WO2020044342A1

WO2020044342A1 - A system, method and computer program product for generating and controlling a soft skill / personality evaluation and matching platform

Info

Publication number: WO2020044342A1
Application number: PCT/IL2019/050964
Authority: WO
Inventors: Lee JOFFA; David Levy; Michael GURION; Eli PODLESNY
Original assignee: Pitchcareer Ltd
Priority date: 2018-08-30
Filing date: 2019-08-28
Publication date: 2020-03-05
Also published as: IL261498A

Abstract

Skill evaluation system comprising a first processor which presents prompts to candidates, accepts videos documenting candidates' responses to prompts, and stores the videos in association with ID data uniquely identifying each candidate; a second processor which presents the videos and plural bi-polar user input devices to evaluators, accepts, for each of respective plural soft skills, the evaluators' evaluations, using the input devices of individual candidates along each of the threads, and stores the evaluations in association with the individual candidate's ID data. A third processor compares evaluations of individual candidates along each thread, with characteriza- tions of jobs along the bi-polar thread, respectively, including determining an extent to which individual candidates match individual jobs accordingly, along each thread. The processor also combines the extents, for the plural threads, and accordingly selects candidates to present these to an administrator end-user.

Description

A System, Method And Computer Program Product For Generating And Controlling A Soft Skill / Personality Evaluation And Matching Platform FIELD OF THIS DISCLOSURE

The present invention relates generally to software platfonns and more particularly to re- cmitment platfonns.

BACKGROUND FOR THIS DISCLOSURE

Recruitment technology is a growing field. Example patent documents in this field are available here: http://stks.freshpatents.com/Hirevue-Inc-nm 1.php and here: https://www.goode.com/patents/US20040186743.

Existing systems serving job-seeking enterprises, provide individual functionalities from among the following:

- Soft skills matching

- Personality matching platforms including psychometric assessment platforms

- Recruitment marketplaces

- Video Recruitment marketplaces

- AI and automation within the recruitment space (e.g. keywords, natural language programming, algorithmic matching)

- Crowd intelligence platforms

US 8185424 to Hansen describes Soft Skills matching using perceptual mapping where questions are in pairs, based on candidates' self evaluation.

Use of Video in recruitment technology is known. For example, Hirevue uses candidate videos in order to reveal the traits that make candidates successful in their positions.

Video based technologies are able to identify characteristics that determine success through the use of various video audio and psychology based technology in order to identify those traits.

Video based prescreening technologies are using question libraries that are specifically automated to each candidate in order to unlock the soft skills that are required to perform the job. Soft Skills matching is known. Soft skills are a cluster of productive personality traits that characterize one's relationships in a person’s environment. These skills can include social graces, communication abilities, decision making skills, thinking type, language skills, personal habits, cognitive or emotional empathy, time management, teamwork and leadership traits.

Hirevue https://www.hirevue.com/ has A1 driven video recruitment software solutions. The Hirevue platform automates the interview process through using algorithms and machine learning to identify a specific candidate for an open position within a user’s company. The plat- form also automates the interview process through the prediction and sequencing of questions. Furthermore, candidates are graded according to their“greatness” for specific roles. Through the use of sequencing algorithms the platfomi is able to predict the next questions to ask candidates. Additionally they offer assessments and coaching. Hirevue have a product that they use for inter- view schediding which simplifies the administration for this process. Candidates are graded according to their responses in tenns of best fit for a role. The platform is used for both talent acqui- sition and retention.

Other state of the art systems include Intervyo, Hiya, Easy Hire, Webcand, jobufo, Vjobs, Linkedin Jobs, Workey, Woo, Soft Factors, Scoutible, and Talenya.

Commonly used psychometric and personality assessment and evaluation tools include Big 5 personality test (FFM) Myers - Briggs type indicator, 16 personalities test, Enneagram test, OPQ32. Big 5 breaks traits down into 5“core traits” tools developed from this model include the 16 personalities model.

The 16PF questionnaire does not collect video data and relies on self evaluations rather than on independent evaluators.

The disclosures of all publications and patent documents mentioned in the specification, and of the publications and patent documents cited therein directly or indirectly, are hereby incorporated by reference. Materiality of such publications and patent documents to patentability is not

conceded.

SUMMARY OF CERTAIN EMBODIMENTS

According to a Gallup poll: 60% of Millennials are ready to switch jobs. It is believed that the traditional emphasis on hard skills or trained skills may not be the best predictor of employee satisfaction and perfonnance. There is a clear demand for a system that is able to measure, match and recommend candidates for jobs, based on individual personalities or soft skills. CV’s are unable to provide the same information that a real interview or a video profile is able to supply; there is yet to be an effective solution that effectively combines a pre-screening platfonn with an intelligent marketplace.

There is a great demand for a holistic solution that is able to disrupt many of the traditional talent acquisition processes (e.g. sourcing, screening, administration and matching).

Certain embodiments seek to provide a platform for personality assessment and/or soft skill evaluation.

The term“Soft skills” as used herein is intended to include inter alia personality traits, people skills, social skills communication skills, character traits, attitudes, career attributes, so- cial intelligence and emotional intelligence quotients, and other skills that enable people to navi- gate their environment, work well with others, perform well and achieve their goals and combi- nations of any of these .

Certain embodiments seek to provide a platform for identification, evaluation and matching of core personality traits which have a direct relationship with soft skills.

Certain embodiments seek to provide a process and/or method and/or flow configured for evaluating and/or matching soft skills.

It is appreciated that specific questions, total number of questions, individual slider traits and many other system characteristics presented herein by way of example are subject to change. For example, some embodiments may be based on traits known from the well-known online personality test/assessment platform called 16 personalities (https://www.16personalities.com/free- personality-test) and/or The Myers-Briggs Type Indicator (MBTI), an introspective self-report questionnaire, however, this is not intended to be limiting.

Certain embodiments seek to provide a system (aka“pitch system”) that combines some or all of Video, Automatic Soft skills matching/recommending, crowd intelligence and prescreening in a single platform.

Certain embodiments seek to combine a video based soft-skill evaluation of pre-screening platform with an intelligent marketplace. Typically, an aggregated prescreening process is employed for candidate evaluation, yielding the advantage that an aggregation of many decision makers' decisions is more effective than a single human evaluation. An evaluation slider may be employed that allows recruiters to position candidates' soft skills along a spectrum. Using the slider data, the system matches accordingly, thereby to lower biases and decrease variance associ- ated with traditional rating systems. The system's recommendation engine may recommend spe- cific jobs to candidates and candidates to jobs, recruiters, and or companies, based at least partly on the soft skills matching scores generated via the pre-screening processes.

A bipolar slider may be helpful in encouraging evaluators to focus more on soft skills and the way candidates respond to questions, rather than judging the candidates themselves. This oc- curs e.g. due to the bipolar slider curtailing biases associated with individual human ratings, such as but not limited to:

Halo effect / Horn effect (Kahneman) effect: reviewers may be less inclined to rank the candidates either higher or lower, based on aspects such as attractiveness; and/or

Anchoring: The bi-polar slider typically has an emphasis on the centre. If the eye is natu- rally positioned at the centre and both options are placed simultaneously on either side of the centre, anchoring may be reduced.

