WO2020239475A1 - Commande automatique de communication - Google Patents

Commande automatique de communication Download PDF

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Publication number
WO2020239475A1
WO2020239475A1 PCT/EP2020/063626 EP2020063626W WO2020239475A1 WO 2020239475 A1 WO2020239475 A1 WO 2020239475A1 EP 2020063626 W EP2020063626 W EP 2020063626W WO 2020239475 A1 WO2020239475 A1 WO 2020239475A1
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WIPO (PCT)
Prior art keywords
seeker
target
optimizing
profile
proximity
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PCT/EP2020/063626
Other languages
English (en)
Inventor
Michaela ERNSTBERGER
Jürgen Reinhold ERNSTBERGER
Tomal Kanti GANGULY
Original Assignee
Aptera Gmbh
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Publication date
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Publication of WO2020239475A1 publication Critical patent/WO2020239475A1/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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task
    • 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

Definitions

  • the present invention relates to a system and a method to allow and improve communication between nodes.
  • P2P peer-to-peer platforms
  • MS multi-sided platforms
  • Nodes on the platform can have different roles for each transaction, i.e. can have the role of a company, employer, lessor, host, lender, seller or the role of lessee, employer, job seeker etc.
  • individuals that can interact through such P2P platforms do not know each-other prior to the interaction or they do not have any direct or indirect personal relation with each other.
  • P2P platforms are configured to be completely open for all potential persons or entities that can access the platform.
  • the intention usually is that the offerings on those platforms are (shall be) visible to everyone who can access the platform, which can often be accessible through the internet. In many cases, it is even not necessary to register as a user (i.e. become part of the platform), in order to be able to view (or make use of) the offerings.
  • the interaction cannot be achieved directly.
  • one of the parties making the offer is required to accept (or reject) requests for interaction (e.g. bookings) from individuals. This may delay the completion of an interaction - i.e. the interaction cannot be completed instantly (e.g. without the consent of the offeror).
  • the P2P platforms are usually configured to provide an offer posted by a peer to (possibly) all other peers on the platform or even to all other individuals that may access the platform (e.g. through internet) this raises privacy issues as well.
  • Many individuals may not want their offers for interaction to be visible to anyone on the platform, but rather only to peers that the offeror may trust and/or may have a direct or indirect competence. Hence, such users, may hesitate using such platforms or might be deterred by bad experiences.
  • the average rating may be distorted i.e. highly influenced by one rating only. Thus, it may not represent a true rating of the respective party.
  • the ratings may not reflect the true satisfaction of the other party, i.e. people may rate each other very good, just not to appear impolite or in fear to receive a negative rating in response. Or, the host may give the guest an excellent rating (even though he was not satisfied), because the guest gave him also an excellent rating. Further issues may be fake ratings in exchange for payments and/or consulting for improving the rating, which is paid for.
  • WO2013027971 is related to a friend finding method which forms a network by using friend information stored in the phone books of user mobile terminals, and searches for friends by using various nicknames of the users within a set range of the formed network; and a system therefore.
  • US2014222555A1 discloses a social revenue management method with the following features: A system and method of social revenue management is disclosed. A member of an online social network reveals a commercial offer from a third party to their social network. The offer is customized and endorsed by the first user and typically includes details such as the price. A customization app obtained from a party outside the social network is used to create the offer. The offer is typically to reserve a bookable entity for a period of time, typically accommodation or transportation. When one or more of the contacts accepts the offer, an agent of the third party rewards the first user with a commission calculated using the purchase price of the offer.
  • US2006085253A1 utilizes a method and system to utilize a user network within a network- based commerce platform and claims a method and a system to utilize a user network within a network-based commerce platform.
  • the method includes identifying a target group including at least one other user of the network-based commerce system based on at least one group association rule, the at least one group association rule being selected by a first user, communicating transaction information to the identified target group, and facilitating the transaction between at least one target user of the identified target group and the first user, wherein the first user and the identified target group have an existing relationship.
  • WO2016148377A1 is an advertisement platform apparatus qualified by the determination of a platform apparatus in which similarities between advertisements are determined, and, among the advertisements determined to have a high degree of similarity, the recipients of each advertisement are shared with each other in providing advertisements, thereby enlarging the number of people receiving advertisements and thus allowing an increase in advertising effectiveness to be anticipated.
  • W02017027206A1 discloses a social network-based inventory management with an example embodiment that includes a system server configured for social network-based inventory management.
  • the system server includes processors and non-transitory computer-readable storage media communicatively coupled to the processors.
  • the media store instructions that, in response to execution by the processors, cause the processors to perform operations.
  • the operations include receiving category specifications and access category definitions for access categories.
  • the access category definitions include relationship criteria for the access categories.
  • the operations include identifying a relationship between a second user and a first user, comparing an identified relationship with the relationship criteria, and determining which of the access categories the second user is included based on the comparison.
  • the operations include providing a product listing to the second user that is consistent with the category specification of the access category of the second user.
  • a job analysis might be undertaken to document the knowledge, skills, abilities and other characteristics (KSAOs) required or sought for the job and the relevant information may be listed in a personal description or personal specification. After that the sourcing usually takes place. Sourcing is the use of one or more strategies to identify and attract candidates to fill job vacancies.
