# US 12,113,555 B2 · Data compression selection system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,113,555 B2 - Document ID: US12113555B2 - Status: Granted - Kind: B2 - Application number: 17/680,758 - Filed: 2022-02-25 - Published: 2023-08-31 - Granted: 2024-10-08 - Earliest claimed: 2022-02-25 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Shu Chen; Jinzhu Chen; Donald K. Grimm; Mahesh Pottelu - Web page: https://patentgrep.com/patent/US12113555B2 ## CPC - H03M 7/607: {Selection between different types of compressors} - H03M 7/70: {Type of the data to be coded, other than image and sound} - G06F 16/2455: Query execution - H03M 7/6064: {Selection of Compressor} - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive metadata and results including at least one data transmission constraint, perform a search of a k-d tree based on the at least one data transmission constraint, and select at least one data compression process from the k-d tree based on the search. The k-d tree includes a plurality of nodes corresponding to data compression solutions. ## Claims ### Claim 1 A system comprising: a vehicle; a remote network; a computer located within the remote network including a processor and a memory, the memory including instructions such that the processor is programmed to: receive, from the vehicle, metadata and results corresponding to at least one data transmission constraint; perform a search of a k-d tree based on the at least one data transmission constraint; and select at least one data compression process from the k-d tree based on the search, wherein the k-d tree includes a plurality of nodes corresponding to data compression solutions; and transmit the at least one data compression process to the vehicle; and a second computer located in the vehicle including a second processor and a second memory, the second memory including instructions such that the second processor is programmed to: compress vehicle data using the at least one data compression process; and transmit the vehicle data compressed by the at least one data compression process. ### Claim 2 (depends on 1) The system of claim 1, wherein the metadata and the results are transmitted by the vehicle to the remote network. ### Claim 3 (depends on 1) The system of claim 1, wherein the processor is further programmed to perform a gradient-based search when at least one additional node has to be inserted into the k-d tree. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one data compression process comprises at least a lossy data compression process or a lossless data compression process. ### Claim 5 (depends on 1) The system of claim 1, wherein compressing data according to the selected data compression process comprises at least one of transform coding, adaptive sampling, quantization, differential coding, or arithmetic coding. ### Claim 6 (depends on 1) The system of claim 1, wherein the processor is further programmed to select a first data compression process for a first portion of vehicle data and select a second data compression process for a second portion of vehicle data, wherein the second data compression process is different from the first data compression process. ### Claim 7 (depends on 1) The system of claim 1, wherein the data compression constraint comprises at least one of central processing unit (CPU) usage, memory usage, latency, loss rate, input/output (I/O) cost, power consumption limit, or bandwidth limit. ### Claim 8 (depends on 1) The system of claim 1, wherein the processor is further programmed to generate a metric space based on emulating at least computing costs corresponding to a vehicle, wherein the metric space comprises a plurality of data compression process solutions for compressing vehicle data. ### Claim 9 (depends on 8) The system of claim 8, wherein the metric space comprises a Euclidean space defining a distance between at least a first node and a second node of the plurality of nodes. ### Claim 10 A method comprising: receiving, by a remote network, metadata and results corresponding to at least one data transmission constraint; performing a search of a k-d tree based on the at least one data transmission constraint; selecting at least one data compression process from the k-d tree based on the search, wherein the k-d tree includes a plurality of nodes corresponding to data compression solutions; transmitting, by the remote network, the at least one data compression process to a vehicle; implementing, by the vehicle, the at least one data compression process to compress vehicle data; and transmitting, by the vehicle, the compressed vehicle data. ### Claim 11 (depends on 10) The method of claim 10, wherein the metadata and the results are transmitted by the vehicle to the remote network. ### Claim 12 (depends on 11) The method of claim 11, the method further comprising performing a gradient-based search when at least one additional node has to be inserted into the k-d tree. ### Claim 13 (depends on 10) The method of claim 10, wherein the at least one data compression process comprises at least one of a lossy data compression process or a lossless data compression process. ### Claim 14 (depends on 10) The method of claim 10, wherein compressing data according to the selected data compression process comprises at least one of transform coding, adaptive sampling, quantization, differential coding, or arithmetic coding. ### Claim 15 (depends on 10) The method of claim 10, the method further comprising selecting a first data compression process for a first portion of vehicle data and select a second data compression process for a second portion of vehicle data, wherein the second data compression process is different from the first data compression process. ### Claim 16 (depends on 10) The method of claim 10, wherein the data compression constraint comprises at least one of central processing unit (CPU) usage, memory usage, latency, loss rate, input/output (I/O) cost, power consumption limit, or bandwidth limit. ### Claim 17 (depends on 10) The method of claim 10, the method further comprising generating a metric space based on emulating at least computing costs corresponding to a vehicle, wherein the metric space comprises a plurality of data compression process solutions for compressing vehicle data. ### Claim 18 (depends on 17) The method of claim 17, wherein the metric space comprises a Euclidean space defining a distance between at least a first node and a second node of the plurality of nodes.