Patent ID: 12210808

DETAILED DESCRIPTION

Embodiments described herein are directed to systems and methods for investigating interactions between simulated humans. The system builds a model of human behavior that can be assigned to a simulated human. The model of human behavior is trained on a dataset of interactions between humans. Parameters of the model of human behavior may be adjusted by a user. Accordingly, depending on the dataset used to train each model of human behavior and the selective adjustment of one or more parameters of the model, a user can accurately control, isolate, and investigate personality variables of the simulated human. The system also builds a model of a task to be engaged in by a plurality of simulated humans. The task may include a goal to be achieved by each of the plurality of simulated humans. The user may also adjust parameters of the model of the task to be engaged in to allow the user to accurately control, isolate, and investigate environmental or contextual variables in the task to be engaged in. The system simulates interactions between the plurality of simulated humans. Throughout the simulation, the system displays behavior of the simulated humans and information about the interactions between the simulated humans, such as their internal emotional, mental, or physiological states. Various embodiments of system for investigating interactions between simulated humans and the operation of the system are described in more detail herein. Whenever possible, the same reference numerals will be used throughout the drawings to refer to the same or like parts.

Referring now to the drawings,FIG.1schematically depicts an example operating environment of a system100for investigating interactions between simulated humans, according to one or more embodiments shown and described herein. As illustrated,FIG.1depicts a first human112and a second human114interacting within an environment110. Data on the first human112, the second human114, and the environment110may be uploaded to an external device130and/or a user device140through a network120. The user device140may be used to perform one or more user-facing functions, such as receiving one or more inputs from a user or providing information to a user. The user device140may be a cellular phone, tablet, or personal computer. The user device140includes a processor for investigating interactions between simulated humans.

Referring now toFIG.2, non-limiting components of the user device140of the system100for investigating interactions between simulated humans of the present disclosure are schematically depicted, according to one or more embodiments shown and described herein. The user device140includes a controller200including a processor202, a memory module204, and a data storage component206. The user device140may further include an interface module146, a network interface hardware150, and a communication path208. It should be understood that the user device140ofFIG.2is provided for illustrative purposes only, and that other user devices140comprising more, fewer, or different components may be utilized.

Referring now toFIGS.1and2, the processor202may be any device capable of executing machine readable and executable instructions. Accordingly, the processor202may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The controller200, including the processor202, is coupled to the communication path208that provides signal interconnectivity between various modules of the user device140. Accordingly, the communication path208may communicatively couple any number of processors202within the user device140with one another, and allow the modules coupled to the communication path208to operate in a distributed computing environment. Specifically, each of the modules may operate as a node that may send and/or receive data. As used herein, the term “communicatively coupled” means that coupled components are capable of exchanging data signals with one another such as, for example, electrical signals via conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like.

Accordingly, the communication path208may be formed from any medium that is capable of transmitting a signal such as, for example, conductive wires, conductive traces, optical waveguides, or the like. In some embodiments, the communication path208may facilitate the transmission of wireless signals, such as WiFi, Bluetooth®, Near Field Communication (NFC) and the like. Moreover, the communication path208may be formed from a combination of mediums capable of transmitting signals. In one embodiment, the communication path208comprises a combination of conductive traces, conductive wires, connectors, and buses that cooperate to permit the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. Additionally, it is noted that the term “signal” means a waveform (e.g., electrical, optical, magnetic, mechanical or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, vibration, and the like, capable of traveling through a medium.

The controller200of the user device140includes the memory module204. The controller200, including the memory module204, is coupled to the communication path208. The memory module204may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable and executable instructions such that the machine readable and executable instructions can be accessed by the processor202. The machine readable and executable instructions may comprise logic or algorithm(s) written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, for example, machine language that may be directly executed by the processor, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable and executable instructions and stored on the memory module204. Alternatively, the machine readable and executable instructions may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or their equivalents. Accordingly, the methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components.

Still referring toFIGS.1and2, the user device140comprises network interface hardware150for communicatively coupling the user device140to the external device130. The network interface hardware150can be communicatively coupled to the communication path208and can be any device capable of transmitting and/or receiving data via a network. Accordingly, the network interface hardware150can include a communication transceiver for sending and/or receiving any wired or wireless communication. For example, the network interface hardware150may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card, mobile communications hardware, near-field communication hardware, satellite communication hardware and/or any wired or wireless hardware for communicating with other networks and/or devices. In one embodiment, the network interface hardware150includes hardware configured to operate in accordance with the Bluetooth® wireless communication protocol. The network interface hardware150of the user device140may transmit information on the investigation of interactions between simulated humans to the external device130. The network interface hardware150may also receive information and data relating to the investigation of interactions between simulated humans from the external device130.

