FR3093201A1 - PROCEDURE FOR PROVIDING RELEVANT INFORMATION ASSOCIATED WITH A PATENT - Google Patents

Abstract

PROCESS FOR PROVIDING RELEVANT INFORMATION ASSOCIATED WITH A PATENT The method (10) for providing relevant information associated with a patent which comprises a step of entering (11) a patent identifier, comprises the following steps: - conversion (12) of the identifier into a first set of keywords representing a technical field, in a first database associating an identifier with a set of keywords; - creation (13) of a request for the collection of 'statistical information, as a function of the keywords resulting from the conversion and, for each said information, a second set of keywords associated with said information, - comparison (14) of the sets of keywords, - of assignment (15 ) a weight for each item of information collected as a function of the result of the comparison, - prioritization (16) of the set of statistics as a function of the weight of each item of information, - selection (17) of a set of statistics from among the sets of statistics hierarchical and re (18) of each statistic in the selected statistic set. Figure for abstract: figure 1

Description

PROCEDURE FOR PROVIDING RELEVANT INFORMATION ASSOCIATED WITH A PATENT

The present invention relates to a method for providing relevant information associated with a patent. It applies, in particular, to research in databases and to the enrichment of queries in patent databases with national statistical data. In addition, the present invention applies to the automatic detection of user behavior in terms of patent extensions.

The compartmentalization of information of different types leads to difficulties in linking by a user wishing to cross distinct sources to increase his knowledge of a subject.

This compartmentalization results in a risk of non-identification of information from a first source by the user when he searches for information based on information from a second source. There is also a risk of identifying incorrect information or information that is less relevant than possible due to the user's choice in the search for information. Finally, it obviously results from a waste of time linked to the search by the user who must analyze information from a first source to identify a search criterion in a second source, carry out the search, sort the results before to select one.

Databases listing invention patents are not linked to databases of statistical information relating to countries, such as macro-economic information, for example, as soon as this information is not directly linked to statistics dealing with patents. This compartmentalization of information makes it difficult to articulate patent-type information with other information that is nevertheless necessary in the development of a strategic choice related to patents. Such a choice is, for example, the geographical extension of a patent application under cover of the right of priority or during a PCT application exit. Such a choice requires the cross-referencing of information representative of the market potential of the patent application, with budgetary data.

The present invention aims to remedy all or part of these drawbacks.

To this end, the present invention is aimed at a method for providing relevant information associated with a patent comprising a step of entering at least one invention patent identifier, which comprises:
- a step of converting the entered identifier into a first set of keywords representative of at least one technical field of the patent, in a first database associating at least one identifier with a set of keywords;
- a step of creating a request for collecting information representative of a set of national statistics, accessible on a network, according to the keywords resulting from the conversion and, for each said piece of information, a second set of words -keys associated with said information,
- a step of comparing the first set of keywords and each second set,
- a step of assigning a weight to each piece of information representative of the set of national statistics collected according to the result of the comparison,
- a stage of prioritization of the set of national statistics according to the weight of each said piece of information,
- a step of selecting at least one set of national statistics from among the sets of hierarchical national statistics and
- a step of supplying each said national statistics of the set of national statistics selected.

Thanks to these provisions, a query in a patent database is enriched, according to keywords associated with the patent, with corresponding national, supranational or subnational statistics, automatically and reliably so that a user can receive reliable information quickly. Such information makes it possible, in particular, to make an informed choice of international extension of a patent application, for example.

In some embodiments, the method that is the subject of the present invention comprises, upstream of the entry step, a step for constructing the first database which comprises:
- a step of collecting and indexing public identifiers of at least one patent and
- a step of associating, with each patent whose public data is indexed, at least one item of information representative of at least one keyword.

These embodiments make it possible to create a new search database using available public data associated with relevant keywords in the technical field of the invention.

In embodiments, the information is representative of at least one title of a technical class associated with a set of keywords.

These embodiments make it possible to deduce from an international or cooperative class of patents keywords that technically represent the field of application of the patent sought. Such keywords can be associated via a correspondence table, for example.

In embodiments, the information is representative of at least a portion of the content of the patent.

Thanks to these provisions, the keywords are deduced from the content of the patent to correspond more precisely to the patent concerned. The content here can refer to the description, claims or abstract, for example.

In some embodiments, the step of constructing the first database includes a step of creating a tree structure, linking at least two patent identifiers, the information representative of at least one keyword being associated in function of each identifier linked to the identifier entered during the entry step.

These embodiments make it possible to link patents by family of patents, for example according to a priority claimed according to the convention of the Paris union. Patent families can therefore be created, the information contained in the database of a patent being able to enrich a query formulated with regard to a patent of the same family.

In embodiments, the method that is the subject of the present invention which comprises a step of extrapolating a national statistic from a set of national statistics presenting a missing national statistic, each value obtained by extrapolation being associated with the second set of words -key information representative of the set of national statistics of the extrapolated value.

These embodiments make it possible to extrapolate national statistics if one or more national statistics is missing from a set of national statistics.

In some embodiments, the method that is the subject of the present invention comprises a step of building the second database comprising:
- a step of collecting and indexing sets of national statistics,
- a step of associating, with each set of indexed national statistics, at least one item of information representative of at least one keyword.

These embodiments make it possible to construct a database of disparate national statistics.

In some embodiments, the sets of national statistics are organized in at least one tree structure comprising at least two levels, a so-called "upper" level to which is attached a so-called "lower" level comprising several sets of national statistics, the set of statistics value of the higher level being equal to the sum of the sets of values of the lower level.

These embodiments make it possible to match a classification of patents to a classification of national statistical data.

In embodiments, during the extrapolation step, a national statistic is extrapolated based on the higher level set of national statistics.

Thanks to these provisions, the extrapolation of national statistics is consistent with the other sets of national statistics in the same tree structure.

In embodiments, the sets of national statistics are organized according to two distinct trees, a national statistic of one tree being extrapolated according to a set of national statistics of another tree.

