Displaying advertisements in video clips

On video-sharing platforms, users access some video clips primarily for audio rather than video content. In such video clips, the display is idle or otherwise possibly uninteresting to the viewer. The techniques of this disclosure apply machine learning to detect if the visual portion of a video clip is likely not of interest to the user. If the visual portion detected to not be of interest to the user, permission is sought from the user to insert a visual ad into the clip while audio continues playing unchanged. If user permission is obtained, ads are inserted in portions of video clips identified as not being of interest to the user, thereby monetizing the video clip

Robust ADHD testing by applying clustering techniques to survey responses or speech data

Existing tests for attention deficit hyperactivity disorder (ADHD) may exhibit some bias. Also, these tests require filling in a survey with subjective responses, which can lead to misdiagnosis. The techniques described herein reduce bias in ADHD tests by seeking clusters in test-parameter space conditioned on certain characteristics of a person. Clustering is performed using machine learning techniques. With user permission, speech data is obtained via one or more devices such as a phone, smart speaker, etc. an is used to make objective diagnoses of ADHD

Destination Search With User-specified Constraints

When issuing destination-related queries users sometimes include on or more constraints within the query. Even though a search engine can be used to search for data regarding each constraint individually, users need to integrate the individual results manually based on separate searches for different types of data. This disclosure describes techniques to retrieve and present search results for destination-related queries based on user-specified constraints present within a user query. The results are obtained by performing separate searches based on various constraints specified in the user query and combining and filtering the results to include only those results that match all constraints. The results are sorted based on specific criteria prior to presentation to the user

Improving Query Suggestions Based On Search Box Edits

Users often enter terms into a search box and then modify the query. Such modifications may be based on, e.g., the real-time query suggestions offered by the search engine, or the user thinking of a different phrasing for the query. Such changes to entered search terms prior to executing the search are not captured in the search history, and are not taken into account for tailoring post-search query suggestions or search results. This disclosure describes the use of a trained machine learning model to customize query suggestions and/or search results based on terms previously typed in the search box, obtained with the user’s permission

Automatic Alerts Based On Departure From Routine

Research indicates that a task or item that is forgotten is presaged by a change in routine. This disclosure describes techniques to automatically determine that a user may have forgotten a task or item. The determination is made based on analysis of user permitted data such as calendar appointments, location, etc. that indicate a change in routine. Upon determination that the user is likely to forget a task or item, e.g., due to change in routine, an alert is provided to the user

Recipe Recommendations Based on Visual Input of Available Ingredients

It is difficult to choose recipes that can be prepared using ingredients available at home. Manually identifying available ingredients and performing searches for feasible recipes is cumbersome and error prone. This disclosure describes techniques that enable users to obtain recipe recommendations by capturing available ingredients visually as images and/or videos. With user permission, the visual input is analyzed using computer vision and natural language processing techniques to identify the type and quantity of available ingredients. Matching recipes are determined using a search engine and ranked based on the user’s preferences. The user can filter the list based on various criteria as well as save, label, and/or annotate specific recipes

Machine Learning Based Virtual Concierge for Planning Group Activities

When a group of individuals attempt to plan a group activity such as a joint trip to a common destination, the presence of conflicting constraints makes it difficult to arrive at a plan that is agreeable to all. This disclosure describes a virtual concierge that accepts as input multiple, potentially conflicting constraints from multiple individuals planning collective travel (or other group activity) and outputs optimized recommendations tailored for the individuals in the group. The virtual concierge application can leverage large language models (LLM) for language understanding and for natural user interactions. The virtual concierge can generate prompts for an LLM that has been efficiently tuned using techniques such as adapter layers, few-shot prompt tuning, etc. Machine learning (ML) can be used to generate a set of recommendations based on the preferences of different individuals in the group

Semi-automated User Account Profile Generation Using Existing Social Media Data

Online interactive services provide the ability for users to create profiles that specify their preferences and/or interests and showcase their photos. It is burdensome and time-consuming for users to provide manual input of the various pieces of information included in such profiles and to keep the profile updated over time. This disclosure describes techniques for automating the generation of a user profile for an online service. With user permission, a preview of the proposed user profile for a new service is generated automatically based on information about the user aggregated from the user’s other accounts. Data from the other accounts is aggregated and analyzed to determine the user’s interests and other relevant information needed for filling out the profile. The inferred interests and preferences are ranked and specific data are selected to be added to the profile, e.g., based on the likely appeal of that information for the audience on the new service. Once confirmed by the user, the information is posted to the user’s profile for the given online service