DETAILED ACTION
Notice of Pre-AIA or AIA Status
In the present application, filed on or after March 16, 2013, claims 1-16 have been considered and examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/09/2024 are in compliance with the provision of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by Examiner.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the method steps of an alarm management method for an automated analysis system, the alarm management method comprising: a step of detecting that an alarm indicating an occurrence of an abnormality in the automated analysis system is generated; a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm; a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance; and a step of notifying the determined related alarm.
The limitations of detecting that an alarm indicating an occurrence of an abnormality in the automated analysis system is generated; a step of storing a degree of relevance between the detected alarm; a step of determining a related alarm that is in an unhandled state according to the stored degree of relevance; and a step of notifying the determined related alarm, as drafted, is an automated analysis system that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting for “an automated analysis system,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “an automated analysis system” language, “detecting that an alarm indicating an occurrence of an abnormality in the automated analysis system is generated; a step of storing a degree of relevance between the detected alarm; a step of determining a related alarm that is in an unhandled state according to the stored degree of relevance; and a step of notifying the determined related alarm” in the context of this claim encompasses the user manually detect an abnormality, storing a degree of relevance, determining a related alarm and notifying the determined related alarm .
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the detecting, the storing, the determining, and the notifying steps. The processor in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the detecting, the storing, the determining, and the notifying steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform all the detecting, the storing, the determining, and the notifying steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-9, 11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Dejima (Dejima – US 2019/0019580 A1) in view of Williamson et al. (Williamson – US 2010/0109860 A1) and Health (Health – US 8,890,676 B1).
As to claim 1, Dejima discloses an automated analysis system comprising:
a test processing unit (Dejima: Abstract and FIG. 1 the measuring part 100) configured to execute at least a part of processing for a test of a specimen (Dejima: Abstract, [0022], [0024]-[0026], [0029]-[0030], [0035], [0043]-[0046], [0048], and FIG. 1-2: The measuring chamber 20 is provided with a measuring chamber (BASO chamber) 21 for counting basophils, a measuring chamber (from the acronym for Lymphocyte, Monocyte, Neutrophil, Eosinophil, it is called LMNE chamber) 22 for classifying and counting lymphocytes, monocytes, neutrophils and eosinophils, a measuring chamber (RBC chamber) 23 for counting red blood cells, and a measuring chamber (WBC chamber) 24 for counting white blood cells and analyzing HGB (hemoglobin concentration). In the Figure, the symbol 30 is a specimen container, showing the specimen container 30 set in the apparatus); and
a management device (Dejima: FIG. 1 the control part 300) configured to manage the test processing unit (Dejima: [0029]-[0030], [0035], [0043]-[0046], [0048], and FIG. 1-2: the control performed by the measurement control part 310 includes control of the action of each part of the measuring part 100 (action of each part of sampling nozzle driving part, measurement action in each measuring chamber and the like), and calculation processing for the analysis of measurement data sent from the measuring part 100, output, storage and the like of the analysis results, and the like), wherein the management device includes
an alarm detection unit configured to detect that an alarm indicating an occurrence of an abnormality in the test processing unit is generated (Dejima: [0024]-[0027], [0029]-[0030], [0037]-[0044], [0046]-[0047], and FIG. 1-2: The action state management part 320 monitors a signal sent when a failure is detected in the action of each part and constituent elements (drive system, parts, reagent etc.) of the measuring part 100, and performs a process for displaying an alarm of the failure and a recovery operation for canceling the alarm on the display part 200), and
an alarm notification unit (Dejima: Abstract, [0023], [0025]-[0026, [0033]-[0037], [0041]-[0047], and FIG. 1 the display part 200: the alarm display region 51 displays alarms of all failures occurring at the moment. When only one failure has occurred, one alarm of the failure that has occurred is displayed in the alarm display region 51).
Dejima does not explicitly disclose a related alarm management unit configured to store a degree of relevance between the alarm detected by the alarm detection unit, and each of other alarms related to the alarm,
a related alarm determination unit configured to determine, among relate alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the degree of relevance stored in the related alarm management unit, and
an alarm notification unit configured to notify the related alarm determined by the related alarm determination unit.
