Computer systems and processes to assist in filling out online forms

Abstract

The invention relates to computer systems (100) and methods (200, 300) for assisting internet users in filling out online form fields. The general principle of the invention is based on assisting with online form completion. Indeed, rather than automatically filling in online form fields, the invention proposes to invite the internet user to choose the digital object that best corresponds to the form field they wish to complete. In practice, the invention uses machine learning to train a model capable of automatically identifying form fields suitable for receiving digital objects that correspond to a predetermined semantic code. Furthermore, the invention uses a computer architecture that includes digital vaults to store the digital objects. Thus, data is no longer stored in the web browser, but securely on the internet. FIG. 1

Description

Title of the invention: Computer systems and methods for assisting in filling out online forms Technical field

[0001] The present invention relates to the field of online form filling. More specifically, it relates to computer systems and methods for assisting an Internet user in filling out online form fields. Prior art

[0002] An online form is a page on a website that allows an organization to retrieve digital objects (administrative files, plans, maps, invoices, videograms, telephone recordings, personal data, etc.).

[0003] To make it easier to fill out order or registration forms on the Internet, web browsers offer an autofill feature. In practice, when we insert digital objects into an online form, browsers offer to save them. We then use the pre-saved digital objects to fill out new forms elsewhere on the Internet.

[0004] However, despite the significant time savings, such functionality can pose serious security problems with regard to digital objects. Indeed, without the Internet user's consent, a malicious person can recover these digital objects. Indeed, the browser stores certain digital objects in clear text, in a file. Sometimes, the file is encrypted using encryption functions present on the computer on which the Web browser is installed. In this case, a virus that infects the computer will be able to access the encryption functions and thus the digital objects present in the file.

[0005] In view of the legal risks incurred by organizations that operate websites, it is appropriate to offer a solution that allows them to save time in filling out online forms and to preserve the security of digital objects belonging to Internet users. Summary of the invention

[0006] The present invention therefore aims to overcome the aforementioned drawbacks. For this, a first aspect of the invention proposes a computer system for assisting an Internet user in filling in at least one online form field on a Web page.

[0007] Next, a second aspect of the invention proposes a method of assisting an Internet user in filling in at least one online form field on a Web page.

[0008] Then, a third aspect of the invention proposes a method for training a machine learning model to identify in a web page a predetermined semantics of at least one online form field.

[0009] Finally, a fourth aspect of the invention provides a non-transitory computer-readable storage medium storing at least one program comprising instructions which, upon execution of the program, make it possible to cause the computer to carry out the second aspect of the invention and the third aspect of the invention.

[0010] Thus, the invention relates to a computer system for assisting an Internet user in filling out at least a first online form field on a first web page, the first online form field being designed to receive a first digital object from the Internet user, furthermore, the first online form field being associated with at least one piece of metadata. The computer system comprises: - at least one browser to visit the first web page through a communication network, - a script to be inserted into the first web page, - at least one digital safe which is associated with at least one second web page, which is configured to store at least one first digital object of the Internet user originating from the second web page and which is arranged to communicate with the script, and - an application programming interface, hereinafter referred to as API, which is arranged to communicate with the script, and which includes a database describing the semantics of the digital objects stored in the digital safe, and in which, - the script, when executed, - acquires the metadata, - creates a first vector from the metadata, - sends a first message to the API including the vector, - the API, when executed, in response to the first message, - identifies a first predetermined semantics associated with the first online form field, hereinafter called SEMI, from the metadata and a pre-trained rule-based machine learning model, also called rule-based machine learning in English, - identifies all digital safes in which SEMI is located, hereinafter referred to as compatible digital safes, and - sends a second message to the script including the SEMI and the list of compatible digital safes, - the script, when executed, in response to the second message, queries all or some of the compatible digital safes from SEMI, so that each of said digital safes returns to the script at least one first digital object of the Internet user corresponding to the SEMI, and - in response to receiving at least a first digital object from the Internet user, the script, when executed, modifies the first web page to invite the Internet user to fill in the first online form field with at least the first digital object from the Internet user.

