Prediction of business outcomes by analyzing voice samples of users

A method and a system for predicting business outcomes by analyzing voice data of users are provided. The method includes generation of predictor models based on test data of test users. The test data includes historical data of the test users, voice samples of the test users, and answers provided by the test users to psychometric questions. The predictor models are then used to predict psychometric features and business outcomes based on target data of a target user. The target data includes voice samples of the target user, historical data of the target user, and answers provided by the target user to the psychometric questions.

FIELD

Various embodiments of the disclosure relate generally to business enhancement using machine learning. More specifically, various embodiments of the disclosure relate to prediction of business outcomes by analyzing voice samples of users.

BACKGROUND

There are always risks associated with initiating new endeavors, especially in a business. The severity of these risks, however, may be substantially minimized if potential outcomes, both positive and negative, are predicted. For an individual, it may include getting suggestion for a suitable job profile, while for an organization, such as an e-commerce service provider, it may include identifying purchase behavior of customers to suitably adjust their inventory to target the customers. Likewise, for increasing work productivity and efficiency of employees, a business organization may determine job affinity of the employees and accordingly allocate different work profiles and tasks to the employees.

Psychometric analysis, further, plays an important role in identifying potential business outcomes for users and organizations. The conventional ways of psychometric analysis involve interviewing with psychologists, counselors, or therapists who observe conscious, subconscious, and semiconscious behavior of their interviewees. Such, interviews may be subject to personal judgement and bias of an interviewer. For example, different interviewers have different judgment capabilities. Hence, it is impractical to solely rely on their judgment for accurate and precise prediction results. Moreover, it may not be feasible to manually conduct psychometric analysis of a large number of users; for example, employees in an organization. Another known technique of conducting psychometric analysis involves analyzing psychosocial reactions of users to tests that simulate artificially created situations, such as Thematic Apperception Test (TAT), Word Association Test (WAT), and the like. However, such tests fail to consider recent activities and behavioral changes of the users for psychometric analysis, thus making the results of psychometric analyzes less accurate, which in turn results in identification of inaccurate business outcomes.

In light of the foregoing, there exists a need for a solution that overcomes aforementioned problems and provides accurate business outcomes.

SUMMARY

Prediction of business outcomes by analyzing voice samples of users is provided substantially as shown in, and described in connection with, at least one of the figures, as set forth more completely in the claims.

These and other features and advantages of the disclosure may be appreciated from a review of the following detailed description of the disclosure, along with the accompanying figures in which like reference numerals refer to like parts throughout.

DETAILED DESCRIPTION

Certain embodiments of the disclosure may be found in a disclosed apparatus for predicting business outcomes by analyzing voice samples of users. Exemplary aspects of the disclosure provide methods and systems for predicting business outcomes for users. The method includes retrieving, by a server, historical data of at least one test user, a first set of voice samples of the test user, and a first set of answers provided by the test user to a set of psychometric questions. The first set of answers and the first set of voice samples are analyzed by the server. The server may be configured to analyze the first set of answers for deriving one or more psychometric features of the test user. The server may be configured to analyze the first set of voice samples for extracting a first set of feature values corresponding to a set of voice features from the first set of voice samples. One or more predictor models are generated by the server based on the historical data of the test user, the first set of feature values, and the one or more psychometric features of the test user. One or more business outcomes for a target user are predicted by the server based on the one or more predictor models and a second set of voice samples associated with the target user.

Another embodiment provides the system for predicting business outcomes for users. The system includes a server that may be configured to retrieve historical data of at least one test user, a first set of voice samples associated with the test user, and a first set of answers provided by the test user to a set of psychometric questions. The server may be configured to analyze the first set of answers and the first set of voice samples. The first set of answers is analyzed for deriving one or more psychometric features of the test user. The first set of voice samples is analyzed for extracting a first set of feature values corresponding to a set of voice features from the first set of voice samples. The server may be configured to generate one or more predictor models based on the historical data of the test user, the first set of feature values, and the one or more psychometric features of the test user. The server may be configured to predict one or more business outcomes for a target user based on the one or more predictor models and a second set of voice samples associated with the target user.

The disclosure involves the prediction of business outcomes by analyzing voice, which accurately reflects one's subconscious mind. As the subconscious mind is responsible for a majority of decision-making tasks and is directly related to an individual's psychometric orientation, the disclosure yields more accurate results in comparison to related techniques. In one exemplary scenario, the predicted business outcomes may be used by an organization to improve marketing strategies and, in turn, expanding business. For example, the organization may target a specific group of customers that have high purchase affinity (i.e., a predicted business outcome) for a product launched by the organization. In another exemplary scenario, the predicted business outcomes may be used by an organization to improve resource management. For example, electronic commerce (e-commerce) industries may use the predicted business outcomes (such as predicted purchase trend) to manage their inventory. Likewise, airline industry may use the predicted business outcomes (such as predicted travel trend) to customize ticket prices to attract more customers.

FIG.1is a block diagram that illustrates an exemplary environment100for prediction of business outcomes by analyzing voice samples of users, in accordance with an embodiment of the disclosure. The environment100includes test users102a-102n(hereinafter designated and referred to as “the test users102”), test-user devices104a-104n(hereinafter designated and referred to as “the test-user devices104”), an application server106, and a database server108. The environment100further includes a target user110and a target-user device112. The test-user devices104, the application server106, the database server108, and the target-user device112may communicate with each other by way of a communication network114or any other communication means established therebetween.

The test users102are individuals, whose test data may be used by the application server106for generating predictor models that predict business outcomes. The test data of each test user102may include historical data of the corresponding test user102, voice samples of the corresponding test user102, and answers provided by the corresponding test user102to various psychometric questions. The historical data of the test users102may refer to data collected based on past events pertaining to the test users102. The historical data may include data generated either manually or automatically by the test users102. For example, the voice samples of the test user102amay include voice recordings of the test user102recorded in real-time by way of the test-user device104a. The voice samples of the test user102amay further include previously recorded voice recordings that are uploaded on the internet or a social media platform by the test user102a. The historical data of the test user102amay include, but is not limited to, educational qualifications, past and present job profiles, purchase history, and travel history of the test user102a. The historical data of the test user102amay further include an activity log of the test user102aon the internet and various social media platforms. For example, with consent of the test user102a, the historical data may be extracted from a social media profile of the test user102a. The answers to the psychometric questions may be provided by the test user102awhen the psychometric questions are presented to the test user102athrough various online tests (such as, but not limited to, the multiple intelligence quiz, the BIG 5, or the personal globe inventory) on the test-user device104a. The voice samples of the test user102amay include voice recordings of the test user102a.