Bias towards Social Status: e.g. through prioritizing personalities matching over traditional recruitment methodologies (including keyword matching by university, location, nationality and age)

Cognitive Bias (law of small numbers/law of big numbers.): e.g. using power of multiple opinions (crowd intelligence) in improving matching rates.

It is appreciated that a soft-skill evaluation system which reduces bias, results in more accurate categorization of candidates, and a more fair recommendation engine based on candidates' actual soft skills.

Certain embodiments seek to algorithmically solve the problem of how to match applicants' soft skills to the soft skills required by jobs e.g. using a feedback loop that le verages crowd intel- ligence to evaluate and match according to soft skills scores linked both to candidates' evaluated soft skills and to job requirements.

Certain embodiments of the present invention seek to provide circuitry typically compris- ing at least one processor in communication with at least one memory, with instructions stored in such memory executed by the processor to provide functionalities which are described herein in detail. Any functionality described herein may be firmware-implemented or processor-imple- mented, as appropriate.

The system may include a mobile or web application for candidates (e.g. Ios, Android, other mobile OS) and/or a web or mobile application for hiring managers (e.g. Web, ios. Android or other platforms).

It is appreciated that any reference herein to, or recitation of, an operation being per- fonned is, e.g. if the operation is perfonned at least partly in software, intended to include both an embodiment where the operation is performed in its entirety by a server A, and also to include any type of“outsourcing” or“cloud” embodiments in which the operation, or portions thereof, is or are performed by a remote processor P (or several such), which may be deployed off-shore or“on a cloud”, and an output of the operation is then communicated to, e.g. over a suitable computer network, and used by, server A. Analogously, the remote processor P may not, itself, perform all of the operations, and, instead, the remote processor P itself may receive output/s of portion/s of the operation from yet another processor/s P', may be deployed off-shore relative to P, or“on a cloud”, and so forth.

The present invention typically includes at least the following embodiments:

Embodiment 1. A soft skill evaluation system comprising:

a first processor which presents prompts to candidate end users, accepts videos document- ing the candidate end users' responses to the prompts, and stores the videos in computer storage in association with ID data uniquely identifying each of the candidate end users;

a second processor which presents said videos and plural bi-polar (typically virtual) user input devices to evaluator end users, accepts, for each of respective plural soft skills aka bi-polar threads, the evaluator end users' evaluations, using said input devices of individual candidate end users along each of said threads, and stores the evaluations in computer memory in association with the individual candidate's said ID data; and

a third processor which compares said evaluations of individual candidate end us- ers along each individual thread from among said threads, with characterizations of jobs along said individual bi-polar thread, respectively, including determining an extent to which individual candidate end users match individual jobs accordingly, along each individual thread from among said threads, and wherein the processor also combines said extents, for said plural threads, and accordingly selects candidate end users to present these to an administrator end-user responsible for accepting a candidate for a given j ob .

It is appreciated that a single physical processor may perform all of the above functionali- ties i.e. The first, second and third processors may be implemented in a single physical proces- sor. Alternatively, plural processors may perform any one of or subset of the above functionalities .

It is appreciated that administrator end-users responsible for accepting a candidate for a given job J, maybe evaluators of candidates forjob J and/or of candidates for jobs otherthan J. It is appreciated that acting as an evaluator end-user may allow the administrator end-user to accumulate credits or incentives or bounty. For example, administrators may be able to reduce or eliminate subscription fees for use of the platform, or may be entitled to more points, or to view more videos, or to use the platform for more minutes or hours per month, by acting as evaluator for certain numbers of candidates. Or, an administrator may be prevented from the system flow from requesting to see candidates for the job she has posted, until she has evaluated C random candidates who may have nothing to do with the job the administrator has posted. And/or, each administrator may be required to assess candidate n, before being given access t to candidate n + 1.

Typically, at least some evaluators of candidates, are unaware of the job/s for which the candidate is applying or being considered. This reduces bias which might result e.g. If an evaluator (a) is aware that candidate c is being considered for job J; (b) is aware of the "right" pole for that job, for a given bipolar thread, and (c) is afflicted by a halo effect hence allows her or his positive or negative overall impression of the candidate to contaminate her or his assessments of the can- didate along the bi-polar threads, by selecting the "right" vs. "wrong" pole, respectively, if his overall impression of the candidate is positive vs. Negative for some or all threads. Inter alia, even if an administrator is assessing candidates being presented to him as (based on accumulated assessments) being suitable for the job the administrator is trying to fill, these suitable candidates may be randomly interspersed with at least one unsuitable candidate. Alternatively or in addition, an administrator's assessments of candidates selected for the job the administrator is trying to fill, may be weighted differently than assessments generated by randomly selected evaluators. For ex- ample, the system may find that the random evaluators rate better (e.g. closer to the eventual crowd wisdom) than administrators viewing selected candidates for job/s they posted— perhaps because the random evaluators are not contaminated by their knowledge of what the job is. Or, the , the system may find that the random evaluators rate less well (e.g. further from the eventual crowd wisdom) than administrators viewing selected candidates for job/s they posted— perhaps because the random evaluators are less motivated, relative to the administrators, to do a good job.

Any suitable technical process may be employed, to allow candidates to send in their vid- eos e.g. candidates could record video using App and their device camera, Upload the video from a computer storage or send a link to a video.

Embodiment 2. A system according to any of the preceding embodiments wherein the third processor averages evaluations along each individual thread, over plural evaluators, thereby to benefit from crowd wisdom.

Embodiment 3. A system according to any of the preceding embodiments wherein said characterizations of jobs along said bi-polar threads are based at least partly on characteriza- tions of respective jobs posted by respective administrator end-users, collected respectively from said respective administrator end-users. Embodiment 4. A system according to any of the preceding embodiments wherein characterization of at least one job j 1 along said bi-polar threads is based at least partly on characterizations of at least one similar job j2 posted by at least one respective administrator end-user a2, collected from the at least one administrator end-user a2 in the past.

Embodiment 5. A system according to any of the preceding embodiments wherein said at least one respective administrator end-user a2 comprises plural administrator end-users a2 thereby to benefit from crowd wisdom in characterizing job j l .

Job similarity may be determined using any suitable known technology or combination thereof e.g. Hard skill matching (computing a distance metric between hard skills, requirements or prerequisites indicated by a job's administrator or advertisement as being required by different jobs (e.g. X vs. Y years of experience, z vs. W academic degrees, etc.), natural language processing of job descriptions, actual similarity of job titles ("sanitation worker" "software engineer", etc.) And so forth.

Embodiment 6. A system according to any of the preceding embodiments wherein each user input device comprises a slider which may be presented on a touch (or non-touch) screen in which case the evaluator end-user may input her or his evaluation of candidate x along thread y (say: introvert-extrovert) by sliding her or his finger (or mouse) along the slider.

