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 Art

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

[0002] Behavior refers to the actions and attitudes that an individual takes in connection with themselves and their environment. Humans have the ability to learn new behaviors. Some learning may be immediately triggered by a single event. However, most learning is based on knowledge accumulated over time and repeated experiences. Learning new behaviors is a complex process and often requires repeated effort and practice.

Summary of the Invention

[0003] Embodiments of the present disclosure relate to a behavior training system that enables a user to achieve the execution of a new behavior using a combination of applied behavior analysis and modified video self-modeling.

[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 a target behavior from a user electronic computing device, constructing a visual representation of the user environment, the visual representation of the user environment including at least one stimulus object, transmitting the constructed visual representation of the user environment to the user electronic computing device, determining a series of steps for the target behavior, generating a behavior clip related to the execution of the target behavior, the behavior clip including a visualization of some but not all of the determined series of steps, receiving a selection of a stimulus object, and transmitting the generated behavior 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 on a target behavior is disclosed. The system comprises a processor and a memory that, when executed by the process, causes the processor to: receive a selection of a target behavior from a user computer; 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 computer; determine a set of steps for the target behavior; generate a behavior clip relating to the execution of the target behavior, the behavior clip including visualizations of some but not all of the steps of the determined set of steps; receive a selection of a stimulus object; and, in response to receiving the selection of a stimulus object, transmit the generated behavior clip to the user computer.

[0006] In a third embodiment, a system for training a user on a target behavior is disclosed. The system comprises a display device, a processor, and a memory that, when executed by the processor, causes the processor to: display on the display device a behavior training user interface including one or more user-selectable options related to a target behavior; receive one or more selections related to the target behavior from the user; transmit one or more selections related to the target behavior to a server computer; receive a constructed visual representation of the user environment from the server computer, 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 computer; receive a behavior clip related to the execution of the target behavior in response to transmitting the selection of the stimulus object, the behavior clip does not include content related to the execution of all steps of the target behavior; receive a behavior clip related to the execution of the target behavior in response to transmitting the selection of the stimulus object, the behavior clip does not include content related to the execution of all steps of the target behavior; receive a behavior clip, the behavior clip does not include content related to the execution of all steps of the target behavior, and, in response to transmitting the selection of the stimulus object, receive one or more rewards and display one or more rewards on the display device to encourage the user to continue engaging with the behavior training user interface.

[0007] Details of one or more of these technologies are described in the accompanying drawings and the following description. Other features, purposes, and preferably, the claims of these technologies will become apparent from the description, drawings, and claims. [Brief explanation of the drawing]

[0008] The following drawings illustrate specific embodiments of the Disclosure and are not intended to limit the scope of the Disclosure. The drawings are not to scale and are intended to be used in conjunction with the descriptions in the following detailed description. Embodiments of the Disclosure are described below in conjunction with the accompanying drawings, where similar figures indicate similar elements. [Figure 1] Figure 1 shows an example of the configuration of the behavioral learning system according to this disclosure. [Figure 2] Figure 2 shows an example of the configuration of the behavioral training engine of the system in Figure 1. [Figure 3] Figure 3 shows an example of the configuration of the behavioral construction module of the behavioral training engine shown in Figure 2. [Figure 4] Figure 4 shows an example of a method for learning new actions as described in this disclosure, which can be performed using the system in Figure 1. [Figure 5] Figure 5 shows an example of a visual representation of the behavioral training user interface from Figure 1, displaying the constructed environment. [Figure 6] Figure 6 shows an example of a visual representation of the behavioral training user interface from Figure 1, displaying a constructed environment that integrates elements related to the selected target behavior. [Figure 7] Figure 7 shows an example of a visual representation of the behavior training user interface from Figure 1, which displays snippets of behavior clips. [Figure 8] Figure 8 shows an example of a visual representation of the behavioral training user interface shown in Figure 1, which displays rewards. [Figure 9] Figure 9 shows an example of a visual representation of the behavioral training user interface from Figure 1, illustrating modifications to the elements of the integrated build environment. [Figure 10] Figure 10 shows another visual representation of the behavioral training user interface from Figure 1, illustrating other modifications to the elements of the integrated build environment. [Figure 11] Figure 11 shows an example of the physical components of the computing device shown in Figure 1. [Modes for carrying out the invention]

[0009] Various embodiments will be described in detail with reference to the drawings, but similar reference figures represent similar parts and assemblies throughout some of the figures. References to various embodiments are not intended to limit the claims appended herein. Furthermore, the exemplary embodiments herein are not intended to be limiting, but merely to illustrate some of the many possible embodiments of the appended claims.

[0010] Generally, the subject of this disclosure relates to a platform for learning new behaviors that enables users to achieve the execution of new behaviors in real life, using a combination of the principles of applied behavior analysis, generalization, and modified video self-modeling.

[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 instances, the disclosed behavioral training system can be used to support users with other types of disabilities, such as autism, attention deficit / hyperactivity disorder, and intellectual disability. Furthermore, even users without cognitive or behavioral rigidity or impairment may experience difficulties learning new behaviors because learning methods vary from user to user, based on user characteristics, diagnosis, function, and age. For example, the complex nature of the behavior, its inability to be imitated, the cognitive understanding of the reasons and consequences, and the social understanding of the behavior may all contribute to the difficulties.

[0012] Difficulty acquiring new behaviors may or may not be associated with a disability. Often, there are environmental, social, medical, and / or developmental reasons for needing to learn certain behaviors. Other reasons for learning behaviors are also possible. Systems that assist such users in training new behaviors would help individuals who have difficulty learning them.

[0013] In one example, a user with a medical condition may need to learn new actions related to wearing equipment for the treatment or rehabilitation of their condition. For instance, they may need to wear a helmet for rehabilitation. However, due to sensory problems or a lack of ability to understand or visualize the benefits of wearing a helmet, they may not necessarily wear it to protect themselves in case of a head injury. In other examples, users may need to unlearn specific learned behaviors by learning new alternative behaviors. Users may need to learn how to move from one place to another, transition to new activities, give and receive toys and playthings, use the toilet, exercise food selection, and avoid violence and self-harm. Other examples related to learning new behaviors are also possible.

[0014] In other cases, younger children or those exhibiting strong cognitive rigidity or other behavioral characteristics may have difficulty entering new spaces, toilet training, coping with environmental changes, wearing new clothing, or sharing items with others. Generally, user characteristics that make it difficult to acquire new behaviors can be improved in a clinical setting through therapeutic techniques such as video self-modeling and chaining.

