or otherwise interact with the object, triggering the prompt. The type of prompt may also be personalized to the user based on the user's past behavior.

[0028] In some examples, the behavioral education platform can also provide personalized rewards as reinforcement for the user triggering the stimulus. Examples of rewards include, but are not limited to, stickers, emoticons, digital points, video clips of the user's favorite television show or movie, video clips of the user's family or friends, a video clip of the user's mother laughing, and other familiar rewards. In addition to various types of reward options, the behavioral education platform can also provide multiple tiers of rewards such that the user is presented with higher tier rewards as the user progresses in learning the target behavior in real life.

[0029] For example, a reward that provides positive reinforcement for one user may not necessarily work as positive reinforcement for another user. Similarly, the type of prompt that works for one user may not always work for another user. Thus, the behavioral education platform may determine the type of prompt(s) and / or reward(s) that are more conducive to triggering a stimulus based on the preferences of a particular individual user.

[0030] The behavioral education platform may use artificial intelligence and machine learning to learn the types of prompts and rewards that are likely to work for general users, as well as the types of prompts and rewards that are likely to work for specific users, and the types of prompts and / or rewards that are likely to work for specific types of target behaviors as opposed to other types. The artificial intelligence and machine learning models used by the behavioral education platform may also study the effects of a user's age, real-life environment, type of diagnosis, level of functioning, type of user equipment used, etc., on the pace and success at which the user learns new target behaviors. The artificial intelligence and machine learning models may then use the collected data in predicting how to improve the built and real-life environment, prompts, and rewards to improve the pace at which the user learns the target behavior. In other words, the artificial intelligence and machine learning models may be used by the behavioral education platform to build a personalized platform to improve the likelihood that a particular user will be successful in learning new behaviors and learn at a faster pace.

[0031] In addition to personalized triggers and prompts, the behavioral education platform can also use machine learning and artificial intelligence to learn about a user's interactions with the behavioral education platform. For example, the behavioral education platform can learn that a particular user is distracted by all the elements in the built environment, e.g., other objects in a room, and misses or takes too long to trigger a stimulus. In such cases, the behavioral education platform can remove the elements in the built environment to focus the user's attention on the prompt.

[0032] 1 is a diagram illustrating an example configuration of a behavioral training system 100. System 100 includes a user electronic computing device 102, a network 106, a server computer 108, and one or more data stores 112. In this example, server computer 108 includes a behavioral training engine 110. More, fewer, or different modules may be used.

[0033] In some examples, the user electronic computing device 102 is a user's electronic computing device. In some examples, the electronic computing device may be a desktop computer, a laptop computer, a virtual reality user device, or a mobile electronic computing device such as a smartphone or tablet computer. In some examples, the user electronic computing device 102 allows the user to access the server computer 108 over the network 106 and display data in the behavioral training user interface 104. In some examples, the user may be an individual experiencing cognitive rigidity. In other examples, the user may be an individual experiencing ADHD. In still other examples, the user may not have any cognitive issues and experience difficulty learning new behaviors. The user may suffer from an autism spectrum disorder, or the user may be an individual seeking to improve cognitive flexibility. Although a single user electronic computing device 102 is shown, the system 100 allows hundreds, thousands, or multiple computing devices to connect to the server computer 108.

[0034] The network 106 in some examples is a computer network such as the Internet. A user of the user electronic computing device 102 can access the server computer 108 via the network 106.

[0035] As a non-limiting example, the server computer 108 is a server computer of an entity, such as an entity that provides services related to improving cognitive flexibility. Although a single server is shown, in practice the server computer 108 may be implemented with multiple computing devices, such as a server farm, or a cloud-based server computer. Many other configurations are possible, as will be apparent to those skilled in the art.

[0036] In one embodiment, behavioral training engine 110 is configured to receive input related to the user's environment and a goal behavior that the user desires to achieve or that the user's therapist(s) or caregiver(s) desires the user to achieve, and behavioral training engine 110 is configured to generate and present one or more behavioral clips in response to stimuli that aid the user in learning the desired behavior. Implementation of behavioral training engine 110 is described in further detail in conjunction with Figures 2-11.

[0037] Examples of data store 112 may include one or more electronic databases that may store data related to users and / or the behavioral training engine 110. The data store 112 may be maintained by the same entity that maintains the server computer 108 or one or more external companies associated with the entity that maintains the server computer 108. The data store 112 may be accessed by the server computer 108 to obtain relevant data related to users and the behavioral training engine 110. The data store 112 may be accessed by the server computer 108 to obtain relevant data about a plurality of users to determine, for example, types of prompts and / or rewards that are more likely to work for certain types of target behaviors as opposed to others based on the success rates of those prompts and / or rewards among groups of users in the system.

[0038] Figure 2 illustrates an example configuration of the behavioral training engine 110 of Figure 1. In some examples, the behavioral training engine 110 may be configured to include a background building module 202, a behavior building module 204, and a personalization module 206. In other examples, the behavioral training engine 110 may be configured to include more or multiple modules.

[0039] In some examples, the background construction module 202 is configured to receive input data related to the user's background, including information related to the user's preferences and the user's environment, from the user electronic computing device 102. For example, the background construction module 202 can be configured to generate a behavioral training user interface 104 for display on the user electronic computing device 102.

[0040] The background building module 202, through the generated behavioral training user interface 104, can request the user or the user's therapist(s) or caregiver(s) to input information related to the user and the user's environment. For example, the requested data can include information about the user, including background information such as the user's name, age, current cognitive ability, rigidity level, information related to the user's social skills, the user's health history, the user's preferences, information related to the user's care provider, the user's diagnosis, the user's functional level, etc.

