Intercepting inadvertent conversational disclosure of personal information

By intercepting a natural language communication of a protected party, the communication is monitored, wherein the protected party is a human being. Within the monitored communication using a natural language processing engine, a natural language interaction between the protected party and a second party is detected. To determine an interaction pattern, the natural language interaction is analyzed. The interaction pattern includes data derived from the monitored communication, metadata of the protected party, and metadata of the second party. Using the interaction pattern and an interaction behavior model, an adverse result of the natural language interaction is predicted, wherein the adverse result comprises an economic loss to the protected party. By notifying the protected party, the predicted adverse result is intercepted.

TECHNICAL FIELD

The present invention relates generally to a method, system, and computer program product for conversation monitoring. More particularly, the present invention relates to a method, system, and computer program product for intercepting inadvertent conversational disclosure of personal information.

BACKGROUND

Marketing of products and services is often performed via a telecommunication network (for example, by telephone) or in person (for example, by visiting a marketing target at home). Typically, the marketer initiates the communication. In a successful marketing interaction, the marketer persuades a marketing target to make a purchase, enroll in a service, enter a promotional drawing in return for providing personal or financial information, and the like. In addition, while many marketing interactions have a legitimate purpose, some are fraudulent. Non-marketing interactions can also have a fraudulent purpose, such as an attempt to obtain private information for an illegitimate reason.

SUMMARY

The illustrative embodiments provide a method, system, and computer program product. An embodiment includes a method that monitors, by intercepting a natural language communication of a protected party, the communication, wherein the protected party is a human being. An embodiment detects, within the monitored communication using a natural language processing engine, a natural language interaction between the protected party and a second party. An embodiment analyzes, to determine an interaction pattern, the natural language interaction, the interaction pattern comprising data derived from the monitored communication, metadata of the protected party, and metadata of the second party. An embodiment predicts, using the interaction pattern and an interaction behavior model, an adverse result of the natural language interaction, wherein the adverse result comprises an economic loss to the protected party. An embodiment intercepts, by notifying the protected party, the predicted adverse result.

DETAILED DESCRIPTION

The illustrative embodiments recognize that those responsible for the welfare of others want to protect those others from making an unneeded purchase, enrolling in an unneeded service, assenting to a potentially harmful action, or providing personal or financial information to others who should not have these types of information. For example, an elderly person or a child may not realize the imprudence of providing personal information, such as a date of birth, identification number, or bank account information, in a situation where providing such information is not actually necessary. As another example, an elderly person, a child, or a person with impaired cognition may be easily persuaded to make an unneeded purchase in response to a sales call.

As used herein, a conversation is a natural language interaction between at least two parties, a protected party and another. A protected party is a human being who, using the illustrative embodiments, is being prevented from inadvertently disclosing personal information or making an undesired commitment in conversation. The other party may be a human being or a machine. A conversation may be in voice form, text form, another form of natural language communication, or a combination of one or more forms of natural language communication. As well, a conversation may be in-person or mediated by a telecommunications network.

The illustrative embodiments recognize that there is an unmet need to monitor a protected party's conversation, predict a trend in the conversation, and intercept the conversation before inadvertent disclosure of personal information occurs.

The illustrative embodiments recognize that the presently available tools or solutions do not address these needs or provide adequate solutions for these needs. The illustrative embodiments used to describe the invention generally address and solve the above-described problems and other problems related to intercepting inadvertent conversational disclosure of personal information.

An embodiment can be implemented as a software application. The application implementing an embodiment can be configured as a modification of an existing environment monitoring or telecommunications system, as a separate application that operates in conjunction with an existing environment monitoring or telecommunications system, a standalone application, or some combination thereof.

Particularly, some illustrative embodiments provide a method for monitoring a protected party's conversation, predicting a trend in the conversation, and intercepting the conversation before inadvertent disclosure of personal information occurs.