The test-user devices104may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for providing the test data of the test users102a-102nto the application server106. In one exemplary scenario, the test-user devices104may refer to communication devices of the test users102. The test-user devices104may be configured to allow the test users102to communicate with the application server106and the database server108. The test-user devices104may be configured to serve as an interface for providing the test data of the test users102to the application server106. In one embodiment, the test-user device104amay be configured to run or execute a software application (e.g., a mobile application or a web application), which may be hosted by the application server106, for presenting various psychometric questions to the test user102afor answering. The test-user device104amay be configured to communicate the answers provided by the test user102ato the psychometric questions to the application server106. The test-user device104amay be further configured to run or execute the software application for accessing various voice recordings of the test user102athat are stored in a memory (not shown) of the test-user device104aor uploaded on the social media profile of the test user102a. The test-user device104amay be further configured to access, with the consent of the test user102a, the social media profile of the test user102afor retrieving the historical data of the test user102a. Likewise, the test-user devices104of the other test users102may be configured to provide the test data to the application server106. Examples of the test-user devices104may include, but are not limited to, mobile phones, smartphones, laptops, tablets, phablets, or other devices capable of communicating via the communication network114.

The application server106may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for predicting business outcomes. The application server106may be a physical or cloud data processing system on which a server program runs. The application server106may be implemented in hardware or software, or a combination thereof. The application server106may be configured to host the software application which may be accessible on the internet for providing a personality and business outcomes prediction service. The application server106may be configured to utilize the software application for retrieving the test data of the test users102. The application server106may be further configured to use a tracker or a web crawler to track the activities of the test users102on the internet and the social media platforms for retrieving the test data. The application server106may be configured to implement a learning phase based on the test data for generating the predictor models. The predictor models may be statistical predictive models generated by means of machine learning algorithms. Examples of the algorithms used for generating the predictor models may include, but are not limited to, a Support Vector Machine (SVM), a Logistic Regression model, a Bayesian Classifier model, a Decision Tree Classifier, a Copula-based Classifier, a K-Nearest Neighbors (KNN) Classifier, a Random Forest (RF) Classifier, or Artificial neural networks.

After the generation of the predictor models, the application server106may be configured to utilize the predictor models during a prediction phase to predict the business outcomes for the target user110based on various inputs received from the target user110(the inputs received from the target user110are hereinafter designated and referred to as “target data”). In one embodiment, the business outcomes may include employment suggestions, compatibility match, product purchase affinity, color affinity, work affinity, and/or the like. In another embodiment, the business outcomes may include work affinity of employees, inventory suggestions, travel trend, purchase trend, and/or the like.

The application server106may be realized through various web-based technologies, such as, but not limited to, a Java web-framework, a .NET framework, a PHP framework, or any other web-application framework. Examples of the application server106include, but are not limited to, a computer, a laptop, a mini-computer, a mainframe computer, a mobile phone, a tablet, and any non-transient, and tangible machine that can execute a machine-readable code, a cloud-based server, or a network of computer systems. Various functional elements of the application server106have been described in detail in conjunction withFIG.2. Generation of the predictor models is described later inFIG.3.

The database server108may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for managing and storing data, such as the test data of the test users102, the target data of the target user110, and the predictor models. The database server108may be configured to receive a query from the application server106to extract the data stored in the database server108. Based on the received query, the database server108may be configured to provide the requested data to the application server106over the communication network114. In one embodiment, the database server108may be configured to implement as a local memory of the application server106. In another embodiment, the database server108may be configured to implement as a cloud-based server. Examples of the database server108may include, but are not limited to, MySQL® and Oracle®.

The target user110may be an individual, whose target data may be used as input to the predictor models for predicting business outcomes. In one exemplary scenario, the target user110may be an individual interested in determining a compatibility match or an individual seeking suggestion regarding employment. In another exemplary scenario, the target user110may be a representative of an organization who wants to know future business outcomes pertaining to a new policy implementation. In another exemplary scenario, the target user110may be an employee of the organization, whose employment affinity (i.e., a business outcome) is of interest to the organization. In another exemplary scenario, the target user110may be a customer in conversation with a sales representative of an organization, such as an e-commerce organization. The target data may include historical data of the target user110, voice samples of the target user110, and/or answers provided by the target user110to the psychometric questions. The application server106may be configured to obtain the target data in a manner that is similar to obtaining the test data of the test users102. In one embodiment, the application server106may be configured to retrieve the voice samples of the target user110in real time, while the target user110is indulged in a conversation accessible to the application server106.

The target-user device112may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for providing the target data of the target user110to the application server106. In one exemplary scenario, the target-user device112may refer to a communication device of the target user110. The target-user device112may be configured to allow the target user110to communicate with the application server106and the database server108. In one embodiment, the target-user device112may be configured to provide the target data to the application server106. For example, the target-user device112may be configured to run or execute the software application, which is hosted by the application server106, for presenting the psychometric questions to the target user110for answering. The target-user device112may be configured to communicate the answers provided by the target user110to the application server106. The target-user device112may be configured to obtain a consent of the target user110for retrieving various voice recordings of the target user110stored in the memory of the target-user device112or uploaded on the social media profile of the target user110. The target-user device112may be further configured to communicate the retrieved voice recordings to the application server106. The target-user device112may be further configured to retrieve the historical data of the target user110by accessing the social media profile of the target user110based on a consent of the target user110and provide the retrieved historical data to the application server106. Examples of the target-user device112may include, but are not limited to, mobile phones, smartphones, laptops, tablets, phablets, or other devices capable of communicating via the communication network114.

The communication network114may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to transmit content and messages between various entities, such as the test-user devices104, the application server106, the database server108, and/or the target-user device112. Examples of the communication network114may include, but are not limited to, a Wi-Fi network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and combinations thereof. Various entities in the environment100may connect to the communication network114in accordance with various wired and wireless communication protocols, such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof.

In operation, the application server106may be configured to predict the business outcomes in two phases, such as the learning and prediction phases. The learning phase may focus on generation of the predictor models. During the learning phase, the application server106may be configured to retrieve the test data from the test users102. The test data may include the historical data of the test users102, the voice samples of the test users102, and the answers provided by the test users102to the psychometric questions. During the learning phase, the application server106may be configured to analyze the test data for generating the predictor models. For example, the voice samples corresponding to the test users102may be analyzed to extract the feature values for the voice features. The answers provided by the test users102may be analyzed to derive psychometric features, such as personality attributes and mood attributes, of the test users102. The psychometric features may refer to behavioral qualities or characteristics of an individual's persona. Personality attributes (such as BIG5 attributes and RIASAC Holland occupational themes) are one example of psychometric features. As per BIG5 attributes, the personality attributes may be classified into five areas of: neuroticism, openness, conscientiousness, extraversion, and agreeableness. The mood attributes may be classified as cheerful, sad, disappointed, happy, confident, or the like. As per RIASAC Holland occupational themes, the personality attributes may be classified into six categories: Realistic (Doers), Investigative (Thinkers), Artistic (Creators), Social (Helpers), Enterprising (Persuaders), and Conventional (Organizers). Other examples of psychometric features may include, but are not limited to, Gardener's Multiple Intelligences theory related attributes, emotional attributes, aesthetic preferences, and the like. Likewise, the historical data of each test user102may be cleaned and normalized to remove irrelevant information. The application server106may be further configured to utilize the analyzed test data as input for the machine learning algorithms to generate the predictor models. The analyzed test data and the predictor models may be stored in the database server108.