However, it has been known in the art of handling alarms to implement a related alarm management unit configured to store a degree of relevance between the alarm detected by the alarm detection unit, and each of other alarms related to the alarm, and
a related alarm determination unit configured to determine, among relate alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the degree of relevance stored in the related alarm management unit, as suggested by Williamson, which discloses a related alarm management unit configured to store a degree of relevance between the alarm detected by the alarm detection unit, and each of other alarms related to the alarm (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: A coefficient of correlation is computed between each distinct pair of alarm categories that indicates the probability that an alarm assigned to the second category of the pair occurs coincidently within the alarm history data with an alarm assigned to the first category of the pair, given that an alarm assigned to the first category has occurred), and
a related alarm determination unit configured to determine, among relate alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the degree of relevance stored in the related alarm management unit (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: the pairs of alarm categories in the list of potentially redundant alarm categories 122 are good candidates for further investigation to determine if alarms of one of the alarm categories are redundant, i.e. alarms from one of the categories are likely caused by the same root cause as alarms from the other category. Alarms of categories identified to be redundant may be removed from the alarm stream, since if an alarm of the non-redundant category is investigated and the root cause is removed, there is a high likelihood that the alarm of the redundant category will be resolved as well ).
Therefore, in view of teachings by Dejima and Williamson, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima to include a related alarm management unit configured to store a degree of relevance between the alarm detected by the alarm detection unit, and each of other alarms related to the alarm, and
a related alarm determination unit configured to determine, among relate alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the degree of relevance stored in the related alarm management unit, as suggested by Williamson. The motivation for this is to identify related alarms for diagnostic and troubleshooting issues.
The combination of Dejima and Williamson does not explicitly disclose an alarm notification unit configured to notify the related alarm determined by the related alarm determination unit.
However, it has been known in the art of handling alarms to implement an alarm notification unit configured to notify the related alarm determined by the related alarm determination unit, as suggested by Health, which discloses a related alarm management unit configured to store a degree of relevance between the alarm detected by the alarm detection unit, and each of other alarms related to the alarm (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: A correlation between the first alert and the second alert is determined and, based on the determined correlation, a determination is made that the first fault is a root cause of the first alert and the second alert.),
a related alarm determination unit configured to determine, among relate alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the degree of relevance stored in the related alarm management unit (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: The alarm unit 320 can also be configured to predict which alerts can potentially be triggered in the future due to the identified root cause. The predicted alerts can be included in the set of correlated alerts. In some implementations, at least a subset of alerts from the correlated set of alerts (including predicted alerts as well as alerts that have already been triggered) can be suitably flagged as safe to be ignored because their root cause has been identified and/or addressed. In some cases, at least some of the predicted alerts in the correlated set of alerts can be preemptively stopped from being triggered. Various graphs, charts, or other dependency models can be used in determining the correlated set of alerts and/or predicting future alerts), and
an alarm notification unit configured to notify the related alarm determined by the related alarm determination unit (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: An indication that the first fault is the root cause of the first alert and second alert is provided).
Therefore, in view of teachings by Dejima, Williamson, and Health, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima and Williamson to include an alarm notification unit configured to notify the related alarm determined by the related alarm determination unit, as suggested by Health. The motivation for this is to identify related alarms for diagnostic and troubleshooting issues.
