DETAILED ACTION
This Office Action is in response for Continuation-in-Part Application # 18/176,721 filed on March 01, 2023 in which claims 1-20 are presented for examination.
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 .
Status of claims
Claims 1-20 are pending, of which claims 1-20 are rejected under 35 U.S.C. 103.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dey et al. US 2016/0146709 A1 (hereinafter ‘Dey’) in view of Dominick Tamborra US 2013/0227971 A1 (hereinafter ‘Tamborra’) (IDS Dated 03/01/2023).
As per claim 1, Dey disclose, A method for determining a monitored exposure threshold for a monitored good (Dey: paragraph 0005: disclose target variable cna be indicative of a machine failure, where examiner equates target variable value as threshold and also examiner equates good to a machine. Examiner would also discuss the limitation of monitoring exposure of a good), comprising:
determining, based on an application of first one or more datasets to an exposure prediction neural network (NN) (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure), an average early warning time (AEWT) (Dey: paragraph 0043: disclose the lead time is considered as early warning time, which examiner equates to average early warning time); wherein the AEWT is an average amount of time prior to non-predictive alert times (NPATs) (Dey: paragraph 0041 and paragraph 0042: disclose the alert include a time stamp and the observation time. Examiner argues that average amount of time can be calculated with the teaching of the prior art to train machine models) for dataset exposure events of one or more exposure event datasets of the one or more datasets that the dataset exposure events can be predicted (Dey: paragraph 0050 and Fig. 1 and Fig. 5: disclose the principle of backward windowing for use in failure prediction for a machine. Referring to FIG. 1, the principle of backward windowing is to model machine failures so that sensor data measured before a lead time 502 for a machine is used by the model learning module 156 but sensor data measured during the lead time 502 is not used by the model learning module 156. As such, a machine failure is modeled by the model learning module 156 so that an observation time 504 corresponds with the start of the lead time 502. The lead time 502 then becomes the time interval before the occurrence of the machine failure at time), and wherein the NN identifies the one or more exposure event datasets from among the one or more datasets with a given accuracy (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure);
determining, across the one or more exposure event datasets, an average exposure amount between the NPATs for the one or more exposure event datasets and corresponding times NPAT plus AEWT for the one or more exposure event datasets (Dey: paragraph 0050 and Fig. 1 and Fig. 5: disclose the principle of backward windowing for use in failure prediction for a machine. Referring to FIG. 1, the principle of backward windowing is to model machine failures so that sensor data measured before a lead time 502 for a machine is used by the model learning module 156 but sensor data measured during the lead time 502 is not used by the model learning module 156. As such, a machine failure is modeled by the model learning module 156 so that an observation time 504 corresponds with the start of the lead time 502. The lead time 502 then becomes the time interval before the occurrence of the machine failure at time); and
wherein an exposure event for the monitored good is to be predicted prior to reaching the monitored exposure threshold using the exposure prediction NN (Dey: paragraph 0045: disclose prediction problem defined by Equation 4 can estimate the probability of the occurrence in the future of a failure, ƒ, in a time window starting at a lead time for a machine. The probability can be calculated based on sensor measurements and alerts provided by the machine in a history window size number of units before the predicted occurrence of the failure).
It is noted, however, Dey did not specifically detail the aspects of
setting the monitored exposure threshold for the monitored good based on the average exposure amount as recited in claim 1.
On the other hand, Tamborra achieved the aforementioned limitations by providing mechanisms of
setting the monitored exposure threshold for the monitored good based on the average exposure amount (Tamborra: paragraph 0016 and Fig. 1: disclose poll sensor 12 for its temperature data, or time temperature data at predetermined intervals, such as every ten minutes, every half hour or some appropriate interval for monitoring changes in temperature within the refrigerator environment. Microcontroller 14 also determines whether the time temperature value monitored by sensor 12 has exceeded a predetermined value indicating that the temperature has been in the "temperature danger zone" for a sufficient time to indicate potential or actual food spoilage; i.e., in time to provide a preemptive ‘predictive’ warning, or to indicate a temperature violation).
