# US 2026/0099501 A1 · Search Processing Based On Routing Predictions For Searching Across Local And Server Datasets > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099501 A1 - Document ID: US20260099501A1 - Status: Published application - Kind: A1 - Application number: 18/909,548 - Filed: 2024-10-08 - Published: 2026-04-09 - Earliest claimed: 2024-10-08 - Assignees: Google Inc. - Applicants: Google LLC - Inventors: Arash Sadr; Timothy Youngjin Sohn; Maryam Gholami - Web page: https://patentgrep.com/patent/US20260099501A1 ## CPC - G06F 16/248: Presentation of query results - G06F 9/547: {Remote procedure calls [RPC]; Web services} - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods for performing user data search can include obtaining a search query, processing the search query with one or more machine-learned planning model to generate one or more specialized queries and instructions to search one or more specific user datasets with the one or more specialized queries, and processing the obtained search results with a generative response model to generate a response to the search query. The systems and methods can search across local databases and server databases. An on-device generative response model can be utilized for device local search results, while a server generative response model may be utilized for server-based search results. ## Claims ### Claim 1 A computing system for cross-application search, the system comprising: one or more on-device generative response models tuned to process queries and result datasets to generate predicted responses, wherein the one or more on-device generative response models are stored on a user computing device; a memory comprising a plurality of different application-specific index datasets, wherein the memory is stored on the user computing device; one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining a search query via the user computing device associated with a particular user; transmitting the search query to one or more machine-learned planning models stored on one or more server computing systems, wherein the one or more machine-learned planning models were tuned to determine one or more particular datasets to search of the plurality of different application-specific index datasets; receiving, via the user computing device, one or more application programming interface calls from the one or more server computing systems, wherein the one or more application programming interface calls comprise instructions for searching the one or more particular datasets based on one or more refined queries generated with the one or more machine-learned planning models; searching, at an operating system level of the user computing device, the one or more particular datasets based on the one or more refined queries to determine one or more search results; processing the one or more search results and the search query with the one or more on-device generative response models to generate one or more model-generated responses; and providing the one or more model-generated responses for display. ### Claim 2 (depends on 1) The system of claim 1, wherein the operations further comprise: obtaining location data from one or more location sensors of a user computing device; and wherein the one or more search results are determined based on the location data. ### Claim 3 (depends on 1) The system of claim 1, wherein the operations further comprise: processing the search query and the one or more search results with the machine-learned planning model to generate a follow-up planning output. ### Claim 4 (depends on 3) The system of claim 3, wherein the follow-up planning output comprises instructions to transmit the search query and the one or more search results to the one or more on-device generative response models based on determining the one or more search results comprises details descriptive of information that is directly responsive to the search query. ### Claim 5 (depends on 3) The system of claim 3, wherein the follow-up planning output comprises one or more follow-up queries and instructions for searching one or more second datasets based on the one or more follow-up queries; wherein the operations further comprise: searching, at the operating system level of the user computing device, the one or more second datasets based on the one or more follow-up queries to determine one or more follow-up search results; processing the one or more search results, the one or more follow-up search results, and the search query with the one or more on-device generative response models to generate one or more second model-generated responses; and providing the one or more second model-generated responses for display. ### Claim 6 (depends on 1) The system of claim 1, wherein the one or more on-device generative response models were trained via distillation learning with one or more teacher models, wherein the one or more teacher models were trained based on a synthetic training dataset comprising a plurality of synthetic personal data associated with a synthetic user profile; and wherein a subset of parameters of the one or more on-device generative response models were fine-tuned on an on-device-specific training dataset while remaining parameters of the one or more on-device generative response models were fixed, wherein the on-device-specific training dataset comprises training examples specific to on-device search tasks. ### Claim 7 (depends on 1) The system of claim 1, wherein the plurality of different application-specific index datasets were generated with one or more indexing engines, wherein the one or more indexing engines are configured to: obtain content data, wherein the content data is descriptive of at least one of: content provided for display to the particular use; or content generated by the particular user; process the content data to generate a centerpiece dataset descriptive of a central focus of the at least one of: content provided for display to the particular use; or content generated by the particular user; process the centerpiece dataset with one or more embedding model to generate one or more content embeddings; and store the one or more content embeddings. ### Claim 8 (depends on 7) The system of claim 7, wherein the one or more indexing engines comprise one or more generative language models configured to process one or more content items to generate semantic understanding output and the one or more embedding models configured to generate feature representations based on at least one of the content items or the semantic understanding output. ### Claim 9 (depends on 7) The system of claim 7, wherein the one or more indexing engines comprise one or more vision language models configured to process one or more images to generate one or more respective image captions