  • appropriate media such as job portals, local or national newspapers, social media (such as Linkedln or RiteSite), business media, specialist recruitment media, professional publications, window advertisements, job centers, or in a variety of ways via the internet.
  • employers may use recruitment consultancies, recruiting agencies or independent recruiters to find otherwise scarce candidates who, in many cases, may be content in the current positions and are not actively looking to move. This initial research for candidates - also called name generation - produces contact information for potential candidates, whom the recruiter can then discreetly contact and screen.
  • KSAOs literacy, technological skills, personal traits, language skills, etc.
  • Assessments are also available to measure physical ability.
  • recruiters and agencies may use applicant tracking systems to filter candidates, along with software tools for psychometric testing and performance-based assessment. In many countries, employers are legally mandated to ensure their screening and selection processes meet equal opportunity and ethical standards.
  • Employers are likely to recognize the value of candidates who encompass soft skills such as interpersonal or team leadership. Many companies, including multinational organizations and those that recruit from a range of nationalities, are also often concerned about whether a candidate fits the prevailing company culture. Companies and recruitment agencies are now turning to video screening as a way to notice these skills without the need to invite the candidates in person. Screening as a practice for hiring has undergone continual change over the years and often organizations are using video to maintain the aforementioned standards, they set for themselves and the industry.
  • US 5164897 A relates to an automated method for selecting personnel which includes the step of selecting a first set of employees having qualifications matching a first job criteria from a first data file where the first data file includes a first plurality of records and each record includes a first job selection criteria, such as job titles, and a corresponding employee code.
  • a second step comprises selecting a second plurality of employees having qualifications matching a second job criteria from a second data file which includes a second plurality of records wherein each record includes a second job selection criteria, such as industrial experience, and a corresponding employee code.
  • a third selection is made from yet a third data file including records having a third job selection criteria, such as special skills, with a corresponding employee code. This results in three groups of selected records.
  • the method of the invention then requires selecting the records of those personnel whose employee codes occur at least once in each of the three employee sets.
  • US 7805382 B2 describes a match-based employment system and method of operation are provided.
  • the match-based employment system collects a plurality of employer seeker and employee seeker profiles, bi-directionally matches the employer seeker and said employee seeker profiles and displays at least a portion of the results to an employer seeker or an employee seeker.
  • the match-based employment system can also order the bi-directional matching results based on a bi-directional match score and display the bi directional matching results according to the ordering.
  • the match-based employment system can also perform the matching such that approximately 70% of a matching score depends upon the quality of the match between employee seeker desires and employment seeker attributes and approximately 30% of the matching score depends upon the quality of the match between employment seeker desires and employee seeker attributes.
  • US 8660871 B2 is directed to systems, methods, distributed networks, and computer- readable media are provided that relate to recruiting and employment services. Background information associated with talent-capability attributes is received from talent. Job description information is received from employers. Prospective matches are identified between employers and talent, and employers and talent are given an opportunity to consent to exchange of talent contact information.
  • an object of the present invention to overcome or at least alleviate the shortcomings of the prior art. More particularly, it is an object of the present invention to provide a method and a corresponding system to automatically determine proximity and to automatically enable communication between nodes accordingly.
  • the present invention relates to a method for automatically controlling communication between nodes comprising a plurality of steps.
  • the method comprises controlling the step of automatically providing target data of targets comprising a plurality of target IDs and a target profile assigned to each target ID.
  • the method comprises controlling the step of automatically providing seeker data of seekers comprising a plurality of seeker IDs and a seeker profile assigned to each seeker ID.
  • the method comprises controlling the step of automatically determining a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile.
  • the method comprises controlling the step of automatically analyzing the target and the seeker profiles and automatically suggesting at least one optimizing step to improve the proximity value.
  • the method may also provide a proximity component, which may be configured to automatically determine a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile.
  • a proximity component may be configured to automatically determine a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile.
  • this may advantageous, as it may allow to determine proximity between, for example, one target profile and one seeker profile, which may be related to the automatic assignation of proximity values, e.g. a proximity value may be assigned in a range of 1 to 100, where smaller values may be assigned to higher proximity between, for example, a target profile and a seeker profile, which allow to automatically infer in correlations between profiles such as between a given target profile and a given seeker profile.
  • the method may further provide an optimizing component, which may be configured to automatically analyze the target and the seeker profiles and to automatically suggest at least one optimizing step to improve the proximity value.
  • an optimizing component which may be configured to automatically analyze the target and the seeker profiles and to automatically suggest at least one optimizing step to improve the proximity value.
  • the method may provide an optimizing component, which may be configured to automatically suggest at least one optimizing step for optimizing the seeker profile to improve the proximity value.
  • an optimizing component which may be configured to automatically suggest at least one optimizing step for optimizing the seeker profile to improve the proximity value.
  • this may be advantageous, as it may allow providing an improved seeker profile, which may result to match better a given target profile, i.e. automatically suggesting at least one optimizing step may facilitate improving the proximity value between one or more given profiles.
  • the method may further provide an optimizing component, which may be configured to suggest at least on optimizing step for optimizing the seeker profile to improve the proximity value upon request of the respective seeker and/or the target.
  • an optimizing component which may be configured to suggest at least on optimizing step for optimizing the seeker profile to improve the proximity value upon request of the respective seeker and/or the target.