In some embodiments, the user device140may be communicatively coupled to the external device130by the network120. In one embodiment, the network120may include one or more computer networks (e.g., a personal area network, a local area network, or a wide area network), cellular networks, satellite networks and/or a global positioning system and combinations thereof. Accordingly, the user device140can be communicatively coupled to the network120via a wide area network, via a local area network, via a personal area network, via a cellular network, via a satellite network, etc. Suitable local area networks may include wired Ethernet and/or wireless technologies such as, for example, wireless fidelity (Wi-Fi). Suitable personal area networks may include wireless technologies such as, for example, IrDA, Bluetooth®, Wireless USB, Z-Wave, ZigBee, and/or other near field communication protocols. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM.

The external device130may be any database server or electronic device belonging to a user of the user device140or a third party. For instance, the external device130may contain one or more storage devices for storing data pertaining to the operation of the system100for investigating interactions between simulated humans. The external device130may function as a general database for transmitting data relating to the first human112, the second human114, and the environment110the first human112and the second human114interact in. That is, the external device130stores and transmits data related to real-world interactions between one or more humans. As used herein, a real-world interaction between one or more humans may be also be referred to as a trial. A single trial relates to a period of interaction between two or more humans contextualized by a constant purpose. For instance, the first human112may be an individual wishing to buy a car, and the second human114may be an individual wishing to sell a car. The first human112and the second human114may communicate, verbally or non-verbally, for a period of ten minutes. If, for the entire ten minutes, the first human112and the second human114communicate regarding the sale/purchase of a vehicle in question, then the entire ten minutes may be considered a first trial, as the entire period of interaction is contextualized by the constant purpose of selling/purchasing the vehicle. If, on the other hand, the first human112and the second human114communicate regarding the sale/purchase of the vehicle for the first five minutes, and then communicate about their past weekends for the second five minutes, the first five minutes may be considered a first trial, and the second five minutes may be considered a second trial.

The data stored and transmitted by the external device130may be collected from one or more physiological sensors worn by, or otherwise situated to collect data on, the first human and the second human. The physiological sensors may include a heart rate sensor, a blood pressure sensor, a sweat sensor, a facial expression recognition sensor, a pupil dilation sensor, an EKG sensor, an EEG sensor, and the like. The physiological data on the first human112and the second human114may be directly transmitted from the one or more physiological sensors to the external device130through the network120. In other embodiments, the sensors may be associated with one or more network-connected devices, such as a cellular device, smart watch, and the like, of the first human112and/or the second human114. In such cases, the data from the physiological sensors may be directly collected by the one or more network-connected devices, which may then transmit the physiological data to the external device130.

Separate trials between the first human112and the second human114may be time stamped, for instance. In one example, an administrator, or individual who observes the communication trial between the first human112and the second human114, may record when trials begin and end and upload such recordings to the external device130. Therefore, physiological data collected over ten minutes of communication, and two separate trials, between the first human112and the second human114may be correctly associated with the trial the individual trial the physiological data was collected during. The administrator may also label the each trial with its constant purpose, such as, a negotiation for sale, an election of a group leader, and the like.

In addition to, or instead of, the physiological data on the first human112and the second human114, the external device130may also store voice of video recordings of the first human112and the second human114during the trial. For instance, the dialogue and physical interactions of the first human112and the second human114may be recorded by any suitable audio or video recording device and uploaded to the external device130.

The administrator, or individual observing the communication trial between the first human112and the second human114may upload administrator notes on the trial between the first human112and the second human114. The administrator notes may be objective observations of the first human112, the second human114, and/or the environment110. For instance, if the trial is contextualized by the purpose of the purchase/sale of a vehicle, the administrator may record where the trial is taking place (at a car lot, at a home of the first human112or the second human114, etc.), the number of people surrounding the first human112and the second human114, the weather, and any other observable variable in the environment110. The administrator notes may also relate to the interactions communications between the first human112and the second human114. For instance, the administrator may record the order of offer/counteroffer between the first human112and the second human114, the change in price of the vehicle with each offer/counteroffer, and the like.

