System and method for selecting and managing interview participants

Abstract

A method and system for managing an interview for an interview question answerer. The system comprising a database containing a plurality of interviewers with their features, an interface operable to receive an entry from an interviewee. The system further comprises a processor is configured match the interviewer and interviewee by corresponding features. The method comprise receiving, through the interface at least one entry from a question answerer, utilizing the processor running a natural language processing model, identifying at least one question answerer feature of the question answerer from the at least one entry and matching using the processor in real time, at least one potential question asker from the plurality of question askers with the question answerer by matching at least one question asker feature corresponding to one of the at least one question answerer feature.

Description

[0001] SYSTEM AND METHOD FOR SELECTING AND MANAGING INTERVIEW PARTICIPANTS

[0002] BACKGROUND

[0003] 1. Technical Field

[0004] This disclosure relates generally to interview management and in particular to a system and method for managing and coordinating interviewers and interviewees.

[0005] 2. Description of Related Art

[0006] Scheduling of interviews is a common occurrence for many different purposes, including, without limitation, job recruitment, market research and surveys. Such scheduling and matching of interviewers and interviewees can be a complicated process of assessing characteristics of the participants, selecting a best fit, and then providing and means of coordinating schedules and providing and selecting available times.

[0007] It is known that some survey or research participants may have a preference or greater comfort level with an interviewer or researcher having particular characteristics or backgrounds. In particular, in areas where the participants, the subject matter, or the questions to be asked are of a sensitive or personal nature, the selection of an interviewer or researcher having particular characteristics may be critical to ensuring the desired information is disclosed or obtained during that interview. However, the task of selecting a best interviewer for a particular participant has traditionally been one performed by an individual, relying on their experience and judgement assigning an interviewer based on availability of that interviewer with little other considered factors. Such reliance on human selection is both time consuming and subject to error. In particular, a person may overlook some factors while they are obtaining the requisite level of experience or may misinterpret some words or expressions used by one or more participants.SUMMARY OF THE DISCLOSURE

[0008] According to a first embodiment of the present disclosure is a method for managing an interview for an interview question answerer comprising providing a database containing a plurality of question askers, wherein each question asker has at least one question asker feature associated therewith, receiving, through an interface in communication with a processor, at least one entry from a question answerer, utilizing the processor identifying at least one question answerer feature of the question answerer from the at least one entry and matching using the processor in real time, at least one potential question asker from the plurality of question askers with the question answerer by matching at least one question asker feature corresponding to one of the at least one question answerer feature.

[0009] The processor may run a natural language processing model configured to perform the steps thereof. The at least one entry may comprise self descriptive questions or input fields. The at least one entry may comprise answers to discrete questions. The at least one question asker may comprise a plurality of potential question askers.

[0010] The method may further comprise presenting the plurality of potential question askers to the question answerer and receiving a selection of a selected question asker from the question answerer.

[0011] The method may further comprise scheduling a time between the question answerer and selected question asker with a scheduling module by looking up in the selected question esker’s calendar, at least one available time, providing the at least one available time to the question answerer and receiving the selected time from the question answerer. The method may further comprise forwarding a calendar invite to each of the question answerer and selected question asker.

[0012] The method may further comprise receiving an image of the question answerer and with the processor, identifying the at least one feature of the question answerer. Receiving the image comprises scanning an image of the questionanswerer. The scanned image may be utilized by the processor to verify the identity of the question answerer against a database of known question answerers.

[0013] According to a further embodiment of the present disclosure is a system for managing an interview for an interview question answerer comprising a database containing a plurality of question askers each having at least one feature associated therewith, an interface operable to receive at least one entry from a selected question specific question answerer and a processor in communication with the interface. The processor is configured to identify in real time, at least one question answerer feature of the question answerer from the at least one entry and match, in real time at least one potential question askerfrom the plurality of question askers with the question answerer by matching at least one question asker feature corresponding to one of the at least one question answerer feature.

