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| Overview of AI features within the Compass: <br/> | | Overview of AI features within the Compass: <br/> |
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| * [[EN:Text_search#Semantic_Search|AI supported semantic search]] | | * [[EN:Text_search#AI_-_Full-Text_Search|AI supported semantic search]] |
| * [[EN:Folders#Folders_and_AI|AI and folders - automatic creation and filling of folders]] | | * [[EN:Folders#Folders_and_AI|AI and folders - automatic creation and filling of folders]] |
| * [[EN:AI_Innovator|AI Innovator - question a result list (LLM)]] | | * [[EN:AI_Innovator|AI Innovator - question a result list (LLM)]] |
| * [[EN:DetailView_AI|AI in the detail view - simplify reading patents]] | | * [[EN:DetailView_AI|AI in the detail view - simplify reading patents]] |
| | | * [[EN:Highlighting#AI_-_Synonyms|AI and Highlighting - finding synonyms]] |
| | * [[EN:Text_search#AI_-_Add_Synonyms|AI and text search - finding synonyms]] |
| | * [[EN:Applicant#AI_-_Associated_Companies|AI and applicant search - finding associated companies]] |
| | *[[EN:Result_List#AI|AI sorting of results]] |
| ---- | | ---- |
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| | * [[EN:AI_Averbis|Averbis AI - folder classification]] |
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| <pre style="color: red" >
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| The following functions are currently still in the pilot phase.
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| Individual functions, interfaces and scope may change in the future.
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| </pre>
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| The following AI functions are '''not''' available in Compass by default. <br/>
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| It is an additional module that can be activated by IP7 on request. <br/>
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| There are additional costs for the AI module. <br/>
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| The AI from [https://averbis.com Averbis] has been integrated into Compass. <br/>
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| The AI can (in future) be used for various areas in Compass: <br/>
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| * Folder assignment or automatic classification of patents
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| * Automatic sorting out of "not relevant" hits (available soon!)
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| * Sorting of the results list (coming soon!) | |
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| The main feature of an AI is that it is trained on the basis of predefined data. <br/>
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| The AI should improve or "learn" through constant human correction and renewed training. <br/>
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| AI is therefore always dependent on human performance. <br/>
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| The quality of the AI is therefore always dependent on this given data or on a human correcting the AI. <br/>
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| == Folder assignment ==
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| Folder structures are created in Compass to classify patents according to individual criteria. <br/>
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| For example, a technology tree can be created to assign patents to specific technologies. <br/>
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| With the "Automatic classification" function, the AI can take over this assignment task. <br
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| === Classifier ===
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| [[File:AI_classifier.jpg|800px]] | |
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| Before the AI can assign patents to folders, the AI must learn or be trained based on existing assignments. <br/>
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| An existing folder structure with already correctly assigned patents is required.<br/>
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| A "classifier" can then be created.<br/>
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| Some settings are defined here, whereby many of the settings affect the training: <br/>
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| *Activate
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| Here you can specify whether the classifier can be used or whether regular automatic training takes place.<br/>
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| *Name
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| *Training frequency
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| This defines how often the classifier is trained automatically. <br/>
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| As the contents of the folders change over time, it makes sense to have the classifier trained regularly. <br/>
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| *Max. # Documents per folder
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| The maximum number of documents per folder that the AI can use for training. <br/>
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| Too few documents per folder will reduce the quality of the training. <br/>
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| Too many documents per folder and the training will take a lot of time. <br/>
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| <br/>
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| Ideally, the AI should always receive the same number of documents for each folder. <br/>
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| For example, if there are a few folders with approx. 500 assignments and many folders with approx. 50 assignments. <br/>
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| Then the value should be set to 50 so that training can be balanced and better results can be achieved. <br/>
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| The limit value here is a maximum of 1,000 documents. <br/>
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| If the number in a folder is higher, 1,000 documents are randomly selected from this folder. <br/>
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| *Min. # documents required for training
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| What is the minimum number of documents that must be in the folder for them to be used in training? <br/>
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| If there are only 3 documents in a folder, for example, the AI will not be able to draw any good conclusions from them. <br/>
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| A minimum value of 10 documents must be specified. <br/>
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| If the number in a folder is smaller, these folders/documents are not used. <br/>
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| *Min. confidence level
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| "Confidence Level" (in per cent) describes how confident the AI is with the assignment. <br/>
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| Everything below the minimum value ends up in the "unclassifiable" folder. <br/>
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| This is defined later in the automatic classification. <br/>
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| *Folder
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| Here you can define which folders are used for training. <br/>
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| The "automatic classification" will later make the assignments in these folders. <br/>
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| The AI can then also assign a patent or patent family to multiple folders.<br/>
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| A minimum of 3 folders/sub-folders have to be selected. <br/>
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| A maximum of 500 folders can be selected. <br/>
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| *Data
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| Here you define which data the AI receives for the training: <br/>
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| Title, Summary, Claims, Description, IPC, CPC <br/>
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| If Description is selected, the AI receives significantly more data for training. <br/>