[0015] Video self-modeling is a therapeutic technique used to teach users new behaviors or skills, and it involves using video recordings as a model. New behaviors or skills that the user has not yet mastered, or that the user or their caregiver hopes the user will master, may be called "target behaviors." In video self-modeling, for example, a user can be shown a video recording of themselves successfully performing all the steps related to a target behavior, without benefiting from the video recording itself, in order to successfully complete the target behavior or skill at a later date.

[0016] For example, if the target behavior is to drink from a cup, the user can be shown a video of themselves moving their hand to the cup, grasping the handle, picking up the cup, bringing the cup to their mouth, and sipping from the cup. The video of the user performing the target behavior is typically created by first capturing footage of the user performing the 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 final resulting video appears to show the user performing the target behavior independently. Video self-modeling is used to help users learn the steps to successfully perform the target behavior. However, showing a series of steps to achieve a target behavior can take a long time before the user is able to successfully complete the target behavior or skill. For example, individuals with autism spectrum disorder may have difficulty concentrating on video for extended periods without interruption, may become confused if the action involves several steps, and may become fixated on or stumped by minor and / or non-essential details in the video recording.

[0017] Generalization is another technique for teaching users new behaviors. Generalization refers to a learner's ability to perform a skill or new behavior under different conditions (generalization of stimuli), their ability to apply the skill in different ways (generalization of responses), and their ability to continue performing the skill over a longer period (retention). Generalization is a method of teaching new behaviors in a way that allows users to learn the behavior itself outside of the components of the environment or stimulus. For example, to continuously teach how to use the toilet, showing the user photos or videos of the main toilet in their home might reveal that the main toilet has a yellow tiled floor, blue wallpaper, a sink to the left of the toilet, and the toilet door to the left of the sink.

[0018] Generalization provides a way for users to learn the behavior of going to the toilet, for example, without needing environmental cues associated with a particular toilet environment. In other words, generalization provides a way for users to learn the behavior of going to the toilet, regardless of the environment in which the toilet is located.

[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 varied at regular or a constant pace based on the user's progress in learning the target behavior that enables the user to learn the behavior without depending on environmental cues to start or complete the behavior.

[0020] Chaining is another technique for teaching a user a new behavior based on task analysis. Chaining breaks the task into small steps and teaches each step. For example, a person who wants to learn to wash their hands may start by turning on the faucet. Once this first skill is mastered, the next skill might be to wet their hands. Chaining may be effective in assisting with daily tasks. However, since chaining is a technique that focuses on learning each step related to the behavior in sequence, it may take a long time to acquire the target behavior.

[0021] In some embodiments, the disclosed behavior 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 behavior training system may be a computer-implemented platform accessible to the user using an electronic computing device. The disclosed behavior training system can construct a visual representation of the user's real-life environment using a photograph or digital representation of the user's environment, the user, and optionally, a photograph or digital representation of the user's therapist(s), caregiver(s), parent, friend, teacher, or other individual who is or may be helpful as part of the process of the user learning the target behavior.

[0022] Furthermore, the disclosed behavior learning platform can teach users new behaviors by reinforcing new behaviors on the platform rather than reinforcing behaviors when they are performed in reality. For example, the disclosed behavior learning platform can use a new approach to the principle of differential reinforcement of alternative behaviors by reinforcing different behaviors such as a user's engagement with the behavior learning platform that shows a digital avatar of the user achieving the behavior rather than the user's behavior in real life in order to help achieve a change in the user's reality.

[0023] For example, a behavior training system can use a user's photo or digital representation, and the user's real-world environment, to construct a visual representation of the user within a constructed environment. Next, the behavior training system can display a video of the visual representation of the user as they begin to perform the target behavior. However, rather than showing all the steps necessary to complete the target behavior, as in video self-modeling, the behavior training system can, for example, skip one or more intermediate steps or simply show the visual representation of the user performing the last step of the target behavior or the visual representation of the user completing the target behavior.

[0024] As a first step in acquiring the target behavior, the behavior training system prompts the user to interact with the constructed environment. In some examples, the behavior training system can use personalized rewards to reinforce the user's progress in successfully acquiring the target behavior.

[0025] For example, for the target 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. When the behavioral education platform receives a trigger, it may display an image of the user walking towards a cup on a table in the constructed environment. Prompts may be used to help the user understand what stimuli trigger the behavior. In the case of the target behavior of drinking from a cup, prompts may include the user touching, clicking, or otherwise selecting an image of a cup on a display screen that shows the constructed environment.

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

[0027] In some cases, behavioral education platforms can use visual or auditory cues to encourage users to elicit stimuli. For example, in the case of the target behavior of drinking from a cup, the size of the cup could be made larger so that it appears disproportionately large in the constructed environment. By increasing the size of an object, the user may be more intuitive to select the object or interact with it in other ways, thus eliciting a stimulus. The type of prompt may also be personalized to the user based on their past behavior.

[0028] In some cases, behavioral education platforms can also offer personalized rewards as reinforcement for users who trigger stimuli. Examples of rewards include, but are not limited to, stickers, emoticons, digital points, video clips of the user's favorite TV shows or movies, video clips of the user's family or friends, video clips of the user's mother laughing, and other well-known rewards. In addition to various types of reward options, behavioral education platforms can also offer multiple tiers of rewards so that users are presented with higher tiers of rewards as they make progress in learning target behaviors in real life.

[0029] For example, a reward that provides positive reinforcement to one user may not necessarily function as positive reinforcement to another user. Similarly, a type of prompt that works for one user may not always work for another. Therefore, a behavioral education platform can determine the type of prompt(s) and / or reward(s) that more readily trigger stimuli based on the preferences of specific individual users.

[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 a general user, as well as the types of prompts and rewards that are likely to work for a specific user, as well as the types of prompts and / or rewards that are likely to work for a specific type of target behavior, in contrast to other types. The artificial intelligence and machine learning models used by the behavioral education platform may also study how factors such as the user's age, real-life environment, type of diagnosis, level of function, and type of user device used affect the pace and success of the user in learning new target behaviors. The artificial intelligence and machine learning models can then use the collected data to predict how to improve the constructed and real-life environments, prompts, and rewards in order to improve the pace at which the user learns target behaviors. In other words, the artificial intelligence and machine learning models used by the behavioral education platform can build a personalized platform that improves the likelihood that a particular user will succeed in learning new behaviors and learn at a faster pace.