[0014] The processor may run a natural language processing model configured to perform the steps thereof. The processor may furthermore be configured to schedule a time between the question answerer and selected question asker with a scheduling module by looking up in the selected question answerer’s calendar, at least one available time, providing the at least one available time to the question answerer and receiving the selected time from the question answerer.

[0015] The system may further comprise include a scanner operable to scan a question answerer image. The scanner may be configured to scan the image of the question answerer in real time. The processor is configured to verify the identity of the question answerer.

[0016] Other aspects and features of the present disclosure will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures.

[0017] BRIEF DESCRIPTION OF THE DRAWINGSThe accompanying drawings constitute part of the disclosure. Each drawing illustrates exemplary aspects wherein similar characters of reference denote corresponding parts in each view,

[0018] Figure 1 is an illustration of a system for coordinating information to and from an individual and at least one computer system.

[0019] Figure 2 is a schematic of the system of Figure 1.

[0020] Figure 3 is a flow chart of an exemplary method for use in the system of Figure 1.

[0021] Figure 4 is a representation of a screen of a potential participant showing a field operable to receive an input from the potential participant. Figure 5 is an illustration of a categorizing step in the method of Figure 3 sorting potential participants into one sanitized classes.

[0022] Figure 6 is an illustration of a categorizing step in the method of Figure 3 selecting potential participants from each of the sanitized classes. Figure 7 is an illustration of an interviewer selection step in the method of Figure 3 selecting potential question askers having a characteristic conforming to the question answerer.

[0023] Figure 8 is a representation of a screen of a participant showing multiple potential moderators for their selection.

[0024] Figure 9 is a representation of a screen of a participant showing a scheduling module for scheduling a

[0025] Figure 10 is a representation of a screen of a question asker receiving a summary from the question asker about the interview.

[0026] Figure 11 is a representation of a screen of a transcript of the interview

[0027] DETAILED DESCRIPTION

[0028] Aspects of the present disclosure are now described with reference to exemplary apparatuses, methods and systems. Referring to Figure 1, an exemplary system for coordinating an interview between participants and a selected moderator with one of a plurality of computer systems according to a first embodiment is shown generally at 10. The system 10 includes a processor 12 and at least one database 14 which may optionally be contained within common computer system with the processor 12. It will be appreciated that thedatabase 14 may comprise a plurality of databases each of which contains different data fields, storage systems and the like. The system 10 is also in communication with a plurality of users through their independent user devices 8 through a network interface device 16.

[0029] The system 10 is adapted to analyse the identity or one other feature of a question answerer and identify at least one question askerfrom the database that may be suitable for the question answerer. The question answerer may then select their preferred question asker wherein the system 10 then coordinates an question answering time between the two through a scheduling module. The system 10 may optionally include one or more additional participant analysing features, including, without limitation, identity traits of the participant both before and after scheduling and selecting as will be further set out below.

[0030] As utilized herein, the term user may mean any of a person asking questions, also referred to as a moderator, interviewer or question asker or a person answering such questions, also referred to as a participant, interviewee or question answerer.

[0031] Turning now to Figures 2, the system 10 comprises the processor 12, and database 14 and optional memory 18. The memory 18 may stores machine instructions that, when executed by the processor 12, cause the processor 12 to perform one or more of the operations and methods described herein. The memory 18 of any known type including a cache memory unit for temporary local storage of instructions, data, or computer addresses. The system 10 may further include the database 14 either internally or externally and may be of any conventional type operable to store the information and fields required by the processor information. As outlined above, the processor 12 is adapted to interface with one or more user devices 8 through a network interface 16 as are known. In particular, the network interface may be of any known type including wired, wireless network systems, including without limitation, cellular, internet protocol, Bluetooth or the like.More generally, in this specification, the term "processor" is intended to broadly encompass any type of device or combination of devices capable of performing the functions described herein, including (without limitation) other types of microprocessors, microcontrollers, other integrated circuits, other types of circuits or combinations of circuits, logic gates or gate arrays, or programmable devices of any sort, for example, either alone or in combination with other such devices located at the same location or remotely from each other. Additional types of processor(s) will be apparent to those ordinarily skilled in the art upon review of this specification, and substitution of any such other types of processor(s) is considered not to depart from the scope of the present invention as defined herein. In various embodiments, the processor 12 can be implemented as a single-chip, multiple chips and / or other electrical components including one or more integrated circuits and printed circuit boards.

