Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-33 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 12,118,821. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are either anticipated by or obvious variants of the patent claims. The following table shows the corresponding limitations between representative claims and representative patent claims.
Present Application Claims
Patent claims
1. A method, comprising: determining a textual identifier that describes at least one facial feature of a human face based on two-dimensional (2D) image data representing the human face; generating a first prompt for a first generative machine learning model, the first prompt comprising information corresponding to the textual identifier that describes the at least one facial feature of the human face; and obtaining, from the first generative machine learning model and based on the first prompt, a first output indicative of a set of false eyelashes that suit the human face.
2. The method of claim 1, wherein the first prompt is generated using a three-dimensional (3D) model of the human face, the 3D model generated using the 2D image data.
3. The method of claim 1, wherein the textual identifier further describes a relationship associated with two or more facial features of the human face.
4. The method of claim 3, wherein the relationship associated with the two or more facial features comprises a relationship of an eye and an eyebrow associated with the eye.
5. The method of claim 1, further comprising: identifying, from a database, information related to one or more sets of false eyelashes; and wherein the first prompt comprises the information related to the one or more sets of false eyelashes.
6. The method of claim 1, wherein the set of false eyelashes comprises a set of artificial lash extensions.
7. The method of claim 1, further comprising: providing, to a first trained machine learning model, information identifying 2D image data representing the human face; and wherein determining the textual identifier comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier.
8. The method of claim 7, wherein the first trained machine learning model is a second generative machine learning model.
9. The method of claim 8, wherein the second generative machine learning model comprises a visual language model (VLM).
10. The method of claim 1, further comprising: providing an indication of the set of false eyelashes for display at a graphical user interface (GUI) of a client device.
11. The method of claim 2, further comprising: determining, using the 2D image data, the 3D model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
12. The method of claim 11, further comprising: identifying a landmark on the 3D model, the landmark identifying the facial feature of the human face.
13. The method of claim 12, wherein determining the textual identifier based at least in part on the 3D model, comprises: determining the textual identifier that corresponds to the landmark.
14. The method of claim 12, wherein determining the textual identifier based at least in part on the 3D model, comprises: identifying one or more points of the 3D model; determining one or more relationships associated with the one or more points; identifying the landmark on the 3D model based on the one or more relationships; and generating one or more geometric measurements based on one or more geometric features represented in the 3D model, wherein the textual identifier is based on the one or more geometric measurements.
15. The method of claim 11, wherein determining the textual identifier based at least in part on the 3D model, comprises: providing, to a second trained machine learning model, information representing the 3D model of the human face; and obtaining, from the second trained machine learning model, one or more outputs identifying an indication that the textual identifier corresponds to a landmark on the 3D model.
16. A system, comprising: a memory; and a processing device operatively coupled with the memory, the processing device to: determine a textual identifier that describes at least one facial feature of a human face based on two-dimensional (2D) image data representing the human face; generate a first prompt for a first generative machine learning model, the first prompt comprising information corresponding to the textual identifier that describes the at least one facial feature of the human face; and obtain, from the first generative machine learning model and based on the first prompt, a first output indicative of a set of false eyelashes that suit the human face.
17. The system of claim 16, wherein the first prompt is generated using a three-dimensional (3D) model of the human face, the 3D model generated using the 2D image data.
18. The system of claim 16, wherein the textual identifier further describes a relationship associated with two or more facial features of the human face.
19. The system of claim 18, wherein the relationship associated with the two or more facial features comprises a relationship of an eye and an eyebrow associated with the eye.
20. The system of claim 16, wherein the processing device is further to: identify, from a database, information related to one or more sets of false eyelashes; and wherein the first prompt comprises the information related to the one or more sets of false eyelashes.
21. The system of claim 16, wherein the set of false eyelashes comprises a set of artificial lash extensions.
22. The system of claim 16, wherein the processing device is further to: provide, to a first trained machine learning model, information identifying the 2D image data representing the human face; and wherein determining the textual identifier comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier.
23. The system of claim 17, wherein the processing device is further to: determine, using the 2D image data, the 3D model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
24. The system of claim 23, wherein the processing device is further to: identify a landmark on the 3D model, the landmark identifying the facial feature of the human face, wherein to determine the textual identifier based at least in part on the 3D model, the processing device is to: determine the textual identifier that corresponds to the landmark.
25. The system of claim 23, wherein to determine the textual identifier based at least in part on the 3D model, the processing device is to: provide, to a second trained machine learning model, information representing the 3D model of the human face; and obtain, from the second trained machine learning model, one or more outputs identifying an indication that the textual identifier corresponds to a landmark on the 3D model.
26. A non-transitory computer-readable storage medium comprising instructions that, responsive to execution by a processing device, cause the processing device to perform operations, comprising: determining a textual identifier that describes at least one facial feature of a human face based on two-dimensional (2D) image data representing the human face; generating a first prompt for a first generative machine learning model, the first prompt comprising information corresponding to the textual identifier that describes the at least one facial feature of the human face; and obtaining, from the first generative machine learning model and based on the first prompt, a first output indicative of a set of false eyelashes that suit the human face.
27. The non-transitory computer-readable storage medium of claim 26, wherein the first prompt is generated using a three-dimensional (3D) model of the human face, the 3D model generated using the 2D image data.
28. The non-transitory computer-readable storage medium of claim 26, wherein the textual identifier further describes a relationship associated with two or more facial features of the human face.
29. The non-transitory computer-readable storage medium of claim 28, wherein the relationship associated with the two or more facial features comprises a relationship of an eye and an eyebrow associated with the eye.
30. The non-transitory computer-readable storage medium of claim 26, wherein the operations further comprise: identifying, from a database, information related to one or more sets of false eyelashes; and wherein the first prompt comprises the information related to of the one or more sets of false eyelashes.
31. The non-transitory computer-readable storage medium of claim 26, wherein the set of false eyelashes comprises a set of artificial lash extensions.
32. The non-transitory computer-readable storage medium of claim 26, wherein the operations further comprise: providing, to a first trained machine learning model, information identifying the 2D image data representing the human face; and wherein determining the textual identifier comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier.
33. The non-transitory computer-readable storage medium of claim 27, wherein the operations further comprise: determining, using the 2D image data, the 3D model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model; identifying one or more points of the 3D model; determining one or more relationships associated with the one or more points; identifying a landmark on the 3D model based on the one or more relationships; and generating one or more geometric measurements based on one or more geometric features represented in the 3D model, wherein the textual identifier is based on the one or more geometric measurements.
1. A method, comprising: receiving two-dimensional (2D) image data corresponding to a 2D image of a human face of a subject; determining a textual identifier that describes a facial feature of the human face represented by the 2D image; providing, to a first generative machine learning model, a first prompt comprising information identifying the textual identifier that describes the facial feature of the human face; and obtaining, from the first generative machine learning model, a first output identifying, among a plurality of sets of false eyelashes, a set of false eyelashes selected to suit the human face of the subject based on the facial feature.
10. The method of claim 1, further comprising: determining, using the 2D image data, a three-dimensional (3D) model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
2. The method of claim 1, wherein the textual identifier further describes a relationship between two or more facial features of the human face.
3. The method of claim 2, wherein the relationship between two or more facial features comprises a relationship between an eye and an eyebrow of the eye.
4. The method of claim 1, further comprising: identifying, from a database, information related to at least some of the plurality of sets of false eyelashes; and generating the first prompt comprising the information related to at least some of the plurality of sets of false eyelashes and the information identifying the textual identifier that describes the facial feature of the human face.
5. The method of claim 1, wherein the set of false eyelashes comprises a set of artificial lash extensions designed for an application at an underside of natural eyelashes of the subject.
6. The method of claim 1, further comprising: providing, to a first trained machine learning model, a second input comprising information identifying 2D image data corresponding to the 2D image of the human face; and wherein determining the textual identifier that describes the facial feature of the human face comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier that describes the facial feature of the human face.
7. The method of claim 6, wherein the first trained machine learning model is a second generative machine learning model.
8. The method of claim 7, wherein the second generative machine learning model comprises a visual language model (VLM).
9. The method of claim 1, further comprising providing an indication of the set of false eyelashes for display at a graphical user interface (GUI) of a client device.
10. The method of claim 1, further comprising: determining, using the 2D image data, a three-dimensional (3D) model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
11. The method of claim 10, further comprising: identifying a landmark on the 3D model, the landmark identifying the facial feature of the human face.
12. The method of claim 11, wherein determining the textual identifier that describes the facial feature of the human face based at least in part on the 3D model, comprises: determining the textual identifier that corresponds to the landmark on the 3D model.
13. The method of claim 11, wherein determining the textual identifier that describes the facial feature of the human face based at least in part on the 3D model, comprises: identifying a subset of a plurality of points of the 3D model; determining one or more relationships between the subset of points of the 3D model; identifying the landmark on the 3D model based on the one or more relationships; and measuring one or more geometric features represented in the 3D model to generate one or more geometric measurements, wherein the textual identifier is created based on the one or more geometric measurements.
14. The method of claim 10, wherein determining the textual identifier that describes the facial feature of the human face based at least in part on the 3D model, comprises: providing, to a second trained machine learning model, a first input, the first input comprising information representing the 3D model of the human face; and obtaining, from the second trained machine learning model, one or more outputs identifying (i) an indication that the textual identifier that describes the facial feature of the human face corresponds to a landmark on the 3D model, and (ii) a level of confidence that the textual identifier corresponds to the landmark on the 3D model.