An embodiment monitors natural language communications of a protected party by intercepting such communications. One embodiment monitors both the protected party and another party during a voice conversation over a communication network. Another embodiment monitors both the protected party and another party during a text-based conversation over a communication network. A conversation over or using a communications network is referred to herein as a network-mediated conversation. To monitor a network-mediated conversation, an embodiment uses any suitable application. Non-limiting examples of a suitable application include a telephone communications application and a text communications application.

Another embodiment monitors a protected party's environment, including both network-mediated and in-person conversations of the protected party, using any communications means the embodiment is equipped to monitor. One embodiment uses a microphone to monitor audio in the protected party's environment. For example, a voice assistant application typically includes a suitable monitoring capability because the voice assistant typically monitors a user's environment to respond to the user's voice commands. As another example, because a hearing aid can enhance audio in a user's environment, such a hearing aid could include a suitable monitoring capability. Another embodiment uses a video camera to monitor another type of communication of the protected party, for example text or sign language. One environment-monitoring embodiment only monitors the protected party's portion of a network-mediated conversation, while another environment-monitoring embodiment monitors another party to a network-mediated conversation as well as the protected party.

Within the monitored communication, an embodiment uses a natural language processing (NLP) engine to detect that a natural language interaction is occurring between the protected party and another party. To detect that a natural language interaction is occurring, an embodiment uses factors such as caller identification (for a network-mediated interaction), an identification of a voice other than the protected party's voice (for an in-person interaction monitored by a microphone), a detection of a presence of a person other than the protected party (for an in-person interaction monitored by a camera), and the like. An embodiment also converts detected speech to text, if necessary, and uses an NLP engine to determine that both the protected party and the other party are participating in the interaction.

An embodiment analyzes the natural language interaction, while it is occurring, to determine an interaction pattern. An interaction pattern includes results obtained by analyzing the natural language interaction, including data derived from the monitored communication, metadata of the protected party, and metadata of the second party. Some non-limiting examples of data derived from the monitored communication are a topic of the interaction, and one or more specific natural language phrases extracted from the interaction.

An interaction pattern also includes, if possible, an identification of the party interacting with the protected party. To identify the other party to a network-mediated interaction, an embodiment can use caller identification information such as a telephone number of a caller to the protected party and dialing information if the protected party initiated the communication. An embodiment can also match a telephone number associated with the other party with directory information such as a name associated with the other party. To identify the other party to an in-person interaction monitored by a microphone, an embodiment matches a voice monitored by the microphone with stored voice data. To identify the other party to an in-person interaction monitored by a camera, an embodiment matches a face or body monitored by the camera with stored face or body data.

An interaction pattern also includes, if possible, an interaction history of the protected party with the identified other party. For example, if the other party has called the protected party multiple times previously, each time using the same communication script, this interaction history can indicate a persistent sales caller that the protected party should not continue to interact with. On the other hand, if the other party has a telephone number matching that of the protected party's physician's office, the party's interaction with the protected party is not likely to lead to a problematic disclosure.

An embodiment includes an interaction behavior model. In one embodiment, an interaction behavior model includes a set of rules. Each entry in the set of rules includes a natural language pattern, or a natural language text that, if matched in an interaction of a protected party, could indicate that an adverse result is about to occur. An adverse result, as used herein, is a result that includes, or could lead to an economic loss to the protected party. One example of an adverse result is the purchase of an unwanted or unnecessary item or service. Another example of an adverse result is an unauthorized charge on a credit card, resulting from providing credit card information to an untrustworthy party. A third example of an adverse result is identity theft, resulting from providing personal information, such as date of birth, address, or identification number, to an untrustworthy party.

