WO2020113122A1 - Marché de travail en ligne holistique - Google Patents

Marché de travail en ligne holistique Download PDF

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Publication number
WO2020113122A1
WO2020113122A1 PCT/US2019/063778 US2019063778W WO2020113122A1 WO 2020113122 A1 WO2020113122 A1 WO 2020113122A1 US 2019063778 W US2019063778 W US 2019063778W WO 2020113122 A1 WO2020113122 A1 WO 2020113122A1
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WIPO (PCT)
Prior art keywords
attributes
user
score
job
attribute
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PCT/US2019/063778
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English (en)
Inventor
Kim N. KELLEY
Malahara R. PINNELLI
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Pepelwerk, Llc
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Publication of WO2020113122A1 publication Critical patent/WO2020113122A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources
    • G06Q10/1053—Employment or hiring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance
    • G06Q50/2053—Education institution selection, admissions, or financial aid

Definitions

  • the Internet has facilitated the ability of Talent (e.g., job seekers), Employers, and Educators to provide opportunities to enhance our working economy.
  • Talent can use the Internet as a primary source of finding work and finding educational opportunities in various fields and free from geographic limitations. This creates a new way to access work, the new definition of what work is, how work is done and how we approach education as a means for work readiness.
  • Employers can use the Internet to search for Talent anywhere in the world to fill the needs of the company.
  • FIG. 1 is a schematic diagram of a system for connecting talent, employers, coaches, and educators in accordance with embodiments of the present disclosure.
  • FIG. 2 is a schematic block diagram of match algorithms in accordance with embodiments of the present disclosure.
  • FIG. 3 is a process flow diagram for matching various participants in accordance with embodiments of the present disclosure.
  • FIG. 4 is a process flow diagram illustrating a tomorrow match algorithm in accordance with embodiments of the present disclosure.
  • FIGS. 5A-B are schematic diagrams illustrating example implementation details for supporting match algorithms in accordance with embodiments of the present disclosure.
  • FIGS. 6-10 are schematic diagrams of a work center in accordance with embodiments of the present disclosure.
  • FIG. 11 is a schematic diagram of the experience interface in accordance with embodiments of the present disclosure.
  • This disclosure describes a cloud based global Talent Marketplace.
  • Systems, methods, and tools described herein can be used to connect talented individuals with work they can do today, help prepare talented individuals for the work of tomorrow and connect them with resources, benefits and tools to enhance their work life.
  • Embodiments can include online tools, such as a software-as-a-solution (SAAS) platform supported by data center server features, cellular and data networks, user devices, etc.
  • SAAS software-as-a-solution
  • This disclosure describes systems, methods, and tools for allowing Talent to actively participate in the decisions that impact their work life.
  • the systems, methods, devices, and computer program products described herein can match talent with potential employers, coaches, educators, and other resources to gain a better understanding of employment landscape they are currently qualified for and how to prepare themselves for the jobs of the future.
  • FIG. 1 is a schematic diagram of a system for connecting talent, employers, coaches, and educators in accordance with embodiments of the present disclosure.
  • FIG. 1 illustrates an example system level view of the various participants of the talent marketplace.
  • the participants also referred to as users
  • the term“talent” or“talented individual” can include a user who wants to work.
  • An employer is a person or entity providing work.
  • An educator is a person or entity providing education and/or training.
  • a coach is a person or entity (or AI program) that provides advice and other guidance to allow a talent to find work, advance in a career, be successful, etc.
  • the talent marketplace can be facilitated by one or more servers hosted by a data center 110.
  • the services provided by the data center include, but are not limited to, database services 130, storage services to store user data, authentication services 116, security services 118, payment processing services (e.g., payment processing offered by third party vendors or payment processing provided by the data center services), location services 134, algorithm hosting and processing 114, graphical user interface 132, API, natural language processing (NLP) 122, etc.
  • the data center servers 110 can be located together or can be geographically distributed.