[0032] Computer code comprising instructions for the processor(s) to carry out the various embodiments, aspects, features, etc. of the present disclosure may reside in the memory 18, within the processor^ or be obtained from outside the system. The code may be broken into separate routines, products, etc. to carry forth specific steps disclosed herein. In various embodiments, the processor 12 can be implemented as a single-chip, multiple chips and / or other electrical components including one or more integrated circuits and printed circuit boards. The processor 12 together with a suitable operating system may operate to execute instructions in the form of computer code and produce and use data. By way of example and not by way of limitation, the operating system may be Windows-based, Macbased, or Unix or Linux-based, among other suitable operating systems. Operating systems are generally well known and will not be described in further detail here. In particular, the processor 12 may be include functions of voice and text recognition and natural language processing. Optionally, the processor 12 may include adaptive algorithms including by way of non-limiting example, machine learning algorithms programmed to communicate with an ability to interpret answers from the users and provide further analysis and interpretationthereof. Such algorithms may include regression analysis, natural language processing, or any other machine learning methods as are available.

[0033] Memory 18 may include various tangible, non-transitory computer-readable media including Read-Only Memory (ROM) and / or Random-Access Memory (RAM). As is well known in the art, ROM acts to transfer data and instructions unidirectionally to the processor 12, and RAM is used typically to transfer data and instructions in a bi-directional manner. In the various embodiments disclosed herein, RAM includes computer program instructions that when executed by the processor 12 cause the processor 12 to execute the program instructions described in greater detail below. More generally, the term “memory” as used herein encompasses one or more storage mediums and generally provides a place to store computer code (e.g., software and / or firmware) and data. It may comprise, for example, electronic, optical, magnetic, or any other storage or transmission device capable of providing the processor 12 with program instructions. Memory 18 may further include a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ASIC, FPGA, EEPROM, EPROM, flash memory, optical media, or any other suitable memory from which processor 12 can read instructions in computer programming languages.

[0034] It will be appreciated that the user device 8 may be any commonly known user device, such as, by way of non-limiting example, a tablet, laptop computer, smartphone, PDA, ultra mobile PC (UMPC), desktop computer, server, etc. It will be understood that the architecture herein is provided for example purposes only and does not limit the scope of the various implementations of the communication systems and methods. As will be further described below, the user device 8 is adapted access the processor through the network either a through a program installed thereon or operated through a web application as are commonly known. The user device 16 may communicate with the processor 12 via a network as are commonly known including local area networks (LAN), wide area networks (WAN), internet and cellular networks and radio transmissions. The system 10 includes a transmitter / receiver 15 for transmitting a message to one or more of the users. In particular, the transmitter / receiver may comprise anyknown transmitter / receiver including for transmission via text, email, short message service (SMS), multimedia messaging service (MMS) or the like. In particular the user device 16 may include audio and / or video capabilities operable to transmit such audio and / or video signals to the processor 12 for recording and analysis as further set out below.