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

[0032] Figure 1 shows an example configuration of the behavioral training system 100. The system 100 includes a user computer 102, a network 106, a server computer 108, and one or more data stores 112. In this embodiment, the server computer 108 may include a behavioral training engine 110. More modules, fewer modules, or different modules can be used.

[0033] In some examples, the user computer 102 is the user's computer. In some examples, the computer may be a desktop computer, a laptop computer, a virtual reality user device, or a mobile computer such as a smartphone or tablet computer. In some examples, the user computer 102 allows the user to access the server computer 108 via the network 106 and display data on 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 yet another example, the user may have no cognitive problems at all but experience difficulty learning new behaviors. The user may have autism spectrum disorder, or the user may be an individual seeking to improve cognitive flexibility. Although a single user computer 102 is shown, the system 100 allows hundreds, thousands, or more computers to connect to the server computer 108.

[0034] In some examples, network 106 is a computer network such as the Internet. Users of user electronic computer 102 can access server computer 108 via network 106.

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

[0036] In one embodiment, the behavioral training engine 110 is configured to receive inputs related to the user's environment and target behaviors that the user wishes to achieve, or that the user's therapist(s) or caregiver(s) wish for the user to achieve. The behavioral training engine 110 is configured to generate and present one or more behavioral clips in response to stimuli that help the user learn the desired behaviors. The implementation of the behavioral training engine 110 will be described in more detail with reference to Figures 2 to 11.

[0037] An example of a data store 112 may include one or more electronic databases that can store data related to users and / or the behavior training engine 110. The data store 112 may be maintained by the same entity that maintains the server computer 108, or by one or more external companies related to the entity that maintains the server computer 108. The data store 112 can be accessed by the server computer 108 to retrieve relevant data related to users and the behavior training engine 110. The data store 112 can be accessed by the server computer 108 to retrieve relevant data about multiple users, for example, to determine which types of prompts and / or rewards are more likely to work for a particular type of target behavior, in contrast to others, based on the success rates of those prompts and / or rewards among user groups in the system.

[0038] Figure 2 shows an example configuration of the behavior training engine 110 in Figure 1. In some examples, the behavior 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 behavior training engine 110 may be configured to include more or more modules.

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

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

[0041] Furthermore, the background building module 202 may also request, through the generated behavioral training user interface 104, the user, or the user's therapist(s) or caregiver(s)(s) to 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, one or more photographs or digital representations of the environment could include photographs or digital representations of the main rooms the user spends time in within their home. The photographs or digital representations could also include all other environments related to the user, such as rooms within the user's home, the user's school, workplace, playground, and vehicles the user uses.

[0042] The background construction module 202 uses photographs or digital representations received from the user, or the user's therapist(s) or caregiver(s)(s)(s) to construct a visual representation of the user's primary environment in which the user is located. 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, and the user's photograph or digital representation can be overlaid on the image of the preschool classroom. Similar constructions of other user-related environments are also generated and can be stored in the data store 112.

[0043] The background build module 202 can take into account the user's age, diagnosis, and function level while building the constructed environment. For example, for a user with a low function level, the background build module 202 may build a constructed environment that does not include all objects present in the corresponding real-world environment. Instead, the background build module 202 can create a simple constructed environment without objects that it deems too distracting for the user. For a user with a higher function level, the background build module 202 may build a constructed environment that includes most, if not all, of the objects found in the corresponding real-world environment.

[0044] The constructed environment typically uses photographic or digital representations of the real environment and the real user, rather than creating an animation or portrait 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 constructed environment. An example of an environment constructed by the background construction module 202 will be described in more detail with reference to Figure 5.

[0045] In some examples, the behavior construction module 204 is configured to receive input data from the user computer 102 related to target behaviors that the user is trying to achieve, or that the user's therapist(s) or caregiver(s) want the user to achieve. Based on the received input regarding the target behaviors, the behavior construction module 204 can select, adjust, and personalize the target behaviors and the environment constructed for the user, generate behavior clips related to the target behaviors, prompt the user to trigger the playback of the behavior clips, have the user perform the playback of the behavior clips, and generate personalized rewards when the user successfully engages with the constructed environment and behavior clips. User engagement includes any way the user interacts with the behavior clips, including digital interactions such as mouse clicks or touch inputs related to the behavior clips, and real-world interactions such as the user looking at, touching, or otherwise interacting with the behavior clip display, or attempting to copy the behaviors shown in the behavior clips. The configuration and operation of the behavior construction module will be described in more detail with reference to Figure 3.

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

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

[0048] For example, a particular user working on a target behavior, such as toilet training, may experience both progress and setbacks. Personalization module 206 can track user progress data in relation to the types of elements used in the constructed environment, the types of prompts used, and the rewards presented to a particular user, in order to determine what works and what doesn't. Based on this determination, personalization module 206 can change the types of elements used in the constructed environment, the types of prompts to initiate engagement, and the types of rewards offered for the successful completion of some or all of the target behavior. Personalization module 206 can also adjust how the elements, prompts, and rewards used to construct the environment are modified based on the user's functional level. For example, personalization module 206 can adjust how the types of elements used in the constructed environment, the types of prompts to initiate engagement, and the types of rewards offered for the successful completion of some or all of the target behavior are modified based on whether a particular user is highly functional or less functional.

[0049] For example, a machine learning model is trained to predict which prompts, environment configurations, and rewards will lead to successful user engagement. If a user experiences difficulty interacting with the constructed environment or selecting stimuli, the prompts may be modified according to what the machine learning model predicts will lead to a successful interaction from that particular user.

[0050] Machine learning models can be trained to learn what elicits successful engagement from specific users, what is effective for the average user of a behavioral training system, and what is effective for specific types of target behaviors. The machine learning model can then be provided with inputs to personalize the constructed environment, including prompts, elements within the environment, and rewards, thereby improving user engagement.

[0051] For example, some users respond to text prompts, others to voice prompts, and still others to visual prompts or a combination of different prompts. An example of a text prompt is a message within the behavioral training user interface 104, generated by the behavioral training system 100 and displayed on the user computer 102, which may include a message that prompts the user to click or otherwise select a specific element in a constructed environment, generated by the behavioral construction module 204 and displayed on the behavioral training user interface 104. An example of a voice prompt is a voice message prompting the user to make a choice instead of a text message. The voice message may, in some cases, include a recording of a caregiver. An example of a visual prompt is an arrow that moves and / or points towards a choice the user should make. Other examples of visual prompts include zooming in on elements, highlighting elements, or highlighting elements by contrast in the behavioral training user interface. Other types of prompts are also possible.

