Behavioral analysis for smart agents

A system and computer-readable storage medium perform a method for contextual inferring capacity for triggering a financial transaction by monitoring, via user device(s), objective contextual data of location, temporal, and volitional transaction information associated with an authorized user of a financial system. Subjective contextual data of personal calendar events, physiological data, and pacing of user interactions with the user device(s) is monitored. The objective and subjective contextual data is analyzed to create scenario(s) correlated with performing a volitional transaction. If not predictive a volitional transaction, a layer of security protocol is added for authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, a determination is made whether the subjective contextual data satisfies criterion for incapacity to perform a volitional transaction. In response to determining incapacity to perform the volitional transaction, access to the financial system is limited.

BACKGROUND

The present application relates to systems and methods for authenticating and characterizing a human operator prior to facilitating financial transactions.

Smart agents as used by smartphones or other devices (e.g., Amazon Alexa, Google Now, iPhone's Siri) respond to specific requests for information, and may remind users about upcoming items on that user's calendar. More sophisticated smart agents are starting to make attempts to predict information that a user might need. For example, when an email is received including flight information, information about the flight might be presented to the user on the day of the flight. Smart agents apparently do not, however, understand emotions or context when performing actions on behalf of users.

BRIEF DESCRIPTION

This brief description is provided to introduce a selection of concepts in a simplified form that are described below in the detailed description. This brief description is not intended to be an extensive overview of the claimed subject matter, identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

In one or more embodiments, the present disclosure provides a method for contextual inferring capacity for triggering a financial transaction. In one or more embodiments, the method includes monitoring, via one or more user devices, objective contextual data comprising more than one of: (i) location information; (ii) temporal information; and (iii) volitional transaction information associated with an authorized user of a financial system. The method includes monitoring, via one or more user devices, subjective contextual data comprising more than one of: (i) personal calendar events; (ii) physiological data; and (iii) pacing of user interactions with the one or more user devices. The method includes analyzing the objective and subjective contextual data to create one or more scenarios that are correlated with performing a volitional transaction. The method includes determining whether a current context is one of the one or more scenarios that is correlated with and predictive of the volitional transactional information. In response to determining that the current context is not predictive a volitional transaction, the method adding a layer of security protocol for user authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, the method includes determining whether the subjective contextual data satisfies at least one criterion for incapacity to perform a volitional transaction. In response to determining an incapacity to perform the volitional transaction, the method includes limiting access to the financial system.

In one or more embodiments, the present disclosure provides a system including an objective contextual monitoring component that is in communication with a one or more user devices. The object contextual monitoring component receives objective contextual data comprising more than one of: (i) location information; (ii) temporal information; and (iii) volitional transaction information associated with an authorized user of a financial system. A subjective contextual monitoring component is in communication with the one or more user devices. The subjective contextual monitoring component receives subjective contextual data comprising more than one of: (i) personal calendar events; (ii) physiological data; and (iii) pacing of user interactions with the one or more user devices. A contextual analyzing component receives the objective and subjective contextual data to create one or more scenarios that are correlated with performing a volitional transaction. A predictive engine determines whether a current context is one of the one or more scenarios that is correlated with and predictive of the volitional transactional information. A financial transaction controller, in response to determining that the current context is not predictive a volitional transaction, adds a layer of security protocol for user authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, the financial transaction controller determines whether the subjective contextual data satisfies at least one criterion for incapacity to perform a volitional transaction. In response to determining an incapacity to perform the volitional transaction, the financial transaction controller limits access to the financial system.

In one or more embodiments, the present disclosure provides a computer-readable storage medium includes computer-executable instructions. When executed via a processing unit on a computer, the computer-readable storage medium performs acts including monitoring, via one or more user devices, objective contextual data comprising more than one of: (i) location information; (ii) temporal information; and (iii) volitional transaction information associated with an authorized user of a financial system. The computer-readable storage medium monitors, via one or more user devices, subjective contextual data comprising more than one of: (i) personal calendar events; (ii) physiological data; and (iii) pacing of user interactions with the one or more user devices. The computer-readable storage medium analyzes the objective and subjective contextual data to create one or more scenarios that are correlated with performing a volitional transaction. The computer-readable storage medium determines whether a current context is one of the one or more scenarios that is correlated with and predictive of the volitional transactional information. In response to determining that the current context is not predictive a volitional transaction, the computer-readable storage medium adds a layer of security protocol for user authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, the computer-readable storage medium determines whether the subjective contextual data satisfies at least one criterion for incapacity to perform a volitional transaction. In response to determining an incapacity to perform the volitional transaction, the computer-readable storage medium limits access to the financial system.