  • such feature may be advantageous, as it may supply processes with more individualized characteristics as well as reducing the amount of process data and consequent processing time via processing only required information to yield a given requested seeker profile with improved proximity values.
  • the method may further provide an optimizing component, which may be configured to automatically suggest at least one distinct optimizing step to improve the proximity value to compensate any difference between the target and seeker profiles or to overfulfill the difference.
  • an optimizing component which may be configured to automatically suggest at least one distinct optimizing step to improve the proximity value to compensate any difference between the target and seeker profiles or to overfulfill the difference.
  • the complexity value may be assigned to at least one, preferably all optimizing steps. In some instances, this may be advantageous, as it may allow to determine prioritization frameworks that may further supply means to determine future complexity values, define implementation priorities and/or processes execution priorities.
  • each target ID may be assigned a target node and each seeker ID may be assigned a seeker node.
  • the method may provide optimizing component, which may be configured to automatically analyze or determine the difference between a seeker profile and a target profile and to automatically check and read out relevant optimizing steps from a first database and to feed back the relevant optimizing steps to a target node and/or a seeker node.
  • optimizing component may be configured to automatically analyze or determine the difference between a seeker profile and a target profile and to automatically check and read out relevant optimizing steps from a first database and to feed back the relevant optimizing steps to a target node and/or a seeker node.
  • the data of the targets and the data of the seekers may be supplied from a second database.
  • first and/or the second databases and/or the first database may be cloud- based. It will be understood that a first database may, for example, be an optimizing data base, and it will also be understood that a second database may, for instance, be a safe database. It will be further be understood that a first and a second database may be part or not of a same cloud-based server.
  • the method may further comprise enabling communication via a communication component that may be configured to enable the communication between a target node assigned to a target and a seeker node assigned to a seeker in case the respective proximity value is exceeding a threshold value.
  • the threshold value can be set by each target node individually. In another embodiment, the threshold value can be set by each seeker node individually.
  • the threshold value may a dynamic value that can be adapted to at least one of the number of targets and number of seekers.
  • the threshold value may be automatically adapted by a neural network.
  • the threshold value may be automatically adapted by an artificial- intelligence-implemented data analyzer.
  • the threshold value may be automatically adapted by a computer-implemented data analysis of inner products.
  • the communication component may be configured to provide a graphic representation of the plurality of seekers to one target.
  • the communication component may be configured to sort the seekers according to the proximity value.
  • the communication component may be configured to sort the seekers in correlation to the proximity value.
  • the communication component may be configured to sort the seekers according to the complexity value.
  • the communication component may be configured to sort the seekers in counter-correlation to the complexity value.
  • the communication component may be configured to sort the seekers according to the proximity value and the complexity value.
  • At least one of the seeker profiles and the target profile may contain data representative of a plurality of different profile categories.
  • the different profile categories may comprise any of certificates, credentials, badges and analytical data originating from the seeker.
  • the optimizing component may be configured to access data from an optimizing database, the optimizing database containing and distinct programs to optimize seeker profiles to target profiles and wherein the optimizing component may be configured to suggest one or more programs to optimize seeker profiles to target profiles.
  • the communication component may be configured to enable communication between a target and a seeker after a distinct program from the optimizing database has been booked.
  • the communication component may be configured to enable communication between the target and the seeker after a distinct program from the optimizing database has been booked by a seeker.
  • the seeker profiles may safely be stored and certified by a second database.
  • the safe database may be blockchain-based.
  • the blockchain may be a consortium blockchain, which may be advantageous, as it may allow implementation of shared resources as well as (semi)decentralized execution of processes and a better granting of access to information, for instances, there may be access granted only to a given seeker profile or target profile, while profiles no gathering the required specifications may have the access denied.
  • the block chain may be a private blockchain, which may be advantageous, as it may allow granting trusted partners to run a communication node, which may bring along a plurality of further advantages, such as, but not limited to, reduction of verification of node updates, higher security which allow providing data confidentiality, etc.
  • the optimizing steps may be automatically generated by an optimizing generator.
  • the optimizing generator may generate an optimizing step when there is more than one common difference between a target profile and a seeker profile.
  • the invention in a second aspect, relates to a system for automatically controlling communication between nodes, the system comprising : at least one proximity component configured to automatically determine a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile; at least one optimizing component configured to automatically analyze the target and the seeker profiles and to automatically suggest at least one optimizing step to improve the proximity value.
  • the optimizing component may be configured to automatically suggest at least on optimizing step for optimizing the seeker profile to improve the proximity value.
  • the optimizing component may be configured to suggest at least on optimizing step for optimizing the seeker profile to improve the proximity value upon request of the respective seeker and/or the target.
  • the optimizing component may be configured to automatically suggest at least one distinct optimizing step to improve the proximity value to compensate any difference between the target and seeker profiles or to overfulfill the difference.
  • a complexity value may be assigned to at least one, preferably all optimizing steps.
  • each target ID may be assigned a target node and each seeker ID may be assigned a seeker node.
  • the optimizing component may be configured to automatically analyze or determine the difference between a seeker profile and a target profile and to automatically check and read out relevant optimizing steps from a first database and to feed back the relevant optimizing steps to a target node and/or a seeker node.