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

[0053] In some examples, the elements that act as triggers for the playback of clips generated by the behavior construction module 204 are related to the target behavior. For example, if the target behavior is drinking from a cup, an image of a cup in the constructed environment may act as the stimulus object. If the target behavior is toilet training, an image of a toilet in the constructed environment may act as the stimulus object.

[0054] Depending on the user's current functional level, it may be difficult for the user to select a specific stimulus object within the constructed environment displayed in the behavioral training user interface 104. The personalization module 206 can learn the user's limitations and strengths and modify one aspect of the stimulus object, constructed environment, or prompt type to increase the user's likelihood of engaging with the constructed environment and selecting a 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. In some examples, the data request may take the form of a questionnaire presented on the behavioral training user interface 104. Other forms of data requests are also possible. 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, and the user's strengths and weaknesses. Other types of personal information may also be collected. The behavioral training engine 110 can store the received data in the data store 112.

[0056] In some embodiments, the personalization module 206 may retrieve data about the user's current functional level from the data store 112 for use with a learning model to modify one aspect of the stimulus object, constructed environment, or prompt type to increase the probability that the user will engage with the constructed environment and select a stimulus object.

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

[0058] Examples of personalized build environments that may be displayed in the behavioral training user interface 104 are described in more detail in relation to Figures 9-10.

[0059] Figure 3 shows an example configuration of the behavior building module 204 in Figure 2. In this embodiment, the behavior building 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 building module 204 may be configured to include more modules or fewer modules.

[0060] In some embodiments, the behavior integration submodule 302 is configured to receive input data from the user computer 102 related to a target behavior that the user, or the user's therapist(s) or caregiver(s) is trying to achieve. The behavior integration submodule 302 is associated with the behavior training system 100 and can be caused to display one or more questions and / or choices related to the target behavior on the behavior training user interface 104 displayed on the user computer 102. For example, the behavior training user interface 104 can ask the user, or the user's therapist(s) or caregiver(s) to select a target behavior from a pre-compiled list of target behaviors. The behavior training user interface 104 can also ask the user, or the user's therapist(s) or caregiver(s) to input additional information related to the target behavior, including places where the user is likely to perform the target behavior, photographs or digital representations of each of these places, and photographs or digital representations of objects related to the target behavior in each of these places. For example, in the case of a target behavior such as toilet training, the user or their caregiver may be asked to upload not only a photograph or digital representation of the user's primary bathroom, such as the bathroom at the user's home, but also photographs or digital representations of other bathrooms that the user is likely to use, such as the bathroom at the user's school or the bathroom of the user's grandparents.

[0061] Based on the received input regarding the target behavior, the behavior integration submodule 302 may select an appropriate constructed environment related to the target behavior, which has been constructed by the background construction module 202 and stored in the data store 112. The behavior integration submodule 302 may then adjust the environment constructed by the background construction module 202 to include elements related to the target behavior, including images or digital representations of objects related to the target behavior, which have been input by the user, or the user's therapist(s) or caregiver(s).

[0062] For example, if the target behavior involves learning to sit in a new chair, the behavior integration submodule 302 can prompt the user, or the user's therapist or caregiver, to provide information about the place where the user typically performs the target behavior, a photograph or digital representation of that place, and, for example, a photograph or digital representation of the user's chair. The user's therapist or caregiver can provide the necessary details, and the behavior integration submodule 302 can use the received information to select a suitable constructed environment, such as the user's preschool classroom, and overlay one or more new elements, such as an image of the new chair, onto the image of the constructed environment.

[0063] In another example, if the target behavior involves learning to use the toilet, the behavior integration submodule 302 can select a constructed environment related to the user's toilet. Alternatively, the behavior integration submodule 302 can also select and adjust a constructed environment in other ways depending on the target behavior.

[0064] As a user becomes habitually successful in completing a specific target behavior in a real-world environment that matches the initially constructed environment, the behavior integration submodule 302 can adjust the constructed environment to secondary environments to help the user learn to achieve the target behavior in other environments. For example, if the target behavior is to use the toilet properly, initially the behavior integration submodule 302 can construct an environment that matches the user's primary environment (the user's home in this embodiment). However, as time passes and the user becomes proficient at using the toilet in their own home, the behavior integration submodule 302 can use the principle of generalization to adjust the constructed environment to include other toilet environments the user might be exposed to, such as the toilet at their school or their grandparents' toilet.

[0065] In some examples, the behavior integration submodule 302 can also be configured to assign one or more elements or objects within the constructed environment as stimulus objects. The behavior integration submodule 302 can prompt the user to select a stimulus object or otherwise engage in order to trigger the playback of a behavior clip. For example, in the case of the target behavior of sitting in a new chair, the stimulus object may be an image of a chair in the constructed environment displayed on the behavior training user interface 104. By selecting the image of the chair on the behavior training user interface 104 or engaging in other ways, the user plays a behavior clip related to the target behavior. The generation and playback of behavior clips will be described in more detail in relation to the behavior clip generation submodule 304.

[0066] One aspect relating to the constructed environment, prompts, and stimulus objects may be modified by the behavior integration submodule 304 based on predictions and feedback from a machine learning model configured by the personalization module 206.

[0067] Once an appropriate environment is established for the selected target behavior, the behavior clip generation module 306 may be configured to construct a clip of the behavior using the established environment. In this embodiment, the clip includes a series of images or a short video snippet of a visual representation of the user performing, for example, the start and end steps of the target behavior within the established environment, and one or more intermediate steps may be omitted. The user images in the clip include photographic images or other digital representations of the user so that when the user views the clip, they can see themselves performing the target behavior within that environment.

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

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

[0070] A clip of a target action can be configured so that the clip contains only a subset of the target action. In this embodiment, the subset includes the start and end of the target action. In other embodiments, the subset may include 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. Making the clip more efficient by not including one or more intermediate steps of the target action in this embodiment limits the length of the clip. Generally, at least a significant portion of users who have difficulty learning new actions, 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 start and end steps of the target action reduces confusion and makes it easier for the user to understand the ultimate goal of the action. Generally, once a user sees themselves successfully performing the target action and understands the ultimate goal, they gain confidence in attempting and perfecting the ultimate goal of the target action. In such cases, intermediate steps related to the goal behavior become easier for the user to achieve without them having to look at each step and / or becoming obsessed with perfecting each intermediate step.

[0071] For example, a clip related to the goal behavior of drinking from a cup could include a series of images or a short video snippet, and could only include the start and end steps of the goal behavior. In another example, a clip related to the goal behavior of sitting in a new chair could include a series of images or a short video snippet of images of the user walking towards the new chair and the user already sitting in the chair.