  • GUI graphical user interface
  • the users can use a graphical user interface (GUI) 132 provided either as an application (e.g., downloaded application onto a mobile device, tablet PC, smart phone, etc.) or a website interface or web portal.
  • the users can use the GUI to create login credentials, build a profile, enter user data, browse matches, communicate with matches, and upload and view files, such as photos, resumes, reference letters, work authorization documentation, etc.
  • the data center servers 110 can store user-provided data 124 as profile information to permit a user to login via secure authentication, run matching algorithms, display match percentages, display matches, display recommendations for attributes that can increase talent matches and visibility, etc.
  • Authentication credentials 116 can be used not only for security purposes, but also to establish tiers of features available to a user based on pricing. Users can sign up at different price points, each price point providing different levels of features.
  • a payment processing service can be used to facilitate secure monetary transactions on behalf of the user, including credit card transactions, bank draft transactions, non-traditional currency
  • Users can enter data 124 related to various attributes, such as skills, domain, location, distance, work type, experience, industry, job type, availability, desired job, personality characteristics, leadership skills, desired salary or rates, etc.
  • Skills describe a person’s ability to use one's knowledge effectively and readily in execution or performance, things they have the dexterity or coordination especially in the execution of learned physical tasks or a learned power of doing something competently or a developed aptitude or ability.
  • Domain is the area of subject matter expertise or field of knowledge.
  • Location is the physical space the person lives and where they want to work.
  • Distance is the miles or kilometers between the work location and the living location.
  • Work type describes employment status and includes terms defined by the department of labor and social terms including but not limited to, Full-Time, Part-Time, Contractor, Intern.
  • a match algorithm 114 can process data to return match percentages to the user and to return matches.
  • the match percentages can be displayed graphically against matches. Also displayed with matches (particularly, matches that fall outside the scoring range of a user) are lacking attributes so that the user can see which attributes the user can improve upon to increase match percentages. Match percentages can be updated on-demand or periodically as users update their profiles or as user attributes change over time.
  • Employers can also build profiles for each job post.
  • the job post profiles can make use of a library of attributes or can enter new attributes that are required and/or desired (must haves vs wants).
  • An algorithm such as one powered by AI, can construct a match percentage scoring threshold.
  • the threshold can be used to alert potential Talent of matching jobs and/or jobs that would require additional attributes.
  • a job posting score threshold can be a minimum score need to qualify for a job posting. Meeting or exceeding a score threshold can trigger the algorithm to display a job posting to a Talent.
  • Talent whose scores do not meeting or exceed a job posting score threshold can be provided with a list of attributes needed to meet or exceed the job posting score threshold.
  • the granularity of how the algorithm provides missing attributes is implementation specific. For example, the algorithm can simply list attributes, provide attributes that are must haves vs wants for a job posting, and/or include a list of mechanisms by which Talent can gain those attributes. Additional attributes can be experience, education, relocation, etc.
  • Talent will only see jobs that match or exceed Talent scores. For example, Talent with a score falling below a specific job scoring threshold will not see that job; in that case, Talent might see a summary of how more jobs can become available if Talent had additional attributes.
  • AI can also be used to link Talent with Educators.
  • Talent that has been presented with lacking attributes associated with education can be provided with a list of educators matching the Talent’s needs. Those needs can be determined by evaluating the current list of attributes, such as educational experience, location, etc.
  • Educators can also build profiles using sets of attributes, which can then be used to match Talent with appropriate Educators.
  • Mentors and coaches can be matched with Talent in a similar way.
  • Another match algorithm 114 can use user-provided data as well as match percentage results to help the user understand how to increase match percentages and connect to educators and coaches to facilitate the match percentage improvements.
  • This second match algorithm can ran on-demand or at predetermined periods of time, to update the match percentages as user attributes change over time.
  • a natural language processing service 122 can be used to parse natural language inputs, such as skills input or experience inputs, to convert those natural language inputs into a match percentage or score. Natural language processing service 122 can also parse documents written in various languages to extract attribute information, e.g., from uploaded documents, resumes, diplomas, etc. The extracted attributes can be used to derive a score by which match algorithms 114 can derive a match score.