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| This will also have a corresponding effect on the training time. <br/>
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| *Status
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| The current status of the classifier is displayed here: <br/>
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| "Training required" <br/>
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| After the classifier has been created, training is required. <br/>
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| "Training" <br/>
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| The classifier is currently being trained. <br/>
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| During training, automatic classifications that use this classifier cannot be executed. <br/>
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| "Ready" <br/>
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| The classifier is ready for use in an automatic classification. <br/>
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| This also means that the last training session was carried out successfully. <br/>
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| "Error" <br/>
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| An error has occurred. <br/>
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| Successful training is a prerequisite for use in automatic classification. <br/>
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| *Last training <br/>
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| This shows when the last (successful) training session was carried out. <br/>
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| The current status can be retrieved using the Refresh button: <br/>
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| [[File:AI_classifier_Status_refresh.jpg|500px]]
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| As soon as the classifier has finished training (status "Ready"), it can be used in an automatic classification. <br/>
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| ==== Training statistics ====
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| Evaluation of the last training run. <br/>
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| [[File:AI_classifier_Status_analysis.jpg|500px]]
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| Further information on the terms Precision, F1Score and Recall: <br/>
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| https://en.wikipedia.org/wiki/Precision_and_recall
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| ==== Limitations ====
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| In total (across all folders used in the classifier), a maximum of 10,000 patents are used for training. <br/>
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| If there are more than 10,000 patents in the folders, these are reduced proportionally. <br/>
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| If a folder is then below the minimum number of patents, the selected patents are increased to the minimum number. <br/>
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| This means that the limit is not exactly 10,000. <br/>
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| The classifier requires at least 3 folders/subfolders. <br/>
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| === Automatic classification ===
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| [[File:AI_automatic_classification.jpg|800px]]
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| The following settings are available for automatic classification: <br/>
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| *Activate
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| Here you can specify whether classification is to be carried out automatically. <br/>
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| This option can be used to quickly stop an active automatic classification in the event of problems. <br/>
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| *Name
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| *Monitored folders
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| The "Incoming folders" are defined here. <br/>
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| All patents in these folders are then classified by the AI. <br/>
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| And, logically, all patents that are assigned to these folders in the future. <br/>
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| <br/>
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| Not to be confused with the folders of the classifier: <br/>
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| The folders into which the AI classifies/assigns the patents are defined in the classifier. <br/>
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| *Non-classifiable" folder
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| All patents that cannot be classified by the AI are assigned here. <br/>
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| *Classifier
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| The previously created classifier is selected here. <br/>
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| *Status
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| "idle" - automatic classification is currently not running. <br/>
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| "running" - automatic classification is currently running. <br/>
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| *Last checked on
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| The system regularly checks whether "new" patents are available for automatic classification. <br/>
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| If "new" patents are available, automatic classification is started. <br/>
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| The date indicates when a check was last carried out. <br/>
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| A check can also be triggered manually using the "Execute" button. <br/>
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| A classifier can theoretically be used for several automatic classifications. <br/>
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| An example of this: <br/>
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| There are several vehicle types that are monitored and should then be assigned to a technology tree: <br/>
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| "1, vehicle types" -> "bicycle" and "motorbike" <br/>
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| There is a folder structure or technology tree in which all vehicle types (vehicles with 2 wheels) are to be assigned: <br/>
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| "2, two wheel technologies" <br/>
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| However, the hits that the AI cannot classify should be saved separately. <br/>
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| For this reason, an automatic classification is created for "bicycle" and "motorbike". <br/>
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| In this case, however, the classifier only needs to be created once. <br/>
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| ==== Limitations ====
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| A maximum of 5,000 hits/patents can be classified in one run. <br/>
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| If there are more than 5,000, automatic classification is not carried out and is deactivated. <br/>
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| If the run is triggered manually, a corresponding warning is displayed. <br/>
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| If the run is then started anyway, only up to 5,000 patents are classified. <br/>
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| Patents that have been classified in a run do not have to be removed from the input folder. <br/>
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| They are recognised as already classified in the next run and are not classified again. <br/>
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| To have one or more patents reclassified, they must be removed from the folder and then reassigned. <br/>
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| This reassignment means that they are no longer recognised as already classified. <br/>
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