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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 21, 23-27, and 30-36 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Garden et al., US Patent Application Publication No. 2017/0290345 (hereinafter Garden).
Regarding Claims 21, 23-27, and 30-36, Garden discloses all the claimed limitations, as outlined below.
Claim 21. 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 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).
Claim 23. The food processing line according to claim 21, 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).
Claim 24. The food processing line according to claim 21, 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).
Claim 25. The food processing line according to claim 21, 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).
Claim 26. The food processing line according to claim 25, 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).
Claim 27. The food processing line according to claim 26, 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).
Claim 30. 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 (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).
Claim 31. The method according to 30, 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).
Claim 32. The method according to claim 20, 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).
Claim 33. The method according to claim 30, 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).
Claim 34. The method according to claim 30, further comprising 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).
Claim 35. The method according to 34, 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).
Claim 36. The method according to claim 30, subsequent to step G, further 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).
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.
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 Ishikura et al., US Patent No. 5,721,001 (hereinafter Ishikura).
Regarding claim 22, Garden discloses all the limitations of the base claims as outlined above.
Garden 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 Garden, 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 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, Garden discloses all the limitations of the base claims as outlined above.
Garden 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 Garden, 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).
Claims 29 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 29 and 39-40, Luo discloses all the limitations of the base claims as outlined above.
Garden fails to clearly specify 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; and wherein the labelling algorithm comprises a failure mode & effect analysis (FMEA) labelling algorithm or a statistical data correlation analysis
However, Luo 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; and wherein the labelling algorithm comprises a failure mode & effect analysis (FMEA) labelling algorithm or a statistical data correlation analysis (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).
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 Thiele, US Patent No. 8,200,346 (hereinafter Thiele).
Regarding claim 37, Garden discloses all the limitations of the base claims as outlined above.
Garden 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 Garden, 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:
Gunasekaran, Sundaram. "Computer vision technology for food quality assurance." Trends in Food Science & Technology 7.8 (1996): 245-256.
Pereira, Ana C., Marco S. Reis, and Pedro M. Saraiva. "Quality control of food products using image analysis and multivariate statistical tools." Industrial & Engineering Chemistry Research 48.2 (2009): 988-998.
Peng, Yankun, and Sagar Dhakal. "Optical methods and techniques for meat quality inspection." Transactions of the ASABE 58.5 (2015): 1371-1386.
Minz, P. S., C. Singh, and I. K. Sawhney. "Machine vision technology in food processing industry: Principles and applications—A review." Engineering interventions in agricultural processing (2017): 3-31.
Teimouri, Nima, et al. "On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach." Biosystems engineering 167 (2018): 8-20.
Meng, Zhaozong, Zhipeng Wu, and John Gray. "Microwave sensor technologies for food evaluation and analysis: Methods, challenges and solutions." Transactions of the Institute of Measurement and Control 40.12 (2018): 3433-3448.
Steenwinckel, Bram. "Adaptive anomaly detection and root cause analysis by fusing semantics and machine learning." European Semantic Web Conference. Cham: Springer International Publishing, 2018.
Massaro, Alessandro, and Angelo Galiano. "Re-engineering process in a food factory: an overview of technologies and approaches for the design of pasta production processes." Production & Manufacturing Research 8.1 (2020): 80-100.
Benatia, Mohamed Amin, Anne Louis, and David Baudry. "Alarm correlation to improve industrial fault management." IFAC-PapersOnLine 53.2 (2020): 10485-10492.
Conclusion
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119