3:20-cv-04332
Syclone IP LLC v. ASUS Computer Intl
I. Executive Summary and Procedural Information
- Parties & Counsel:
- Plaintiff: Syclone IP LLC (Texas)
- Defendant: ASUS Computer International (California)
- Plaintiff’s Counsel: Insight, PLC
- Case Identification: 3:20-cv-04332, N.D. Cal., 06/29/2020
- Venue Allegations: Venue is asserted as proper in the Northern District of California because the Defendant is a resident of the district.
- Core Dispute: Plaintiff alleges that Defendant’s ZenFone 5Z smartphone, which includes an "AI-powered intelligent charging" feature, infringes a patent related to methods for managing battery charging to extend battery lifespan.
- Technical Context: The technology concerns battery management systems in electronic devices, specifically strategies to mitigate battery degradation caused by maintaining a full charge for extended periods, a significant issue for consumer electronics like smartphones.
- Key Procedural History: The complaint does not mention any prior litigation, Inter Partes Review (IPR) proceedings, or licensing history related to the patent-in-suit.
Case Timeline
| Date | Event |
|---|---|
| 2011-05-27 | ’363 Patent Priority Date |
| 2015-01-27 | ’363 Patent Issue Date |
| 2020-06-29 | Complaint Filing Date |
II. Technology and Patent(s)-in-Suit Analysis
U.S. Patent No. 8,941,363 - “Device Battery Management,” issued January 27, 2015
The Invention Explained
- Problem Addressed: The patent addresses the technical trade-off in rechargeable batteries, particularly lithium-ion types. It notes that maintaining a battery at a full 100% charge for extended periods (e.g., overnight) can reduce its useful lifetime, while limiting the charge to a lower level (e.g., 85-90%) may shorten the device's usable time between recharges (’363 Patent, col. 1:9-19).
- The Patented Solution: The invention proposes a multi-stage charging method. First, it charges the battery to a "less than full" level to preserve its health. It then determines a "top off charge trigger," which is an event or prediction indicating the user will soon disconnect the device. In response to this trigger, the system provides a final "top off charge" to bring the battery to a fully or nearly-fully charged state just before it is needed, balancing battery longevity with maximum user utility (’363 Patent, col. 2:21-34; Fig. 4).
- Technical Importance: This approach allows for preserving the long-term health of a battery without compromising the available charge for the user at the beginning of a usage cycle.
Key Claims at a Glance
- The complaint asserts independent claims 1 (method), 14 (machine-readable medium), and 21 (device) (Compl. ¶34).
- Independent Claim 1 (Method):
- charging the device battery to a less than full charge using the device charger;
- determining a device battery top off charge trigger associated with the device and the device battery;
- maintaining the less than full charge until a top off charge is to be provided; and
- providing the top off charge to the device battery in response to the device battery top off charge trigger.
- Independent Claim 14 (Machine-Readable Medium): A non-transitory medium with instructions to perform the four steps listed in Claim 1.
- Independent Claim 21 (Device): A device comprising a battery, a charger, a processor, and a machine-readable medium with instructions to perform the four steps listed in Claim 1.
- The complaint does not explicitly reserve the right to assert dependent claims.
III. The Accused Instrumentality
Product Identification
The "Accused Instrumentality" is the ASUS "ZenFone 5Z" smartphone (Compl. ¶7).
Functionality and Market Context
The complaint focuses on the phone's "AI-powered intelligent charging" functionality, which includes features described as "AI Charging" and "Scheduled charging" (Compl. ¶12). This system is alleged to perform the patented method by first charging the battery to 80%, then pausing (Compl. ¶13, ¶15). Based on "AI-learned user behavior" or a set schedule, it allegedly determines when to resume charging to 100% "just before the charger is disconnected" (Compl. ¶16). A screenshot from Defendant's website describes this as "dynamically adjusting the charging rate, which slows down the battery ageing process" (Compl. p. 4). Another visual from a support page states, "For instance, AI makes the phone stops charging at 80% battery life at night. It will not recharge until two hours before you gets up to keep battery activity" (Compl. p. 5).
