DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 4 and 13 have been cancelled. Claims 1-3, 5-12 and 14-20 are currently pending. Claims 19 and 20 are newly added.
Response to Arguments
Applicant’s arguments with respect to claim(s) rejected in the official action dated 05/07/2025 have been considered but are moot because the new ground of rejection does not rely on the same combination of references applied in the prior rejection.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5-12 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over FUKISHIMA (US 2018/0189242) in view of REITER (US 2017/0365137).
Regarding claims 1 and 8,
FUKUSHIMA teaches a method for determining, generating, and issuing a contextual alert message to a user regarding an alert condition of a process system, the method comprising the steps of:
(i) determining a first set and second set of variable data from a sensor associated with a process system ([0038] teaches attaching any “n” sensors to the one prototype mfp and acquires “k” pieces of sensor data from each sensor as learning data. The “k” pieces of sensor data are acquired at timings when the first to k-th points of time at predetermined time intervals (every ten minutes, every one hour, or the like) arrive, respectively, interpreted as corresponding to "a first set of variable data"; [0038] further teaches “p” pieces of sensor data are acquired at the predetermined time intervals during normal operation of the mfp, and, successively, “q” pieces of sensor data are acquired at the predetermined time intervals in a state that a fault has been intentionally caused to the mfp; [0040] teaches "for each of the sensors 1 to “n”, the “p” pieces of sensor data during normal operation and the “q” pieces of sensor data at the time of anomaly are shown." thus, "p" and "q" data are interpreted as corresponding to "a second set of variable data");
(ii) transmitting the first set of variable data from the sensor to a server ([0033] teaches that the system is provided with a processing server 1) and using the server to develop an alert classification model using the first set of variable data ([0033] teaches that the processing server 1 performs construction of a classification model using sensor data attached to a monitoring target), wherein the alert classification model contains contextual information about an alert condition of the process system ([0033] teaches that the processing server 1 performs construction of a classification model using acquired sensor data; [0042] teaches that for each sensor, a condition for acquiring sensor data i.e., contextual information, is automatically selected from among the first to k-th conditions. In a classification model, only sensor data acquired when the selected condition is satisfied is used);
(iii) transmitting the second set of variable data from the sensor to the server and using the server to compare the second set of variable data ([0040] teaches in combination with FIG. 4, waveform data which shows transition of a value along time of various sampling points of data from a sensor) to a threshold value to determine if the process system is approaching the alert condition ([0129] teaches calculating a probability of the monitoring target being normal or anomalous; [0130] teaches that the performance of anomaly detection is initiated when selected conditions are satisfied- the presence of said satisfied conditions is interpreted as corresponding to “approaching the alert condition”);
wherein if the process system is determined in step (iii) to be approaching an alert condition, then:
(iv) using the server to compare the second set of variable data to the alert classification model (see any one of data which is detected by sensors 2-n in fig. 3 as discussed in [0033], said data being compared to a threshold for use in anomaly determination) to determine contextual information about the approaching alert condition of the process system ([0042] teaches that a condition for acquiring sensor data is automatically selected from among the first to k-th conditions, said conditions corresponding to contextual information; [0130] teaches that the performance of anomaly detection is initiated when selected conditions are satisfied- the presence of said satisfied conditions is interpreted as corresponding to “approaching the alert condition”); and
generating, and issuing an alert message to the user regarding the approaching alert condition of a process system ([0101] teaches that the output information generator 14 generates information required to be presented to a user as output information on the basis of the classification model).
FUKUSHIMA fails to expressly teach (v) using the server to generate and issue the contextual alert message to a user containing the contextual information about the approaching alert condition of the process system.
REITER teaches a method and apparatus for alert validation using at least in-part, a server to generate and issue the contextual alert message to a user containing the contextual information about the approaching alert condition of the process system ([0013] teaches that the system provides an alert validation text including a level of validation of an alert, which may include a situational analysis text, and which may provide not only an indication of the validity of an alert condition, but additional text that provides an explanation and the context in which the validity decision was made; [0055] teaches generating additional messages including data relating to contextual information, such as, the key events, significant events, historical data, said context information including data that would enable a decision maker to make a decision or understand a current situation, information to be used by a mental model (e.g. satisfy a user's internal list of items to check when validating an alert), information that enables a user to override a decision and/or the like; also see steps 414-418 in fig. 4).
Before the effective filing date of the invention, it would have been obvious to modify the teachings of Fukushima per the teachings of Reiter such that the system of Fukushima also generate and issue the contextual alert message to a user containing the contextual information about the approaching alert condition of the process system, for the purpose of providing the user with an adequate awareness of the current condition or situation, so that they may determine whether the alert validation decision was the correct decision, and also to aid in decision making with regard to addressing the state which caused the alert.
Regarding claims 2 and 12,
Fukushima teaches that the contextual alert information comprises the approaching alert condition and contextual information about the approaching alert condition selected from the group consisting of: why/when/how the alert condition was/will be reached; the root cause/timing of the alert condition; whether the process system is currently in/out of the alert condition; the current variable data of sensor measurement; the trajectory of the variable data; and data or information received or interpreted from other sensors ([0050] teaches using sensor data acquired from each of “n” sensors and state data indicating a state of a monitoring target - the state data includes a classification label of the state of the monitoring target. Specifically, a label indicating a normal class is included when the state of the monitoring target is normal, and a label indicating an anomalous class is included when the state is anomalous), or wherein the contextual alert information comprises a root cause message based on classification of primary root causes of process systems alerts and current process conditions which then are subsequently combined together.
Regarding claims 3 and 9,
Fukushima teaches that variable data is received and used by the server from a second/plurality of sensor ([0033] teaches using sensor data acquired from any “n” sensors (n is at least 2 or larger)), in developing the alert classification model and in determining the contextual information about the approaching alert condition ([0130]).
Regarding claims 6 and 15,
Fukushima teaches that the process system is located in a facility selected from the group consisting of: a laboratory, medical facility, and a manufacturing facility ([0047] teaches using sensor data obtained by in-hospital rehabilitation as learning data).
Regarding claims 7 and 16,
Fukushima teaches that the sensor associated with the process system is an environmental variable sensor positioned to determine variable environmental data about or within the process system ([0036] teaches that the sensors comprise: acceleration sensor, a geomagnetic sensor, an image sensor, a humidity sensor, a temperature sensor and a piezoelectric element.)
Regarding claim 10,
Reiter teaches transmitting a user response and/or input to the server regarding the variable data and/or accuracy of the contextual alert message generated in step (v), and using the user responses and/or input by the server in the development of the alert classification model ([0055] teaches generating additional messages including data relating to contextual information, such as, the key events, significant events, historical data, said context information including data that would enable a decision maker to make a decision or understand a current situation, information to be used by a mental model (e.g. satisfy a user's internal list of items to check when validating an alert) – thus corresponding to “using the user responses”, information that enables a user to override a decision and/or the like).
Regarding claim 11,
Fukushima teaches that the user response or input comprises data/alert rules, data labels, and/or data/alert classification ([0059] The learning data table and the classification label data may be inputted from the input/output device 2).
Regarding claims 5 and 14,
Fukushima teaches that the contextual alert message contains prediction information regarding the approaching alert condition of the process system and/or that the process system is on a trajectory to achieve and/or the approaching alert condition ([0043] teaches basing performance of alert detection on sensor data acquired when a selected condition is satisfied, for a classification model; [0053] teaches that a learning data ID is associated with each piece of learning data wherein a learning data ID corresponds to information identifying a condition under which corresponding learning data has been detected; [0129] further discusses that the condition data is used in making a probability determination. These disclosures of reliance upon condition information in determining a probability of anomaly, are interpreted as corresponding to at least the determination that the process system is approaching an alert condition).
Regarding claims 17 and 18,
Fukushima teaches an apparatus for determining and generating and/or issuing a predictive contextual alert message to a user regarding an approaching alert condition of a process system, the apparatus comprising programmed circuitry comprising instructions for performing the steps of claim 8 (see the rejection of claims 1 and 8 above. Furthermore, [0129] teaches a processor 101 calculating a probability (i.e., predictive) of the monitoring target being normal or anomalous; [0130] teaches that the performance of anomaly detection is initiated when selected conditions are satisfied- the presence of said satisfied conditions is interpreted as corresponding to “approaching the alert condition”).
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over FUKISHIMA (US 2018/0189242) in view of REITER (US 2017/0365137) as applied to claims 18 and 19, and further in view of LOWENSTEIN (US 2008/0184719).
Regarding claims 19 and 20,
Fukishima, modified by Reiter, teaches the apparatus of claims 17 and 18. Reiter further teaches in [0014] that the system may be utilized for machine monitoring, but the combination of references fails to expressly teach that the process system comprises a cold storage unit (CSU) wherein the sensor is associated with the CSU.
LOWENSTEIN teaches a process system comprises a cold storage unit (CSU) wherein the sensor is associated with the CSU ([0063] teaches a cold storage device provided with a sensor).
Before the effective filing date of the invention, it would have been obvious to further modify the invention of Fukishima such that the issuance of contextual alerts is utilized in combination with a cold storage unit, since Lowenstein teaches that there is a recognized need in CSU devices for determining whether a temperature fault has occurred, and furthermore, if a temperature fault has occurred, then the system will display, print or announce an appropriate error message (step 620).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689