DETAILED DESCRIPTION

A system and computer-readable storage medium perform a method for contextual inferring capacity for triggering a financial transaction by monitoring, via user device(s), objective contextual data of location, temporal, and volitional transaction information associated with an authorized user of a financial system. Subjective contextual data of personal calendar events, physiological data, and pacing of user interactions with the user device(s) is monitored. The objective and subjective contextual data is analyzed to create scenario(s) correlated with performing a volitional transaction. If not predictive a volitional transaction, a layer of security protocol is added for authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, a determination is made whether the subjective contextual data satisfies criterion for incapacity to perform a volitional transaction. In response to determining incapacity to perform the volitional transaction, access to the financial system is limited.

Embodiments or examples, illustrated in the drawings are disclosed below using specific language. It will nevertheless be understood that the embodiments or examples are not intended to be limiting. Any alterations and modifications in the disclosed embodiments, and any further applications of the principles disclosed in this document are contemplated as would normally occur to one of ordinary skill in the pertinent art.

The following terms are used throughout the disclosure, the definitions of which are provided herein to assist in understanding one or more aspects of the disclosure.

As used herein, the term “infer” or “inference” generally refer to the process of reasoning about or inferring states of a system, a component, an environment, a user from one or more observations captured via events or data, etc. Inference may be employed to identify a context or an action or may be employed to generate a probability distribution over states, for example. An inference may be probabilistic. For example, computation of a probability distribution over states of interest based on a consideration of data or events. Inference may also refer to techniques employed for composing higher-level events from a set of events or data. Such inference may result in the construction of new events or new actions from a set of observed events or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources.

Turning to the Drawings,FIG. 1illustrates a system100has an objective contextual monitoring component102that is in communication with a one or more user devices104associated with an authorized user105. The objective contextual monitoring component102receives objective contextual data comprising more than one of: (i) location information; (ii) temporal information; and (iii) volitional transaction information associated with an authorized user of a financial system. A subjective contextual monitoring component106is in communication with the one or more user devices104to receive subjective contextual data comprising more than one of: (i) personal calendar events; (ii) physiological data; and (iii) pacing of user interactions with the one or more user devices. A contextual analyzing component108receives the objective and subjective contextual data and create scenario(s) that are correlated with performing a volitional transaction. A predictive engine110determines whether a current context is one of the scenario(s) that is correlated with and predictive of the volitional transactional information. A financial transaction controller112acts on predictive analyses. In response to determining that the current context is not predictive a volitional transaction, the financial transaction controller112adds a layer of security protocol for user authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, the financial transaction controller112determines whether the subjective contextual data satisfies at least one criterion for incapacity to perform a volitional transaction. In response to determining an incapacity to perform the volitional transaction, the financial transaction controller112limits access to the financial system.

In one or more embodiments,FIG. 2illustrates a system200executed at least in part by one or more user devices, illustrated as a smartphone202, blue tooth earpiece204, and smart watch206that form a personal access network (PAN)208. A number of Internet of Things (IoT) devices can provide information to an IoT contextual tracking module210. For example, the smartphone202can contain or be in communication one or more of the following sources of contextual information: (i) physiological data sensors212; (ii) audio sensor214; (iii) infrared sensor216; (iv) location sensor218; (v) personal calendar220; (vi) camera monitor222; (vii) transaction monitor224; (viii) communication tracking226; and (ix) biometric sensor228.

Based on the information collected by the IoT contextual tracking module210, a number of learned contexts can be amassed by a learned context module230, such as a “Town A work context”, “Town B home context”, and “Town B shopping context”, “Town C vacation context”230can have certain contexts that are too incomplete for doing predictive analyses, represented by “Town D Unfamiliar” context.

Subjective aspects of a historical or current context can be determined by an emotional and cognitive state analysis module232. Examples of constituent capabilities of module232are illustrated as a facial recognition component234, a voice recognition/stress analyzer component236, a remote temperature sensor238, a context correlator240, and a user interface pacing component242.

An executive contextual control module244can act upon the objective and subjective contextual information to trigger an authentication component246, a transaction agent248, and a report component250as needed. For example, the executive contextual control module244can determine that a current contextual scenario fall within a multidimensional space defined within an access control module252. For example, the contextual response can be a function of an inferred user authentication value, an inferred emotional state value and an inferred cognitive capacity value. For example, when fully authenticated with high values of cognitive and emotional state, full transactional access can be granted. When not authenticated, additional layers of security can be imposed. When in a reduced capacity, transactions can be limited, such as authorizing transportation for an inebriated user but not authorizing large banking transactions. When in an agitated state, the system can be discrete in what types of financial transactions or advertisements are offered to not exacerbate the situation.

