DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04-22-2026 has been entered.
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 14-22 are currently pending. Claims 21 and 22 are newly added.
Response to Arguments
Applicant’s arguments with respect to claim(s) rejected in the official action dated 10/28/2025 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. The present action sets forth a new rejection based upon a different combination of references. Accordingly, Applicant’s argument directed to the particular combination of teachings relied upon in the previous rejection are moot and shall not be addressed individually.
The Examiner notes that the Applicant characterizes Fukushima’s disclosure as being limited to the design phase. While Fukushima does teach sensor selection and constructing a classification model for the purpose of supporting a designer, Fukushima also teaches the subsequent real-time use of the selected sensors and generated classification model during actual operation. Specifically [0129]-[0130] teach acquiring sensor data from sensors attached to the monitoring target, using the generated classification model to make a normal/anomalous judgement based upon the acquired sensor data, and upon determining an anomalous condition, transmitting a message to an administrator and/or sounding an alarm ([0129]). Paragraph [0130] teaches that this processing occurs in its disclosure of, “At the time of actual operation of the classification model, data is collected from the selected sensors when the selected conditions are satisfied.” Thus, Fukushima’s disclosure is not limited to selecting sensors during a design phase, as argued by the Applicant. Rather, Fukushima’s disclosure extends to operational monitoring and anomaly determination using the resulting classification model.
Applicant’s arguments concerning Lowenstein for claims 19 and 20 are not persuasive.
The Applicant submits, “Lowenstein's temperature monitoring involves no classification model, no contextual root cause analysis, no "approaching" detection, and no contextual alert messages of any kind.” The applicant cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The applicant further submits that the examiner's conclusion of obviousness is based upon improper hindsight reasoning. It must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The Applicant’s characterization of Lowenstein as merely an unrelated pharmaceutical-container tracking system, is not persuasive. Regardless of Lowenstein’s broader purpose, the relied upon disclosure is also drawn to monitoring a temperature condition associated with a cold storage unit and determining whether a temperature fault has occurred. Thus Lowenstein provides evidence that monitoring sensor data to identify an undesirable condition in a cold storage environment was a known application of sensor-based condition monitoring. Reliance upon Lowenstein is thus maintained.
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, 14-18, 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over TASHMAN (US 2020/0265331) in view of FUKUSHIMA (US 2018/0189242) in view of REITER (US 2017/0365137).
Regarding claims 1 and 8,
TASHMAN 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 ([0021] teaches that each asset 106 may include one or more sensors 107 that may be strategically arranged around the asset 106 to measure various parameters of the asset 106; [0038] teaches receiving historical sensor data 306 associated with assets in one or more factories 105) and second set of variable data from a sensor associated with a process system ([0065] teaches receiving new sample data streams from factory equipment);
(ii) transmitting the first set of variable data from the sensor to a server ([0022] teaches that each sensor 107 may be configured to periodically sample a respective parameter of the asset and to communicate the information as sample data directly or indirectly to the FDS 102) and using the server to develop an alert classification model using the first set of variable data (see steps 200 and 205 in fig. 2 ), wherein the alert classification model contains contextual information about an alert condition of the process system ([0034] teaches that a root cause analysis engine 134 may be configured to identify probable root-cause of a failure event and its relationship to the data observed);
(iii) transmitting the second set of variable data from the sensor to the server to determine if the process system is approaching the alert condition ([0066] teaches failure time prediction wherein as new sample data is streamed in, an estimate of the asset “health” state over a certain future horizon is calculated with the health inference circuitry 130 as well as the most probable time at which the piece of factory equipment will enter the “failure” state (terminal state) as calculated by the root cause analysis circuitry 134); and
(iv) using the server to compare the second set of variable data to the alert classification model ([0064] teaches that observations may be handled in the ML model 316 by conditioning the ML model 316 on the possible states the particular piece of factory equipment can be in at each point in time. For example, the ML model 316 may be conditioned using end of life predictions to provide all non-terminal (non-end of life) observation probability states of the asset) to determine contextual information about the approaching alert condition of the process system ([0067] teaches in FIGS. 2 and 3, at operation 210, the root cause analysis circuitry 134 may include a root cause model in the AI subsystem 115 trained to infer the root cause of failure in a piece of factory equipment. This is further illustrated in section 315 of FIG. 3 by the root cause analysis circuitry 134).
Tashman fails to expressly teach comparing the second set of variable data to a threshold value to determine if the process system is approaching the alert condition.