These embodiments allow extrapolation against other sets of related national statistics, but in two separate trees.

In embodiments, the first database comprises, for each patent, an identification of at least one holder and a code representative of the geographical scope, the method further comprises:
- a step of associating at least one other patent from the first database corresponding to the first set of keywords of the identifier entered and/or to the holder of the title,
- a step of counting the number of occurrences of each code representative of the geographical scope as a function of each other patent associated patent whose identifier is entered;
- a step of weighting each national statistic of the set of national statistics selected according to the number of occurrences,
- during the supply stage, the set of national statistics is classified by decreasing weighting.

Thanks to these provisions, the behavior of managers in similar situations is modeled and makes it possible to deduce, for the manager in charge of the industrial property title studied, an appropriate strategy according to the behavior observed to limit costs and optimize the decisions taken.

In embodiments, each national statistic is associated with a geographic area of scope, the method further comprises;
- a step of defining a predetermined limit value,
- a stage of classification of each national statistic of the set of national statistics selected by geographical area and by decreasing value,
- an iterative step of calculating the sum of each national statistic, in order of classification, until the value of the sum is greater than or equal to the predetermined limit value, and
- during the supply step, the national statistics provided are the national statistics used during the last iteration of the calculation step.

These embodiments make it possible to enrich the query with geographical areas.

Other advantages, aims and particular characteristics of the invention will emerge from the non-limiting description which follows of at least one particular embodiment of the method which is the subject of the present invention, with reference to the appended drawings, in which:

represents, schematically and in the form of a flowchart, a particular succession of steps of a first method embodiment which is the subject of the present invention,

represents, schematically and in the form of a flowchart, a particular succession of steps of a second method embodiment which is the subject of the present invention,

represents, schematically and in the form of a flowchart, a particular succession of steps of a third method embodiment which is the subject of the present invention,

represents, schematically and in the form of a flowchart, a particular succession of steps of a fourth method embodiment which is the subject of the present invention,

represents, schematically, a graphic representation of the objects manipulated by the method which is the subject of the present invention.

DESCRIPTION OF EXAMPLES OF EMBODIMENT OF THE INVENTION

This description is given on a non-limiting basis, each characteristic of an embodiment being able to be combined with any other characteristic of any other embodiment in an advantageous manner.

Note that the figures are not to scale.

It should be noted here that the term "patent" designates both a patent application, a granted patent, a utility certificate and any other industrial property title classified according to one of the technical patent classifications, including in particular the CIB (for “International Classification of Patents”) and the CCB (for “Cooperative Classification of Patents”, known in English under the acronym CPC, for “Cooperative Patent Classification”).

It should be noted that the term “national statistics” refers to any data characterizing a country or a group of countries. This data can be representative of an economic activity, a political or legal state or representative of the population of a country, for example. These examples are given without limitation. By extension, a set of national statistics is a set of numerical values representing the same entity, over several time periods and/or geographical areas. Note that a time period is a duration of predefined length, for example a year or a month. We also note that a geographical area is defined by the delimitation of the borders of a country or a group of countries.

“Machine learning” is any statistical approach technology that allows a computer program to improve its performance in solving a task without this increase in performance being the result of explicit programming. The machine learning considered can implement any algorithm known to those skilled in the art to achieve the objective for which it is applied.

It is noted that the first and the second computer terminal can be confused.

FIG. 1 shows a flowchart of steps of a first particular embodiment of the method 10 which is the subject of the present invention. The method 10 for supplying relevant information associated with a patent which comprises a step 11 of inputting at least one invention patent identifier, comprises:
- a step 12 of converting the entered identifier into a first set of keywords representative of at least one technical field of the patent, in a first database associating at least one identifier with a set of keywords;
- a step 13 of creating a request for collecting information representative of a set of national statistics, accessible on a network, according to the keywords resulting from the conversion and, for each said piece of information, a second set of keywords associated with said information,
- a comparison step 14 of the first set of keywords and of each second set,
- a step 15 of assigning a weight to each item of information representative of the set of national statistics collected according to the result of the comparison,
- a step 16 for prioritizing the set of national statistics according to the weight of each said piece of information,
- a step 17 of selecting at least one set of national statistics from among the sets of hierarchical national statistics and
- a step 18 of supplying each said national statistics of the set of national statistics selected.

The input step 11 is performed by means of a first computer terminal comprising a man/machine interface, for example a keyboard or a touch screen. During input step 11, an operator inserts into an input field a sequence of alphanumeric characters representing a patent identifier. An operator enters, for example, a patent application or publication number. For example, the patent identifier can be a code composed of two letters representing a geographical area and a set of numeric characters representing an application number.

In embodiments, the identifier corresponds to the publication number, application number or grant number of a patent or patent application. Preferably, each industrial property title of the first database comprises a field representative of the geographical area, country or region, covered by the title. The form of a patent identifier is known to those skilled in the art. The entry step 11 may include a step for displaying a predictive entry (otherwise called "intuitive entry") known to those skilled in the art.

During the conversion step 12, the identifier entered is associated with at least one keyword in the first database. The construction of the first database is described with regard to the flowchart of figure 2.

The construction step 21 of the first database includes:

- a step of collecting 22 and indexing public identifiers of at least one patent and

- an association step 23, with each patent whose public data is indexed, at least one piece of information representative of at least one keyword.

The collection step 22 is carried out, for example, by the implementation of an electronic calculation circuit, of the computer or computer server type, configured to receive via a means of communication, such as a network card for example, identifiers patent public.

During the collection 22 and indexing step, several public patent databases of patents are collected and assembled. Each patent is defined by computer data representing a unique identifier.

In some embodiments, the method that is the subject of the present invention comprises a plurality of data collection steps 22 . Such a plurality of collection steps 22 can correspond either to the successive collection of an identical type of data and/or to the collection of several distinct types of data.