The administrator may also upload administrator analysis on the trial between the first human112and the second human114. The administrator analysis may be subjective observations of the first human112, the second human114, and/or the environment110. For instance, the administrator may observe the vocal inflections, facial expressions, and the like of the first human112and the second human114and take notes on whether the first human112and the second human114appear disgruntled, excited, insulted, and the like throughout the trial between the first human112and the second human114.

The data stored and transmitted by the external device130may also be self-reported analysis from the first human112and the second human114. For instance, during, or following a communication trial between the first human112and the second human114, the first human112and/or the second human114may provide a self-analysis of their mental and emotional state during the trial. For instance, the first human112may report that they were insulted by a certain comment by the second human114, that they were excited by the initial price of a vehicle in question but subdued the expressions of the excitement, that they did not trust the second human114, and the like.

The above-described data, including the physiological data, voice or visual recordings, the administrator notes, the administrator analysis, and the self-reported analysis may collectively be referred to as trial data or stimulus data, herein. That is, the above-described data generally relates to the mental, emotional, and physiological response of the first human112, for instance, in response to outside stimuli (such as the second human114, the environment110, etc.). The stimulus data, therefore, provides data on a human's reaction and behavior in response to different stimuli, when interacting with different humans, when interacting in different environments with different purposes, and the like.

The data stored and transmitted by the external device130on each of the first human112and the second human114may be stored in a profile for each of the first human112and the second human114. That is, all data collected from all trials including the first human112, whose name may be John Doe, may be stored in a profile specific to John Doe. Personal profile data of John Doe may also be stored in the John Doe profile. For instance, John Doe's sex, gender, age, religion, education, income level, marital status, and the like can be stored in the John Doe profile.

The John Doe profile may also include data on the values, personality traits, and the like of the John Doe. Such data may be collectively referred to as personality data herein. While the trial data may track John Doe's behavior, mental state, emotional state, thought, and the like during a communication trial with a second human114, for instance, the personality data provides information on John Doe's baseline mental and emotional qualities. For instance, the personality data may include a quantification, on a scale of 0-10, for instance, on whether John Doe is trusting, easily insulted, frugal, spendthrift, honest, and the like. The personality data may be collected from a personality test completed by John Doe. The personality data may also be self-reported by John Doe. For instance, John Doe may be asked to rank how honest he is on a scale of 0-10. The personality data may also be observational data reported by the administrator. For instance, the administrator may observe John Doe to assemble subjective observational data on John Doe's baseline personality traits.

The above-described personal profile data and personality data may be described as baseline data herein, as the personal profile data and personality data relate to the starting qualities and traits of the first human112, for instance, independent of any outside stimuli.

As noted, data may be uploaded to the external device130through one or more network connected devices connected to the network120. For instance, an administrator, first human112, or second human114may input data on their respective personal devices and upload the data to the external device130. In some embodiments, the external device130includes an interface module, such as a display screen, that allows for an administrator or other individual to directly provide data to the external device130through the interface module of the external device130.

It should be appreciated, however, that the external device130is not required for operation of the system100. For instance, the trial data, personal profile data, personality data, and the like may be directly uploaded to and stored on the user device140.

Still referring toFIGS.1-2, the user device140comprises the interface module146. The interface module146may be coupled to the communication path208. The interface module146includes one or more user/machine interfaces to allow presentation of data or information to a user and/or allow for input of user information to the user device140. For instance, the interface module146may include a visual interface144. The visual interface144may be, for example, a cathode ray tube, light emitting diodes, a liquid crystal display, a plasma display, or the like. Moreover, the visual interface144may be a touchscreen that, in addition to providing an optical display, detects the presence and location of a tactile input upon a surface of or adjacent to the visual interface144. The interface module146may also include audial interface142. The audial interface142may include one or more speakers to output an audio message to a user. The audial interface142may also include a microphone to receive audio input, such as vocal commands, from a user.

Referring again to the memory module204of the controller200of the user device140, the programming instructions stored in the memory module204may be embodied as a plurality of software logic modules, where each logic module provides programming instructions for completing one or more tasks. Each of the logic modules may be embodied as a computer program, firmware, or hardware, as an example. Illustrative examples of logic modules present in the memory module204include, but are not limited to, data receiving logic210, human behavior model logic212, task model logic214, parameter logic216, simulation logic218, display logic220, and training logic222.