The learning phase may be followed by the prediction phase. During the prediction phase, the application server106may be configured to retrieve the target data of the target user110. The target data may include one or more voice samples corresponding to the target user110, answers provided by the target user110to the psychometric questions, and/or the historical data of the target user110. The application server106may be further configured to analyze the target data for predicting the business outcomes. For example, the answers provided by the target user110may be analyzed to derive the psychometric features, such as personality and mood attributes, of the target user110and the voice samples of the target user110may be analyzed to extract feature values corresponding to the voice features. In one embodiment, the application server106may be further configured to analyze the voice sample and the historical data of the target user110to predict psychometric features of the target user110. The application server106may be further configured to use the derived and predicted psychometric features, the extracted feature values, and/or the analyzed historical data as input to the predictor models for predicting the business outcomes. The learning phase is explained in detail in conjunction withFIG.3and the prediction phase is explained in detail in conjunction withFIGS.4-7.

FIG.2is a block diagram that illustrates the application server106, in accordance with an embodiment of the disclosure. The application server106may include a first processor202, an audio processor204, a memory206, and an input/output (I/O) module208. The first processor202, the audio processor204, the memory206, and the I/O module208may communicate with each other by means of a communication bus210.

The first processor202may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for implementing the learning and prediction phases. The first processor202may be configured to obtain the test data of the test users102and the target data of the target user110. The first processor202may be configured to analyze the answers provided by the test users102and the target user110to the psychometric questions to derive psychometric features for the test users102and the target user110, respectively. Examples of the psychometric features may include, but are not limited to, skills and knowledge, abilities, attitudes, mood attributes, aesthetic preferences, and personality attributes. The first processor202may include multiple functional blocks, such as: a model generator212, a data filtration and normalization module214, and a prediction module216. Examples of the first processor202may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), and the like.

The audio processor204may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to execute one or more operations for voice analysis. The audio processor204may include a feature extraction module218. The feature extraction module218may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to extract feature values for various voice features from the voice samples associated with the test users102and the target user110. The voice features may include, but are not limited to, paralinguistic features, linguistic features, sound features, and the like. The paralinguistic features may be associated with paralanguage aspect of spoken communication and include tone, prosody, volume, or the like. The linguistic features, such as phonemes or the like, are associated with language aspect of spoken communication. The sound features may include analysis of pitch, signal energy, mel-spectrum, cepstral coefficients, formants, or the like. Examples of the audio processor204may include, but are not limited to, a digital signal processor (DSP), an ASIC processor, a RISC processor, a CISC processor, an FPGA, and the like.

The model generator212and the filtration and normalization module214may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to implement the learning phase for generating the predictor models. During the learning phase, the test data may be received and analyzed. For example, the model generator212may be configured to analyze the answers provided by the test users102for deriving the psychometric features of the test users102, the data filtration and normalization module214may be configured to analyze the historical data of the test users102, and the feature extraction module218may be configured to analyze the voice samples associated with the test users102. The model generator212may be configured to use the normalized and filtered historical data, the derived psychometric features, and the extracted feature values for generating the predictor models. For the generation of the predictor models, the model generator212may be configured to use various machine learning algorithms such as, but not limited to, regression based predictive learning and neural networks based predictive leaning. In one embodiment, the model generator212may be further configured to update the predictor models to improve its prediction accuracy based on a feedback provided by the target user110on relevance of the predicted business outcomes.

The data filtration and normalization module214may be configured to normalize and filter the historical data of the test users102and the target user110. For example, the data filtration and normalization module214may be configured to filter the commonly used words (such as “the”, “is”, “at”, “which”, “on”, and the like) as irrelevant information from the historical data and normalize the remaining historical data to make it more meaningful. In another example, the historical data may be filtered to parse specific keywords such as, but not limited to, identifying a stream of numbers that may represent a mobile number, extracting keywords related to personality, mood, job, likes, dislikes, and the like. In another example, the historical data may be filtered to extract one or more named entities which are related to specific objects or actions (for example, identifying full name of an institution by recognizing informal name of the institution in a post) and to recognize one or more activities which are mentioned indirectly (for example, recognizing a type of sport activity by referring a place description or a club name in a post).

The prediction module216may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to implement the prediction phase for predicting the business outcomes by using the target data as input to the predictor models. In one embodiment, the prediction module216may be configured to use the predictor models to predict psychometric features based on the analyzed historical data and the extracted feature values of the voice features extracted from the voice samples associated with the target user110. The predicted psychometric features may also be used for predicting the business outcomes. The feature extraction module218may be configured to analyze the voice samples of the test users102and the target user110to extract feature values for various voice features.

The memory206may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to store the instructions and/or code that enable the first processor202and the audio processor204to execute their operations. In one embodiment, the memory206may be configured to store the test data, the target data, and the predictor models. Examples of the memory206may include, but are not limited to, a random-access memory (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive (HDD), a flash memory, a solid-state memory, and the like. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to realizing the memory206in the application server106, as described herein. In another embodiment, the memory206may be realized in form of a cloud storage working in conjunction with the application server106, without departing from the scope of the disclosure.

The I/O module208may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to transmit and receive data to (or form) various entities, such as the test-user devices104, the target-user device112, and/or the database server108over the communication network114. Examples of the I/O module208may include, but are not limited to, an antenna, a radio frequency transceiver, a wireless transceiver, a Bluetooth transceiver, an Ethernet port, a universal serial bus (USB) port, or any other device configured to transmit and receive data. The I/O module208may be configured to communicate with the test-user devices104, the target-user device112, and the database server108using various wired and wireless communication protocols, such as TCP/IP, UDP, LTE communication protocols, or any combination thereof.

FIG.3is a block diagram that illustrates an exemplary scenario300for generating predictor models, in accordance with an exemplary embodiment of the disclosure. The exemplary scenario300involves the test users102, the application server106, and the database server108. The application server106may be configured to retrieve test data302of the test users102as a part of the learning phase. The test data302may include voice data304of the test users102, historical data306of the test users102, and answers308provided by the test users102to various psychometric questions. For the sake of simplicity, the retrieval of the test data302is explained with respect to the test user102a. However, it will be apparent to a person of ordinary skill in the art that the application server106may be configured to retrieve the test data302of other test users102b-102nin a similar manner as described for the test user102a.

With reference to the test user102a, the voice data304may include voice samples of the test user102aand corresponding date and time markers of the voice samples. The date and time markers of a voice sample indicate a date and time of a recording of the voice sample by a test user, such as the test user102a. The application server106may be configured to utilize the software application that runs on the test-user device104ato retrieve, with the consent of the test user102a, the voice data304stored in the memory of the test-user device104. For example, the software application may retrieve, with the consent of the test user102a, the voice recordings of the test user102astored in the memory of the test-user device104a. In another example, the software application may be further configured to access and retrieve, with the consent of the test user102a, the voice recordings of the test user102athat the test user102ahas posted on the test users social media profile. In another example, the software application may be configured to prompt the test user102ato record and upload the test user's102avoice samples to the application server106.