FUKUSHIMA teaches in [0129] a post design, real-time operational process comprising evaluation of the condition of a monitored system based upon sensor data, wherein sensor data acquired from sensors attached to a monitoring target, is used to calculate a probability concerning the condition of the monitoring target. The calculated probability is compared with a predetermined threshold to determine the state of the monitored target.
Before the effective filing date of the invention, it would have been obvious to modify the Tashman system’s failure-prediction process to further compare a value determined by the acquired operational sensor(s) to a predetermined threshold as taught by Fukushima, for the purpose of providing a defined decision criteria by which the monitored asset condition may be evaluated when determining whether the equipment is progressing toward failure.
Tashman teaches in [0096] that the computer system 1000 may include a touchscreen, configured to allow a user to interact with components of system 1000. The combined disclosures of Tashman and 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 system wherein if the process system is determined to be approaching an alert condition ([0016] teaches a system wherein an alert condition may be received from an alert source), then:
(iv) using the server to compare the second set of variable data to the alert classification model ([0016] teaches that in response to an identified alert condition, sensor/process data associated with the alert condition is subject to additional analysis to determine information concerning the alert condition; [0053] teaches that analysis can use historical data, domain models, rules, relationships between channels and machine learning constructs to characterize the detected alert relationship between them, at least the domain models/machine-learning model corresponding to utilizing a classification model) to determine contextual information about the approaching alert condition of the process system ([0055] teaches that the system generates messages containing key events, significant events, historical data and information enabling a decision maker to understand the current situation i.e., contextual information ); and
(v) generating, and issuing information for presentation to a user regarding the approaching alert condition of a process system ([0053]-[0055] that following alert detection performing a further analysis of the data associated with the alert and generates alert validation text including contextual information concerning the alert condition).
Before the effective filing date of the invention, it would have been obvious to further modify the anomaly detection and notification system of Tashman so as to include the messaging taught by Reiter, for the purpose of providing Tashman’s user with additional information concerning the circumstances associated with the approaching alert condition, thus improving the user’s understanding of the detected condition and aiding in the ability to determine an appropriate cause of action.
Regarding claims 2 and 12,
Tashman 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 (see [0034]; [0061] teaches prediction of a failure actually occurs at time 250, as illustrated by “X” in the sensor values 802 may be predicted with the root cause analysis circuitry 134); 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, 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 ([0130]) and Tashman teaches in determining the contextual information about the approaching alert condition.
Regarding claims 5 and 14,
Tashman 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 ([0062] teaches that forward probability calculations may be used to facilitate online incorporation of historical information for failure time distribution prediction; [0064] teaches that the ML model 316 may be conditioned using end of life predictions to provide all non-terminal (non-end of life) observation probability states of the asset).
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; Tashman teaches in [0019] and FIG. 1, an exemplary environment 100 that includes various systems/devices that facilitate predicting equipment/asset failure events and optimizing manufacturing operations.)
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 17 and 18,
Tashman 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 analysis of claims 1 and 8, above; furthermore, [0062] teaches that forward probability calculations may be used to facilitate online incorporation of historical information for failure time distribution prediction; [0064] teaches that the ML model 316 may be conditioned using end of life predictions to provide all non-terminal (non-end of life) observation probability states of the asset).
Regarding claims 21 and 22,
Tashman teaches that determining comprises analyzing a trajectory of the variable data during a non-alert state to predict that the alert condition will occur within a future time period ([0065]-[0066] teaches that as new sample data is streamed in, an estimate of the asset “health” state over a certain future horizon is calculated with the health inference circuitry 130 as well as the most probable time at which the piece of factory equipment will enter the “failure” state (terminal state) as calculated by the root cause analysis circuitry 134.)
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over TASHMAN (US 2020/0265331) in view of FUKUSHIMA (US 2018/0189242) and 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,
Tashman (modified), teaches the apparatus of claims 17 and 18. Tashman further identifies temperature as one of the parameters monitored by its failure-prediction system ([0003], [0004]) for the purpose of monitoring equipment condition and predicting failure (also see [0021], [0022]). 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 that temperature monitoring is applicable to a cold storage unit (CSU) and that temperature data may be used to determine whether a temperature fault has occurred in the cold storage unit ([0009] teaches utilizing sensors to indicate that there has been a power failure or an adverse temperature change; [0063] teaches a cold storage device provided with a sensor; 620, fig. 6).
Before the effective filing date of the invention, it would have been obvious to modify Tashman’s monitored process system to comprise a cold storage unit having an associated temperature sensor for the purpose of monitoring the operating temperature of the cold storage unit and determining whether a temperature fault has occurred or shall occur. Such a modification would result in the Tashman system, if a temperature fault has occurred, displaying, printing or announcing an appropriate error message.
Conclusion
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
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