[0041] Additionally, the background building module 202, through the generated behavioral training user interface 104, may also request that the user or the user's therapist(s) or caregiver(s) provide one or more photographs or digital representations of the user's primary environment and one or more photographs or digital representations of the user. For example, the one or more photographs or digital representations of the environment may include a photograph or digital representation of a primary room in the user's home where the user spends time. The photographs or digital representations may also include any other environments associated with the user, such as rooms in the user's home, the user's school, work, playground, vehicles used by the user, etc.

[0042] The background construction module 202 constructs a visual representation of the user's primary environment using photographs or digital representations received from the user or the user's therapist(s) or caregiver(s) to construct a visual representation of the user's primary environment in which the user is located within the primary environment. For example, if the user spends a significant amount of time in a preschool classroom, the constructed primary environment may include a photograph or digital representation of the preschool classroom, overlaying a photograph or digital representation of the user within the image of the preschool classroom. Similar constructions of other user-associated environments may also be generated and stored in the data store 112.

[0043] The background construction module 202 may take into account the user's age, diagnosis, and functional level while constructing the constructed environment. For example, for a user with a lower functional level, the background construction module 202 may construct a constructed environment that does not include all of the objects present in the corresponding real-world environment. Instead, the background construction module 202 may create a constructed environment that is simple and free of objects that are deemed too distracting to the user. For a user with a higher functional level, the background construction module 202 may construct a constructed environment that may include most, if not all, of the objects found in the corresponding real-world environment.

[0044] Built environments typically use photographs or digital representations of the real environment, and real photographs or digital representations of the user, rather than creating cartoons or portraits of the environment or the user. In these preferred embodiments, the use of realistic visual representations of the user and the user's environment helps the user relate to and engage with the built environment. Examples of environments constructed by the background construction module 202 are described in further detail in conjunction with FIG. 5.

[0045] In some examples, the behavior building module 204 is configured to receive input data from the user electronic computing device 102 related to a goal behavior that the user is attempting to achieve or that the user's therapist(s) or caregiver(s) would like the user to achieve. Based on the received input related to the goal behavior, the behavior building module 204 can select, tailor, and personalize the goal behavior and the built environment for the user, generate behavior clips related to the goal behavior, prompt the user to trigger playback of the behavior clips, cause playback of the behavior clips to occur, and generate personalized rewards when the user successfully engages with the built environment and the behavior clips. User engagement can include the user interacting with the behavior clip in any manner, including digital interactions such as mouse clicks or touch inputs associated with the behavior clips, or real-world interactions such as the user viewing, touching, or otherwise interacting with the display of the behavior clip or attempting to copy the behavior depicted in the behavior clip. The configuration and operation of the behavior building module are described in further detail in conjunction with FIG. 3.

[0046] In some examples, the personalization module 206 is configured to personalize built environment elements, prompts, and rewards for a particular user using historical data from the particular user, artificial intelligence and / or machine learning models.

[0047] For example, the personalization module 206 can use historical data associated with a particular user to inform whether different elements for the built environment, different prompts, or different rewards would lead to greater progress by a particular use in learning the behavior in question. For example, the personalization module 206 can analyze historical data related to the types of elements used in the built environment, the types of prompts used to initiate engagement from a particular user, and the types of rewards provided for successful completion of part or all of the target behavior. If the personalization module 206 detects a delay or lag associated with a particular user's progress in learning the target behavior, the personalization module 206 can modify the types of elements used in the built environment, the types of prompts used to initiate engagement from a particular user, and the types of rewards provided for successful completion of part or all of the target behavior.

[0048] For example, a particular user working on a goal behavior, such as potty training, may experience progress and delays. The personalization module 206 may track a user's progress data in relation to the types of elements used in the built environment, the types of prompts used, and the rewards presented to the particular user to determine what works and what does not work. Based on this determination, the personalization module 206 may modify the types of elements used in the built environment, the types of prompts to initiate engagement, and the types of rewards provided for successful completion of part or all of the goal behavior. The personalization module 206 may also adjust how the elements, prompts, and rewards for building the environment are modified based on the user's level of functioning. For example, the personalization module 206 may adjust how the types of elements used in the built environment, the types of prompts to initiate engagement, and the types of rewards provided for successful completion of part or all of the goal behavior are modified based on whether the particular user is high-functioning or low-functioning.

[0049] For example, a machine learning model may be trained to predict which prompts, environmental configurations, and rewards will result in successful user engagement. If a user experiences difficulty engaging with the built environment or selecting stimuli, the prompts may be modified according to what the machine learning model predicts will result in successful engagement from the particular user.

[0050] The machine learning model can be trained to learn what elicits successful engagement from a particular user, what works for an average user of the behavioral training system 100, and what works for a particular type of target behavior. The machine learning model can then provide input for personalizing the built environment, including prompts, elements within the environment, and rewards, to improve user engagement.

[0051] For example, some users may respond to text prompts, some to audio prompts, and some to visual prompts or a combination of different prompts. An example of a text prompt may include a message in the behavioral training user interface 104 generated by the behavioral training system 100 and displayed on the user electronic computing device 102, which asks the user to click or otherwise select a particular element in the built environment, generated by the behavioral building module 204 and displayed on the behavioral training user interface 104. An example of an audio prompt may include an audio message instead of a text message, prompting the user to make a selection. The audio message may, in some cases, include a recording of the caregiver. An example of a visual prompt may include an arrow that moves and / or points toward the selection the user is to make. Other examples of visual prompts include magnifying an element in the behavioral training user interface, highlighting an element, or highlighting an element by contrast. Other types of prompts are possible.