In one or more embodiments,FIG. 3illustrates a communication system300having a network302that couples sources of data and processing capabilities to perform contextual collection, analysis and prediction of a user304. One or more financial transaction sources306provide past or current financial transactions, either open for completion by a human operator304or previously completed. A system manager308can obtain relevant financial transactions310from the financial transactions sources306and provide filtered financial transactions312to the transaction analyzing component314of a simulacrum engine316. The simulacrum engine316can create and maintain a predictive model318for the human operator304that is combined with contextual information. The predictive model318is contained in a simulacrum engine database320. Mobile application322executed on a user device324can provide a user interface326for providing recommendations328for and for receiving activation of control affordances330from the human operator304and for collecting objective and subjective contextual data. The user device324itself can contain sensors and communication channels that enable monitoring of a capacity status of the human operator304. For clarity, external sources of information regarding the health, location, activity level, cognitive interactions, etc., by the human operator206are illustrated as a camera332, a smart watch334, and a blue tooth device336, or other Internet of Things (IoT) devices. Thus, an ability is provided to capture health and mobility levels of the human operator. This ability could leverage other devices used to monitor an individual's health in general (e.g., activity trackers, heart rate monitors). The mobile application322can communicate via a communication channel338to a node340that in turn is communicatively coupled to the network302. Based on this information, the system300can differentiate situations such as full capable user304, an imposter342, an incapacitated user344, and an enraged, irrational user346.

Generally, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media as will be discussed below. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform one or more tasks or implement one or more abstract data types. Typically, the functionality of the computer readable instructions are combined or distributed as desired in various environments.

FIG. 4illustrates a system400including a computing device412configured to implement one or more embodiments provided herein. In one configuration, computing device412includes at least one processing unit316and memory418. Depending on the exact configuration and type of computing device, memory418may be volatile, such as RAM, non-volatile, such as ROM, flash memory, etc., or a combination of the two. This configuration is illustrated inFIG. 4by dashed line414.

In other embodiments, device412includes additional features or functionality. For example, device412may include additional storage such as removable storage or non-removable storage, including, but not limited to, magnetic storage, optical storage, etc. Such additional storage is illustrated inFIG. 4by storage3420. In one or more embodiments, computer readable instructions to implement one or more embodiments provided herein are in storage420. Storage420may store other computer readable instructions to implement an operating system, an application program, etc. Computer readable instructions may be loaded in memory418for execution by processing unit416, for example.

Device412includes input device(s)424such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, or any other input device. Output device(s)422such as one or more displays, speakers, printers, or any other output device may be included with device412. Input device(s)424and output device(s)422may be connected to device412via a wired connection, wireless connection, or any combination thereof. In one or more embodiments, an input device or an output device from another computing device may be used as input device(s)424or output device(s)422for computing device412. Device412may include communication connection(s)426to facilitate communications via a network428with one or more other computing devices430.

Certain functionalities can be performed by software applications resident in memory418, such as a transactional analyzing component432, simulacrum engine434, communications monitoring component436, and a simulacrum avatar438.

Still another embodiment involves a computer-readable medium including processor-executable instructions configured to implement one or more embodiments of the techniques presented herein. An embodiment of a computer-readable medium or a computer-readable device devised in these ways is illustrated inFIG. 5, wherein an implementation500includes a computer-readable medium508, such as a CD-R, DVD-R, flash drive, a platter of a hard disk drive, etc., on which is encoded computer-readable data506. This computer-readable data506, such as binary data including a plurality of zero's and one's as shown in506, in turn includes a set of computer instructions504configured to operate according to one or more of the principles set forth herein. In one such embodiment500, the processor-executable computer instructions304may be configured to perform a method502, such as method600ofFIG. 6. In another embodiment, the processor-executable instructions504may be configured to implement a system, such as the system400ofFIG. 4. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein.

FIG. 6illustrates a method600of contextual inferring capacity for triggering a financial transaction. In one or more embodiments, the method600begins monitoring, via one or more user devices, objective contextual data comprising more than one of: (i) location information; (ii) temporal information; and (iii) volitional transaction information associated with an authorized user of a financial system (block602). Method600includes monitoring, via one or more user devices, subjective contextual data comprising more than one of: (i) personal calendar events; (ii) physiological data; and (iii) pacing of user interactions with the one or more user devices (block604). Method600includes analyzing the objective and subjective contextual data to create one or more scenarios that are correlated with performing a volitional transaction (block606). Method600includes determining whether a current context is one of the one or more scenarios that is correlated with and predictive of the volitional transactional information (decision block608). In response to determining that the current context is not predictive a volitional transaction, method600includes adding a layer of security protocol for user authentication prior to executing the volitional transaction (block610). Then method600ends. In response to determining that the current context is predictive of a volitional transaction in decision block608, method600includes determining whether the subjective contextual data satisfies at least one criterion for incapacity to perform a volitional transaction (decision block612). In response to determining an incapacity to perform the volitional transaction, method600includes limiting access to the financial system (block614). Then method600ends. In response to not determining an incapacity to perform the volitional transaction in decision block612, method600ends.