The data receiving logic210includes one or more programming instructions for receiving data from the external device130. That is, the data receiving logic210includes programming to cause a connection between the network interface hardware150and the external device130such that data transmitted by the external device130is received by the controller200. Further, the data transmitted by the external device130may be stored (e.g., within the data storage component206). The data transmitted by the external device130may include any and all of the data discussed above.

The human behavior model logic212includes one or more programming instructions for building a model of human behavior for a simulated human. The model of human behavior may be constructed from data transmitted from the external device130. That is, the model of human behavior may be trained on the dataset of interactions, or trials, between humans stored in the external device130. The model of human behavior may be representative of the behavior of a single human. For instance, a user, through the interface module146, may specify for the model of human behavior to be trained on the data specific to John Doe. Such data would include John Doe's baseline data and stimulus data (i.e. physiological, mental, emotional data) on John Doe in different trials.

The model of human behavior may be representative of the behavior of more than one human. For instance, a user, through the interface module146, may specify for the model of human behavior to be trained on the specific data related to John Doe and the specific data related to Jane Doe. The model of human behavior may then represent an imaginary person that is a combination of the personality traits, or baseline data, of John Doe and Jane Doe, as well as a combination of the stimulus data of John Doe and Jane Doe (i.e. how they both react in response to different stimuli).

The user may also specify for the model of human behavior to be trained on data related to all persons in a certain category. For instance, the user may build a model of human behavior that is representative, or a combination of, all males 25-30 years old. Therefore, the controller200may receive all baseline and stimulus data on males aged 25-30 years old from the external device130to train the model of human behavior on. The model of human behavior is, therefore, representative of the average male in the age range of 25-30 years old. That is, the model of human behavior represents both the average baseline personality traits and stimulus data of males in the age range of 25-30 years old.

The task model logic214includes one or more programming instructions for building a model of a task to be engaged in by a plurality of simulated humans. The model of the task to be engaged in may include the goal of each simulated human in the model of the task to be engaged in. For instance, a goal of a first simulated human may be to purchase a car, and the goal of a second simulated human may be to sell a particular car. The model of the task to be engaged in may also include the context surrounding the goals of the first simulated human and the second simulated human, for instance. The context may be the internal monetary budget of the simulated human with the goal of purchasing the vehicle, the demand from external purchasers for the vehicle in question, the weather or time of year while the first simulated human and second simulated human interact, and the like.

The parameter logic216includes one or more programming instructions for receiving and executing instruction from the user to adjust one or more parameters of the model of human behavior and/or the model of a task to be engaged in by a plurality of simulated humans. For instance, before or after training the model of human behavior on a dataset of baseline data and stimulus data related to a single human, a combination of known humans, or a combination of humans in a population pool, the user may manually adjust and set a parameter of the model. The parameter may be honesty, irritability, or any other desirable personality or behavioral trait, for instance. Similarly, the user may adjust one or more parameters of the model of a task to be engaged in by a plurality of simulated humans. The parameters of the model of the task to be engaged in may generally relate to the context surrounding the goals of the first simulated human and the second simulated human. For instance, the user may manually adjust or set the internal budget of the simulated human with the goal of purchasing a vehicle.

The parameter logic216also includes one or more programming instructions for learning the parameters of the model of the task to be engaged in. For instance, the task to be engaged in may be the purchase/sale of a vehicle. The controller200may access all data stored in the external device collected during trials related to the sale/purchase of an item, or more specifically the sale/purchase of a vehicle. The data may be analyzed to learn the specific parameters that provide context for sale/purchase tasks. For instance, it may be learned that parameters that provide context for the task, or will influence the interactions between the simulated humans engaging in the task, are the budget of the purchaser, the number of interested third party buyers, whether the vehicle being purchased in new or used, whether the seller is a professional car salesman, whether the seller is a neighbor of the purchaser, and the like. It should be appreciated that the user may set the value of any or all of the learned parameters as desired.

