Patent Drafting Analysis of Hyundai Mobis’s Autonomous Vehicle Control Authority Method | US 2023/0234618 A1
Patent Drafting Analysis of Hyundai Mobis's Autonomous Vehicle Control Authority Method | US 2023/0234618 A1
A structural and strategic analysis of US 2023/0234618 A1, examining claim architecture, drafting quality signals, critical gaps, and prosecution positioning for Hyundai Mobis's driver-risk-adaptive control authority allocation system.
Spec Words
7,200
Across 5 sections
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Total Claims
15
3 independent · 12 dependent
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Figure Sheets
7
System architecture, vehicle layout, module block diagrams, flow charts
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Published by PatSnap Insights Team · · 12 min read Verified by PatSnap Eureka Data
Overview
Structural Overview
The detailed description dominates at approximately 62% of total specification words (~4,800 words), with extensive tabular data (Tables 1–13) supporting the risk-level determination framework. The claim set comprises 15 claims total — 3 independent (Claims 1, 9, and 16) and 12 dependent — providing method, apparatus, and CRM coverage. Seven figure sheets span system architecture (FIG. 1), vehicle layout (FIG. 2), module diagrams (FIGS. 3, 5, 6), and process flowcharts (FIGS. 4, 7), giving reasonable but not exhaustive visual coverage of the disclosed embodiments.
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 7 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1 | Overall block diagram of the autonomous driving control system showing the Autonomous Driving Integrated Controller 600, driving information input interface 101, traveling information input interface 201, sensor unit 500, passenger output interface 301, and vehicle control output interface 401.Search in Eureka ↗ | System architecture |
| FIG. 2 | Top-view diagram of the autonomous vehicle showing physical placement of sensors (LiDAR 511/521, cameras 531–534, internal camera 535), microphones (internal 551, external 552), speaker 310, display 320, control panel 120, and interfaces 101/201/301/401 within the vehicle chassis.Search in Eureka ↗ | Key embodiment |
| FIG. 3 | Block diagram of the controller 320 showing its constituent modules: weather condition analysis module 310, driver proficiency analysis module 320, road condition analysis module 330, control authority determination module 340, driver information/statistics analysis module 350, road congestion analysis module 360, vehicle state checking module 370, risk analysis module 380, and plural databases 390.Search in Eureka ↗ | System architecture |
| FIG. 4 | Flowchart illustrating the method for controlling the autonomous vehicle starting from driving (S401) through vehicle and driver information collection (S402), driver proficiency analysis (S403–S404), weather condition analysis (S405–S407), road condition analysis (S408–S410), risk comprehensive decision (S411), control authority decision (S412), and changing control authority to driver (S413) or autonomous vehicle (S414).Search in Eureka ↗ | Flow diagram |
| FIG. 5 | Software block diagram of the AI robot embedded system 500 showing (a) system components including AI robot 510, control authority determination module 520, and vehicle state checking module 530, and (b) AI robot 510 functions: checking driver's condition, learning driver's condition, controlling vehicle functions, and learning driving patterns.Search in Eureka ↗ | Key embodiment |
| FIG. 6 | Hardware block diagram of the AI robot H/W configuration 600 showing biometric sensor 610, MCU 620, MIC 630, IR sensor 640, speaker 650, and tilt motor 660 as constituent hardware components.Search in Eureka ↗ | Claim support |
| FIG. 7 | Flowchart illustrating the method of distributing vehicle control authority based on driver's conditions, starting from driving (S701) through checking (S702), learning (S703), analyzing driver condition (S704), checking driver information (S705), learning driving pattern (S706), analyzing driving pattern (S707), comprehensively determining risk level (S708), determining control authority (S709), and shifting to driver (S710) or autonomous vehicle (S711).Search in Eureka ↗ | Flow diagram |
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Claims
Claim Architecture Analysis
The patent contains 3 independent claims: Claim 1 (method), Claim 9 (autonomous vehicle apparatus), and Claim 16 (non-transitory computer-readable medium/CRM). The dependent:independent ratio of 4:1 is below the automotive/autonomous driving industry norm of approximately 5–8:1, indicating thinner fallback layering than typical. Notably, Claim 8 was cancelled during prosecution, reducing the effective dependent count, and the tripartite structure of method/apparatus/CRM in Claims 1, 9, and 16 provides essential enforcement coverage across all claim types.