15. A system, comprising: a memory; and a processing device operatively coupled with the memory, the processing device to: receive two-dimensional (2D) image data corresponding to a 2D image of a human face of a subject; determine a textual identifier that describes a facial feature of the human face represented by the 2D image; provide, to a first generative machine learning model, a first prompt comprising information identifying the textual identifier that describes the facial feature of the human face; and obtain, from the first generative machine learning model, a first output identifying, among a plurality of sets of false eyelashes, a set of false eyelashes selected to sui the human face of the subject based on the facial feature.
21. The system of claim 15, wherein the processing device is further to: determine, using the 2D image data, a three-dimensional (3D) model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
16. The system of claim 15, wherein the textual identifier further describes a relationship between two or more facial features of the human face.
17. The system of claim 16, wherein the relationship between two or more facial features comprises a relationship between an eye and an eyebrow of the eye.
18. The system of claim 15, wherein the processing device is further to: identify, from a database, information related to at least some of the plurality of sets of false eyelashes; and generate the first prompt comprising the information related to at least some of the plurality of sets of false eyelashes and the information identifying the textual identifier that describes the facial feature of the human face.
19. The system of claim 15, wherein the set of false eyelashes comprises a set of artificial lash extensions designed for an application at an underside of natural eyelashes of the subject.
20. The system of claim 15, wherein the processing device is further to: provide, to a first trained machine learning model, a second input comprising information identifying 2D image data corresponding to the 2D image of the human face; and wherein determining the textual identifier that describes the facial feature of the human face comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier that describes the facial feature of the human face.
21. The system of claim 15, wherein the processing device is further to: determine, using the 2D image data, a three-dimensional (3D) model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model.
22. The system of claim 21, wherein the processing device is further to: identify a landmark on the 3D model, the landmark identifying the facial feature of the human face, wherein to determine the textual identifier that describes the facial feature of the human face based at least in part on the 3D model, the processing device is to: determine the textual identifier that corresponds to the landmark on the 3D model.
23. The system of claim 21, wherein to determine the textual identifier that describes the facial feature of the human face based at least in part on the 3D model, the processing device is to: provide, to a second trained machine learning model, a first input, the first input comprising information representing the 3D model of the human face; and obtain, from the second trained machine learning model, one or more outputs identifying (i) an indication that the textual identifier that describes the facial feature of the human face corresponds to a landmark on the 3D model, and (ii) a level of confidence that the textual identifier corresponds to the landmark on the 3D model.
24. A non-transitory computer-readable storage medium comprising instructions that, responsive to execution by a processing device, cause the processing device to perform operations, comprising: receiving two-dimensional (2D) image data corresponding to a 2D image of a human face of a subject; determining a textual identifier that describes a facial feature of the human face represented by the 2D image; providing, to a first generative machine learning model, a first prompt comprising information identifying the textual identifier that describes the facial feature of the human face; and obtaining, from the first generative machine learning model, a first output identifying, among a plurality of sets of false eyelashes, a set of false eyelashes selected to suit the human face of the subject based on the facial feature.
29. The non-transitory computer-readable storage medium of claim 24, wherein the operations further comprise: providing, to a first trained machine learning model, a second input comprising information identifying 2D image data corresponding to the 2D image of the human face; and wherein determining the textual identifier that describes the facial feature of the human face comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier that describes the facial feature of the human face.
25. The non-transitory computer-readable storage medium of claim 24, wherein the textual identifier further describes a relationship between two or more facial features of the human face.
26. The non-transitory computer-readable storage medium of claim 25, wherein the relationship between two or more facial features comprises a relationship between an eye and an eyebrow of the eye.
27. The non-transitory computer-readable storage medium of claim 24, wherein the operations further comprise: identifying, from a database, information related to at least some of the plurality of sets of false eyelashes; and generating the first prompt comprising the information related to at least some of the plurality of sets of false eyelashes and the information identifying the textual identifier that describes the facial feature of the human face.
28. The non-transitory computer-readable storage medium of claim 24, wherein the set of false eyelashes comprises a set of artificial lash extensions designed for an application at an underside of natural eyelashes of the subject.
29. The non-transitory computer-readable storage medium of claim 24, wherein the operations further comprise: providing, to a first trained machine learning model, a second input comprising information identifying 2D image data corresponding to the 2D image of the human face; and wherein determining the textual identifier that describes the facial feature of the human face comprises: obtaining, from the first trained machine learning model, a second output identifying the textual identifier that describes the facial feature of the human face.
30. The non-transitory computer-readable storage medium of claim 24, wherein the operations further comprise: determining, using the 2D image data, a three-dimensional (3D) model of the human face, wherein the determining the textual identifier is based at least in part on the 3D model; identifying a subset of a plurality of points of the 3D model; determining one or more relationships between the subset of points of the 3D model; identifying a landmark on the 3D model based on the one or more relationships; and measuring one or more geometric features represented in the 3D model to generate one or more geometric measurements, wherein the textual identifier is created based on the one or more geometric measurements.
Conclusion
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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