EP3323099A1 - Procédé de traitement d'une demande de recommandation et moteur de recommandation - Google Patents

Procédé de traitement d'une demande de recommandation et moteur de recommandation

Info

Publication number
EP3323099A1
EP3323099A1 EP15797387.6A EP15797387A EP3323099A1 EP 3323099 A1 EP3323099 A1 EP 3323099A1 EP 15797387 A EP15797387 A EP 15797387A EP 3323099 A1 EP3323099 A1 EP 3323099A1
Authority
EP
European Patent Office
Prior art keywords
user
ranking
items
rating server
internal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP15797387.6A
Other languages
German (de)
English (en)
Inventor
Catherine NOHRA CHINA
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
B2cloud
Original Assignee
B2cloud
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by B2cloud filed Critical B2cloud
Publication of EP3323099A1 publication Critical patent/EP3323099A1/fr
Ceased legal-status Critical Current

Links

Classifications

    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the field of this invention is that of recommendation engines.
  • the invention relates to a method for processing a recommendation request.
  • Recommendation engines are real time information filtering systems that aim at automatically identifying items/contents which could interest users, for example as a function of their past choices.
  • (B2C) context for example to increase sales of electronic commerce platforms.
  • the most important feature of recommendation engines is reactivity, i.e. the promptness of the system for taking into account, user preferences, interactions with the system, etc.
  • Such user-centric recommendation system is referred to as “memory- based", because the ratings database is persistently maintained so as to be used directly for generate recommendations towards active users.
  • model-based i.e. item-centric
  • data are pre-treated offline.
  • le “learnt” model is used for predictions.
  • B2B Business-to-Business
  • the engine should maximize the quality, the trustworthiness and the transparency of recommendations over the reactivity. Moreover, the engine should assure a good resilience against noise, corrupted data and spam.
  • the present invention provides a method for processing recommendation request from a user of an equipment connected to a recommendation platform through a public network, the recommendation platform being also connected through a secured network to at least one item server referencing a plurality of items and to at least one internal rating server, the recommendation platform being also connected through the public network to at least one external rating server, the platform comprising a storage unit storing a preference profile of said user, and a processing unit,
  • the method being characterized in that it comprises performing by the processing unit steps of: (a) receiving from the equipment the recommendation request, comprising at least one attribute of a requested item, an identifier of the user and a time length;
  • step (c) comprising calculating a plurality of rankings and aggregating these rankings
  • each of the ranking is associated with a weight representing a trust level of the ranking or of the providing internal or external rating server;
  • each weight is provided by the internal or external rating server associated with the ranking of the weight
  • ⁇ step (d) comprises measuring by the processing unit the precision and/or the recall of a ranking, and dynamically updating the associated weight accordingly to said measurement.
  • calculating a ranking associated with an internal or external rating server comprises determining scores of items of the selected subset from rating data; • determining scores of items of the selected subset from data published by an external rating server (5) comprises verifying said data, so as to discard data identified as fake or corrupted;
  • step (d) consists of iteratively calculating a plurality of successive rankings, each successive ranking being calculated as a function of at least the previous ranking, the preference profile of the user and scores provided by said internal and external rating servers;
  • • calculating ranking also comprises aggregating a plurality of plurality of rankings into the current ranking.
  • the invention provides a recommendation platform connected through a public network to at least one equipment and at least one external rating server, and also connected through a secured network to at least one item server referencing a plurality of items and to at least one internal rating server, the platform comprising a storage unit storing at least the user of equipment of said user and a processing unit, characterized in that the processing unit is configured to perform:
  • reception module for receiving a recommendation request from the equipment, the request comprising at least one attribute of a requested item, an identifier of the user and a time length;
  • a selection module selecting a subset of the items referenced by the item servers matching said attribute and the preference profile of the user;