  • the data of the targets and the data of the seekers may be supplied from a second database.
  • first and/or the second databases and/or the first database may be cloud-based .
  • the system may comprise a communication component that may be configured to ena ble communication between a ta rget node assigned to a ta rget and a seeker node assigned to a seeker in case the respective proximity value is exceeding a threshold value.
  • the threshold value can be set by each target node individually. In a further embodiment, the threshold value can be set by each seeker node individually.
  • the threshold value is a dynamic value that can be adapted to at least one of the number of targets and number of seekers.
  • system may be configured to automatically adapt the threshold value by a neural network.
  • system may be configured to automatically adapt the threshold value by an artificial-intelligence-implemented data analyzer.
  • system may be configured to automatically adapt the threshold value by a computer-implemented data analysis of inner products.
  • the communication component may be configured to provide a graphic representation of the plurality of seekers to one ta rget.
  • the communication component may be configured to sort the seekers according to the proximity value.
  • the communication component may be configured to sort the seekers in correlation to the proximity value.
  • the communication component may be configured to sort the seekers according to the complexity value.
  • the communication component may be configured to sort the seekers in counter-correlation to the complexity value.
  • the communication component may be configured to sort the seekers according to the proximity value and the complexity value.
  • At least one of the seeker profiles and the target profile may contain data representative of a plurality of different profile categories.
  • the different profile categories may comprise any of certificates, credentials, badges and analytical data originating from the seeker.
  • the optimizing component may be configured to access data from an optimizing database, the optimizing database containing and distinct programs to optimize seeker profiles to target profiles and wherein the optimizing component may be configured to suggest one or more programs to optimize seeker profiles to target profiles.
  • the communication component may be configured to enable communication between a target and a seeker after a distinct program from the optimizing database has been booked.
  • the communication component may be configured to enable communication between the target and the seeker after a distinct program from the optimizing database has been booked by a seeker.
  • the seeker profiles may be safely stored and certified by a second database.
  • the safe database may be blockchain-based.
  • the blockchain may be a consortium blockchain.
  • the block chain may be a private blockchain.
  • system may further comprise an optimizing generator to automatically generate the optimizing steps.
  • the optimizing generator may generate an optimizing step when there is more than one common difference between a target profile and a seeker profile.
  • the invention relates to the use of the system for carrying out the method recited herein.
  • the present invention relates to a system for communication between a plurality of nodes, which may also be referred as communication system.
  • the system can be configured for facilitating at least two nodes for participating in a communication or interaction with each-other.
  • the present invention is directed to a method for automatically controlling communication between nodes.
  • Communication is intended to comprise any activities to send or exchange information, signals, control signals, sensor signals, documents, certificates electronically.
  • the method can comprise the following method steps.
  • One step can be providing target data of targets.
  • the targets can be any entity that usually offers products or services or labor.
  • the target data can comprise a plurality of target IDs and a target profile assigned to each target ID. That is, each target has a target ID and the respective profile assigned to it. That can be a static profile.
  • the profile can be made subject of to change so whenever there is a new attribute or certificate, it will be added to the target profile.
  • Each target can have assigned to it a target node, meaning a point or interface of enabling communication such as a work station, a computer, a handheld device (smart phone or tablet) etc.
  • the step of automatically controlling communication between nodes may also imply an automated computer-implemented step of coordinating communication between nodes as consequence of the step of automatically controlling the communication between nodes.
  • a seeker can be any entity that usually seeks products or services or labor.
  • the seeker data can provide a plurality of seeker IDs and a seeker profile assigned to each seeker ID.
  • Each seeker can have assigned to it a seeker node, meaning a point or interface of enabling communication such as a work station, a computer, a handheld device (smart phone or tablet) etc.
  • the present invention can further provide the step of automatically determining a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile. This can be realized by a proximity component.
  • the invention provides the steps of automatically analyzing the target and the seeker profiles and of automatically suggesting at least one optimizing step to improve the proximity value. This can be realized by an optimizing component.
  • a proximity component (lb) is provided and configured to automatically determine a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile.
  • each target ID is assigned a target node (20-23) and each seeker ID is assigned a seeker node (10-12).
  • M9 The method according to any of the preceding method embodiments wherein the optimizing component (lc) is configured to automatically analyze or determine the difference between a seeker profile and a target profile and to automatically check and read out relevant optimizing steps from a first database (3) and to feed back the relevant optimizing steps to a target node (20-23) and/or a seeker node (10-12).
  • the method according to any of the preceding method embodiments comprising a communication component (la) that is configured to enable communication between a target node (20-23) assigned to a target and a seeker node (10-12) assigned to a seeker in case the respective proximity value is exceeding a threshold value.
  • a communication component (la) that is configured to enable communication between a target node (20-23) assigned to a target and a seeker node (10-12) assigned to a seeker in case the respective proximity value is exceeding a threshold value.
  • the threshold value is a dynamic value that can be adapted to at least one of the number of targets and number of seekers.
  • threshold value is automatically adapted by a neural network.
  • M27 The method according to any of the preceding method embodiments wherein the optimizing component is configured to access data from an optimizing database, the optimizing database containing and distinct programs to optimize seeker profiles to target profiles and wherein the optimizing component is configured to suggest one or more programs to optimize seeker profiles to target profiles.