Dey and Tamborra are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Machine Learning Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Dey and Tamborra because they are both directed to machine learning systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Tamborra with the method described by Dey in order to solve the problem posed.
The motivation for doing so would have been to determine the current status of the time temperature clock, thus increasing the exposure of the refrigerator's contents to danger zone inducing conditions, and if one is away from the refrigerator, the alarm cannot be monitored (Tamborra: paragraph 0007).
Therefore, it would have been obvious to combine Tamborra with Dey to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Dey did not specifically detail the aspects of
wherein setting the monitored exposure threshold for the monitored good based on the average exposure amount comprises raising a user-provided value by the average exposure amount as recited in claim 2.
On the other hand, Tamborra achieved the aforementioned limitations by providing mechanisms of
wherein setting the monitored exposure threshold for the monitored good based on the average exposure amount comprises raising a user-provided value by the average exposure amount (Tamborra: paragraph 0009: disclose monitor also determines a time during which the temperature is above a predetermined value. The monitor provides an output to a transceiver and sends one of a temperature signal at predetermined time intervals, and an alarm signal when an accumulated time value exceeds a predetermined value. Examiner equates the predetermined value to user-provided value).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Dey disclose, predicting the exposure event for the monitored good based on an application of the exposure prediction NN to a monitored dataset corresponding to the monitored good (Dey: paragraph 0024: disclose machine learning “Neural Network’ algorithms and for the training of a failure prediction model); and sending an alert to a user in response to predicting the exposure event (Dey: paragraph 0060: disclose used to model the sensor data and predict machine failures).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Dey disclose, training the exposure prediction NN using a second one or more datasets (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Dey disclose, wherein the exposure prediction NN is configured to predict the exposure event for the monitored good by analyzing an exposure trend for the monitored good in the monitored dataset (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1 and 5 above.
It is noted, however, Dey did not specifically detail the aspects of
wherein the exposure prediction NN is further configured to predict the exposure event for the monitored good by analyzing one or more of a temperature reading trend in the monitored dataset and a temperature trend for the monitored good in the monitored dataset as recited in claim 6.
On the other hand, Tamborra achieved the aforementioned limitations by providing mechanisms of
wherein the exposure prediction NN is further configured to predict the exposure event for the monitored good by analyzing one or more of a temperature reading trend in the monitored dataset and a temperature trend for the monitored good in the monitored dataset (Tamborra: paragraph 0016 and Fig. 1: disclose poll sensor 12 for its temperature data, or time temperature data at predetermined intervals, such as every ten minutes, every half hour or some appropriate interval for monitoring changes in temperature within the refrigerator environment. Microcontroller 14 also determines whether the time temperature value monitored by sensor 12 has exceeded a predetermined value indicating that the temperature has been in the "temperature danger zone" for a sufficient time to indicate potential or actual food spoilage; i.e., in time to provide a preemptive ‘predictive’ warning, or to indicate a temperature violation).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Dey did not specifically detail the aspects of
wherein the given accuracy is specified by a user as recited in claim 7.
On the other hand, Tamborra achieved the aforementioned limitations by providing mechanisms of
wherein the given accuracy is specified by a user (Tamborra: paragraph 0026: report of a danger zone condition, such as temperature above a determined value such as 41.degree).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Dey disclose, wherein the NPAT plus AEWT comprises an equivalent average prediction exposure threshold (EAPET) (Dey: paragraph 0045: disclose prediction problem defined by Equation 4 can estimate the probability of the occurrence in the future of a failure, ƒ, in a time window starting at a lead time for a machine. The probability can be calculated based on sensor measurements and alerts provided by the machine in a history window size number of units before the predicted occurrence of the failure).