The simulation logic218includes one or more programming instructions for simulating an interaction between a plurality of simulated humans based on the model of human behavior built for each simulated human and the model of the task to be engaged in by the plurality of simulated humans. The simulation logic218includes one or more programming instructions for building an avatar for each of the simulated humans. A model of human behavior may be applied to each avatar. The simulation logic218includes one or more programming instructions for simulating dialogue and/or non-verbal communication between the plurality of simulated humans as they engage in the task to be engaged in. The simulation logic218also includes one or more programming instructions for simulating both the physical appearance and internal emotional and mental states of the plurality of simulated humans. It should be appreciated that the internal emotional or mental state of a simulated human may, but not in all cases, manifest itself as a change in physical appearance of the simulated human.

The display logic220includes one or more programming instructions for displaying the simulation of the interaction between the plurality of simulated humans as they engage in the task to be engaged in. The simulation of the interaction between the plurality of simulated humans as they engage in the task to be engaged in may be displayed on the interface module146. The display may be visual, audial, or a combination. Avatars may be displayed for each of the simulated humans. The appearance of the avatars may be updated and adjusted based on the simulation of the physical appearance and internal emotional and mental states of the simulated humans. Dialogue between the simulated humans may be visually displayed in a text box or audibly output through a speaker of the user device140. The task to be engaged in may also be displayed on the interface module146in any desired detail. For instance, if the task to be engaged in the sale/purchase of a vehicle, the vehicle being negotiated over may be displayed. Additionally, any other contextual parameters of the task to be engaged in, such as the weather, environmental setting, and the like, may be displayed. One or more graphs, scales, or measurements may also be displayed. For instance, a numerical value or other graphical representation of an internal state, such as an emotion, of the simulated humans may be displayed and updated as the simulated humans engage in the task to be engaged in.

The training logic222includes one or more programming instructions for utilizing a neural network or other machine learning model to adjust or improve the operation of one or more other logic modules of the memory module204. For instance, the training logic222may include programming to train the parameter logic216to improve the determination of contextual parameters for different tasks to be engaged in.

Still referring toFIGS.1and2, data storage component206may generally be a storage medium. Data storage component206may contain one or more data repositories for storing data that is received and/or generated. The data storage component206may be any physical storage medium, including, but not limited to, a hard disk drive (HDD), memory, removable storage, and/or the like. While the data storage component206is depicted as a local device, it should be understood that the data storage component206may be a remote storage device, such as, for example, a server computing device, cloud based storage device, or the like. Illustrative data that may be contained within the data storage component206includes, but is not limited to, human behavior data, task data, parameter data, simulation data, display data, and training data.

The human behavior data may generally be data that is used by the controller200to build a model of human behavior. The task data may generally be data that is used by the controller200to build a model of a task to be engaged in. The parameter data may generally be data that is used by the controller200to learn parameters of a task and present parameters to a user to adjust. The simulations data may generally be data that is used by the controller200to simulate interactions between a plurality of simulated humans. The display data may generally be data that is used by the controller to display the simulated interactions between a plurality of simulated humans. The training data may generally be data that is generated as a result of one or more machine learning processes used to improve the accuracy of the model of human behavior, for instance.

FIG.3depicts flowchart for a method300for investigating interactions between simulated humans. The method300may be executed based on instructions stored in the memory module204that are executed by the processor202.FIG.4schematically depicts example user interactions with the user device140through the interface module146(FIG.2), andFIG.5schematically depicts an example display of a simulated interaction between simulated humans through the interface module146(FIG.2) according to the method300of operation of the system100.

Referring now toFIGS.1-4, at block302of the method300, the system100builds a model of human behavior to assign to a simulated human. More particularly, the system100builds a model of human behavior to assign to each simulated human in the simulated interaction, respectively. A user may specify the stimulus and baseline data that each model of human behavior should be trained on respectively. For instance, as shown inFIG.4, for a first simulated human, the user may specify that the model of human behavior should be trained on baseline and stimulus data collected on males that are 25-30 years old and college graduates. While three data specifiers (e.g. male, 25-30, college grad) are shown inFIG.4, it should be appreciated that the user may select more or fewer data specifiers for the model of human behavior to be trained on. The data specifiers may be selected by the user from a drop-down menu, typed into the interface module146, spoken into the interface module146, or otherwise specified or selected in any desirable manner.

