DETAILED ACTION
Claims 21-40 are pending.
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 .
Response to Arguments
Applicant’s arguments, filed 05/15/2026, have been fully considered but are moot in view of the new ground(s) of rejection.
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 21, 23-27, 29-36, and 39-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garden et al., US Patent Application Publication No. 2017/0290345 (hereinafter Garden) in view of Luo et al., US Patent Application Publication No. 2019/0294484 (hereinafter Luo).
Regarding claims 21, 23-27, 29-36, and 39-40, the combination of Garden and Luo, teaches all the claimed limitations, as outlined below:
Regarding claim 21, Garden teaches a food processing line for processing a food product, comprising: a plurality of processing stations in which the food product is subjected to one or more processing operations; at least one utility supply station providing a processing utility to one or more of the processing stations; at least one food product sensor configured to acquire a food product condition measure (Para 0108-0114- - for example sensing food products);
at least one utility sensor configured to acquire a utility condition measure (Para 0108-0114- - for example monitoring temperature and humidity);
at least one processing sensor configured to acquire a processing station condition measure (0108-0114 - - monitor and control conveyors);
a processing line controller for controlling the food processing line, comprising: a data collection module for collecting sensor information, configured to: receive sensor information from the at least one food product sensor, the at least one utility sensor and the at least one processing sensor; store sensor information on a storage means; communicate stored sensor information via an electronic communication line (Para 0108-0114 - - Using the controller to receive information, determine patterns of movement and supply control signals);
input means for specifying at least one desired food product output characteristic; input means for specifying a nominal operating condition for the utility supply station and for the processing station; an anomaly detection module configured to in operation detect an anomaly from the nominal operating condition based on the collected sensor information; a root cause module configured to in operation determine a root cause of the detected anomaly using a statistical data analysis (Para 0102-0103 - - controlling to correct anomalies/”out of thresholds”);
a corrective measure module configured to in operation determine a corrective measure in response to a detected anomaly based on the determined root cause, and to provide the corrective measure to at least one physical actuator in the food processing line in order to control the food processing line such that the food product is processed in accordance with the desired food product output characteristic (Para 0024-0088, 0108-0114, and Figs 1, 5, and 7).
Garden fails to clearly specify wherein the statistical data analysis comprises supervised learning algorithms based on statistical data correlation analysis and/or Failure Mode & Effect Analysis (FMEA) for calculating the probability for each detected anomaly that the anomaly is caused by a specific root cause.
However, Luo teaches wherein statistical data analysis comprises supervised learning algorithms based on statistical data correlation analysis and/or Failure Mode & Effect Analysis (FMEA) for calculating the probability for each detected anomaly that the anomaly is caused by a specific root cause (Para 0057 and 0060- - using supervised machine learning, using statistical data, and labeled root cause training data).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to food producing and processing.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above on-demand food assembly, as taught by Garden, and incorporating the anomaly detection techniques, as taught by Luo.
One of ordinary skill in the art would have been motivated to do this modification in order to provide accurate identifications of malfunction causes, as suggested by Luo (Para 0005 and 0057).
Regarding claim 23, Garden further teaches wherein at least one of the at least one food product sensor is configured to acquire one of the groups consisting of core temperature, surface temperature, weight, a product color, a product dimension and a product appearance characteristic (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 24, Garden further teaches wherein the at least one processing sensor is configured to acquire one of the groups consisting of a climate characteristic at one of the pluralities of processing stations and a dwell time of the product at one of the plurality of processing stations (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 25, Garden further teaches wherein the processing line controller comprises an electronic actuator controller module for in operation controlling the at least one physical actuator in response to a corrective measure provided to the electronic actuator controller module (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 26, Garden further teaches wherein the processing line controller comprises a predictor module configured for determining in operation an estimated prediction of at least one food product output characteristic based on sensor information from the collection module (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 27, Garden further teaches wherein the estimated prediction of at least one food product output characteristic from the prediction module relates to the at least one desired food product output characteristic (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 29, Luo further teaches utilizing a supervised machine learning algorithm, wherein the detected anomaly is labelled with a root cause label, using the collected sensor information of the data collection module and a failure mode & effect analysis (FMEA) labelling algorithm (Para 0057 and 0060- - using supervised machine learning, using statistical data, and labeled root cause training data).
Regarding claim 30, Garden teaches a method for controlling a food processing line, the processing line comprising: a plurality of physically separate processing stations in which a food product is subjected to one or more processing operations; at least one utility supply station providing a processing utility to one or more processing stations; a plurality of food product sensors configured to observe a food product condition (Para 0108-0114- - for example sensing food products);
at least one utility sensor configured to observe a utility condition (Para 0108-0114- - for example monitoring temperature and humidity);
at least one processing sensor configured to observe a processing station condition (0108-0114 - - monitor and control conveyors);
a processing line controller for controlling the food processing line, comprising a data collection module for collecting sensor information, the method comprising the steps of: A) providing at least one desired food product output characteristic to the processing line controller; B) providing a nominal operating condition for the utility supply station and for the processing station; C) collecting sensor information from the plurality of food product sensors and the at least one utility sensor and the at least one processing sensor into the data collection module (Para 0108-0114 - - Using the controller to receive information, determine patterns of movement and supply control signals);
D) detecting an anomaly from the nominal operating condition by analyzing the sensor information; E) determining a root cause of the anomaly; F) determining a corrective measure to correct for the anomaly based on the determined root cause (Para 0102-0103 - - controlling to correct anomalies detected anomalies/”out of thresholds”);
G) providing the corrective measure to at least one actuator in the food processing line in order to control the food processing line such that the food product is processed in accordance with the desired food product output characteristic (Para 0024-0088, 0108-0114, and Figs 1, 5, and 7).