The historical data306of the test user102amay include, but is not limited to, the curriculum information, the education particulars, the travel history, the employment details, the purchase history of the test user102a, and one or more posts that are shared, followed, and liked by the test user102aon the internet and the social media platform. For example, the test-user device104a, executing the software application hosted by the application server106, may be configured to access the activity log of the test user102aon the internet to obtain the travel history and the purchase history of the test user102a. Based on a consent of the test user102a, the test-user device104amay be configured to access the social media profile (for example LinkedIn®, Facebook®, or the like) of the test user102ato retrieve the education and job particulars of the test user102aon the social media profile. The application server106may be configured to communicate a questionnaire to the test-user device104aregarding the historical data of the test user102a. The test-user device104amay be configured to communicate to the application server106a response provided by the test user102ato the questionnaire and the application server106may be configured to the include the response of the test user102ain the historical data306.

The application server106may be further configured to prompt the test user102aby way of the test-user device104ato take one or more online tests (such as, but not limited to, the multiple intelligence quiz, the BIG 5, or the personal globe inventory) that include the psychometric questions. The answers308to the psychometric questions are then provided by the test user102aand communicated to the application server106by the test-user device104a. In one exemplary scenario, the psychometric questions may include one hundred questions each of which is associated with a linear scale. For example, the linear scale may be scored from 0 to 9, where score ‘0’ means there is no correlation between the test user102aand a question statement and score ‘9’ means the test user102aand the question statement completely corelate. In this scenario, the answer308to each psychometric question may be a score selected by the test user102afrom the linear scale. In another exemplary scenario, the psychometric questions may include one hundred questions each of which is associated with a set of options, such as four options, having a specific score associated thereto. The test user102amay be required to select one or more options from the set of options for each psychometric question as the answer. It will be apparent to a person of skill in the art that the abovementioned examples are for illustrative purpose and should not be construed to limit the scope of the disclosure. In another embodiment, the application server106may be configured to retrieve the answers308from third-party servers (not shown) that conduct psychometric analysis of various users via online tests.

After retrieving the test data302of the test users102, the application server106may be configured to process the test data302. Processing of the test data302may involve filtering and normalizing the historical data306(as represented by block310). The historical data306retrieved from the test users102may include irrelevant information. Thus, the data filtration and normalization module214may be configured to filter and normalize the historical data306so that only relevant information is processed further. For example, the data filtration and normalization module214may be configured to filter the commonly used words (such as “the”, “is”, “at”, “which”, “on”, and the like) as irrelevant information from the historical data306and normalize the remaining historical data to make it more meaningful. In another example, the data filtration and normalization module214may be configured to parse specific keywords, such as, but not limited to, identifying a stream of numbers that may represent a mobile number, extracting keywords related to personality, job, likes, dislikes, or the like, in the historical data306. In another example, the data filtration and normalization module214may be configured to extract one or more named entities which are related to specific objects or actions (for example, identifying full name of an institution by recognizing informal name of the institution in a post) from the historical data306and recognize one or more activities which are mentioned indirectly (for example, recognizing a type of sport activity by referring a place description or a club name in a post) in the historical data306.

Processing of the test data302may further involve analyzing the voice data304. For analyzing the voice data304, the feature extraction module218may be configured to perform audio processing (as represented by block312) followed by feature value extraction (represented by block314). In one scenario, the application server106may use the date and time markers of the voice sample for audio processing and feature value extraction. In other words, the application server106may be configured to perform audio processing and feature value extraction in a chronological order based on the date and time markers. For example, the application server106may process a voice sample that was obtained one month ago before another voice sample that was obtained one day ago. The feature values are extracted for voice features, such as, paralinguistic features, linguistic features, sound features, or the like (as represented by block316). In one embodiment, the extracted feature values may correspond to a multidimension vector. In one embodiment, the feature extraction module218may be configured to combine the extracted feature values corresponding to the voice samples of the voice data304. For example, the feature extraction module218may normalize and adjust the extracted feature values corresponding to the voice samples of each test user102to obtain a specific set of feature values for each test user102. The feature extraction module218may be configured to store the extracted feature values corresponding to the voice data304in the database server108.

Processing of the test data302may further involve analyzing the answers308to derive psychometric features of the test users102. For the sake of ongoing description, the psychometric features are assumed to include personality and mood attributes, such as, but not limited to, neuroticism, openness, conscientiousness, extraversion, amused, cheerfulness, realistic, investigative, artistic, social, enterprising, and conventional (as represented by block318). The first processor202may be configured to analyze the answers308corresponding to each test user102for deriving the personality and mood attributes (i.e., the psychometric features) of each test user102. When the first processor202receives the answers308, the first processor202may be configured to determine the psychometric score for the test user102a. In one example, when the answers308provided by the test user102ainclude a score selected by the test user102afrom the linear scale associated with each psychometric question, the psychometric score may be equal to a cumulative sum of the scores selected by the test user102a. In another example, when the answers308provided by the test user102ainclude one or more options selected by the test user102afrom the set of options associated with each psychometric question, the psychometric score may be equal to a cumulative sum of the scores associated with the options selected by the test user102a. For deriving the personality and mood attributes318of the test user102a, the first processor202may be configured to determine the confidence score for each personality and mood attribute318based on the determined psychometric score of the test user102a. It will be apparent to a person of skill in the art that the abovementioned exemplary scenario is for illustrative purpose and should not be construed to limit the scope of the disclosure. The first processor202may derive the personality and mood attributes318from the answers308by using by any technique known in the art.

After the test data302is processed, the model generator212may be configured to use the analyzed historical data, the extracted feature values, the combined feature values extracted from the voice data304, and the derived psychometric features as inputs for predictor model generation (as represented by block320). The model generator212may be configured to use one or more machine learning algorithms, such as regression based predictive learning, neural networks based predictive leaning, and the like, for generating predictor models322. During the generation of the predictor models322, the model generator212may be configured to map the voice features and analyzed historical data with the derived personality and mood attributes based on the extracted feature values and generate links therebetween. In other words, a linear combination of voice features is linked to each personality and mood attribute based on the extracted feature values. For example, in a linear regression model, for a first set of feature values extracted from the voice samples of the test user102a, the voice features may be mapped to the confidence scores of each of the personality and mood attributes318derived for the test user102a. For a second set of feature values extracted from the voice samples of the test user102b, the voice features may be mapped to the confidence scores of each of the personality and mood attributes318derived for the test user102b. Likewise, the analyzed historical data may be mapped with the personality and mood attributes318. The model generator212may be configured to assign weights to the generated links. The assigned weights indicate the strength of association between the specific voice feature and the personality and mood attributes318. For example, the model generator212may assign a first set of weights to a first set of links between the voice features and the personality and mood attributes318derived for the test user102a. In one scenario, when the second set of feature values extracted from the voice samples of the test user102bare same as the first set of feature values and the confidence scores of the personality and mood attributes318derived for the test user102bare same as of the user102a, the model generator212may be configured to increase the first set of weights assigned to the first set of links. However, if the second set of feature values are different from the first set of feature values and/or the confidence scores of the personality and mood attributes318derived for the test user102bare not same as of the user102a, the model generator212may be configured to adjust the first set of weights assigned to the first set of links and may generate a second set of links having a second set of weights between the voice features and the personality and mood attributes318derived for the test user102b. Similarly, the model generator212may assign weights to the links generated between the voice features and the personality and mood attributes318derived for other test users102c-102n. The model generator212may be configured to generate the predictor models322by using the weighted links. It will be apparent to a person of ordinary skill in the art that the abovementioned examples are for illustrative purpose, the model generator212may use other complex models of mapping the voice features to the personality and mood attributes318without deviating from the scope of the disclosure.