IV. Analysis of Infringement Allegations
’363 Patent Infringement Allegations (Claim 1)
| Claim Element (from Independent Claim 1) | Alleged Infringing Functionality | Complaint Citation | Patent Citation |
|---|---|---|---|
| charging the device battery to a less than full charge using the device charger | The Accused Instrumentality, using AI, charges the device's battery to an 80% level. A visual states "AI makes the phone stops charging at 80% battery life at night." | ¶13, ¶19 | col. 6:40-46 |
| determining a device battery top off charge trigger associated with the device and the device battery | The device "detects a user's charging behavior and estimates when the user will disconnect the charger" to determine when to initiate the final charge. | ¶14, ¶20 | col. 7:11-14 |
| maintaining the less than full charge until a top off charge is to be provided | The device maintains the 80% charge level "until it is time to resume charging, based on the AI-learned user behavior." | ¶15, ¶21 | col. 9:16-19 |
| providing the top off charge to the device battery in response to the device battery top off charge trigger | In response to the trigger, the device charges the battery to 100% "just before the charger is disconnected." | ¶16, ¶22 | col. 10:1-3 |
’363 Patent Infringement Allegations (Claim 14)
| Claim Element (from Independent Claim 14) | Alleged Infringing Functionality | Complaint Citation | Patent Citation |
|---|---|---|---|
| A machine readable non-transitory medium having stored therein instructions that, when executed, cause the machine to provide battery management by: | The ZenFone 5Z includes non-transitory storage, such as its 256GB ROM, which stores the software for the AI-powered intelligent charging features. A visual from the Defendant's website shows the device specifications including "Up to 256GB ROM." | ¶18; p. 7 | col. 13:58-62 |
| [The remaining four limitations are substantively identical to Claim 1] | [The alleged infringing functionality for the remaining limitations mirrors that described for Claim 1, as described in paragraphs 19-22 of the complaint] | ¶19-22 | col. 16:26-47 |
Identified Points of Contention
- Scope Questions: A central question will be whether the accused "AI Charging," which "dynamically adjust[s] the charging rate based on your previous charging behavior" (Compl. p. 4), falls within the scope of "determining a device battery top off charge trigger" as claimed. The patent describes various triggers, from predicting removal time based on past behavior to detecting external events like an alarm clock (’363 Patent, col. 7:27-40; col. 8:30-40). The court will need to determine if the alleged "AI-learned user behavior" constitutes such a trigger.
- Technical Questions: What evidence demonstrates that the "AI Charging" feature functions by predicting a specific removal event, as required by the patent's teachings, rather than operating on a more generalized or pre-set schedule? The complaint presents a visual for "Scheduled charging" that operates on a fixed time window (10:00 PM to 7:00 AM) alongside the more adaptive "AI Charging" (Compl. p. 6). A potential dispute may arise over the precise mechanism of the "AI Charging" and whether it is distinct from, or merely a variation of, the fixed schedule approach.
V. Key Claim Terms for Construction
- The Term: "determining a device battery top off charge trigger"
Context and Importance
This term is the core inventive step that differentiates the claimed method from simply pausing a charge. Its construction will dictate whether the accused AI's learning process infringes. Practitioners may focus on this term because the case hinges on whether ASUS's algorithm, which learns from "previous charging behavior," constitutes the specific act of "determining a... trigger" as envisioned by the patent.
Intrinsic Evidence for Interpretation
- Evidence for a Broader Interpretation: The specification provides numerous and varied examples of what can constitute a trigger, including "predicting a removal time of the device from the device charger" based on "past behavior" (’363 Patent, col. 7:36-40), receiving a user command (’363 Patent, col. 9:8-10), determining proximity to a destination (’363 Patent, col. 7:63-65), and receiving an indicator from a networked appliance (’363 Patent, col. 8:51-66). This variety may support a broad construction covering any predictive or responsive logic.
- Evidence for a Narrower Interpretation: A party could argue that the term requires the determination of a discrete, identifiable event. Specific embodiments describe triggers based on detecting an "audio alarm," the "lights in the room... being turned on," or an indicator from a refrigerator that its "door has been opened" (’363 Patent, col. 8:30-40, 64-66). This could support a narrower construction that requires more than a generalized learning algorithm based on past charging patterns.
VI. Other Allegations
Indirect Infringement
The complaint alleges active inducement, stating Defendant "sells, offers to sell and advertises the Accused Instrumentality through websites" with the intent that customers use the infringing features (Compl. ¶42). The factual basis relies on Defendant's marketing materials and website descriptions of the AI charging functionality (Compl. pp. 4-6).
Willful Infringement
The complaint does not allege pre-suit knowledge of the ’363 patent. It alleges knowledge only "at least as of the service of the present complaint" (Compl. ¶33, ¶41). This pleading may only support a claim for post-suit willfulness.
VII. Analyst’s Conclusion: Key Questions for the Case
The resolution of this dispute will likely depend on the court's answers to two central questions:
A core issue will be one of definitional scope: Can the phrase "determining a device battery top off charge trigger," which the patent illustrates with examples like predicting a specific removal time or detecting an external event, be construed to cover the accused product's "AI Charging" feature that learns generally from "previous charging behavior"?
A key evidentiary question will be one of technical mechanism: Does the accused "AI Charging" feature actually perform the specific function of predicting a user's disconnection event, as the patent requires, or does it operate on a more generalized, time-based algorithm that may not align with the specific teachings of the claims? The distinction between the product's "AI Charging" and "Scheduled Charging" features will be a critical factual inquiry.