EP3555719A1 - Device and method for determining the pinpoint-ability of potential errors of one or more components - Google Patents
Device and method for determining the pinpoint-ability of potential errors of one or more componentsInfo
- Publication number
- EP3555719A1 EP3555719A1 EP17832478.6A EP17832478A EP3555719A1 EP 3555719 A1 EP3555719 A1 EP 3555719A1 EP 17832478 A EP17832478 A EP 17832478A EP 3555719 A1 EP3555719 A1 EP 3555719A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- diagnostic
- predetermined
- matrix
- error
- result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/0272—Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Definitions
- the invention relates to a device and a method for determining whether a possible error of one or more components is "pinpoint-capable", ie whether the error can be unambiguously determined on the basis of the results of predetermined diagnostic options
- the components are, in particular, components of a motor vehicle ,
- DMA Diagnostic capability analysis
- a method of determining whether a potential failure of one or more components is uniquely determinable based on results of given diagnostic capabilities and thus pinpointable comprises according to one embodiment of the invention: (a) selecting the components to be tested;
- the error to be identified is solely by the diagnostic result associated with that row clearly identified. The method can therefore be terminated for the relevant error to be identified at this point.
- each of the selected lines contains at least two parameters which are greater than the predetermined third limit value, the error to be identified is still due to the diagnostic result associated with the selected lines not clearly identifiable and the following additional steps are performed:
- a device for determining whether a possible error of one or more components can be determined unambiguously on the basis of results of predetermined diagnostic options and is thus pinpointable comprises, according to one embodiment of the invention:
- a memory device which is designed to have numerical parameters which, for each combination of a possible error of one of the components and a possible diagnosis result, are a measure of whether the respective error is possible (may be present) if the respective diagnostic result is present, to store in a matrix, wherein the rows of the matrix are each assigned to one of the possible diagnostic results, and wherein the columns of the matrix are each assigned to one of the possible errors;
- a selector configured to select rows of the matrix that contain a parameter greater than a predetermined first threshold for a diagnostic result consistent with an error to be identified
- a computing device configured to calculate a result vector by multiplying the selected rows of the matrix element by element;
- a determining device configured to determine whether the result vector contains at least one element that is not less than a predetermined second limit value, wherein other elements of the result vector that are smaller than the second limit value are not greater than a predetermined one third limit are.
- the first limit may be equal to the second limit.
- the first and the second limit value may be equal to one ("1") and the third limit value may in particular be equal to zero (“0").
- DMA diagnostics capability analysis
- the DMA can include the complete and relevant information needed to create the guided troubleshooting (gFS).
- an online gFS can be generated, which consists of the
- Field feedback can be dynamically generated by updated DMA results.
- Such an automatically generated gFS can then be used on workshop testers with network access.
- a connection to a central server is established and the required information is dynamically generated and transmitted there.
- Embodiments of the invention make it possible, for example, the
- the DMA also provides information as to which errors originate at which starting point, which is predetermined by the error memory entry, for example. With complete information about all starting points and complete information about all available diagnostic options, troubleshooting can be determined directly. If the diagnostic possibilities get along without additional tools, it is also possible to automate a vehicle into one
- the method comprises determining whether the result vector contains exactly one element that is not less than the predetermined second limit, all other elements of the result vector being no greater than the predetermined third limit. In this case, the error can be determined uniquely based on the selected diagnostic results.
- the method includes determining if multiple elements of the result vector greater than the predetermined second
- the parameters are either one ("1") or zero ("0").
- the process is particularly easy to perform and requires little storage space to store the parameters. In particular, we only need one bit for each parameter.
- the parameters may have any value, in particular a value between zero ("0") and one ("1").
- 0 zero
- 1 one
- the quality of the decision as to whether a diagnostic option or a combination of diagnostic options is "pinpoint-capable" can be further improved.
- each diagnostic option is assigned an expense parameter which describes, in particular, the expenditure of time and / or the costs of the respective diagnostic option.
- the method includes in this
- Diagnostic options whose parameters are multiplied together to sum. This allows the total effort of each selected Determine a combination of diagnostic options and select the combination of diagnostic options with the lowest total effort to perform the diagnosis with the least possible effort.