It should be appreciated that whileFIG.4depicts the user specifying the data that the model of human behavior for the first simulated human is trained on, the user may take similar steps to specify the data that the model of human behavior for the second simulated human to be trained on. The data to train the model of human behavior for the second simulated human may be the same data as that used for the first simulated human, or different data. For instance, the model of human behavior for the second simulated human may be specified to be baseline and stimulus data for females, a specific person (i.e. John Doe), and the like. Similarly, the model of human behavior for the second simulated human may be trained on data including, but not limited to, the data the model of human behavior for the first simulated human is trained on. For instance, the user may specify that the model of human behavior should be trained on baseline and stimulus data collected on males that are 25-35 years old and college graduates.

Referring now toFIGS.1-5, at block304of the method300, the system100builds a plurality of simulated humans. For instance, as shown inFIG.4, the user specifies for two simulated humans to be built. The system100may then build a first simulated human502and a second simulated human504depicted inFIG.5. The simulated humans502and504may be avatars taking the general likeness of humans.

Still referring toFIGS.1-5, at block306of the method300, the system100applies a model of human behavior to each simulated human502,504, respectively. The model of human behavior for the first simulated human502may be applied to the first simulated human502, and the model of human behavior for the second simulated human504may be applied to the second simulated human504. The models of human behavior determine the baseline and stimulus attributes of the simulated humans502,504. For instance, the model of human behavior for the first simulated human502determines if, and to what extent, the first simulated human502is trusting, outspoken, confrontational, and the like. When applying the model of human behavior for the first simulated human502to the first simulated human502, for instance, the system100may adjust the appearance of the first simulated human502, to the extent possible, to represent the model of human behavior for the first simulated human502. More specifically, the appearance of the first simulated human502may be adjusted to appear as a male in the 25-30 year old age range. The appearance of the second simulated human504may be similarly adjusted based on the model of human behavior applied to the second simulated human504.

Still referring toFIGS.1-5, at block308of the method300, the system100builds a model of a task to be engaged in by the plurality of simulated humans502,504. The model of the task to be engaged in by the plurality of simulated humans502,504includes the overarching type of interaction between the simulated humans502,504. For instance, the user may specify that the simulated humans502,504engage in a negotiation. The user may further specify the type of negotiation to be engaged in. For instance, the model of the task to be engaged in will vary depending on whether the negotiation involves the sale/purchase of an item or a trade of items between the simulated humans502,504. The user may further specify the item to be negotiated over by the simulated humans502,504. As shown inFIG.4, the item to be negotiated over is a vehicle. When building the model of the task to be engaged in, the user may further specify the roles of each simulated human502,504, and more particularly, the unique objective of each simulated human502,504in the task (e.g. buy a car, sell a car).

The above is merely an example of a model of a task for the simulated humans502,504to engage in. The task built by the system100may generally be any circumstance that the user wishes to observe the simulated humans502,504navigate together. For instance, model of the task to be engaged in by the plurality of simulated humans502,504may be an employment interview process, an allocation of jobs/roles in a group assignment, and the like. The model of the task to be engaged in by the plurality of simulated humans502,504may also be purely social in nature. That is, the task need not result in a tangible allocation of goods, items, or positions among the plurality of simulated humans502,504. Instead, the task may merely be to engage in “small talk,” for the simulated humans502,504to acquaint themselves with each other, to build goodwill with each other, and the like. Therefore, the interactions of the simulated humans502,504may be investigated in any desirable task.

Still referring toFIGS.1-5, at block310of the method300, the system100adjusts one or more parameters of the models of human behavior for the simulated humans502,504and/or the model of the task to be engaged in by the simulated humans502,504. The system100may adjust the one or more parameters of the models of human behavior for the simulated humans502,504and/or the model of the task to be engaged in by the simulated humans502,504based on input received from the user. The parameters of the models of the human behavior for the simulated humans502,504may be initially determined by the data each model of human behavior is trained on respectively. Parameters of the models of human behavior generally include the baseline and stimulus characteristics of the simulated human that the model is applied to. For example, parameters of the models of human behavior may include qualities such as, forthcoming, perceptive, trusting, economical, and impressionable. Specifically, the model of simulated human behavior for the first simulated human502, trained on stimulus and baseline data from 25-30 year old male college graduates, may result in the parameters depicted inFIG.4. That is, the simulated human502, which the model is applied to, may have a forthcoming score of 4, on a scale of 0-10. The parameters need not be represented by numerical values. For instance, the parameters may be displayed by any visual or audial representation that can conveys a weight or magnitude of a parameter. The user may then selectively adjust all or any of the parameters of the models of human behavior, as desired. For instance, while the average 25-30 year old male college graduate may have a perceptive score of 6 out of 10, the user may selectively adjust the parameter of the model to a 4 out of 10, therefore decreasing the perceptiveness of the simulated human502that the model is applied to.