  • M28 The method according to the preceding method embodiment wherein the communication component is configured to enable communication between a target and a seeker after a distinct program from the optimizing database has been booked.
  • a system for automatically controlling communication between nodes comprising
  • At least one communication component (la) configured to enable communication between a target node (20-23) assigned to a target and a seeker node (10-12; at least one proximity component (lb) configured to automatically determine a proximity value that is representative for the proximity between at least one target profile and at least one seeker profile; and
  • At least one optimizing component (lc) configured to automatically analyze the target and the seeker profiles and to automatically suggest at least one optimizing step to improve the proximity value.
  • the optimizing component (lc) is configured to automatically suggest at least one distinct optimizing step to improve the proximity value to compensate any difference between the target and seeker profiles or to overfulfill the difference.
  • a complexity value is assigned to at least one, preferably all optimizing steps.
  • each target ID is assigned a target node (20-23) and each seeker ID is assigned a seeker node (10-12).
  • the optimizing component (lc) is configured to automatically analyze or determine the difference between a seeker profile and a target profile and to automatically check and read out relevant optimizing steps from a first database (3) and to feed back the relevant optimizing steps to a target node (20-23) and/or a seeker node (10-12).
  • the data of the targets and the data of the seekers are supplied from a second database (2).
  • the first and/or the second databases (2,3) and/or the first database (3) are cloud-based.
  • the system according to any of the preceding system embodiments comprising a communication component (la) that is configured to enable communication between a target node (20-23) assigned to a target and a seeker node (10-12) assigned to a seeker in case the respective proximity value is exceeding a threshold value.
  • the system according to the preceding system embodiment wherein the threshold value can be set by each target node (20-23) individually.
  • the system according to any of the two preceding embodiments wherein the threshold value can be set by each seeker node (10-12) individually.
  • the threshold value is a dynamic value that can be adapted to at least one of the number of targets and number of seekers.
  • the communication component (la) is configured to provide a graphic representation of the plurality of seekers (10-12) to one target (20-23).
  • the system according to any of the preceding system embodiments wherein the communication component (la) is configured to sort the seekers according to the proximity value.
  • the communication component (la) is configured to sort the seekers in correlation to the proximity value.
  • the system according to any of the preceding system embodiments wherein the communication component (la) is configured to sort the seekers according to the complexity value The system according to the preceding system embodiment wherein the communication component (la) is configured to sort the seekers in counter correlation to the complexity value.
  • at least one of the seeker profile and the target profile contain data representative of a plurality of different profile categories.
  • the different profile categories comprise any of certificates, credentials, badges and analytical data originating from the seeker
  • the optimizing component is configured to access data from an optimizing database, the optimizing database containing and distinct programs to optimize seeker profiles to target profiles and wherein the optimizing component is configured to suggest one or more programs to optimize seeker profiles to target profiles
  • the communication component is configured to enable communication between a target and a seeker after a distinct program from the optimizing database has been booked.
  • the communication component is configured to enable communication between the target and the seeker after a distinct program from the optimizing database has been booked by a seeker.
  • the seeker profiles are safely stored and certified by a second database (2). 529.
  • the safe database is blockchain-based.
  • Fig. 1 schematically depicts an embodiment of a method for controlling communication between nodes, according to embodiments of the present invention
  • Fig. 2 schematically depicts a constellation of component distribution according to embodiments of the present invention
  • Fig. 3 schematically depicts an interaction scenario between components of the present invention according to embodiments of the present invention
  • FIG. 4 schematically depicts an application of the present invention according to embodiments of the present invention.
  • FIG. 5 schematically depicts a more complex scenario where embodiments of the present invention may encounter applications according to embodiments of the present invention
  • Fig. 6 schematically depicts another application of the present invention according to embodiments of the present invention.
  • Fig. 1 schematically depicts an embodiment of method for communication between nodes, according to embodiments of the present invention.
  • Fig. 1 depicts a component 1, which is just shown for the purpose of illustration centrally.
  • the component 1 can comprise a communication component la, a proximity component lb and an optimizing component lc together as depicted in Fig. 1. These components can be combined or any of the before-mentioned sub-components can be provided separately. Separately can mean non-integral or even arranged in different spots or locations or the cloud (a network with remote components). In more simple words, in one embodiment, for example, as shown in Fig. 1, the component 1 may comprise a solely component comprising the three components (communication component la, proximity component lb and optimizing component lc).
  • the component 1 can have access to a database 2 that can comprise some or all of the data needed.
  • a separate optimizing database 3 can be provided as well.
  • the optimizing database 3 can be combined with database 2 or parts thereof.
  • the component 1 can bilaterally communicate with the databases 2, 3. Alternatively, at least one of the databases 2, 3 can be arranged to just submit data to the component. Moreover, the component can be directly or indirectly be connected with target nodes 20- 23 and/or with seeker nodes 10-12. Any part of the connections shown are shown bilaterally. However, the connection can be unilaterally as well. In the embodiment shown in Fig. 1, the component 1 is configured to communicate with all elements mentioned before. However, a selected communication with just some or one of the elements shown can also be realized.
  • Fig. 2 schematically depicts a constellation of component distribution according to embodiments of the present invention.
