Adaptive document curation

1. A method comprising:

training a machine learning model on a first library of documents to assign staleness scores to documents, each staleness score indicating a confidence that the document to which it is assigned should be archived;

receiving an identification of a first document not included in the first library of documents;

applying the machine learning model to the first document to generate a first staleness score;

based upon a determination that the first staleness score is at least equal to a first threshold, identifying the first document as a candidate to be archived;

confirming that the first document should be archived; and

responsive to confirming that the first document should be archived, archiving the first document.

2. The method of claim 1 , wherein confirming that the first document should be archived comprises:

presenting, to a user, the first document and an indication that the first document should be archived; and

receiving a confirmation from the user that the first document should be archived.

3. The method of claim 2 , wherein the indication that the first document should be archived includes a representation of the staleness score.

4. The method of claim 2 , further comprising:

adjusting the machine learning model based upon the confirmation from the user that the first document should be archived.

5. The method of claim 1 , wherein the confirming that the first document should be archived comprises:

determining that no override condition exist for the first document that indicate the first document should not be archived;

wherein the first document is automatically archived upon determining that no override condition exists.

6. The method of claim 1 , wherein, to generate the staleness score, the machine learning model uses one or more attributes selected from the group consisting of: a creation date of the first document, a last edit date of the first document, a count of unique edits of the first document during a time period, a count of unique editors of the first document during a time period, a count of unique viewers of the first document during a time period, a count of unique non-editor viewers of the first document during a time period, a count of other documents linked from the first document, an average staleness score of documents linked to the first document, a count of documents that link to the first document, and an aggregate staleness score of a folder containing the first document.

7. The method of claim 1 , further comprising

receiving an identification of a second document not included in the first library of documents;

applying the machine learning model to the second document to generate a second staleness score;

based upon a determination that the second staleness score is at least equal to the first threshold, identifying the second document as a candidate to be archived;

presenting, to a user, the second document and an indication that the second document should be archived; and

receiving an indication from the user that the second document should not be archived.

8. The method of claim 7 , further comprising:

adjusting the machine learning model based upon the indication from the user that the second document should not be archived.

9. A non-transitory computer readable medium having instructions that when performed on at least one processor cause the at least one processor to perform the steps comprising:

training a machine learning model on a first library of documents to assign staleness scores to documents, each staleness score indicating a confidence that the document to which it is assigned should be archived;

receiving an identification of a first document not included in the first library of documents;

applying the machine learning model to the first document to generate a first staleness score;

based upon a determination that the first staleness score is at least equal to a first threshold, identifying the first document as a candidate to be archived;

confirming that the first document should be archived; and

responsive to confirming that the first document should be archived, archiving the first document.

10. The non-transitory computer readable medium of claim 9 , wherein confirming that the first document should be archived comprises:

presenting, to a user, the first document and an indication that the first document should be archived; and

receiving a confirmation from the user that the first document should be archived.

11. The method of claim 10 , wherein the indication that the first document should be archived includes a representation of the staleness score.

12. The non-transitory computer readable medium of claim 10 , further comprising:

adjusting the machine learning model based upon the confirmation from the user that the first document should be archived.

13. The non-transitory computer readable medium of claim 9 , wherein the confirming that the first document should be archived comprises:

determining that no override condition exist for the first document that indicate the first document should not be archived;

wherein the first document is automatically archived upon determining that no override condition exists.

14. The non-transitory computer readable medium of claim 9 , wherein, to generate the staleness score, the machine learning model uses one or more attributes selected from the group consisting of: a creation date of the first document, a last edit date of the first document, a count of unique edits of the first document during a time period, a count of unique editors of the first document during a time period, a count of unique viewers of the first document during a time period, a count of unique non-editor viewers of the first document during a time period, a count of other documents linked from the first document, an average staleness score of documents linked to the first document, a count of documents that link to the first document, and an aggregate staleness score of a folder containing the first document.

15. The non-transitory computer readable medium of claim 9 , further comprising

receiving an identification of a second document not included in the first library of documents;

applying the machine learning model to the second document to generate a second staleness score;

based upon a determination that the second staleness score is at least equal to the first threshold, identifying the second document as a candidate to be archived;

presenting, to a user, the second document and an indication that the second document should be archived; and

receiving an indication from the user that the second document should not be archived.

16. The non-transitory computer readable medium of claim 15 , further comprising:

adjusting the machine learning model based upon the indication from the user that the second document should not be archived.