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

[0073] Once an action clip is generated, the action clip generation submodule 304 can store the clip in the data store 112. Upon receiving a trigger from the user, the action clip generation submodule 304 can cause the action clip to be played and displayed on the action training user interface display 104. In some embodiments, the trigger may include the user selecting or otherwise engaging with a stimulus image on a constructed environment displayed on the action training user interface 104. In other examples, other methods of triggering the playback of the action clip are possible, as will be readily apparent to those skilled in the art. The user's progress in learning the target behavior may depend heavily on the number of times the user repeatedly views and engages with the generated action clip. Therefore, as a form of repetitive practice, the action clip generation submodule 304 can provide reminders for the user or the user's caregiver to repeatedly trigger the playback of the action clip so that the user can view the clip many times to become familiar with the target behavior.

[0074] When the behavior clip generation submodule 304 receives input indicating that the user has engaged with a stimulus object or otherwise selected a stimulus object, the behavior clip generation submodule 304 can cause the generated behavior clip to be played back on the behavior training user interface 104 on the user computer 102.

[0075] For example, in the case of the target behavior of drinking from a cup, the cup itself can be the stimulus. Clip playback may be triggered when the user selects a cup within the displayed build environment. Playing an behavior clip 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 drinking from the cup.

[0076] In some embodiments, the reward generation submodule 306 is configured to generate a reward for the user after the user has successfully engaged with the constructed environment and the playback of an action clip has been triggered. The reward generation submodule 306 can generate one or more rewards for display on the action training user interface 104. Rewards may include digital text, images, audio, and / or video objects that can actively reinforce the user to continue engaging with the constructed environment. Rewards may also encourage the user to reflect the target behavior in real life. For example, rewards may include images of the user's favorite anime characters, emoticons, clapping sound clips, GIF (Graphics Interchange Format) snippets, images and videos of the user's family and friends, or videos of the user's favorite TV shows or movies. Other types of digital rewards are also possible. Although not necessary to successfully train the user in new behaviors, the disclosed action training system may also use external real-world rewards to incentivize the user. For example, external real-world rewards may include a points system in which the user receives points for certain types of engagement that can be exchanged for prizes. Prizes may include access to digital content or physical objects, or monetary rewards.

[0077] The reward generation submodule 306 may be personalized for users so that it can generate rewards for different types of user engagement for different users. For example, for users who find it difficult to engage with the behavior training user interface 112 at all, the reward generation submodule 306 can generate rewards for simply clicking, or, in the case of a touch interface, for touching any part of the constructed environment.

[0078] As the user progresses through training, the reward generation submodule 306 can generate rewards only for clicks or touches on any part of the constructed environment that is outside the user's image within the constructed environment. Ultimately, the reward generation submodule 306 may generate rewards only when the user clicks or touches a stimulus object within the constructed environment.

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

[0080] Figure 4 shows an example of a method 400 for learning a new behavior. In an exemplary behavior 402, the background building module 202 of the behavior training engine 110 can receive background data from the behavior training user interface 104 of the user computer 102. For example, background data may include user-related information such as history, social, health, and family-related information; user photographs; information related to the user's therapist or caregiver; places where the user spends time; interior photographs of the user's room; and photographs of other rooms in the user's home, school, or workplace.

[0081] In exemplary operation 404, the background build module 202 of the behavior training engine 110 may build an environment for display on the behavior training user interface 102 from photographs or digital representations of the user and the user's environment. For example, the built environment may be a realistic visual representation of the user and the user's environment, rather than an animation or caricature of the user and the user's environment, so that the user can visualize themselves completing the task. In some embodiments, the built environment may be stored in the data store 112 and / or the built environment may be transmitted to the user computer 102 for display on the behavior training user interface 104.

[0082] In exemplary operation 406, the behavioral construction module 204 of the behavioral training engine 110 can receive information related to target behaviors from the behavioral training user interface 104 of the user computer 102. For example, target behavior information may be entered or selected by the user or the user's therapist or caregiver on the behavioral training user interface 104. Target behavior information may include a selection of target behaviors that the user is trying to achieve or that the user's therapist or caregiver wants the user to achieve, from a pre-compiled list of target behaviors, and photographs or digital representations of objects or places related to the target behaviors. Other types of information related to target behaviors may also be received from the user computer 102, as will be readily apparent to those skilled in the art.

[0083] In the exemplary operation 408, the behavior building module 204 of the behavior training engine 110 may integrate elements related to the target behavior into the environment built from operation 404. For example, the behavior integration submodule 302 of the behavior building module 204 may integrate elements related to the target behavior into the environment built from operation 404. The process of integrating elements related to the target behavior into the built environment will be described in more detail in relation to the behavior integration submodule 302.

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

[0085] In example 412, the behavior construction module 204 of the behavior training engine 110 may generate behavior clips related to the target behavior. For example, the behavior clip generation submodule 304 of the behavior construction module 204 may generate behavior clips based on images of a user performing the start and end steps related to the target behavior. The behavior clip generation submodule 304 may use an image editing or video editing algorithm to generate a series of images or a short video snippet of a user performing the start and end steps of the target behavior within the constructed environment. Intermediate steps are not included in the generated clips. Once the behavior clips are generated, the behavior clip generation submodule 304 stores the generated clips in the data store 112. The generation of behavior clips will be described in more detail in relation to the behavior clip generation submodule 304 in Figure 11.

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

[0087] In some embodiments, the selection can be completed when the user clicks on an image of a stimulus object using a mouse. In other embodiments, if the user computer 102 includes a touch-sensitive display screen, the selection may be completed when the user touches an image of a stimulus object displayed on the behavior training user interface 104. The user's selection is sent from the user computer 102 to the behavior clip generation submodule 304 of the behavior training engine 110.

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

[0089] When the user computer 102 receives an action clip, it can cause the action training user interface 104 to display and automatically play the action clip. The generation and playback of action clips will be described in more detail in relation to the action clip generation submodule 304 shown in Figure 3.

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

[0091] One or more rewards can be personalized based on the type of reward that actively reinforces a particular user to continue learning a target behavior, including when the user touches, selects, or otherwise engages with any part of the integrated build environment displayed within the behavior training user interface 104 other than the user's image within the integrated build environment, and / or when the user touches, selects, or otherwise engages with a stimulus object within the integrated build environment. Reward generation and personalization are further described in relation to the reward generation submodule 306 in Figure 3.

