US 12,254,039 B2
User interface including personalized feed with dynamically generated prompts
Yaw Oduro Amoateng, Redmond, WA (US); Roberta Cannerozzi, Bellevue, WA (US); Jeremy Michael Grubaugh, Kirkland, WA (US); Graham Kent, Seattle, WA (US); Adam Douglas Troy, Seattle, WA (US); Aaron John Cronin, Oslo (NO); Ola Natvig, Trondheim (NO); Åsmund Grammeltvedt, Tromso (NO); Roman Werpachowski, Arneberg (NO); Wei-Han Chang, Seattle, WA (US); and Maya Angele Pelichet, Seattle, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Mar. 23, 2023, as Appl. No. 18/125,286.
Prior Publication US 2024/0320257 A1, Sep. 26, 2024
Int. Cl. G06F 16/435 (2019.01); G06F 3/0482 (2013.01); G06F 16/2457 (2019.01); G06F 16/438 (2019.01); G06F 40/166 (2020.01); G06F 40/40 (2020.01); G06T 11/00 (2006.01)
CPC G06F 16/435 (2019.01) [G06F 3/0482 (2013.01); G06F 16/24578 (2019.01); G06F 16/438 (2019.01); G06F 40/166 (2020.01); G06F 40/40 (2020.01); G06T 11/00 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a device including at least one memory having processor-executable code stored therein, and at least one processor that is adapted to execute the processor-executable code, wherein the processor-executable code includes processor-executable instructions that, in response to execution, enable the device to perform actions, including:
submitting a request via a feed service device to request ranked content that is associated with a first user;
responsive to requesting the ranked content, receiving the ranked content, at the feed service device, such that the ranked content is ranked based on a relevance of content among the ranked content to the first user;
accessing a first profile that is associated with the first user, wherein the first profile includes a plurality of key-value pairs, each key-value pair of the plurality of key-value pairs including two linked elements, the two linked elements including a key that is an identifier of the key-value pair and a value that is a corresponding value for the key;
selecting at least some of the plurality of key-value pairs from the first profile for transmission to a large language model;
generating a first prompt such that the first prompt includes natural-language text instructions for the large language model;
providing the first prompt, the plurality of selected key-value pairs, and the ranked content to the large language model as input;
using the large language model to generate a plurality of pill prompts associated with the ranked content and a response to each pill prompt of the plurality of pill prompts, such that the pill prompts are personalized information requests that are personalized to have relevance to the first user based on the plurality of selected key-value pairs, and the response to each pill prompt includes content that corresponds to the requested information;
transmitting, via the feed service device, a content feed to a user device of the first user; causing the content feed to be displayed on the user device, including causing a plurality of selectable pills to be displayed on the user device as part of the displayed content feed such that each selectable pill of the plurality of selectable pills includes a corresponding pill prompt of the plurality of pill prompts;
receiving a selection of one of the selectable pills of the plurality of selectable pills via the user device;
responsive to the selection of one of the selectable pills, causing the response to the pill prompt that corresponds to the selection to be displayed on the user device; and
using the large language model to determine logical groupings of feed items among the feed items such that the logical groupings are based on commonalities among the feed items, wherein causing the content feed to be displayed on the user device includes causing the content feed to be displayed on the user device such that logical groupings of feed items are displayed together, and wherein each logical grouping of the logical groupings has a summary that summarizes the logical grouping, the actions further including: based on the ranked content and the first profile, using the large language model to generate the summaries of the logical groupings such that the summaries are unique to the first user and such that the summaries are personalized to the first user.