[0011] In a first implementation, the computer system further comprises a training engine for the pre-trained machine learning model, and in which: - the script, when executed following the Internet user's refusal to fill in the form field with the Internet user's first digital object, but rather with a second digital object of the Internet user coming from the digital safe and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI, - creates a second vector from SEM2 and the metadata, and - sends the current second vector to the training engine, - in response to receiving the second vector, the training engine trains the pre-trained machine learning model from the second vector and a rule-based model learning algorithm.

[0012] In a second implementation, the script is a scripted bookmark, also called a bookmarklet in English.

[0013] The invention also covers a method for assisting an Internet user in filling out at least one first online form field of a first web page, the first online form field being designed to receive a first digital object from the Internet user, furthermore, the first online form field being associated with at least one metadata. The method comprises the following steps: - providing at least one browser to visit the first web page through a communication network, - provide a script to be inserted into the first web page, - providing at least one digital safe which is associated with at least one second web page, which is configured to store at least one first digital object of the Internet user originating from the second web page, and which is arranged to communicate with the script, and - provide an application programming interface, hereinafter referred to as API, which is arranged to communicate with the script, and which includes a database describing the semantics of the digital objects stored in the vault digital, and the method further comprising the following steps, - instruct the script to, when executed - acquire the metadata, - create a first vector from the metadata, - send a first message to the API including the vector, - instruct the API to, when executed, in response to the first message, - identify a first predetermined semantics associated with the first online form field, hereinafter called SEMI, from the metadata and a pre-trained rule-based machine learning model, also called rule-based machine learning in English, - identify all digital safes in which SEMI is located, hereinafter referred to as compatible digital safes, and - send a second message to the script including the SEMI and the list of compatible digital safes, - instruct the script to, when executed, in response to the second message, query all or part of the compatible digital safes from SEMI, so that each of said digital safes returns to the script at least a first digital object of the Internet user corresponding to SEMI, and - in response to receiving at least a first digital object from the Internet user, when the script is executed, modify the first web page to invite the Internet user to fill in the first online form field with at least the first digital object from the Internet user.

[0014] In a first implementation, the method further comprises the following steps: - instruct the script to, when executed following the Internet user's refusal to fill in the form field with the Internet user's first digital object, but rather with a second digital object of the Internet user from the digital safe and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI, - create a second vector from SEM2 and the metadata, and - send the second current vector to a training engine of the pre-trained machine learning model, - in response to receiving the second vector, training, by the training engine, the pre-trained machine learning model from the second vector and a rule-based model learning algorithm, also called rule-based machine learning in English.

[0015] In a second implementation, the script is a scripted bookmark, also called bookmarklet in English.

[0016] The invention also covers a non-transitory computer-readable storage medium storing at least one program comprising instructions, which upon execution of the program, make it possible to cause the computer to assist an Internet user in filling in at least a first online form field of a first web page according to the second aspect of the invention.

[0017] The invention also covers a method for training a machine learning model, to identify in a web page, a predetermined semantics of at least one online form field. The method comprising the following steps, for each of a plurality of training online form fields from a plurality of training web pages, each training online form field being associated with at least one metadata: - associate the training online form field with a predetermined semantics, hereinafter called SEM, - acquire at least one metadata associated with the training online form field, - create a third vector from SEM and metadata, - train the machine learning model using the third vector and a rule-based machine learning algorithm, so as to obtain a pre-trained machine learning model.

[0018] In one implementation, the rule-based model learning algorithm uses one of the approaches selected from: association rules, classifier systems, and artificial immune systems.

[0019] The invention also covers a non-transitory computer-readable storage medium storing at least one program comprising instructions, which upon execution of the program, enable training a machine learning model to identify in a web page a predetermined semantics of at least one online form field according to a third aspect of the invention. Brief description of the drawings

[0020] Other characteristics and advantages of the invention will be better understood on reading the description which follows and with reference to the appended drawings, given for illustrative purposes and in no way limiting.

[0021] [Fig.l] [Fig.l] represents a computer system according to the invention.

[0022] [Fig.2] [Fig.2] represents a first method according to the invention.

[0023] [Fig.3] [Fig.3] represents a second method according to the invention.

[0024] [Fig.4] [Fig.4] represents an online form.

[0025] For reasons of clarity, the elements shown are not to scale with respect to each other, unless otherwise stated. Description of the embodiments

[0026] The general principle of the invention is based on assistance in filling out online forms. Indeed, rather than automatically filling in online form fields, the invention proposes to invite the Internet user to choose the digital object which best corresponds to the form field which he wishes to fill out.

[0027] A digital object is understood to mean a bit stream, such as for example: a file, a group of files, a file and metadata that allows it to be described, a file accompanied by an electronic signature, an encrypted file or personal data. Personal data is understood to mean any information relating to an identified or identifiable natural person, directly or indirectly by reference to an identification number or to one or more elements specific to them. For example, personal data may be a name, an identification number, location data, an online identifier, or to one or more specific elements specific to their physical, physiological, genetic, psychological, economic, cultural or social identity.

[0028] In practice, the invention uses machine learning to train a model capable of automatically identifying form fields capable of receiving digital objects that correspond to a predetermined semantics. Semantics means what gives meaning to a digital object.

[0029] Furthermore, the invention uses a computer architecture that includes digital safes to store digital objects. Thus, the data is no longer stored in the web browser, but in a safe place on the Internet.

[0030] A digital safe is understood to mean a component of an information system consisting of software or a software / hardware combination that allows the integrity of digital objects to be preserved over time. The digital safe can be accessible through an online service such as the following digital safe services: Coffreo, OFSAD, Darwin, E-folia, Digiposte, PeopleDoc or Xambox. In the invention, a digital safe is personal to an Internet user. Thus, only the Internet user can access his digital objects thanks, in particular, to an authentication means such as, for example, an identifier and a password or an electronic certificate or biometric information.

[0031] [Fig.l] illustrates a computer system 100 according to the invention. The computer system 100 is provided to assist an Internet user in filling in at least a first online form field 21 of a first Web page 20. In a particular implementation, the first Web page 20 is defined according to a markup language hypertext of the XML type, HTML such as, for example, HTML5. In the remainder of the description, by way of illustration, the first Web page 20 is an Internet page which allows a user profile to be created on a merchant site of the Cdiscount or Amazon type.

[0032] In the invention, the first online form field 21 is provided to receive a first digital object from the Internet user.

[0033] [Fig.4] illustrates an example of an online form which comprises first online form fields 21 capable of receiving digital objects.

[0034] In the invention, the first online form field 21 is associated with at least one metadata 22, also called meta tag or meta data in English. Metadata is understood to mean any information describing a form field such as the first online form field 21. In other words, metadata is data that is used to define or describe another data item. In particular, the metadata can be used to identify (e.g. title, identifier, origin), classify / prioritize (e.g. style class, keywords, visual accessibility tags, semantic annotation) or provide technical information (e.g. data format, encoding, compression) to the first online form field 21.

[0035] In one example, the first form field in line 21 is represented by the following HTML5 code: clabel for="name">Name: cinput type="text" id="name" name="username" placeholder="Enter your name" class="formatting_username sensitive_data">. In this case, metadata 22 corresponds at least to the name and / or content of one of the following attributes of the tags <label>And <input> : 'type', 'id', 'name', 'placeholder' and 'class'.

[0036] Of course, other attributes can be used without requiring substantial modification of the invention. In one example, the address of the Web Page can be considered as metadata according to the invention. In another example, the invention will also consider as metadata the HTML5 attributes that are associated with microdata, also called microdata in English, such as 'itemid', 'itemscope', 'itemtype' and 'itemprop'. Reference can be made to the site www.w3.org / TR / microdata / to obtain more information on the W3C standard concerning microdata.

[0037] Returning to [Fig.l], the computer system 100 comprises at least one web browser 110, a script 120, an application programming interface, hereinafter called API 130 also called application programming interface in English, and at least one digital safe 140, also called digital safe in English.

[0038] First, each of the components of the computer system 100 will be described. Then, an operation of the computer system 100 will be presented.