[0052] In some examples, if the behavior building module 204 does not receive a user selection on or engagement with the behavioral training user interface 104 for a threshold time, the behavior building module 204 can change the type of prompt displayed on the behavioral training user interface 104. For example, if the behavior building module 204 initially generates a text prompt for a user to select a particular element in the behavioral training user interface 104, but does not receive a user selection or any other user engagement with the behavioral training user interface 104 for the threshold time, the behavior building module 204 may change the prompt to an audio prompt. The threshold amount of time can be variable and can range from a few seconds to a few minutes. The threshold amount of time can be personalized to a user based on the amount of time it typically takes a particular user to make a selection or otherwise engage with the behavior building user interface 104.

[0053] In some examples, the element that serves as a trigger for the playing of a clip generated by the behavior construction module 204 is related to the goal behavior. For example, if the goal behavior is drinking from a cup, an image of a cup in the built environment may serve as the stimulus object. If the goal behavior is toilet training, an image of a toilet in the built environment serves as the stimulus object.

[0054] Depending on the user's current level of functionality, the user may have difficulty selecting a particular stimulus object within the built environment displayed within the behavioral training user interface 104. The personalization module 206 can learn the user's limitations and strengths and modify an aspect of the stimulus object, the built environment, or the type of prompt to increase the likelihood that the user will engage with the built environment and select the stimulus object.

[0055] For example, the behavioral training engine 110 may request the user or the user's caregiver to provide personal information related to the user. The data request in some examples may be in the form of a questionnaire presented on the behavioral training user interface 104. Other forms of data requests are possible. The personal information may include details about the user, such as the user's name, address, age, medical history, educational history, family history, health history, the user's functional level, the user's strengths and weaknesses, etc. Other types of personal information may also be collected. The behavioral training engine 110 may store the received data in the data store 112.

[0056] In some examples, the personalization module 206 can obtain data about the user's current level of functionality from the data store 112 for use with the learning model to modify an aspect of the stimulus object, the built environment, or the type of prompt to increase the likelihood that the user will engage with the built environment and select the stimulus object.

[0057] For example, the machine learning model may predict, based on the user's prior behavior, that the user is more likely to respond to a prompt if the stimulus object is larger in size or if distracting elements in the built environment are removed. Thus, the personalization module 206 may interact with the behavior building module to modify and personalize the built environment, prompts, rewards, and number of steps used to train the user for a new behavior based on a particular user's preferences, predictions about what types of behaviors a particular user may find difficult to learn, or the type of targeted behavior.

[0058] Examples of personalized building environments that may be displayed in the behavioral training user interface 104 are described in more detail in connection with FIGS.

[0059] Fig. 3 is a diagram showing an example of the configuration of the behavior construction module 204 in Fig. 2. In this embodiment, the behavior construction module 204 may be configured to include a behavior integration submodule 302, a behavior clip generation submodule 306, and a reward generation submodule 308. In other examples, the behavior construction module 204 may be configured to include more or fewer modules.

[0060] In some embodiments, the behavioral integration submodule 302 is configured to receive input data from the user electronic computing device 102 related to a target behavior that the user or the user's therapist(s) or caregiver(s) are trying to achieve. The behavioral integration submodule 302 can cause a behavioral training user interface 104 associated with the behavioral training system 100 and displayed on the user electronic computing device 102 to display one or more questions and / or options related to the target behavior. For example, the behavioral training user interface 104 can request the user or the user's therapist(s) or caregiver(s) to select a target behavior from among a list of multiple pre-compiled target behaviors. The behavioral training user interface 104 can also request the user or the user's therapist(s) or caregiver(s) to input additional information related to the target behavior, including locations where the user is likely to perform the target behavior, and photographs or digital representations of each of the locations where the user is likely to perform the target behavior, and photographs or digital representations of objects related to the target behavior in each such location. For example, for the goal behavior of toilet training, the user or the user's caregiver may be prompted to upload photos or digital representations of the user's primary bathroom, such as the bathroom at the user's home, as well as photos or digital representations of other bathrooms the user is likely to use, such as a bathroom at the user's school, the user's grandparents' bathroom, etc.

[0061] Based on the received input regarding the goal behavior, the Behavior Integration sub-module 302 may select an appropriate constructed environment associated with the goal behavior that was constructed by the background construction module 202 and stored in the data store 112. The Behavior Integration sub-module 302 may then adjust the environment constructed by the background construction module 202 to include elements associated with the goal behavior, including images or digital representations of objects associated with the goal behavior, input by the user or the user's therapist(s) or caregiver(s).

[0062] For example, if the target behavior includes learning to sit in a new chair, the Behavior Integration sub-module 302 may prompt the user, or the user's therapist(s) or caregiver(s), to provide information about where the user normally performs the target behavior, a photo or digital representation of that location, and, for example, a photo or digital representation of the user's chair. The user's therapist or caregiver may provide the necessary details, and the Behavior Integration sub-module 302 may use the received information to select an appropriate built environment, such as the user's preschool classroom, and overlay one or more new elements, such as an image of a new chair, within the image of the built environment.

[0063] In another example, if the goal behavior includes learning to use a toilet, the behavior integration submodule 302 may select a built environment that is related to the user's toilet. The behavior integration submodule 302 may also select and adjust the built environment in other ways depending on the goal behavior.

[0064] Once the user becomes routinely successful in completing a particular target behavior in a real-world environment that matches the primary built environment, the behavior integration submodule 302 can adjust the built environment to secondary environments to help the user learn to accomplish the target behavior in other environments. For example, if the target behavior is to successfully use the toilet, initially, the behavior integration submodule 302 can build an environment that matches the user's primary environment (the user's home in this example). However, over time, as the user becomes successful in using the toilet in the user's home, the behavior integration submodule 302 can use principles of generalization to adjust the built environment to include other toilet environments to which the user may be exposed, such as the user's school toilet, the user's grandparents' toilet, etc.