- Figure 1 is a schematic view of a device according to a
- Figure 2 shows a matrix of parameters which define the relationship between possible errors of different components and the results of different diagnostic possibilities.
- FIG. 3 shows a matrix with parameters which are suitable for a specific
- FIG. 1 shows a schematic view of a device 1 according to one exemplary embodiment of the invention.
- the device 1 comprises an input device 2, which is provided for inputting data, in particular parameters, which define the relationship between possible errors of different components and the results of different diagnostic options.
- the input device 2 may comprise, for example, a keyboard, a mouse, a touchscreen and / or an electronic interface for the electronic input of data.
- the device 1 further comprises a memory device 4, which is designed to store parameters that are a measure of each combination of a possible error of a component and of a possible diagnosis result provide that the respective error is present when the respective diagnostic result is present.
- the memory device 4 is in particular designed to store the parameters in a matrix 20, wherein each row of the matrix 20 is associated with a possible diagnostic result and each column is associated with a possible error.
- the diagnostic results may be associated with the columns of the matrix 20 and the possible errors associated with the rows of the matrix 20.
- the device 1 also comprises a selection device 6, which is designed to select at least two lines of the matrix 20 that are suitable for a
- Diagnostic result which is compatible with an error to be identified, contain a parameter that is greater than a predetermined first limit value.
- a computing device 8 is configured by multiplying the selected rows of the array 20 element by element, i. Multiply the elements of the rows of the matrix 20 that are in the same column of the matrix 20 to calculate a result vector.
- a determination device 10 configured to determine whether the result vector contains at least one element that is not less than a predetermined second limit value and that the other elements of the result vector are not greater than a predetermined third limit value.
- the device 1 further comprises an output device 12, which is designed to output a result determined by the determination device 10.
- the output device 12 may include a screen, a printer, and / or an electronic interface for electronically outputting the results.
- FIG. 2 shows a matrix 20 showing the relationships between possible ones
- Each diagnostic option DM1, DM2, DM3, DM4 provides a positive result
- the parameter "1" in the matrix means that the error Fl1, F12, F13, F21, F22, F31, F41 indicated by the respective column may be present in the diagnosis result corresponding to the respective row.
- the parameter "0" in the matrix means that the error Fl1, F12, F13, F21, F22, F31, F41 designated by the respective column can not be present in the diagnosis result corresponding to the respective row.
- the first diagnostic option DM1 provides a negative (n.i.O.) result (-) (line 1)
- at least one of the errors Fll, F12 of the first component Kl or the error F22 of the second component K2 may be present.
- the errors F13 of the first component K1, F21 of the second component K2 and F31, F41 of the third and fourth components K3, K4 can be excluded.
- the first diagnostic option DM1 provides a positive (OK) result (+) (line 2)
- at least one of the errors F12, F13 of the first component K1, F21 of the second component K2, F31 of the third component K3, or F41 of FIG fourth component K4 be present.
- the errors F12 of the first component K1 and F22 of the second component K2 can be excluded.
- the result of the first diagnostic option Dl alone is therefore not sufficient to uniquely identify an error Fll, F12, F13, F21, F22, F31, F41 or else only one faulty component K1, K2, K3, K4.
- the lines which correspond to the diagnostic results of different diagnostic options are multiplied together element by element, ie those parameters the lines to be multiplied that are in the same column are multiplied together.
- the results of these multiplications form the result vector (the result row). Lines which contain the parameter "0" in the column of the selected error F1, F12, F13, F21, F22, F31, F41 can be disregarded because the selected error F1, F12, F13, F21, F22, F31, F41 is by definition excluded from these diagnostic results.
- a combination (element-wise multiplication) of the diagnostic results DM1 (-) (line 1) and DM2 (+) (line 4) results, for example, in the result vector (1/0/0/0/1/0 / 0).
- This result vector contains the parameter "1" twice, by combining the diagnostic results
- This result vector also contains the parameter "1" twice, so it is not possible to decide whether the error Fll or the error F12 is present Since the two possible errors Fll, F12 are errors of the first component Kl, the error Fll , F12, F13,
- F21, F22 are limited to the first component Kl by the combination of the diagnostic results DM1 (-) and DM3 (+).