[0039] In the example of [Fig. 1], each web browser 110 is configured to visit the first web page 20 through a wired or wireless communication network 10.

[0040] Furthermore, in the example of [Fig.l], the script 120 is inserted into the first web page 20. In one example, the script 120 is a scripted bookmark, also called a bookmarklet in English. The script 120 can be written in an interpreted language such as Jscript (Javascript), VBScript or any ActiveScripting compatible language. However, depending on the needs, a compiled language can also be used. In a particular implementation, the script 120 can include computer code which, when executed, prompts the Internet user to authenticate before being able to use the functionalities of the invention described subsequently.

[0041] Additionally, in the example of [Fig.l], the API 130 is arranged to communicate with the script 120 via the communication network 10. In one implementation of the invention, the API 130 is a set of rules programmed in a programming language that allow dynamic interaction and data exchange with the script 120. Furthermore, the API 130 includes or can access a file, a directory or a database 160 that describes the types of data stored in the digital safe 140.

[0042] Finally, in the example of [Fig.l], the digital safe 140 is associated with at least one second Web page 30, in which the Internet user has previously entered at least one first digital object. In the remainder of the description, by way of illustration, the second Web page 30 is a networking Internet page of the Linkedin type, on which the Internet user has a previously entered user profile. In practice, the digital safe 140 is configured to store the first digital object entered by the Internet user in the second Web page 30.

[0043] When the computer system 100 is in operation, its components interact with each other as indicated below to assist the Internet user in filling in the first online form field 21 of the first Web page 20.

[0044] First, the script 120, when executed, acquires at least one metadata 22 associated with a first online form field 21. Next, the script 120 creates a first vector from the metadata 22. Then, the script 120 sends to the API 130 a first message that includes the vector. In response to the first message, the API 130 first identifies a first predetermined semantic that is associated with the first online form field 21, hereinafter referred to as SEMI. In practice, SEMI is identified from the metadata 22 and a pre-trained rule-based machine learning model 500. In light of [Fig. 2], more details will be provided below regarding the training of a machine learning model 400. to obtain the pre-trained machine learning model 500. Next, the API 130 identifies the set of digital safes 140 in which SEMI is located, hereinafter referred to as compatible digital safes. Finally, the API 130 sends to the script 120 a second message that includes SEMI and the list of compatible digital safes 140.

[0045] Subsequently, the script 120 queries all or part of the compatible digital safes 140 from SEMI. Then, in response to the query from the script 120, each of said digital safes 140 returns to the script 120 at least one first digital object of the Internet user which corresponds to SEMI.

[0046] Finally, in response to receiving one or more first digital objects from the Internet user, the script 120, when executed, modifies the first web page 20 to prompt the Internet user to fill in the first online form field 21 with at least one first digital object from the Internet user. In one example, modifying the first web page 20 may include presenting, at the first online form field 21, a drop-down list that includes several first digital objects from the Internet user and that come from one or more digital vaults 140.

[0047] In a particular implementation of the preceding operation phase, the computer system 100 further comprises a training engine 150 for continuously training the pre-trained machine learning model 500. This particular implementation concerns the case of refusal of the Internet user to fill the first online form field 21 with the first digital object, but rather with a second digital object of the Internet user which comes from the digital safe 140 and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI. For example, the first digital object corresponds to a surname while the second digital object corresponds to a first name.In this case, the modification of the first web page 20 may consist of presenting, at the level of the first online form field 21, a drop-down list which includes several first and second digital objects of the Internet user which come from one or more digital safes 140.

[0048] In this particular implementation, the script 120, when executed, creates a second vector from SEM2 and the metadata 22. Then, the script 120 sends the current second vector to the training engine 150. In response to receiving the second vector, the training engine 150 trains the pre-trained machine learning model 500 from the second vector and a rule-based model learning algorithm.

[0049] [Fig.2] illustrates a method 200 according to the invention. The method 200 allows to train a machine learning model 400, to identify in a web page a predetermined semantics of at least one online form field. The method 200 can also be used to carry out the continuous training of the pre-trained machine learning model 500 as indicated above.

[0050] The method 200 requires a plurality of training web pages that each include at least one training online form field that is associated with at least one metadata 22.

[0051] The method 200 firstly consists, in step 210, in associating the training online form field with a predetermined semantics, hereinafter called SEM. The SEMs can be defined manually according to the implementation needs. The SEMs can also be obtained in an assisted manner, from one or more of the metadata 22. For example, the content of the 'name' attribute can be used to identify the semantics of the digital object. Indeed, it is quite likely that the 'name' attribute associated with an online form field that designates the surname of the Internet user will include one or more of the following texts: 'NAME', 'SURNAME', 'FAMILY NAME'. These text values ​​can therefore be used to establish the SEMs.

[0052] Returning to method 200, in step 220, at least one metadata 22 associated with the training online form field is acquired. Then, in step 230, a third vector is created from the SEM and the metadata 22. Finally, in step 240, the machine learning model 400 is trained from the third vector and a rule-based model learning algorithm, also called rule-based machine learning in English, so as to obtain a pre-trained machine learning model 500.

[0053] In a particular implementation, the rule-based model learning algorithm uses an unsupervised classification approach. For example, one may use one of the approaches selected from: association rules, classifier systems, and artificial immune systems.

[0054] The example below describes an implementation of a rule-based model learning algorithm that uses the association rule approach. One advantage of the association rule approach is that the input data can be of variable length.

[0055] The association rules approach is a data mining technique. This approach is traditionally linked to the distribution sector, which uses it to search for associations between products on receipts. However, this approach is also used in many sectors of activity in which potential groupings of products or services are sought: banking services, telecommunications services, for example. In Furthermore, it is also used in the medical sector for the search for complications due to drug combinations or for the search for fraud by searching for unusual associations. In the field of distribution, this approach searches for which products tend to be purchased together. The association rules approach is said to be unsupervised, because only the description of the purchases is available as input. In the example, the invention proposes to use this approach to determine which metadata 22 are associated with the semantics of a digital object in a web page.

[0056] Table 1, below, is an example of input data that can be used with the association rules approach:

[0057] [Tables 1] Metadata A Metadata eB Metadata eC Metadata eD SEM A SE MB SE MC Training form field 1 XXX Training form field 2 XXXX Training form field 3 XX Training form field 4 XXX Training form field 5 XXX

[0058] In Table 1, the input data of the association rules approach comprises, for each training inline form field, a list which comprises the associated metadata 22 and the predetermined semantics associated with the training inline form field, hereinafter called SEMA, SEMB and SEMC. Subsequently, two examples are given to understand how to read Table 1. In a first example of Table 1, the training inline form field 1 is associated with the metadata A and D as well as the predetermined semantics SEMA. In another example of Table 1, the inline form field training 3 is associated with metadata D as well as predetermined semantics SEMC.

[0059] Applying the association rules approach to Table 1 produces a set of association rules. An association rule is a rule of the form: 'If condition then result'.

[0060] In a particular implementation, one can limit oneself to rules where the condition is a conjunction of appearance of metadata 22 and the result includes a predetermined semantics. For example, one can obtain a rule of the form: If ('metadata A' and 'metadata D') then 'SEMC'. Semantically, such a rule is stated: If metadata A and D appear simultaneously in the attributes of a form field then said form field is of the SEMC type.

[0061] To choose the most relevant association rules, the person skilled in data mining may use conventional digital metrics such as the support index, the confidence index and the improvement index, respectively called support, confidence and lift in English.

[0062] In another particular implementation, the rule-based model learning algorithm uses a supervised classification approach. In this case, a vector is labeled that includes all the metadata 22 associated with each training online form field. In practice, this means that each vector is accompanied by a label that identifies the phenomenon to be classified, namely one of the predetermined semantics such as SEMA, SEMB and SEMC. In this context, the machine learning model used is a so-called predictive model in which significant correlations are discovered between the metadata 22 and the predetermined semantics, and in which one seeks to generalize these correlations to cases not yet observed. An example of this particular implementation can be based on the decision tree forest method, also called Random Forest in English.