Embodiment 7. A system according to any of the preceding embodiments wherein the system maintains a stored indication of how many assessments have been collected for each candidate end user and wherein a candidate end user who has accumulated N assessments is no longer presented to assessors.

Embodiment 8. A system according to any of the preceding embodiments wherein, responsive to a session in which an evaluator indicates readiness to evaluate a candidate end user, the system selects a candidate end user who has accumulated less than N assessments, for the evaluator to assess, at least partly randomly.

N may for example be a few dozen. It is appreciated that accumulating N e.g. 5 or 10 or a few dozen or more assessments per candidate, allows the system to benefit from crowd wisdom and allows an average assessment per thread to be computed per candidate.

Any suitable background process may be employed to rate assessors (e.g. Assessors who have to date consistently rated candidates close to the crowd wisdom eventually accumulated for these candidates may be rated high, relative to outlier assessors who have to date consistently rated candidates far from the crowd wisdom (perhaps these outlier assessors are randomly moving the slider, for example, rather than actually making the effort to correctly assess the videos they are presented with). Incentives may be given to candidates who rate high. Also, average assessments may be weighted such that highly rated assessors' assessments of a given candidate are highly weighted whereas outlier assessors' assessments of the same candidate are assigned low weights.

Embodiment 9. A system according to any of the preceding embodiments wherein when the system selects a candidate end user who has accumulated less than N assessments, for the evaluator to assess, selection takes into account how many assessments candidate end users have accumulated and prioritizes candidate end users who have accumulated less assessments, over candidate end users who have accumulated more assessments.

For example, the system may randomly select a candidate to be evaluated, from among all candidates having less than M < N evaluations.

Embodiment 10. A computerized soft skill evaluation method comprising:

presenting prompts to candidate end users, accepting videos documenting the candidate end users' responses to the prompts and storing the videos in computer storage in association with ID data uniquely identifying each of the candidate end users;

presenting the videos and plural bi-polar user input devices to evaluator end users , ac- cepting, for each of respective plural soft skills aka bi-polar threads, the evaluator end users' eval- uations, using the input devices of individual candidate end users along each of the threads, and storing the evaluations in computer memory in association with the individual candidate's the ID data; and

using a processor, comparing the evaluations of individual candidate end users along each individual thread from among the threads, with characterizations of jobs along the individual bi- polar thread, respectively, including detennining an extent to which individual candidate end us- ers match individual jobs accordingly, along each individual thread from among the threads, and wherein the processor also combines the extents, for the plural threads, and, accordingly, selects candidate end users to present to an administrator end-user responsible for accepting a candidate for a given job.

Embodiment 11. A computer program product, comprising a non-transitory tangible computer readable medium having computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a computerized soft skill evaluation method comprising:

presenting prompts to candidate end users, accepting videos documenting the candidate end users' responses to the prompts and storing the videos in computer storage in association with ID data uniquely identifying each of the candidate end users; presenting the videos and plural bi-polar user input devices to evaluator end users, accept- ing, for each of respective plural soft skills aka bi-polar threads, the evaluator end users' evaluations, using the input devices of individual candidate end users along each of the threads, and storing the evaluations in computer memory in association with the individual candidate's the ID data; and

using a processor, comparing the evaluations of individual candidate end us- ers along each individual thread from among the threads, with characterizati ons of jobs along the individual bi-polar thread, respectively, including determining an extent to which individual candidate end users match individual jobs accordingly, along each individual thread from among the threads, and wherein the processor also combines the extents, for the plural threads, and, accordingly, selects candidate end users to present to an administrator end-user responsible for accepting a candidate for a given job.

Also provided, excluding signals, is a computer program comprising computer program code means for performing any of the methods shown and described herei n when the program is run on at least one computer; and a computer program product, comprising a typically non-tran- sitory computer-usable or -readable medium e.g. non-transitory computer -usable or -readable storage medium, typically tangible, having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement any or all of the methods shown and described herein. The operations in accordance with the teachings herein may be performed by at least one computer specially constructed for the desired purposes or general purpose computer specially configured for the desired purpose by at least one computer program stored in a typically non-transitory- computer readable storage medium. The term "non-transitory" is used herein to exclude transitory, propagating signals or waves, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.

Any suitable processor/s, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and apparatus shown and described herein; the above processor/s, display and input means including computer programs, in accordance with some or all of the embodiments of the present invention. Any or all functionalities of the inven- tion shown and described herein, such as but not limited to operations within flowcharts, may be performed by any one or more of: at least one conventional personal computer processor, workstation or other programmable device or computer or electronic computing device or processor, either general-purpose or specifically constructed, used for processing; a computer display- screen and/or printer and/or speaker for displaying; machine-readable memory such as optical disks, CDROMs, DVDs, BluRays, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting. Modules shown and described herein may include any one or combination or plurality of: a server, a data processor, a memory/computer storage, a communication interface, a com- puter program stored in memory/computer storage.

The term "process" as used above is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g. electronic, phenomena which may occur or reside e.g. within registers and /or memories of at least one computer or processor. Use of nouns in singular form is not intended to be limiting; thus the term processor is intended to include a plurality of processing units which may be distributed or remote, the tenn server is intended to include plural typically interconnected modules running on plural respective servers, and so forth.

The above devices may communicate via any conventional wired or wireless digital communication means, e.g. via a wired or cellular telephone network or a computer network such as the Internet.

The apparatus of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instruc- tions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in ad- dition, the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may, wherever suitable, operate on signals representative of physical objects or substances.

The embodiments referred to above, and other embodiments, are described in detail in the next section.

Any trademark occurring in the text or drawings is the property of its owner and occurs herein merely to explain or illustrate one example of how an embodiment of the invention may be implemented.

Unless stated otherwise, terms such as, "processing", "computing", "estimating", "selecting", "ranking", "grading", "calculating", "determining", "generating", "reassessing", "classifying", "generating", "producing", "stereo-matching", "registering", "detecting", "associating", "superimposing", "obtaining", "providing", "accessing", "setting" or the like, refer to the action and/or processes of at least one computer/s or computing system/s, or processor/s or similar elec- tronic computing device/s or circuitry, that manipulate and/or transform data which may be rep- resented as physical, such as electronic, quantities e.g. within the computing system's registers and/or memories, and/or may be provided on-the-fly, into other data which may be similarly rep- resented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices or may be provided to external factors e.g. via a suitable data network. The term“computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting exam- ple, personal computers, servers, embedded cores, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing de- vices. Any reference to a computer, controller or processor is intended to include one or more hardware devices e.g. chips, which may be co-located or remote from one another. Any controller or processor may for example comprise at least one CPU, DSP, FPGA or ASIC, suitably configured in accordance with the logic and functionalities described herein.

