“Feller is the rare combination of street smarts and strategic acumen. He is a serial entrepreneur with an uncanny sense of what will be big next. Big or small, Feller is at home leading in both environments. And he has good taste in wine. In short, kickass.”
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Highly analytical operator and entrepreneur with 25+ years of experience building…
Activity
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I'm looking for an incredible marketing manager at Quilt - please send referrals! This is a new role reporting to me that will get to touch all…
I'm looking for an incredible marketing manager at Quilt - please send referrals! This is a new role reporting to me that will get to touch all…
Liked by David Feller
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I see startups every day juggling whether they should or shouldn't make a hire. "Hire slow, fire fast" is always the old mantra, but if you wait too…
I see startups every day juggling whether they should or shouldn't make a hire. "Hire slow, fire fast" is always the old mantra, but if you wait too…
Liked by David Feller
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A few months ago Ann introduced me to Jae and Soyoung at Twelve Labs. I was blown away by the team, mission, tech and hustle (reminds me of early…
A few months ago Ann introduced me to Jae and Soyoung at Twelve Labs. I was blown away by the team, mission, tech and hustle (reminds me of early…
Liked by David Feller
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Volunteer Experience
Patents
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Recipe Recommendation
Issued US 9,824,152
User activity data describing how a user interacts with recipes posted on a web page or provided by an application is received. A first set of recommended recipes for the user is generated based on the user activity data. A content model that aligns recipe features extracted from the content of the recipes is built based on content of the recipes. A second set of recommended recipes is generated based on the content model. The first set of recommended recipes and the second set of recommended…
User activity data describing how a user interacts with recipes posted on a web page or provided by an application is received. A first set of recommended recipes for the user is generated based on the user activity data. A content model that aligns recipe features extracted from the content of the recipes is built based on content of the recipes. A second set of recommended recipes is generated based on the content model. The first set of recommended recipes and the second set of recommended recipes are merged and transmitted for display to the user.
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Prediction of recipe preparation time
Issued US 9,797,873
Embodiments infer a total preparation time of a recipe. A recipe including preparation steps describing how to prepare a food item is obtained. A recipe server identifies preparation features in the preparation steps, where the preparation features represent portions of the preparations steps that are correlated with time to prepare the recipe. The recipe server obtains preparation times associated with the preparation features and combines these preparation times to estimate to total…
Embodiments infer a total preparation time of a recipe. A recipe including preparation steps describing how to prepare a food item is obtained. A recipe server identifies preparation features in the preparation steps, where the preparation features represent portions of the preparations steps that are correlated with time to prepare the recipe. The recipe server obtains preparation times associated with the preparation features and combines these preparation times to estimate to total preparation time of the recipe. The estimated total preparation time is stored or transmitted to a client device in response to a request for the recipe. The estimated total preparation time is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on total preparation time. The estimated total preparation time is used to select recipes for recommendation to a user based on that user's preferences.
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Inferring temporal attributes of a recipe
Issued US 9,639,805
Embodiments infer a temporal attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with temporal attributes. The recipe server determines whether the recipe is associated with a temporal attribute based on the attribute features and obtained attribute parameters…
Embodiments infer a temporal attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with temporal attributes. The recipe server determines whether the recipe is associated with a temporal attribute based on the attribute features and obtained attribute parameters corresponding to the attribute features. A temporal attribute determined to be associated with a recipe is transmitted to a client device in response to a request for the recipe. The estimated temporal attribute is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on the temporal attribute. The estimated temporal attribute is used to select recipes for recommendation to a user based on a current time.
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Inferring Recipe Difficulty
Issued US 9,489,377
Embodiments infer a difficulty attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with difficulty attributes. The recipe server determines whether the recipe is associated with a difficulty attribute based on the attribute features and obtained attribute…
Embodiments infer a difficulty attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with difficulty attributes. The recipe server determines whether the recipe is associated with a difficulty attribute based on the attribute features and obtained attribute parameters corresponding to the attribute features. A difficulty attribute determined to be associated with a recipe is transmitted to a client device in response to a request for the recipe. The estimated difficulty attribute is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on the difficulty attribute. The estimated difficulty attribute is used to select recipes for recommendation to a user based on that user's preferences.