  • a determination module determining an initial ranking of the items of said subset as a function of the preference profile of the user and scores provided by said internal rating server and said external rating server;
  • an update module dynamically updating said initial ranking so as to take into account the evolution of the preference profile of the user and of the scores provided by said internal rating server and said external rating server);
  • the invention proposes a system comprising a recommendation platform according to the second aspect and at least one equipment.
  • FIG. 1 represents a system for performing the method according to the invention
  • FIG. 2 is a diagram representing steps of the method according to the invention.
  • the present method aims at providing a user with a "recommendation report" from a recommendation request, i.e. providing the user with a list of items which can fit a need of the user expressed through the search request.
  • the present "items" may be goods, products, multimedia contents, services, and generally anything that could be solution to a user's problem.
  • the present method is performed by a platform 2, implementing a recommendation engine.
  • the platform 2 comprises a processing unit 21 (for example a processor, on any other data processing means) and a storage unit 22 (a memory, typically a hard drive) storing among others a database of preference profiles of users.
  • a processing unit 21 for example a processor, on any other data processing means
  • a storage unit 22 a memory, typically a hard drive
  • the platform 2 is connected to numerous equipment and servers through a secured network 20 (in particular a Virtual Private Network, VPN), which is preferably under Internet Protocol, within a public IP network 10
  • a secured network 20 in particular a Virtual Private Network, VPN
  • VPN Virtual Private Network
  • each user uses an equipment 1 , typically a personal workstation, a smartphone or a tablet.
  • Each equipment 1 comprises its own processing unit, a Human-computer interface, HCI (typically a screen, a keyboard, a mouse, etc.), and is connected to the platform 2 through the network 10.
  • HCI Human-computer interface
  • the user is invited to create an account and to fill a search preference profile using said HCI.
  • This interface also enables the user to generate a recommendation request (see later) and to be delivered the results (said recommendation report).
  • the preference profile may for example included relevancy preferred scores, response time preferences, type of external rates, internal rates from expert knowledge bases, etc.
  • At least one item server 3 At least one internal rating server 4, also called knowledge server (both are local servers connected to the platform 2 through the secured local network 20), and at least one external rating server 5.
  • at least one item server 3 At least one internal rating server 4, also called knowledge server (both are local servers connected to the platform 2 through the secured local network 20), and at least one external rating server 5.
  • the item servers 3 are local servers each referencing a plurality of items which could possibly suits the user, i.e. candidate items. They include a data quality base of the items. Among them, there are "eligible items” i.e. potentially matching items, as it will be explained later.
  • the item servers 3 are typically presenting products or services.
  • Each candidate item is defined by a plurality of attributes enabling to compare the items together.
  • the recommendation report presents a (ranked) subset of these candidate items so as to respond to a search request generated by a user requesting items.
  • Each of the internal rating servers 4 and external rating servers 5 is a source of data from which a ranking of the eligible items can be determined. More precisely, these internal and external servers 4, 5 are respectively local and remote third-party servers providing various ratings/scorings/rankings of some of the referenced items.
  • the servers 5 might be those of consumers' forums, experts' websites, social networks, etc. It is to be noted that an item server 3 can act as an internal rating server 4.
  • the processing unit 21 consequently processes numerous and heterogeneous sources to calculate different rankings of eligible items.
  • these sources are generally classified into four groups:
  • S1 Eligible content database (i.e. data from item servers 3) and preference profiles;
  • S2 Various rating, scoring and performance key indicators (from external rating servers 5);
  • S3 Expert's knowledge and rating (from internal rating servers 4)
  • S4 User's feedback and user's interactions (from internal rating servers 4 and external rating server 5 such as a social network).
  • a ranking may be determined by group of sources (i.e. a ranking by set S1 , S2, etc.), and/or by source (i.e. each internal or external ranking server 4, 5).
  • scores may be directly provided by servers 4 and 5, or deduced by the platform 2 from data published by each of these servers.