As to claim 3, Dejima, Williamson, and Health disclose the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the alarm notification unit is configured to notify alarm detailed information related to details of the alarm and the related alarm (Dejima: Abstract, [0023], [0025]-[0026, [0033]-[0037], [0041]-[0047], and FIG. 1 the display part 200: the alarm display region 51 displays alarms of all failures occurring at the moment. When only one failure has occurred, one alarm of the failure that has occurred is displayed in the alarm display region 5, Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: an illustrative operating environment 100 and several software components for generating a list of potentially redundant alarms is shown, according to embodiments. The environment 100 includes alarm history data 102. The alarm history data 102 consists of alarm records 104 representing individual alarms or other events captured over a period of time from a stream of alarms or events generated by devices or components comprising a network or other complex system…Each alarm record 104 may include a device ID 106 identifying the device or component that generated the alarm, a device type 108 identifying the type of the device or component that generated the alarm, an alarm condition 110 indicating the type of condition represented by the alarm, and a timestamp 112. According to one embodiment, the timestamp 112 may indicate the time when the alarm occurred. In another embodiment, the timestamp 112 may indicate the time when the alarm was received by an alarm management system. The alarm history data 102 may be stored in a database to permit statistical computations to be carried out against the data as well as allow other analysis and reporting to be performed and Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: a display can be used to render visually an identification of the root cause and possibly the correlated set of alerts associated with the root cause. For example, when a visual indication is provided of the alerts, the root cause may be represented in a different color, brightness or size than the downstream alerts to show their relationship. In some implementations, alerts that have not been set off, but that are predicted based on the identification of the root case are displayed as potential future alerts. In some implementations, the alerts are audio rendered alerts. In some implementations, providing indication of the root cause can further include suppressing at least some of the associated set of correlated alerts).
As to claim 4, Dejima, Williamson, and Health disclose the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the test processing unit includes an analysis device configured to perform an analysis of the specimen (Dejima: Abstract, [0022], [0024]-[0026], [0029]-[0030], [0035], [0043]-[0046], [0048], and FIG. 1-2: The measuring chamber 20 is provided with a measuring chamber (BASO chamber) 21 for counting basophils, a measuring chamber (from the acronym for Lymphocyte, Monocyte, Neutrophil, Eosinophil, it is called LMNE chamber) 22 for classifying and counting lymphocytes, monocytes, neutrophils and eosinophils, a measuring chamber (RBC chamber) 23 for counting red blood cells, and a measuring chamber (WBC chamber) 24 for counting white blood cells and analyzing HGB (hemoglobin concentration). In the Figure, the symbol 30 is a specimen container, showing the specimen container 30 set in the apparatus) and a peripheral device thereof, and the peripheral device includes at least one or more of a pretreatment device configured to perform a pretreatment of the specimen,
a consumable storage device configured to store a consumable including a reagent (Dejima: [0002], [0006], [0022], [0026], [0030], [0032], [0035], [0040], [0046], and FIG. 1: The piping also contains various pumps, electromagnetic valve device, liquid cleansing agent tank, dilution liquid tank and reagent tank (these are not shown) ), or
a robot device configured to operate in a test room.
As to claim 5, Dejima, Williamson, and Health disclose the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the related alarm determination unit is configured to determine the degree of relevance based on the number of times of duplicate generations between the alarm and the related alarm (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: In one embodiment, the coefficient of correlation R.sub.A,B between a distinct pair of alarm categories A and B is calculated by dividing the number of times an alarm of category B occurred coincidentally with an alarm of category A by the number of time an alarm of category A occurred in the alarm history data 102).
As to claim 6, Dejima, Williamson, and Health disclose the limitations of claim 5 further comprising the automated analysis system according to claim 5, wherein the related alarm determination unit is configured to determine the degree of relevance based on the number of times of duplicate generations (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: In one embodiment, the coefficient of correlation R.sub.A,B between a distinct pair of alarm categories A and B is calculated by dividing the number of times an alarm of category B occurred coincidentally with an alarm of category A by the number of time an alarm of category A occurred in the alarm history data 102) within a predetermined period (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: The alarm history data 102 consists of alarm records 104 representing individual alarms or other events captured over a period of time from a stream of alarms or events generated by devices or components comprising a network or other complex system. For example, the alarm history data 102 may contain hundreds of thousands of alarm records 104 collected over a two year period from devices in a complex network operated by a network service provider).
As to claim 7, Dejima, Williamson, and Health disclose the limitations of claim 5 further comprising the automated analysis system according to claim 5, wherein the related alarm determination unit determines the degree of relevance based on a magnitude relationship between the number of times of duplicate generations and a predetermined threshold (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: Each alarm in a compilation of alarm history data is assigned to an alarm category. A coefficient of correlation is computed between each distinct pair of alarm categories that indicates the probability that an alarm assigned to the second category of the pair occurs coincidently within the alarm history data with an alarm assigned to the first category of the pair, given that an alarm assigned to the first category has occurred. Finally, a list of potentially redundant alarms is created consisting of pairs of alarm categories having a coefficient of correlation equal to or exceeding a threshold value ).