Core inventive concept: The claims address the problem that autonomous vehicle control authority is not adaptively shifted based on a driver's real-time physical and mental condition combined with driving proficiency. The solution, expressed across Claims 1 and 9, is a multi-factor risk allocation mechanism that determines a first risk level from the driver's physical condition, a second risk level from the driver's mental or conscious condition, and a driver proficiency level, then allocates control authority to either the driver or the autonomous vehicle according to the combined determination of these three parameters.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A method for changing a control authority of an autonomous vehicle | comprising | determining to allocate a control authority of the autonomous vehicle to a driver; checking if a reaction of the driver is detected for a predetermined time; in response that the reaction is not detected for the predetermined time, allocating the control authority of the autonomous vehicle to the autonomous vehicleSearch prior art ↗ |
| Claim 9 | An autonomous vehicle | comprising | at least one sensor configured to obtain driving information or detect an inside or outside object; a controller configured to: determine to allocate a control authority to a driver; check if a reaction of the driver is detected for a predetermined time; in response that the reaction is not detected, allocate the control authority of the autonomous vehicle to the autonomous vehicleSearch prior art ↗ |
| Claim 16 | A non-transitory computer-readable medium having stored thereon a computer program configured to perform the method defined in one of claim 1 | comprising | Incorporates by reference all limitations of Claim 1 — method for changing control authority including reaction-detection-based fallback to autonomous vehicleSearch prior art ↗ |
Claim Dependency Tree
1 Method for changing control authority of autonomous vehicle — reaction-detection-based authority allocationSearch Claim 1 prior art ↗
2 Adds: determining first risk level (physical), second risk level (mental/conscious), and driving proficiency level; allocating authority per all three levels; second risk level via camera sensingSearch in Eureka ↗
3 Further: determining second risk level by analyzing mental condition via AI speaker conversationSearch in Eureka ↗
4 Further: using autonomous vehicle in any one of autonomous driving levels 3 to 5Search in Eureka ↗
5 Adds: determining third risk level (weather), fourth risk level (road), and driver proficiency level; allocating per all four levelsSearch in Eureka ↗
6 Further: determining driver proficiency level using accident occurrence risk, acceleration/deceleration pattern, and lane change patternSearch in Eureka ↗
7 Further: accident occurrence risk using risk of collision with preceding vehicle, side-lane vehicle, and following vehicleSearch in Eureka ↗
9 Autonomous vehicle apparatus — sensor and controller for reaction-detection-based authority allocationSearch Claim 9 prior art ↗
10 Adds: controller determines driver proficiency level using accident occurrence risk, acceleration/deceleration pattern, and lane change patternSearch in Eureka ↗
11 Further: controller determines accident occurrence risk using collision risk with preceding, side-lane, and following vehiclesSearch in Eureka ↗
12 Adds: autonomous vehicle used in any one of autonomous driving levels 3 to 5Search in Eureka ↗
13 Adds: controller determines first risk level (physical), second risk level (mental/conscious), and driving proficiency; allocates per all three; senses conscious condition via cameraSearch in Eureka ↗
14 Further: controller analyzes mental condition via AI speaker conversationSearch in Eureka ↗
15 Adds: controller determines third risk level (weather), fourth risk level (road), driver proficiency; allocates per all four levelsSearch in Eureka ↗
16 Non-transitory computer-readable medium performing method of Claim 1Search Claim 16 prior art ↗
| Metric | This Application | Automotive / Autonomous Driving Norm |
|---|---|---|
| Total claims | 15 | 15 – 25 |
| Independent claim count | 3 | 2 – 4 |
| Dependent : Independent ratio | 4.0 : 1 | 5 – 8 : 1 |
| Method claims present? | Yes — Claim 1 | Always |
| System / apparatus claims? | Yes — Claim 9 | Always |
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Drafting Quality
Drafting Quality Signals
The claims demonstrate deliberate tripartite coverage (method/apparatus/CRM in Claims 1, 9, and 16), and the detailed description contains extensive numerical database tables (Tables 1–13) that provide unusually strong quantitative support for the risk-level determination limitations. However, the base independent Claims 1 and 9 are drafted at an unusually high abstraction level — reciting primarily only a reaction-detection fallback mechanism — while the more commercially significant multi-risk-level allocation is entirely buried in dependent Claims 2 and 13, creating a significant vulnerability if the independent claims are invalidated.
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Antecedent Basis
Antecedent basis is generally clean across the claim set. In Claim 1, "a control authority" is introduced in the first limitation and properly referenced as "the control authority" thereafter. In Claim 9, "at least one sensor" and "a controller" are properly introduced and referenced with "the" in subsequent claim body elements. Claim 2's "the first risk level" and "the second risk level" have clear antecedents in the same claim's body. No orphaned "the" references were identified across the 15 claims.
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Spec–Claim Consistency
Key claim limitations are well-anchored to specific spec sections. The reaction-detection fallback mechanism in Claims 1 and 9 is directly supported by paragraphs [0121] and [0136]. The three-risk-level allocation in Claim 2 maps to FIG. 3 (modules 310–380) and Tables 11–13. The driver proficiency determination using accident occurrence risk, acceleration/deceleration pattern, and lane change pattern in Claims 6 and 10 is specifically supported by Tables 2–7 and paragraphs [0079]–[0083]. No independent claim limitation lacks explicit written description support.
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Transition Word Usage
All independent and dependent claims use "comprising" as the transition word, which is strategically appropriate for this autonomous vehicle technology domain. The open-ended transition preserves coverage against implementations that add additional sensors, modules, or processing steps beyond those recited. There is no instance of the more restrictive "consisting of" or "consisting essentially of," and no missed opportunity to use "comprising" where a narrower transition was inadvertently chosen.