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Clustering and Display of Recipes
Issued US 9,483,547
Recipes are hierarchically clustered into groups based on features of the recipes. Candidate clusters with a threshold number of clustered recipes having at least one feature in common are found by traversing the hierarchy. A plurality of clusters is selected for display to a user from among the candidates based on an objective function that considers the relevancy of the cluster as well as diversity of the clusters. A plurality of recipes within each selected cluster is selected for display to…
Recipes are hierarchically clustered into groups based on features of the recipes. Candidate clusters with a threshold number of clustered recipes having at least one feature in common are found by traversing the hierarchy. A plurality of clusters is selected for display to a user from among the candidates based on an objective function that considers the relevancy of the cluster as well as diversity of the clusters. A plurality of recipes within each selected cluster is selected for display to a user from among the recipes within the cluster based on an objective function that considers the quality of the recipe as well as the diversity of the recipes within the cluster. At least one feature that all of the recipes in a respective cluster have in common is used to generate a name for the cluster.
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Recipe Text and Image Extraction
Issued US 9,311,568
Embodiments process a recipe from structured data to extract recipe text and select an image representative of the recipe. Recipes in structured data are retrieved and sequenced into segments to facilitate further processing. A recipe parser generates features corresponding to the segments. These generated features are inputs to a recipe model to classify the segments into components. This recipe model is trained according to classified training recipes. The trained model may then determine…
Embodiments process a recipe from structured data to extract recipe text and select an image representative of the recipe. Recipes in structured data are retrieved and sequenced into segments to facilitate further processing. A recipe parser generates features corresponding to the segments. These generated features are inputs to a recipe model to classify the segments into components. This recipe model is trained according to classified training recipes. The trained model may then determine classifications for segments of the recipe. The classified recipe text is used to select the representative image for the recipe. To select this image, candidate images for a recipe are retrieved and filtered to remove unacceptable images. Features corresponding to these candidate images are generated and used as inputs to an image model to select the representative image. This image model is trained using representative training images of training recipes.
Other inventors -
Services Scheduling
Issued US 11/095,934
A system and a method to receive a price submission in connection with a service at an incremental time block is described. Schedule information is to be received from a service provider. The information indicates at least one incremental time block at which the service provider is available to provide the service. The schedule information is to be published to a service consumer. A price submission in connection with at least one incremental time block is to be received from the service…
A system and a method to receive a price submission in connection with a service at an incremental time block is described. Schedule information is to be received from a service provider. The information indicates at least one incremental time block at which the service provider is available to provide the service. The schedule information is to be published to a service consumer. A price submission in connection with at least one incremental time block is to be received from the service consumer.
Other inventors
Honors & Awards
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Webby Award - Best Food & Drink Website
Webby Awards
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Webby Award - Best User Experience
Webby Awards
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App Store - "Essentials" Designation - Yummly iPhone, iPad, and Apple TV applications
Apfel
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Gold Award - Yummly iPhone App - Best Visual Design - Aesthetic
Davey Awards
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Gold Award Winner - Yummly iPhone App - Mobile Features - Best Visual Design - Function
W3 Awards
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Appy Award
MediaPost
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Official Honoree
The Webby Awards
2015, Mobile Sites & Apps, Food & Drink
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App Store Best of 2014
Apfel
The App Store Best of 2014 celebrates the year’s most innovative and entertaining apps and games.
Recommendations received
13 people have recommended David
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Since I was a kid, I've always loved the Olympics. Paris 2024 was the 13th Games where I have had the privilege of working with Olympians, and it is…
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Introducing the new Yahoo Fantasy App - available now on iOS and Android. It is a bright, fun, and modern update... and includes a first-of-it's-kind…
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Happy to be co-hosting a meet and greet at former Mountain View Mayor Michael Kasperzak's home next Saturday for my friends, Jessica Speiser, running…
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Very proud of my daughter Sydney. She was featured by Emory’s student athlete departmentrun a profile on her summer research and shadowing activities.
Very proud of my daughter Sydney. She was featured by Emory’s student athlete departmentrun a profile on her summer research and shadowing activities.
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Premiering next week, the "Elevate Your AIQ" podcast will be the destination for thought-provoking insights on how you can best prepare yourself…
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Today, we are presenting our poster paper at CUI 2024, the ACM SIGCHI Conversational User Interfaces Conference in Luxembourg 🇱🇺, on “Prompt…
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