  • the communication component la and the proximity component lb may represent a unique component and the optimizing component lc may be an independent component. It may also be possible that component la-lb is in bidirectional communication with the component lc.
  • the communication component la may operate as an independent component in bidirectional component communication with a compound component lb-lc comprising the component lb and the optimizing component lc.
  • the proximity component lb may be the component operating as an independent component in communication with the other components (communication component la and optimizing component lc).
  • the communication between components either communication component la, proximity component lb and/or optimizing component lc, or any of distribution of them thereof, may be either bidirectional or unidirectional.
  • Such communication approach of components distribution may be advantageous, as it may allow to make better use of the databases 2 and 3, which may further yield a more efficient data processing and more processing automated controlling of communication between nodes.
  • Fig. 3 schematically depicts an interaction scenario between components of the present invention according to embodiments of the present invention.
  • Fig. 3 depicts a component la-lc which comprises a communication component la and an optimizing component lc, which is, for example, bidirectionally communicated with a proximity component lb.
  • the component la-lc may retrieve information either from a plurality of target nodes, such as node 20, and a plurality of seeker nodes, such as node 10.
  • the retrieval of information may be carry out via, for example, the communication component, which further (internally) transfer the retrieved data to the optimizing component lc, which may, for example, pre-process the retrieved data and afterwards transferring the pre-processed data to the communication component la, which may further delivery the pre-processed to the proximity component lb, which may be in charge of further processing and analyzing the pre-processed data.
  • the proximity component lb may for instance, either retrieved the processed data to the component la-lc or may also provide a different set of data containing the information requested for example, by a seeker profile.
  • the component la-lc may, for instance, receive a seeker data from one or more seeker nodes, such as node 10, which the received seeker data may further comprise a plurality of seeker IDs and a seeker profile assigned to each seeker ID,
  • each seeker can have assigned to it a seeker node, meaning a point or interface of enabling communication such as a work station, a computer, a handheld device (smart phone or tablet), etc., which granted to the final processed data, while the part of the data processing remains totally inaccessible, for example, the processes executed in proximity component lb, which may in some instances, for example, be a component remote from the component la-lc.
  • Fig. 4 schematically depicts an application according to embodiments of the present invention.
  • Fig. 4 depicts a computer-implemented data processing method 100 (not depicted), which may also be referred to simply as method 100.
  • the method 100 may, for example, comprise a plurality of nodes, which may, inter alia, be target nodes assigned with target ID such as the conceptually identified by reference numeral 20, 22, 24, and seeker nodes assigned with a seeker ID such as the conceptually identified by reference numeral 10, 12.
  • a node depicted in Fig. 4 are for demonstrative purposes only, and any other nodes may further be present as part of the scenario described in Fig. 1. It should be noted that the selection of a node as a target node or seeker node may also differ for different scenarios, for instance, a seeker node described in Fig. 4 may be a target node for a different seeker profile. Thus, a node may follow a plurality of assignations within a diverse categorization, for example, a target node may be considered a first node if, for instance, the target node generates a communication, and may be considered a second node when is does not generate a communication but participates in a node communication, etc.
  • Fig. 4 schematically depicts two application scenarios of the method 100 according to embodiments of the present invention.
  • a seeker node e.g. seeker node 10
  • a seeker profile fulfilled only by the target profile of one target node e.g. target node 22
  • a seeker node e.g. seeker node 12
  • a seeker profile fulfilled by more than one target node e.g. nodes 20 and 24.
  • a seeker node 10 may be assigned a seeker profile, which may comprise a plurality of characteristics to be fulfilled. Such characteristics may, for example, be a desired or expected property of a system which may be supplied or satisfied by one or more target nodes.
  • the bridge may be required to exhibit minimum characteristics, such as, for example, a minimum bearing capacity.
  • minimum bearing capacity it may also be advantageous to know the maximum bearing capacity of the bridge, as it may allow, for example, planning of alternative routes during high traffic times.
  • a seeker node 10 may be a computer-implemented testing method, such as a diagnostic load testing, which may be required to be carried out in order to assess the bearing capacity of a bridge in current service, such as the carrying capacity of a bridge that is regularly being used for circulation of diverse type of vehicles.
  • the bearing capacity of the bridge may comprise a plurality of minimum and/or ideal requirements, which may need to be fulfilled from a plurality of existent features such as the conceptually identified in Fig. 4 by reference numerals 30, 32, 34, 36, 38, 40, 42 and 44.
  • Such required features may, for example, be properties 30, 36, 38, 42, and 44.
  • the seeker profile 10 may then evaluate a target profile such as the target node 22, which may possess the required features or a combination of features making the target node 22 suitable for the seeker profile of the seeker node 10.
  • the method 100 may automatically score the suitability of the target node 22 for the seeker profile of the seeker node 10 and a proximity value may be generated.
  • a proximity evaluation it may be the case that one of the properties, for example, feature 42, is not present or is rendered inadequate for the seeker profile of the seeker node 10.
  • the method 100 may automatically provide an evaluation, which may be referred to as proximity evaluation.
  • the step of evaluating the proximity of the target node 22 with the seeker node 10 may further comprise the automatic analysis of data to generate an automated proximity improvement alternative, which may be used to improve the proximity value of the target node 22 to the seeker node 10, and which may be referred to as step of automatically suggesting an optimizing step via an optimizing generator.