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§112(f) Means-Plus-Function Risk
The CRM Claim 16 uses the phrase "a computer program configured to perform the method defined in one of claim 1" — this functional claiming language without structural definition of the program's architecture creates potential §112(f) ambiguity, as it does not identify any structural algorithm. More significantly, the apparatus Claim 9 recites a "controller" that is "configured to" perform multiple functions without defining the controller's structural algorithm, which in software-implemented systems can attract §112(f) means-plus-function treatment. The specification does provide algorithmic structure (FIG. 7 flowchart, Tables 1–13), which may provide the required algorithmic disclosure, but the linkage is implicit rather than explicit.
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§101 Eligibility Risk
The base independent Claims 1 and 16 recite a method and CRM for "determining to allocate," "checking if a reaction is detected," and "allocating" — steps that an examiner may characterize as an abstract idea (decision-making/data processing) under Alice Step 1. The hardware tie-in in Claim 9 ("at least one sensor" and an autonomous vehicle apparatus) provides a stronger §101 defense for the apparatus claim. However, Claims 1 and 16 lack any explicit recitation of physical sensors or actuators performing the steps, leaving them exposed to a Step 2A Prong 1 rejection. The spec's extensive AI robot hardware (FIG. 6) and sensor arrays are not incorporated into the independent method claim limitations.
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Dependent Claim Fallback Quality
The dependent claims provide substantive but structurally redundant fallback: Claims 2 and 13 are parallel mirror claims adding the same three-risk-level framework to Claims 1 and 9 respectively, meaning that invalidation of the independent claims does not create independent survival for these dependents as distinct claim lines. Claims 5 and 15 similarly mirror each other by adding weather/road risk levels to Claims 1 and 9. The strongest independent fallback value comes from Claims 6–7 (specific proficiency quantification via TTC-based collision risk metrics) and Claim 3 (AI speaker mental condition analysis), which add genuinely novel technical limitations. Cancelled Claim 8 was not replaced, leaving a gap in the fallback ladder.
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Abstract Quality
The abstract accurately describes the high-level method but overemphasizes the multi-risk-level feature ("determining a first risk level of a physical condition," "determining a second risk level") while omitting the commercially distinctive AI robot driver condition monitoring and the reaction-detection-based fallback mechanism that forms the actual independent Claim 1. An examiner reading only the abstract may focus examination on the risk-level determination framework and miss the simpler but broader reaction-detection limitation that defines the independent claims — a mismatch that could complicate prosecution if the examiner cites prior art on risk-level systems rather than reaction-detection systems.
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Figure Support Quality
Overall figure support is strong for the apparatus and flowchart limitations. FIG. 1 directly supports the sensor unit (500) and controller (600) elements of Claim 9. FIG. 3 maps directly to the module-level limitations in Claims 5/15 (weather condition analysis module 310, road condition analysis module 330). FIG. 6 supports the biometric sensor and AI speaker limitations of Claims 2–3 and 13–14. The one gap is that no figure illustrates the reaction-detection-for-predetermined-time mechanism that forms the core of Claims 1 and 9 — this limitation is described only textually at [0121], without a dedicated flowchart step or timing diagram.
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Scorecard
Strategic Intent Scorecard
Multi-dimensional assessment of this application's patent strategy quality, based on claim structure, specification depth, and prosecution positioning.
Claim Breadth
3
Prosecution Defensibility
3.2
Spec–Claim Consistency
4
Dependent Claim Coverage
3
Claim Type Diversity
4.5
Figure Support Quality
3.5
Key observation: Claim Type Diversity (Score 4.5) is the strongest dimension — the tripartite method/apparatus/CRM structure across Claims 1, 9, and 16 ensures enforcement flexibility across multiple legal theories, which is particularly valuable in the automotive OEM licensing context. The weakest dimension is Claim Breadth (Score 3.0): the independent Claims 1 and 9 are drafted at a high level of abstraction with the reaction-detection fallback mechanism, but this abstraction cuts both ways — the claims are easily designed around by implementing proactive rather than reactive authority transfer, a scenario clearly contemplated in the spec (¶[0099]–[0103]) but not claimed. Practitioners should consider a continuation filing directed to the proactive weather/road-based authority transfer described in the spec but absent from the independent claims.
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Critical Gaps
3 Critical Gaps in This Claim Set
A senior-attorney lens on the three highest-priority structural weaknesses — what each exposes in prosecution and litigation, and what a stronger filing would have done differently.
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3 Critical Gaps in This Claim Set
See the full attorney-level analysis of what this application leaves unprotected — and how to draft it more defensively for your own filings.
Core innovation buried in dependents Missing figure for reaction-detection mechanism Proactive weather/road transfer unclaimed
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US 2023/0234618 A1 — key questions answered
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Disclaimer: This analysis is generated by PatSnap Eureka AI based on publicly available patent data from the USPTO. It does not constitute legal advice and should not be relied upon as such. Patent data may be subject to change as prosecution progresses. Scores and assessments reflect automated analysis and may not capture all relevant legal or technical nuances. Always consult a qualified patent attorney for formal legal opinions on patentability, freedom to operate, or infringement.
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