As per claim 9, Dey disclose, A system (Dey: paragraph 0004: disclose computing system ) for determining a monitored exposure threshold for a monitored good (Dey: paragraph 0005: disclose target variable cna be indicative of a machine failure, where examiner equates target variable value as threshold and also examiner equates good to a machine. Examiner would also discuss the limitation of monitoring exposure of a good), comprising:
a memory (Dey: paragraph 0029: disclose one or more memory devices and server memory) to store an exposure prediction neural network (NN) (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure); and
one or more processors in communication with the memory, the one or more processors configured to (Dey: paragraph 0029: disclose one or more processors): remaining limitations in this claim 9 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 10, limitations of this claim are similar to claim 2. Therefore, examiner rejects claim 10 limitations under the same rationale as claim 2.
As per claim 11, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 11 limitations under the same rationale as claim 3.
As per claim 12, limitations of this claim are similar to claim 4. Therefore, examiner rejects claim 12 limitations under the same rationale as claim 4.
As per claim 13, limitations of this claim are similar to claim 5. Therefore, examiner rejects claim 13 limitations under the same rationale as claim 5.
As per claim 14, limitations of this claim are similar to claim 6. Therefore, examiner rejects claim 14 limitations under the same rationale as claim 6.
As per claim 15, limitations of this claim are similar to claim 7. Therefore, examiner rejects claim 15 limitations under the same rationale as claim 7.
Claims 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dominick Tamborra US 2013/0227971 A1 (hereinafter ‘Tamborra’) (IDS Dated 03/01/2023) in view of Dey et al. US 2016/0146709 A1 (hereinafter ‘Dey’).
As per claim 16, Tamborra disclose, A method for environmental monitoring and performing environmental-related calculations (Tamborra: paragraph 0016: disclose monitoring changes in temperature within the refrigerator environment), comprising:
receiving a plurality of temperature readings taken at a temperature-controlled area (Tamborra: paragraph 0009: disclose the monitor provides an output to a transceiver and sends one of a temperature signal at predetermined time intervals, and an alarm signal when an accumulated time value exceeds a predetermined value, which is equates to receiving plurality of temperature readings);
applying a timestamp to each one of the plurality of temperature readings (Tamborra: paragraph 0009: disclose storing temperature data as a time stamped entry);
determining, based on the plurality of temperature readings, a temperature of a monitored good stored within the temperature-controlled area at each timestamp (Tamborra: paragraph 0009: disclose storing temperature data as a time stamped entry, the remote monitor providing an output to a cellular phone transceiver for transmitting the data received from the monitoring unit to a remote device);
calculating, using the temperature of the monitored good at each timestamp, an exposure of the monitored good at each timestamp (Tamborra: paragraph 0021: disclose calculate a lapsed time as well as actual time in order to determine intervals and time stamps for monitored events).
It is noted, however, Tamborra did not specifically detail the aspects of
predicting, based on the exposure of the monitored good at each timestamp, that the exposure of the monitored good will exceed a monitored exposure threshold; and
sending an alert to a first user in response to the predicting that the exposure of the monitored good will exceed the monitored exposure threshold as recited in claim 16.
On the other hand, Dey achieved the aforementioned limitations by providing mechanisms of
predicting, based on the exposure of the monitored good at each timestamp, that the exposure of the monitored good will exceed a monitored exposure threshold (Dey: paragraph 0045: disclose prediction problem defined by Equation 4 can estimate the probability of the occurrence in the future of a failure, ƒ, in a time window starting at a lead time for a machine. The probability can be calculated based on sensor measurements and alerts provided by the machine in a history window size number of units before the predicted occurrence of the failure); and
sending an alert to a first user in response to the predicting that the exposure of the monitored good will exceed the monitored exposure threshold (Dey: paragraph 0060: disclose used to model the sensor data and predict machine failures, which equated to exceed the threshold).
Dey and Tamborra are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Machine Learning Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Dey and Tamborra because they are both directed to machine learning systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Tamborra with the method described by Dey in order to solve the problem posed.