[0065] In some examples, the behavioral integration submodule 302 may also be configured to assign one or more elements or objects in the built environment as a stimulus object. The behavioral integration submodule 302 may prompt the user to select or otherwise engage with the stimulus object to trigger the playback of a behavioral clip. For example, for a goal behavior of sitting in a new chair, the stimulus object may be an image of a chair in the built environment displayed on the behavioral training user interface 104. The user selects or otherwise engages with the image of the chair on the behavioral training user interface 104, causing a behavioral clip associated with the goal behavior to play. Generation and playback of behavioral clips is described in further detail in connection with the behavioral clip generation submodule 304.

[0066] Aspects related to the built environment, prompts, and stimulus objects may be modified by the behavioral integration submodule 304 based on predictions and feedback of the machine learning models configured by the personalization module 206.

[0067] Once an appropriate environment has been constructed for a selected target behavior, the behavioral clip generation module 306 may be configured to construct a clip of the behavior using the constructed environment. The clip in this example includes a series of images or short video snippets of a visual representation of the user performing, for example, the beginning and ending steps of the target behavior within the constructed environment, with one or more intermediate steps omitted. The image of the user in the clip may include a photographic image or other digital representation of the user, so that when the user views the clip, they can see themselves performing the target behavior within the environment.

[0068] In some examples, the behavioral clip generation module 304 may determine the sequence of steps associated with the selected target behavior by accessing data regarding the selected target behavior from the data store 112. For example, the sequence of steps associated with each of a pre-compiled list of target behaviors selectable by the behavioral training system 100 may be obtained by the behavioral training system 100 from an internal or external data source and stored in the data store 112.

[0069] In one example, the behavioral clip generation module 304 can access the data store 112 to retrieve one or more stock images or video clips associated with the selected target behavior. The behavioral clip generation module 304 can then edit the stock images or video clips to replace the stock images of the user, the user's background, the stimulus objects, and other elements in the user's background with images of the particular user, images of the particular user's background, images of the stimulus objects of the particular user, and images of elements in the particular user's background. Other methods of generating a behavioral clip for a selected target behavior are possible.

[0070] The clip of the target behavior can be configured such that the clip includes only a subset of the target behavior. In this example, the subset includes the beginning and end of the target behavior. In another example, the subset includes some, but not all, of the intermediate steps. The number of steps included in the clip may be based on the user's actual progress as reported by the user or the user's caregiver. In this embodiment, one or more intermediate steps are not included in the clip. In this example, streamlining the clip by not including one or more intermediate steps of the target behavior is to limit the length of the clip. In general, at least a significant portion of users who have difficulty learning new behaviors, including but not limited to users with cognitive rigidity, such as users on the autism spectrum, may have difficulty concentrating on long clips that contain complex details. Limiting the clip to the beginning and end steps of the target behavior reduces confusion and makes it easier for the user to understand the end goal of the behavior. Generally, when a user understands the end goal by seeing themselves successfully perform the target behavior, the user gains confidence in attempting and perfecting the end goal of the target behavior. In such cases, intermediate steps related to the goal behavior become easier for the user to accomplish without the user having to look at each step and / or become obsessed with perfecting each intermediate step.

[0071] For example, a clip associated with the goal behavior of taking a drink from a cup may include a sequence of images or a short video snippet and may include only the start and end steps of the goal behavior. In another example, a clip associated with the goal behavior of sitting in a new chair may include a sequence of images or a short video snippet of an image of a user walking to a new chair and an image of the user already sitting in the chair.

[0072] The level of detail within the short video snippet, the length of the short video snippet, and the number of intermediate steps depicting the target behavior within the short video snippet may depend on the user's current functional level as determined by the behavioral training engine 110 via reports provided by the user or the user's caregiver related to the user's diagnosis and progress. For example, a user with a lower functional level may require a video snippet depicting multiple intermediate steps depicting the target behavior, while a user with a higher functional level may only require a video snippet that does not depict any of the intermediate steps depicting the target behavior.

[0073] Upon generating the behavioral clip, the behavioral clip generation submodule 304 can store the clip in the data store 112. The behavioral clip generation submodule 304 can cause the playback and display of the behavioral clip on the behavioral training user interface display 104 upon receiving a trigger from the user. In some examples, the trigger may include the user selecting or otherwise engaging with a stimulus image on the built environment displayed on the behavioral training user interface 104. In other examples, other methods of triggering the playback of the behavioral clip are possible, as would be readily understood by one of ordinary skill in the relevant art. The progress of the user in learning the target behavior may be highly dependent on the number of times the user repeatedly views and engages with the generated behavioral clip. Thus, the behavioral clip generation submodule 304 can provide reminders for the user or the user's caregiver to repeatedly trigger the playback of the behavioral clip so that the user can view the clip multiple times to familiarize themselves with the target behavior as a form of repetitive practice.

[0074] When the behavioral clip generation sub-module 304 receives input indicating that a user has engaged with or otherwise selected a stimulus object, the behavioral clip generation sub-module 304 can cause playback of the generated behavioral clip on the behavioral training user interface 104 on the user electronic computing device 102.

[0075] For example, for a goal behavior of drinking from a cup, the cup itself may be the stimulus. Clip playback may be triggered when the user selects the cup within the displayed built environment. Behavioral clip playback may include displaying an image or video snippet of the user reaching for the cup's handle, followed by an image or video snippet of the user already taking a sip from the cup.

[0076] In some embodiments, the reward generation submodule 306 is configured to generate a reward for the user after the user successfully engages with the built environment and the playback of the behavioral clip is triggered. The reward generation submodule 306 can generate one or more rewards for display in the behavioral training user interface 104. The rewards can include digital text, images, audio, and / or video objects that can positively reinforce the user to continue to engage with the built environment. The rewards can also encourage the user to mirror the goal behavior in real life. For example, the rewards can include images of the user's favorite animated characters, emoticons, hand clapping audio clips, GIF (Graphics Interchange Format) snippets, images and videos of the user's family and friends, videos of the user's favorite television shows and movies, and the like. Other types of digital rewards are possible. Although not necessary to successfully train a user in a new behavior, the disclosed behavioral training system can also use external real-world rewards to incentivize the user. For example, external real-world rewards can include a points system where the user receives points for certain types of engagement that the user can redeem for prizes. The prizes can also include access to digital content or physical objects, or monetary rewards.

