IP Library › Granted Patent US 12,724,918
Granted Patent B2
US 12,724,918 · App. 18/502,309 · Granted Sep 1, 2026

Protecting confidential information in a neural-computer interface system

Inventors: Sara E Berger (Beaverton, OR); Marc P. Yvon (Antony, FR); Neil Delima (Scarborough, CA)
Assignee: International Business Machines Corporation
G06F21/6245G06F1/163H04L63/0227H04L63/04
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,724,918
App. No.
18/502,309
Filed
Nov 6, 2023
Granted
Sep 1, 2026
Kind
B2
Art Unit
3791
USPC
600/544
Abstract

A method, system, and computer program product detect and react to neural signals. The method includes monitoring the neural signals, which are collected from a user by a neural-computer interface (NCI) device. The method further includes detecting, based on the monitoring, an inhibitory signal in the neural signals and determining, in response to the detecting, that the inhibitory signal is aligned with an emotionally salient signal. Additionally, the method includes generating a notification in response to the determining that the inhibitory signal is aligned with the emotionally salient signal. The notification is displayed at an output device.

Claims (46)

1 . A method, comprising:

monitoring, by a processor communicatively coupled to a memory, neural signals collected from a user by a neural-computer interface (NCI) device, wherein the monitoring comprises classifying the neural signals as speech-, motor-, or emotion-related;

detecting, by the processor and based on the monitoring, a potential disclosure of confidential information, wherein the detecting comprises:

detecting an inhibitory signal in the neural signals classified as speech- or motor-related;

in response to the detecting the inhibitory signal, aligning the inhibitory signal with a co-occurring neural signal from the neural signals classified as emotion-related; and

determining that the co-occurring neural signal is aligned with an emotionally salient signal;

generating, by the processor and in response to the detecting the potential disclosure of confidential information, a notification;

displaying, at an output device, the notification; and

deleting, by the processor, a portion of the neural signals from a time range selected based on the potential disclosure of confidential information.

2 . The method of claim 1 , further comprising receiving a user-input response to the notification.

3 . The method of claim 2 , wherein the response comprises instructions to delete the portion of the neural signals.

4 . The method of claim 1 , wherein the NCI device is a wearable device.

5 . The method of claim 1 , wherein the monitoring comprises monitoring a type of signal selected from the group consisting of brainwaves, hemodynamic response, event-related potential (ERP), skin conductance response (SRP), and cortical activity.

6 . The method of claim 1 , wherein the deleting is carried out automatically based on stored user preferences.

7 . The method of claim 1 , further comprising detecting, by the processor and based on the monitoring, an additional potential disclosure of confidential information.

8 . The method of claim 7 , further comprising automatically storing a portion of the neural signals from a time range selected based on the additional potential disclosure of confidential information in a temporary collection cache.

9 . The method of claim 1 , further comprising automatically stopping the collection of the neural signals in response to the detecting the potential disclosure of confidential information.

10 . A system, comprising:

a memory; and

a processor communicatively coupled to the memory, wherein the processor is configured to perform a method comprising:

monitoring, by the processor, neural signals collected from a user by a neural-computer interface (NCI) device, wherein the monitoring comprises classifying the neural signals as speech-, motor-, or emotion-related;

detecting, by the processor and based on the monitoring, a potential disclosure of confidential information, wherein the detecting comprises:

detecting an inhibitory signal in the neural signals classified as speech- or motor-related;

in response to the detecting the inhibitory signal, aligning the inhibitory signal with a co-occurring neural signal from the neural signals classified as emotion-related; and

determining that the co-occurring neural signal is aligned with an emotionally salient signal;

generating, by the processor and in response to the detecting the potential disclosure of confidential information, a notification;

displaying, at an output device, the notification; and

deleting, by the processor, a portion of the neural signals from a time range selected based on the potential disclosure of confidential information.

11 . The system of claim 10 , further comprising receiving a user-input response to the notification.

12 . The system of claim 11 , wherein the response comprises instructions to delete the portion of the neural signals.

13 . The system of claim 10 , wherein the NCI device is a wearable device.