  • a location service 134 can use zip code information, location services, global positioning services, user-entered location preferences, etc., to match users based on geographical preferences. Geographical preferences can be used in deriving scores for positions, education, mentor matches, etc. The scores derived from user entries for matches can use geographical preferences to determine best matches between users. For example, a user who does not wish to relocate can be matched with other users within a predefined search area. Employers who cannot pay for relocation can be matched with local Talent. Educational needs that are not available to a user can be augmented by educational services that are online or virtual.
  • AI 120 can be used to derive match scores and percentages, as well as identify matches, lacking attributes, educational services, and other features. As user scenarios and patterns start to be recognized over the lifetime of the product the system will be able to recognize patterns and the use of the service. AI 120 can make use of training sets, machine learning, deep learning, and other intelligence. The machine learning will not only capture common terms and phases but also natural language. The combination of patterns and the machine learning will provide opportunities for artificial intelligence to take over some aspects of the matching and coaching service by responding to context and engaging in conversation to deliver the intended goals of the exchange. This use of AI is to support Talent consistently on recurring issues and bring more complex conversations to our human coaches, thereby increasing the quality of human touch in the process. [0025] FIG.
  • A“today” match algorithm 202 can be used to match talent with employers (and vice versa) to connect people for current job openings.
  • A“tomorrow” match algorithm 204 can be used to help talent prepare for the future. For example, the tomorrow match algorithm 204 can use user-provided data and pre-existing match percentages to identify areas that talent can improve or augment attributes to allow talent to quality for jobs, and/or to identify reasons for why talent did not match with one or more employers.
  • the tomorrow match algorithm 204 can also connect talent with educators.
  • the educators can be third party educators, including individuals or entities that can provide education.
  • the educator can also be an internal educator associated with the employer. For example, talent that is currently working for an employer can use educational or training resources available through the employer to add skills or other attributes to qualify for advancement, increased pay, etc.
  • An educator match algorithm 206 can use user-provided data to connect talent with appropriate educators that can help the talent improve or augment attributes.
  • the educators are match based on the Talents likelihood to match with a job of tomorrow, and various data elements captured in their tomorrow match (interests, domain, learning delivery preferences, etc.). Preferences are set in the Talent’s Learner tile and Educators in their Educator module.
  • a coach match algorithm 208 can use user-provided data to connect talent with coaches. Coaches are matched with Talent based on the availability of coaches. Preferences are set in the Talent’s Coach tile and Coaches in their Coach module.
  • FIG. 3 is a process flow diagram 300 for matching various participants in accordance with embodiments of the present disclosure.
  • a user can enter relevant data (302), either as a talented individual, employer, educator, or coach.
  • the user can use a GUI interface through an app or website.
  • the (today) match algorithm can process the data to provide a score for the user and matches.
  • the data can be entered using a GUI interface, which can provide fields for input, selectable attributes, natural language inputs, document uploading interface, voice command interface, etc.
  • the today match algorithm 204 can use weights to assign scores for each attribute. For example, certain attributes are weighted differently than others. Employers can adjust weights for each attribute as desired. For example, one employer may value years of experience over educational experience, and therefore assign a higher weight to experience than to education.
  • the algorithm can use skills, geography, location, experience, job type, work type, availability, domain, and industry as inputs, as well as other inputs, to generate a weighted score for the user (304).
  • the match algorithm can compare scores generated for a user against other scores from other users (306, 312). More specifically, Talent scores can be compared against Employer job posting score thresholds. A matching score can be a Talent score that meets or exceeds a job posting score threshold. The matches can be displayed to the user (310). For example, talent can view all employers above a 75% match.
  • the algorithm can use a user’s membership type (e.g., based on authentication credentials) to determine what to display to a user (308).
  • a user can use a user’s membership type (e.g., based on authentication credentials) to determine what to display to a user (308).
  • a first membership type can grant the user a list of jobs that meet or exceed a threshold score.