[0077] The reward generation submodule 306 may be personalized for a user such that rewards can be generated for different types of user engagement for different users. For example, the reward generation submodule 306 may generate rewards for simply clicking, or in the case of a touch interface, touching any part of the built environment, for a user who has difficulty engaging with the behavioral training user interface 112 at all.

[0078] As the user progresses through training, the reward generation submodule 306 may generate rewards only for clicks or touches of any parts of the built environment that are outside of the user's image within the built environment. Eventually, the reward generation submodule 306 may generate rewards only when the user clicks or touches a stimulus object within the built environment.

[0079] The personalization module 206 may be trained to learn a user's progress and reward preferences to personalize when to reward the user and the type of rewards generated for the user.

[0080] 4 illustrates an example of a method 400 for learning new behaviors. In an example operation 402, the background building module 202 of the behavioral training engine 110 can receive background data from the behavioral training user interface 104 of the user electronic computing device 102. For example, the background data can include information related to the user, such as biographical, social, health, and family-related information, photos of the user, information related to the user's therapist or caregiver, places where the user spends time, photos of locations, such as interior photos of the user's room, photos of other rooms in the user's residence, school, work, etc.

[0081] In an example operation 404, the background construction module 202 of the behavioral training engine 110 may construct an environment for display in the behavioral training user interface 102 from a photograph or digital representation of the user and the user's environment. For example, the constructed environment may be a realistic visual representation of the user and the user's environment rather than a cartoon or caricature of the user and the user's environment to allow the user to visualize themselves completing a task. In some embodiments, the constructed environment may be stored in the data store 112 and / or the constructed environment may be transmitted to the user electronic computing device 102 for display in the behavioral training user interface 104.

[0082] In an exemplary operation 406, the behavioral building module 204 of the behavioral training engine 110 can receive information related to the target behavior from the behavioral training user interface 104 of the user electronic computing device 102. For example, the target behavior information can be entered or selected by the user or the user's therapist or caregiver on the behavioral training user interface 104. The target behavior information can include a selection of the target behavior that the user is trying to achieve or that the user's therapist or caregiver wants to achieve from a list of pre-compiled target behaviors, and a photograph or digital representation of an object or location associated with the target behavior. Other types of information related to the target behavior can also be received from the user electronic computing device 102, as would be readily understood by one of ordinary skill in the relevant art.

[0083] In an example operation 408, the behavior building module 204 of the behavioral training engine 110 may integrate elements associated with the goal behavior into the built environment from operation 404. For example, the behavior integration sub-module 302 of the behavior building module 204 may integrate elements associated with the goal behavior into the built environment from operation 404. The process of integrating elements associated with the goal behavior into the built environment is described in more detail in connection with the behavior integration sub-module 302.

[0084] In an example operation 410, the behavior construction module 204 of the behavioral training engine 110 may send the integrated constructed environment from operation 406 to the user electronic computing device 102 for display on the behavioral training user interface 104. For example, the behavior integration submodule 302 may retrieve the integrated constructed environment including the personalized prompts associated with the target behavior stored in the data store 112 in operation 408 and send it to the user electronic computing device 102 for display on the behavioral training user interface 104.

[0085] In an example operation 412, the behavioral construction module 204 of the behavioral training engine 110 may generate a behavioral clip associated with the target behavior. For example, the behavioral clip generation submodule 304 of the behavioral construction module 204 may generate the behavioral clip based on an image of a user performing the start and end steps associated with the target behavior. The behavioral clip generation submodule 304 may use image or video editing algorithms to generate a sequence of images or short video snippets of an image of a user performing the start and end steps of the target behavior in the built environment. Intermediate steps are not included in the generated clip. Once the behavioral clip is generated, the behavioral clip generation submodule 304 stores the generated clip in the data store 112. Generation of behavioral clips is described in more detail in connection with the behavioral clip generation submodule 304 of FIG. 11.

[0086] In an example operation 414, the behavioral construction module 204 of the behavioral training engine 110 may receive a selection of a stimulus object from the user electronic computing device 102. The behavioral clip generation submodule 304 may receive the selection of the stimulus object in response to a personalized prompt, the selection being made by the user on the user electronic computing device 102 via the behavioral training user interface 104. In one embodiment, for a target behavior of sitting in a new chair, an image of a chair in the integrated built environment serves as the stimulus object. Based on the personalized prompt displayed on the behavioral training user interface 104, the user, or the user's therapist or caregiver, on the user electronic computing device 102, may select an image of a chair on the behavioral training user interface 104.

[0087] In some embodiments, the selection may be completed when the user clicks on the image of the stimulus object using a mouse. In other examples, if the user electronic computing device 102 includes a touch-sensitive display screen, the selection may be completed when the user touches the image of the stimulus object displayed in the behavioral training user interface 104. The user's selection is communicated from the user electronic computing device 102 to the behavioral clip generation sub-module 304 of the behavioral training engine 110.

[0088] In an exemplary operation 416, the behavioral building module 204 of the behavioral training engine 110 may transmit the behavioral clip to the user electronic computing device 102 for display on the behavioral training user interface 104. For example, in response to receiving a selection of a stimulus object, the behavioral clip generation submodule 304 may retrieve the behavioral clip generated and stored in the data store 112 in operation 412 and transmit the generated behavioral clip to the user electronic computing device 102 for display on the behavioral training user interface 104. Receiving a selection of a stimulus object by the user or the user's therapist or caregiver in operation 414 may trigger the retrieval and transmission of the behavioral clip in operation 416.