14 . The system of claim 10 , wherein the monitoring comprises monitoring a type of signal selected from the group consisting of brainwaves, hemodynamic response, event-related potential (ERP), skin conductance response (SRP), and cortical activity.

15 . The system of claim 10 , wherein the deleting is carried out automatically based on stored user preferences.

16 . A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause a device to perform a method, the method comprising:

monitoring, by the processor, neural signals collected from a user by a neural-computer interface (NCI) device, wherein the monitoring comprises classifying the neural signals as speech-, motor-, or emotion-related;

detecting, by the processor and based on the monitoring, a potential disclosure of confidential information, wherein the detecting comprises:

detecting an inhibitory signal in the neural signals classified as speech- or motor-related;

in response to the detecting the inhibitory signal, aligning the inhibitory signal with a co-occurring neural signal from the neural signals classified as emotion-related; and

determining that the co-occurring neural signal is aligned with an emotionally salient signal;

generating, by the processor and in response to the detecting the potential disclosure of confidential information, a notification;

displaying, at an output device, the notification; and

deleting, by the processor, a portion of the neural signals from a time range selected based on the potential disclosure of confidential information.

17 . The computer program product of claim 16 , further comprising receiving a user-input response to the notification, wherein the response comprises instructions to delete a portion of the neural signals.

18 . The computer program product of claim 16 , wherein the NCI device is a wearable device.

19 . The computer program product of claim 16 , wherein the monitoring comprises monitoring a type of signal selected from the group consisting of brainwaves, hemodynamic response, event-related potential (ERP), skin conductance response (SRP), and cortical activity.