Preferably, each patent is associated with at least one of the following characteristics:
- computer data representative of a holder of the title called "holder",
- computer data representative of at least one technical class called "class",
- an agent,
- a priority number,
- a priority date,
- the identification of at least one inventor,
- a filing date,
- a deposit number,
- a publication date,
- a publication number,
- a title,
- at least one document cited in the patent examination procedure,
- an abstract,
- a description,
- at least one claim and/or
- any other information known to those skilled in the art and present on at least a database of industrial property titles.

The computer data representative of a holder may represent a name, an address, a telephone number, a legal form, a unique identification number issued by a government administration, and/or any other information representative of a company or of an individual.

Each computer data representative of at least one technical class may correspond to an element of a patent classification such as the "Cooperative Patent Classification" (acronym "CPC") or the "International Patent Classification" (acronym “IPC”). Each code representing a technical field of the CPC and the IPC includes at least, in the following order:
- a letter representing a section,
- two figures representing a class,
- a second letter representing a subclass,
- at least one figure representing a group.

In embodiments, each code further comprises at least two other digits separated from each digit representing a group by the symbol “/”. Said other two digits represent a subgroup. For example, the alphanumeric string of data representative of a technical class would be "A21C1/06".

The association step 23 is carried out, for example, by the implementation of an electronic calculation circuit, of the computer or computer server type, configured to directly or indirectly associate at least one keyword with a patent identifier . An indirect association is made, for example, by associating a corresponding technical class indicator, via a correspondence table, with a set of keywords.

In embodiments, during the association step 23, the information is representative of at least one title of a technical class associated with a set of keywords. For example, the technical class is associated, in another database, with a title. Key words can be extracted from said title.

In embodiments, the information is representative of at least a portion of the content of the patent. For example, the information is representative of at least part of a claim, aggregate, title or description.

In embodiments, the information is representative of at least one activity of at least one holder. For example, each holder is associated with an economic field of activity in another database. The technical field of activity may be represented by a code according to the French activity nomenclature (acronym "NAF") or any equivalent in other countries.

In embodiments, the information is representative of a combination of the above information.

In some embodiments, the construction step 20 comprises a step 24 for creating a tree structure, linking at least two patent identifiers, the information representative of at least one keyword being associated according to each identifier linked to the identifier entered during the entry step.

The construction step 24 is carried out, for example, by the implementation of an electronic calculation circuit, of the computer or computer server type, configured to associate in a database several patent identifiers according to a determined rule.

In embodiments, one or more patents may be cited during the granting procedure of the patent whose identifier is entered, linking said patents to the patent whose identifier has been entered. Each of said patents is identified by a unique identifier associated with information representing at least one keyword. In embodiments, two patent identifiers are linked by at least one common priority claim, or by a common owner, for example.

In particular embodiments, such as that represented in FIG. 2, the method 20 comprises, upstream of the conversion step 12, a step of reading data representative of a content of the patent, the step of conversion 12 being carried out according to the data read.

Such a reading step can be carried out, for example, by the implementation of an electronic calculation circuit configured to execute a computer program for reading documents.

This representative data is, for example, a representative text of the abstract of the patent on a database, such as a patent register. This textual content is then, for example, added to the first set of keywords, so that the calculation is then linked to the patent which contextualizes the class to the application targeted by the patent.

During the conversion step 12, each piece of information representative of at least one keyword is obtained by consulting the first database. Then, at least one keyword associated with the information is obtained. Preferably, each keyword obtained is integrated into the first set of keywords. The conversion step 12 is carried out, for example, by the computer server in a database associating, for at least one patent identifier, at least one technical class. This database is, for example, an online patent register, such as, for example, the French patent register kept by the INPI (for "Institut National de la Propriété Industrielle"), the European patent register kept by the EPO (for "European Patent Office") or the United States Patent Register held by the USPTO (for "United States Patent and Trademarks Office", translated by Office des Brevets et des Marques des Etats -United). This database is preferably the DocDB database maintained by the EPO.

In embodiments, the converting step includes a step of removing duplicates from the first set of keywords.

In embodiments, at least one keyword for which the representative information is at least one of a part of a claim, title, abstract and/or description is extracted from said element.

In embodiments, at least one keyword for which the representative information is at least one technical class is extracted from the title of the technical class.

In embodiments, at least one keyword is associated with a relevance value, this relevance value being calculated by semantic and/or statistical analysis of the candidate keywords for conversion.

In embodiments in which a tree structure is created, the set of keywords includes at least partially the keywords of a patent whose identifier is linked to the patent whose identifier is entered. There is then an accumulation of keywords for more precision during the search.

The method for extracting keywords is known to those skilled in the art.

The method 10 comprises a step 13 of creating a request for collecting information representative of a set of national statistics, accessible on a network, according to the keywords resulting from the conversion and, for each said information, a second set of keywords associated with said information.

An information gathering request, also known as a “search request”. During step 13 of creating a query, the first set of keywords is used by an information search engine in the second database. A query is a set of words associated with logical operators such as “AND”, “OR”, or even “AND/OR”. The search engine digs into the data at its disposal to find the words according to the defined logical operators.

Preferably, during the creation step 13, the set of words is associated with an “AND/OR” operator, that is to say that the set of words can appear simultaneously or alternately in a search field .

The second database is detailed with regard to the construction step 31 represented in FIG. 3. The construction step 31 of the second database comprises:
- a step of collecting and indexing 32 sets of national statistics,
- A step 33 of association, with each set of indexed national statistics, at least one item of information representative of at least one keyword.

Step 32 of collecting and indexing sets of national statistics is carried out, for example, by a human operator consulting an open database of national statistical data and recording all or part of the data accessible on a computer server or on a computer terminal, such as a computer. This collection step 32 can also be automatic and carried out by connecting a computer server or collection computer to an open database. Alternatively, national statistical data can be entered by a human operator, via a man-machine interface, on a computer server or collection computer.

Each national statistical data thus collected includes, at the very least, a set of keywords associated with a numerical value. For example, a national statistical data collected is: "number of stethoscopes sold in Europe" associated with the number 175,372.