The predictor models322generated by the model generator212may include at least three predictor models. The first predictor model may be capable of predicting psychometric features (such as the personality and mood attributes) by using feature values extracted from a voice sample as input. The second predictor model may be capable of predicting psychometric features (such as the personality and mood attributes) by using analyzed historical data as input. The third predictor model may be capable of predicting business outcomes by using predicted and derived psychometric features (such as the predicted and derived personality and mood attributes) and feature values extracted from one or more voice samples as input. The model generator212may be further configured to store the three predictor models322in the database server108. The predictor models322may be used by the prediction module216for predicting business outcomes described in conjunction withFIGS.4-7.

It will be apparent to a person of ordinary skill in the art that the voice features (as represented by block316) and the personality and mood attributes (as represented by block318) are shown for illustrative purpose. Thus, the voice features can include any voice feature known in the art and the personality and mood attributes may include any attribute known in the art, without deviating from the scope of the disclosure.

FIG.4is a block diagram that illustrates an exemplary scenario400for predicting business outcomes, in accordance with an exemplary embodiment of the disclosure. The exemplary scenario400involves the target user110who may provide target data402, the application server106, and the database server108that may store the predictor models322. The exemplary scenario400illustrates a scenario where the target data402includes voice data404of the target user110, historical data406of the target user110, and answers408provided by the target user110to the psychometric questions.

For retrieving the voice data404of the target user110, the application server106may be configured to utilize the software application running on the target-user device112for retrieving, with the consent of the target user110, the voice recordings stored in the target-user device112. The application server106may be configured to utilize the software application running on the target-user device112for retrieving the voice recordings uploaded or posted by the target user110on the social media profile. In another scenario, the application server106may be configured to prompt the target user110, through the software application running on the target-user device112, to record and upload the test user's102voice sample in real time for providing to the application server106. The voice data404may further include corresponding date and time markers of the voice samples.

The historical data406of the target user110may include, but is not limited to, the curriculum information, the education particulars, the travel history, the employment details, and the purchase history of the target user110. For example, the target-user device112, executing the software application hosted by the application server106, may be configured to access the activity log of the target user110on the internet and social media platforms to obtain the travel history and the purchase history of the target user110. Based on a consent of the target user110, the target-user device112may be further configured to access the social media profile (for example LinkedIn®, Facebook®, or the like) of the target user110for obtaining the education and job particulars of the target user110and one or more posts that are shared, liked, or followed by the target user110on the social media profile. The application server106may be further configured to communicate a questionnaire to the target user110, regarding the historical data406of the target user110through the software application accessed by the target user110on the target-user device112. The target-user device112may be configured to communicate to the application server106a response provided by the target user110to the questionnaire and the application server106may be configured to the include the response of the target user110in the historical data406.

The application server106may be further configured to prompt the target user110through the software application that runs on the target-user device112to take one or more online tests (such as, but not limited to, the multiple intelligence quiz, the BIG5, or the personal globe inventory) that include the psychometric questions. The answers408to the psychometric questions are then provided by the target user110. In another embodiment, the application server106may be configured to retrieve the answers408from the third-party servers that conduct the psychometric analysis of users via online tests.

After retrieving the target data402, the application server106may be configured to process the target data402. Processing of the target data402may involve filtering and normalizing the historical data406(as represented by block410). Processing of the target data402may further involve analyzing the voice data404. For analyzing the voice data404, the feature extraction module218may be configured to perform audio processing (as represented by block412) followed by feature value extraction (as represented by block414) on the voice data404. During feature value extraction, the feature extraction module218may be configured to extract the feature values corresponding to the voice features (as represented by block316). The voice features may include, but are not limited to, paralinguistic features, linguistic features, and sound features. The feature extraction module218may be configured to store the extracted feature values corresponding to the voice data404in the database server108. Processing of the target data402may further involve analyzing the answers408by the first processor202for deriving personality and mood attributes416(hereinafter, referred to as “derived personality and mood attributes416”) of the target user110.

After the target data402is processed, the prediction module216may be configured to query the database server108to retrieve the predictor models322. The prediction module216may be configured to use the feature values extracted from the voice data404and the analyzed historical data as input to the first and second predictor models, respectively, for psychometric prediction (as represented by block418). The psychometric prediction may yield predicted personality and mood attributes420of the target user110as output. In one embodiment, the prediction module216may be configured to predict personality and mood attributes separately for each voice sample of the voice data404by using the first predictor model. After the personality and mood attributes are predicted for each voice sample of the voice data404, the prediction module216may be configured to normalize and adjust the personality and mood attributes to yield the predicted personality and mood attributes420. In another embodiment, the prediction module216may be configured to normalize and combine the feature values extracted from the voice samples of the voice data404and use the normalized and combined feature values as input to the first predictor model for obtaining the predicted personality and mood attributes420. In another example, the prediction module216may be configured to predict the personality and mood attributes420by using the first predictor model in two stages. The first stage may include predicting the mood attributes by using the normalized and combined feature values extracted from the voice samples of the voice data404as input to the first predictor model and the second stage may include predicting the personality attributes by using the predicted mood attributes as input.

The prediction module216may be further configured to use the combined feature values extracted from the voice data404, the derived personality and mood attributes416, and the predicted personality and mood attributes420as input to the third predictor model for predicting business outcomes (as represented by block422). The application server106may be configured to store the predicted business outcomes in the database server108. In an embodiment, the business outcomes may include, but are not limited to, job suggestions, purchase suggestions, targeted advertisements, compatibility match, and the like. In one embodiment, due to chronological processing of the voice data404based on the date and time markers, the application server106may be capable of predicting the business outcomes as per behavioral changes exhibited by the target user110over a period of time. The application server106may be configured to communicate the predicted business outcomes to the target user110. Thus, based on the predicted business outcomes, intelligent and informed decisions (such as accepting or denying a job offer, and the like) may be made by the target user110. In another embodiment, the business outcomes may include purchase trend of various commodities, affinity of the target user110for one or more activities, and the like. The application server106may communicate the predicted business outcomes to an organization, such as a social media provider, an e-commerce provider, or the like. Thus, based on the predicted business outcomes, intelligent and informed decisions (such as providing relevant job suggestions to the target user110on the social media profile of the target user110or customizing the social media profile of the target user110based on the interests of the target user110) may be made by the social media provider. Likewise, based on the predicted business outcomes, an e-commerce platform may make intelligent decisions, such as updating their inventory based on the purchase trend. The e-commerce platform may divide customers into different groups based on their common purchase interests (i.e., business outcomes). Moreover, deep personalization of a customer (i.e., analyzing voice samples of the customer) to understand more complex patterns of customer behavior (i.e., business outcomes) and preferences may help the e-commerce platform to grow.