  • the platform 2 may use tools which are able to extract from the servers 4 and 5 the rating data. For example, these tools may perform text analysis so as to read comments and identify drawback or advantages of items, and from this they may elaborate a score.
  • a first step (a) the user first generates a recommendation request, and transmits it to the platform 2.
  • the recommendation request defines a need expressed by the user, in particular in natural language.
  • the search request may be expressed as directly defining one or more features of a requested item (filtering).
  • the search request shall comprise at least one attribute of the item requested by the user.
  • the search request comprises a time length, i.e. the duration of a time period.
  • the present recommendation method defines a "dynamic session" with a predefined length, for example a week.
  • This time length is chosen by the user, along with the attributes of the item he wishes to find.
  • this time length defines the duration the platform 2 has at disposal for providing the recommendation report.
  • the processing unit 21s of the platform 2 processes data during this time length so as to refine the results and make them more accurate.
  • the quality of the results is preferred over the reactivity, and this time length is accepted by the user. Moreover, the user can arbitrate between reactivity and quality by changing the time length: the longer the duration, the higher the quality.
  • the processing unit 21 selects a subset of the items referenced by the item servers 3 matching said attribute and the preference profile of the user, i.e. identifying the eligible items among the candidate items.
  • This step consists in particular in a filtering, wherein items which are obviously not suitable for user are discarded.
  • the platform 2 applies predetermined eligibility rules to identity the eligible items.
  • a third step (c) the processing unit 21 determines an initial ranking of the items of said subset as a function of the preference profile of the user and scores provided by said internal rating server 4 and external rating server 5.
  • ranking it is meant a sorting of the eligible items: from the highest score (i.e. the item is the most recommended to the user) to the lowest score (i.e. the item is the least recommended to the user).
  • This initial ranking is a first ranking, immediately available if the user wants, but not the optimal one.
  • This ranking may be obtained by known ranking algorithms.
  • rating data from internal sources preference profile, knowledge base
  • external sources external rating servers, users feedback
  • a "supervised ensemble-ranking" method may be applied to merge the different obtained rankings.
  • the processing unit 21 of the platform 2 processes different rankings defined on the same set of items, and computes a ranking with least possible disagreement with each of input rankings.
  • R ⁇ R1 Rn ⁇ a set of n different rankings defined over the same set of items (i.e Ri are permutations defined over the set of items).
  • An ensemble ranking method computes a new permutation R* out of the set R such as:
  • R * argmin ⁇ M d ⁇ R*,Ri) .
  • d is a distance function defined over rankings.
  • Each used source S1 , S2, S3, S4 (or each internal or external rating server 4, 5) processed by the processing unit 21 of the platform 2 could thus be associated with a weight representing the trust value of the ranking provided by that source (and more generally the trust level of the source).
  • the weight of each source is advantageously updated, preferably in function of its induced precision and recall.
  • This monitoring process allows detecting spammed or ill-performing ranking sources (ex. fake external ratings).
  • the fact of using a weighted ensemble-ranking method allows the system to be less sensitive to errors induced by few rankings sources. This enables also to avoid the "cold start” phenomenon since initial rankings can always be calculated with enough workable data from the knowledge base source.
  • the present method gives the ability to always adjust the weight of various data sources and ranking, according to their completeness and to strengthen the engine scalability and reliability
  • an external rating is preferably further checked by the processing unit 21 so as identity fake users and pseudo-ratings. Suspicious external ratings are not taken into account for calculating a ranking.
  • the initial ranking is dynamically updated, in other words successive rankings are calculated.
  • the ranking evolves through iterations taking into account evolutions of the items specifications (as provided by items servers 3), evolutions of the user preference profile, evolutions of internal rating servers 4 and external rating servers 5 (sources can be added or removed), and evolutions of the data from these sources.
  • weights associated with sources can vary over time.
  • the step (d) advantageously comprises dynamically updating the weights and consequently dynamically updating the plurality of rankings to aggregate.