to be embedded by the one or more embedding models. ### Claim 10 (depends on 7) The system of claim 7, wherein the one or more indexing engines comprise one or more document understanding models tuned to process documents to generate document representations based on content features and layout features of the documents and one or more segmentation models tuned to segment portions of the documents based on the document representations. ### Claim 11 A computer-implemented method for personal data indexing and search, the method comprising: obtaining, by a computing system comprising one or more processors, a search query from a user computing device associated with a particular user; processing, by the computing system, the search query with one or more machine-learned planning models to generate one or more first application programming interface calls, wherein the one or more first programming interface calls comprise one or more first queries and instructions to interface with one or more first application-specific index datasets of a plurality of different application-specific index datasets; performing, by the computing system, the one or more first application programming interface calls to obtain one or more first result sets; processing, by the computing system, the search query and the one or more first result sets with the one or more machine-learned planning models to generate one or more second application programming interface calls, wherein the one or more second programming interface calls comprises one or more second queries and instructions to interface with one or more second application-specific index datasets of the plurality of different application-specific index datasets; performing, by the computing system, the one or more second application programming interface calls to obtain one or more second result sets; processing, by the computing system, the search query, the one or more first result sets, and the one or more second result sets with one or more generative response models to generate one or more model-generated responses to the search query, wherein the one or more model-generated responses comprise details of the one or more first result sets and the one or more second result sets in a natural language response to one or more prompts of the search query; and transmitting, by the computing system, the one or more model-generated responses to the user computing device associated with the particular user. ### Claim 12 (depends on 11) The method of claim 11, further comprising: obtaining, by the computing system, a plurality of personal datasets from a plurality of different applications associated with a plurality of different profiles associated with the particular user; processing, by the computing system, the plurality of personal datasets to generate the plurality of different application-specific index datasets; and storing, by the computing system, plurality of different application-specific index datasets in association with one or more personal identifiers associated with the particular user. ### Claim 13 (depends on 12) The method of claim 12, wherein the one or more personal identifiers are associated with a centralized profile of the particular user, wherein the centralized profile comprises information descriptive of biographical data of the particular user. ### Claim 14 (depends on 13) The method of claim 13, wherein the one or more machine-learned planning models are communicatively connected with the centralized profile, and wherein predictions of the one or more machine-learned planning models are conditioned on the information of the centralized profile. ### Claim 15 (depends on 11) The method of claim 11, wherein the one or more first application programming interface calls comprise instructions for interfacing with indexed email data associated with the particular user. ### Claim 16 (depends on 15) The method of claim 15, wherein the one or more second application programming interface calls comprise instructions for interfacing with indexed image data associated with the particular user, wherein the indexed image data was obtained from a native image gallery application on the user computing device. ### Claim 17 A server computing system for cross-application search, the system comprising: one or more machine-learned planning models configured to process a query to generate predicted planning data descriptive of particular tools to utilize and particular datasets to search; one or more server-side generative response models tuned to process queries and result datasets to generate predicted responses; a memory comprising a plurality of different application-specific index datasets, wherein the plurality of different application-specific index datasets are descriptive of personal data instances across a plurality of different application profiles associated with a particular user; one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining a search query from a user computing device associated with the particular user; processing the search query with the one or more machine-learned planning models to generate one or more refined queries and to determine one or more particular subsets of the plurality of different application-specific index datasets to search; searching, via one or more personal data intelligence models based on one or more planning model outputs, the one or more particular subsets of the plurality of different application-specific index datasets based on the one or more refined queries to determine one or more search results; processing the search query and the one or more search results with the one or more server-side generative response models to generate one or more model-generated responses to the search query, wherein the one or more model-generated responses comprise details of the one or more search results in a natural language response to a prompt of the search query; and transmitting the one or more model-generated responses to the user computing device associated with the particular user. ### Claim 18 (depends on 17) The system of claim 17, further comprising: one or more application programming interfaces configured to interface with one or more application indexes based on outputs of the one or more machine-learned planning models. ### Claim 19 (depends on 18) The system of claim 18, wherein the one or more application programming interfaces interface with the personal data intelligence model to perform the search of the one or more particular subsets of the plurality of different application-specific index datasets. ### Claim 20 (depends on 17) The system of claim 17, wherein the one or more particular subsets of the plurality of different application-specific index datasets comprise an email index dataset and a photo gallery index dataset.