  • a second, higher membership type can provide a list of attributes the Talent is missing in order to meet or exceed job posting score thresholds.
  • a user can be provided with a list of missing or lacking attributes to provide the user with reasons for why certain jobs desired did not match.
  • the user can be provided with missing or lacking attributes that the user can obtain to increase match percentages, whether the user matched or not. For example, if the user just meets a score threshold, the user might be able to increase a score by taking more classes or gaining some experience in a desired area. Score thresholds, therefore, can be met by meeting the“must haves” and can be exceed by including some desired attributes that are optional. Other implementation choices are also contemplated.
  • FIG. 4 is a process flow diagram 400 illustrating a tomorrow match algorithm in accordance with embodiments of the present disclosure.
  • the tomorrow match algorithm can connect Talent with one or more educatorsors to allow Talent to prepare for a Job in the future.
  • the tomorrow match algorithm can be used by talent to improve match percentages.
  • Talent can use inputted user data or the algorithm can pull previously entered user data from a user’s profile to execute the tomorrow algorithm.
  • a user can input data in a similar way as described above (402).
  • a match percentage or score can be generated, as described above (404).
  • the match percentages or scores can be used to identify and report attributes that Talent that get to can improve match percentages or scores for jobs the talent desires or to prepare for jobs, advancements, salaries, etc. (406, 408).
  • Talent might be qualified in two programming languages, but if the talent learned a third, the talent could qualify for advancement and more income.
  • Talent might be below a threshold score for a programming job, but if Talent learned a certain programming language, then Talent would exceed a score threshold for that job.
  • the tomorrow match algorithm can indicate that to the talent.
  • the tomorrow match algorithm can also connect the talent with educators so that the talent can add or improve attributes to increase match percentages (410).
  • algorithms and other computer-program products tangibly embodied on non-transient computer readable media can include operations that when executed cause a computer to perform certain functions.
  • algorithm functions can include providing a graphical user interface (GUI) to a user.
  • GUI graphical user interface
  • the GUI can be an application installed on a computer or mobile device, a website interface, a web portal, or other interface.
  • the GUI can provide a way for a user to enter data that is stored in a database or repository in a server or servers.
  • a user profile can be received from the user across the GUI, the user profile indicating a desired employment position.
  • the user profile can be built by the user entering information using the GUI or uploading information through the GUI.
  • a plurality of attributes, the plurality of attributes can be received from the user.
  • the plurality of attributes can include one or a combination of educational attributes, experiential attributes, personality attributes, geographic attributes, demographic attributes, and aspirational aptitudes.
  • the algorithm can generate a user score based on the received plurality of attributes.
  • the user score can be derived from assigning a value to each attribute.
  • the value can be a weighted value depending on the attribute, the job position sought, or other factors.
  • the AI can assign weights to the attributes to generate a score that corresponds to relevant job postings. For example, if a desired job is in a technical field, an attribute provided that is not relevant to technical skills may be weighed less than an attribute that is valued for a technical field. Other mechanisms for calculating a user’ s score are also contemplated.
  • the algorithm can identify one or more available employment positions based on the desired employment position and the user score. Job matching can be accomplished by determining that the user score is below a threshold score for a subset of the available employment positions.
  • the algorithm can identify one or more attributes that the user is lacking for the desired employment position based, at least in part, on the desired employment position, the plurality of attributes received from the user, and the user score. The one or more attributes that the user is lacking to the user on the GUI.
  • receiving the plurality of attributes can include, for each attribute, receiving a natural input corresponding to an attribute from the user through the GUI; translating the natural language input into a recognizable attribute; and using the recognizable attribute to generate the user score.
  • Some embodiments also can include identifying a validation attribute from the plurality of attributes, the validation attribute including an attribute that is to be validated; prompting the user to provide validation materials using the GUI; receiving a validation document from the user across the GUI; and validating the validation attribute using the validation document.
  • one of the attributes that the user is lacking includes an educational attribute.