20 . The computer program product of claim 16 , wherein the deleting is carried out automatically based on stored user preferences.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 6, 2023
From: BERGER, SARA E; YVON, MARC P.; DELIMA, NEIL
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 065466/0433 →
Continuity (1)
Related Publication 20250148123A1 · May 8, 2025
References Cited (109)
US 7418290B2 · Devlin · 2008 [cited by examiner]
US 12105785B2 · Maizels · 2024 [cited by examiner]
US 12130901B2 · Wexler · 2024 [cited by examiner]
US 12131739B2 · Maizels · 2024 [cited by examiner]
US 12141262B2 · Wexler · 2024 [cited by examiner]
US 12142280B2 · Maizels · 2024 [cited by examiner]
US 12142281B2 · Wexler · 2024 [cited by examiner]
US 12142282B2 · Maizels · 2024 [cited by examiner]
US 12147521B2 · Wexler · 2024 [cited by examiner]
US 12154572B2 · Wexler · 2024 [cited by examiner]
US 12204627B2 · Wexler · 2025 [cited by examiner]
US 12205595B2 · Maizels · 2025 [cited by examiner]
US 12216749B2 · Maizels · 2025 [cited by examiner]
US 12216750B2 · Wexler · 2025 [cited by examiner]
US 12505190B2 · Maizels · 2025 [cited by examiner]
US 20030013981A1 · Gevins · 2003 [cited by examiner]
US 20050043774A1 · Devlin · 2005 [cited by examiner]
US 20080167571A1 · Gevins · 2008 [cited by examiner]
US 20140228701A1 · Chizeck et al. · 2014 [cited by applicant]
US 20140316230A1 · Denison · 2014 [cited by examiner]
US 20160287166A1 · Tran · 2016 [cited by applicant]
US 20160302711A1 · Frank · 2016 [cited by examiner]
US 20170095157A1 · Tzvieli et al. · 2017 [cited by applicant]
US 20240070251A1 · Maizels · 2024 [cited by examiner]
US 20240070252A1 · Maizels · 2024 [cited by examiner]
US 20240071364A1 · Maizels · 2024 [cited by examiner]
US 20240071386A1 · Maizels · 2024 [cited by examiner]
US 20240073219A1 · Maizels · 2024 [cited by examiner]
US 20240079011A1 · Maizels · 2024 [cited by examiner]
US 20240079012A1 · Maizels · 2024 [cited by examiner]
US 20240087361A1 · Wexler · 2024 [cited by examiner]
US 20240096328A1 · Wexler · 2024 [cited by examiner]
US 20240119937A1 · Wexler · 2024 [cited by examiner]
US 20240119938A1 · Wexler · 2024 [cited by examiner]
US 20240119961A1 · Maizels · 2024 [cited by examiner]
US 20240127816A1 · Wexler · 2024 [cited by examiner]
US 20240127817A1 · Wexler · 2024 [cited by examiner]
US 20240127824A1 · Wexler · 2024 [cited by examiner]
CN 113425047A · 2021 [cited by applicant]
CN 111956219B · 2023 [cited by applicant]
WO WO2006073384A1 · 2006 [cited by examiner]
WO WO2009129480A2 · 2009 [cited by examiner]
Alicart et al., “Gossip information increases reward-related oscillatory activity”, Neuroimage, Apr. 15, 2020, 210:116520, . Epub Jan. 7, 2020, 9 pages, https://doi.org/10.1016/j.neuroimage.2020.116520. [cited by applicant]
Anonymous, “BCI milestone: New research from UCSF with support from Facebook shows the potential of brain-computer interfaces for restoring speech communication”, Meta, Jul. 14, 2021, 17 pages, <https://tech.facebook.co… [cited by applicant]
Anonymous, “Brain Filters”, Think Social Publishing, Inc., Copyright © 2021, 2 pages, <https://www.socialthinking.com/>. [cited by applicant]
Anonymous, “How the Brain Prepares For Action”, Neuroscience News, Feb. 7, 2019, 8 pages, <https://neurosciencenews.com/brain-action-preparation-10710/>. [cited by applicant]
Anonymous, “Machine Learning”, 1 page, downloaded from the Internet on Sep. 13, 2023, https://www.frontiersin.org/files/Articles/272182/fnbot-11-00038-HTML/image_m/fnbot-11-00038-g001.jpg>. [cited by applicant]
Anonymous, “N200 (neuroscience)”, Wikipedia, This page was last edited on Aug. 15, 2023, 6 pages. [cited by applicant]
Anonymous, “neurosynth.org”, downloaded from the Internet on Sep. 14, 2023, 2 pages, <neurosynth.org>. [cited by applicant]
Anonymous, “Real and Imagined Movements Are Controlled by the Brain in the Same Way”, Neuroscience News, Apr. 24, 2018, 9 pages, <https://neurosciencenews.com/real-imagined-movement-8868/>. [cited by applicant]
Anonymous, “A real emotion evaluation system based on wearable emotion recognition device,” IP.com No. IPCOM000251219D, IP.com Electronic Publication IP.com Electronic Publication Date: Oct. 26, 2017, 5 pages. [cited by applicant]
Anonymous, “An Intelligent Alert System for Negative Emotions,” An IP.com Prior Art Database Technical Disclosure, IP.com No. IPCOM000268929D, IP.com Electronic Publication Date: Mar. 9, 2022, 3 pages. [cited by applicant]
Anonymous, “Confidential Information privacy using a Cognitive System,” IP.com No. IPCOM000252890D, IP.com Electronic Publication Date: Feb. 20, 2018, 4 pages. [cited by applicant]
Anonymous, “Method and System for AR-Based Visualization of the Consequences of Steps That Workers Intend to Perform,” IP.com No. IPCOM000271673D, IP.com Electronic Publication Date: Jan. 27, 2023, 4 pages. [cited by applicant]
Baca, Arnold, “Methods for Recognition and Classification of Human Motion Patterns—A Prerequisite for Intelligent Devices Assisting in Sports Activities”, IFAC Proceedings Volumes, vol. 45, Issue 2, 2012, pp. 55-61, htt… [cited by applicant]
Belkacem, Abdelkader Nasreddine , “Cybersecurity Framework for P300-based Brain Computer Interface”, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Oct. 11-14, 2020. Toronto, Canada, pp. 1-6,… [cited by applicant]
Bianchin et al., “Decision Preceding Negativity in the Iowa Gambling Task: An ERP study”, Brain and Recognition 75 (2011), pp. 273-280, doi:10.1016/j.bandc.2011.01.005. [cited by applicant]
Brosch et al., “Additive effects of emotional, endogenous, and exogenous attention: Behavioral and electrophysiological evidence”, Neuropsychologia 49.7 (2011): pp. 1779-1787. [cited by applicant]
Candia-Rivera et al., “Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal”, PNAS, May 19, 2022, 27 pages, https://doi.org/10.1073/pnas.2119599119. [cited by applicant]
Carver et al., “Anger Is an Approach-Related Affect: Evidence and Implications”, Psychological Bulletin, American Psychological Association, 2009, vol. 135, No. 2, pp. 183-204, DOI: 10.1037/a0013965. [cited by applicant]
Chmura et al., “Classification of Movement and Inhibition using a Hybrid BCI”, frontiers in Neurorobotics, published Aug. 15, 2017, 7 pages, https://doi.org/10.3389/fnbot.2017.00038. [cited by applicant]
Choi et al., “Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke”, MDPI, Electronics 2019, 8, 1466, Published: Dec. 2, 2019, 15 pages, doi:10.3390/electronics8121466. [cited by applicant]
Cui et al., “The brain-computer interface based robot gives spinal cord injury patients a full-cycle active rehabilitation”, 2021 9th International Winter Conference on Brain-Computer Interface (BCI), 5 pages, DOI: 10.1… [cited by applicant]
Datta et al., “The P300 as a Marker of Waning Attention and Error Propensity”, Hindawi Publishing Corporation, Computational Intelligence and Neuroscience, vol. 2007, Article ID 93968, 9 pages, doi:10.1155/2007/93968. [cited by applicant]
Del Giudice et al., “The Voice of Anger: Oscillatory EEG Responses to Emotional Prosody”, PLoS One, Published online Jul. 21, 2016, 11 pages, doi: 10.1371/journal.pone.0159429. [cited by applicant]
Deniz et al., “Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset”, Abstract only, Springer Link, Published: Nov. 10, 2022, 12 pages, <https://link.springer.com/article… [cited by applicant]
Dillon et al., “Inhibition of Action, Thought, and Emotion: A Selective Neurobiological Review”, NIH Public Access, Published in final edited form as: Appl Prev Psychol. Dec. 2007; 12(3): 99-114, <https://www.ncbi.nlm.n… [cited by applicant]
Durham, Emily, “First-ever noninvasive mind-controlled robotic arm”, Carnegie Mellon University, College of Engineering, downloaded from the Internet on Sep. 13, 2023, 3 pages, <https://engineering.cmu.edu/news-events/n… [cited by applicant]
Eijlers et al., “Implicit measurement of emotional experience and its dynamics”, PLoS One, 14(2): e0211496, Feb. 5, 2019, 15 pages, https://doi.org/10.1371/journal.pone.0211496. [cited by applicant]
Farwell et al., “Brain fingerprinting classification concealed information test detects US Navy military medical information with P300”, Frontiers in Neuroscience, published: Dec. 23, 2014,22 pages, doi: 10.3389/fnins.2… [cited by applicant]
