Pairing fonts for presentation

• US 10,115,215 B2
• Filed: 04/17/2015
• Issued: 10/30/2018
• Est. Priority Date: 04/17/2015
• Status: Active Grant

• Current Assignee: Monotype Imaging (Monotype Imaging Holdings, Inc.)
• Original Assignee: Monotype Imaging (Monotype Imaging Holdings, Inc.)
• Inventors: Matteson, Steve, Kaasila, Sampo Juhani, Scronce, Evan, Vijay, Anand, Bansal, Jitendra Kumar
• Primary Examiner(s): Chu, David H
• Application Number:
  US14/690,260
• Publication Number:
  US 20160307347A1
• Time in Patent Office: 1,292 Days
• Field of Search: None
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 40/109 Font handling; Temporal or kinetic typography
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06V 30/245 Font recognition

Abstract

A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor to execute the instructions to perform operations that include attaining data representing features of a font capable of representing one or more glyphs. Operations also include determining a rating for pairing the font and at least one other font using machine learning, the features of the font, and one or more rules included in a set of rules.

Claims

1. A computing device implemented method comprising:

attaining data representing features of a font capable of representing one or more glyphs; and

determining a rating for pairing the font and at least one other font using a machine learning system and the data representing the features of the font, wherein at least one of the features or at least one category of a set of categories is identified to represent the font by the machine learning system using the features of the font, and wherein the machine learning system produces a vector of numerical values, each numerical value represents one of the features or one of the categories in the set of categories.

2. The computing device implemented method of claim 1, further comprising:

initiating presentation of the pairing of the font and the at least one other font.

3. The computing device implemented method of claim 2, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon customer interest.

4. The computing device implemented method of claim 3, wherein customer interest includes transaction information of the pairing of the font and the at least one other font.

5. The computing device implemented method of claim 2, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon a stochastic process.

6. The computing device implemented method of claim 2, wherein initiating presentation includes providing a user interface that presents the pairing of the font and the at least one other font.

7. The computing device implemented method of claim 1, wherein the machine learning system uses a deep learning technique.

8. The computing device implemented method of claim 1, wherein at least one category is not pairable with another of the categories.

9. The computing device implemented method of claim 1, wherein the features are derived based upon separating two or more categories.

10. The computing device implemented method of claim 1, wherein the features are derived in an automatic manner.

11. The computing device implemented method of claim 1, wherein a portion of the features are derived in an automatic manner and a portion of the features are derived based upon separating two or more categories.

12. The computing device implemented method of claim 1, wherein determining the rating for pairing the font and the at least one other font uses a set of rules.

13. The computing device implemented method of claim 1, wherein determining the rating for pairing the font and the at least one other font uses a ruling provided by the machine learning system.

14. The computing device implemented method of claim 1, wherein the features are attained from one or more bitmaps of one or more characters of the font.

15. The computing device implemented method of claim 1, wherein the features are attained from one or more outlines of one or more characters of the font.

16. The computing device implemented method of claim 1, wherein the features of the font are employed to determine font similarity.

17. The computing device implemented method of claim 1, wherein one of the features of the font represents squareness of one or more characters of the font.

18. A system comprising:

a computing device comprising;

a memory configured to store instructions; and

a processor to execute the instructions to perform operations comprising;

attaining data representing features of a font capable of representing one or more glyphs; and

determining a rating for pairing the font and at least one other font using a machine learning system and the data representing the features of the font, wherein at least one of the features or at least one category of a set of categories is identified to represent the font by the machine learning system using the features of the font, and wherein the machine learning system produces a vector of numerical values, each numerical value represents one of the features or one of the categories in the set of categories.

19. The system of claim 18, the operations further comprising:

initiating presentation of the pairing of the font and the at least one other font.

20. The system of claim 19, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon customer interest.

21. The system of claim 20, wherein customer interest includes transaction information of the pairing of the font and the at least one other font.

22. The system of claim 19, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon a stochastic process.

23. The system of claim 19, wherein initiating presentation includes providing a user interface that presents the pairing of the font and the at least one other font.

24. The system of claim 18, wherein the machine learning system uses a deep learning technique.

25. The system of claim 18, wherein at least one category is not pairable with another of the categories.

26. The system of claim 18, wherein the features are derived based upon separating two or more categories.

27. The system of claim 18, wherein the features are derived in an automatic manner.

28. The system of claim 18, wherein a portion of the features are derived in an automatic manner and a portion of the features are derived based upon separating two or more categories.

29. The system of claim 18, wherein determining the rating for pairing the font and the at least one other font uses a set of rules.

30. The system of claim 18, wherein determining the rating for pairing the font and the at least one other font uses a ruling provided by the machine learning system.

31. The system of claim 18, wherein the features are attained from one or more bitmaps of one or more characters of the font.

32. The system of claim 18, wherein the features are attained from one or more outlines of one or more characters of the font.

33. The system of claim 18, wherein the features of the font are employed to determine font similarity.

34. The system of claim 18, wherein one or the features of the font represents squareness of one or more characters of the font.

35. One or more non-transitory computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:

attaining data representing features of a font capable of representing one or more glyphs; and

determining a rating for pairing the font and at least one other font using a machine learning system and the data representing the features of the font, wherein at least one of the features or at least one category of a set of categories is identified to represent the font by the machine learning using the features of the font, and wherein the machine learning system produces a vector of numerical values, each numerical value represents one of the features or one of the categories in the set of categories.

36. The non-transitory computer readable media of claim 35, the operations further comprising:

initiating presentation of the pairing of the font and the at least one other font.

37. The non-transitory computer readable media of claim 36, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon customer interest.

38. The non-transitory computer readable media of claim 37, wherein customer interest includes transaction information of the pairing of the font and the at least one other font.

39. The non-transitory computer readable media of claim 36, wherein initiating presentation of the pairing of the font and the at least one other font includes prioritizing the pairing for presentation based upon a stochastic process.

40. The non-transitory computer readable media of claim 36, wherein initiating presentation includes providing a user interface that presents the pairing of the font and the at least one other font.

41. The non-transitory computer readable media of claim 35, wherein the machine learning system uses a deep learning technique.

42. The non-transitory computer readable media of claim 35, wherein at least one category is not pairable with another of the categories.

43. The non-transitory computer readable media of claim 35, wherein the features are derived based upon separating two or more categories.

44. The non-transitory computer readable media of claim 35, wherein the features are derived in an automatic manner.

45. The non-transitory computer readable media of claim 35, wherein a portion of the features are derived in an automatic manner and a portion of the features are derived based upon separating two or more categories.

46. The non-transitory computer readable media of claim 35, wherein determining the rating for pairing the font and the at least one other font uses a set of rules.

47. The non-transitory computer readable media of claim 35, wherein determining the rating for pairing the font and the at least one other font uses a ruling provided by the machine learning system.

48. The non-transitory computer readable media of claim 35, wherein the features are attained from one or more bitmaps of one or more characters of the font.

49. The non-transitory computer readable media of claim 35, wherein the features are attained from one or more outlines of one or more characters of the font.

50. The non-transitory computer readable media of claim 35, wherein the features of the font are employed to determine font similarity.

51. The non-transitory computer readable media of claim 35, wherein one or more features of the font represents squareness of one or more characters of the font.