  • the "up-to-date” ranking is therefore calculated according to previously calculated rankings, the preference profile of the user and scores provided by said internal rating servers 4 and external rating servers 5. More precisely, this is not a simple recalculation of the ranking, but an iterative calculation of it, depending from previous values.
  • the ranking slowly "converges" towards an optimum.
  • the formulas and rules used to calculate the ranking are first predetermined according to a model, an then inferred by automatic learning, so as to not limit it and gradually improve its quality.
  • the recalculation of a ranking may be either periodically performed, or at each evolution of the data quality base.
  • the processing unit 21 interrupts the calculation of the ranking and prepared said recommendation report. More precisely, it transmits as "final ranking" the current ranking to the equipment 1 , for outputting.
  • the final ranking may be accompanied with synthesis and/or graphical representation of the recommendation data. Should the user not satisfied of the result, the session may be prolonged by a new time length. It is to be noted that at any moment before the end of the given time period, the user may requests interrupting the updating, and the current ranking become the final ranking, even if such ranking could have been further refined.
  • the final report is transmitted to the user as a message, such as an e-mail, a SMS (Short message service etc.).
  • "updated reports” may be transmitted to the user if he wished to.
  • Such reports are notifications sent to the equipment 1 containing a current ranking. For example, it may be decided that at each time the ranking changes (i.e. if at least one item has its rank modified after an update), then a report is transmitted to the user for notifying it. Therefore, the user is aware of the gradual improvement of the recommendation he has requested.
  • the invention proposes the platform 2 for performing the method according to the first aspect.
  • said recommendation platform 2 is connected through a secured network 20 to at least one item server 3 referencing a plurality of items, and to at least one internal rating server 4. Said recommendation platform 2 is also connected to at least one equipment 1 and to at least one external rating server 5 through a public network 10.
  • the platform 2 comprises a storage unit 22 storing a preference profile of at least the user of equipment 1 and a processing unit 21
  • the processing unit 21 is configured to perform:
  • reception module for receiving a recommendation request from the equipment 1 , the request comprising at least one attribute of a requested item, an identifier of the user and a time length;
  • a selection module selecting a subset of the items referenced by the item servers 3 matching said attribute and the preference profile of the user;
  • a determination module determining an initial ranking of the items of said subset as a function of the preference profile of the user and scores provided by said internal rating server 4 and/or external rating server 5;
  • a transmission module transmitting the current ranking as a final ranking to the equipment 1 when said time limit is over or when the user requests interrupting the updating.
  • the processing means may also perform as explained a supervision module monitoring the weight of sources/rankings.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un procédé permettant de traiter une demande de recommandation d'un utilisateur d'un équipement (1) connecté à une plateforme de recommandation (2) également connectée à au moins un serveur d'articles (3) référençant une pluralité d'articles, à au moins un serveur d'évaluation interne (4) et à au moins un serveur d'évaluation externe (5), la plateforme (2) comprenant une unité de stockage (22) pour stocker un profil de préférences dudit utilisateur et une unité de traitement (21 ). Le procédé est caractérisé en ce qu'il consiste, au moyen de l'unité de traitement (21), à : (a) recevoir de l'équipement (1 ) la demande de recommandation comprenant au moins un attribut d'un article demandé, un identifiant de l'utilisateur et une durée ; (b) sélectionner un sous-ensemble des articles référencés par les serveurs d'articles (3) pour apparier ledit attribut et le profil de préférence de l'utilisateur ; (c) déterminer un classement initial des articles dudit sous-ensemble en fonction du profil de préférences de l'utilisateur et des scores fournis par ledit serveur d'évaluation interne (4) et ledit serveur d'évaluation externe (5) ; (d) mettre à jour ledit classement initial de façon dynamique ; (e) lorsque ladite durée est terminée ou lorsque l'utilisateur demande l'interruption de la mise à jour, transmettre le classement actuel en tant que classement final à l'équipement (1). L'invention concerne également un système permettant de mettre en œuvre ledit procédé.
EP15797387.6A 2015-07-16 2015-07-16 Procédé de traitement d'une demande de recommandation et moteur de recommandation Ceased EP3323099A1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/IB2015/001818 WO2017009683A1 (fr) 2015-07-16 2015-07-16 Procédé de traitement d'une demande de recommandation et moteur de recommandation