The comparison step 14 is carried out, for example, by the computer implementation of an algorithm for measuring lexical and/or semantic proximity between two sets of keywords. Such an algorithm is, for example, an algorithm for measuring a distance:
- from Hamming,
- from Levenshtein,
- from Damerau-Levenshtein, the edit distance and the edit distance with transposition and/or
- Jaro-Winkler distance.

These examples of distances are well known from the prior art and from the field of text mining, aim to evaluate the distance between two text strings, such as class keywords or national statistical data for example.

This measurement of distance, or of similarity index on the contrary, takes place for at least one pair formed of a set, or subset, of keywords of a class and of a set, or under - set of keywords of national statistical data.

Classes and sets of national statistics can belong to a tree structure, called classification, made up of nodes and branches. Two such trees are illustrated in Figure 5:
- a technical 505 classification on the one hand,
- a classification 510 of national statistical data on the other hand.

The 505 technical classification is made up of two levels of nodes, 540 and 515, with node 540 thematically encapsulating node 515. For example, node 540 corresponds to agricultural techniques, and node 515 to fruit growing.

Two terminal 520 classes are also represented and attached to node 515. These classes correspond to the endings of the tree structure and are, for example, “orchard cultivation techniques” and “greenhouse cultivation techniques”. We call, in this document, “class” both a node and an end of the classification.

Classification 510 of national statistical data is formed of two levels of nodes, 525 and 530, node 525 thematically encapsulating node 530. For example, node 525 corresponds to the agricultural production values of fruits and vegetables, and node 530 to the agricultural production value of fruits.

Two terminal 535 classes are also represented and attached to node 530. These classes correspond to the endings of the tree structure and are, for example, "fruit produced under greenhouse" and "other fruit production". In this document, “national statistical data” refers to both a node and an end of such a classification of national statistical data.

When at least one of the data among classes or national statistical data is classified according to a determined tree structure, the comparison step 13 can be modified as follows: the similarity is measured between pairs of classes – national statistical data is carried out at different levels of at least one tree structure so as to prioritize the national statistical data according to the level for which the similarity index is maximum.

Thus, preferably, this index is measured starting either from the highest or the lowest level of a classification and going to the other end.

In the example of Figure 5, for example, a similarity index is first measured between node 540 and node 525, then between node 540 and node 530, then between node 540 and each terminal data 535 . The same cycle begins again with the children of node 540, and so on, so as to produce a matrix of similarity indices.

This matrix makes it possible to identify, for a given class, the most relevant national statistical data, whatever the level in the tree structure of this national statistical data. Preferably, during the calculations associated with a node, all or part of the keywords of the children of said node are included in the keywords of the node.

In the example of Figure 5, for the class “greenhouse cultivation techniques”, the most similar national statistical data is “fruit produced under greenhouse”. For the class “orchard cultivation techniques”, the most similar national statistical data is, in this example, “agricultural production values of fruit”.

Preferably, the lower a national statistical data associated with a class is in the economic tree structure, the more its similarity index is increased.

In variants, during a selection step, a user can request, via a man-machine interface, to go up to a higher technical classification level or to go down to a lower technical classification level, in this case, the national statistical data are adapted to correspond to the level of classification intended by the user.

When no similarity index has been calculated for a given pair, the similarity index is determined as minimum, for example.

The method 10 comprises a step of assigning a weight to each item of information representative of the set of national statistics collected as a function of the result of the comparison. The assigned weight is representative of at least one element, or a combination among:
- the similarity measurement carried out if necessary,
- the number of keywords common to both sets,
- the classification level,
- the completeness of the set of national statistics,
- the origin of the national statistical data,
- a relevance or non-relevance factor given by the user by means of a man-machine interface.

In particular embodiments, such as that represented in FIG. 1, the method 10 the step of weighting a set of national statistics is carried out at least partially as a function of data representative of a nationality of an applicant of the patent. This representative datum can be read, for example, from an online database, such as a patent register for example.

A step 16 for prioritizing the set of national statistics according to the weight of each said piece of information. Ranking step 16 is carried out, for example, by sorting the national statistics in relation to a given class as an ascending or descending function of the assigned weight. This sorting is carried out, for example, by a computer program. This hierarchization step 16 aims to facilitate the selection by a human user or by an automatic selection process carried out, for example, by an artificial intelligence.

In particular embodiments, such as that represented in FIG. 1, the step 16 of hierarchizing sets of national statistics is carried out, when at least two classes are read, according to at least one class associated with at least national statistical data.

In variants, the computer server associates with at least one class an indicator representative of an application or technological character, the hierarchy 16 being carried out so that the national statistical data associated with an application class are placed higher hierarchically.

The method 10 comprises a step 17 of selecting at least one set of national statistics from among the sets of hierarchical national statistics.

The selection step 17 is carried out, for example, by a human operator selecting, via a man-machine interface and on a computer terminal, at least one national statistical datum to be associated with at least one class. Alternatively, this selection step 17 is carried out automatically as soon as a pair has a weight greater than a predetermined or determined limit value and learned by an artificial intelligence. Such learning is described with respect to an optional learning step described below.

In some embodiments, the method 10 comprises a recording step, implemented, for example, by a computer server connected to the terminal used by the human operator to record each selection made by the operator so as to gradually constitute a database of links between classes and national statistical data. This registration is intended to facilitate the selection made during a second selection stage.

In some embodiments, a connection step is carried out, for example, by implementing a data network, such as the Internet or a cellular network for example, between the second computer terminal and the computer server recording the associations between classes and national statistical data.

The second selection step is carried out, for example, by a human operator selecting, via a man-machine interface and on a computer terminal, at least one association from among the registered associations. Alternatively, this selection step is performed automatically based on user behavior learned by artificial intelligence. Such learning is described with respect to an optional learning step described below.

At the end of this process 10, a patent has been associated with a set of national statistics, such an association being provided, for example, displayed or used in a subsequent process.

In particular embodiments, the method 10 includes a step of registering at least one association selected during the second selection step on a computer server.