[0063] [Fig. 3] illustrates a method 300 according to the invention. The method 300 makes it possible to assist an Internet user in filling in the first online form field 21 in the first Web page 20. In the method 300, the Web browser 110, the script 120, the API 130, and the digital safe 140 are defined in the same way as in the example of [Fig. 1].

[0064] The method 300 consists first of all, as indicated above, in instructing the script 120 to, when it is executed: - acquire, in step 310, at least one metadata 22, and - create, in step 320, a first vector from the metadata 22 and send it to the API 130, in a first message.

[0065] The method 300 then consists, as indicated above, in response to the first message, to instruct API 130 to, when executed: - identifying, in step 330, a first predetermined semantics associated with the first online form field 21, hereinafter called SEMI, from the metadata 22 and a pre-trained rule-based machine learning model 500, also called rule-based machine learning in English. Then, the set of digital safes 140 in which SEMI is located, hereinafter called compatible digital safes, is identified, and - send, at step 340, to the script 120 a second message which includes the SEMI and the list of compatible digital safes 140.

[0066] Then, in response to receiving the second message, the method 300 instructs, in step 350, the script 120 to query all or part of the compatible digital safes 140 from SEMI.

[0067] Then, in response to the query from the script 120, the method 300 instructs, in step 360, each of said digital safes 140 to return to the script 120 at least one first digital object of the Internet user which corresponds to SEMI.

[0068] Finally, in response to the receipt of one or more first digital objects from the Internet user, when the script 120 is executed, the method modifies, in step 380, the first Web page 20 to invite the Internet user to fill in the first form field 21 with at least the first digital object.

[0069] In a particular implementation of step 380, the case of refusal by the Internet user to fill in the first form field 21 with the first digital object of the Internet user is considered, but rather with a second digital object of the Internet user which comes from the digital safe 140 and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI.

[0070] In this implementation, the method 300 firstly consists of instructing the script 120 to, when executed - create, at step 381, a second vector from SEM2 and metadata 22, and - sending, in step 382, ​​the second current vector to a training engine of the pre-trained machine learning model 500.

[0071] Then, in response to receiving the second vector, the pre-trained machine learning model 500 is trained again, in step 390, by a training engine, based on the second vector and a rule-based model learning algorithm, also called rule-based machine learning in English, as indicated above.

[0072] In a particular embodiment of the invention, computer program instructions implement the steps of methods 200, 300. Consequently, the invention also relates to a program with at least one computer program code fixed on a non-transitory storage medium, and which is sus capable of performing the steps of methods 200, 300 when the computer program is loaded into the computer or executed in the computer.

[0073] The invention has been described and illustrated in the present detailed description and in the figures. However, the present invention is not limited to the embodiments presented. Thus, other variants and embodiments may be deduced and implemented by a person skilled in the art upon reading the present description and the appended figures. For example, the computer system 100 may be implemented in a mobile application, a web application or a voice assistant.< / label>

Claims

1. Claims Computer system (100) for assisting an Internet user in filling out at least a first online form field (21) of a first web page (20), the first online form field being designed to receive a first digital object from the Internet user, furthermore, the first online form field being associated with at least one metadata (22), the computer system comprising: - at least one browser (110) for visiting the first web page through a communication network (10), - a script (120) to be inserted into the first web page, - digital safes (140) which are associated with second web pages (30), each digital safe being configured to store at least a first digital object of the Internet user entered in the associated second web page and being arranged so as to communicate with the script, and - an application programming interface, hereinafter referred to as API (130), which is arranged to communicate with the script, and which comprises a database describing the semantics of the digital objects stored in the digital safe, and in which, - the script, when executed, - acquires the metadata, - creates a first vector from the metadata, - sends a first message to the API including the vector, - the API, when executed, in response to the first message, - identifies a first predetermined semantics associated with the first online form field, hereinafter called SEMI, from the metadata and a pre-trained rule-based machine learning model (500), - identifies the set of digital safes in which SEMI is located, hereinafter called compatible digital safes, and - sends to the script a second message comprising the SEMI and the list of compatible digital safes, - the script, when executed, in response to the second message, queries all or part of the compatible digital safes from SEMI, so that each of said digital safes returns to the script at least a first digital object of the Internet user corresponding to the SEMI, and - in response to receiving at least a first digital object from the Internet user, the script, when executed, modifies the first web page to prompt the Internet user to fill in the first online form field with at least the first digital object from the Internet user.