Filimon et al., “Human cortical representations for reaching: mirror neurons for execution, observation, and imagery”, NeuroImage 37 (2007) 1315-1328, Available online Jun. 18, 2007, <https://europepmc.org/article/med/1… [cited by applicant]
Gable et al., “The motivational dimensional model of affect: Implications for breadth of attention, memory, and cognitive categorisation”, Psychology Press, Cognition and Emotion 2010, 24 (2), pp. 322-337, DOI: 10.1080/… [cited by applicant]
Gannouni et al., “Emotion detection using electroencephalography signals and a zero time windowing based epoch estimation and relevant electrode identification”, Nature Scientific Reports, (2021) 11:7071, 17 pages, http… [cited by applicant]
Garofalo et al., “Mediofrontal negativity signals unexpected omission of aversive events”, Scientific Reports, 4 : 4816, 8 pages, DOI: 10.1038/srep04816. [cited by applicant]
Guo et al., “Effect of Virtual Reality on Fear Emotion Base on EEG Signals Analysis”, 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), Nanjing, China, 2019, pp. 1-4, doi: 10.1109/IMBIOC.2019.87778… [cited by applicant]
Jiang et al., “Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy”, Frontiers in Physiology, published: Jan. 7, 2022, 13 pages, doi: 10.3389/fphys.2021.823013. [cited by applicant]
Khalil et al., “Implementation of Machine Learning in BCI Based Lie Detection”, 2022 IEEE World AI IoT Congress (AIIoT), pp. 213-217, doi:10.1109/AIIoT54504.2022.9817162. [cited by applicant]
Kimmatkar et al., “Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques”, Computers 2021, 10, 37, 20 pages, https://doi.org/10.3390/computers10030037. [cited by applicant]
Kimmatkar et al., “The Study of Emotional Brain to Detect Emotions Using Brain EEG Signals and Improving Accuracy of Emotion Detection System Using Feature Selection Techniques”, Abstract only, ICMVA '22: Proceedings of… [cited by applicant]
Maithri et al., “Automated emotion recognition: Current trends and future perspectives”, Computer Methods and Programs in Biomedicine, vol. 215, Mar. 2022, 106646, 30 pages, <https://doi.org/10.1016/j.cmpb.2022.106646>. [cited by applicant]
Mandal et al., “SoK: Your Mind Tells a Lot About You: On the Privacy Leakage via Brainwave Devices”, WiSec '22, May 16-19, 2022, Session 5: Wearable and Cellular Security, San Antonio, TX, USA, pp. 175-187, https://doi.… [cited by applicant]
Martinescu et al., “Self-Evaluative and Other-Directed Emotional and Behavioral Responses to Gossip About the Self”, Frontiers in Psychology, Jan. 4, 2019, Sec. Organizational Psychology, vol. 9, Article 2603, 16 pages,… [cited by applicant]
Miller et al., “Cortical activity during motor execution, motor imagery, and imagery-based online feedback”, PNAS, Mar. 2, 2010, vol. 107, No. 9, 6 pages, www.pnas.org/cgi/doi/10.1073/pnas.0913697107. [cited by applicant]
Napolitano, Anna, “Study casts new light on mirror neurons”, Nature, Aug. 24, 2021, 3 pages, <https://www.nature.com/articles/d43978-021-00101-x>. [cited by applicant]
Pandarinath et al., “High performance communication by people with paralysis using an intracortical brain-computer interface”, eLIFE Sciences, Feb. 21, 2017, 27 pages, <https://doi.org/10.7554/eLife.18554>. [cited by applicant]
Pandarinath et al., “The Science and Engineering Behind Sensitized Brain-Controlled Bionic Hands”, Physiological Reviews, vol. 102, Issue 2, Published Sep. 20, 2021, pp. 511-1158, doi/10.1152/physrev.00034.2020. [cited by applicant]
Pfurtscheller et al., “Rehabilitation with Brain-Computer Interface Systems”, Published by the IEEE Computer Society, Oct. 2008, 8 pages. [cited by applicant]
Rahim et al., “Emotion Charting Using Real-time Monitoring of Physiological Signals”, 2019 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan, 2019, pp. 1-5, doi: 10.1109/ICRAI… [cited by applicant]
Rahman et al., “Recognition of human emotions using EEG signals: A review”, Computers in Biology and Medicine, vol. 136, Sep. 2021, 104696, 18 pages, <https://www.sciencedirect.com/science/article/abs/pii/S0010482521004… [cited by applicant]