This registration step is carried out, for example, in a manner similar to the registration step, via the connection of the computer server to the second terminal. Preferably, this association is registered in a user profile of the user who carried out the second selection step.

In these variants, the step of assigning a weight can be adapted according to a context of the user, or an indicator of relevance or not, and to reduce the field of associations applicable to the user . Thus, the associations recorded during the step of assigning a weight 17 can be contextually neutral while the associations recorded during the registration step take into account the context specific to the user.

For example, national statistical data can associate a technical class of “electronic control means” with both national statistical data of “automatic door controls” and “automatic gas valve controls”. These two associations may have been selected during the first step 17 of selection since they target many electrical control means. During the second stage of selection, the user, manager of a portfolio of patents in the field of gas, selects only the automatic controls of gas valves so as to visualize national statistical data for his use of the patents, modifying thus the weight assigned to the sets of national statistics.

In some embodiments, at least one selection step 17 is carried out by an automatic learning algorithm.

In particular embodiments, such as that represented in FIG. 1, the method 10 comprises, downstream of the registration step, a step 150 of learning a selection behavior during the second selection step, the machine learning algorithm selecting an association based on at least one learned behavior.

The learning step 150 is carried out, for example, in a probabilistic manner by successive reading of the selections made by a user to determine a behavior of the user with regard to the first and second sets of keywords. The following description considers that a first set is defined by the title of a class and that a second set is defined by the title of a set of national statistics. When a class – national statistics set association frequency is lower than a determined limit frequency, the class – national statistics set association is either withdrawn for this user, or pre-selected. When an association frequency is low for a determined number of distinct users, the weight assigned between the class and the set of national statistics can be reduced so that during the selection step, this association is prioritized at a lower level or, if the selection is automated, this association is erased.

In deeper learning models, the machine learning algorithm is designed to detect, in a set of keywords not selected by a user against a set of national statistics, the composition of this set so to establish a rule according to the successive behaviors of the user.

The supply step 18 is carried out, for example, by implementing a display on a screen of the second terminal.

Specific steps and specific embodiments of the step for constructing the second database are described below.

In some embodiments, the method that is the subject of the present invention comprises, downstream of the collection step 32, a step of normalizing at least part of the collected data.

The problem to be solved is the following: a collected data set can include data classified according to several distinct systematic approaches (taxonomy type for example), making the data inconsistent. In such a set, each datum includes both a value and an indicator of the classification system used. However, it results from the plurality of systematic approaches used an inconsistency between the representative data, however, of the same object or concept.

An illustration of this example is, for example, the classification of a monetary value of shoe production changing from one year to the next. In this illustration, this monetary value can be positioned in a class named “A” of a first system for a given year and in a class named “B” of a second system for another year. However, for the first year determined, class “B” corresponds in the first system used to the monetary value of soap production, resulting in an inconsistency in the data collected.

This illustration can be schematized as follows, for data representative of a single object and/or concept:

Footwear production 2010 2000 Class of 2010 AT Classification system 2010 1 Footwear production 2011 2500 Class of 2011 B Classification system 2011 2

Thus, during the normalization step, a set of collected data is translated according to a unified systematic approach. This normalization step is carried out, for example, by an electronic calculation device configured to assign a value to each entry of a database comprising the set of data, said value being associated with a unique classification system indicator used .

The classification system used can be determined by a user, via a man-machine interface. The user chooses, for example, a classification system from a list of all classification systems for which an indicator is detected among the data in the dataset.

The classification system used can be determined automatically, for example, by the electronic calculation device configured to statistically determine a number of data associated with each classification system and to select the classification system whose number of occurrences is the higher. Alternatively, this statistical approach can be carried out on a sample of data corresponding to states of the object and/or concept between two determined dates, for example two years before the date of execution of the harmonization step .

Alternatively, the electronic calculation device is configured to statistically determine a number of data associated with each classification system for data corresponding to the last state of the object and/or concept that this data represents and to select the classification system with the highest number of occurrences.

Once the classification system used has been determined, the standardization step preferably implements a correspondence table between classification systems to replace the classification system indicators not used by the classification system indicators. For example, in the table below, the entries corresponding to the lines “class 2010 – A” and “classification system 2010 – 1” are replaced or supplemented by lines “class 2010 – B” and “classification system 2010 – 2”.

The correspondence table can be predetermined, collected or dynamic and obtained by the use of an automatic learning algorithm configured to determine a semantic proximity between objects of at least two different classification systems and, according to a value from this semantic proximity, determining a correspondence between the classes corresponding to said objects in the classification systems.

For example, in the table above, the values for 2010 and 2011 are both representative, regardless of the classification system, of the production value of footwear. The proximity between these two labels is total since these labels are identical: a correspondence can thus be automatically generated between classes A, of the 2010 classification system and B of the 2011 classification system.

In variants, when this correspondence table associates several classes in a non-used system with a class in the system used, the values associated with these classes are added to produce a composite value. For example, if a value in 2010 for class "A" is 10 and the value in 2010 for a class "B" is 20 and the lookup table maps "A" to class "C" and " B” to class “C”, the value in class “C” for 2010 and in the classification system of “C” is 30.

Sometimes such an approach is not possible, especially when a class from a classification system that predates the classification system in use is split into two. In these cases, it is impossible to know what part of a value associated with a class of the previous classification system is to be assigned to each of the two new classes in the classification system used. In such cases, the electronic calculation device assigns either a value of 0 in the classification system used, or a value determined according to a determined proportion between the two classes of the classification system used.

In these variants, the method that is the subject of the present invention comprises a step 34 of extrapolating a national statistic from a set of national statistics presenting a missing national statistic, each value obtained by extrapolation being associated with the second set of keywords information representative of the set of national statistics of the extrapolated value. This extrapolation step 34 is carried out, for example, by the electronic calculation device calculating an average of the relative proportion of known values for classes of the classification system used forming, in a combined manner, a class of the previous classification system for another known dataset. For example, here is a data table:

Footwear production 2010 2000 Class of 2010 AT Classification system 2010 1 Footwear production 2011 3000 Class of 2011 B Classification system 2011 2 Footwear production 2011 1000 Class of 2011 VS Classification system 2011 2

In this example, the value associated with class "A" in the 2010 classification system corresponds partly to the value associated with class "B" and partly to the value associated with class "C" of the 2011 classification system The value associated with classes “B” and “C” in the 2011 classification system represent respectively 75% and 25% of the total of these two values.