In one or more embodiments, the criterion for incapacity is intoxication; and method600includes limiting access to the financial system by: (i) presenting a cognitive sobriety test via at least one user device; and (ii) preventing access to the financial system in response to failing the cognitive sobriety test.

In one or more embodiments, the criterion for incapacity is emotional agitation. Method600includes limiting access to the financial system by blocking at least one category of financial transaction associated with a policy having a condition precedent of being in a rational state of mind. In a particular embodiments, the criterion further includes detecting a third party who is at least one of: (i) verbally interacting with the authorized user; and (ii) maintaining close proximity to the authorized user indicative of the authorized user being under duress. Method600includes limiting access to the financial system by: (i) presenting on a user interface execution of the financial transaction; and (ii) placing a provisional hold on actual completion of the financial transaction for a period of time.

In one or more embodiments, the criterion for incapacity is emotional agitation. Method600includes limiting access to the financial system by blocking at least one category of financial transaction having a condition precedent of being in a rational state of mind.

In one or more embodiments, the criterion for incapacity comprises emotional agitation. Method600includes limiting access to the financial system by: (i) blocking an advertisement, via the one or more user devices, that has a correlation with causing additional agitation to the authorized user; and (ii) presenting another advisement, via the one or more user devices, that has a correlation with soothing the emotional agitation of the authorized user.

In one or more embodiments, method600includes: (i) monitoring the pacing of user interactions with the one or more user devices comprises determining a baseline pacing of user interactions; and (ii) determining whether the subjective contextual data satisfies the at least one criterion for incapacity comprises determining whether a current pacing is below a threshold fraction of the baseline pacing. In a particular embodiment, the criterion for incapacity is emotional agitation. Method600includes limiting access to the financial system by: (i) blocking an advertisement, via the one or more user devices, that has a correlation with causing additional agitation to the authorized user; and (ii) presenting another advisement, via the one or more user devices, that has a correlation with soothing the emotional agitation of the authorized user.

According to aspects of the present innovation, behavioral analysis is provided for smart devices that leverages the fact that everyone has a unique communication footprint. The information gleaned therefrom can be used for fraud prevention, authentication, creation of user profiles, and adjusting smart device behavior for different individuals. For example, a smart device might attempt to get an “emotional read” on a person. Based on knowing when the person is upset or tired, the system adjusts smart device behavior or marketing messages accordingly.

In one or more aspects, a smart agent (or virtual assistant) acts in a more sophisticated manner by incorporating context into actions performed on behalf of a user. Using context for fraud prevention, authentication improves services performed by the smart agent on behalf of a user and can provide more effective marketing messages.

According to aspects of the present innovation, a smart agent acts in a more sophisticated manner by incorporating context into actions performed on behalf of a user, as would an actual human being. For example, a smart agent might perform an analysis over the course of weeks or months to determine certain traits associated with the user.

In an illustrative example, a smart agent can determine the following traits: (i) The user habitually calls a relative each Sunday night; (ii) The user tends to pay bills each Thursday evening; (iii) The user tends to access television listing information at the “tvlistings.com” website when the television is first turned on; (iv) the user tends to become angry when talking to a particular person or when a particular topic is discussed.

Based on such contextual learning, the system can perform authentication. If the user does not perform a task he or she normally performs on Sunday evenings (e.g., calling a relative), the smart agent might require additional authentication credentials to ensure that the user is the authorized user

As another example, the system can provide fraud prevention. If the user attempts to pay bills at an unusual time (e.g., other than on Thursday evening), further authentication could be required before granting access to financial information

As an additional example, the system can provide services related to context. When the smart agent determines, based its audio sensors, that the television was just turned on, the smart agent might automatically provide television listing information from the tvlistings.com website.

As a further example, the system can provide enhanced marketing. When the smart agent determines that the user is angry or emotional, the smart agent might refrain from presenting marketing messages. By benefit of the present disclosure, contextual learning is used to determine behaviors or traits for authentication, and fraud prevention. Use of determined emotions can better leverage marketing content for appropriate situations.

One or more embodiments may employ various artificial intelligence (AI) based schemes for carrying out various aspects thereof. One or more aspects may be facilitated via an automatic classifier system or process. A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class. In other words, f(x)=confidence (class). Such classification may employ a probabilistic or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed.

One or more embodiments may employ classifiers that are explicitly trained (e.g., via a generic training data) as well as classifiers which are implicitly trained (e.g., via observing user behavior, receiving extrinsic information). For example, SVMs may be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, a classifier may be used to automatically learn and perform a number of functions, including but not limited to determining according to a predetermined criteria.

Various operations of embodiments are provided herein. The order in which one or more or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated based on this description. Further, not all operations may necessarily be present in each embodiment provided herein.

Although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur based on a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims.