Temporal copy using embedding content database

1 . A method for capturing content items into a content database, the method comprising:

receiving a capture request to capture a plurality of content items, the plurality of content items including a first content item of a first content type and a second content item of a second content type, wherein the first content type is different than the second content type;

capturing the plurality of content items with metadata associated with the plurality of content items;

generating a plurality of semantic embeddings of the plurality of content items and the metadata, using one or more semantic models, the plurality of semantic embeddings including a first semantic embedding generated based on the first content item of the first content type and a second semantic embedding generated based on the second content item of the second content type;

storing the plurality of semantic embeddings in the content database;

providing captured content history stored in the content database, the captured content history including a list of captured content items;

receiving a user query to search the captured content history;

providing search results based on a semantic measure between the plurality of semantic embeddings in the content database and the user query;

determining if an action associated with one or more of the plurality of content items exists based on the metadata; and

in response to determining that the action exists, performing the action associated with the one or more of the plurality of content items.

2 . The method of claim 1 , wherein the plurality of content items includes one or more texts, documents, images, pictures, photos, videos, or audios.

3 . The method of claim 1 , wherein the metadata for one or more content items of the plurality of content items includes a date and time when the one or more content items was captured, a type of the one or more captured content items, a file name and a path of a file associated with the one or more captured content items, a computing device used to capture the one or more content items, a location of the computing device where the one or more content items was captured, an application used to capture the one or more content items, and any information extracted from the one or more content items.

4 . The method of claim 1 , wherein the user query is a natural language query.

5 . The method of claim 1 , wherein providing the search results based on the user query includes:

generating an input embedding based on the user query; and

retrieving semantic embeddings associated with one or more previously captured content items from the content database based on a similarity to the input embedding.

6 . The method of claim 1 , further comprising filtering the search results based on the metadata associated with content items in the search results.

7 . The method of claim 1 , wherein the one or more semantic models includes a generative large language model (LLM).

8 . A computing device for generating a design difference caption, the computing device comprising:

a processor; and

a memory having a plurality of instructions stored thereon that, when executed by the processor, causes the computing device to:

receive a capture request to capture a plurality of content items, the plurality of content items including a first content item of a first content type and a second content item of a second content type, wherein the first content type is different than the second content type;

capture the plurality of content items with metadata associated with the plurality of content items;

generate a plurality of semantic embeddings of the plurality of content items and the metadata, using one or more semantic models, the plurality of semantic embeddings including a first semantic embedding generated based on the first content item and a second semantic embedding generated based on the second content item;

store the plurality of semantic embeddings in a content database;

provide captured content history stored in the content database, the captured content history including a list of captured content items;

receive a user query to search the captured content history;

provide search results based on a semantic measure between the plurality of semantic embeddings in the content database and the user query;

determine if an action associated with one or more of the plurality of content items exists based on the metadata; and

in response to determining that the action exists, perform the action associated with the one or more of the plurality of content items.

9 . The computing device of claim 8 , wherein the plurality of content items includes one or more texts, documents, images, pictures, photos, videos, or audios.

10 . The computing device of claim 8 , wherein the metadata for one or more content items of the plurality of content items includes a date and time when the one or more content items was captured, a type of the one or more captured content items, a file name and a path of a file associated with the one or more captured content items, a source computing device used to capture the one or more content items, a location of the computing device where the one or more content items was captured, an application used to capture the one or more content items, and any information extracted from the one or more content items.

11 . The computing device of claim 8 , wherein to provide the search results based on the user query includes to:

generate an input embedding based on the user query; and

retrieve semantic embeddings associated with one or more previously captured content items from the content database based on a similarity to the input embedding.

12 . The computing device of claim 8 , wherein the plurality of instructions, when executed, further cause the computing device to filter the search results based on the metadata associated with content items in the search results.

13 . The computing device of claim 8 , wherein the one or more semantic models includes a generative large language model (LLM).

14 . A non-transitory computer-readable medium storing instructions for generating a design difference caption, the instructions when executed by one or more processors of a computing device, cause the computing device to:

receive a capture request to capture a plurality of content items, the plurality of content items including a first content item of a first content type and a second content item of a second content type, wherein the first content type is different than the second content type;

capture the plurality of content items with metadata associated with the plurality of content items;

generate a plurality of semantic embeddings of the plurality of content items and the metadata, using one or more semantic models, the plurality of semantic embeddings including a first semantic embedding generated based on the first content item and a second semantic embedding generated based on the second content item;

store the plurality of semantic embeddings in a content database;

provide captured content history stored in the content database, the captured content history including a list of captured content items;

receive a user query to search the captured content history;

provide search results based on a semantic measure between the plurality of semantic embeddings in the content database and the user query;

determine if an action associated with one or more of the plurality of content items exists based on the metadata; and

in response to determining that the action exists, perform the action associated with the one or more of the plurality of content items.

15 . The non-transitory computer-readable medium of claim 14 , wherein the plurality of content items includes one or more texts, documents, images, pictures, photos, videos, or audios, and wherein the metadata for one or more content items of the plurality of content items includes a date and time when the one or more content items was captured, a type of the one or more captured content items, a file name and a path of a file associated with the one or more captured content items, a source computing device used to capture the one or more content items, a location of the computing device where the one or more content items was captured, an application used to capture the one or more content items, and any information extracted from the one or more content items.

16 . The non-transitory computer-readable medium of claim 14 , wherein to provide the search results based on the user query includes to:

generate an input embedding based on the user query; and

retrieve semantic embeddings associated with one or more previously captured content items from the content database based on a similarity to the input embedding.

17 . The non-transitory computer-readable medium of claim 14 , wherein the instructions, when executed, further cause the computing device to filter the search results based on the metadata associated with content items in the search results.