Thus, the value associated with class “A” in the 2010 classification system can receive corresponding values in classes “B” and “C” of the 2011 classification system respectively the values “1500” and “500”.

In some embodiments, the method that is the subject of the present invention comprises a step of calculating an aggregated value as a function of values collected during the collection step 32, harmonized during the harmonization step and/or or extrapolated during step 34 of extrapolation. Such an aggregated value corresponds, for example, to a value of an economic indicator for a patent office, this economic indicator value being obtained by adding the values of the economic indicator for the member countries of this patent office.

In some embodiments, the extrapolation step 34 consists of filling in missing values in a sequence of data.

For example, if the dataset only has values for the years 2010, 2011, 2013 and 2014, which value should be considered for the year 2012? This continuity of data is essential to detect a trend in many uses.

Several extrapolation methods can be used here. In a first variant, a linear interpolation is used to determine a mathematical function which comprises two points surrounding a missing datum and by calculating, from this function, the value for the missing point. For example, if the value associated with 2011 is "10" and the value associated with 2013 is "30", the value for 2012 can be estimated as "20", which is the average of "10" and "30".

Such a method, however, is limited to cases where a value after the desired value is known and, moreover, loses a great deal of precision when several successive values are missing. Finally, such a method prevents the value thus calculated from leaving the interval between the minimum value and the maximum value of the values surrounding the value to be estimated.

To overcome these drawbacks, an alternative extrapolation method used may consist of trend extrapolation.

In some embodiments, the sets of national statistics are organized in at least one tree structure comprising at least two levels, a so-called "upper" level to which is attached a so-called "lower" level comprising several sets of national statistics, the set of statistics value of the higher level being equal to the sum of the sets of values of the lower level. In these embodiments, a curve fitting function may be employed on a set of values prior to a value to be filled. Once this curve has been obtained, the calculation of the missing value can be carried out according to one of the coordinates of this value, such as a year for example.

However, such a method can generate curves locally presenting growth rates that are too high to be consistent with real values measured a posteriori.

To avoid such an inconvenience, a value to be filled can be determined by weighting the results of different extrapolation methods, such as linear interpolation and curve fitting methods.

In embodiments, in which the sets of national statistics are organized in at least one tree structure comprising at least two levels, a so-called "upper" level to which is attached a so-called "lower" level comprising several sets of national statistics, the set of national statistics of the higher level being equal to the sum of the sets of values of the lower level.

In embodiments, the sets of national statistics are organized according to two distinct trees, a national statistic of one tree being extrapolated according to a set of national statistics of another tree. In these embodiments, extrapolation methods consist of applying a trend from another set of values to the set of values for which a value must be filled, during a tree comparison step . This other set of values can come from a step 32 of collecting a set of distinct national statistics.

These methods consist in determining a rate of variation of a value from a rate of variation measured for other known values of another set of values.

For example, in a very simplified way, if between the years 2010 and 2011, the growth rate of the Gross Domestic Product is 5%, a production value of shoes in 2011, to be filled, can be calculated by using the formula “shoe production value in 2010 x 105%”.

The set of values used to determine this variation can be predetermined or, preferably, determined automatically.

In variants where the selected set of values is determined automatically, a determination step is carried out during the method that is the subject of the present invention. This determination step is carried out, for example, by an electronic calculation device.

During this determination step, at least one set of values is selected from a set of available sets of values. For each such set of values selected, an evaluation of the relevance of the set of values is carried out. This evaluation is carried out, for example, by the comparison between a variation over time of the set of values and a variation over time of the set of values of which a value is to be filled. If the signatures of the variations correspond, i.e. have a correlation index greater than a determined value, the set of values evaluated is validated.

The rate of change corresponding to a known value in the known set of values corresponding to an unknown value in the set of values to be filled is then applied to the set of values to be filled to determine the unknown value.

In variants, known values of the set of values to be filled are virtually considered as unknown, so that the prediction reliability can be evaluated during the evaluation step from real values.

In variants, whether the extrapolation is carried out to fill in a missing value in a sequence of values or to calculate a value at the end of a set of values, the method that is the subject of the present invention implements an automatic learning algorithm configured to from a set of known values and a determined number of parameters associated with these values, determining a weighting between values associated with parameters of other sets of values.

Below, an example of an extrapolation step is provided.

In this example, we consider that the values collected come from different sources and that these values are representative of different ways of characterizing a country. For exemple :
- a first source of data provides a set of values representative of a number of births, GDP and energy production from renewable sources for France, the United Kingdom and Germany,
- a second source of data provides a set of values representative of a monetary value representative of imports and exports of recyclable infant diapers for France and the United Kingdom and
- a third source of data provides a set of values representative of a monetary value representative of the production of infant diapers for France and Germany.

Each value set has a value for each of the years 2010, 2011, and 2012, for example. In this example, the production value of infant nappies is unknown for the UK in any year.

To determine the production values of infant nappies in the United Kingdom for 2010, 2011 and 2012, the electronic calculation device evaluates the evolution of the GDP of France between 2010 and 2011 and between 2011 and 2012 and compares this evolution with, respectively, the change in the representative monetary value of the production of infant diapers for France between 2010 and 2011 and between 2011 and 2012. If these changes have a higher correlation coefficient than, for example, the change in energy production from renewable sources of France between 2010 and 2011 and between 2011 and 2012 compared with, respectively, the evolution of the representative monetary value of the production of infant diapers for France between 2010 and 2011 and between 2011 and 2012, then the calculation device determines that the evolution of GDP is more representative than the evolution of energy production from renewable sources to estimate the value of the product value ction of infant diapers. Knowing the GDP of the United Kingdom and its evolution over time, the electronic calculation device determines a rule of three applicable in France and Germany between GDP and the production of infant nappies and applies this rule of three to the value of GDP of the United Kingdom for each year to determine the infant production value for the years 2010, 2011 and 2012.