As to claim 8, Dejima, Williamson, and Health discloses the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the related alarm determination unit determines, as the related alarm, an alarm related to a device the same as a device for which the alarm is generated (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: the pairs of alarm categories in the list of potentially redundant alarm categories 122 are good candidates for further investigation to determine if alarms of one of the alarm categories are redundant, i.e. alarms from one of the categories are likely caused by the same root cause as alarms from the other category. Alarms of categories identified to be redundant may be removed from the alarm stream, since if an alarm of the non-redundant category is investigated and the root cause is removed, there is a high likelihood that the alarm of the redundant category will be resolved as well and Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: The system alert model 325 can also include a machine learning system such as a Bayesian classifier. In some implementations, the system alert model 325 includes a history of previous alerts and their root causes. If a machine learning system is used in the system alert model 325, such historical data can be used as training data for the machine learning system. In some implementations, the system alert model 325 can include user-defined rules created, for example, based on experience or known dependencies).
As to claim 9, Dejima, Williamson, and Health discloses the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the related alarm determination unit determines, as the related alarm, an alarm related to another device provided in a test room (Dejima: [0029]-[0030], [0035], [0043]-[0046], [0048], and FIG. 1-2: the control performed by the measurement control part 310 includes control of the action of each part of the measuring part 100 (action of each part of sampling nozzle driving part, measurement action in each measuring chamber and the like), and calculation processing for the analysis of measurement data sent from the measuring part 100, output, storage and the like of the analysis results, and the like) the same as a test room provided with a device for which the alarm is generated (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: the pairs of alarm categories in the list of potentially redundant alarm categories 122 are good candidates for further investigation to determine if alarms of one of the alarm categories are redundant, i.e. alarms from one of the categories are likely caused by the same root cause as alarms from the other category. Alarms of categories identified to be redundant may be removed from the alarm stream, since if an alarm of the non-redundant category is investigated and the root cause is removed, there is a high likelihood that the alarm of the redundant category will be resolved as well and Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: The system alert model 325 can also include a machine learning system such as a Bayesian classifier. In some implementations, the system alert model 325 includes a history of previous alerts and their root causes. If a machine learning system is used in the system alert model 325, such historical data can be used as training data for the machine learning system. In some implementations, the system alert model 325 can include user-defined rules created, for example, based on experience or known dependencies).
As to claim 11, Dejima, Williamson, and Health discloses the limitations of claim 1 further comprising the automated analysis system according to claim 1, wherein the related alarm determination unit determines an alarm related to an abnormality of hardware as the related alarm (Williamson: Abstract, [0015]-[0018], [0020], [0029]-[0033], and FIG. 1-4: For example, a particular device within a system may begin to report a low memory condition, which is followed by a failure of the device 20 minutes later. Other devices or components in the system that rely on the failed device may then begin to report related failure conditions. In this example, an incidence interval of at least 20 minutes would be required to capture the correlation between the low memory alarm and the other failure alarms ultimately dependent on the low memory alarm and Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: the first alert indicates a first fault related to a first component of the multiple components and the second alert that indicates a second fault related to a second component of the multiple components. The first component affects the second component such that the first fault caused the second fault).
As to claim 13, Dejima discloses an alarm management method for an automated analysis system, the alarm management method comprising:
a step of detecting that an alarm indicating an occurrence of an abnormality in the automated analysis system is generated (Dejima: [0024]-[0027], [0029]-[0030], [0037]-[0044], [0046]-[0047], and FIG. 1-2: The action state management part 320 monitors a signal sent when a failure is detected in the action of each part and constituent elements (drive system, parts, reagent etc.) of the measuring part 100, and performs a process for displaying an alarm of the failure and a recovery operation for canceling the alarm on the display part 200).
Dejima does not explicitly disclose a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm;
a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance; and
a step of notifying the determined related alarm.