The present invention may be described, merely for clarity, in terms of terminology spe- cific to, or references to, particular programming languages, operating systems, browsers, sys- tem versions, individual products, protocols and the like. It will be appreciated that this termi- nology or such reference/s is intended to convey general principles of operation clearly and briefly, by way of example, and is not intended to limit the scope of the invention solely to a particular programming language, operating system, browser, system version, or individual product or protocol. Nonetheless, the disclosure of the standard or other professional literature defining the programming language, operating system, browser, system version, or individual product or protocol in question, is incorporated by reference herein in its entirety.

Elements separately listed herein need not be distinct components and alternatively may be the same structure. A statement that an element or feature may exist is intended to include (a) embodiments in which the element or feature exists; (b) embodiments in which the element or feature does not exist; and (c) embodiments in which the element or feature exist selectably e.g. a user may configure or select whether the element or feature does or does not exist.

Any suitable input device, such as but not limited to a sensor, may be used to generate or otherwise provide information received by the apparatus and methods shown and described herein. Any suitable output device or display may be used to display or output information generated by the apparatus and methods shown and described herein. Any suitable processor/s may be employed to compute or generate information as described herein and/or to perform function- alities described herein and/or to implement any engine, interface or other system described herein. Any suitable computerized data storage e.g. computer memory may be used to store in- formation received by or generated by the systems shown and described herein. Functionalities shown and described herein may be divided between a server computer and a plurality of client computers. These or any other computerized components shown and described herein may com- municate between themselves via a suitable computer network.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present invention are illustrated in the following drawings; in the block diagrams, arrows between modules may be implemented as APIs and any suitable tech- nology may be used for interconnecting functional components or modules illustrated herein in a suitable sequence or order e.g. via a suitable API/Interface. For example, state of the art tools may be employed, such as but not limited to Apache Thrift and Avro which provide remote call support. Or, a standard communication protocol may be employed, such as but not limited to HTTP or MQTT, and may be combined with a standard data format, such as but not limited to JSON or XML.

Fig. 1 is a simplified flow diagram of an example flow for a method for soft skill evaluation and matching. The method of Fig. 1 typically comprises all or any subset of the illustrated opera- tions, suitably ordered e.g. as shown.

Fig. 2 is a pictorial diagram useful in understanding the slider embodiment shown and described herein.

Fig. 3 is a table storing example candidate data.

Fig. 4 is a pictorial diagram useful in understanding the matching operations shown and described herein.

Fig. 5 is an example matching computation performed according to certain embodiments.

Fig. 6 is an example graph useful in demonstrating scalability according to certain embodiments.

Methods and systems included in the scope of the present invention may include some (e.g. any suitable subset) or all of the functional blocks shown in the specifically illustrated implementations by way of example, in any suitable order e.g. as shown. Computational, functional or logical components described and illustrated herein can be implemented in various fonns, for example, as hardware circuits such as but not limited to custom VLSI circuits or gate arrays or programmable hardware devices such as but not limited to FPGAs, or as software program code stored on at least one tangible or intangible computer read- able medium and executable by at least one processor, or any suitable combination thereof. A specific functional component may be fomied by one particular sequence of software code, or by a plurality of such, which collectively act or behave or act as described herein with reference to the functional component in question. For example, the component may be distributed over sev- eral code sequences such as but not limited to objects, procedures, functions, routines and pro- grams and may originate from several computer files which typically operate synergistically.

Each functionality or method herein may be implemented in software (e.g. for execution on suitable processing hardware such as a microprocessor or digital signal processor), firmware, hardware (using any conventional hardware technology such as Integrated Circuit technology) or any combination thereof.

Functionality or operations stipulated as being software-implemented may alternatively be wholly or fully implemented by an equivalent hardware or firmware module and vice-versa. Firmware implementing functionality described herein, if provided, may be held in any suitable memory device and a suitable processing unit (aka processor) may be configured for executing firmware code. Alternatively, certain embodiments described herein may be implemented partly or exclusively in hardware in which case some or all of the variables, parameters, and computa- tions described herein may be in hardware.

Any module or functionality described herein may comprise a suitably configured hard- ware component or circuitry. Alternatively or in addition, modules or functionality described herein may be performed by a general purpose computer or more generally by a suitable micro- processor, configured in accordance with methods shown and described herein, or any suitable subset, in any suitable order, of the operations included in such methods, or in accordance with methods known in the art.

Any logical functionality described herein may be implemented as a real time application if and as appropriate, and which may employ any suitable architectural option such as but not limited to FPGA, ASIC or DSP or any suitable combination thereof.

Any hardware component mentioned herein may in fact include either one or more hardware devices e.g. chips, which may be co-located or remote from one another.

Any method described herein is intended to include within the scope of the embodiments of the present invention also any software or computer program performing some or all of the method’s operations, including a mobile application, platform or operating system e.g. as stored in a medium, as well as combining the computer program with a hardware device to perform some or all of the operations of the method.

Data can be stored on one or more tangible or intangible computer readable media stored at one or more different locations, different network nodes or different storage devices at a single node or location.

It is appreciated that any computer data storage technology, including any type of storage or memory and any type of computer components and recording media that retain digital data used for computing for an interval of time, and any type of information retention technology, may be used to store the various data provided and employed herein. Suitable computer data storage or information retention apparatus may include apparatus which is primary, secondary, tertiary or off-line; which is of any type or level or amount or category of volatility, differentia- tion, mutability, accessibility, addressability, capacity, performance and energy use; and which is based on any suitable technologies such as semiconductor, magnetic, optical, paper and others.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Computerized systems which rely on human evaluations of soft skills need to be designed and configured such that the resulting architecture overcomes the statistical weaknesses of human evaluation; otherwise system outputs will not be valuable to the system users. Also, if it is desired to present a "learning" system, again the architecture needs to be properly designed to establish what information is learned from whom or from what, when and how.

Typically, plural (e.g. 5 or more or less in the illustrated examples herein) questions are asked to the candidates, each question being selected by a human expert e.g. psychologist to reveal a soft skill. For example:“tell about a time you had to deal with a difficult colleague. What did you do to communicate properly?” Additionally, information may be gathered from recruiters' individual perceptions from the videos themselves. Human beings have an innate ability to gather information from small data sets and make accurate assessments. This ability is known as thin- slicing. Video is an excellent vehicle via which the platform may provide humans with non-verbal cues such as body language, tonality, eye contact, pupil dilation, speech patterns etc. Having been provided this information, humans are then able to provide the platfonn with effective character judgements, evaluations, and assessments. Candidates need not be asked to do self-assessments, since candidates may be predisposed to incorrectly evaluate themselves e.g. as per Daniel Khaneman's optimism bias, whereby in standardized self-evaluation techniques, candidates are unable to truly measure themselves against the average, and are predisposed to gaming the system, whereby there is a tendency to answer ques- tions in a way that that the results can be manipulated. This is more challenging with a video based platform as so much non-verbal communication is communicated to the evaluators.

Typically, a bi-polar slider is used to evaluate soft skills, typically using a "lid for every pot" rationale according to which there are no good or bad candidates, but rather candidates x are better suited to job y based upon their individual soft skills z.