- the result vector only receives the parameter "1" once, so the error Fl1 of the first component K1 can be uniquely identified by the combination of the diagnostic results DM1 (-), DM2 (+) and DM3 (+) also for the other errors F12, F13,
- the parameters in the matrix 20 are binary parameters, ie are called parameters which can assume only one of the two values "1" or "0".
- the parameters may also assume values between "0" and "1", for example 0.1 or 0.9.
- a value of 0.9 would mean, for example, that the respective error Fll, F12, F13, F21, F22 can be present with a 90% probability if the relevant diagnostic result is available.
- a combination of diagnostic results is uniquely identified as an error Fll, F12, F13, F21, F22 when an element of the
- Resulting vector exceeds a predetermined limit, for example, a value of 0.8, while all other elements of the result vector, a further predetermined limit value, for example, a value of 0.2
- an expense parameter is stored for each diagnostic option DM1, DM2, DM3, which represents the time and / or financial expense, which is associated with the implementation of the respective diagnostic option DM1, DM2, DM3.
- a diagnostic selector 14 may select the combination with the least amount of time and / or expense. In this way, the diagnosis can be carried out as quickly (least expenditure of time) and / or as cost-effectively (lowest financial expenditure) as possible.
- the low pressure sensor K2 indicates too high a value
- DM3 Measuring the flow rate of the fuel pump
- the relationships between the possible error sources Fx and the available diagnostic options DMx are symbolized by the parameters in the matrix 20 shown in FIG.
- Pinpointing is possible because visually checking the high pressure trail (DM1 (-)) alone will result in a result vector (1/0/0/0) that meets the Pinpoint requirements.
- the low pressure sensor K2 indicates too high a value (F2):
- Pinpointing is possible because already checking the flow rate (DM4 (+)) results in a result vector (0/0/1/0) that meets the pinpoint requirements.
- Path 1 "visually checking the high pressure rail (+) and testing the flow rate (-) and measuring the return flow rate (+)":
- Path 2 "visually checking the high pressure trail (+) and testing the high pressure trail (+)
- visual check of the high pressure trail Kl may be performed first, as it will immediately result in pinpointing if the result is negative (-).
- the preferred order can be derived from the occurrence probabilities of the individual errors F1, F2, F3, F4, which are recorded in the DMA, or dynamically generated from field feedbacks.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Analysis (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Human Computer Interaction (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
- Tests Of Electronic Circuits (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102016225081.7A DE102016225081A1 (en) | 2016-12-15 | 2016-12-15 | Apparatus and method for determining the pinpoint capability of possible errors of one or more components |
PCT/EP2017/082213 WO2018108809A1 (en) | 2016-12-15 | 2017-12-11 | Device and method for determining the pinpoint-ability of potential errors of one or more components |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3555719A1 true EP3555719A1 (en) | 2019-10-23 |
Family
ID=61007642
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP17832478.6A Withdrawn EP3555719A1 (en) | 2016-12-15 | 2017-12-11 | Device and method for determining the pinpoint-ability of potential errors of one or more components |
Country Status (5)
Country | Link |
---|---|
US (1) | US11409273B2 (en) |
EP (1) | EP3555719A1 (en) |
CN (1) | CN110050241B (en) |
DE (1) | DE102016225081A1 (en) |
WO (1) | WO2018108809A1 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113722659B (en) * | 2021-08-30 | 2023-04-18 | 北京智盟信通科技有限公司 | Fault mode-based substation main equipment diagnosis method and system |
Family Cites Families (24)
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US5544308A (en) * | 1994-08-02 | 1996-08-06 | Giordano Automation Corp. | Method for automating the development and execution of diagnostic reasoning software in products and processes |