The motivation for doing so would have been to predict some future problems that may occur with the machinery and to prevent the problems before they occur by performing preventative maintenance (Dey: paragraph 0003).
Therefore, it would have been obvious to combine Tamborra with Dey to obtain the invention as specified in instant claim 16.
As per claim 17, most of the limitations of this claim have been noted in the rejection of claim 16 above. In addition, Tamborra disclose, wherein the predicting that the exposure of the monitored good will exceed the monitored exposure threshold is further based on one or more of the plurality of temperature readings and the temperature of the good at each timestamp (Tamborra: paragraph 0016 and Fig. 1: disclose poll sensor 12 for its temperature data, or time temperature data at predetermined intervals, such as every ten minutes, every half hour or some appropriate interval for monitoring changes in temperature within the refrigerator environment. Microcontroller 14 also determines whether the time temperature value monitored by sensor 12 has exceeded a predetermined value indicating that the temperature has been in the "temperature danger zone" for a sufficient time to indicate potential or actual food spoilage; i.e., in time to provide a preemptive ‘predictive’ warning, or to indicate a temperature violation).
As per claim 18, most of the limitations of this claim have been noted in the rejection of claim 16 above.
It is noted, however, Tamborra did not specifically detail the aspects of
training a neural network (NN) using a plurality of datasets; the training comprising analyzing, at the NN, patterns found in the datasets; said training to be used by the NN for the predicting that the exposure of the monitored good will exceed the monitored exposure threshold as recited in claim 18.
On the other hand, Dey achieved the aforementioned limitations by providing mechanisms of
training a neural network (NN) using a plurality of datasets (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure); the training comprising analyzing, at the NN, patterns found in the datasets (Dey: paragraph 0059: disclose model learning module 156 use the indications for the target variables 408a-c to learn machine state patterns based on the values for the input variables); said training to be used by the NN for the predicting that the exposure of the monitored good will exceed the monitored exposure threshold (Dey: paragraph 0047 and Fig. 1: disclose define ‘determine’ a data set ‘datasets’ for use as training data and as input to a machine learning model ‘Neural Network’ (e.g., the model learning model 156) paragraph 0055: disclose model learning for use in predicting a machine failure).
As per claim 19, most of the limitations of this claim have been noted in the rejection of claim 16 above.
It is noted, however, Tamborra did not specifically detail the aspects of
determining an average amount of exposure between times of exposure events of a plurality of exposure event datasets and corresponding times of the exposure events plus an average early warning time (AEWT) associated with the plurality of exposure event datasets; and setting the monitored exposure threshold using the average amount of exposure as recited in claim 19.
On the other hand, Dey achieved the aforementioned limitations by providing mechanisms of
determining an average amount of exposure between times of exposure events of a plurality of exposure event datasets and corresponding times of the exposure events plus an average early warning time (AEWT) associated with the plurality of exposure event datasets; and setting the monitored exposure threshold using the average amount of exposure (Dey: paragraph 0043: disclose the lead time is considered as early warning time, which examiner equates to average early warning time).
As per claim 20, most of the limitations of this claim have been noted in the rejection of claims 16 and 19 above. In addition, Tamborra disclose, wherein the monitored exposure threshold is set using the average exposure amount by raising a user-provided value the average exposure amount (Tamborra: paragraph 0009: disclose monitor also determines a time during which the temperature is above a predetermined value. The monitor provides an output to a transceiver and sends one of a temperature signal at predetermined time intervals, and an alarm signal when an accumulated time value exceeds a predetermined value. Examiner equates the predetermined value to user-provided value).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2014/0200870 A1 disclose “LARGE-SCALE MULTI-DETECTOR PREDICTIVE MODELING”
US Pat. US 9,638,544 B2 disclose “Sensor terminal and sensor network system”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAVAN MAMILLAPALLI whose telephone number is (571)270-3836. The examiner can normally be reached on M-F. 8am - 4pm, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached on (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159