Typically the two poles of each slider are identified by semantically neutral natural language labels such as introvert vs. extrovert thereby to minimize cognitive bias (in assessing candidates and/or jobs) rather than by labels (e.g. highly adaptive vs. non-adaptive, intelligent vs. unintelligent) which imply one pole is good and the other bad, since such labels may be expected to increase cognitive bias. Labels can be pre-tested statistically, on human users, to establish they are semantically neutral.

Typically, the system leams as it evolves over time, hence becomes better at recommending and matching with greater data input e.g. as described herein.

Typically, all or plural candidates are evaluated for the same set of soft skills e.g. 5 (or more or less) bi-polar threads in the example embodiment herein. This enhances crowd wisdom since there is a large pool of candidates and evaluators for each soft skill, relative to a system in which the soft skills depend on the job the candidate is applying for.

According to certain embodiments, two poles of each slider may be identified by neutral natural language labels such as thinking vs. feeling, intuitive vs. observant, e.g. to minimize cognitive bias (in assessing candidates and/or jobs) rather than by labels (e.g. highly adaptive vs. non- adaptive) which imply one pole is good and the other bad, thereby introducing bias.

An example flow for a method for soft skill evaluation and matching is shown in fig. 1: the method of fig. 1 may include all or any subset of the following operations, suitably ordered e.g. As shown:

Operation 5: offline, questions and characteristics of each thread from among 5 (or more or less) (e.g.) bi-polar threads, are initialized from a back office by an admin human team.

Operation 10: each candidate downloads mobile application from google store or app store, or other services, or opens a web version or connects to a URL address and then logs in to register and gain access to the application (Web or app) Operation 20. A mobile application for candidates, or other subsystem, typically con- trolled by a first processor, prompts candidates to input standard data such as but not limited to all or any subset of: name, academic degrees, contact particulars, and copy of cv, some or all of which may be stored as the candidate's record.

Operation 30: mobile or web application for candidates presents each candidate with typi- cally 5 (or more or less) (e.g.) natural language prompts e.g. questions. These are typically standard for all candidates, corresponding to 5 (or more or less) (e.g.) bi-polar threads or characteristics.

Typically, each prompt or question is selected by a human expert to result in a video which will allow the candidate to be assessed along one of the threads. The question may even present to the end-user the two poles and ask him to describe himself in those terms e.g. "are you a hunter or a farmer?". Or, the question could be more general e.g. "how would you go about doing your job properly - what would you normally do to ensure your job gets done well? " Alternatively, one video could be used to allow the candidate to be assessed along more than one thread (soft skill slider). And/or an evaluator may be presented with slider 1 - 5 (or more or less) after viewing videos 1 - 5 respectively however, the evaluator may be entitled to tweak his assessment along one slider, even after s/he has gone on to view a video associated with a later presented slider. For example, after the evaluator views all videos, the evaluator may see all 5 (or more or less) (say) sliders and may be given an opportunity to tweak his assessments on any of the sliders.

Operation 40: subsequent to each question, candidate is prompted to video tape his answer, thereby to generate (e.g. using his cellphone camera to image himself) 5 videotapes (or more or less) for each candidate; the videotapes are stored e.g. by the first processor, in computer storage in association with the candidate's record.

Operation 50: each manager accesses a suitable subsystem e.g. enters a URL on a web browser then logs in or registers to access a hiring manager web interface, at the URL, or downloads a mobile application.

Operation 60: web interface prompts hiring manager to characterize the job he/she is offering (typically, inter alia along each of the 5 (or more or less) (say) bi-polar threads)

Operation 64: for each job, some or all assessed candidates in the system may be ordered from most to least suitable for that job by a first machine learning algorithm on the server.

For example, candidates whose non-soft-skill characteristics render them clearly unsuitable for the job (e.g. wrong geographical area, wrong level of education, wrong amount of experience) are typically ruled out and not ordered. Operation 70: hiring managers aka job administrators may browse through candidates, accessing the candidates e.g. according to a random candidate selection algorithm performed by the server or according to any manager-desired criterion or logical combination thereof.

Alternatively, candidates selected by the first machine learning) algorithm may be presented to managers in a more-suitable-first (i.e. candidates best matching the job presented before candi- dates which match the job less well) order. The first machine learning/ algorithm may be used to find the best matching candidate and a job, using data provided by candidates and the manager to compute a match.

Managers may browse candidates in a job title, in a new candidates section of the hiring manager web interface.

Operation 76: candidate videos accessed during this browsing operation are human-char- acterized, thereby to generate spatial (position along a slider e.g.) or numerical evaluations of individual candidates along individual ones of the 5 threads. Humans may be rewarded for performing such characterizations.

For example, initial characterizations of candidates may be provided online by hiring man- agers (humans) e.g. to win points, say because each manager is required to evaluate C (perhaps 3, 5, 10 or 15) candidates in order to (have enough points to) upgrade functionality of platfomi (e.g. all or any subset of number of viewed candidates per day, amount of active jobs, other features). Typically, all initial characterizations of candidates are human (not machine) generated. Altema- lively or in addition, initial characterizations of candidate videos along the 5 threads, may be col- lected, typically off-line, from human or machine evaluators.

Typically, each such characterization (along the 5 (or more or less) threads) is accumulated in computer storage.

Typically, the internal flow of operation 76 is controlled by a second processor.

Operation 80: server or a 3^rd processor selects candidates whose characterizations along the 5 bi-polar threads match the hiring manager's characterization of the job she or he is offering, e.g. by computing differences between candidate and job characterizations, for each thread, computing a weighted sum of these differences, and ranking candidates as being a good match for a job to the extent that candidate's weighted sum of differences, for that job, is small and, conversely, ranking candidates as being a poor match for the job to the extent that candidate's weighted sum of differences, for that job, is large. Weights may initially all be equal, may be preset manually and intuitively or may be preset by any other suitable method. Hiring managers are presented, by the server, with these candidates and for each, the server generates a graphic display of the candi- date's characterization along each thread, vis a vis the job's characterization along that thread. Operation 90: hiring managers enter their hiring decision such as pitched (interested in considering this candidate) and/or passed (not interested in this candidate) and/or placed (candidate has been given the job).

Operation 100: using a second (machine learning) algorithm, weights may be updated e.g. to reflect that some threads are more correlated than others, to hiring decisions, for the entire pop- ulation or for subsets of the population.

Or, weights employed by the first (machine learning) algorithm may be updated to reflect that some threads are more correlated than others, to hiring decisions, for the entire population, or for subsets of the population.

An example implementation of soft skills evaluating and matching processes, including evaluation and definition of candidate skills, is now described in detail. This may be used, say, to implement operation 80. The flow may be controlled by a third processor.