[0035] T urning now to Figure 3, one exemplary embodiment of the present system and method are illustrated for facilitating and conducting an interview. As illustrated in Figure 3, the system 10 receives entries of potential participants Such potential participants may be obtained from a database 14, or may be uploaded through a web portal, or any other known means, either in batches or individually. The processor 12 may then optionally conduct an initial screening of the potential participants to determine one or more desired characteristics of the question answerers or to identify the individual being interviewed. In particular the processor 12 may utilize video captured by the user device 8 to identify the potential participant or to identify one or more feature of the participant. The processor 12 may conduct such analysis using any feature of the potential participant’s appearance, language or the like. By way of nonlimiting example, the processor 12 may analyze the language, word usage, age, gender or demographic including ethnicity, or the like. In particular, the processor may utilize such identified features to ensure an even distribution of demographics or to target the research being conducted to particular demographics of interest. Additionally the processor 12 may analyze the language used by the participant to features of such participant using analysis of their answers to open ended questions requiring a text based answer from the user. Examples of such analysis may include analyzing and interpreting one or more of the word choice, frequency, themes or sentiment expressed by the potential participant. The processor may furthermore conduct further processing on such language used to remove punctuation, correct spelling or grammar errors or extrapolate themes or sentiments from such answers. The processor 12 may also be configured to conduct a regression model analysis from the words, themes or expressions used to determine a sentiment to a particular issue among potential participants with one or more features. Theartificial intelligence model may be adapted to improve ability to predict age range, ethnicity, gender or any other desired demographic information for use in the present model. Optionally, a potential participant may be able to select between audio, video or text base answer submissions.

[0036] Turning now to Figure 4, one embodiment of the initial screening process 104 is illustrated. As illustrated in Figure 4, the initial participant or question answerer input may comprise an open ended questions with a space 40 for the individual to enter a string of text. It will be appreciated that although the illustrated question in Figure 4 is a general request to describe themselves, this question and input field may be any desired question and intended to have a potential participant input a text string within this field. The processor 12 is then adapted to utilized natural language processing and word recognition and one or more machine learning algorithms to recognized one or more key words or phrases 42, 44 and 46 by way of non-limiting example. In particular the processor and algorithm in the present system are adapted to recognize identifying words, phrases, tones or sentiment to identify and categorize the potential participant into one or more classes as will be further described below. Furthermore, the system 10 may be configured to receive the open ended answer shown in Figure 4, through audio or video input. Similar machine learning algorithms may be applied by the processor to such input to categorize the potential participant.

[0037] Turning now to Figure 5, the processor 12 is configured to utilize one or more learning algorithms to identify key words 50 expressed by the potential participant to identify one or more key or trigger words, expressions, phrases, tones, or sentiments, either express or implied. The processor may be configured to classify the potential participants into one of a plurality of predefined sanitized classes of a particular feature. As illustrated in Figures 5, the processor 12 is configured, by way of non-limiting example, to categorize whether and what type of disability each participant has. The processor uses the identified key words to select which class 52, 54 or 56 most appropriately conforms to the key words utilized by the potential participant in their openended answer 40 above. As such it will be appreciated that the transforms the user’s open ended answer or self identification or description 40 into standardized or sanitized classes 52, 54 or 56 for each of the characteristics required. It will be appreciated that the processor 12 may apply such identification and categorization to the supplied inputs from the potential participant for a plurality of classes so as to determine one or more classifications of that feature for the individual.

[0038] Furthermore, the processor 10 may also be utilized to verify the identity of the potential participant in step 106 as being within the indicated age, gender or other demographic group using visual recognition. Such verification may be operable to confirm the age, gender or other feature of the potential participant. Furthermore, it will be appreciated that some verification results may be unclear or negative and such results may then be provided to an operator for further verification in step 108 before being included in a list of study participants. The processor 12 may also be configured to be able to detect the use of Al or computer programs for generating such responses.