Non-limiting examples of entries in a rule set, when spoken by a protected party, are “my credit card number is”, “my date of birth is”, and “my bank account information is”, indicating that the protected party is about to disclose the corresponding information. Additional examples of entries in a rule set, when spoken by a protected party, include the first digits of the protected party's birthdate, credit card number, or identification number, indicating that the protected party has already started to disclose the corresponding information. Non-limiting examples of entries in a rule set, when spoken by a party other than a protected party, are “What's your social?” and “How do you want to pay?”, because in response to questions like these, a protected party is likely to respond with the requested information. Additional non-limiting examples of entries in a rule set, when spoken by a party other than a protected party, are “Do I have your permission to access your bank records” and “Do you agree to an ongoing subscription to our newsletter at $79 a month?” because in response to questions like these, a protected party is likely to respond with an unwise agreement.

In addition to the set of rules, an interaction behavior model includes a set of exceptions to the rules. An exception is a natural language or other pattern that represents a circumstance in which a rule, although matching an interaction of a protected party, does not apply because an adverse result is unlikely to occur.

One reason an adverse result is unlikely to occur is because the protected party is interacting with another party that is trusted, i.e. a party with which the protected party can safely interact. To implement an exception based on interactions with a trusted party, an embodiment maintains a set of identifications corresponding to trusted parties. Example identifications corresponding to a trusted party include a telephone number of a trusted party (for example, obtained using the caller ID feature of a telephone network) and a voice matching a voice sample of a trusted party (for example, obtained by monitoring an in=person interaction of a protected party).

Another reason an adverse result is unlikely to occur could be because the protected party initiated the interaction. One embodiment assumes that if a protected party initiated an interaction, the protected party intended the interaction and understands the consequences of disclosing information during the interaction. However, another embodiment recognizes that there are situations in which a protected party initiated the interaction, for example in response to an email or previous call instructing the protected party to initiate an interaction. This embodiment does not assume that if a protected party initiated the interaction an adverse result is unlikely to occur.

For example, although the example rule “my date of birth is” could result in the inappropriate disclosure of personal information if the protected party were interacting in a telephone conversation with a sales caller who had initiated the call, the same would not be the case if the protected party had initiated a call to make an appointment at a physician's office, where such information is both needed for medical records and protected from disclosure. As a result, two example exceptions to this rule might be that the protected party initiated the conversation and that the identification information for the second party to the conversation matches identification information for a trusted party.

In another embodiment, an interaction behavior model is implemented using a learning model, for example a neural network. Before being used to perform a task, a learning model must be trained to perform the task. Thus, one method of training a learning interaction behavior model of an embodiment is to supply the model with sample interactions, both those that result in a party's personal information disclosure and those that do not. During training, the model learns to recognize interaction patterns with a corresponding probability value of resulting in personal information disclosure.

An embodiment uses the interaction pattern and interaction behavior model to predict a future course of the interaction. In an embodiment using a set of rules and optional exceptions to one or more rules, an embodiment determines whether the interaction pattern is above a threshold value of matching one or more rules. For example, consider an interaction pattern including the phrase “what's your credit card number?” Here, an interaction behavior model includes a rule specifying that an interrogatory statement including a string related to payment information (e.g. “credit card”). As a result, the rule matches the interaction pattern with a value of 95 percent, above an example threshold of 75 percent.

As another example, consider an interaction pattern including the phrase “what's your date of birth?” Here, an interaction behavior model includes a rule specifying that an interrogatory statement including a string related to date of birth information (e.g. “date of birth” or “birthday”). However, the model also includes a rule specifying that if the protected party is interacting with a trusted party, the date-of-birth rule does not apply. Here, the interaction pattern includes the information that the protected party is interacting with a party having caller identification matching an entity in the set of trusted parties. As a result, although the interaction pattern matches the rule with a value of 99 percent, above an example threshold of 75 percent, the interaction pattern also matches the exception with a value of 98 percent, above an example threshold of 80 percent. As a result, the embodiment concludes that the protected party is interacting with a trusted party, and an adverse result is unlikely to result.

In an embodiment using a learning interaction behavior model, an embodiment determines whether the interaction pattern is above a threshold value of matching one or more patterns that the model has learned are likely to result in personal information disclosure. For example, for the interaction pattern including the phrase “what's your credit card number?”, a learning interaction behavior model has learned that a pattern including the phrase “what's your” followed by one of several variations of strings related to payment information (e.g. “credit card”) predicts an adverse result. As a result, when the learned pattern matches the interaction pattern with a value of 80 percent, above an example threshold of 65 percent, the embodiment predicts an adverse result for the interaction pattern.

As another example, for the interaction pattern including the phrase “what's your date of birth?” and the information that the protected party is interacting with a trusted party, a learning interaction behavior model has learned that a pattern including the phrase “what's your” followed by one of several variations of strings related to date of birth information (e.g. “date of birth” or “birthday”), but the protected party is interacting with a trusted party, does not predict an adverse result. As a result, when the learned pattern matches the interaction pattern with a value of 80 percent, above an example threshold of 65 percent, the embodiment does predicts an adverse result for the interaction pattern.

Thus, based on either a set of rules or a set of learned patterns, an embodiment determines a probability that an interaction pattern matches a pattern likely to result in an adverse result. If the future course of the interaction includes a result that is likely to be adverse to the protected party, an embodiment attempts to prevent the predicted result by alerting the protected party or taking another prevention or mitigation step. For example, an alert can be an audible tone or warning message, a text warning message, a visual alert such as a flashing light, or another signal or message.

An example prevention step for a network-mediated audio communication is to mute the protected party's microphone, preventing the protected party's next words from reaching the other party. An example prevention step for a network-mediated text communication is to not send the protected party's text communication, or to send the protected party's text communication only once an embodiment has determined that the text communication does not include information the protected party should not share.

An example mitigation step is to report the interaction, including any personal information the protected party disclosed, to another party responsible for the protected party, such as a caregiver or family member. Once notified, the responsible party can act to mitigate the results of the disclosure. For example, if notified that the protected party revealed credit card information, a family member could cancel the credit card, preventing future unauthorized charges. Similarly, if notified that the protected party had committed to an unneeded service, a caregiver could cancel the service, preventing future losses. Other example mitigation steps include automatically canceling a revealed credit card or unneeded service, without an intermediary.

In addition, an embodiment is configurable to explain a reason for an alert or prevention or mitigation step. For example, an embodiment alerting a protected party to an imminent text disclosure of credit card information could provide a message to the protected party, explaining that disclosing credit card information in the particular circumstances is unwise.

An embodiment uses the interaction pattern, the interaction behavior model, and any predicted or actual adverse results to identify and monitor a trend in the protected party's behavior. One example trend might be that the number of telephone calls from unknown callers a protected party answers is increasing. Another example trend might be that the lengths of a protected party's conversations with callers not a list of trusted callers are increasing. If a trend exceeds a threshold value, or is changing at more than a threshold rate, an embodiment is configurable to report the trend to another party responsible for the protected party, such as a caregiver or family member. A trend could indicate an increasing propensity to disclose personal information. A trend could also indicate a new or worsening symptom of the protected party. For example, an increasing number of long (above a threshold time) conversations with untrusted parties could be an early sign of a cognitive illness, or a worsening cognitive condition, indicating that the protected party needs additional medical attention or another form of assistance.

An embodiment also uses the interaction pattern, the interaction behavior model, and any predicted or actual adverse results to identify and monitor a trend in behavior targeting the protected party. For example, an increase in a number of sales calls to patients at a caregiving facility could indicate that the facility is a particular target for these types of calls, and that the facility should take prevention or mitigation steps. As another example, an increase in calls from a particular telephone number, a particular pattern of telephone numbers, or in calls that use a particular interaction script could also indicate a particular pattern of caller behavior.

An embodiment using a learning interaction behavior model updates the model based on an identified interaction pattern and any predicted or actual adverse results. In particular, an embodiment uses a difference between predicted and actual results for an interaction pattern to adjust the model so as to be more likely to make an accurate prediction if the same or a similar interaction pattern recurs. An embodiment also uses actual results for a previously-unknown interaction pattern to train the model to recognize such an interaction pattern.

The manner of intercepting inadvertent conversational disclosure of personal information described herein is unavailable in the presently available methods in the technological field of endeavor pertaining to natural language interaction prediction. A method of an embodiment described herein, when implemented to execute on a device or data processing system, comprises substantial advancement of the functionality of that device or data processing system in monitoring a protected party's conversation, predicting a trend in the conversation, and intercepting the conversation before inadvertent disclosure of personal information occurs.

The illustrative embodiments are described with respect to certain types of parties, patterns, interactions, trends, thresholds, predictions, responses, adjustments, sensors, measurements, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the invention. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments.

With reference to the figures and in particular with reference toFIGS.1and2, these figures are example diagrams of data processing environments in which illustrative embodiments may be implemented.FIGS.1and2are only examples and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. A particular implementation may make many modifications to the depicted environments based on the following description.

Application105implements an embodiment described herein. Application105uses NLP engine136. Application105and NLP engine136can execute in any of servers104and106, clients110,112, and114, and device132. In addition, application105and NLP engine136need not execute in the same system. Further, application105makes use of any suitable monitoring sensor, for example but not limited to microphone134, which need not be collocated with a system on which application105executes.

Servers104and106, storage unit108, and clients110,112, and114, and device132may couple to network102using wired connections, wireless communication protocols, or other suitable data connectivity. Clients110,112, and114may be, for example, personal computers or network computers.

With reference toFIG.2, this figure depicts a block diagram of a data processing system in which illustrative embodiments may be implemented. Data processing system200is an example of a computer, such as servers104and106, or clients110,112, and114inFIG.1, or another type of device in which computer usable program code or instructions implementing the processes may be located for the illustrative embodiments.

The hardware inFIGS.1-2may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted inFIGS.1-2. In addition, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system.

With reference toFIG.3, this figure depicts a block diagram of an example configuration for intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. Application300is an example of application105inFIG.1and executes in any of servers104and106, clients110,112, and114, and device132inFIG.1.

Protected party monitoring module310monitors natural language communications of a protected party by intercepting such communications. Module310is configurable to monitor both the protected party and another party during a voice or text-based conversation over a communication network, using data from an application such as a telephone communications application or a text communications application. Module310is also configurable to monitor a protected party's environment, including both network-mediated and in-person conversations of the protected party, using any sensor to which application300has access. For example, module310can use a microphone to monitor audio in the protected party's environment, or a video camera to monitor another type of communication of the protected party, for example text or sign language.

Within the monitored communication, module310uses a natural language processing (NLP) engine to detect that a natural language interaction is occurring between the protected party and another party. To detect that a natural language interaction is occurring, module310uses factors such as caller identification (for a network-mediated interaction), an identification of a voice other than the protected party's voice (for an in-person interaction monitored by a microphone), a detection of a presence of a person other than the protected party (for an in-person interaction monitored by a camera), and the like. An embodiment also converts detected speech to text, if necessary, and uses an NLP engine to determine that both the protected party and the other party are participating in the interaction.

Module310analyzes the natural language interaction, while it is occurring, to determine an interaction pattern. Module310uses the interaction pattern and interaction behavior model320to predict a future course of the interaction. One implementation of interaction behavior model320includes a set of rules. Each entry in the set of rules includes a natural language pattern, or a natural language text that, if matched in an interaction of a protected party, could indicate that an adverse result is about to occur. A rule-based implementation of interaction behavior model320also includes a set of exceptions to the rules. An exception is a circumstance in which a rule, although matching an interaction of a protected party, does not apply because an adverse result is unlikely to occur. Another implementation of interaction behavior model320uses a learning model, for example a neural network, that is trained to recognize interaction patterns with a corresponding probability value of resulting in personal information disclosure.

In particular, module310determines a probability that an interaction pattern matches a pattern likely to result in an adverse result. If the future course of the interaction includes a result that is likely to be adverse to the protected party, warning module340attempts to prevent the predicted result by alerting the protected party or taking another prevention or mitigation step. An alert can be an audible tone or warning message, a text warning message, a visual alert such as a flashing light, or another signal or message. One prevention step for a network-mediated audio communication mutes the protected party's microphone, preventing the protected party's next words from reaching the other party. A prevention step for a network-mediated text communication does not send the protected party's text communication, or sends the protected party's text communication only once module340has determined that the text communication does not include information the protected party should not share. In a mitigation step, module340reports the interaction, including any personal information the protected party disclosed, to another party responsible for the protected party, such as a caregiver or family member. In addition, warning module340is configurable to explain a reason for an alert or prevention or mitigation step. For example, an embodiment alerting a protected party to an imminent text disclosure of credit card information could provide a message to the protected party, explaining that disclosing credit card information in the particular circumstances is unwise.

Trend monitoring module330uses the interaction pattern, the interaction behavior model, and any predicted or actual adverse results to identify and monitor a trend in the protected party's behavior. One example trend might be that the number of telephone calls from unknown callers a protected party answers is increasing. Another example trend might be that the protected party's conversations with callers not a list of trusted callers is increasing. If a trend exceeds a threshold value, or is changing at more than a threshold rate, warning module340reports the trend to another party responsible for the protected party, such as a caregiver or family member. A trend could indicate an increasing propensity to disclose personal information, or a new or worsening symptom of the protected party.

With reference toFIG.4, this figure depicts another block diagram of an example configuration for intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. In particular,FIG.4depicts more detail of protected party monitoring module310inFIG.3.

Speech to text module410converts speech, monitored using a microphone, to text. If application300is equipped with a visual monitoring capability, such as a video camera, module410is also configurable to convert visual information, such as American sign language, to text as well.

Party identification module420performs caller identification (for a network-mediated interaction), an identification of a voice other than the protected party's voice (for an in-person interaction monitored by a microphone), a detection of a presence of a person other than the protected party (for an in-person interaction monitored by a camera), and the like. To identify the other party to a network-mediated interaction, module420can use caller identification information such as a telephone number of a caller to the protected party and dialing information if the protected party initiated the communication. Module420can also match a telephone number associated with the other party with directory information such as a name associated with the other party. To identify the other party to an in-person interaction monitored by a microphone, module420matches a voice monitored by the microphone with stored voice data. To identify the other party to an in-person interaction monitored by a camera, module420matches a face or body monitored by the camera with stored face or body data.

Pattern matching module430analyzes the natural language interaction, while it is occurring, to determine an interaction pattern. Module430also uses the interaction pattern and interaction behavior model320to predict a future course of the interaction. In an rule-based implementation of model320, module430determines whether the interaction pattern is above a threshold value of matching one or more rules. In a learning model based implementation of model320, module430determines whether the interaction pattern is above a threshold value of matching one or more patterns that the model has learned are likely to result in personal information disclosure.

With reference toFIG.5, this figure depicts an example configuration for intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. The example can be implemented using application300inFIG.3.

In particular,FIG.5depicts example rule set510and example exception set530, for a rule-based implementation of an interaction behavior model.

Rule set510includes entries512,514,516,518, and520, each including a natural language pattern, or a natural language text that, if matched in an interaction of a protected party, could indicate that an adverse result is about to occur. For example, entry512, “My credit card number is”, is a natural language pattern indicating that the speaker is likely to complete the sentence by reciting credit card information. A protected party's reciting of credit card information could indicate that the protected party is making a purchase or providing personal information to another party that should not have this particular personal information.

