Suggesting a domain name from digital image metadata
Systems and methods of the present invention provide for a server computer coupled to a network and configured to: receive an image; transmit the digital image to an API operated by at least one metadata generation service, and receive a metadata data set about the digital image, prioritize a plurality of keywords within the data set, generate a list of candidate domain names including a keyword, insert a second candidate domain name into the list comprising a keyword replacing or concatenated to the keyword and associated with a lower priority than the keyword; and transmit the list to a client computer for display.
1. A system comprising a server hardware computing device coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the system to:
decode a transmission encoding a digital image, wherein the transmission encoding the digital image is generated from a client hardware computing device coupled to the network;
utilize an application programming interface (API) operated by at least one service to generate a metadata from the digital image;
determine a dataset of keywords from the metadata;
utilize a trained neural network model to generate, from the dataset of keywords a dataset of prioritized keywords that have been prioritized based on a significance hierarchy that is indicative of a significance ranking of each respective keyword within the digital image;
wherein the trained machine learning model is trained at least in part using at least one feedback input received from the client hardware computing device;
wherein the significance hierarchy comprises at least one keyword associated with a high priority determined, according to at least one of:
i) a font size, weight, or emphasis of the at least one keyword within the digital image,
ii) a frequency of the at least one keyword within a description or a tag associated with one or more objects identified within the digital image, or
iii) a size of the at least one keyword relative to at least one additional text string extracted from the digital image using optical character recognition (OCR);
identify at least one higher-significance keyword from the dataset of prioritized keywords based on the significance hierarchy;
encode a list of candidate domain names comprising a first candidate domain name including the at least one keyword;
insert into the list of candidate domain names a second candidate domain name comprising at least one additional lower-significance keyword replacing or concatenated to the at least one higher-significance keyword, wherein the at least one additional lower-significance keyword is associated with a lower significance than the at least one higher-significance keyword based on the significance hierarchy; and
transmit the list of candidate domain names to the client hardware computing device for display to a user operating the client hardware computing device.
2. The system of claim 1 , wherein the client hardware computing device is configured to crawl the digital image to identify at least one metadata, associated with or embedded within the digital image, and transmit the at least one metadata to the server hardware computing device or the at least one service, the at least one metadata comprising:
a filename for the digital image;
a source of the digital image;
a location at which the digital image was captured; or
a date on which the digital image was captured.
3. The system of claim 1 , wherein the at least one service comprises a metadata generation service including:
a reverse image lookup service; or
an object identification service.
4. The system of claim 1 , wherein the at least one service comprises a metadata generation service including:
a metadata tagging service;
a sentence building service; or
an optical character recognition service.
5. The system of claim 1 , wherein the at least one service comprises a metadata generation service including:
a facial recognition service;
a logo recognition service; or
a color recognition service.
6. The system of claim 1 , wherein the at least one service comprises a metadata generation service including a geolocation service.
7. The system of claim 1 , wherein the data set of metadata generated from the at least one service comprises:
a facial recognition data;
a name for an entity associated with the digital image;
an industry category for the entity;
a plurality of keywords or additional text content describing the entity;
a contact data for the entity; or
a trade dress, comprising a trade logo or one or more trade colors, for the entity.
8. The system of claim 1 , wherein:
the data set of metadata is organized into at least one parent data associated with a higher priority entity data and at least one child data associated with the parent data; and
the server hardware computing device stores the at least one child data in a database in association with the at least one parent data.
9. The system of claim 1 , wherein the specific computer-executable instructions further cause the system to automatically encode at least one product web page from the data set of metadata.
10. The system of claim 1 , wherein the specific computer-executable instructions further cause the system to automatically populate at least one electronic form using the data set of metadata.
11. A method comprising the steps of:
decoding, by a server hardware computing device coupled to a network and comprising at least one processor executing computer-executable instructions within a memory, a transmission encoding a digital image, wherein the transmission encoding the digital image is generated from a client hardware computing device coupled to the network;
utilizing, by the server hardware computing device, an application programming interface (API) operated by at least one service to generate a metadata from the digital image;
determining a dataset of keywords from the metadata;
utilizing a trained neural network model to generate, from the dataset of keywords a dataset of prioritized keywords that have been prioritized based on a significance hierarchy that is indicative of a significance ranking of each respective keyword within the digital image;
wherein the trained machine learning model is trained at least in part using at least one feedback input received from the client hardware computing device;
wherein the significance hierarchy comprises at least one keyword associated with a high priority determined, according to at least one of:
i) a font size, weight, or emphasis of the at least one keyword within the digital image,
ii) a frequency of the at least one keyword within a description or a tag associated with one or more objects identified within the digital image, or
iii) a size of the at least one keyword relative to at least one additional text string extracted from the digital image using optical character recognition (OCR);
identifying at least one higher-significance keyword from the dataset of prioritized keywords based on the significance hierarchy;
encoding a list of candidate domain names comprising a first candidate domain name including the at least one keyword;
inserting into the list of candidate domain names a second candidate domain name comprising at least one additional lower-significance keyword replacing or concatenated to the at least one higher-significance keyword, wherein the at least one additional lower-significance keyword is associated with a lower significance than the at least one higher-significance keyword based on the significance hierarchy; and
transmitting the list of candidate domain names to the client hardware computing device for display to a user operating the client hardware computing device.
12. The method of claim 11 , further comprising the steps of crawling, by the client hardware computing device, the digital image to identify at least one metadata, associated with or embedded within the digital image, and transmitting the at least one metadata to the server hardware computing device or the at least one service, the at least one metadata comprising:
a filename for the digital image;
a source of the digital image;
a location at which the digital image was captured; or
a date on which the digital image was captured.
13. The method of claim 11 , wherein the at least one service comprises a metadata generation service including:
a reverse image lookup service; or
an object identification service.
14. The method of claim 11 , wherein the at least one service comprises a metadata generation service including:
a metadata tagging service;
a sentence building service; or
an optical character recognition service.
15. The method of claim 11 , wherein the at least one service comprises a metadata generation service including:
a facial recognition service;
a logo recognition service; or
a color recognition service.
16. The method of claim 11 , wherein the at least one service comprises a metadata generation service including a geolocation service.
17. The method of claim 11 , wherein the data set of metadata generated from the at least one service comprises:
a facial recognition data;
a name for an entity associated with the digital image;
an industry category for the entity;
a plurality of keywords or additional text content describing the entity;
a contact data for the entity; or
a trade dress, comprising a trade logo or one or more trade colors, for the entity.
18. The method of claim 11 , wherein:
the data set of metadata is organized into at least one parent data associated with a higher priority entity data and at least one child data associated with the parent data; and
the server hardware computing device stores the at least one child data in a database in association with the at least one parent data.
19. The method of claim 11 , wherein the specific computer-executable instructions further cause the system to automatically encode at least one product web page from the data set of metadata.
20. The method of claim 11 , further comprising the step of automatically populating, by the at least one server hardware computing device, at least one electronic form using the data set of metadata.