  • the proximity evaluation may be used to generate an automated rejection of a target node when the proximity value between the target node, e.g. target node 22, and the seeker node, e.g. seeker node 10, exceeds a certain threshold.
  • a rejection may only be generated when the proximity value of the nodes and that the automated rejection may one of the suggested optimizing steps i.e.
  • an optimizing step suggested by the method 100 may be the rejection of a given target node. It will be understood that the term improvement and optimization as well as optimizing steps are used interchangeably and convey the same meaning, which is intended to comprise the actions and/or steps to be executed and/or perform in order to yield result closer to an expected result, e.g. in order to improve the proximity between nodes.
  • another seeker node for example, seeker node 12 may be assigned a seeker profile, which may comprise a plurality of features, such as features 30 to 44.
  • the method 100 may automatically prompt assessment of nodes 20 and 24 to subsequently determine the proximity value of each target node, nodes 20 and 24, to the seeker node 12. It will be understood that entirely or partial fulfillment of features by the target nodes 20 and 24 does not necessarily imply that their proximity values to the seeker node 12 are identical.
  • the node proximity assessment may generate a node proximity value, which may further score the node proximity of nodes 20 and 24.
  • the node proximity assessment may be adapted to provide a proximity score to the nodes 20 and 24.
  • the proximity score may be intended to numerically represent the proximity between the target nodes and a given seeker node via assignation of any real number. For example, a lower scoring may be represented with a higher real number, which may be intended to represent a lower proximity between a target node and the seeker node 12. Put it differently, the higher the score of a proximity evaluation between two given nodes, the closer these nodes may be respect to each other, which may further mean that the target profile of a target node matches more closely the seeker profile of the seeker node 12. Whenever a proximity score of a target node is below a minimum value, it may be possible that the method 100 would render the target node as inadequate.
  • the method 100 may further supply an automated suggestion for improvement, which may be intended to comprise the actions' necessaries to bring the features of a target node to standards necessary to satisfy the features of the seeker profile of the seeker node 12, and which may also be referred to as optimizing step.
  • optimizing step only the target node with the lowest score may be rejected and the target node with the second lower score (or highest, but still insufficient score) may be subjected to improvement, i.e. subjected to a plurality of suggested optimizing steps.
  • This approach may be advantageous as, it may allow to always have at least one target node that may be rendered suitable for the seeker node 12. For instance, if the seeker target 12 is now evaluating profiles of a plurality of bridges connecting different areas of a city that is unevenly divided by a natural cause of water, e.g. a river, which may have installed along the river a plurality of bridges, e.g. bridge 1 and 2, it may be possible that the different bridges have different proximity scoring when contrasted with the seeker node 12.
  • the seeker profile of seeker node 12 may comprise features related, inter alia, to traffic density, the types of vehicles circulating at given times such as peak hours, weight the vehicles circulating at a given time as well as the load per given time supported by the bridge. Furthermore, the seeker profile may define distances between areas of the city on one and another side of the river respect to the city center, which may render the bridges to be located non- equidistantly respect to the defined center.
  • the bridge 1 may be intended to be represented as target node 20 with given characteristics defined by a target profile
  • the bridge 2 may be intended to be identified as target node 24 with given features defined in its corresponding target profile.
  • the score of one or both bridges do not gather the minimum required to qualify as suitable for the seeker profile of the seeker node 12.
  • the bridges 1 and 2 are the only available bridges to connect one area of the city with the area on the other side of the river.
  • improvement actions may be required, for which the method 100 may automatically prompt a plurality of computer-implemented processes to be executed to generate an automated suggestion of optimizing steps.
  • the suggestion for improvement may contain a plurality of feasible actions to be implemented to bring at least one of the target nodes to satisfy the minimum required characteristics of the seeker node 12.
  • a required property may be that the bridge should be able to allocate vehicle of great freightage during peak hours.
  • bridge 1 has a partially inadequate feature, e.g. it may be that its metallic structure is over a certain number of years and due to atmospheric corrosion, the structure may represent a risk of collapse.
  • the method may automatically suggest optimizing steps which necessary to bring the bridge to optimal conditions or at least alleviate the risk of collapse to safer levels, e.g. structural reinforcement of certain or all pillars of the bridge.
  • the term automatically is intended to refer to execution of steps independently and without human control.
  • automate is intended to refer to making a process, e.g. in a factory or office, operated in order to reduce the amount of work done by humans and the time taken to do the work. It should be noticed that these are the typical definitions of automatically and automate.
  • the terms automatically and/or automate is intended to refer to steps' performance without involvement of humans, but solely via a component configured to execute and/or perform each step, for example, a communication component adapted for communication between nodes, optimizing component for optimization of nodes proximities, etc.
  • the terms are intended to describe a (semi)autonomous execution of steps via components configured for computer- implementation of the steps described herein.
  • Fig. 5 schematically depicts a more complex scenario where embodiments of the present invention may encounter applications.
  • Fig. 5 depicts a plurality of target nodes conceptually identified by reference numeral 20 to 29 and 50 to 63, each of which may have assigned an ID and a profile which may be referred to as target profile.
  • Fig. 5 depicts, as an example, two seeker nodes, nodes 10 and 12, each of which in turn may also have assigned an ID and a profile which may be referred to as target profile.