However, it has been known in the art of handling alarms to implement a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm; and
a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance, as suggested by Williamson, which discloses a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm; (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: A coefficient of correlation is computed between each distinct pair of alarm categories that indicates the probability that an alarm assigned to the second category of the pair occurs coincidently within the alarm history data with an alarm assigned to the first category of the pair, given that an alarm assigned to the first category has occurred), and
a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: the pairs of alarm categories in the list of potentially redundant alarm categories 122 are good candidates for further investigation to determine if alarms of one of the alarm categories are redundant, i.e. alarms from one of the categories are likely caused by the same root cause as alarms from the other category. Alarms of categories identified to be redundant may be removed from the alarm stream, since if an alarm of the non-redundant category is investigated and the root cause is removed, there is a high likelihood that the alarm of the redundant category will be resolved as well ).
Therefore, in view of teachings by Dejima and Williamson, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima to include a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm; and
a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance, as suggested by Williamson. The motivation for this is to identify related alarms for diagnostic and troubleshooting issues.
The combination of Dejima and Williamson does not explicitly disclose a step of notifying the determined related alarm.
However, it has been known in the art of handling alarms to implement a step of notifying the determined related alarm, as suggested by Health, which discloses
a step of storing a degree of relevance between the detected alarm; and each of other alarms related to the alarm (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: A correlation between the first alert and the second alert is determined and, based on the determined correlation, a determination is made that the first fault is a root cause of the first alert and the second alert.)
a step of determining, among related alarms each having a predetermined degree of relevance with the alarm, a related alarm that is in an unhandled state according to the stored degree of relevance (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: The alarm unit 320 can also be configured to predict which alerts can potentially be triggered in the future due to the identified root cause. The predicted alerts can be included in the set of correlated alerts. In some implementations, at least a subset of alerts from the correlated set of alerts (including predicted alerts as well as alerts that have already been triggered) can be suitably flagged as safe to be ignored because their root cause has been identified and/or addressed. In some cases, at least some of the predicted alerts in the correlated set of alerts can be preemptively stopped from being triggered. Various graphs, charts, or other dependency models can be used in determining the correlated set of alerts and/or predicting future alerts); and
a step of notifying the determined related alarm (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: An indication that the first fault is the root cause of the first alert and second alert is provided).
Therefore, in view of teachings by Dejima, Williamson, and Health, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima and Williamson to include a step of notifying the determined related alarm, as suggested by Health. The motivation for this is to identify related alarms for diagnostic and troubleshooting issues.
As to claim 14, Dejima, Williamson, and Health disclose the limitations of claim 13 further comprising the alarm management method according to claim 13, wherein in the step of determining the related alarm, the degree of relevance is determined based on the number of times of duplicate generations between the alarm and the related alarm (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: In one embodiment, the coefficient of correlation R.sub.A,B between a distinct pair of alarm categories A and B is calculated by dividing the number of times an alarm of category B occurred coincidentally with an alarm of category A by the number of time an alarm of category A occurred in the alarm history data 102).
As to claim 15, Dejima, Williamson, and Health disclose the limitations of claim 14 further comprising the alarm management method according to claim 14, wherein in the step of determining the related alarm, the degree of relevance is determined based on the number of times of duplicate generations (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: In one embodiment, the coefficient of correlation R.sub.A,B between a distinct pair of alarm categories A and B is calculated by dividing the number of times an alarm of category B occurred coincidentally with an alarm of category A by the number of time an alarm of category A occurred in the alarm history data 102) within a predetermined period (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: The alarm history data 102 consists of alarm records 104 representing individual alarms or other events captured over a period of time from a stream of alarms or events generated by devices or components comprising a network or other complex system. For example, the alarm history data 102 may contain hundreds of thousands of alarm records 104 collected over a two year period from devices in a complex network operated by a network service provider).