Ramirez et al., “Detecting Emotion from EEG Signals Using the Emotive Epoc Device”, Abstract only, International Conference on Brain Informatics, BI 2012: Brain Informatics, pp. 175-184, Part of the Lecture Notes in Com… [cited by applicant]
Robbins et al., “Who Gossips and How in Everyday Life?”, Abstract only, Social Psychological and Personality Science, First published online May 2, 2019, 13 pages, https://doi.org/10.1177/1948550619837000. [cited by applicant]
Ryding et al., “Silent Speech Activates Prefrontal Cortical Regions Asymmetrically, as Well as Speech-Related Areas in the Dominant Hemisphere”, Brain and Language, vol. 52, Issue 3, Mar. 1996, pp. 435-451, <https://www… [cited by applicant]
Shu et al., “A Review of Emotion Recognition Using Physiological Signals”, Sensors, Published: Jun. 28, 2018, 41 pages, http://dx.doi.org/10.3390/s18072074. [cited by applicant]
Stachowski et al., “Spinal Inhibitory Interneurons: Gatekeepers of Sensorimotor Pathways”, International Journal of Molecular Sciences, 2021, 22, 2667, Published: Mar. 6, 2021, 17 pages, https://doi.org/10.3390/ijms2205… [cited by applicant]
Szameitat et al., “Motor imagery of complex everyday movements. An fMRI study”, NeuroImage, vol. 34, Issue 2, Jan. 15, 2007, pp. 702-713, https://doi.org/10.1016/j.neuroimage.2006.09.033. [cited by applicant]
Tam et al., “Human motor decoding from neural signals: a review”, BMC Biomedical Engineering, Published: Sep. 3, 2019, 22 pages, https://doi.org/10.1186/s42490-019-0022-z. [cited by applicant]
Tivatansakul et al., “Emotion Recognition using ECG Signals with Local Pattern Description Methods”, International Journal of Affective Engineering, Dec. 2015, 12 pages, DOI: 10.5057/ijae.IJAE-D-15-00036. [cited by applicant]
Trafton, Anne, “How we tune out distractions”, MIT News, Jun. 12, 2019, 6 pages, <https://news.mit.edu/2019/how-brain-ignores-distractions-0612>. [cited by applicant]
Vazquez et al., “Neural signals implicated in the processing of appetitive and aversive events in social and non-social contexts”, Frontiers in Systems Neuroscience, Aug. 3, 2022, vol. 16—2022, 18 pages, https://doi.org… [cited by applicant]
Wahab et al., “EEG signals for emotion recognition”, Abstract only, Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2009,… [cited by applicant]
Wang et al., “Multi-modal emotion recognition using EEG and speech signals”, Computers in Biology and Medicine, vol. 149, Oct. 2022, 105907, 13 pages, <https://doi.org/10.1016/j.compbiomed.2022.105907>. [cited by applicant]
Willett et al., “High-performance brain-to-text communication via imagined handwriting”, bioRxiv preprint, this version posted Jul. 2, 2020, 21 pages, https://doi.org/10.1101/2020.07.01.183384. [cited by applicant]
Wilson et al., “Listening to speech activates motor areas involved in speech production”, Abstract only, Nature Neuroscience vol. 7, pp. 701-702 (2004), <https://www.nature.com/articles/nn1263>. [cited by applicant]
Xue et al., “Common Neural Substrates for Inhibition of Spoken and Manual Responses”, Cerebral Cortex, Aug. 2008;18:1923—1932, doi: 10.1093/cercor/bhm220, Advance Access publication Jan. 31, 2008. [cited by applicant]
Yuen et al., International Journal of Integrated Engineering (Issue on Electrical and Electronic Engineering), 2009, 10 pages. [cited by applicant]
Chikara et al. “Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model”, Sensors (Basel), Sep. 1, 2019, p. 3791 (18 pages), vol. 19, No. 17. [cited by applicant]
International Searching Authority, “Notification of Transmittal of the International Search Report and the Written Opinion of the International Searching Authority, or Declaration,” Patent Cooperation Treaty, Nov. 25, 2… [cited by applicant]
Neupane et al. “Brain Hemorrhage: When Brainwaves Leak Sensitive Medical Conditions and Personal Information”, 2019 17th International Conference on Privacy, Security And Trust (PST), IEEE, Aug. 26, 2019, pp. 1-10. [cited by applicant]
Hammond, D., “What is Neurofeedback: An Update,” Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, Nov. 30, 2011, vol. 15 No. 4 , pp. 305-336. [cited by applicant]