[0092] Figures 5-8 show visual examples of the behavioral training user interface 104 during different actions of a behavioral training system related to the target behavior of sitting in a new chair within a classroom environment.

[0093] Figure 5 shows an example visual representation 500 of the behavioral training user interface 104 that displays the constructed environment. The exemplary visual representation 500 shows an image of the constructed environment 502 along with an image of the user 504 superimposed 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 computer 102 for display on the behavioral training user interface 104 before the user selects a target behavior.

[0094] The background construction module 202 may construct the image of the constructed environment 502 from one or more photographs or digital representations of the user's classroom to resemble the user's actual 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 the actual user. The background construction module 202 may overlay the user's image on top of the image of the constructed environment to create a visual representation of the user within the constructed environment.

[0095] In some cases, the environment being constructed may be defined by the target behavior itself. For example, in the case of a target behavior related to toilet training, the background construction module 202 may require the user to provide a photograph or digital representation of a toilet in the user's primary environment, such as the user's home.

[0096] Figure 6 shows an example visual representation 600 of the behavior training user interface 104 that displays a constructed environment integrating elements related to the selected target behavior. The exemplary visual representation 600 shows an image of the constructed environment 502 with images of the user 504 and one or more target behavior elements 602 superimposed on the constructed environment 502.

[0097] For example, the background integration submodule 302 of the behavior training engine 110 integrates one or more target behavior-related elements into a constructed environment in operations 406-408 of Figure 4. The integrated constructed environment may contain stimulus objects that, when triggered in response to a prompt, cause the playback of a behavior clip.

[0098] Visual representation example 600 relates to the target behavior of learning to sit in a new chair. In visual representation example 600, the image of the chair functions as both a stimulus object and one or more target behavior elements 602. Other configurations are possible, as will be apparent to those skilled in the art.

[0099] Figure 7 shows a visual representation example 700 of the behavioral training user interface 104 that displays snippets of behavioral clips. The visual representation example 700 shows snippets from behavioral clips generated by the behavioral clip generation submodule 304 of the behavioral training engine 110 and transmitted to the user computer 102 for display on the behavioral training user interface 104.

[0100] In visual representation example 700, the snippet from the action clip includes the completion step related to the target action, which is part of the generated action clip. For a target action related to sitting in a new chair, the action clip generated by the action clip generation submodule 304 includes multiple image or video snippets showing the start and end steps of the target action. For example, the start step of the target action includes the user beginning to turn towards the new chair, and the end step of the target action includes the user already sitting in the new chair.

[0101] In some embodiments, the generated action clip may include two images: a first image showing the user facing the new chair, and a second image showing the user sitting in the new chair. Visual representation example 700 shows the completion step of the target action, which includes an image of the user sitting in the new chair.

[0102] Figure 8 shows an exemplary visual representation 800 of the behavioral training user interface 104 that displays rewards. The visual representation example 800 shows the reward 802 generated by the reward generation submodule 306 of the behavioral training engine 110 and transmitted to the user computer 102 in response to the user triggering the selection of a stimulus object on the behavioral training user interface 104.

[0103] In visual representation example 800, reward 802 includes a sticker of a firefighter giving a "thumbs up" signal. Reward 802 is personalized to provide positive reinforcement to a specific user. The process of generating rewards is described in more detail in relation to the reward generation submodule 306 in Figure 3.

[0104] Figure 9 shows an example of a visual representation 900 of the behavioral training user interface 104 that displays modifications to elements of the integrated build environment. The behavioral training engine 110 learns the types of elements in the integrated build environment 902 using a machine learning model configured by the personalization module 206, resulting in improved 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 build 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, potentially leading the user to select the stimulus object 904 or otherwise engage with the stimulus object 904.

[0105] Figure 10 shows a visual representation 1000 of another example of the behavioral training user interface 104, which displays other modifications to elements of the integrated build environment. The behavioral training engine 110 uses a machine learning model configured by the personalization module 206 to learn the types of elements in the integrated build environment 1002 that may prevent a particular user from engaging with the stimulus object 1004. For example, elements in the integrated build environment 1002 may be removed to prevent a particular user from being distracted and preventing the user from focusing their attention on the stimulus object 1004. Removing distracting elements from the integrated build environment may help increase the likelihood that a particular user will select the stimulus object 1004. The personalization module 206 can predict the types of modifications to the integrated build environment and / or stimulus object that would help increase the engagement of a particular user. Personalization of the integrated build environment and stimulus object will be further described in relation to the personalization module 206 in Figure 2.

[0106] For example, the personalization module 206 can predict, based on the user's past engagement with the behavioral training user interface 104, that the user may be distracted by brightly colored elements in the integrated build environment. The behavioral build module 204 can then remove all brightly colored elements other than stimulus objects from the behavioral training user interface 104.

[0107] In other embodiments, based on the user's past engagement with the behavior training user interface 104, it may be predicted that the user will select or engage with an element in the upper right corner of the behavior training user interface 104. The behavior construction module 204 may then remove all background elements from the upper right corner of the behavior training user interface 104. Alternatively, the behavior construction module 204 in this embodiment may move a stimulus object to the upper right corner of the behavior training user interface 104 to increase the probability that the user will engage with the stimulus object.

[0108] Figure 11 shows an example of the physical components of the computing device in Figure 1. As in the example in Figure 11, the server computer 108 includes at least one central processing unit ("CPU") 1102, system memory 1108, and a system bus 1122 connecting the system memory 1108 to the CPU 1102. The system memory 1108 includes random access memory ("RAM") 1110 and read-only memory ("ROM") 1112. A basic input / output system, including basic routines that help transfer information between elements within the server computer 108, such as during startup, 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 related to a software application 1116. Some or all of the components of the server computer 108 can also be included in a user electronic computer 102.

[0109] The mass storage device 1114 is connected to the CPU 1102 via a mass storage controller (not shown) connected to the system bus 1122. The mass storage device 1114 and its associated computer-readable data storage medium provide the server computer 108 with non-volatile, non-transient storage. While the description of computer-readable data storage medium included herein refers to mass storage devices such as hard disks or solid-state disks, it will be understood by those skilled in the art that the computer-readable data storage medium may be any available, non-transient physical device or product from which a central processing unit can read data and / or instructions.

[0110] Computer-readable data storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing 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 technologies, CD-ROM, digital versatile disks ("DVD"), other optical storage media, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be used to store desired information and 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 a logical connection to a remote network device via 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 via a network interface unit 1104 connected to a system bus 1122. It should be understood that the network interface unit 1104 can also be used 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 types of input devices. Similarly, the input / output controller 1106 can provide outputs to a touch user interface display screen or other types of output devices.