2. A computer system according to claim 1, further comprising a training engine (150) for the pre-trained machine learning model, and wherein: - the script, when executed following the refusal of the Internet user to fill in the form field with the first digital object of the Internet user, but rather with a second digital object of the Internet user coming from the digital safe and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI, - creates a second vector from SEM2 and the metadata, and - sends the second current vector to the training engine, - in response to receiving the second vector, the training engine trains the pre-trained machine learning model (500) from the second vector and a rule-based model learning algorithm, also called rule-based machine learning in English.

3. Computer system according to one of the preceding claims, in which the script is a scripted bookmark, also called a bookmarklet in English.

4. Method (300) for assisting an Internet user in completing at least one first online form field (21) of a first web page (20), the first online form field being designed to receive a first digital object of the Internet user, furthermore, the first online form field being associated with at least one metadata (22), the method comprising the following steps: - providing at least one browser (110) for visiting the first web page through a communication network (10), - providing a script (120) to be inserted into the first web page, - providing digital safes (140) which are associated with second web pages (30), each digital safe being configured to store at least one first digital object of the Internet user entered in the associated second web page, and being arranged to communicate with the script, and - providing an application programming interface, hereinafter called

5. API (130), which is arranged to communicate with the script, and which comprises a database describing the semantics of the digital objects stored in the digital vault, and the method further comprising the following steps, - instructing the script to, when executed - acquire (310) the metadata, - create (320) a first vector from the metadata, - send a first message to the API including the vector, - instruct the API to, when executed, in response to the first message, - identify a first predetermined semantics associated with the first online form field, hereinafter called SEMI, from the metadata and a pre-trained rule-based machine learning model (500), - identify all the digital safes in which SEMI is located, hereinafter called compatible digital safes, and - send to the script a second message comprising the SEMI and the list of compatible digital safes, - instruct the script to, when executed, in response to the second message, query all or part of the compatible digital safes from SEMI, so that each of said digital safes returns to the script at least a first digital object of the Internet user corresponding to SEMI, and - in response to receiving at least a first digital object from the Internet user, when the script is executed, modify (380) the first web page to invite the Internet user to fill in the first online form field with at least the first digital object from the Internet user. The method of claim 4, further comprising the steps of: - instruct the script to, when executed following the Internet user's refusal to fill in the form field with the Internet user's first digital object, but rather with a second digital object of the Internet user from the digital safe and which is associated with a second predetermined semantics, hereinafter called SEM2, different from SEMI, - create (381) a second vector from SEM2 and the metadata, and - send (382) the second current vector to a drive motor of the pre-trained machine learning model, - in response to receiving the second vector, training (390), by the training engine, the pre-trained machine learning model (500) from the second vector and a rule-based model learning algorithm, also called rule-based machine learning in English.

6. Method according to one of claims 4 to 5, in which the script is a scripted bookmark, also called a bookmarklet in English.

7. Method according to one of claims 4 to 6, wherein the pre-trained machine learning model is obtained by training a machine learning model (400), the training of the machine learning model (400) comprising the following steps, for each of a plurality of training online form fields from a plurality of training web pages, each training online form field being associated with at least one metadata (22): - associating (210) the training online form field with a predetermined semantics, hereinafter called SEM, - acquiring (220) at least one metadata associated with the training online form field, - creating (230) a third vector from SEM and the metadata, - training (240) the machine learning model from the third vector and a rule-based model learning algorithm,so as to obtain the pre-trained machine learning model (500).,

8. The method of claim 7, wherein the rule-based model learning algorithm uses one of the approaches selected from: association rules, classifier systems, and artificial immune systems.

9. A non-transitory computer-readable storage medium storing at least one program comprising instructions, which upon execution of the program, enable the computer to assist an Internet user in filling out at least one first online form field of a first web page according to one of claims 4 to 8.

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