Assmne for the purposes of simplicity that the data includes 5 threads, each associated with a slider and each represented by number between 0 and 20 representing a position along the slider (where 0, 20 are the poles of the slider). Each thread, then, is a representation of a candidate’s position between a pair of opposite skills or poles e.g. as shown in Fig. 2. A candidate notation may be computed by any suitable soft skill evaluation e.g. may be a weighted average of the 5 threads T where W = Weight. Typically, a thread is a notation of a candidate for a particular soft skills pair.

Any suitable candidate skill initialization process may be perfonned, since, initially, anew candidate has no notation. For example, the system may use bounty or other human (or even machine) evaluations to initialize the evaluation of a candidate. To be initialized, the system may predetermine that a candidate needs to be evaluated by a minimum of , say, 4 or 5 or 6 or 10 companies (recruiters).

"Bounty" may comprise initial characterizations of candidates e.g. along the 5 (say) threads, generated by human end-users e .g . in return for incentives, and accumulated by the system in computer memory, thereby to provide crowd wisdom to the system.

Typically, bounty is accumulated by a subsystem which shows candidates’ videos, for can- didates typically at least partly randomly selected, typically selected by a process which does not take into account the job that the recruiter seeks to fill, or even deliberately selects candidates unsuitable for the job the recruiter seeks to fill e.g. candidates with the“wrong” hard skills. Thus the system accumulates evaluations generated by the recruiters (e.g. along sliders as described herein) and the recruiters may, say, accumulate points. The software may have a predetermined minimum of candidate evaluations that must be accumulated, before the matching process may be applied to a given candidate e.g. Joe must be evaluated 4 or 5 or 6 or 10 times, before being matched to jobs.

In order to incentivize bounty evaluators to aim for quality evaluations, the system may compare the difference between the average evaluations for a particular candidate to their individ- ual evaluations. Typically, the closer to the average ratings, the more points are allocated to the evaluator.

Further evaluation may be provided by human recruiters who watch the candidate profile. Each candidate s 5 (say) threads are evaluated by the recruiter. Each time a recruiter evaluates a candidate, the software updates the data of the candidate.

Any suitable weighting initialization may be employed. For example, initially the software may start from equal weights e.g. the initial weight of each thread is 20%.

Weighting of threads may be measured using machine learning technology. The weighting may be global across the software, and may not be linked to a specific candidate.

The table of Fig. 3 represents 3 candidates’ evaluations along each of five threads T1, T2. T3, T4,T5. The weighting may be evaluated on occasion or periodically e.g. each time a candidate is pitched, passed or placed by a recruiter or administrator for a specific job, where:

Pitched : Action (by recruiter) that confirms recruiter’s interest in continuing recruitment process with a particular candidate.

Passed : Action(by recruiter) that confirms that recruiter is not interested in continuing recruitment process with a particular candidate.

Placed : Action(by recruiter) that confinns that recruiter has successfully finished the recruitment campaign with a particular candidate.

In the illustrated example, the first two lines of the table of Fig. 3 show that T 2 is more important than other threads, as candidate has moved further by process (Pitched and Placed). So the weighting of T 2 may be increased. The three lines taken together show that T 1 is also im- portant, but less important than T 2. Weighting order after these conclusions may thus be:

T 2 > T 1 > T3

T 2 > T 1 > T4

T 2 > T 1 > T 5

In the illustrated example, the software may decline to adjust weighting of T 3, T 4, T 5 on the grounds that more data is needed to evaluate these. Typically, as pitched , passed or placed data accumulates, the software refines the weighting of the threads further. An example Weighting Algorithm may be:

Any suitable matching process may be employed to match evaluated candidates to evalu- ated jobs. To evaluate a job, for example, the system may prompt the recruiter or administrator, during the process of setting up a job post, to set values required for a job along the 5 threads. The software may then compute a difference between candidate values and job-required values, for each thread, thereby to yield a matching result. Weighting may be used to ensure a more accurate result. The grey curve reflects the evaluation of the candidate by the current recruiter. This data may be used to update candidates’ evaluation.

Fig. 5 is an example matching equation where R represents a job-required Value along a gi ven thread for a specific job and C represents a Candidate Value. In the illustrated embodiment, the

Match is a number between 0 and 20, however of course this is not intended to be limiting. The software may represent the matching result to a user as a coloured icon e.g. a zig-zag curve interconnecting the relevant positions along the 5 threads. Colour may be used to represent a rough indication of match quality e.g. a 3 -colour code to represent 3 quality levels such as:

Green - Good Match : Match > 14

Orange - Average Match : 10 < Match <= 14

Red - Poor Match : 0 <= Match <= 10

It is appreciated that computer storage may generall y include any digital storage or memory in any location/s (not necessarily a candidate or hiring manager's device) including storage on a cloud or in a datacenter. For example, video may be stored in a local server or a virtual server on a cloud/datacenter. Non-video data such as, say, candidates' profile, weighting, profile of recruiting company/entity, may be stored in a database accessible by the server which may also be stored on the cloud/datacenter.

The system typically incorporates suitably privacy measures. For example, candidate end users may be pre-informed that their videos will be presented to evaluators, and are asked to confirm their approval. Also, evaluators may be shown the identity of a candidate they are about to evaluate, and may be asked to confirm that they are not personally acquainted with her or him before the video data for that candidate is presented to the evaluator.

It is appreciated that the processors shown and described herein may be incorporated within several types of systems, such as but not limited to:

1) a dating application or system operative to match couples based on bi-polar personality attributes

2) a recruitment market place

3) systems configured to assess employee internal job fit

4) a system that measures/rates or reviews soft skills/personality attributes, whereby entrepreneurs would apply for funding opportunities

5) an educational or learning system or application

6) other soft skills matching platforms. Advantages of embodiments herein include:

The platform is typically configured for leveraging data from multiple recruiters through crowd intelligence to improve decision making processes vs. single agent decision making in tra- ditional recruitment in which individual external recruiters may be biased due to economic incen- fives that will come into effect in the event of a placement, hence are naturally predisposed to encourage recruiters to recommend his or her particular candidate. The platform is typically operative to decrease variance upon soft skills matching/decision making, will improve the overall quality of decisions, and allow improved job matching to take place from both internal and external recruiters.

The platform and/or its flow are typically configured for allowing individual soft skill evaluators to remain independent of each-other so as to not contaminate each others' decisions, which enhances the ability of crowd intelligence to be effective.

Traditionally, an interview has been the typical route to be able to identify soft skills. But according to certain embodiments, the video component of the platform architecture allows pre- screening (e.g. soft skill evaluation) to be scaled, mass-evaluated and aggregated. Through the use of standardized cross industry soft skills, this enables recruiters from different fields of expertise to make candidate evaluations. Thin-slicing is typically applied here in order to encourage recruiters to make clear and fast candidate evaluations. The greater the number of individual evaluations, the lower the variance. This flow facilitates candidate evaluation before the candidate meets his or her hiring recruiter e.g. as shown in the graph of Fig. 6 which is useful in demonstrating scalability.