[0112] As briefly mentioned above, the mass storage device 1114 and RAM 1110 of the server computer 108 can store software instructions and data related to the software application 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 functions of the server computer 108 described in this document. For example, the mass storage device 1114 and / or RAM 1110 can, when executed by the CPU 1102, store software instructions that cause the server computer 108 to display received data on the display screen of the server computer 108.

[0113] While various embodiments are described herein, those skilled in the art will understand that many modifications are possible within the scope of this disclosure. Therefore, the scope of this disclosure is not intended to be limited in any way by the examples provided. [Aspect 1] A computer implementation method for training users on target behavior, Receiving the selection of the target action from the user's computer, To construct a visual representation of a user environment, wherein the visual representation of the user environment includes at least one stimulus object. Transmitting the visual representation of the user environment to the user computer, Determining a series of steps for the aforementioned target action, To generate an action clip related to the execution of the target action, wherein the action clip includes some, but not all, of the steps in the determined set of steps. Receiving the selection of the aforementioned stimulus object, In response to receiving the selection of the stimulus object, the generated behavior clip is transmitted to the user's computer. Computer implementation methods including [Aspect 2] The computer implementation method according to Embodiment 1, wherein the target behavior is a behavior or action pattern that the user or the user's caregiver wants the user to learn. [Aspect 3] Constructing the visual representation of the user environment is Receiving one or more photographs or digital representations of the user's real-life environment, wherein the one or more photographs or digital representations of the real-life environment include one or more background elements. Receiving one or more photographs or digital representations of the aforementioned user, Receiving one or more photographs or digital representations of one or more objects related to the aforementioned target behavior, The method involves overlaying the user's image and the one or more images of the one or more objects onto an image of the real-life environment to generate a visual representation of the user's environment, The image of the user is generated from one or more photographs or digital representations of the user. The one or more images of the one or more objects are generated from the one or more photographs or digital representations of the one or more objects. The image of the real-life environment is generated from one or more photographs or digital representations of the real-life environment. To generate, A computer implementation method according to embodiment 1, including the method described in embodiment 1. [Aspect 4] The computer implementation method according to embodiment 3, wherein at least one of the images of the one or more objects superimposed on the image of the real-life environment is designated as the stimulus object. [Aspect 5] The computer implementation method according to embodiment 3, wherein the one or more photographs or digital representations of the user's real-life environment include one or more photographs or digital representations of a place occupied by the user. [Aspect 6] The computer implementation method according to embodiment 3, further comprising removing at least one of the one or more background elements from the image of the real-life environment based on the level of user engagement with the image of the one or more objects superimposed on the image of the real-life environment. [Aspect 7] The generated action clips are stored in a data store, In response to receiving the selection of the stimulus object, the action clip generated from the data store is retrieved, The computer implementation method according to embodiment 1, further comprising: [Aspect 8] The further includes transmitting one or more rewards to the user computer in response to receiving the selection of the stimulus object, The computer implementation method described in Embodiment 1. [Aspect 9] The computer implementation method according to embodiment 8, wherein the visual representation of the user environment, the stimulus object, and at least one of the one or more rewards are based on the user's personal preferences. [Aspect 10] A system for training users on target behaviors, Processor and When executed by the aforementioned processor, the aforementioned processor will Receiving the selection of the target action from the user's computer, To construct a visual representation of a user environment, wherein the visual representation of the user environment includes at least one stimulus object. Transmitting the visual representation of the user environment to the user computer, Determining a series of steps for the aforementioned target action, To generate an action clip related to the execution of the target action, wherein the action clip includes a visualization of some but not all of the steps of the determined set of steps. Receiving the selection of the aforementioned stimulus object, In response to receiving the selection of the stimulus object, the generated behavior clip is transmitted to the user's computer. Memory containing instructions to execute, A system that includes these features. [Aspect 11] The system according to embodiment 10, wherein the target behavior is a behavior or manner that the user or the user's caregiver wishes the user to learn. [Aspect 12] Constructing the visual representation of the user environment is Receiving one or more photographs or digital representations of the user's real-life environment, Receiving one or more photographs or digital representations of the aforementioned user, Receiving one or more photographs or digital representations of one or more objects related to the aforementioned target behavior, The visual representation of the user environment is Overlaying the user's image and one or more images of one or more of the one or more objects onto the image of the real-life environment. By generating, Includes, The image of the user is generated from one or more photographs or digital representations of the user. The one or more images of the one or more objects are generated from the one or more photographs or digital representations of the one or more objects. The image of the real-life environment is generated from one or more photographs or digital representations of the real-life environment. The system described in embodiment 10. [Aspect 13] The system according to embodiment 12, wherein at least one image of the one or more images of the one or more objects superimposed on the image of the real-life environment is designated as the stimulus object. [Aspect 14] The system according to embodiment 12, wherein the one or more photographs or digital representations of the real-life environment include one or more photographs or digital representations of a place occupied by the user. [Aspect 15] When the aforementioned instruction is executed by the processor, the processor further: Based on the level of user engagement with the image of one or more objects superimposed on the image of the real-life environment, remove at least one of the one or more background elements from the image of the real-life environment. The system described in embodiment 12, which causes the system to perform the following actions. [Aspect 16] When the aforementioned instruction is executed by the processor, the processor further: The generated action clips are stored in a data store, In response to receiving the selection of the stimulus object, the action clip generated from the data store is retrieved, The system according to embodiment 10, which causes the execution of the following: [Aspect 17] When the aforementioned instruction is executed by the processor, the processor further: In response to receiving the selection of the stimulus object, transmit one or more rewards to the user's computer. The system according to embodiment 10, which causes the system to perform the following actions. [Aspect 18] The system according to embodiment 8, wherein at least one of the visual representation of the user environment, the stimulus object, and the reward is based on the user's personal preferences. [Aspect 19] A system for training users on target behaviors, Display device and Processor and When executed by the aforementioned processor, the aforementioned processor will Displaying a behavioral training user interface on the display device that includes 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 related to the target behavior to the server computing device, Receiving a constructed visual representation of the user environment from the server computing device and based on the selection, wherein the constructed visual representation of the user environment includes stimulus objects. The display device displays a visual representation of the constructed user environment and prompts requesting the user to trigger the stimulus object. Receiving the selection of the stimulus object from the user, To transmit the selection of the stimulus object to the server computing device, Receiving, in response to transmitting the selection of the stimulus object, an action clip relating to the performance of the target behavior, wherein the action clip does not contain any content relating to the performance of the target behavior. The display device will display the action clip and play it automatically. Receiving one or more rewards in response to the selection of the stimulus object, Displaying one or more of the aforementioned rewards on the display device to encourage the user to continue engaging with the behavioral training user interface, Memory containing instructions to execute, A system that includes these features. [Aspect 20] The system according to embodiment 19, wherein the visual representation, stimulus object, prompt, and at least one of the one or more rewards constructed in the user environment are tailored to the user.

