Patent Description:
However, current methods for customer identification and authentication are cumbersome and may be susceptible to manipulation by bad actors. For example, with respect to identification, a bad actor may spoof the phone numberof a known customer and may pretend to be the customer when speaking with the agent. With respect to authentication, the bad actor may have learned the answer to the secret questions or other customer information using social engineering. While two factor authentication provides enhanced authentication, many customers have not set it up, or may find it cumbersome to use, which may lead to customer frustration and a poor call center experience. Therefore, what is needed is a way to improve customer identification and authentication, without providing additional burdens on the customer. <CIT> discloses customer authentication by matching a voice sample from a call to a voiceprint. The voiceprint belongs to a customer associated with the call.

In one embodiment, certain words or phrases spoken by customers during calls to a call center are used to identify or authenticate the user. Words or phases such as a customer's name, or an account number or telephone number, are selected for a customer. Recordings of the selected words or phrases spoken by the customer during previous calls are used to generate voiceprints that are stored and associated with the customer. Later, when the customer calls the call center, instances of the customer speaking the selected words are extracted from the call (referred to herein as "audio-of-interest") and are compared against the voiceprints stored for the customer. If the voiceprints match the audio-of-interest the customer is authenticated. The extracted audio-of-interest can be further used to identify the customer by matching the extracted audio-of-interest against voiceprints stored for multiple customers. The extracted audio-of-interest can be further compared against voiceprints stored for known bad actors or "fraudsters" to identify and prevent fraud.

As will be discussed further below, the embodiments described herein provide many benefits for authentication and identification. First, by authenticating or identifying a customer using audio-of-interest extracted from the call, the customer is not required to actively participate in the authentication or identification process during the call. For example, the customer is no longer expected to provide information such as their address or answers to long forgotten security questions. Second, because the voiceprints for each customer may be generated automatically for a customer based on customer interactions, the customer is not required to participate in a separate training phase or session where the customer is asked to repeatedly speak selected words and phrases. As a result of the benefits of the embodiments described herein, the overall call experience for customers is improved, the average call length is reduced, and the detection of fraudulent activities is improved.

In one embodiment, a system for authenticating and identifying customers is provided according to claim <NUM>.

Embodiments may include some or all of the following features. The biometrics module may further include instructions that when executed by the one or more processors cause the one or more processors to: if it is determined that the customer is not authenticated, perform a secondary authentication. Determining if the customer is authenticated may include: determining that one or more of the one or more voiceprints associated with the customer matches at least a portion of the audio-of interest with a confidence that satisfies a threshold confidence; and determining that the customer is authenticated in response to determining that the confidence satisfies the threshold confidence. The interest module may further include instructions that when executed by the one or more processors cause the one or more processors to: detect that audio-of-interest is being spoken by the customer; and extract the audio-of-interest from the received call in response to detecting that the audio-of-interest is being spoken. The interest module may further include instructions that when executed by the one or more processors cause the one or more processors to: determine that audio-of-interest is likely to be spoken by the customer based on a workflow associated with the call; and extract the audio-of-interest from the received call in response to determining that the audio of interest is likely to be spoken by the customer. The audio-of-interest may include one or more words or phrases spoken by the customer during the call. The audio-of-interest may include a non-consecutive sequence of words or phrases spoken by the customer during the call. The biometrics module may further include instructions that when executed by the one or more processors cause the one or more processors to: receive the audio-of-interest; retrieve one or more voiceprints associated with each customer of a plurality of customers; based on the one or more voiceprints and the audio-of-interest, identify the customer associated with the call from among the plurality of customers; and transmit a message that the customer is identified. The biometrics module may further include instructions that when executed by the one or more processors cause the one or more processors to: receive the audio-of-interest; retrieve one or more voiceprints associated with fraudulent customers of a plurality of customers; based on the one or more voiceprints and the audio-of-interest, identify the customer associated with the call as a fraudulent customer; and transmit a message that the customer is a fraudulent customer. Extracting audio-of-interest from the received call may include extracting audio-of-interest from a recording of the received call. Extracting audio-of-interest from the received call comprises extracting audio-of-interest from the received call in real time.

In an embodiment, a method for authenticating and identifying customers is provided according to claim <NUM>.