In an exemplary scenario, the application server106may be implemented by a customer helpdesk of an organization. Thus, when the target user110places a call to the customer helpdesk for a query, conversation between the target user110and a customer helpdesk executive may be monitored and analyzed for predicting business outcomes for the target user110. The customer helpdesk executive may use the predicted business outcomes and the personality and mood attributes of the target user110to provide relevant insights to the target user110, thereby achieving high customer satisfaction.

In another exemplary scenario, the target user110may be a person to be hired by an organization. In this scenario, voice samples of the target user110may be obtained and analyzed by using the predictor models322to get accurate prediction of the personality and mood of the target user110, without asking any question to the target user110. In another exemplary scenario, the target user110may be an employee of the organization, whose employment affinity (i.e., a business outcome) is of interest to the organization, such as for employee retention and engagement. In another exemplary scenario, the disclosure may be implemented to achieve emotional intelligence within robots, i.e., improving working efficiency with which robots learn emotional attributes.

It will be understood by a person of ordinary skill in the art that the abovementioned business outcomes are listed for exemplary purpose and should not be construed to limit the scope of the disclosure. In other embodiments, the predictor models322may be utilized to predict business outcomes that are different from the business outcomes mentioned above.

In one embodiment, the application server106may be configured to render a user interface (UI) on the target-user device112for presenting the predicted business outcomes to the target user110. In one example, the application server106may render the UI through the software application that runs on the target-user device112. A feedback (for example, a common score or an individual score for each business outcome) may be provided by the target user110to indicate a relevance of the predicted business outcomes. For example, when the business outcomes have high relevance to the target user110, a positive feedback may be provided by the target user110. In another example, when the business outcomes have low relevance to the target user110, a negative feedback may be provided by the target user110. The model generator212may use the feedback provided by the target user110to update the predictor models322for improving the prediction accuracy. The model generator212may be configured to adjust the weight of links between the voice features and the personality and mood attributes based on the feedback.

FIG.5is a block diagram that illustrates an exemplary scenario500for predicting business outcomes, in accordance with another embodiment of the disclosure. The exemplary scenario500involves the target user110who may provide target data502, the application server106, and the database server108that may store the predictor models322. The exemplary scenario500illustrates a scenario where the target data502includes the voice data404of the target user110and the historical data406of the target user110. The application server106may be configured to retrieve the target data502in a manner similar to the retrieval of the target data402as explained inFIG.4.

After retrieving the target data502, the application server106may be configured to process the target data502. Processing of the target data502may involve filtering and normalizing the historical data406(as represented by block504). Processing of the target data502may further involve analyzing the voice data404. The feature extraction module218may be configured to analyze the voice data404by performing audio processing (as represented by block506) followed by feature value extraction (as represented by block508). During feature value extraction, the feature extraction module218may be configured to extract the feature values corresponding to the voice features (as represented by block316). The voice features may include, but are not limited to, paralinguistic features, linguistic features, and sound features. The feature extraction module218may be configured to store the extracted feature values corresponding to the voice data404in the database server108. Since the target data502does not include answers to psychometric questions, the first processor202does not derive any personality and mood attributes of the target user110.

After the target data502is processed, the prediction module216may be configured to query the database server108to retrieve the predictor models322. The prediction module216may be configured to use the feature values extracted from the voice data404and the analyzed historical data as input to the first and second predictor models, respectively, for psychometric prediction (as represented by block510). The psychometric prediction may yield predicted personality and mood attributes512of the target user110as output.

The prediction module216may be further configured to use the feature values extracted from the voice data404and the predicted personality and mood attributes512as input to the third predictor model for predicting the business outcomes (as represented by block514). The predicted business outcomes may be relevant to the target user110and/or an organization as described inFIG.4. The application server106may be configured to store the predicted business outcomes in the database server108.

FIG.6is a block diagram that illustrates an exemplary scenario600for predicting business outcomes, in accordance with another embodiment of the disclosure. The exemplary scenario600involves the target user110who provides target data602, the application server106, and the database server108that stores the predictor models322. The exemplary scenario600illustrates a scenario where the target data602includes the voice data404of the target user110and the answers408provided by the target user110to the psychometric questions. The application server106may be configured to retrieve the target data602in a manner similar to the retrieval of the target data402as explained inFIG.4.

After retrieving the target data602, the application server106may be configured to process the target data602. Processing of the target data602may involves analyzing the voice data404. The feature extraction module218may be configured to perform audio processing (as represented by block604) followed by feature value extraction (as represented by block606). During feature value extraction, the feature extraction module218may be configured to extract the feature values corresponding to the voice features (as represented by block316). The voice features may include, but are not limited to, paralinguistic features, linguistic features, and sound features. The feature extraction module218may be configured to store the extracted feature values corresponding to the voice data404in the database server108. Processing of the target data602may further involve analyzing the answers408by the first processor202for deriving personality and mood attributes608(hereinafter, referred to as “derived personality and mood attributes608”) of the target user110.

After the target data602is processed, the prediction module216may be configured to query the database server108to retrieve the predictor models322. The prediction module216may be configured to use the feature values extracted from the voice data404as input to the first predictor model for psychometric prediction (as represented by block610). The psychometric prediction may yield predicted personality and mood attributes612as output.

The prediction module216may be further configured to use the feature values extracted from the voice data404, the derived personality and mood attributes608, and the predicted personality and mood attributes612as input to the third predictor model for predicting the business outcomes (as represented by block614). The predicted business outcomes may be relevant to the target user110and/or an organization as described inFIG.4. The application server106may be configured to store the predicted business outcomes in the database server108.

FIG.7is a block diagram that illustrates an exemplary scenario700for predicting business outcomes, in accordance with an embodiment of the disclosure. The exemplary scenario700involves the target user110who may provide target data702, the application server106, and the database server108that stores the predictor models322. The exemplary scenario700illustrates a scenario where the target data702includes only the voice data404of the target user110. The application server106may be configured to retrieve the target data702in a manner similar to the retrieval of the target data402as explained inFIG.4.

After retrieving the target data702, the application server106may be configured to process the target data702. Processing of the target data702may involve analyzing the voice data404. The feature extraction module218may be configured to perform audio processing (as represented by block704) followed by feature value extraction (as represented by block706). During feature value extraction, the feature extraction module218may be configured to extract the feature values corresponding to the voice features (as represented by block316). The voice features may include, but are not limited to, paralinguistic features, linguistic features, and sound features (as represented by block316). The feature extraction module218may be configured to store the extracted feature values corresponding to the voice data404in the database server108. Since the target data702does not include answers to psychometric questions, the first processor202does not derive any personality and mood attributes of the target user110.