As to claim 16, Dejima, Williamson, and Health disclose the limitations of claim 14 further comprising the alarm management method according to claim 14, wherein in the step of determining the related alarm, the degree of relevance is determined based on a magnitude relationship between the number of times of duplicate generations and a predetermined threshold (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], [0041]-[0042], and FIG. 1-4: Each alarm in a compilation of alarm history data is assigned to an alarm category. A coefficient of correlation is computed between each distinct pair of alarm categories that indicates the probability that an alarm assigned to the second category of the pair occurs coincidently within the alarm history data with an alarm assigned to the first category of the pair, given that an alarm assigned to the first category has occurred. Finally, a list of potentially redundant alarms is created consisting of pairs of alarm categories having a coefficient of correlation equal to or exceeding a threshold value ).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Dejima (Dejima – US 2019/0019580 A1) in view of Williamson et al. (Williamson – US 2010/0109860 A1) and health (Health – US 8,890,676 B1) and further in view of Kazama et al. (Kazama – US 2020/0271677 A1).
As to claim 2, Dejima, Williamson, and Health disclose the limitations of claim 1 except for the claimed limitations of the automated analysis system according to claim 1, wherein the management device further includes an alarm management table for managing the generated alarm, and
the alarm management table includes information indicating whether the alarm is handled or unhandled.
However, it has been known in the art of handling alarms to implement wherein the management device further includes an alarm management table for managing the generated alarm, and
the alarm management table includes information indicating whether the alarm is handled or unhandled, as suggested by Kazama, which discloses wherein the management device further includes an alarm management table for managing the generated alarm (Kazama: Abstract, [0142]-[0143], and FIG. 5-8: FIGS. 5 to 8 illustrate correspondence tables in which the correlation between a combination of a plurality of data alarms and an output used for output control in the automated analyzer 1 is defined. In particular, a correspondence table for the middle level data alarm described above is illustrated. The analysis control unit 50 and the output control function perform output control processing according to the rules of such a correspondence table), and
the alarm management table includes information indicating whether the alarm is handled or unhandled (Kazama: Abstract, [0142]-[0156], and FIG. 5-8: The “absence” (value 2) represents that automatic retesting is unnecessary (absence). As the value of the fifth column “condition”, there are three types of values of “same” (value 1), “decrease” (value 2), and “increase” (value 3). The “same” (value 1) represents the same condition (remeasurement condition, and the like) as in the previous measurement. The “decrease” (value 2) represents that the condition is changed to a condition for decreasing the amount of sample 2 with respect to the condition at the time of the previous measurement. The “increase” (value 3) represents that the condition is changed to a condition for increasing the amount of sample 2 with respect to the condition at the time of the previous measurement).
Therefore, in view of teachings by Dejima, Williamson, Health, and Kazama, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima, Williamson, and Health to include wherein the management device further includes an alarm management table for managing the generated alarm, and
the alarm management table includes information indicating whether the alarm is handled or unhandled, as suggested by Kazama. The motivation for this is to provide information of alarms for handling alarm issues.
As to claim 12, Dejima, Williamson, and Health discloses the limitations of claim 1 further comprising the automated analysis system according to claim 1, further comprising:
a display device configured to display an analysis result and an operation state in the test processing unit (Dejima: Abstract, [0023], [0025]-[0026, [0033]-[0037], [0041]-[0047], and FIG. 1 the display part 200: the alarm display region 51 displays alarms of all failures occurring at the moment. When only one failure has occurred, one alarm of the failure that has occurred is displayed in the alarm display region 51), wherein the display device displays an alarm determined to be the related alarm by the related alarm determination unit (Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: An indication that the first fault is the root cause of the first alert and second alert is provided), and the related alarm information is configured to select and display a related alarm having a predetermined characteristic among the related alarms (Williamson: Abstract, [0015]-[0018], [0020], [0030]-[0033], and FIG. 1-4: the pairs of alarm categories in the list of potentially redundant alarm categories 122 are good candidates for further investigation to determine if alarms of one of the alarm categories are redundant, i.e. alarms from one of the categories are likely caused by the same root cause as alarms from the other category. Alarms of categories identified to be redundant may be removed from the alarm stream, since if an alarm of the non-redundant category is investigated and the root cause is removed, there is a high likelihood that the alarm of the redundant category will be resolved as well and Health: Abstract, column 6 lines 27-column 7 lines 55, column 8 lines 39-62, column 9 lines 42-column 10 lines 67, and FIG. 3-5: The alarm unit 320 can also be configured to predict which alerts can potentially be triggered in the future due to the identified root cause. The predicted alerts can be included in the set of correlated alerts. In some implementations, at least a subset of alerts from the correlated set of alerts (including predicted alerts as well as alerts that have already been triggered) can be suitably flagged as safe to be ignored because their root cause has been identified and/or addressed. In some cases, at least some of the predicted alerts in the correlated set of alerts can be preemptively stopped from being triggered. Various graphs, charts, or other dependency models can be used in determining the correlated set of alerts and/or predicting future alerts).