[0039] After potential participants have been identified, the processor may be utilized to determine the features of the distribution of participants. In particular, the demographics can be customized to get those desired according to parameters imputed from a user including, without limitation, diverse or specific. As shown in Figure 6, the processor may be configured to select a particular category, such as presence of disability in Figure 6 and identify which of the potential participants has been pre-located within each class 52, 54 or 56, as described above with a grouping of participants 62, 64 and 66 in each class. The processor may then select the desired number of participants 62a, 64a and 66a, in the example where one participant from each class is desired for participation in the study. It will be appreciated that the processor 12 may be configured based on input from a user or study supervisor to select any combination of participants, including, by way of non-limiting example, an even distribution from each class, a proportionate representation from each class or a single class.Once a potential participant has been selected to be a question answerer, the processor may identify one or more features of the question answerer, the processor 12 may then order or select a list of potential question askers form the database 14 in step 112. In particular, such potential question askers may be selected so as to have one or more common features with the question answerer, including without limitation, age, gender, ethnic background, geographic origin, language similarities or common interests. As illustrated in Figure 7, each participant 70 is represented in the database as having a plurality of characteristics, as located in the sanitized classes as set out above. The processor selects the desired characteristic 72 and then selects the potential question askers 74 having that the corresponding characteristic so as to provide a match for that characteristic between the question asker and answerer. By way of non-limiting example, the processor may select a list of female question askers for a female question answerer. In some embodiments, the processor may be configured to select the potential interviewers based on other criteria as defined by an administrator or based on any other criteria, including, without limitation randomly. In other embodiments, the processor 12 may determine, based upon the results and learning of the algorithm, that a particular characteristic of the question answerer is most suited by a question asker having a specific but different characteristic. By way of non-limiting example, the processor may, through analyzing feedback, as set out below, determined that young male question answers respond best to female question askers for a particular study type. The processor 12 will therefore select only female question askers for use in such a study. Thus the type of question askers is configured by the processor according to what has been determined to be most successful in past interviews.

[0040] The potential question askers 80 and 82 may be presented to the question answerer as illustrated in Figure 8 along with details of that question asker. The question answerer may then select their preferred question answerer whereupon a schedule of availability 84 is presented to the question answerer as illustrated in Figure 5. Once the question answerer selects their preferredtime 86, the processor may optionally provide one or more means of organizing the interview time by sending any manner of coordinating, including without limitation, calendar invites, video conference meeting links or the like. It will be appreciated that in many environments and situations, the time available to provide the present matching of interviewer and interviewees is limited, such as in the case of market and product research or where participants are volunteers. It will therefore be further appreciated that timely and accurate matching of each side of the interview is important to maintain the participating of such participants. In such situations, the ability of a processor running a suitable model to perform such matching in real time ensures that such participants are not lost due to delay while manual matching and selection of interviewers and interviewees is performed for alter presentation. The ability to provide options to a question answerer also advantageously increases participant comfort and therefore the end results of such an interview.

[0041] The system 10 may also optionally permit the interview between the question asker and question answerer to be conducted through the system with the processor 12 operable to record such interview for further analysis. Such further analysis may include providing summaries, transcripts or insights into the interview, such as, by way of non-limiting example, providing themes expressed, sentiment trends, orfeedback for a question asker on questions that may be asked in different manners or different questions based upon the information contained within the database from previous interviews. Video recognition may also be utilized to interpret and determine feelings or truthfulness of the question answerer during the interview using video and facial recognition techniques.

[0042] 90question asker during the interview by providing information to them in advance of the interview, such as briefing materials, including, by way of nonlimiting example video, background materials or briefing memos. The processor may also optionally provide question suggestions to the question asker during the interview based on the real time analysis conducted on question answerer responses using text, audio or video analysis. It will beappreciated that any suggested questions or briefing materials may be prepared based on the prior knowledge of the features of the question answerer as well as application of machine learning to past results of past interviews. The processor may also be configured to provide an indication or information to the question asker as to the timing or duration of the interview so as to be operable to assist with the timely completion of the interview such as, by way of non-limiting example, visual, auditory indications of any suitable information, including, by way of non-limiting example, elapsed time, time remaining, time compared to allocated time or the like. Such post interview analysis may also be used to update the models used for such suggestions using known machine learning techniques.

