Guiding Capture of New Photos Based On Published Photos

This disclosure describes techniques for presenting recommendations that guide users to capture high-quality photos based on existing crowdsourced, popular, and/or shared photos. Machine learning models are used to find high-quality public photos that have similar attributes to a photo the user wishes to capture. These public photos are displayed as examples for the user and are used to guide the capture location, photo framing, and/or camera settings during the photo taking process. Described features inform and guide a user in a process of capturing high-quality photos and are easily accessed in a device used to capture the photos

Improved Message Facilitator For On-Time And Quick Response

Increasing reliance on written electronic communication has created a need for automated ways for users to efficiently manage and respond to the various emails, texts, and other electronic messages that they receive. Some solutions have already been introduced to help remind users respond to messages after some number of days have passed, and to help users compose messages. However, through the use of natural language processing, these features can be improved so that users are kept aware of pending response deadlines, and presented with proposed responses that take account of the context of a given message, as well as any questions being asked therein

Identifying Impaired State for a Driver

Generally, the present disclosure is directed to improving driver safety by identifying impaired drivers. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a driver’s impairment state based on driving data

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

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

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

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