[0089] Once the user electronic computing device 102 receives the behavioral clip, the user electronic computing device 102 can cause the behavioral training user interface 104 to display and automatically play the behavioral clip. Generation and playback of behavioral clips is described in further detail in connection with the Behavioral Clip Generation sub-module 304 of FIG.

[0090] In an exemplary operation 418, the behavioral building module 204 of the behavioral training engine 110 may transmit the reward to the user electronic computing device 102 for display in the behavioral training user interface 104. In some examples, upon receiving the selection of the stimulus object in operation 414, the reward generation submodule 306 of the behavioral building module 204 may generate one or more personalized rewards and transmit the one or more rewards to the user electronic computing device 102. In other examples, the reward generation submodule 306 may pre-generate and store one or more personalized rewards in the data store 112 and upon receiving the selection of the stimulus object in operation 414, retrieve the personalized rewards from the data store 112 and transmit the one or more rewards to the user electronic computing device 102.

[0091] The one or more rewards can be personalized based on the type of reward that positively reinforces a particular user to continue learning the target behavior, including when the user touches, selects, or otherwise engages with any portion of the integrated built environment displayed within the behavioral training user interface 104, when the user touches, selects, or otherwise engages with any portion of the integrated built environment displayed within the behavioral training user interface 104 other than an image of the user in the integrated built environment, and / or when the user touches, selects, or otherwise selects a stimulus object within the integrated built environment. Reward generation and personalization is further described in connection with reward generation sub-module 306 of FIG. 3.

[0092] 5-8 show example visual representations of the behavioral training user interface 104 during different operations of the behavioral training system associated with the target behavior of sitting in a new chair within a classroom environment.

[0093] 5 illustrates an example visual representation 500 of the behavioral training user interface 104 displaying a constructed environment. The example visual representation 500 shows an image of a constructed environment 502 with an image of a user 504 overlaid on the environment. The visual representation 500 is constructed by the background construction module 202 of the behavioral training engine 110 and sent to the user electronic computing device 102 for display in the behavioral training user interface 104 before the user selects a target behavior.

[0094] The background construction module 202 may construct an image of the built environment 502 from one or more photographs or digital representations of the user's classroom to resemble the user's real classroom. The background construction module 202 may also construct an image of the user 504 from one or more photographs or digital representations of the user to resemble a real user. The background construction module 202 may overlay an image of the user over an image of the built environment to create a visual representation of the user within the built environment.

[0095] In some instances, the environment that is constructed may be defined by the goal behavior itself: for example, for a goal behavior related to toilet training, the background construction module 202 may request the user to provide a photo or digital representation of a toilet within the user's primary environment, such as the user's home.

[0096] 6 illustrates an example visual representation 600 of the behavioral training user interface 104 displaying a built environment 502 integrating elements related to a selected goal behavior. The example visual representation 600 illustrates an image of the built environment 502 with an image of a user 504 and one or more goal behavior elements 602 overlaid on the built environment 502.

[0097] For example, the background integration submodule 302 of the behavioral training engine 110 integrates one or more goal behavior-related elements into a built environment in operations 406-408 of Figure 4. The integrated built environment may include a stimulus object that, when triggered in response to a prompt, causes playback of a behavioral clip to occur.

[0098] The example visual representation 600 is associated with a goal behavior of learning to sit in a new chair. In the example visual representation 600, an image of a chair serves as one of one or more goal behavior elements 602 as well as a stimulus object. Other configurations are possible, as will be apparent to one of ordinary skill in the relevant art.

[0099] 7 illustrates an example visual representation 700 of the behavioral training user interface 104 displaying snippets of a behavioral clip. The example visual representation 700 illustrates snippets from a behavioral clip generated by the behavioral clip generation sub-module 304 of the behavioral training engine 110 and transmitted to the user electronic computing device 102 for display in the behavioral training user interface 104.

[0100] In the example visual representation 700, snippets from the behavioral clip include an end step associated with the target behavior that is part of the generated behavioral clip. The behavioral clip generated by the behavioral clip generation submodule 304 for a target behavior associated with sitting in a new chair includes multiple image or video snippets showing the start and end steps of the target behavior. For example, the start step of the target behavior includes the user beginning to turn toward the new chair, and the end step of the target behavior includes the user already sitting in the new chair.

[0101] In some embodiments, the generated behavior clip may include two images: a first image of the user facing the new chair and a second image of the user sitting in the new chair. The example visual representation 700 displays the end step of the goal behavior including an image of the user sitting in the new chair.

[0102] 8 illustrates an example visual representation 800 of the behavioral training user interface 104 displaying a reward. The example visual representation 800 illustrates a reward 802 generated by the reward generation submodule 306 of the behavioral training engine 110 and transmitted to the user electronic computing device 102 in response to a user triggering a selection of a stimulus object on the behavioral training user interface 104.

[0103] The reward 802 in the example visual representation 800 includes a sticker of a firefighter with a "thumbs up" signal. The reward 802 is personalized to provide positive reinforcement to a particular user. The process of generating rewards is described in further detail in connection with the reward generation sub-module 306 of FIG. 3.

[0104] FIG. 9 illustrates an example visual representation 900 of the behavioral training user interface 104 displaying modifications to elements of the integrated built environment. The behavioral training engine 110 uses the machine learning model configured by the personalization module 206 to learn the types of elements in the integrated built environment 902, resulting in increased user engagement. For example, the personalization module 206 can predict that if the size of the stimulus object 904 is increased, the user is more likely to engage with the integrated built environment 902 and select the stimulus object 904. Based on the prediction generated by the personalization module 206, the behavioral integration submodule 302 can adjust the size of the stimulus object 904 in the built environment. Increasing the size of the stimulus object (in this case, an image of a new chair) may draw the user's attention to the stimulus object 904, leading to the user selecting or otherwise engaging with the stimulus object 904.