  • the algorithm can include identifying one or more educational programs the user can access to acquire the educational attribute based, at least in part, on other educational attributes the user indicated; and providing the one or more educational programs to the user.
  • generating a user score based on the received plurality of attributes can include processing the plurality of attributes using machine learning, the machine learned trained with a training set of attributes associated with one or more of a specific job, a job category, a job level, or a location preference.
  • generating a user score based on the received plurality of attributes comprises assigning a weighted score to each attribute to calculate the user score, the weighted score generated based on machine learning from a training data set.
  • Some embodiments can include calculating the threshold score, the threshold score calculated by identifying one or more job attributes provided by a job provider for a specific job; assigning a weighted score for each job attribute; and generating the threshold score based on a combination of the weighted scores for each attribute.
  • the job attributes can include a priority score set by the job provider, and the weighted scores are determined based on the priority score set by the job provider.
  • Some embodiments can include receiving a plurality of job attributes from a job provider; and determining that the user is lacking one or more attributes based on a comparison of the user’s attributes and the job attributes from the job provider.
  • FIGS. 5A-B are schematic diagrams illustrating example implementation details for supporting match algorithms in accordance with embodiments of the present disclosure.
  • the system architecture is built to be secure, internet scalable, resilient, distributed and cloud enabled.
  • the system uses the latest open source technology components to give the platform speed and scale.
  • Data Layer -
  • the data layer is part of the system stack that includes data reception and storage.
  • the data layer can use various types of interface and storage components, such as a distributed server and storage network.
  • One example implementation is Cassandra.
  • Cassandra can be used as primary data source, which allows the platform to store unlimited data using distributed nodes.
  • Cassandra can be combined with Elastic search for quick data access for some of the most used data elements for matching.
  • Kafka can be used for the critical messaging between the components to give robustness to the communication among components of the system.
  • Computation Layer provides computational power for executing algorithms and complex calculations.
  • Apache Spark can be used for computation to ran the matching algorithms, which can run massive parallel in-memory processing for scale and speed.
  • Application Layer - The application layer provides application services for user interface. On example is Node with Express, which can be used to build microservice architecture to provide application services to user interface.
  • the application layer interacts with data layer elements (e.g., Kafka) for any messaging needs and works as gate way to all the 3 rd party integrations.
  • Presentation Layer - the presentation layer provides a user with a user experience through single page applications and Native Mobile apps, such as via React and React Native.
  • FIGS. 6-10 are schematic diagrams of a work center in accordance with embodiments of the present disclosure.
  • FIGS. 6-10 are drawings of the work center, giving access to Talent, Employers, and Educators access to users only information to better enhance the work they produce, the tools to complete the work and the education demanded in the market to improve course quality and outcomes.
  • the Talent work center gives choice in payroll, healthcare benefits, products and services exclusive to Members.
  • the Employer work center gives choice in payroll, onboarding and other people management services with the ability to choose what to cost-share with Talent they match with and hire on the marketplace.
  • the Educator work center gives access to tools to enhance their delivery methods and resources to keep courses relevant.
  • FIG. 7 is a schematic diagram of the experience interface in accordance with embodiments of the present disclosure.
  • the experience interface illustrates the talent, employer, and educator dashboards (or interface).
  • the software interface 702 can provide various layouts and features depending on the user and the membership level. A free membership for example can provide fewer features than a“featured” membership.
  • the interface and features can be tailored based on, e.g., the number of employees at the company and whether the company is searching for 1099 staffing or W2 staffing or other types of staffing.
  • features 704 can be provided (features 704 represent an example subset of features and are not limiting).
  • the features can be provided by internal systems or by vendor integration.
  • FIG. 8 illustrates example vendor integration 802.
  • background check vendors can be partnered with to provide aptitude assessment analysis by performing various background check functions, such as attribute validation, credit checks, criminal record checks, etc. Payroll services can be used for payroll functions. Other vendors are contemplated to provide for various features.
  • Vendor integration 802 can include support services, such as customer support.
  • FIG. 9 illustrates linkages between customer support and various features.