The user may also adjust the parameters of the model of the task to be engaged in by the simulated humans502,504. The parameters of the model of the task to be engaged in may be user generated. For instance, the user may self-generate interested third party buyers as a parameter affecting the task to be engaged in by the simulated humans502,504and input a value for the parameter (e.g. no, yes, and/or number of third party buyers). The system100may also learn parameters that affect the task to be engaged in, and present the parameters to the user for the user to set as desired. For instance, the system100may learn that the number of interested third party buyers affects the task to be engaged in and present that parameter to the user for the user to set, as desired. The task to be engaged in parameters include parameters external to the simulated humans502,504, such as interested third party buyers. The task to be engaged in parameters may include parameters internal to the simulated humans502,504, such as an amount of money the first simulated human502will not spend over and an amount of money the second simulated human504will not sell for less than.

Still referring toFIGS.1-5, at block312of the method300, the system100simulates interactions between the plurality of simulated humans502,504within the task to be engaged. At block314of the method300, the system100displays behavior of the simulated humans502,504and information about the interactions between the simulated humans502,504during the simulated interactions. The system100may also display information on the task to be engaged in by the simulated humans. For instance, the system100may display a vehicle506and any other contextual or environmental parameters affecting the task to be engaged in.

The system100may simulate both verbal and non-verbal interactions between the simulated humans502,504. For instance, the simulation may begin with a first “turn.” In a first turn, the first simulated human502or the second simulated human504may begin the interaction with some action or communication. As shown inFIG.5, the first simulated human502asks the second simulated human504, “How much for the car?” This communication may be displayed by the system100as a text box or may be output through speakers of the interface module146.

At the beginning of the first turn, the first simulated human502and the second simulated human504may begin the simulation based on the baseline data incorporated into the respective models of human behavior applied to the first simulated human502and the second simulated human504. These internal mental, emotional, or physiological states may be displayed by the system100. For instance, the system may graphically display scales showing the overall mood of the first simulated human502and the second simulated human504, and the trust each simulated human502,504has in the other. The system100also displays a numerical value for the heart rate of each simulated human502,504.

After the completion of the first turn, a second turn in the simulation may begin with the second simulated human504responding to the question posed by the first simulated human502in the first turn. While each turn is depicted inFIG.5as a discrete or still display, this need not be the case. For instance, the display of the simulation may fluidly change with each turn and action taken by the simulated humans502,504. In other words, the display of the first turn may change into the display of the second turn, and so on. In such cases, the first turn does not remain as a discrete visual display on the user device140.

As the simulation continues, the internal mental, emotional, or physiological states of the simulated humans502,504are updated based on the communications of the other of the simulated humans502,504and possible changes in the context of the task to be engaged in. For instance, the graphics depicting the mood, trust, and heart rate of the of the simulated human502,504will update as the simulated humans502,504become angry, distressed, insulted, hopeful for a resolution to the task, and the like. The avatar of the simulated humans502,504may also be updated in appearance during the simulation. The change in appearance of the avatars may reflect the internal mental, emotional, or physiological state of each simulated human502,504. The change in appearance may also relate to a non-verbal action taken by the simulated humans502,504. For instance, the first simulated human502may be excited by the price the second simulated human504is offering on the vehicle. However, the first simulated human502may not wish to physically express such excitement to the second simulated human504. In such cases, the avatar of the first simulated human502will take the outward appearance as intended by the first simulated human502during the negotiation. The system100may also simulate concrete physical actions, such as one of the simulated humans502,504walking away from the other.

The system therefore allows a user to investigate and observe, in real time, how two simulated humans, possessing desired characteristics, interact with each other during a simulated task, also possessing desired characteristics. The user may control all variables or parameters of the models of human behavior and model of the task to be engaged in except one, to determine the affect that particular variable has on the interactions between the simulated humans502,504. It should be appreciated that each simulation and display of the simulation may be stored on the data storage component206for future play back and statistical analysis, for instance.