After the target data702is processed, the prediction module216may be configured to query the database server108to retrieve the predictor models322. The prediction module216may be configured to use the feature values extracted from the voice data404as input to the first predictor model for psychometric prediction (as represented by block708). The psychometric prediction may yield predicted personality and mood attributes710as output. The prediction module216may be further configured to use the feature values extracted from the voice data404and the predicted personality and mood attributes710as input to the third predictor model for predicting the business outcomes (as represented by block712). The predicted business outcomes may be relevant to the target user110and/or an organization as described inFIG.4. The application server106may be configured to store the predicted business outcomes in the database server108.

FIG.8Ais block diagram800A that illustrates a UI802rendered on the test-user device104aby the application server106for receiving the test data302of the test user102a, in accordance with an embodiment of the disclosure. The UI802may include a first input box804, where a name (for example, “John Doe”) is required to be entered by the test user102a. The UI802may further include first through third options806-810pertaining to prediction inputs (i.e., the test data302) required from the test user102a. The first through third options806-810may be selectable by the test user102a. If the first option806is selected by the test user102a, the application server106may be configured to retrieve the voice data304of the test user102a. If the second option808is selected by the test user102a, the application server106may be configured to retrieve the answers provided by the test user102ato the psychometric questions. If the third option810is selected by the test user102a, the application server106may be configured to retrieve the historical data of the test user102a. The retrieval of the voice data304of the test user102a, the answers provided by the test user102ato the psychometric questions, and the historical data306has been described inFIG.3. The UI802may further include a submit button812, which may be selected by the test user102ato submit the test data302to the application server106.

It will be apparent to a person of ordinary skill in the art that the UI802is shown for illustrative purposes and should not be construed to limit the scope of the disclosure. In another embodiment, the application server106may render the UI802on the target-user device112for retrieving the target data (such as the target data402,502,602, or702) of the target user110. The application server106may be configured to retrieve the target data (as described inFIGS.4-7) based on the selection performed by the target user110. For example, if the second option808is not selected and the third option810is selected by the target user110, the application server106may retrieve only the voice data404and the historical data of the target user110.

FIG.8Bis a block diagram800B that illustrates a UI814rendered on the target-user device112by the application server106for presenting predicted business outcomes, in accordance with an embodiment of the disclosure. The UI814may include a first field816, where the name of the target user110is displayed (for example, “John Doe”). The UI814may further include a first table818that may display personality and mood attributes of the target user110and corresponding confidence scores. For example, the personality and mood attributes of the target user110are neuroticism, openness, conscientiousness, extraversion, amused, cheerful, realistic, investigative, social, enterprising, and conventional having the confidence scores as 0.1, 0.59, 0.05, 0.01, 0.037, 0.223, 0.2, 0.1, 0.14, 0.3, 0.05, and 0.09, respectively. The UI814may further include a second table820that may display various job suggestions (such as Accountant, IT, and Business analyst) for the target user110and corresponding confidence scores. Likewise, the UI814may include additional tables (not shown) that display relevant business outcomes, such as product purchase suggestions, travel suggestions, or the like, to the target user110. The UI814further includes a feedback button822. The target user110may select the feedback button822for providing a feedback, such as a common score or an individual score per business outcome, to the application server106indicating the relevance of the predicted business outcomes displayed in the second table820.

FIGS.9A-9E, collectively represent a flow chart900that illustrates a method for predicting business outcomes, in accordance with an embodiment of the disclosure. With reference toFIGS.9A-9C, at902, the historical data306of the test users102, the voice samples (i.e., the voice data304) associated with the test users102, and the answers provided by the test users102to the psychometric questions (i.e., the test data302as described byFIG.3) are retrieved. The application server106may retrieve the historical data306, the voice samples (i.e., the voice data304) associated with the test users102, and the answers308. At904, the historical data306of the test users102is filtered and normalized (as described inFIG.3). At906, the answers308provided by the test users102are analyzed for deriving psychometric features of the test users102(as described inFIG.3). At908, the voice samples (i.e., the voice data304) of each test user102are analyzed for extracting feature values for the voice features (as represented by block316ofFIG.3). The application server106may be configured to analyze the voice samples by selecting one item at a time from the voice data304.

Referring now toFIG.9D, at908a, the voice sample is recognized in the voice data304. At908b, it is determined whether the voice sample is found in the database server108. If at908b, it is determined that the selected voice sample is not present in the database server108, control passes to908c. At908c, the feature values are extracted from the voice sample for the voice features. At908d, the extracted feature values are written in the database server108. At908e, it is determined whether the voice data304includes more items. If at908e, it is determined that the voice data304includes one or more items that are not yet processed, control passes to908a.

If at908b, it is determined that the voice sample is found in the database server108, control passes to908f. At908f, the stored feature values from the external database server108are read. Control passes to908e. If at908e, it is determined that all the items of the voice data304are analyzed, control passes to908g. At908g, the feature values corresponding to all items (voice sample) in the voice data304are combined. At908h, the combined feature values are returned. Control passes to910.

Referring back toFIGS.9A-9C, at910, the predictor models322for prediction of business outcomes are generated (as described inFIG.3). The application server106generates the predictor models322based on the test data302of the test users102.

Referring now toFIG.9E, at910a, each voice feature is mapped with each psychometric feature of a test user (e.g., any test user102) to generate link therebetween. The application server106may be configured to map each voice feature with a confidence score of each psychometric feature derived for the test user (e.g., any test user102). At910b, a weight is assigned to the link between each voice feature and each psychometric feature for generating the predictor models322. The application server106may be configured to assign the weight based on the extracted feature values. At910c, it is determined whether the voice features are mapped for all the test users102. If at910c, it is determined that the voice features are not mapped for all the test users102, control passes to910c. The application server106may be configured to perform910a-910cuntil the voice features are mapped for all the test users102. If at910c, it is determined that voice features are mapped for all the test users102, control passes to910d. At910d, the predictor models322are returned to the application server106.

Referring back toFIGS.9A-9C, at912, the predictor models322are stored in the database server108. At914, the target data (such as the target data402,502,602, or702) is received from the target user110. At916, it is determined whether the target data includes voice samples of the target user110. If at916, it is determined that the target data does not include the voice samples (i.e., the voice data404) of the target user110, control passes to914. The application server106performs914again until the voice samples (i.e., the voice data404) of the target user110are received. If at916, it is determined that the target data includes the voice samples (i.e., the voice data404) of the target user110, control passes to918. At918, the voice samples (i.e., the voice data404) of the target user110are analyzed to extract feature values for the voice features (as represented by block316ofFIG.3). The process of extracting feature values from the voice data404is same as that performed for the voice data304of the test users102comprising908a-908eofFIG.9D. At920, the psychometric features are predicted for the target user110by using extracted feature values as input to the first predictor model.