  • Customer support 902 can be a vendor-provided service or can be a self-provided service 904.
  • Customer support can include communications access, such as email for membership accreditation and authentication, conference services for interviews and other meetings, document storage, telecommunications services integration, web hosting, etc.
  • FIG. 10 is an example diagram 1000 of how the platform can use various algorithms for intended functions.
  • Talent 1002 can use the today match algorithm and the tomorrow match algorithm to match with employers 1004 and educators 1006, identify job postings, and identify gap recommendations. Gap recommendations can lead Talent 1002 to identify educators 1006, as well as courses offered by various matching educators that can satisfy job posting requirements.
  • An education match algorithm similar to the today match and tomorrow match, can use attributes from a user to identify matches between the user and an educator. The matches between a user and an educator can be based on a weighted scoring of attributes as well as gap recommendations (e.g., lacking or missing attributes).
  • FIG. 11 is a block diagram illustrating an example interface 1100 in accordance with embodiments of the present disclosure.
  • the interface 1100 illustrates a dashboard for Talent 1102, Employers 1104, Educators 1106, and Coaches 1108.
  • the talent dashboard 1102 is illustrated as an account profile for Talent.
  • the employer dashboard 1104 can provide information and an interface for job postings, talent matches, calendars, documents, membership information, etc.
  • the educator dashboard 1106 can provide similar information about Talent, such as class offerings, student loans, rosters, etc.
  • the educator dashboard 1106 can link to an education center 1108. Any of the dashboards can link to other centers, such as talent center, 1114, educator center 1108 or coaching center 1110 or technik center 1112. These centers can provide consolidated information associated with linked partners, matches, or other entities or functions used by each of the users.
  • a host and device may be implemented, which are equipped with functionality to implement authentication and measurement architectures as discussed in the examples above, in any one of a variety of computing architectures (e.g., using any one of a variety of different interconnects or fabrics).
  • a host may connect to a device supporting the authentication architecture within a personal computing system (e.g., implemented in a laptop, desktop, mobile, smartphone, Internet of Things (IoT) device, smart appliance, gaming console, media console, etc.).
  • IoT Internet of Things
  • a host may connect to a device supporting the authentication architecture within a server computing system (e.g., a rack server, blade server, tower server, rack scale server architecture or other disaggregated server architecture), among other examples.
  • a server computing system e.g., a rack server, blade server, tower server, rack scale server architecture or other disaggregated server architecture

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Abstract

Des systèmes, des procédés et des produits programmes d'ordinateur peuvent comprendre la réception d'un profil d'utilisateur provenant de l'utilisateur, le profil d'utilisateur indiquant une position d'utilisation souhaitée ; la réception, en provenance de l'utilisateur utilisant la GUI, d'une pluralité d'attributs, la pluralité d'attributs comprenant des attributs éducatifs, des attributs expérimentaux, des attributs de personnalité, des attributs géographiques, des attributs démographiques et des aptitudes aspirationnelles ; la génération d'un score d'utilisateur sur la base de la pluralité reçue d'attributs ; l'identification d'une ou de plusieurs positions d'utilisation disponibles sur la base de la position d'utilisation souhaitée et du score d'utilisateur ; la détermination du fait que le score d'utilisateur est inférieur à un score de seuil pour un sous-ensemble des positions d'utilisation disponibles ; l'identification d'un ou de plusieurs attributs dont l'utilisateur est dépourvu pour la position d'utilisation souhaitée sur la base, au moins en partie, de la position d'utilisation souhaitée, de la pluralité d'attributs reçus de l'utilisateur, et du score d'utilisateur ; et l'affichage de l'attribut ou des attributs dont l'utilisateur est dépourvu à l'utilisateur.
PCT/US2019/063778 2018-12-01 2019-11-27 Marché de travail en ligne holistique WO2020113122A1 (fr)

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US11797942B2 (en) 2022-03-09 2023-10-24 My Job Matcher, Inc. Apparatus and method for applicant scoring

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