Granted Patent B2

US 12,333,501 · App. 17/935,060 · Granted Jun 17, 2025

Inventors: Liwei Wu (Davis, CA); Lichao Ni (Sunnyvale, CA); Mikaela Makalinao Guerrero (Downey, CA); Yanen Li (Hillsborough, CA)

Assignee: Microsoft Technology Licensing, LLC

G06Q10/1053

Abstract

Claims (72)

1. A method comprising:

extracting an input text from an online system, the input text comprising a job title;

applying an unsupervised generative text machine learning model to the input text;

generating, by the generative text machine learning model, a plurality of sentences based on the job title;

extracting one or more skills from the plurality of sentences, wherein the extracted one or more skills correspond to one or more skills in a skill taxonomy;

generating a frequency distribution over the extracted one or more skills;

ranking each skill of the extracted one or more skills based on the frequency distribution;

comparing the frequency distribution to a threshold skill distribution;

in response to determining that the frequency distribution does not satisfy the threshold skill distribution, generating additional sentences from the input text;

generating an additional frequency distribution using the plurality of sentences and the additional sentences;

comparing the additional frequency distribution to the threshold skill distribution;

in response to determining that the additional frequency distribution satisfies the threshold skill distribution, ranking each skill of the extracted one or more skills based on the additional frequency distribution;

generating a subset of the extracted one or more skills based on the ranking and a threshold number of skills; and

providing the subset of the extracted one or more skills to a downstream operation, process, or service of the online system.

2. The method of claim 1 , wherein extracting the one or more skills from the plurality of sentences comprises:

identifying one or more skills in the plurality of sentences that do not correspond to the one or more skills in the skill taxonomy; and

adding the identified one or more skills to the skill taxonomy.

3. The method of claim 1 , further comprising generating a skill recommendation based on comparing the subset of the extracted one or more skills with a set of skills identified in an entity profile.

4. The method of claim 1 , further comprising training the generative text machine learning model by applying unsupervised machine learning to a domain-independent set of unlabeled and unstructured training data.

5. The method of claim 1 , wherein extracting the input text from the online system comprises:

receiving an input seed phrase from a user system; and

determining the input text based on the input seed phrase.

6. The method of claim 1 , further comprising training the generative text machine learning model as a causal text generator that generates a set of sentences from a seed word or a job title.

7. A method comprising:

extracting, using a string search, an input text from an online system, the input text comprising a job title;

applying an unsupervised generative text machine learning model to the input text;

generating, by the generative text machine learning model, a plurality of sentences from the job title;

extracting one or more skills from the plurality of sentences, wherein the extracted one or more skills correspond to one or more skills in a skill taxonomy;

generating a frequency distribution over the extracted one or more skills, the frequency distribution generated by aggregating a number of occurrences for each skill of the extracted one or more skills;

ranking each skill of the extracted one or more skills based on the frequency distribution;

comparing the frequency distribution to a threshold skill distribution;

in response to determining that the frequency distribution does not satisfy the threshold skill distribution, generating additional sentences from the input text;

generating an additional frequency distribution using the plurality of sentences and the additional sentences;

comparing the additional frequency distribution to the threshold skill distribution;

in response to determining that the additional frequency distribution satisfies the threshold skill distribution, ranking each skill of the extracted one or more skills based on the additional frequency distribution;

generating a subset of the extracted one or more skills by selecting the subset using the ranking and a threshold number of skills, wherein the threshold number of skills define the number of skills in the subset; and

providing the subset of the extracted one or more skills to a downstream operation, process, or service of the online system.

8. The method of claim 7 , wherein extracting the one or more skills from the plurality of sentences comprises:

identifying one or more skills that do not correspond to one or more skills in the skill taxonomy; and

adding the identified one or more skills to the skill taxonomy.

9. The method of claim 7 , further comprising generating a recommended skill based on comparing the subset of the extracted one or more skills with a set of skills identified in an entity profile.

10. The method of claim 7 further comprising configuring the generative text machine learning model as a causal text generator that generates a set of sentences from a seed word or a job title.

11. The method of claim 7 , wherein extracting the input text from the online system comprises:

receiving an input seed phrase from a user system; and

determining the input text from the input seed phrase.

12. The method of claim 7 , further comprising training the generative text machine learning model by applying unsupervised machine learning to a domain-independent set of unlabeled and unstructured training data.

13. A system comprising:

at least one memory device; and

a processing device, operatively coupled to the at least one memory device, to:

extract an input text from an online system, the input text comprising a job title;

apply an unsupervised generative text machine learning model to the input text;

generate, by the unsupervised generative text machine learning model, a plurality of sentences based on the job title;

extract one or more skills from the plurality of sentences, wherein the extracted one or more skills correspond to one or more skills in a skill taxonomy;

generate a frequency distribution over the extracted one or more skills;

compare the frequency distribution to a threshold skill distribution;

in response to determining that the frequency distribution does not satisfy the threshold skill distribution, generate additional sentences from the input text;

generate an additional frequency distribution using the plurality of sentences and the additional sentences;

compare the additional frequency distribution to the threshold skill distribution; and

provide the extracted one or more skills to a downstream operation, process, or service of the online system.

14. The system of claim 13 , wherein to extract the input text from the online system, the processing device is to:

receive an input seed phrase from a user system; and

determine the input text from the input seed phrase.

15. The system of claim 13 , wherein the processing device trains the generative text machine learning model as a causal text generator that generates a set of sentences from a seed word or a job title.

16. The system of claim 13 , wherein the processing device generates a skill recommendation based on comparing the extracted one or more skills with a set of skills identified in an entity profile.

17. The system of claim 13 , wherein to extract the one or more skills from the plurality of sentences, the processing device is caused to:

identify one or more skills that do not correspond to a skill in the skill taxonomy; and

add the identified one or more skills to the skill taxonomy.

18. The system of claim 13 , wherein the processing device is caused to:

in response to determining that the additional frequency distribution satisfies the threshold skill distribution, rank each skill of the extracted one or more skills based on the additional frequency distribution; and

generate a subset of the extracted one or more skills based on the ranking.

19. The system of claim 13 , wherein the processing device is caused to train the generative text machine learning model by applying unsupervised machine learning to a domain-independent set of unlabeled and unstructured training data.

20. The system of claim 13 , wherein the processing device is caused to generate the frequency distribution by aggregating a number of occurrences for each skill of the extracted one or more skills.

Assignments (1)

Continuity (1)

References Cited (18)