At922, it is determined whether the target data includes the answers408to the psychometric questions. If at922, it is determined that the target data includes the answers408, control passes to924. At924, the answers408are analyzed to derive psychometric features of the target user110(as described inFIG.4). Control passes to926. If at922, it is determined that the target data does not include the answers408, control passes to926. At926, it is determined whether the target data includes the historical data406. If at926, it is determined that the target data includes the historical data406, control passes to928. At928, the historical data406of the target user110is filtered and normalized. At930, the psychometric features are predicted for the target user110by using the analyzed historical data406as input to the second predictor model. Control passes to932. If at926, it is determined that the target data does not include the historical data406, control passes to932. At932, the derived and predicted psychometric features are combined. At934, the business outcomes for the target user110are predicted by using the combined psychometric features and extracted feature values as input to the third predictor model.

FIG.10is a flow chart1000that illustrates a method for updating the predictor models322, in accordance with an embodiment of the disclosure. At1002, the UI814is rendered on the target-user device112. The application server106may be configured to render the UI814to present the predicted business outcomes and predicted psychometric features (e.g., the personality and mood attributes) to the target user110. At1004, a feedback is received from the target user110. The application server106may be configured to receive the feedback indicating relevancy of the predicted business outcomes and the predicted psychometric features from the target user110. At1006, the weight assigned of the link between each voice feature and each psychometric feature is adjusted based on the feedback. The application server106may be configured to increase or decrease the weight based on a positive or negative feedback from the target user110. At1008, each voice feature is re-mapped with each psychometric feature of the test user102abased on the adjusted weight of the link between each voice feature and each psychometric feature.

FIG.11is a block diagram that illustrates system architecture of a computer system1100, in accordance with an embodiment of the disclosure. An embodiment of disclosure, or portions thereof, may be implemented as computer readable code on the computer system1100. In one example, the test-user and target-user devices104and112and the database server108ofFIG.1may be implemented in the computer system1100using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the method ofFIGS.9A-9E and10.

The computer system1100may include a processor1102that may be a special-purpose or a general-purpose processing device. The processor1102may be a single processor, multiple processors, or combinations thereof. The processor1102may have one or more processor cores. In one example, the processor1102is an octa-core processor. The processor1102may be further connected to a communication infrastructure1104, such as a bus, message queue, multi-core message-passing scheme, and the like. The computer system1100may further include a main memory1106and a secondary memory1108. Examples of the main memory1106may include RAM, ROM, and the like. The secondary memory1108may include a hard disk drive or a removable storage drive, such as a floppy disk drive, a magnetic tape drive, a compact disk, an optical disk drive, a flash memory, and the like. The removable storage drive may further read from and/or write to a removable storage device in a manner known in the art. In one example, if the removable storage drive is a compact disk drive, the removable storage device may be a compact disk. In an embodiment, the removable storage unit may be a non-transitory computer readable recording media.

The computer system1100may further include an input/output (I/O) interface1110and a communication interface1112. The I/O interface1110may include various input and output devices that are configured to communicate with the processor1102. Examples of the input devices may include a keyboard, a mouse, a joystick, a touchscreen, a microphone, and the like. Examples of the output devices may include a display screen, a speaker, headphones, and the like. The communication interface1112may be configured to allow data to be transferred between the computer system1100and various devices that are communicatively coupled to the computer system1100. Examples of the communication interface1112may include a modem, a network interface, i.e., an Ethernet card, a communication port, and the like. Data transferred via the communication interface1112may correspond to signals, such as electronic, electromagnetic, optical, or other signals as will be apparent to a person skilled in the art. The signals may travel via a communication channel (not shown) which may be configured to transmit the signals to devices that are communicatively coupled to the computer system1100. Examples of the communication channel may include, but are not limited to, cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, and the like. The main memory1106and the secondary memory1108may refer to non-transitory computer readable mediums that may provide data that enables the computer system1100to implement the method illustrated inFIGS.9A-9E.

Various embodiments of the present disclosure include the application server106for predicting business outcomes for the target user110. The application server106may retrieve the historical data306of the test users102, voice samples304of the test users102, and answers308provided by the test users102to a set of psychometric questions. The first processor202may analyze the answers308and the audio processor204may analyze the voice samples304. The answers308may be analyzed for deriving one or more psychometric features318of the test users102. The voice samples304may be analyzed for extracting a first set of feature values corresponding to a set of voice features from the voice samples304. The model generator212may generate the predictor models322based on the historical data306of the test users102, the first set of feature values, and the one or more psychometric features318of the test users102. The prediction module216may predict one or more business outcomes for the target user110based on the one or more predictor models322and voice samples404associated with the target user110.

In one embodiment, a non-transitory computer readable medium having stored thereon, computer executable instructions, which when executed by a computer, cause the computer to execute operations for predicting business outcomes for the target user110(as described inFIGS.9A-9E). The operations include retrieving the historical data306of at least one test user (for example, the test user102a), a first set of voice samples (i.e., the voice data304) associated with the test user102a, and a first set of answers308provided by the test user102ato a set of psychometric questions. The first set of answers308is analyzed by the first processor202and the first set of voice samples304is analyzed by the audio processor204. The first set of answers308is analyzed for deriving one or more psychometric features318of the test user102a. The first set of voice samples304is analyzed for extracting a first set of feature values corresponding to a set of voice features from the first set of voice samples304. The model generator212may be configured to generate one or more predictor models322based on the historical data306of the test user102a, the first set of feature values, and the one or more psychometric features318of the test user102a. The prediction module216may be configured to predict one or more business outcomes for the target user110based on the one or more predictor models322and a second set of voice samples (i.e., the voice data404) of the target user110.

Various embodiments of the disclosure include the application server106which may enable the prediction of business outcomes by analyzing the voice data of the target user110, The voice samples of the target user110accurately reflect the subconscious mind of the target user110at any given time instance. The predictor models322generated by the application server106are trained based on the test data302of multiple test users102. The test data302includes the voice data304, the historical data306, and the answers308provided by the test users102, which reflect the subconscious mind of the test users102. Due to chronological processing of the voice data404based on the date and time markers, behavioral changes exhibited by the target user110over a period of time may be accurately monitored. As the subconscious mind is responsible for majority of decision making and directly related to the psychometric orientation, the prediction accuracy of the predictor models322is very high. Thus, the disclosure yields more accurate results in comparison to the related techniques. The ability of the predictor models322to accurately predict psychometric orientation and business outcomes may provide competitive edge to a service company, utilizing the predictor models322, over its competitors. For example, the service company may utilize the technological improvements provided by the predictor models322to provide targeted services to the customers. Similarly, the technological improvements provided by the predictor models322allows an organization to efficiently keep track of behavioral changes and mental health of corresponding employees by periodically analyzing employees' voice samples, rather than hire a psychiatrist or conduct time consuming psychometric tests. The technological improvements provided by the predictor models322may be utilized to concurrently predict business outcomes for multiple target users, thereby reducing the time spent by organizations on data analytics for various operations, such as hiring, or the like. The disclosure has applicability in all such areas that are customer and employee centric. For example, e-commerce industries, business ventures, customer helpdesks, travel industries, financial industries, insurance agencies, or the like.

A person of ordinary skill in the art will appreciate that embodiments and exemplary scenarios of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. The operations may be further described as a sequential process, however some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter.