Lenovo Patent Portfolio
40 US patents · Average 25 claims per patent
Technology Focus Areas
Patent Details
| Patent # | Title | Granted | Expires | Claims | Status |
|---|---|---|---|---|---|
| 10004142 | Method for multi-layer channel estimation in cloud communications | Aug 2, 2024 | Dec 14, 2041 | 24 | 15.7y left |
| 10004143 | Method for efficient machine learning inference using photonic | Jul 23, 2014 | Apr 12, 2030 | 24 | 4.0y left |
| 10004144 | Method for dynamic machine learning inference using quantum | May 26, 2024 | Apr 20, 2040 | 10 | 14.0y left |
| 10004145 | System and method for adaptive data processing using nano-scale | Aug 15, 2022 | Apr 14, 2039 | 6 | 13.0y left |
| 10004146 | System for multi-layer neural network processing with quantum | Feb 26, 2023 | Jan 20, 2041 | 18 | 14.8y left |
| 10004147 | Method for configurable machine learning inference using RF | May 8, 2021 | Aug 16, 2037 | 21 | 11.4y left |
| 10004148 | System for improved signal transmission in CMOS networks | May 19, 2005 | Aug 25, 2023 | 19 | Expired |
| 10004149 | Method for multi-layer wireless communication using lidar | Nov 19, 2012 | Jun 26, 2029 | 18 | 3.2y left |
| 10004150 | System for adaptive neural network processing with nano-scale | May 18, 2011 | Mar 20, 2029 | 39 | 3.0y left |
| 10004151 | Computer-implemented method for high-performance analog optimization | Feb 16, 2018 | May 21, 2034 | 33 | 8.1y left |
| 10004152 | Method for integrated machine learning inference using 5G | Apr 17, 2020 | Jan 19, 2037 | 46 | 10.8y left |
| 10004153 | System and method for optimized data processing using CMOS | Sep 5, 2006 | Jul 26, 2022 | 38 | Expired |
| 10004154 | System for high-performance neural network processing with blockchain | Aug 6, 2016 | Nov 7, 2032 | 29 | 6.6y left |
| 10004155 | Method for configurable channel estimation in neural communications | Nov 10, 2027 | Sep 6, 2043 | 35 | 17.4y left |
| 10004156 | System for low-latency neural network processing with RF | Nov 2, 2005 | Sep 7, 2021 | 41 | Expired |
| 10004157 | Method for integrated machine learning inference using lidar | Aug 27, 2025 | Nov 8, 2042 | 42 | 16.6y left |
| 10004158 | Apparatus for high-performance data encoding in quantum systems | Feb 4, 2025 | Oct 11, 2043 | 6 | 17.5y left |
| 10004159 | Method for modular wireless communication using RF | Jan 13, 2003 | Jul 28, 2021 | 35 | Expired |
| 10004160 | System for optimized signal transmission in AI-driven networks | May 3, 2026 | Aug 8, 2044 | 22 | 18.3y left |
| 10004161 | Method for integrated machine learning inference using MEMS | Apr 21, 2025 | Mar 28, 2041 | 42 | 15.0y left |
| 10004162 | Method for configurable machine learning inference using digital | Mar 2, 2011 | Apr 6, 2028 | 6 | 2.0y left |
| 10004163 | System for enhanced signal transmission in quantum networks | Dec 23, 2019 | Feb 14, 2036 | 33 | 9.9y left |
| 10004164 | Apparatus for optimized computational operations in edge environments | Mar 4, 2004 | Mar 19, 2021 | 39 | Expired |
| 10004165 | Method for enhanced wireless communication using RF | Aug 3, 2020 | Dec 19, 2037 | 22 | 11.7y left |
| 10004166 | System and method for modular data processing using AI-driven | Feb 13, 2024 | Jun 7, 2040 | 16 | 14.2y left |
| 10004167 | Computer-implemented method for optimized nano-scale optimization | Mar 26, 2019 | Apr 11, 2035 | 31 | 9.0y left |
| 10004168 | System for autonomous signal transmission in neural networks | Nov 17, 2017 | Jan 24, 2035 | 40 | 8.8y left |
| 10004169 | Method for multi-layer wireless communication using edge | Oct 15, 2028 | Jun 20, 2044 | 22 | 18.2y left |
| 10004170 | Computer-implemented method for adaptive neural optimization | Jun 10, 2024 | Mar 21, 2040 | 16 | 14.0y left |
| 10004171 | Method for enhanced channel estimation in 5G communications | Jun 10, 2017 | Jan 15, 2035 | 24 | 8.8y left |
| 10004172 | Method for configurable machine learning inference using blockchain | Jan 6, 2013 | Sep 23, 2031 | 33 | 5.5y left |
| 10004173 | Method for efficient wireless communication using lidar | Jun 1, 2022 | Jul 11, 2039 | 11 | 13.3y left |
| 10004174 | Method for high-performance machine learning inference using MEMS | Jan 25, 2026 | Feb 16, 2042 | 46 | 15.9y left |
| 10004175 | System for efficient signal transmission in blockchain networks | Jun 4, 2021 | Aug 8, 2038 | 6 | 12.3y left |
| 10004176 | System and method for dynamic data processing using analog | May 28, 2005 | Feb 20, 2022 | 23 | Expired |
| 10004177 | System and method for configurable data processing using quantum | Nov 27, 2021 | Jan 17, 2037 | 37 | 10.8y left |
| 10004178 | Method for scalable channel estimation in quantum communications | May 20, 2021 | Jan 6, 2039 | 31 | 12.8y left |
| 10004179 | Computer-implemented method for autonomous digital optimization | Feb 27, 2022 | Jan 10, 2040 | 6 | 13.8y left |
| 10004180 | Method for scalable wireless communication using neural | Feb 24, 2014 | Sep 13, 2032 | 12 | 6.4y left |
| 10004181 | Method for distributed wireless communication using analog | Mar 8, 2027 | Aug 11, 2044 | 16 | 18.3y left |
Frequently Asked Questions
Lenovo holds 40 US patents with an average of 25 claims per patent. The portfolio has a Patent Strength Score of 40/100 (Grade D).
Lenovo has 1 patents expiring within 2 years and 4 patents expiring within 5 years. These expirations may create opportunities for competitors and generic entrants.
Lenovo's key technology focus areas include Telecommunications, Computing & Data Processing. The portfolio spans 2 distinct technology classifications (CPC codes).
The Patent Strength Score (0-100, A-F) benchmarks a company's patent portfolio quality based on portfolio size (30%), claims breadth (25%), time remaining to expiration (25%), and portfolio diversity across technology areas (20%).
Patent Strength Score is based on portfolio size, claims breadth, time to expiration, and technology diversity using CPC classifications.
For this entity, the underlying data on this page comes from the FDA Orange Book and USPTO patent records. The breakdown above is the federal record; the paragraphs below add the per-entity context that makes the headline numbers usable for a real decision rather than just a data lookup.
Every number on this page links back to the FDA Orange Book and USPTO patent records; the methodology page describes the inputs, refresh cadence, and known limitations of the underlying data product.
Practical use of this page is in combination with the comparison and ranking pages elsewhere on the site, which surface the same data for this entity’s peers within U.S. brand-name drugs. A single-entity reading without peer context can be misleading when an entity is an outlier on one axis but typical on another.
Source: USPTO patent search, 2026.