  • the number of target nodes and seeker nodes are merely exemplary, as in some instances the number of nodes, target and/or seeker nodes, may be in the range of hundreds or even thousands.
  • the seeker node 10 has a plurality of potential target nodes to fulfilled the requirements of its seeker profile.
  • the potential target nodes may differ in proximity to the seeker node 10.
  • the target nodes 51 and 24 may exhibit similar features and may also be the best available matches for the seeker node 10, however, the target node 51 may, for example, have a better scoring, i.e. a closer proximity to the seeker node 10. If the case were that the seeker node 10 requires only one target node to fulfill all features, then the method 100 may automatically suggest the target node 51 as the target node to be chosen. On the other hand, if the seeker node 10 were to require more than one target node to execute a given process, i.e.
  • the method 100 may, inter alia, automatically suggest optimizing steps to bring the inadequate target node, e.g. target node 24, to match the minimum features to qualify for the seeker profile of the seeker node 10.
  • optimizing steps e.g. actions to be taken, features to be added, etc., which may consequently allow to improve the proximity of the target nodes to a seeker node.
  • a computer-implemented analysis method which may further comprise a plurality of different and independent computer- implemented modules.
  • the amount of computer- implemented analysis method and their corresponding modules are exceeding the thousand options and that there is no proven application of a combination of method and/or modules.
  • Fig. 6 depicts schematically an application of embodiments of the present invention.
  • Fig. 6 depicts a system 200 adapted according to embodiments of the present invention to perform any of the embodiments of the method 100 (not depicted).
  • the system 200 may comprise, for example, a component la-c which in turn comprise a plurality of modules, e.g. la, lb, lc, etc.
  • the system 200 may be configured to stablish a node communication between a plurality of seeker nodes and target nodes, as shown in Fig. 6.
  • the system 200 may determine the proximity of each target node to a seeker, for example in the case of the bridge engineering and design, the system 200 may stablish a communication between a seeker node (the project that is in charge of constructing the bridge) with a plurality of target nodes (computer-implemented analysis methods, their modules and/or combinations thereof).
  • the system 200 may then determine the proximity value of each target-seeker node set and automatically suggest the most convenient target nodes i.e. the target nodes with the lowest proximity.
  • Such application of the present invention may allow to determine which computer-implemented methods and/or modules are required for the steps of engineering and designing the bridge. Furthermore, the system 200 may also suggest optimizing steps such improvement of certain modules in order to fulfill the task.
  • node is intended to convey the meaning of a given node or nodes within a plurality of nodes participating in a node communication. Therefore, in some instances, the term node may be used interchangeably with a plurality of other terms according to the nature of either the target node or seeker node in a given process, without any prejudice to the meaning of the term node. In simple words, the term node may also be intended to refer to nodes involved in a node communication, which can in turn comprise plurality of processes, for instance, processes relating to job application and/or recruitment of labor.
  • the target node may be considered a vacant in a job category, which may have associated a plurality of features or characteristics, which may further be referred to as required skills.
  • the seeker node may comprise a job applicant, which have their own set of required characteristics, i.e. a seeker profile.
  • the method 100 may allow to assess the proximity of the seeker node (job applicant) and the target node (job vacant). The same may possible to implement in vice versa, where the seeker node may be a recruiter and the target node(s) may be one or a plurality of applicants. In any of the given cases, the method 100 may, inter alia, score the proximity between nodes and/or suggest optimizing steps.
  • the term "at least one of a first option and a second option" is intended to mean the first option or the second option or the first option and the second option.
  • step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).
  • step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).

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Abstract

La présente invention concerne un procédé de commande automatique de communication entre des nœuds. La présente invention concerne également un système et une utilisation d'un tel système. Le procédé comprend les étapes consistant à : fournir des données de cibles correspondant à des cibles comprenant i) une pluralité d'ID de cibles et ii) un profil cible attribué à chaque ID de cible ; fournir des données de recherche de demandeurs comprenant i) une pluralité d'ID de chercheur et ii) un profil de chercheur attribué à chaque ID de chercheur ; déterminer automatiquement une valeur de proximité qui est représentative de la proximité entre au moins un profil de cible et au moins un profil de chercheur ; et l'analyse automatique des profils de cible et de chercheur, et la suggestion automatique d'au moins une étape d'optimisation pour améliorer la valeur de proximité. Le système comprend au moins un composant de communication (1a) configuré pour permettre une communication entre un nœud de cible (2023) attribué à une cible et un nœud de chercheur (10-12); au moins un composant de proximité (1b) configuré pour déterminer automatiquement une valeur de proximité qui est représentative de la proximité entre au moins un profil de cible et au moins un profil de chercheur ; et au moins un composant d'optimisation (1c) configuré pour analyser automatiquement les profils de la cible et du chercheur et suggérer automatiquement au moins une étape d'optimisation pour améliorer la valeur de proximité.
PCT/EP2020/063626 2019-05-29 2020-05-15 Commande automatique de communication WO2020239475A1 (fr)

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US5164897A (en) 1989-06-21 1992-11-17 Techpower, Inc. Automated method for selecting personnel matched to job criteria
US8660871B2 (en) 2002-03-19 2014-02-25 Career Destination Development, Llc Apparatus and methods for providing career employment services
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