[0105] FIG. 10 illustrates a visual representation 1000 of another example of the behavioral training user interface 104 displaying other modifications to elements of the integrated built environment. The behavioral training engine 110 uses the machine learning model configured by the personalization module 206 to learn the types of elements in the integrated built environment 1002 that may prevent a particular user from engaging with the stimulus object 1004. For example, elements in the integrated built environment 1002 may be removed to distract a particular user and prevent the user's attention from being focused on the stimulus object 1004. Removing distracting elements from the integrated built environment may help increase the likelihood that a particular user will select the stimulus object 1004. The personalization module 206 may predict the types of modifications to the integrated built environment and / or the stimulus object that will help increase the engagement of a particular user. Personalization of the integrated built environment and the stimulus object is further described in connection with the personalization module 206 of FIG. 2.

[0106] For example, the personalization module 206 may predict that a user may be distracted by bright colored elements in the integrated built environment based on the user's past engagement with the behavioral training user interface 104. The behavioral building module 204 may then remove all bright colored elements from the behavioral training user interface 104 except for the stimulus object.

[0107] In another embodiment, based on the user's past engagement with the behavioral training user interface 104, the behavior building module 204 may predict that the user will select or engage with an element in the upper right corner of the behavioral training user interface 104. The behavior building module 204 may then remove all background elements from the upper right corner of the behavioral training user interface 104. The behavior building module 204 in this embodiment may also move the stimulus object to the upper right corner of the behavioral training user interface 104 to increase the probability that the user will engage with the stimulus object.

[0108] FIG. 11 is a diagram illustrating an example of the physical components of the computing device of FIG. 1. As in the example of FIG. 11, the server computer 108 includes at least one central processing unit ("CPU") 1102, a system memory 1108, and a system bus 1122 that couples the system memory 1108 to the CPU 1102. The system memory 1108 includes a random access memory ("RAM") 1110 and a read-only memory ("ROM") 1112. A basic input / output system, including basic routines that help to transfer information between elements within the server computer 108, such as during start-up, is stored in the ROM 1112. The server computer 108 further includes a mass storage device 1114. The mass storage device 1114 can store software instructions and data 1116 associated with software applications 1116. Some or all of the components of the server computer 108 can also be included in the user electronic computing device 102.

[0109] The mass storage device 1114 is connected to the CPU 1102 through a mass storage controller (not shown) that is connected to the system bus 1122. The mass storage device 1114 and its associated computer readable data storage media provide non-volatile, non-transitory storage for the server computer 108. Although the descriptions of computer readable data storage media contained herein refer to mass storage devices such as hard disks or solid state disks, those skilled in the art should understand that a computer readable data storage medium may be any available non-transitory physical device or article of manufacture from which a central processing unit can read data and / or instructions.

[0110] Computer-readable data storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable software instructions, data structures, program modules, or other data. Examples of types of computer-readable data storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, digital versatile disks ("DVDs"), other optical storage media, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the server computer 108.

[0111] According to various embodiments of the present invention, the server computer 108 can operate in a networked environment using logical connections to remote network devices over a network 106, such as a wireless network, the Internet, or other types of networks. The server computer 108 can connect to the network 106 through a network interface unit 1104 connected to a system bus 1122. It should be understood that the network interface unit 1104 can also be utilized to connect to other types of networks or remote computing systems. The server computer 108 also includes an input / output controller 1106 for receiving and processing input from a number of other devices, including a touch user interface display screen, or other type of input device. Similarly, the input / output controller 1106 can provide output to a touch user interface display screen or other type of output device.

[0112] As briefly mentioned above, the mass storage device 1114 and RAM 1110 of the server computer 108 may store software instructions and data associated with software applications 1116. The software instructions include an operating system 1118 suitable for controlling the operation of the server computer 108. The mass storage device 1114 and / or RAM 1110 also store software instructions that, when executed by the CPU 1102, cause the server computer 108 to provide the functionality of the server computer 108 described herein. For example, the mass storage device 1114 and / or RAM 1110 may store software instructions that, when executed by the CPU 1102, cause the server computer 108 to display received data on a display screen of the server computer 108.

[0113] While various embodiments are described herein, those skilled in the art will appreciate that many variations are possible within the scope of the present disclosure, and therefore, the scope of the present disclosure is not intended to be limited in any way by the examples provided.

Claims

1. 1. A computer-implemented method for training a user on a target behavior, comprising: receiving a selection of the target behavior from a user electronic computing device; receiving a photograph or digital image of a stimulus object from the user electronic computing device; constructing a visual representation of a user environment based on the selection of the target behavior and the photograph or digital image of the stimulus object, the visual representation of the user environment including the stimulus object; transmitting the constructed visual representation of the user environment to the user electronic computing device, thereby causing the constructed visual representation to be displayed on a display device of the user electronic computing device; determining a sequence of steps for the target behavior; generating a behavioral video clip associated with the performance of the target behavior, the behavioral video clip including visualization of some but not all of the steps in the determined sequence of steps; receiving a selection of the stimulus object, the selection of the stimulus object being performed via a prompt in the constructed visual representation of the user environment displayed on the display device of the user electronic computing device; transmitting the generated behavioral video clip to the user electronic computing device in response to receiving the selection of the stimulus object; A computer-implemented method comprising:

2. The computer-implemented method of claim 1 , wherein the target behavior is a behavior or attitude that the user or a caregiver of the user desires the user to develop.

