Generating a digest memorable highlights from unstructured messaging conversation

Many social media and photo collection applications offer features to generate a personalized digest of photos or structured text. A digest typically includes highlights of content from a larger collection. Such digest generation and viewing capabilities are not currently available for freeform unstructured conversational interactions, such as those that take place via messaging or chat applications. This disclosure describes techniques to automatically generate a digest of such interactions with permission from the conversing parties. The conversational interactions may contain a mixture of text, images, audio, and video. A user can review and save the digest offline for later personal use, share the digest with the other conversation parties, or post it on social media. The described techniques can be applied to direct one-to-one conversations as well as to multi-party group conversations

Triggering virtual assistant actions by live conversation

This disclosure describes virtual assistant techniques that automatically detect, with user permission, a conversation between a user and other individual(s), and provide information relevant to the conversation. For example, a user may be approached by a stranger with a request for directions to a certain business. Based on user-permitted factors such as the ongoing conversation, the user location, the user’s location history, etc., the virtual assistant readies a map relating to the stranger’s query. Further, if the user permits, voice identifier of the participants of a conversation is maintained. If the participants in the conversation meet again, the user is provided with information from the last conversation which serves as a conversation starter

Detecting and Correcting Problems in Photos Using Generative Artificial Intelligence

User captured photos can suffer from a variety of issues such as blurriness, poor lighting, presence of unwanted objects or people, etc. In addition, subjects in the photos can be captured in undesired expressions or poses. This disclosure describes techniques for automatically detecting and fixing problematic aspects of photos by employing generative artificial intelligence (gen AI) techniques. With user permission, a photo can be automatically edited using a suitable generative AI model to fix problems specified in a user-provided prompt. Optionally, a multimodal foundational model can be utilized to detect problems and generate an image correction prompt, along with a prompt that includes the desired corrections. The techniques can be applied to correct a variety of problematic aspects, such as facial expressions, pose, and appearance; lighting; presence of undesired objects or people, etc. Appropriate fairness techniques for machine learning can be incorporated to ensure that the corrections are applied appropriately for diverse photos with different settings and subjects

AI-based Image Synthesis for Enriched Search and Shopping

Across numerous applications, notably in search and shopping for unique items, humans are constrained by what has already been built or designed. This disclosure describes techniques that leverage natural language-based, deep-learning image synthesis to deliver enhanced product search via services such as search engines or e-commerce websites. The synthetically generated products can be custom manufactured upon order. Unconstrained by real world objects, the techniques deliver to the search engine or e-commerce user synthetic objects based on text descriptions provided by the user

SONG SENTIMENT RECOGNITION BASED ON IMPLICIT USER FEEDBACK

Computing devices utilize user feedback (e.g., explicit and/or implicit feedback) to determine a user’s sentiment towards a song or music played by the computing device or another speaker within a given environment. The computing device may determine the user’s sentiment, emotion, reaction, or opinion towards a song and utilize that information when selecting music, tunes, or songs to include in a playlist or otherwise output to the user. In one example, to determine the user’s sentiment towards the song, the computing device executes a sentiment recognition algorithm that utilizes implicit feedback, such as a user singing along with the song, the user moving in time with the beat of the song, etc., which may be captured by one or more sensors of the computing device. In some instances, the computing device may also use explicit feedback provided by the user, such as selecting a “like” or a “dislike” button to determine the user’s sentiment towards the song. If the user enjoys the song, the computing device may recommend, suggest, or output the song or similar songs (e.g., similar artists, genres, etc.) for playback, for example, when selecting songs to be included in an autogenerated playlist

Improving Remote Customer Interaction Experiences Using Machine Learning

A common problem in contact centers is high employee turnover. Artificial intelligence (AI) techniques that have been introduced to smoothen interaction and improve the customer’s experience can have opposite effects, e.g., by requiring the customer to navigate complex menu options. This disclosure describes AI-based techniques applied to agent training and customer calls. The techniques can reduce turnover at contact centers and improve the experience of end users who interact with customer service agents. Per the techniques, suitable AI techniques are implemented to train human customer agents, and human feedback is in turn used to train AI techniques. Human-AI augmentation can be used to mirror the communication styles of customers to improve the interaction experience. The techniques can also be used to improve safety, e.g., by automatically detecting scam calls and alerting users. The techniques enable the creation of scalable, standalone, artificial or human-AI augmented customer service agents

Face Recognition for Fast Information Retrieval and Record Lookup

This publication describes systems and techniques directed to taking a red, green and blue (RGB) image of a customer’s face, identifying landmarks on the face, creating a face box region, thumbnailing the face box region, sending the thumbnail to a face recognition model where face data is embedded into a vector, and using the vector in a multi-dimensional tree searching algorithm to quickly look up or retrieve information or a record relating to the customer in a database. The face image capture may be performed using standard RGB camera technology to enable broad, cost-effective business adoption. Two-factor authentication may be employed to address potential RGB image spoofing. Embedding the vector may be implemented on local technology to minimize network data transmissions of face images and increase lookup speed

The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies

Despite the clinical significance of balanced chromosomal abnormalities (BCAs), their characterization has largely been restricted to cytogenetic resolution. We explored the landscape of BCAs at nucleotide resolution in 273 subjects with a spectrum of congenital anomalies. Whole-genome sequencing revised 93% of karyotypes and demonstrated complexity that was cryptic to karyotyping in 21% of BCAs, highlighting the limitations of conventional cytogenetic approaches. At least 33.9% of BCAs resulted in gene disruption that likely contributed to the developmental phenotype, 5.2% were associated with pathogenic genomic imbalances, and 7.3% disrupted topologically associated domains (TADs) encompassing known syndromic loci. Remarkably, BCA breakpoints in eight subjects altered a single TAD encompassing MEF2C, a known driver of 5q14.3 microdeletion syndrome, resulting in decreased MEF2C expression. We propose that sequence-level resolution dramatically improves prediction of clinical outcomes for balanced rearrangements and provides insight into new pathogenic mechanisms, such as altered regulation due to changes in chromosome topology

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to 300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m 2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years