In a more sophisticated variant, based on machine learning technology, the electronic calculating device assigns weighting factors to each of the data types and to recorded parameters specific to each country from known past data of production values nappies for the UK.

A second embodiment 40 of the method which is the subject of the present invention is observed in FIG. The embodiment represented in FIG. 4 is compatible with all the embodiments described above. Steps 11 to 18 are identical to the embodiments described above.

Method 40 additionally comprises steps 41 to 46.

For the implementation of the method 40, the first database comprises for each patent, an identification of at least one holder and a code representing the geographical scope.

Process 40 includes:
- a step 41 for associating at least one other patent from the first database corresponding to the first set of keywords of the identifier entered and/or to the holder of the title,
- a step 42 of counting the number of occurrences of each code representative of the geographical scope as a function of each other patent associated patent whose identifier is entered;
- a step 43 for weighting each national statistic of the set of national statistics selected according to the number of occurrences,
- During the supply step 18, the set of national statistics is classified by decreasing weighting.

In some embodiments, the association step 41 associates at least one other patent from the first database corresponding to the first set of keywords of the entered identifier. The association step 41 is carried out, for example, by the computer implementation of an algorithm for measuring lexical proximity between two sets of keywords. Such an algorithm is, for example, an algorithm for measuring a distance:
- from Hamming,
- from Levenshtein?
- from Damerau-Levenshtein, the edit distance and the edit distance with transposition and/or
- Jaro-Winkler distance.

These examples of distances are well known from the prior art and from the field of text mining, aim to evaluate the distance between two text strings, such as class keywords or national statistical data for example.

This measurement of distance, or of similarity index on the contrary, takes place for at least one pair formed of a set, or subset, of keywords of a class and of a set, or under - set of keywords of national statistical data.

In embodiments, at least one title of a technical class is associated with a set of keywords. For example, keywords can be extracted from said title.

Preferably, these embodiments comprise a step of evaluating a predominant technical class associated with the entered identifier according to each technical class associated with the entered identifier. Each technical class associated with the identifier entered gives information concerning a technical field to which the industrial property title applies. The evaluation step makes it possible to deduce the predominant technical domain.

Preferably, the evaluation step includes:
- a truncation step for each technical class to obtain a truncated class,
a step for counting the number of occurrences of classes of the same truncation, the evaluated technical field corresponding to the truncated class of majority number of occurrences.

During the truncation step, each class is truncated such that the subclass is the last element considered. For example, the class "A21C1/06" is shortened to "A21C". The truncation step is performed by computer, for example by means of an application server interrogating the first database to retrieve each class. In embodiments, the class may be shortened to the representative digits of the class. For example, the class "A21C1/06" is shortened to "A21". In other words, when the technical classification of patents, for example the CPC or the IPC, is seen as a tree structure, the truncation amounts to going back to a higher node of the tree structure.

Then, for the patent corresponding to the entered identifier, we count the number of classes which, once truncated, are identical. To carry out the calculation by computer, it is possible to use a sorting algorithm known to those skilled in the art.

The first set of keywords is determined based on the title of the truncated class appearing the most times.

Preferably, the evaluation step, the truncation step and the counting step are applied to each other patent in the first database and more particularly those with a corresponding holder.

The term corresponding holder refers to holders whose name is the same and/or whose unique national identifier, for example the number of the business directory identification system (acronym " SIREN ") is the same. In other embodiments, two holders are considered as corresponding if, after application of an algorithm for calculating a semantic proximity, the semantic proximity is greater than a predetermined limit value, for example equal to 90% of the value maximum semantic proximity.

In some embodiments, the method 40 includes a step of standardizing the computer data representative of holders stored in the first database. The standardization step, known to those skilled in the art, can, for example, put each character in lowercase, remove special characters, remove accents.

In embodiments, in the event of equality between the number of occurrences, the evaluation step comprises a step of counting the number of occurrences of each truncated class among each patent, of the first database presenting a corresponding holder. The first set of keywords is determined based on the title of the truncated class appearing the most times.

To associate at least one patent with the patent whose identifier is entered, the evaluation step is applied to each patent in the first database. Then, the predominant class can be stored in a field of the first database. The first set of keywords associated with the patent for which the identifier is entered then presents a high semantic proximity with the other patents of the first database of the same predominant class.

In embodiments, each evaluation step is performed during the construction of the first database. And the field corresponding to the technical field evaluated is updated during updates of at least one class associated with a patent in the first database.

In some embodiments, the association step 41 associates at least one other patent from the first database corresponding to the holder of the patent whose identifier is entered. In these embodiments, a corresponding holder is determined as indicated above.

In some embodiments, the association step 41 associates at least one other patent from the first database corresponding to the first set of keywords of the entered identifier and to a corresponding holder. These embodiments are a combination of the embodiments detailed above.

The method 40 includes a step 42 of counting the number of occurrences of each code representative of the geographical scope according to each other patent associated patent whose identifier is entered.

During the counting step 42, the content of the field comprising the geographical area covered by the patent is subjected to a sorting algorithm known to those skilled in the art to count and classify the geographical areas by decreasing number of occurrences. The ranking is temporarily stored.

In embodiments, the patents submitted to the sorting algorithm are filtered by holder, for example, for the corresponding holders or even for the holders of the same nationality.

In embodiments, the counting step includes:
- a step of selecting a predetermined value X of holders classified by decreasing number of occurrences,
- a counting step 22 of the number of industrial property titles of the first database of the same technical field for each holder selected,
- A classification step 23 of each zone by decreasing number of occurrences.

During the selection step, an operator specifies, by means of a man/machine interface, for example a keyboard, a mouse or a touch screen, the number of holders X to be taken into account. In embodiments, a choice of predetermined values X can be offered to the user, for example, three, five or ten.