Claims

1. A computer-implemented method for training a user on a target behavior, which is performed by a computer. Receiving the selection of the target action from the user's computer, The user's computer receives a photograph or digital image of the stimulus object, Constructing a visual representation of the user environment based on the selection of the target behavior and the photograph or digital image of the stimulus object, wherein the visual representation of the user environment includes the stimulus object. The user computer transmits the constructed visual representation of the user environment to the user computer, thereby causing the constructed visual representation to be displayed on the display device of the user computer. Determining a series of steps for the aforementioned target action, To generate a video clip of an action related to the execution of the aforementioned target action, wherein the video clip of the action includes visualizations of some, but not all, of the steps of the determined set of steps. Receiving the selection of the stimulus object, the selection of the stimulus object being performed via a prompt of the constructed visual representation of the user environment displayed on the display device of the user computer, In response to receiving the selection of the stimulus object, the generated behavioral video clip is transmitted to the user's computer. Computer implementation methods including

2. The computer implementation method according to claim 1, wherein the target behavior is a behavior or attitude that the user or the user's caregiver wants the user to learn.

3. Constructing the visual representation of the user environment is The receiving of one or more photographs or digital images of the user's real-life environment, wherein the one or more photographs or digital images of the real-life environment include one or more background elements. Receiving one or more photographs or digital images from the aforementioned user, The method involves overlaying the user's image and the stimulus object's image onto the image of the real-life environment, wherein the stimulus object is related to the target behavior, and the overlay generates a visual representation of the user's environment. 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 the photograph or digital image of the stimulus object. The image of the real-life environment is generated from one or more photographs or digital images of the real-life environment. To generate, The computer implementation method according to claim 1, including the method described in claim 1.

4. The computer implementation method according to 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 place occupied by the user.

5. The computer implementation method according to 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 the level of user engagement with the image of the stimulus object superimposed on the image of the real-life environment.

6. The generated video clips of the actions are stored in a data store, In response to receiving the selection of the stimulus object, the behavioral video clip generated from the data store is retrieved. The computer implementation method according to claim 1, further comprising:

7. The further includes transmitting one or more rewards to the user computer in response to receiving the selection of the stimulus object, The computer implementation method according to claim 1.

8. The computer implementation method according to claim 7, wherein the visual representation of the user environment, the stimulus object, and at least one of the one or more rewards are based on the user's personal preferences.

9. A system for training users on target behaviors, Processor and When executed by the aforementioned processor, the aforementioned processor will Receiving the selection of the target action from the user's computer, The user's computer receives a photograph or digital image of the stimulus object, Constructing a visual representation of the user environment based on the selection of the target behavior and the photograph or digital image of the stimulus object, wherein the visual representation of the user environment includes the stimulus object. The user computer transmits the constructed visual representation of the user environment to the user computer, thereby causing the constructed visual representation to be displayed on the display device of the user computer. Determining a series of steps for the aforementioned target action, To generate a video clip of an action related to the execution of the target action, wherein the video clip of the action includes a visualization of some, but not all, of the steps of the determined set of steps. Receiving the selection of the stimulus object, the selection of the stimulus object being performed via a prompt of the constructed visual representation of the user environment displayed on the display device of the user computer, In response to receiving the selection of the stimulus object, the generated behavioral video clip is transmitted to the user's computer. Memory containing instructions to execute, A system equipped with these features.

10. The system according to claim 9, wherein the target behavior is a behavior or attitude that the user or the user's caregiver wants the user to learn.

11. Constructing the visual representation of the user environment is Receiving one or more photographs or digital images of the user's real-life environment, Receiving one or more photographs or digital images from the aforementioned user, The visual representation of the user environment is The process involves overlaying the user's image and the stimulus object's image onto the image of the real-life environment, wherein the stimulus object is related to the target behavior, By generating, Includes, 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 the photograph or digital image of the stimulus object. The image of the real-life environment is generated from one or more photographs or digital images of the real-life environment. The system according to claim 9.

12. The system according to 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 place occupied by the user.

13. When the aforementioned instruction is executed by the processor, the processor further: Based on the level of engagement the user shows with respect to the image of the stimulus object superimposed on the image of the real-life environment, at least one of the one or more background elements is removed from the image of the real-life environment. The system according to claim 11, which causes to perform the following.

14. When the aforementioned instruction is executed by the processor, the processor further: The generated video clips of the actions are stored in a data store, In response to receiving the selection of the stimulus object, the behavioral video clip generated from the data store is retrieved. The system according to claim 9, which causes to perform the following.

15. When the aforementioned instruction is executed by the processor, the processor further: In response to receiving the selection of the stimulus object, transmit one or more rewards to the user's computer. The system according to claim 9, which causes the following to be performed.

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

17. A system for training users on target behaviors, Display device and Processor and When executed by the aforementioned processor, the aforementioned processor will Transmitting a photograph or digital image of a stimulus object to a server computer, Displaying a behavioral training user interface on the display device that includes 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 related to the target behavior to the server computing device, Receiving a constructed visual representation of the user environment from the server computing device based on the selection and the photograph or digital image of the stimulus object, wherein the constructed visual representation of the user environment includes the stimulus object. The display device displays a visual representation of the constructed user environment and prompts requesting the user to trigger the stimulus object. Receiving the selection of the stimulus object from the user, To transmit the selection of the stimulus object to the server computing device, Receiving a behavioral video clip related to the performance of the target behavior in response to transmitting the selection of the stimulus object, wherein the behavioral video clip does not contain any content related to the performance of the target behavior. The aforementioned display device will display the aforementioned action video clip and automatically play it back. Receiving one or more rewards in response to the selection of the aforementioned stimulus object, Displaying the aforementioned one or more rewards on the display device, Memory containing instructions to execute, A system equipped with these features.

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

19. The system according to claim 17, wherein the constructed visual representation of the user environment is constructed by superimposing an image of the user and an image of the stimulus object onto an image of the user 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. The system according to claim 19.

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