Patents and Patent Applications:
| Applic./Pat. No. | Filing Date | Status/Type | Patents | |
| 62/037,788 | 8.15.14 | Provisional | System and Method For Matching Food Cravings With Restaurant Recommendations | |
| US 9,323,786 | 4.26.16 | Issued | System and Computer Method for Visually Guiding a User to a Current Interest** | |
| US 9,779,160 | 10.3.17 | Issued | Iterative Image Search Algorithm Informed By Continuous Human-Machine Input Feedback** | |
| US 10,268,702 | 4.23.19 | Issued | C-I-P Of US application 14/827,205 * | |
| Pending Applications | ||||
| 16/363,693 | 3.25.19 | Continuation | Of US application 15/054,979 (filed 2.26.16) *** | |
| 15/688,362 | 8.28.17 | Continuation | Of US application 14/827,205 (filed 8.14.15) ** | |
| 16/162,024 | 10.16.18 | Continuation | Of US application 15/070,371 (filed 3.15.16)** | |
| (TrackOne) | ||||
| 62/745,794 | 10.15.18 | Provisional | Iterative Multi-User Selection and Weighting Recommendation Engine | |
| US2015.045391 | 8.14.15 | P.C.T. | System and Computer Method for Visually Guiding a User to a Current Interest**** | |
| US2017.019674 | 2.27.17 | P.C.T. | Iterative Image Search Algorithm Informed By Continuous Human-Machine Input Feedback | |
| *Claims priority to US 9,323,786, which claims priority to application 62/037,788 filed 8.15.14 | ||||
| **Claim priority to US Provisional Application 62/037,788 filed on 8.15.14 | ||||
| ***Claims priority to US 10,268,702, which claims priority to US 9,323,786, claiming priority to 62/037,788 (8.15.14) | ||||
| ****PCT member countries filed: European Union, China, South Korea, Japan,Australia,Canada | ||||
Pending US and foreign applications
Trademarks
Software
Know-how
Ask Sydney ™ Technology, as described by its registered patents and patent applications is currently practiced by FOODFAVES ®, a consumer- oriented dining assistant first released on the APP Store Sept., 2016. The FOODFAVES ® App was the first of its kind to feature an iterative search algorithm powering its signature “Crave Quiz”, helping users find the solution to their hunger by swiping sequential images of dishes, each subsequent image queued based on user (positive/negative) response to the previous photo. Embedded descriptive tags (metadata) allow the algorithm to learn what the user ‘wants’ in each quiz session. Ask Sydney ™ founders soon realized the diversity of industries potentially transformed by this Visual Search engine, seeking early protection of its underlying and unique intellectual property.
The CraveQuiz software is in essence, an algorithm for estimating the value of a user’s response to an object (i.e. image) or objects presented in a search session. The algorithm was designed to help users find a desired object even if the user is unaware of what they are looking for.
The search algorithm was developed using Ruby on Rails, and employs an SQL database. The data used in the search may be collected from the current session, or taken from various connected sources previously collected from the user. The algorithm begins by presenting media to the user (current implementation, images/photos) containing metadata that assign relevant attributes to the respective media. Such metadata (attributes) are referred to as tags. Upon sequential presentation of media, the user is given a binary choice for each option (image): yes or no. Positive (‘yes’) responses add value to the embedded tags; similarly, negative (‘no’) responses reduce value of tags. The algorithm uses (then) current tag values according to user response to select (queue) the next media/image.
The algorithm is easily modified to present different types of media (i.e. video, audio) and to utilize different metadata applicable to such media and as such may be applied to different applications and data sets. The algorithm’s interaction with the end user is accomplished through an application program interface (API) following a JavaScript Object Notation (JSON) standard. Currently operating on AWS, the software may be deployed on any server.