During counting step 20, the corresponding holders are considered to be the same holder.

The X most recurring holders are selected. A filtering of the first database is performed to exclude holders other than the selected holders.

Then for each patent of at least one of the X selected holders, a counting step similar to the counting step 42 and independent of the patent holder is implemented.

The sorted list is stored in a memory at least temporarily.

During the weighting step 43, each national statistic of the selected national statistics set is weighted according to the number of occurrences. For example, the weighting can follow a linear law according to the number of occurrences.

In embodiments, each national statistic is associated with a geographical area of scope, the method 40 further comprises;
- a step 44 for defining a predetermined limit value,
- a classification step 45 of each national statistic of the set of national statistics selected by geographical area and by decreasing value,
- an iterative step 46 of calculating the sum of each national statistic, in order of classification, until the value of the sum is greater than or equal to the predetermined limit value, and
- During the supply step 18, the national statistics supplied are the national statistics used during the last iteration of the calculation step.

During the definition step 44, an operator enters a predetermined limit value by means of a man/machine interface, for example by moving a slider representing a target market percentage ranging from zero percent to one hundred percent. The first predetermined limit value is then calculated from the market value across all geographical areas.

During the classification step 45, a sorting algorithm is implemented to classify the geographical areas according to the value of the national statistical data in a decreasing manner. The classification step 45 is preferably implemented by an application server.

During calculation step 46, it is checked whether the value of the geographical area classified according to the first rank is greater than the predetermined limit value. If necessary, we go to the supply step 18. Otherwise, we calculate the sum of the national statistic corresponding to the geographical area ranked first with the national statistic corresponding to the geographical area ranked second. If the sum is greater than the predetermined limit value, we move on to supply step 18, otherwise the calculation is repeated with the national statistics corresponding to the geographical area of the third rank. Calculation step 46 is thus iterated until the sum is greater than the predetermined limit value.

The most recurrent geographical areas classified by decreasing recurrence. The sorted list is stored in a memory at least temporarily.

The supply step 18 may include a display step performed on a screen, for example, by means of an internet browser displaying a secure internet page by using the hierarchy provided. The display can be in the form of a map or a list, for example.

Claims (12)

Method (10, 40) for supplying relevant information associated with a patent which comprises a step of entering (11) at least one patent identifier, characterized in that it comprises:
- a step of converting (12) the identifier entered into a first set of keywords representative of at least one technical field of the patent, in a first database associating at least one identifier with a set of keywords ;
- a step of creating (13) a request for collecting national statistical information, accessible on a network, according to the keywords resulting from the conversion and, for each said piece of information, a second set of associated keywords to said information,
- a step of comparing (14) the first set of keywords and each second set,
- a step of assigning (15) a weight to each item of information representative of the set of national statistics collected according to the result of the comparison,
- a step of prioritization (16) of the set of national statistics according to the weight of each said piece of information,
- a selection stage (17) of at least one set of national statistics from among the sets of hierarchical national statistics and
- a step of supplying (18) each said national statistic of the set of national statistics selected. Method (10,40) according to claim 1, which comprises, upstream of the input step (11), a step of constructing the first database which comprises:
- a step (22) of collecting and indexing public identifiers of at least one patent and
- a step (23) of association, with each patent whose public data is indexed, at least one item of information representative of at least one keyword. A method (10,40) according to claim 2, wherein the information is representative of at least one title of a technical class associated with a set of keywords. Method (10,40) according to one of claims 2 or 3, in which the information is representative of at least part of the content of the patent. Method (10,40) according to one of Claims 2 to 4, in which the step of constructing (21) the first database comprises a step of creating (24) a tree structure, linking at least two identifiers patents, the information representative of at least one keyword being associated according to each identifier linked to the identifier entered during the entry step. Method (10,40) according to one of Claims 1 to 5, which comprises a step of extrapolating (34) a national statistic from a set of national statistical values having a missing national statistic, each value obtained by extrapolation being associated with the second set of keywords of the information representative of the set of national statistics of the extrapolated value. Method (10,40) according to one of Claims 1 to 6, which comprises a step of building (31) the second database comprising:
- a step (32) for collecting and indexing sets of national statistics,
- a step (33) for associating, with each set of indexed national statistics, at least one item of information representative of at least one keyword. Method (10,40) according to claim 7, in which the sets of national statistics are organized in at least one tree structure comprising at least two levels, a so-called "upper" level to which is attached a so-called "lower" level comprising several sets of national statistics, the national statistics set of the higher level being equal to the sum of the sets of values of the lower level. A method (10, 40) according to claims 6 and 8, wherein in the extrapolation step (34), a national statistic is extrapolated based on the higher level set of national statistics. Method (10, 40) according to one of Claims 6 and 8 or 9, in which the sets of national statistics are organized according to two distinct trees, a national statistic of a tree being extrapolated (34) according to a set of national statistics from another tree. Method (140) according to one of Claims 1 to 10, in which the first database comprises, for each patent, an identification of at least one holder and a code representative of the geographical scope, the method further comprising :
- a step of associating (41) at least one other patent from the first database corresponding to the first set of keywords of the identifier entered and/or to the holder of the title,
- a step of counting (42) the number of occurrences of each code representative of the geographical scope as a function of each other patent associated patent whose identifier is entered;
- a step of weighting (43) each national statistic of the set of national statistics selected according to the number of occurrences,
- During the supply step (18), the set of national statistics is classified by decreasing weighting. Method (40) according to one of claims 1 to 11, in which each national statistic is associated with a geographical area of scope, the method further comprises;
- a step of defining (44) a predetermined limit value,
- a classification step (45) of each national statistic of the set of national statistics selected by geographical area and by decreasing value,
- an iterative step of calculating (46) the sum of each national statistic, by ranking order, until the value of the sum is greater than or equal to the predetermined limit value, and
- During the supply step (18), the national statistics supplied are the national statistics used during the last iteration of the calculation step.