Publications (1)

Publication Number Publication Date
EP3323099A1 true EP3323099A1 (fr) 2018-05-23

Family

ID=54601824

Family Applications (1)

Application Number Title Priority Date Filing Date
EP15797387.6A Ceased EP3323099A1 (fr) 2015-07-16 2015-07-16 Procédé de traitement d'une demande de recommandation et moteur de recommandation

Country Status (3)

Country Link
EP (1) EP3323099A1 (fr)
CN (1) CN107851263B (fr)
WO (1) WO2017009683A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109104471A (zh) * 2018-07-26 2018-12-28 新疆玖富万卡信息技术有限公司 一种推荐服务的方法、管理服务器和推荐服务器
CN110889747B (zh) * 2019-12-02 2023-05-09 腾讯科技（深圳）有限公司 商品推荐方法、装置、系统、计算机设备及存储介质
CN110968622B (zh) * 2019-12-20 2024-04-02 同济大学 一种会计报告定制方法、平台和终端

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6434546B1 (en) * 1998-12-22 2002-08-13 Xerox Corporation System and method for transferring attribute values between search queries in an information retrieval system
US20030013433A1 (en) * 2001-07-10 2003-01-16 Koninklijke Philips Electronics N.V. Recommender system with user-selectable input limiting factors and output ripeness indicator
US6795793B2 (en) * 2002-07-19 2004-09-21 Med-Ed Innovations, Inc. Method and apparatus for evaluating data and implementing training based on the evaluation of the data
US20110289088A1 (en) * 2010-05-19 2011-11-24 Frank N. Magid Associates, Inc. System and method for ranking content interest
CN101957847B (zh) * 2010-09-21 2011-11-23 百度在线网络技术（北京）有限公司 一种搜索系统及其实现方法
FR2969338A1 (fr) * 2010-12-20 2012-06-22 France Telecom Procede et terminal d'enrichissement de donnees
WO2012138266A1 (fr) * 2011-04-05 2012-10-11 Telefonaktiebolaget L M Ericsson (Publ) Procédés et agencements de création de recommandations personnalisées
IN2014CN02384A (fr) * 2011-10-13 2015-06-19 Koninkl Philips Nv
CN103294692B (zh) * 2012-02-24 2017-10-17 北京搜狗信息服务有限公司 一种信息推荐方法及系统
EP2645324A1 (fr) * 2012-03-30 2013-10-02 Sony Corporation Procédé permettant de recommander des articles et système de recommandation
CN103714084B (zh) * 2012-10-08 2018-04-03 腾讯科技（深圳）有限公司 推荐信息的方法和装置
CN103810626A (zh) * 2012-11-13 2014-05-21 姚德明 网络购物的方法与网购中介
US20150127660A1 (en) * 2013-11-04 2015-05-07 Here Global B.V. Method and apparatus for calculating ranks and trust levels for data sources
CN104463630B (zh) * 2014-12-11 2015-08-26 新一站保险代理有限公司 一种基于网购保险产品特性的产品推荐方法及系统

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
None *
See also references of WO2017009683A1 *

Also Published As

Publication number Publication date
CN107851263A (zh) 2018-03-27
CN107851263B (zh) 2022-07-15
WO2017009683A1 (fr) 2017-01-19

Similar Documents

Publication Publication Date Title
JP5432243B2 (ja) メディアオブジェクト照会の送信および反応
US7703030B2 (en) Method and system for providing customized recommendations to users
US9305092B1 (en) Search query auto-completions based on social graph
US11934474B2 (en) Selective presentation of content types and sources in search
US11188545B2 (en) Automated measurement of content quality
US8650133B2 (en) Adaptive rating system and method
US10255282B2 (en) Determining key concepts in documents based on a universal concept graph
US20090327054A1 (en) Personal reputation system based on social networking
US20130124437A1 (en) Social media user recommendation system and method
US20170371925A1 (en) Query data structure representation
US20120221561A1 (en) Computer system, database and uses thereof
CN109635206B (zh) 融合隐式反馈和用户社会地位的个性化推荐方法及系统
EP2359276A1 (fr) Classement et sélection d entités sur la base de scores de réputation ou d influence calculés
US20200104427A1 (en) Personalized neural query auto-completion pipeline
US20190066054A1 (en) Accuracy of member profile retrieval using a universal concept graph
US11323404B2 (en) System and method for displaying message or user lists
US20190065612A1 (en) Accuracy of job retrieval using a universal concept graph
WO2017003823A1 (fr) Amélioration d'une pertinence de recherche utilisant une réputation de chercheurs passée
WO2017009683A1 (fr) Procédé de traitement d'une demande de recommandation et moteur de recommandation
US8825698B1 (en) Showing prominent users for information retrieval requests
US20120290552A9 (en) System and method for search of sources and targets based on relative topicality specialization of the targets
WO2006076472A2 (fr) Procede et systeme de mise a disposition de recommandations personnalisees
JP2017151741A (ja) 情報処理装置、情報処理方法及びプログラム
WO2015191741A1 (fr) Systèmes et procédés d'exécution de transactions en ligne dépendant d'une relation
WO2016018467A1 (fr) Classement de recherche influencé par le comportement

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20180216

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
RAP1 Party data changed (applicant data changed or rights of an application transferred)

Owner name: B2CLOUD

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20200122

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

REG Reference to a national code

Ref country code: DE

Ref legal event code: R003

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20210915