Intuit, Inc.

Wendi Cui

Damien J Lopez

Colin P Ryan

Qi Shen

Jiaxin Zhang

Beatrice Hendra

This invention relates to systems and methods for performing double-level ranking of documents. The system implements methods for retrieving documents based on a pre-processed user query to generate a document level ranking of one or more documents that are determined to be relevant. The system implements methods for aggregating one or more sub-topic snippets from the document level ranked documents. The system further implements methods for generating a topic level ranking of the one or more sub-topic snippets. Once topic level ranking of the one or more sub-topic snippets has been performed, the system implements methods for transmitting the topic level ranked one or more sub-topic snippets to a user associated with the user query.

Systems and methods for double level ranking

A method includes obtaining a hierarchical document structure of a raw document. The hierarchical document structure includes a multitude of sections arranged in a hierarchy of successive document levels. A hierarchical document graph having a graph hierarchical structure corresponding to the hierarchical document structure of the raw document is constructed. A hierarchical document graph corresponding to the raw document matching a user query is retrieved. A hierarchical search is performed on the hierarchical document graph to obtain a set of relevant nodes. A set of relevant content embeddings is retrieved from the set of relevant nodes. A large language model (LLM) generates a response to the user query from the set of relevant content embeddings. The response is presented in a user application.

Hierarchical graph retrieval augmented generation with context embedding

A method includes receiving a query and applying a classifier to a hierarchical taxonomy and the query to output a query taxonomy array. Each of a number of taxonomy tags has an associated level in a hierarchical taxonomy. The query taxonomy array includes query tags selected, based on a term contained in the query, from the taxonomy tags. The query taxonomy array is compared to article tags associated with articles. The article tags are selected from the taxonomy tags. Shared tags are also identified based on sharing. A list of articles is generated, the list including a subset of articles selected from the articles. The subset of articles are associated with the shared tags. Corresponding scores are assigned to the subset of articles in the list. The list is sorted, based on the corresponding scores, to generate a sorted list of articles. The sorted list is presented.

Taxonomy-based document retrieval

Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.

Managing search engines based on search perform metrics

Systems and methods for a boosted Latent Dirichlet Allocation model with predefined clusters and repelling force. Herein, clusters are pre-populated with seed words with amplified weights to predefine the characteristics of clusters. A repel force is also applied while assigning words to specific clusters in order to assign words more accurately to clusters in instances where clusters have similar characteristics. In addition to generating improved clustering of words, systems and methods may analyze words within queries to provide explainability output related to the performance of content or data.

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Mountain View, CA, United States of America

Wendi Cui