[0043] Turning now to Figure 10, the processor 12 may be adapted to receive an open ended text input 90 from the question asker. The processor may be adapted to, utilizing similar methods to those above, to categorize and analyses the question and answering session. In particular, the processor may be adapted to categorize the results of the interview, including, without limitation, sentiment, truthfulness, answer completeness or interviewer effectiveness. Furthermore, as illustrated in Figure 11, the processor may receive a transcript 92 of the interview including time stamps which may be cross compared with the question asker inputs. It will be appreciated that such feedback mechanisms, may also be received in either audio or video input format in which the processor may be configured to recognize and categorize tone of voice to identify mood, feelings, sentiment, empathy, or any other feeling or qualitative aspect of the interview. The results of such post-interview analysis may be provided to the question asker on an individual interview basis or combined for overall performance and evaluation. The processor 12 may also utilized such post analysis to further refine characteristic matching of the question asker an answerer above, or for suggesting questions having higher probabilities of success to be displayed or presented to the question asker during the interview.While specific embodiments have been described and illustrated, such embodiments should be considered illustrative only and not as limiting the disclosure as construed in accordance with the accompanying claims.

Claims

What is claimed is:

1. A method for managing an interview for an interview question answerer comprising:providing a database containing a plurality of question askers, wherein each question asker has at least one question asker feature associated therewith;receiving, through an interface in communication with a processor, at least one entry from a question answerer;utilizing the processor, identifying at least one question answerer feature of the question answerer from the at least one entry; andmatching using the processor in real time, at least one potential question asker from the plurality of question askers with the question answerer by matching at least one question asker feature corresponding to one of the at least one question answerer feature.

2. The method of claim 1 wherein the processor runs a natural language processing model configured to perform the steps thereof.

3. The method of claim 1 wherein said at least one entry comprises self descriptive questions or input fields.

4. The method of claim 1 wherein said at least one entry comprises answers to discrete questions.

5. The method of claim 1 wherein said at least one question asker comprises a plurality of potential question askers.

6. The method of claim 5 further comprising:presenting the plurality of potential question askers to the question answerer; andreceiving a selection of a selected question asker from the question answerer.

7. The method of claim 1 further comprising scheduling a time between the question answerer and selected question asker with a scheduling module by:looking up in the selected question esker’s calendar, at least one available time;providing the at least one available time to the question answerer; andreceiving the selected time from the question answerer.

8. The method of claim 7 further comprising forwarding a calendar invite to each of the question answerer and selected question asker.

9. The method of claim 1 further comprising receiving an image of the question answerer and identifying with the processor, the at least one feature of the question answerer.

10. The method of claim 9 wherein receiving the image comprises scanning an image of the question answerer.

11. The method of claim 10 wherein the scanned image is utilized by the processor to verify the identity of the question answerer against a database of known question answerers.

12. A system for managing an interview for an interview question answerer comprising;a database containing a plurality of question askers each having at least one feature associated therewith;an interface operable to receive at least one entry from a selected question specific question answerer;a processor in communication with the interface, the processor configured to:identify in real time, at least one question answerer feature of the question answerer from the at least one entry; andmatch, in real time at least one potential question askerfrom the plurality of question askers with the question answerer by matching at least one question asker feature corresponding to one of the at least one question answerer feature.

13. The system of claim 12 wherein the processor runs a natural language processing model configured to perform the steps thereof.

14. The system of claim 12 wherein the processor is furthermore configured to schedule a time between the question answerer and selected question asker with a scheduling module by:looking up in the selected question answerer’s calendar, at least one available time;providing the at least one available time to the question answerer; andreceiving the selected time from the question answerer.

15. The system of claim 12 further comprising include a scanner operable to scan a question answerer image.

16. The system of claim 15 wherein the scanner is configured to scan the image of the question answerer in real time.

17. The system of claim 15 where the processor is configured to verify the identity of the question answerer.

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