Embodiments may include some or all of the following features. The method may further include: if it is determined that the customer is not authenticated, performing a secondary authentication. Determining if the customer is authenticated may include: determining that one or more of the one or more voiceprints associated with the customer matches at least a portion of the audio-of interest with a confidence that satisfies a threshold confidence; and determining that the customer is authenticated in response to determining that the confidence satisfies the threshold confidence. The method may further include: detecting that audio-of-interest is being spoken by the customer; and extracting the audio-of-interest from the received call in response to detecting that the audio-of-interest is being spoken. The method may further include: determining that audio-of-interest is likely to be spoken by the customer based on a workflow associated with the call; and extracting the audio-of-interest from the received call in response to determining that the audio of interest is likely to be spoken by the customer. The audio-of-interest may include one or more words or phrases spoken by the customer during the call. The audio-of-interest may include a non-consecutive sequence of words or phrases spoken by the customer during the call. The method may further include: receiving the audio-of-interest; retrieving one or more voiceprints associated with each customer of a plurality of customers; based on the one or more voiceprints and the audio-of-interest, identifying the customer associated with the call from among the plurality of customers; and transmitting a message that the customer is identified.

In an example, a non-transitory computer-readable medium is provided. The medium includes instructions stored thereon that when executed by a processor cause the processor to: receive a call, wherein the call is associated with a customer; extract audio-of-interest from the received call; retrieve one or more voiceprints associated with the customer; based on the one or more voiceprints and the audio-of-interest, determine if the customer is authenticated; and if it is determined that the customer is authenticated, transmit a message that the customer is authenticated.

In an example, a system for authenticating and identifying customers is provided. The system includes an interest module and a biometrics module. The biometrics module: receives the audio-of-interest; receives zero or more voiceprints for a customer; creates or enhances the voiceprints for said customer to include additional audio-of-interest; and stores the voiceprint for later usage.

Embodiments may include some or all of the following features. The voiceprint may not become active for verification until the associated customer is verified and not a fraudulent actor. The voiceprint may be originally created on passive biometrics using the audio-of-interest comprising of the whole call and later enhanced to include other more specific audio-of-interest voiceprints. The voiceprint may become active for verification based on a <NUM>-factor authentication or other secondary authentication method. The voiceprint may become active after it passes a threshold for accuracy and performance measure. The added voiceprint may be enhanced with audio specific to a device or medium with different audio qualities.

The foregoing summary, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the embodiments, there is shown in the drawings example constructions of the embodiments; however, the embodiments are not limited to the specific methods and instrumentalities disclosed. In the drawings:.

<FIG> is an illustration of an environment <NUM> for authenticating and identifying callers. The environment <NUM> may be implemented by a call center or any other entity that receives calls from customers or clients. A customer <NUM> may use a computing device <NUM> (or a telephone <NUM>) to initiate a call with an agent <NUM> associated with the environment <NUM>. The agent <NUM> may receive the call via a channel <NUM> such as a VOIP line, POTS line, or a cellular channel. Any channel suitable for voice communication may be used. Note as used herein call may refer to the call received from the customer or a copy of the call received from the customer.

The agent <NUM> may receive the call from the customer <NUM> on an agent computing device <NUM>. The agent computing device <NUM> may be equipped with both human and virtual voice agent capabilities.

Besides the agent <NUM>, the call may also be received (at the same time or later) by a computing device <NUM> associated with the call center environment <NUM>. The computing device <NUM> may provide one or more call center services to the customer <NUM> such as interactive voice response services ("IVR") where the user may be presented with an automated system that may determine the optimal agent <NUM> to direct the call, may determine the identity of the customer <NUM>, or may retrieve other information from the customer in an automated way.

As may be appreciated, the computing device <NUM>, agent computing device <NUM>, and the computing device <NUM> may each be implemented by one or more general purpose computing devices such as the computing device <NUM> illustrated with respect to <FIG>. Depending on the embodiment, the computing device <NUM> may be part of a voice recorder or other device performing functions in a call center.

To authenticate and identify the customers <NUM>, the computing device <NUM> may include one or more modules. As illustrated, these modules include an interest module <NUM> and a biometrics module <NUM>. More or fewer modules may be supported. Depending on the implementation, some or all of the modules <NUM> and <NUM> may be implemented the same computing device <NUM>, or by some combination of computing devices <NUM>. In addition, some or all of the modules <NUM> and <NUM> may be implemented by a cloud-based computing system.

In general, one way to authenticate a customer <NUM> using voice is referred to as active voice authentication. Traditionally, for active voice authentication, one or more voiceprints <NUM> are generated for a customer <NUM> by having the customer <NUM> train the system by repeatedly speaking one or more words or phrases to the call center during a training phase. Later, when the customer <NUM> calls the call center the customer <NUM> is asked to repeat the one or more words or phrases in order to authenticate the user. While active voice authentication is effective, requiring the user to train the system is burdensome and leads to a low participation rate.

Another common way to authenticate a customer <NUM> using voice is referred to as passive voice authentication. For passive voice authentication, a recording of the customer <NUM> speaking is used to create a general representation of how the customer <NUM> speaks that is unrelated to any particular word or phrase. When a customer <NUM> calls the call center, their speaking voice is processed with the representation to authenticate the customer <NUM>. Passive voice authentication does not require an active training phase where the customer <NUM> is required to speak certain words or phrases which is an improvement on active voice authentication. However, passive voice authentication requires a large amount of spoken voice data from a customer <NUM> to authenticate, which may result in a significant delay from when a call begins and when the customer <NUM> is authenticated.

To provide authentication and identification services using active voice authentication but without the drawbacks associated with an active training phase, the interest module <NUM> and biometrics module <NUM> may use what are referred to herein as audios-of-interest <NUM>. An audio-of-interest <NUM> may be a word or phrase that is typically spoken by a customer <NUM> during a call. Examples include the name of the customer, the city or address associated with the customer, a telephone number associated with a customer, or an account number associated with a customer. Other examples of audio-of-interest <NUM> may include common words such as "yes", "no", "thank you", or "okay. " The audio-of-interest may not be words in the spoken language. Phonemes (or a basic unit of sound) may be pulled from words matching. English has a finite number (<NUM>) of phonemes which all words are made up from. Using this the training words for a voiceprint need not be the same words used for verification.

The audio-of-interest <NUM> may be used to quickly authenticate and/or identify a customer <NUM> on a call based on the voice of the customer <NUM>. In some embodiments, the audios-of-interest <NUM> may be selected to be a word or phrase that is typically spoken by a customer <NUM> at around the beginning of a call. This may ensure that the customer <NUM> can be quickly authenticated or identified at the beginning of the call.

Depending on the embodiment, the audios-of-interest <NUM> may be consecutive or non-consecutive words or phrases. For example, a consecutive audio-of-interest <NUM> may be an account number, whereas a non-consecutive audio-of-interest <NUM> may be the first, third, and fifth digit of the account number.

In some embodiments, the audio-of-interest <NUM> may be most of, or a large portion of, the call. In such embodiments, the audio-of-interest <NUM> may function similarly as a passive biometric voiceprint.

In some embodiments, each customer <NUM> may use the same audio-of-interest <NUM>. For example, each customer <NUM> may use their name as their associated audio-of-interest <NUM> or may use their account number as their audio-of-interest <NUM>. Alternatively, customers <NUM> may have different audios-of-interest <NUM>. For example, one customer <NUM> may use their first name as their audio-of-interest <NUM>, while another customer <NUM> may use their last name as their audio-of-interest <NUM>. In embodiments where different audios-of-interest <NUM> are used by customers <NUM>, the particular audio-of-interest <NUM> used by a customer <NUM> may be identified in a customer profile <NUM> associated with the customer <NUM>, for example.

In some embodiments, when a customer <NUM> makes their first call with a call center, after authenticating the customer <NUM> using two factor authentication or some other means, the interest module <NUM> may identify and extract voice data from the call corresponding to the audio-of-interest <NUM>. For example, if the audio-of-interest <NUM> is the name of the customer <NUM>, the interest module <NUM> may extract the audio-of-interest <NUM> from the call at or around the time that the customer <NUM> is saying their name. Depending on the embodiment, the interest module <NUM> may extract the voice data (and audio-of-interest <NUM>) from the call in real-time, or near-real time. Alternatively, the interest module <NUM> may extract the voice data from a recording, or partial recording, of the call.

As may be appreciated, the audio-of-interest <NUM> is extracted from the normal back and forth conversation associated with the call. There is no explicit or required training phase where the customer <NUM> is asked to repeat the audio-of-interest <NUM>.

The interest module <NUM> may process the extracted audio-of-interest <NUM> for a customer <NUM> and may generate and store one or more voiceprints <NUM> for the customer <NUM>. A voiceprint <NUM> is a digital structure that is unique to a customer <NUM> and that can be later used to identify or authenticate the customer <NUM> when the customer <NUM> speaks the word or phase that corresponds to the audio-of-interest <NUM> that was used to create the voiceprint <NUM>. Any method or technique for creating voiceprints <NUM> may be used.

In some implementations, rather than extract audio-of-interest <NUM> from a call, the interest module <NUM> may identify where in a particular recording of a call the audio-of-interest <NUM> is located. A pointer to the audio-of-interest <NUM> may then be stored by the interest module <NUM> along with some or all of the recording of the call.

In some implementations, voiceprints <NUM> associated with a customer <NUM> or account may relate to behavioral characteristics about how the customer <NUM> speaks or says certain phrases. For example, a customer <NUM> may have the behavior characteristics of always speaking each number of their phone number (i.e., "three", "zero", "zero", "eight", "zero", five", "six"). Another customer may have the behavior characteristic of grouping certain digits of their phone number into larger numbers (i.e., "three-hundred", "eight", "zero", five", "six"). As another example, a customer <NUM> may always say their zip code first when speaking their address. Other types of behavioral characteristics may be included.

In some embodiments, each audio-of-interest <NUM> for a customer <NUM> or account may be associated with a separate voice print <NUM>. Alternatively, a single voiceprint <NUM> may be used that includes all of the audios-of-interest <NUM> that may be used for a customer <NUM>. The voiceprint <NUM> may also include behavioral characteristics and any other information that may be used to authenticate a customer <NUM> such as face biometrics, fingerprints, etc..

The interest module <NUM> may use the stored voiceprints <NUM> to authenticate and/or identify customers <NUM>. With respect to authentication, when a customer <NUM> initiates a call with the call center, the agent <NUM> (or an interactive voice response system ("IVR")) may initially identify the customer <NUM> on the call based on information indirectly or directly provided by the customer <NUM>. For example, the customer <NUM> may be identified based on the telephone number that they used to place the call, or based on the name, address, or customer number that they provided to the agent <NUM> or IVR system.

The interest module <NUM> may continuously update or improve on the stored voiceprints <NUM> for each customer <NUM>. After a customer <NUM> is authenticated using audio-of-interest <NUM>, the interest module <NUM> may update some or all of the stored voiceprints <NUM> based on the audio-of-interest <NUM>. As may be appreciated, overtime the voice associated with a customer <NUM> may change slightly. By continuously updating the stored voiceprints <NUM> using recent audios-of-interest <NUM> these changes may be reflected in the stored voiceprints <NUM>.

The interest module <NUM> may extract the audio-of-interest <NUM> from the call. Depending on the embodiment and type of audio-of-interest <NUM>, the interest module <NUM> may extract the audio-of-interest <NUM> based on a script or workflow associated with the call. As may be appreciated, most agents <NUM> and IVR systems use a workflow or script that specifies how the agent <NUM> and IVR system interact with the customer <NUM> including the questions that are asked during the call. Accordingly, the interest module <NUM> may use the script or workflow to determine a time or place in the call when the audio-of-interest <NUM> is likely to appear in the call and may extract the audio-of-interest <NUM> at the determined time and place. Any method may be used.

For example, for an audio-of-interest <NUM> of a customer <NUM> name, the workflow may indicate that the agent <NUM> asks the customer <NUM> to speak their name as the first question during the call. Accordingly, the interest module <NUM> may look at or around the time of the first question in the call for audio-of-interest <NUM> that corresponds to the customer <NUM> speaking their name. In another example, the interest module <NUM> may detect that agent computing device <NUM> has just displayed a text box that the agent <NUM> uses to enter the account number of the customer <NUM>. Accordingly, the interest module <NUM> may determine that the customer has likely been asked their account number and may begin extracting audio-of-interest <NUM> from the call.

Alternatively, or in addition to the workflow, the interest module <NUM> may process the call using speech recognition to identify one or more portions of the call that likely correspond to the audio-of-interest <NUM>. For example, where the audio-of-interest <NUM> is the customer number, the interest module <NUM> may use speech recognition to identify portions of the call where the customer is speaking numbers. The interest module <NUM> may extract the audio-of-interest <NUM> from the identified portions.

As may be appreciated, interest module <NUM> may use the workflow-based extraction in combination with the speech-recognition-based extraction. For example, where the audio-of-interest <NUM> is a customer number, the workflow may indicate that the customer <NUM> is asked to recite their customer number as the third question. Accordingly, the interest module <NUM> may use the speech recognition to identify portions of the call where the customer is speaking numbers after the third question.

In some embodiments, the computing device <NUM> may include an optional speech module <NUM>. The speech module <NUM> may work with the interest module <NUM> to process audio and identify the audio-of-interest <NUM>. The speech module <NUM> may use speech recognition to determine what questions are being asked by the agent <NUM> and may then determine when to extract the audio-of-interest <NUM> from the audio. As another example, where the audio-of-interest <NUM> is specific numbers being spoken by the customer <NUM>, the speech module <NUM> may recognize when the specific numbers are being spoken by the customer <NUM> and may extract the numbers from the audio as the audio-of-interest <NUM>.

The biometrics module <NUM> may receive the extracted audio-of-interest <NUM> from the interest module <NUM> and may use the extracted audio-of-interest <NUM> to authenticate the customer <NUM> associated with the call. In some embodiments, the biometrics module <NUM> may retrieve one or more voiceprints <NUM> associated with the customer <NUM> and may determine if the extracted audio-of-interest <NUM> matches any of the retrieved one or more voiceprints <NUM> associated with the customer <NUM>. If the extracted audio-of-interest <NUM> matches any of the retrieved one or more voiceprints <NUM> associated with the customer <NUM>, the biometrics module <NUM> may determine that the customer <NUM> is authenticated.

In other embodiments, the biometrics module <NUM> may determine if the extracted audio-of-interest <NUM> matches all of the retrieved one or more voiceprints <NUM> associated with the customer <NUM>. And only when the extracted audio-of-interest <NUM> matches all of the retrieved one or more voiceprints <NUM> associated with the customer <NUM>, may the biometrics module <NUM> determine that the customer <NUM> is authenticated.

Any method for determining if extracted audio-of-interest <NUM> matches a voiceprint <NUM> may be used. In some implementations, the biometrics module <NUM> may calculate a confidence that the extracted audio-of-interest <NUM> matches a voiceprint <NUM>. If the calculated confidence satisfies a threshold confidence (e.g., is greater than or equal to the threshold confidence), then the biometrics module <NUM> may determine that the extracted audio-of-interest <NUM> matches the voiceprint <NUM>. Else, the biometrics module <NUM> may determine the that the extracted-audio-of-interest <NUM> does not match the voiceprint <NUM>. The threshold confidence may be set by a user or administrator, for example.

The biometrics module <NUM> may generate and transmit a message <NUM> indicating whether the customer <NUM> was authenticated based on the extracted audio-of-interest <NUM>. The message <NUM> may be transmitted to the agent computing device <NUM> and displayed to the agent <NUM>. Depending on the embodiment, if the message <NUM> indicates that the customer <NUM> could not be authenticated based on the audio-of-interest <NUM> the agent <NUM> may attempt to authenticate the customer <NUM> using other methods such as two factor authentication or by using security questions. Any method for authenticating a customer <NUM> may be used.

In addition to authentication, the interest module <NUM> and biometrics module <NUM> may further perform customer <NUM> identification using the stored voiceprints <NUM>. In such embodiments, when a call is initially received by an agent <NUM> or IVR system, the interest module <NUM> may extract the audio-of-interest from the call as described above and may provide the extracted audio-of-interest <NUM> to the biometrics module <NUM>. The biometrics module <NUM> may then identify the customer <NUM> by comparing the extracted audio-of-interest <NUM> with the stored voiceprints <NUM> associated with some or all of the known customers <NUM>. If the extracted audio-of-interest <NUM> matches a voiceprint <NUM>, then the biometrics module <NUM> may identify the customer <NUM> associated with the call as the customer <NUM> associated with the matching voiceprint <NUM>. The biometrics module <NUM> may then send a message <NUM> to the agent <NUM> or IVR system indicating whether the customer <NUM> was identified, and if so, the identity of the customer <NUM>. If the customer <NUM> could not be identified the agent <NUM> or IVR system may identify the customer <NUM> using different methods.

As may be appreciated, one benefit of identifying the customer <NUM> using the audio-of-interest <NUM> and the voiceprints <NUM> is that after the customer <NUM> is identified they are also authenticated. Accordingly, the amount of time required to both identify and authenticate the customer <NUM> is reduced, which may result in a reduced call time and an increased call experience for the customer <NUM>. Reduced call times increases the overall number of calls that a call center can handle and may also reduce the overall number of agents <NUM> needed at the call center.

The interest module <NUM> and biometrics module <NUM> may further perform fraud detection using the stored voiceprints <NUM>. In particular, the biometrics module <NUM> may determine if a customer <NUM> is likely a fraudulent customer <NUM> (i.e., a customer <NUM> or other individual that was previously associated with fraud or fraudulent activities). In such embodiments, when a call is initially received by an agent <NUM> or IVR system, the interest module <NUM> may extract the audio-of-interest <NUM> from the call as described above and may provide the extracted audio-of-interest <NUM> to the biometrics module <NUM>. The biometrics module <NUM> may then compare the extracted audio-of-interest <NUM> with stored voiceprints <NUM> associated with known bad actors or fraudulent customers <NUM>. If the extracted audio-of-interest <NUM> matches a stored voiceprint <NUM>, then the biometrics module <NUM> may identify the customer <NUM> as a fraudulent customer <NUM>. The biometrics module <NUM> may then send a message <NUM> to the agent <NUM> or IVR system indicating that the customer <NUM> is likely a fraudulent customer <NUM>. The agent <NUM> or IVR system may then take actions such as escalating the call to a fraud specialist, terminating the call, or what ever actions may be specified for fraudulent calls by the call center.

In some implementations, when a customer <NUM> cannot be authenticated by the computing device <NUM>, any voiceprints <NUM> associated with the customer <NUM> may be considered by the biometrics module <NUM> when detecting fraud. While the customer <NUM> may not be a known fraudulent customer, the authentication failure may indicate that the customer <NUM> may be attempting to breech one or more accounts. If the biometrics module <NUM> recognizes the customer <NUM> attempting to access another account at a later time, the voiceprints <NUM> associated with the customer <NUM> may be added by the biometrics module <NUM> to the list of known bad actors or fraudulent customers <NUM>.

In some embodiments, where the audio-of-interest <NUM> is passive biometric voiceprint, different thresholds may be used by the biometrics module <NUM> for verification, identification, and fraud detection or prevention.

Note that the authentication and fraud detection processes described above may not be performed in any particular order with respect to each other and may in fact be performed at approximately the same time. Accordingly, when a customer <NUM> calls the agent computing device <NUM>, the computing device <NUM> may begin the authentication process while the computing device <NUM> also begins the fraud detection process.

Note that the use of the computing device <NUM> (and interest module <NUM> and biometrics module <NUM>) is not specifically limited to use within call centers. For example, a user may use a banking app on their smartphone. The banking app may include a biometrics module <NUM> that has access to, or stores, one or more voiceprints <NUM> associated with the user. The voice prints <NUM> may have been learned by the computing device <NUM> at the call center as described above. When the user attempts to use the app, the interest module <NUM> also included in the app may prompt the user to speak or say the audio-of-interest <NUM>. The biometrics module <NUM> may then authenticate the user using the voiceprints <NUM> and the audio-of-interest <NUM>. The user may then be allowed or denied access to the app based on the authentication.

In another example, a customer may go to a bank branch to speak with a teller. As the customer speaks with the teller, a microphone records audio from the conversation. An interest module <NUM> associated with the bank branch or a computing device <NUM> of the teller may extract the audio-of-interest <NUM> from the recorded audio, and the biometrics module <NUM> may authenticate the customer using the audio-of-interest <NUM> and one or more stored voiceprints <NUM> of the customer. Once the customer is authenticated, the computing device <NUM> may display a message to the teller.

As may be appreciated, the authentication system described herein may be incorporated into a variety of applications and scenarios where authenticating users based on voice may be useful. For example, the system could be used to authenticate users before they enter airplanes, trains, buses, or other public transportation systems. The system could similarly be used to authenticate residents of a building, or office workers, before the enter the building. The system could be used to authenticate credit card or ATM transactions.

<FIG> is an illustration of a method <NUM> for authenticating a customer <NUM>. The method <NUM> may be performed by the interest module <NUM> and/or the biometrics module <NUM> of the computing device <NUM>.

At <NUM>, a call is received from a customer. The call may be received from a customer <NUM> by an agent <NUM>, or IVR system, associated with an agent computing device <NUM>. Depending on the embodiment, the agent <NUM> or IVR system may have identified the customer <NUM> using information such as the telephone number associated with the call or using information provided by the customer <NUM> such as a name, address, or account number, for example.

At <NUM>, audio-of-interest <NUM> is extracted from the call. The audio-of-interest <NUM> may be extracted from the call by the interest module <NUM>. The audio-of-interest <NUM> may be extracted from the call in real-time or may be extracted from a recording of the call that was provided to the interest module <NUM>. The audio-of-interest <NUM> may comprise a recording of the customer <NUM> speaking a particular word or phrase that may be used to authenticate the customer <NUM>.

At <NUM>, one or more voiceprints associated with the customer <NUM> are retrieved. The one or more voiceprints <NUM> may be retrieved by the biometrics module <NUM>. Each voiceprint <NUM> may have been generated by the biometrics module <NUM> from previous interactions with the associated customer <NUM>. Depending on the embodiment, the voiceprints <NUM> may have been generated by an active voiceprint <NUM> generation process.

At <NUM>, a determination is made as to whether the customer is authenticated. The determination may be made by the biometrics module <NUM> using the one or more voiceprints <NUM> associated with the customer <NUM> and the audio-of-interest <NUM> extracted for the customer <NUM> from the call. In some embodiments, the customer <NUM> is authenticated if at least a portion (or all) of the extracted audio-of-interest <NUM> matches a voiceprint <NUM> associated with the customer <NUM>. If the customer <NUM> is authenticated, then the method <NUM> may continue at <NUM>. Else, the method <NUM> may continue at <NUM>.

At <NUM>, a message that the customer is authenticated is transmitted. The message <NUM> may be transmitted to the agent <NUM> or IVR system by the biometrics module <NUM>. After receiving the message <NUM> the agent <NUM>, or IVR system, may proceed as if the customer <NUM> has been authenticated.

At <NUM>, secondary authentication is performed. The secondary authentication may be performed by the agent <NUM> or the IVR system. Depending on the embodiment, the secondary authentication may include performing two step authentication or asking one or more security questions.

<FIG> is an illustration of a method <NUM> for identifying a customer <NUM>. The method <NUM> may be performed by the interest module <NUM> and/or the biometrics module <NUM> of the computing device <NUM>.

At <NUM>, a call is received from a customer. The call may be received from a customer <NUM> by an agent <NUM>, or IVR system, associated with an agent computing device <NUM>. The customer <NUM> may not have been identified by the agent <NUM> or IVR system.

At <NUM>, voiceprints associated with customers <NUM> are accessed. The voiceprints <NUM> may be accessed by the biometrics module <NUM>. The voiceprints <NUM> may have been generated by the biometrics module <NUM> for each of the customers <NUM>.

At <NUM>, a determination is made as to whether the customer <NUM> is identified. The determination may be made by the biometrics module <NUM> using the accessed voiceprints <NUM> and the audio-of-interest <NUM> extracted for the customer <NUM> from the call. In some embodiments, the biometrics module <NUM> may attempt to match each of the accessed voiceprints <NUM> with the extracted audio-of-interest <NUM>. If a voiceprint <NUM> matching the extracted audio-of-interest <NUM> is found, then the biometrics module <NUM> may identify the customer <NUM> associated with the call as the customer <NUM> associated with the matching voiceprint <NUM>. If the customer <NUM> is identified, then the method <NUM> may continue at <NUM>. Else, the method <NUM> may continue at <NUM>.

At <NUM>, a message that the customer is identified is transmitted. The message <NUM> may be transmitted to the agent <NUM> or IVR system by the biometrics module <NUM>. Because the customer <NUM> was also authenticated by virtue of the matching voiceprint <NUM>, the message <NUM> may further indicate that the customer <NUM> was also authenticated.

At <NUM>, secondary identification is performed. The secondary identification may be performed by the agent <NUM>, the IVR system, or other modules or systems. For example, the secondary identification may include asking the customer <NUM> to state their name or account number. Other examples the secondary identification may include passive voice biometrics and face biometrics. Other methods for customer identification and/or verification may be used.

<FIG> is an illustration of a method <NUM> for determining if a customer is a fraudulent customer. The method <NUM> may be performed by the interest module <NUM> and/or the biometrics module <NUM> of the computing device <NUM>.

At <NUM>, audio-of-interest <NUM> is extracted from the call. The audio-of-interest <NUM> may be extracted from the call by the interest module <NUM>. The audio-of-interest <NUM> may be extracted from the call in real-time, or may be extracted from a recording of the call that was provided to the interest module <NUM>. The audio-of-interest <NUM> may comprise a recording of the customer <NUM> speaking a particular word or phrase that may be used to authenticate the customer <NUM>.

At <NUM>, one or more voiceprints associated with fraud are retrieved. The one or more voiceprints <NUM> may be retrieved by the biometrics module <NUM>. Each voiceprint <NUM> may have been generated by the biometrics module <NUM> from interactions with known fraudulent customers <NUM>. Depending on the embodiment, the biometrics module <NUM> may maintain a "blacklist" of fraudulent customers <NUM> along with one or more voiceprints <NUM> generated for each fraudulent customer <NUM>. After a call associated with a customer <NUM> is determined to be fraudulent, any voiceprints <NUM> associated with the customer <NUM> may be added to the blacklist. If no such voiceprints <NUM> exist, the biometrics module <NUM> may generate voiceprints <NUM> for the customer <NUM> from the last call recorded for the customer <NUM>.

At <NUM>, a determination is made as to whether the customer is a fraudulent customer. The determination may be made by the biometrics module <NUM> using the retrieved voiceprints <NUM> associated with fraud. In some embodiments, if the extracted audio-of-interest <NUM> matches any of the voiceprints <NUM> associated with fraud, then the customer <NUM> may be determined to be a fraudulent customer <NUM>. If the customer <NUM> is determined to be a fraudulent customer <NUM>, then the method <NUM> may continue at <NUM>. Else, the method <NUM> may continue at <NUM>.

At <NUM>, a message that the customer is a fraudulent customer is transmitted. The message <NUM> may be transmitted to the agent <NUM> or IVR system by the biometrics module <NUM>. After receiving the message <NUM> the agent <NUM>, or IVR system, may end the call, or may escalate the call to an agent <NUM> specializing in fraud.

At <NUM>, a message that the customer is not a fraudulent customer is transmitted. The message <NUM> may be transmitted to the agent <NUM> or IVR system by the biometrics module <NUM>. After receiving the message <NUM> the agent <NUM>, or IVR system, may proceed with the call.

<FIG> shows an exemplary computing environment in which example embodiments and aspects may be implemented. The computing device environment is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality.

Numerous other general purpose or special purpose computing devices environments or configurations may be used. Examples of well-known computing devices, environments, and/or configurations that may be suitable for use include, but are not limited to, personal computers, server computers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, distributed computing environments that include any of the above systems or devices, and the like.

Computer-executable instructions, such as program modules, being executed by a computer may be used. Distributed computing environments may be used where tasks are performed by remote processing devices that are linked through a communications network or other data transmission medium. In a distributed computing environment, program modules and other data may be located in both local and remote computer storage media including memory storage devices.

With reference to <FIG>, an exemplary system for implementing aspects described herein includes a computing device, such as computing device <NUM>. In its most basic configuration, computing device <NUM> typically includes at least one processing unit <NUM> and memory <NUM>. Depending on the exact configuration and type of computing device, memory <NUM> may be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in <FIG> by dashed line <NUM>.

Computing device <NUM> may have additional features/functionality. For example, computing device <NUM> may include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape. Such additional storage is illustrated in <FIG> by removable storage <NUM> and non-removable storage <NUM>.

Computing device <NUM> typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by the device <NUM> and includes both volatile and non-volatile media, removable and non-removable media.

Computer storage media include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Memory <NUM>, removable storage <NUM>, and non-removable storage <NUM> are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device <NUM>. Any such computer storage media may be part of computing device <NUM>.

Computing device <NUM> may contain communication connection(s) <NUM> that allow the device to communicate with other devices. Computing device <NUM> may also have input device(s) <NUM> such as a keyboard, mouse, pen, voice input device, touch input device, etc. Output device(s) <NUM> such as a display, speakers, printer, etc. may also be included. All these devices are well known in the art and need not be discussed at length here.

It should be understood that the various techniques described herein may be implemented in connection with hardware components or software components or, where appropriate, with a combination of both. Illustrative types of hardware components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. The methods and apparatus of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium where, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the presently disclosed subject matter.

Although exemplary implementations may refer to utilizing aspects of the presently disclosed subject matter in the context of one or more stand-alone computer systems, the subject matter is not so limited, but rather may be implemented in connection with any computing environment, such as a network or distributed computing environment. Still further, aspects of the presently disclosed subject matter may be implemented in or across a plurality of processing chips or devices, and storage may similarly be effected across a plurality of devices. Such devices might include personal computers, network servers, and handheld devices, for example.

Claim 1:
A system for authenticating and identifying customers:
one or more processors;
a memory communicably coupled to the one or more processors and storing:
an interest module including instructions that when executed by the one or more processors cause the system to:
receive a call, wherein the call is associated with a customer of a plurality of customers;
retrieve a customer profile associated with the customer;
determine an audio-of-interest associated with the customer based on the customer profile, wherein each customer is associated with a different kind of audio-of-interest;
identify where the audio-of-interest is spoken by the customer in the received call; and
store a pointer to the audio-of-interest with a recording of the received call; and
a biometrics module including instructions that when executed by the one or more processors cause the system to:
receive the audio-of-interest from the received call using the stored pointer;
retrieve one or more voiceprints associated with the customer;
based on the one or more voiceprints and the audio-of-interest, determine if the customer is authenticated; and
if it is determined that the customer is authenticated, transmit a message that the customer is authenticated and the interest module to update the retrieved one or more voiceprints using the audio-of interest.