Garden fails to clearly specify wherein the statistical data analysis comprises supervised learning algorithms based on statistical data correlation analysis and/or Failure Mode & Effect Analysis (FMEA) for calculating the probability for each detected anomaly that the anomaly is caused by a specific root cause.
However, Luo teaches wherein statistical data analysis comprises supervised learning algorithms based on statistical data correlation analysis and/or Failure Mode & Effect Analysis (FMEA) for calculating the probability for each detected anomaly that the anomaly is caused by a specific root cause (Para 0057 and 0060- - using supervised machine learning, using statistical data, and labeled root cause training data).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to food producing and processing.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above on-demand food assembly, as taught by Garden, and incorporating the anomaly detection techniques, as taught by Luo.
One of ordinary skill in the art would have been motivated to do this modification in order to provide accurate identifications of malfunction causes, as suggested by Luo (Para 0005 and 0057).
Regarding claim 31, Garden further teaches wherein steps A and B are provided as an initial value before the food product is subjected to a processing operation in the food processing line (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 32, Garden further teaches wherein steps C and D are performed during the processing of the food product in the food processing line (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 33, Garden further teaches wherein steps E, F and G are executed in case an anomaly is detected in step D (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 34, Garden further teaches determining an estimated prediction of at least one predicted food product output characteristic, using the collected sensor information as input to a prediction algorithm and wherein the at least one predicted food product output characteristic relates to the at least one desired food product output characteristic as provided in step A (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 35, Garden further teaches wherein the prediction algorithm comprises an algorithm from the group of Kalman filter, neural network and machine learning algorithm (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 36, Garden further teaches comprising the step of determining an electronic control signal, in response to the corrective measure of step G and providing the electronic control signal to at least one physical actuator in the food processing line (Para 0024-0088 and Figs 1, 5, and 7).
Regarding claim 39, Luo further teaches utilizing a supervised machine learning algorithm, wherein the detected anomaly is labelled with a root cause label, using the collected sensor information of the data collection module and a labelling algorithm (Para 0057 and 0060- - using supervised machine learning, using statistical data, and labeled root cause training data).
Regarding claim 40, Luo further teaches wherein the labelling algorithm comprises a failure mode & effect analysis (FMEA) labelling algorithm (Para 0057 and 0060- - using supervised machine learning, using statistical data, and labeled root cause training data).
Claim 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garden et al., US Patent Application Publication No. 2017/0290345 (hereinafter Garden) in view of Luo et al., US Patent Application Publication No. 2019/0294484 (hereinafter Luo) in view of Ishikura et al., US Patent No. 5,721,001 (hereinafter Ishikura).
Regarding claim 22, the combination of Garden and Luo teaches all the limitations of the base claims as outlined above.
The combination of Garden and Luo fails to clearly specify wherein the processing utility of the at least one utility supply station is one of the groups consisting of thermal oil, steam and pressurized air.
However, Ishikura teaches wherein the processing utility of the at least one utility supply station is one of the groups consisting of thermal oil, steam and pressurized air (Abstract).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to food producing and processing.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above on-demand food assembly, as taught by the combination of Garden and Luo, and incorporating varied food ingredients and corresponding cooking procedures, as taught by Ishikura.
One of ordinary skill in the art would have been motivated to do this modification in order to provide increase food quality, as suggested by Ishikura (Abstract).
Claims 28 and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garden et al., US Patent Application Publication No. 2017/0290345 (hereinafter Garden) in view of Luo et al., US Patent Application Publication No. 2019/0294484 (hereinafter Luo) in view of Amruthnath, Nagdev, and Tarun Gupta. "A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance." 2018 5th international conference on industrial engineering and applications (ICIEA). IEEE, 2018 (hereinafter Amruthnath).
Regarding claims 28 and 38, the combination of Garden and Luo teaches all the limitations of the base claims as outlined above.
The combination of Garden and Luo fails to clearly specify wherein the anomaly detecting comprises a multivariate statistic process control algorithm and/or an unsupervised machine learning algorithm.
However, Amruthnath teaches wherein the anomaly detecting comprises a multivariate statistic process control algorithm and/or an unsupervised machine learning algorithm (Abstract).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to food producing and processing.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above on-demand food assembly, as taught by the combination of Garden and Luo, and incorporating the anomaly detection techniques, as taught by Amruthnath.
One of ordinary skill in the art would have been motivated to do this modification in order to provide accurate detection, as suggested by Amruthnath (Abstract).
Claim 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garden et al., US Patent Application Publication No. 2017/0290345 (hereinafter Garden) in view of Luo et al., US Patent Application Publication No. 2019/0294484 (hereinafter Luo) in view of Thiele, US Patent No. 8,200,346 (hereinafter Thiele).
Regarding claim 37, the combination of Garden and Luo teaches all the limitations of the base claims as outlined above.
The combination of Garden and Luo fails to clearly specify wherein the electronic control signal is determined using a control algorithm based on at least one of linear PID-controller, model predictive controller, linear quadratic controller, and fuzzy controller.
However, Thiele teaches wherein the electronic control signal is determined using a control algorithm based on at least one of linear PID-controller, model predictive controller, linear quadratic controller, and fuzzy controller (C8 L37-44).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to food producing and processing.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above on-demand food assembly, as taught by the combination of Garden and Luo, and incorporating the local control techniques, as taught by Thiele.
One of ordinary skill in the art would have been motivated to do this modification in order to improve control performance, as suggested by Thiele (Abstract).
Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Steiner, US Patent No. 8,158,176 relates to – meat processing system.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS R ORTIZ RODRIGUEZ whose telephone number is (571)272-3766. The examiner can normally be reached on Mon-Fri 10:00 am- 6:30 pm.
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119