FR2783620B1 (en) * | 1998-09-22 | 2002-03-29 | De Micheaux Daniel Lafaye | MULTIDIMENSIONAL PROCESS AND SYSTEM FOR STATISTICAL PROCESS CONTROL |
DE10107367B4 (en) | 2001-02-16 | 2016-09-01 | Robert Bosch Gmbh | Method and device for diagnosis by error pattern recognition |
JP2005339142A (en) * | 2004-05-26 | 2005-12-08 | Tokyo Electric Power Co Inc:The | Facility maintenance supporting device |
US7409594B2 (en) * | 2004-07-06 | 2008-08-05 | Intel Corporation | System and method to detect errors and predict potential failures |
US20090106593A1 (en) * | 2005-01-19 | 2009-04-23 | Zigmund Bluvband | System and Method for Bouncing Failure Analysis |
DE102007041848A1 (en) * | 2007-09-03 | 2009-03-05 | Robert Bosch Gmbh | Method and device for detecting faulty components of coupled chains of action |
DE102008040461A1 (en) | 2008-07-16 | 2010-01-21 | Robert Bosch Gmbh | Method for determining faulty components in a system |
EP2342683A2 (en) * | 2008-10-03 | 2011-07-13 | BAE Systems PLC | Assisting with updating a model for diagnosing failures in a system |
US8745568B2 (en) * | 2008-12-17 | 2014-06-03 | Advantest (Singapore) Pte Ltd | Method and apparatus for determining relevance values for a detection of a fault on a chip and for determining a fault probability of a location on a chip |
WO2010139146A1 (en) * | 2009-06-05 | 2010-12-09 | 中兴通讯股份有限公司 | Method and device for analyzing alarm correlation, system and method for checking alarm correlation analyzing device |
DE102009026807A1 (en) | 2009-06-08 | 2010-12-09 | Robert Bosch Gmbh | Method and device for fault monitoring of a complete system having multiple systems |
JP5230557B2 (en) * | 2009-08-03 | 2013-07-10 | 日立オートモティブシステムズ株式会社 | Vehicle failure determination device, vehicle failure determination device design program |
US8498776B2 (en) * | 2009-11-17 | 2013-07-30 | GM Global Technology Operations LLC | Fault diagnosis and prognosis using diagnostic trouble code markov chains |
US9740993B2 (en) * | 2009-12-04 | 2017-08-22 | GM Global Technology Operations LLC | Detecting anomalies in field failure data |
US8473330B2 (en) | 2009-12-10 | 2013-06-25 | GM Global Technology Operations LLC | Software-centric methodology for verification and validation of fault models |
CN106342308B (en) * | 2010-01-08 | 2013-10-16 | 中国人民解放军国防科学技术大学 | A kind of diagnostic tree building method based on two heuristic functions |
CN101753382B (en) | 2010-01-25 | 2013-07-24 | 浪潮通信信息系统有限公司 | Method for establishing adaptive network failure monitoring and positioning security model |
JP5765336B2 (en) * | 2010-05-06 | 2015-08-19 | 日本電気株式会社 | Fault analysis apparatus, fault analysis method and program |
US8527441B2 (en) * | 2011-03-10 | 2013-09-03 | GM Global Technology Operations LLC | Developing fault model from service procedures |
US8768668B2 (en) * | 2012-01-09 | 2014-07-01 | Honeywell International Inc. | Diagnostic algorithm parameter optimization |
CN102818948B (en) | 2012-07-16 | 2015-03-25 | 北京航空航天大学 | Synthetic diagnosing method based on fuzzy fault diagnosis and relevancy model diagnosis |
US9274884B2 (en) * | 2012-10-10 | 2016-03-01 | HGST Netherlands B.V. | Encoding and decoding data to accommodate memory cells having stuck-at faults |
US10584986B2 (en) * | 2013-09-06 | 2020-03-10 | International Business Machines Corporation | Detecting device, detecting method, and program |
-
2016
- 2016-12-15 DE DE102016225081.7A patent/DE102016225081A1/en active Pending
-
2017
- 2017-12-11 EP EP17832478.6A patent/EP3555719A1/en not_active Withdrawn
- 2017-12-11 WO PCT/EP2017/082213 patent/WO2018108809A1/en unknown
- 2017-12-11 CN CN201780077728.6A patent/CN110050241B/en active Active
- 2017-12-11 US US16/468,633 patent/US11409273B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
US11409273B2 (en) | 2022-08-09 |
DE102016225081A1 (en) | 2018-06-21 |
WO2018108809A1 (en) | 2018-06-21 |
CN110050241B (en) | 2022-07-12 |
US20200081427A1 (en) | 2020-03-12 |
CN110050241A (en) | 2019-07-23 |
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