Exception set530includes entries532and534, natural language patterns that represents a circumstance in which a rule, although matching an interaction of a protected party, does not apply because an adverse result is unlikely to occur. For example, rule set510includes entry514, “my date of birth is”, because disclosing one's date of birth could result in the inappropriate disclosure of personal information if the protected party were interacting in a telephone conversation with a sales caller who had initiated the call. However, the same would not be the case if the protected party had initiated a call to make an appointment at a physician's office, where such information is both needed for medical records and protected from disclosure. As a result, entry532provides an example exception to rule514: the protected party initiated the conversation. Entry534provides another example exception to rule514: that the identification information for the second party to the conversation matches identification information for a trusted party.

With reference toFIG.6, this figure depicts an example configuration for intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. The example can be implemented using application300inFIG.3. Interaction behavior model320is the same as interaction behavior model320inFIG.3.

In particular,FIG.6depicts example training conversations610and620, for use in training a learning interaction behavior model320. Conversation610has a high probability of resulting in a protected party's disclosing credit card information, and conversation620has a high probability of resulting in a protected party's disclosing bank account information. Thus, if an interaction pattern is above a threshold value of matching conversation610or620, the interaction pattern also has an above-threshold probability of a protected party's disclosing credit card or bank account information.

With reference toFIG.7, this figure depicts an example of intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. The example can be implemented using application300inFIG.3.

In particular,FIG.7depicts example conversations710and720. Application300determines that conversation710has a high probability of resulting in a protected party's disclosing credit card information, either because the interaction pattern corresponding to conversation710is above a threshold value of matching one or more rules, and no exceptions, in a rule-based interaction behavior model, or because conversation710is above a threshold value of matching one or more of a learning interaction behavior model's learned patterns that have a high probability of resulting in a protected party's disclosing bank account information. Thus, conversation710also has an above-threshold probability of a protected party's disclosing credit card or bank account information and application300generates an alert, intercepting the disclosure. However, application300determines that conversation720has a low probability of resulting in a protected party's disclosing date of birth information, either because the interaction pattern corresponding to conversation720is above a threshold value of matching one or more rules and exceptions, in a rule-based interaction behavior model, or because conversation720is below a threshold value of matching one or more of a learning interaction behavior model's learned patterns that have a high probability of resulting in a protected party's disclosing date of birth information to an untrusted party. Thus, conversation720also has an below-threshold probability of a protected party's disclosing date of birth information to an untrusted party and application300allows the disclosure to proceed without generating an alert or interception.

With reference toFIG.8, this figure depicts an example of trend monitoring in intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. The example can be implemented using application300inFIG.3.

As depicted, application300has used the interaction pattern, the interaction behavior model, and any predicted or actual adverse results to identify and monitor two trends in the protected party's behavior. Example trend810depicts a graph indicating that the number of telephone calls from unknown callers a protected party answers is increasing. When the number of answered telephone calls from unknown callers rises above threshold815, application300reports the trend to another party responsible for the protected party, such as a caregiver or family member. Example trend820depicts a graph indicating that the lengths of a protected party's conversations with callers not a list of trusted callers are increasing. When the conversation length rises above threshold825, application300reports the trend to another party responsible for the protected party, such as a caregiver or family member.

With reference toFIG.9, this figure depicts a flowchart of an example process for intercepting inadvertent conversational disclosure of personal information in accordance with an illustrative embodiment. Process900can be implemented in application300inFIG.3.

In block902, the application monitors an intercepted conversation of a protected party. In block904, the application uses a natural language processing engine to detect a natural language interaction between the protected party and another party. In block906, the application analyzes the interaction to determine an interaction pattern. In block908, the application checks whether the interaction pattern matches a pattern or rule requiring adverse result prevention. If yes (“YES” path of block908), in block910the application generates an appropriate alert that intercepts the predicted adverse result, then ends. If not (“NO” path of block908), in block912the application checks whether the interaction pattern matches a pattern or rule requiring trend data collection. If yes (“YES” path of block912), in block914the application collects. trend data corresponding to the pattern or rule, then (also “NO” path of block912) returns to block902to continue the monitoring.