Also, through the use of video combined with bi-polar sliders, the platform is able to gather from the crowd enough data to reduce or eliminate individual cognitive biases (e.g. halo effect, anchoring, cognitive biases, social biases, etc.) from each individual recruiter.

Another advantage of certain embodiments is that the candidates being proposed for a gi ven job J, have already been prescreened and evaluated, using the same soft skill parame ters that are going to be used e.g. by the 3rd processor, to match the candidates to job J. Time saving for recruiters can be immense; each recruiter contributes only a few evaluations, but these evaluations are amassed and used to serve many other recruiters.

Another advantage of certain embodiments is that the platform need not require candidates to provide self-assessments of their own soft skills, since candidates may be predisposed to incor- rectly evaluate themselves. For example, Daniel Khaneman's work on the Optimism bias establishes that in standardized self-evaluation techniques, candidates are unable to truly make sound self-personality assessments due to the nature of candidates answering questions based on the results that they are looking to achieve. Through suitable, typically pre-tested video questions, it becomes increasingly more challenging for candidates to“manipulate” video based answers, as much of the communication that takes place is non-verbal.

Another advantage of certain embodiments is that the platform is configured for leveraging data from multiple recruiters through crowd intelligence, thereby to improve decision making pro- cesses relative to conventional single agent decision making based recruitment. Individual external recruiters may be biased e.g. due to economic incentives conditional on placement, hence may be predisposed to encourage recruiters to recommend their own particular candidate. The platfonn, which, according to certain embodiments, decreases variance of soft skills matching/decision mak- ing, improves overall decision quality and facilitates improved job matching from both internal and external recruiters. Typically, the platform maintains effectivity of crowd intelligence by en- suring that individual contributors to crowd intelligence remain independent of one another so as to not affect each other's decisions (the platform is typically configured such that recruiters make individual candidate evaluations before they see the averaged crowd evaluation). This allows eval- uators to make independent evaluations based upon their individual intuition and assessments. Over time, evaluators may become more skilled at evaluating, thus improving the overall quality of the system’s evaluations.

Also, the use of video in the platform, to collect soft-skills data (typically, the same questions are posed to each candidate, each question being written to reveal one or at least one soft skill), allows the pre-screening process to be scaled (e.g. as described herein with reference to the graph of Fig. 6), mass-evaluated and aggregated, allowing the platform to gather from the crowd enough data to decrease variance and avoid a great number of the biases that individual recruiters suffer from, in conventional platforms or conventional paradigms, in which an interview is the typical vehicle by which soft skills are ostensibly identified. Through the use of video, in- formation is gathered from recruiters' individual perceptions from the videos; recruiters thus be- come more and more adept at identifying these soft skills as they gain experience. It is believed that due to the cognitive ease phenomenon studied by Khaneman, ease mode results in a good mood and trust of intuitions, resulting in casual decision making, whereas a strained mode is associated with more effort, less of a feeling of comfort, but fewer errors. By use of the video and/or bi-polar slider, the platform may yield soft skills data in which evaluators judge candidates based upon how the candidates actually answer the questions, as opposed to the evaluators' indi- vidual ratings of the candidates by“guiding/nudging” evaluators to listen carefully to answers resulting in evaluations that are more thought through. The system is typically configured to allow evaluators to make immediate intuitive judgements, and then, after watching all of the videos, to supplement their intuitions with a deeper overall analysis after receiving a greater overall picture of the candidate. This allows for both type 1 (Intuitive) and type 2 (More cognitive) thinking.

Through the removal of various decision biases, in addition to the power of crowd intelli- gence, the overall decisions are of higher quality(e.g. Khaneman re thinking fast and slow).

According to certain embodiments, there are, initially, three types of algorithms: Matching, Evaluating and Weighting algorithms.

The Matching algorithm typically requires candidate soft skills data as well as a company’s (or a recruiting entity’s) soft skills requirements.

The Ev aluation Algorithm typically requires all or any subset of type of event (Pitch/Pass or Place), most recent evaluation rate (optional), amount of evaluations, and data of each evalua- tion.

The Weighting algorithm typically requires all or any subset of type of event (Pitch/Pass or Place,) most recent weighting of the threads, amount of reweighings done, and the list of all the tables (evaluations).

In the present specification, any references to evaluation may be regarded as referring to assessment (of a soft skill e.g.).

According to certain embodiments, candidates may respond to a specific question using their mobile phones to record a video.

According to certain embodiments, after the Evaluator has reviewed a video he or she will then evaluate the candidates response using the Bi-polar slider as a tool in order to express his / her assessment of the candidates soft skills and/or personality traits.

According to certain embodiments, questions about qualities may be in scale mode, for example: Is the candidate more of an extrovert or an introvert ?

According to certain embodiments, the results of the evaluations may be stored, and may be used in line with the systems requirements for matching and recommendation.

According to certain embodiments, questions may ask interviewees or candidates about daily situations which are designed to encourage the candidates to reveal or display specific personality traits / soft skills and typically do not directly ask the candidates about these traits in an effort to avoid self imposed ratings which are affected by self awareness and defensiveness.

According to certain embodiments, the system flow may permit or prompt evaluators to adjust the assessment of the candidates that evaluators gave after hearing candidate’s response to each question, after all five (say) questions have been evaluated, to allow evaluator assessments to benefit from the human’s overall retrospective view of the candidates in addition to the human evaluator’s impression of how candidates answered individual questions. The methods and systems shown and described herein are useful in processing and/or matching, within bodies of knowledge including hundreds, thousands, tens of thousands, or hun- dreds of thousands of applicants and jobs or vacancies.

It is appreciated that terminology such as "mandatory", "required", "need" and "must" re- fer to implementation choices made within the context of a particular implementation or applica- tion described herewithin for clarity and are not intended to be limiting, since, in an alternative implementation, the same elements might be defined as not mandatory and not required or might even be eliminated altogether.

Components described herein as software may, alternatively, be implemented wholly or partly in hardware and/or firmware, if desired, using conventional techn iques, and vi ce-versa. Each module or component or processor may be centralized in a single physical location or physical device or distributed over several physical locations or physical devices.

Included in the scope of the present disclosure, inter alia, are electromagnetic signals in accordance with the description herein . These may carry computer-readable instructions for per- forming any or all of the operations of any of the methods shown and described herein, in any suitable order including simultaneous performance of suitable groups of operations as appropri- ate; machine-readable instructions for perfonning any or all of the operations of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the operations of any of the methods shown and described herein, in any suitable order i.e. not necessarily as shown, including performing various operations in parallel or con- currently rather than sequentially as shown; a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the operations of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the operations of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programm ed to perform, alone or in combination, any or all of the opera- tions of any of the methods shown and described herein, in any suitable order; electronic de- vices each including at least one processor and/or cooperating input device and/or output device and operative to perform e.g. in software any operations shown and described herein; information storage devices or physical records, such as disks or hard drives, causing at least one computer or other device to be configured so as to carry out any or all of the operations of any of the methods shown and described herein, in any suitable order; at least one program pre-stored e.g. in memory or on an information network such as the Internet, before or after being down- loaded, which embodies any or all of the operations of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; at least one processor configured to perform any combination of the described operations or to execute any combination of the described modules; and hardware which performs any or all of the operations of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software. Any computer-readable or machine-readable media described herein is intended to include non- transitory computer- or machine-readable media.

Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any operation or functionality described herein may be wholly or partially computer-implemented e.g. by one or more processors. The invention shown and de- scribed herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.

The system may, if desired, be implemented as a web-based system employing software, computers, routers and telecommunications equipment as appropriate.

Any suitable deployment may be employed to provide functionalities e.g. software functionalities shown and described herein. For example, a server may store certain applications, for download to clients, which are executed at the client side, the server side serving only as a store- house. Some or all functionalities e.g. software functionalities shown and described herein maybe deployed in a cloud environment. Clients e.g. mobile communication devices such as smartphones may be operatively associated with but external to the cloud.

The scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein , such that even though users of the device may not use the capacity, they are, if they so desire, able to modify- the device to obtain the structure or function.

Any“if -then” logic described herein is intended to include embodiments in which a processor is programmed to repeatedly determine whether condition x, which is sometimes tme and sometimes false, is currently true or false and to perform y each time x is determined to be true, thereby to yield a processor which performs y at least once, typically on an“if and only if" basis e.g. triggered only by determinations that x is true and never by determinations that x is false.

Features of the present invention, including operations, which are described in the con- text of separate embodiments may also be provided in combination in a single embodiment. For example, a system embodiment is intended to include a corresponding process embodiment and vice versa. Also, each system embodiment is intended to include a server-centered“view” or client centered“view”, or“view” from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities per- formed at that sewer or client or node. Features may also be combined with features known in the art and particularly although not limited to those described in the Background section or in publications mentioned therein.

Conversely, features of the invention, including operations, which are described for brev- ity in the context of a single embodiment or in a certain order may be provided separately or in any suitable subcombination, including with features known in the art (particularly although not limited to those described in the Background section or in publications mentioned therein) or in a different order "e.g." is used herein in the sense of a specific example which is not intended to be limiting. Each method may comprise some or all of the operations illustrated or described, suitably ordered e.g. as illustrated or described herein.

Devices, apparatus or systems shown coupled in any of the drawings may in fact be integrated into a single platform in certain embodiments or may be coupled via any appropriate wired or wireless coupling such as but not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, power line communication, cell phone, Smart Phone (e.g. iPhone), Tablet, Laptop, PDA, Black- berry GPRS, Satellite including GPS, or other mobile delivery. It is appreciated that in the de- scription and drawings shown and described herein, functionalities described or illustrated as systems and sub-units thereof can also be provided as methods and operations therewithin, and functionalities described or illustrated as methods and operations therewithin can also be pro- vided as systems and sub-units thereof. The scale used to illustrate various elements in the drawings is merely exemplary and/or appropriate for clarity of presentation and is not intended to be limiting.

Claims

1. A soft skill evaluation system comprising:

a third processor which compares said evaluations of individual candidate end users along each individual thread from among said threads, with characterizations of jobs along said individual bi-polar thread, respectively, including determining an extent to which individual candidate end users match individual jobs accordingly, along each individual thread from among said threads, and wherein the processor also combines said extents, for said plural threads, and accordingly selects candidate end users to present these to an administrator end-user responsible for accepting a candidate for a given job.

2. A system according to claim 1 wherein the third processor averages evaluations along each individual thread, over plural evaluators, thereby to benefit from crowd wisdom.

3. A system according to claim 1 wherein said characterizations of jobs along said bi-polar threads are based at least partly on characterizations of respective jobs posted by respective ad- ministrator end-users, collected respectively from said respective administrator end-users.

4. A system according to claim 1 wherein characterization of at least one job j 1 along said bi- polar threads is based at least partly on characterizations of at least one similar job j2 posted by at least one respective administrator end-user a2, collected from the at least one administrator end- user a2 in the past.

5. A system according to claim 4 wherein said at least one respective administrator end-user a2 comprises plural administrator end-users a2 thereby to benefit from crowd wisdom in characterizing job j l.

6. A system according to claim 1 wherein each user input device comprises a slider which may be presented on a touch (or non-touch) screen in which case the evaluator end-user may input her or his evaluation of candidate x along thread y (say: introvert-extrovert) by sliding her or his finger (or mouse) along the slider.

7. A system according to claim 1 wherein the system maintains a stored indication of how many assessments have been collected for each candidate end user and wherein a candidate end user who has accumulated N assessments is no longer presented to assessors.

8. A system according to claim 7 wherein, responsive to a session in which an evaluator in- dicates readiness to evaluate a candidate end user, the system selects a candidate end user who has accumulated less than N assessments, for the evaluator to assess, at least partly randomly.

9. A system according to claim 8 wherein when the system selects a candidate end user who has accumulated less than N assessments, for the evaluator to assess, selection takes into account how many assessments candidate end users have accumulated and prioritizes candidate end users who have accumulated less assessments, over candidate end users who have accumulated more assessments.

10. A computerized soft skill evaluation method comprising:

presenting prompts to candidate end users, accepting videos documenting the candidate end users' responses to the prompts and storing the videos in computer storage in association with ID data uniquely identifying each of the candidate end users:

presenting said videos and plural bi-polar user input devices to evaluator end users , ac- cepting, for each of respective plural soft skills aka bi-polar threads, the evaluator end users' eval- uations, using said input devices of individual candidate end users along each of said threads, and storing the evaluations in computer memory in association with the individual candidate's said ID data; and

using a processor, comparing said ev aluations of individual candidate end users along each individual thread from among said threads, with characterizations of jobs along said individual bipolar thread, respectively, including determining an extent to which individual candidate end us- ers match individual jobs accordingly, along each individual thread from among said threads, and wherein the processor also combines said extents, for said plural threads, and, accordingly, selects candidate end users to present to an administrator end-user responsible for accepting a candidate for a given job.

11. A computer program product, comprising a non-transitory tangible computer readable me- dium having computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a computerized soft skill evaluation method comprising: presenting prompts to candidate end users, accepting videos documenting the candidate end users' responses to the prompts and storing the videos in computer storage in association with ID data uniquely identifying each of the candidate end users:

presenting said videos and plural bi-polar user input devices to evaluator end users, ac- cepting, for each of respective plural soft skills aka bi-polar threads, the evaluator end users' eval- uations, using said input devices of individual candidate end users along each of said threads, and storing the evaluations in computer memory in association with the individual candidate's said ID data; and

using a processor, comparing said evaluations of individual candidate end us- ers along each individual thread from among said threads, with characterizations of jobs along said individual bi-polar thread, respectively, including determining an extent to which individual candidate end users match individual jobs accordingly, along each individual thread from among said threads, and wherein the processor also combines said extents, for said plural threads, and, accordingly, selects candidate end users to present to an administrator end-user responsible for accepting a candidate for a given job.