3. Constructing the visual representation of the user environment comprises: receiving one or more photographs or digital images of the user's real-life environment, the one or more photographs or digital images of the real-life environment including one or more background elements; receiving one or more photographs or digital images of the user; overlaying an image of the user and an image of the stimulus object onto an image of the real-life environment, the stimulus object being associated with the target behavior, thereby generating the visual representation of the user environment; the image of the user is generated from the one or more photographs or digital images of the user; the image of the stimulus object is generated from the photograph or the digital image of the stimulus object; the image of the real-life environment is generated from the one or more photographs or digital images of the real-life environment. and The computer-implemented method of claim 1 , comprising:

4. The computer-implemented method of claim 3 , wherein the one or more photographs or digital images of the user's real-life environment include one or more photographs or digital images of a location occupied by the user.

5. 4. The computer-implemented method of claim 3, further comprising removing at least one of the one or more background elements from the image of the real-life environment based on a level of engagement the user exhibits with the image of the stimulus object overlaid on the image of the real-life environment.

6. storing the generated behavioral video clips in a data store; retrieving the generated behavioral video clip from the data store in response to receiving the selection of the stimulus object; The computer-implemented method of claim 1 , further comprising:

7. and further comprising transmitting one or more rewards to the user electronic computing device in response to receiving the selection of the stimulus object. The computer-implemented method of claim 1 .

8. The computer-implemented method of claim 7 , wherein at least one of the visual representation of the user environment, the stimulus object, and the one or more rewards is based on personal preferences of the user.

9. 1. A system for training a user on a target behavior, comprising: a processor; When executed by the processor, it causes the processor to: receiving a selection of the target behavior from a user electronic computing device; receiving a photograph or digital image of a stimulus object from the user electronic computing device; constructing a visual representation of a user environment based on the selection of the target behavior and the photograph or digital image of the stimulus object, the visual representation of the user environment including the stimulus object; transmitting the constructed visual representation of the user environment to the user electronic computing device, thereby causing the constructed visual representation to be displayed on a display device of the user electronic computing device; determining a sequence of steps for the target behavior; generating a behavioral video clip associated with the performance of the target behavior, the behavioral video clip including visualization of some but not all of the steps of the determined sequence of steps; receiving a selection of the stimulus object, the selection of the stimulus object being performed via a prompt in the constructed visual representation of the user environment displayed on the display device of the user electronic computing device; transmitting the generated behavioral video clip to the user electronic computing device in response to receiving the selection of the stimulus object; a memory containing instructions for executing the A system comprising:

10. The system of claim 9 , wherein the target behavior is a behavior or attitude that the user or a caregiver of the user desires the user to develop.

11. Constructing the visual representation of the user environment comprises: receiving one or more photographs or digital images of the user's real-life environment; receiving one or more photographs or digital images of the user; the visual representation of the user environment; overlaying an image of the user and an image of the stimulus object onto the image of the real-life environment, the stimulus object being associated with the target behavior; and generating Including, the image of the user is generated from the one or more photographs or digital images of the user; the image of the stimulus object is generated from the photograph or digital image of the stimulus object; the image of the real-life environment is generated from the one or more photographs or digital images of the real-life environment. The system of claim 9.

12. The system of claim 11 , wherein the one or more photographs or digital images of the real-life environment include one or more photographs or digital images of a location occupied by the user.

13. The instructions, when executed by the processor, further cause the processor to: removing at least one of the one or more background elements from the image of the real-life environment based on a level of engagement the user exhibits with the image of the stimulus object overlaid on the image of the real-life environment; The system of claim 11 , wherein the system executes the following:

14. The instructions, when executed by the processor, further cause the processor to: storing the generated behavioral video clips in a data store; retrieving the generated behavioral video clip from the data store in response to receiving the selection of the stimulus object; The system of claim 9 , wherein the system executes the following:

15. The instructions, when executed by the processor, further cause the processor to: transmitting one or more rewards to the user electronic computing device in response to receiving the selection of the stimulus object; The system of claim 9, wherein the system executes the following:

16. The system of claim 15 , wherein at least one of the visual representation of the user environment, the stimulus object, and the one or more rewards is based on personal preferences of the user.

17. 1. A system for training a user on a target behavior, comprising: a display device; a processor; When executed by the processor, it causes the processor to: transmitting a photograph or digital image of the stimulus object to a server computing device; displaying on the display device a behavioral training user interface including one or more user-selectable options related to the target behavior; receiving one or more selections from the user related to the target behavior; transmitting the one or more selections associated with the target behavior to the server computing device; receiving, from the server computing device, a constructed visual representation of a user environment based on the selection and the photograph or the digital image of the stimulus object, the constructed visual representation of the user environment including the stimulus object; displaying on the display device a constructed visual representation of the user environment and a prompt requesting the user to trigger the stimulus object; receiving a selection of the stimulus object from the user; transmitting the selection of the stimulus object to the server computing device; receiving, in response to transmitting the selection of the stimulus object, a behavioral video clip associated with the performance of the target behavior, the behavioral video clip not including content associated with the performance of all of the target behavior; displaying and automatically playing the behavioral video clip on the display device; receiving one or more rewards in response to said selection of said stimulus object; displaying the one or more rewards on the display device; a memory containing instructions for executing the A system comprising:

18. 20. The system of claim 17, wherein at least one of the constructed visual representation of the user environment, the stimulus object, the prompt, and the one or more rewards are tailored to the user.

19. The system of claim 17 , wherein the constructed visual representation of the user's environment is constructed by overlaying an image of the user and an image of the stimulus object onto an image of the user's environment.

20. the image of the user is generated from one or more photographs or digital images of the user; the image of the stimulus object is generated from one or more photographs or digital images of the stimulus object; the image of the user's environment is generated from one or more photographs or digital images of the user's environment; 20. The system of claim 19.

Images