The combination of Dejima, Williamson, and Health does not explicitly disclose wherein the display device is configured to display a related alarm information table and the related alarm information table is configured to select and display a related alarm having a predetermined characteristic.
However, it has been known in the art of handling alarms to implement wherein the display device is configured to display a related alarm information table and the related alarm information table is configured to select and display a related alarm having a predetermined characteristic, as suggested by Kazama, which discloses wherein the display device is configured to display a related alarm information table and the related alarm information table is configured to select and display a related alarm having a predetermined characteristic (Kazama: Abstract, [0142]-[0156], and FIG. 5-8: The “absence” (value 2) represents that automatic retesting is unnecessary (absence). As the value of the fifth column “condition”, there are three types of values of “same” (value 1), “decrease” (value 2), and “increase” (value 3). The “same” (value 1) represents the same condition (remeasurement condition, and the like) as in the previous measurement. The “decrease” (value 2) represents that the condition is changed to a condition for decreasing the amount of sample 2 with respect to the condition at the time of the previous measurement. The “increase” (value 3) represents that the condition is changed to a condition for increasing the amount of sample 2 with respect to the condition at the time of the previous measurement).
Therefore, in view of teachings by Dejima, Williamson, Health, and Kazama, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima, Williamson, and Health to include wherein the display device is configured to display a related alarm information table and the related alarm information table is configured to select and display a related alarm having a predetermined characteristic, as suggested by Kazama. The motivation for this is to provide information of alarms for handling alarm issues.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dejima (Dejima – US 2019/0019580 A1) in view of Williamson et al. (Williamson – US 2010/0109860 A1) and Health (Health – US 8,890,676 B1) and further in view of Manickam et al. (Manickam – US 2020/0089983 A1).
As to claim 10, Dejima, Williamson, and Health disclose the limitations of claim 1 except for the claimed limitations of the automated analysis system according to claim 1, wherein the related alarm determination unit determines an alarm related to an abnormality of software as the related alarm.
However, it has been known in the art of handling alarms to implement wherein the related alarm determination unit determines an alarm related to an abnormality of software as the related alarm, as suggested by Manickam, which discloses wherein the related alarm determination unit determines an alarm related to an abnormality of software as the related alarm (Manickam: Abstract, [0025], [0053]-[0054], FIG. 1, and FIG. 9: The action may be for example, but is not limited to, automatically running an application for resolving the software fault, running an application (e.g. a tool) to perform scanning or diagnostics or debugging of the software fault, a recommendation for resolving the software fault, procedure of resolving the software fault, a recommendation to connect to a technical expert (i.e. a field engineer) and so on).
Therefore, in view of teachings by Dejima, Williamson, Health, and Manickam, it would have been obvious to one of the ordinary skill in the art before eth effective filing date of the claimed invention to implement in the specimen analysis apparatus of Dejima, Williamson, and Health to include wherein the related alarm determination unit determines an alarm related to an abnormality of software as the related alarm, as suggested by Manickam. The motivation for this is to identify alarm issues related to software faults.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Kaneko et al., US 2019/0079107 A1, discloses automatic analyzer.
Fukuma et al., US 2011/0077871 A1, discloses analysis apparatus information processing unit, and an information displaying method.
Bosko et al., US 2016/0020986 A1, dsiclsoes method and system for aggregating diagnostic analyzer related information.
Conclusion
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/QUANG PHAM/Primary Examiner, Art Unit 2685
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