# US 12,118,821 B1 · Using image processing, machine learning and images of a human face for prompt generation related to false eyelashes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,821 B1 - Document ID: US12118821B1 - Status: Granted - Kind: B1 - Application number: 18/631,983 - Filed: 2024-04-10 - Granted: 2024-10-15 - Earliest claimed: 2024-04-10 - Assignees: Lashify, Inc. - Applicants: Lashify, Inc. - Inventors: Sahara Lotti; Jesse Chang - Web page: https://patentgrep.com/patent/US12118821B1 ## CPC - G06V 40/168: {Feature extraction; Face representation} - G06V 10/82: using neural networks - G06V 40/171: {Local features and components; Facial parts (eye characteristics G06V40/18); Occluding parts, e.g. glasses; Geometrical relationships} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A method includes receiving 2D image data corresponding to a 2D image of a human face of a subject. The method further includes determining a textual identifier that describes a facial feature of the human face represented by the 2D image. The method further includes providing, to a first generative machine learning model, a first prompt including information identifying the textual identifier that describes the facial feature of the human face. The method further includes 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the textual identifier further describes a relationship between two or more facial features of the human face. ### Claim 3 (depends on 2) 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. ### Claim 4 (depends on 1) 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. ### Claim 5 (depends on 1) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 6) The method of claim 6, wherein the first trained machine learning model is a second generative machine learning model. ### Claim 8 (depends on 7) The method of claim 7, wherein the second generative machine learning model comprises a visual language model (VLM). ### Claim 9 (depends on 1) 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. ### Claim 10 (depends on 1) 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. ### Claim 11 (depends on 10) The method of claim 10, further comprising: identifying a landmark on the 3D model, the landmark identifying the facial feature of the human face. ### Claim 12 (depends on 11) 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. ### Claim 13 (depends on 11) 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. ### Claim 14 (depends on 10) 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. ### Claim 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. ### Claim 16 (depends on 15) The system of claim 15, wherein the textual identifier further describes a relationship between two or more facial features of the human face. ### Claim 17 (depends on 16) 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. ### Claim 18 (depends on 15) 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. ### Claim 19 (depends on 15) 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. ### Claim 20 (depends on 15) 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. ### Claim 21 (depends on 15) 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. ### Claim 22 (depends on 21) 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. ### Claim 23 (depends on 21) 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. ### Claim 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. ### Claim 25 (depends on 24) 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. ### Claim 26 (depends on 25) 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. ### Claim 27 (depends on 24) 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. ### Claim 28 (depends on 24) 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. ### Claim 29 (depends on 24) 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. ### Claim 30 (depends on 24) 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.