It should be appreciated that the method300discussed above is not limited to the order of steps presented inFIG.3. For instance, in some embodiments, plurality of simulated humans at block304may be built prior to the model of human behavior to assign to a simulated human at block302. It should also be appreciated that steps presented inFIG.3need to not be discrete in all embodiments. That is, the system100may build a plurality of simulated humans at block304and apply a model of human behavior to each simulated human at block306substantially simultaneously, such that blocks308and310may be considered a single step in method300. Moreover, it should be appreciated that one or more steps of the method300depicted inFIG.3may be omitted from the method300. For instance, in some embodiments, the user need not adjust the parameters of the model of human behavior or the model of the task to be engaged in at block310. Additionally, one or more steps not presented in the method300depicted inFIG.3may be completed by the system100

Based on the foregoing, it should now be understood that embodiments shown and described herein relate to systems and methods for investigating interactions between simulated humans. The system builds a model of human behavior that can be assigned to a simulated human. The model of human behavior is trained on a dataset of interactions between humans. Parameters of the model of human behavior may be adjusted by a user. Accordingly, depending on the dataset used to train each model of human behavior and the selective adjustment of one or more parameters of the model, a user can accurately control, isolate, and investigate personality variables of the simulated human. The system also builds a model of a task to be engaged in by a plurality of simulated humans. The task may include a goal to be achieved by each of the plurality of simulated humans. The user may also adjust parameters of the model of the task to be engaged in to allow the user to accurately control, isolate, and investigate environmental or contextual variables in the task to be engaged in. The system simulates interactions between the plurality of simulated humans. Throughout the simulation, the system displays behavior of the simulated humans and information about the interactions between the simulated humans, such as their internal emotional, mental, or physiological states.

As used herein, the term “about” means that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. When the term “about” is used in describing a value or an end-point of a range, the specific value or end-point referred to is included. Whether or not a numerical value or end-point of a range in the specification recites “about,” two embodiments are described: one modified by “about,” and one not modified by “about.” It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

Directional terms as used herein—for example up, down, right, left, front, back, top, bottom—are made only with reference to the figures as drawn and are not intended to imply absolute orientation.

Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order, nor that with any apparatus specific orientations be required. Accordingly, where a method claim does not actually recite an order to be followed by its steps, or that any apparatus claim does not actually recite an order or orientation to individual components, or it is not otherwise specifically stated in the claims or description that the steps are to be limited to a specific order, or that a specific order or orientation to components of an apparatus is not recited, it is in no way intended that an order or orientation be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps, operational flow, order of components, or orientation of components; plain meaning derived from grammatical organization or punctuation, and; the number or type of embodiments described in the specification.

As used herein, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a” component includes aspects having two or more such components, unless the context clearly indicates otherwise.

For the purposes of describing and defining the present subject matter, it is noted that reference herein to a variable being a “function” of a parameter or another variable is not intended to denote that the variable is exclusively a function of the listed parameter or variable. Rather, reference herein to a variable that is a “function” of a listed parameter is intended to be open ended such that the variable may be a function of a single parameter or a plurality of parameters.

It is noted that recitations herein of a component of the present disclosure being “configured” or “programmed” in a particular way, to embody a particular property, or function in a particular manner, are structural recitations, as opposed to recitations of intended use. More specifically, the references herein to the manner in which a component is “programmed” or “configured” denotes an existing physical condition of the component and, as such, is to be taken as a definite recitation of the structural characteristics of the component.

It is noted that terms like “preferable,” “typical,” and “suitable” when utilized herein, are not utilized to limit the scope of the claimed subject matter or to imply that certain features are critical, essential, or even important to the structure or function of the claimed subject matter. Rather, these terms are merely intended to identify particular aspects of an embodiment of the present disclosure or to emphasize alternative or additional features that may or may not be utilized in a particular embodiment of the present disclosure.

For the purposes of describing and defining the present subject matter it is noted that the terms “substantially” and “approximately” are utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. The terms “substantially” and “approximately” are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.

Having described the subject matter of the present disclosure in detail and by reference to specific embodiments thereof, it is noted that the various details disclosed herein should not be taken to imply that these details relate to elements that are essential components of the various embodiments described herein, even in cases where a particular element is illustrated in each of the drawings that accompany the present description. Further, it will be apparent that modifications and variations are possible without departing from the scope of the present disclosure, including, but not limited to, embodiments defined in the appended claims. More specifically, although some aspects of the present disclosure are identified herein as preferred or particularly advantageous, it is contemplated that the present disclosure is not necessarily limited to these aspects.