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1. WO2020107100 - SYSTÈMES INFORMATIQUES ET PROCÉDÉS DE GÉNÉRATION DE DONNÉES D'ÉVALUATION D'UNE SOCIÉTÉ PRIVÉE

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

[ EN ]

Claims:

1. A system for generating valuation data of a private company, the system comprising:

a data merger, the data merger for receiving company data,

the company data including a plurality of company metrics,

wherein at least one company metric of the plurality of company metrics corresponds to a company other than the private company; a model trainer, the model trainer for generating a machine learning model, based on the company data,

the machine learning model including a plurality of variables, each variable of the plurality of variables corresponding to at least one company metric of the plurality of company metrics;

a user input receiver, the user input receiver for receiving a request to generate the valuation data; and

a model predictor, the model predictor for generating the valuation data based on the machine learning model and the request to generate the valuation data.

2. The system of claim 1 , wherein the request to generate the valuation data includes private company data,

the private company data including at least one financial metric of the private company,

the at least one financial metric of the private company corresponding to the at least one variable of the plurality of variables.

3. The system of claim 1 , further comprising:

a data pre-processor, the data preprocessor for normalizing the company data, based on at least one statistical property of at least one company metric of the plurality of company metrics.

4. The system of claim 1 , further comprising:

a data pre-processor, the data pre-processor for:

determining whether the company data includes missing data; and generating replacement data, whereby the replacement data replaces the missing data.

5. The system of claim 1 , further comprising:

a data splitter, the data splitter for apportioning the company data into training data, calibration data, and testing data;

a confidence calibrator, the confidence calibrator for generating a confidence score for at least one company metric of the plurality of company metrics, based on the machine learning model and the calibration data.

6. The system of claim 5, further comprising:

a model tester, the model tester for generating model testing data based on the machine learning model and the testing data.

7. A computer-implemented method for generating valuation data of a private company, the method comprising:

receiving company data,

the company data including a plurality of company metrics,

wherein at least one company metric of the plurality of company metrics corresponds to a company other than the private company; generating a machine learning model, based on the company data,

the machine learning model including a plurality of variables, each variable of the plurality of variables corresponding to at least one company metric of the plurality of company metrics;

receiving a request to generate the valuation data; and

generating the valuation data, based on the machine learning model and the request to generate the valuation data.

8. The method of claim 7, wherein the valuation data includes variable importances that quantify an impact of the at least one company metric on valuation prediction, and wherein the variable importances correspond to a relative effect of the at least one company metric.

9. The method of claim 7, wherein the request to generate the valuation data includes private company data,

the private company data including at least one financial metric of the private company,

the at least one financial metric of the private company corresponding to the at least one variable of the plurality of variables.

10. The method of claim 7, wherein generating the machine learning model includes optimizing a loss function.

1 1 . The method of claim 7, wherein generating the machine learning model includes multi-target learning.

12. The method of claim 7, further comprising:

normalizing the company data, based on at least one statistical property of at least one company metric of the plurality of company metrics.

13. The method of claim 7, further comprising:

determining whether the company data includes missing data; and

generating replacement data, whereby the replacement data replaces the missing data.

14. The method of claim 13, wherein generating replacement data is based on at least one statistical property of at least one company metric of the plurality of company metrics.

15. The method of claim 13, wherein generating replacement data is based on the machine learning model.

16. The method of claim 7, further comprising:

apportioning the company data into training data, calibration data, and testing data; and

generating a confidence score for at least one company metric of the plurality of company metrics, based on the machine learning model and the calibration data.

17. The method of claim 7, further comprising:

generating model testing data based on the machine learning model and the testing data.

18. The method of claim 17, further comprising:

generating a further machine learning model, based on the model testing data, and the machine learning model.

19. The method of claim 7, wherein the valuation data includes comparable company data and company metric importance data.

20. A non-transitory computer-readable medium storing instructions executable on a processor for implementing a method for generating valuation data of a private company, the method comprising:

receiving company data,

the company data including a plurality of company metrics,

wherein at least one company metric of the plurality of company metrics corresponds to a company other than the private company; generating a machine learning model, based on the company data,

the machine learning model including a plurality of variables, each variable of the plurality of variables corresponding to at least one company metric of the plurality of company metrics;

receiving a request to generate the valuation data; and

generating the valuation data, based on the machine learning model and the request to generate the valuation data.