DETAILED ACTION
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-11, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2013/0204417 (Matsumaru) in view of
U.S. Patent Application Publication No. 2019/0034309 (Nayak).
Claim 1:
The cited prior art describes a method of operating an electronic device, the method comprising: (Matsumaru: “The present invention relates to a power consumption output device for an apparatus.” Paragraph 0001; “A sample workflow of how the power consumption output device 100 as one embodiment of the present invention outputs average electric energy consumption per product when a plurality of products are manufactured from an elongated material W, which is an example of a single material, will now be described on the basis of FIGS. 1 and 2.” Paragraph 0046)
receiving factory data related to product production of a factory and energy used in the factory; (Matsumaru: see the count of the number of products S205 and the start S202 and end of the measuring electric power S208 as illustrated in figure 2)
determining an energy stability indicator (ESI) indicating an amount of the energy used for the product production based on the factory data; (Matsumaru: see the output electric energy / the number of products S209 as illustrated in figure 2; “On the basis of the power consumption measured by the power detection means 102, the output means 104 calculates the electric energy consumption of the whole apparatus during the processing operation, divides the electric energy consumption by the number of products counted by the count means 103 to obtain the average electric energy consumption per product, and outputs the average electric energy consumption to the display means 105 (S209).” Paragraph 0055)
Matsumaru does not explicitly describe data processing or outputting as described below. However, Nayak teaches the data processing and output as described below.
generating a normalized energy stability indicator distribution (ESID) with respect to a preset time period based on the ESI; (Nayak: see the normalization 804 of energy data 802 over a time period as illustrated in figure 8; “The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system.” Paragraph 0202)
determining a distribution difference by comparing the generated ESID and a reference distribution predetermined for energy consumption of the factory; and (Nayak: “The second dimension may be calculated similarly, using consumption and NLA information for the same timeline the previous year (step 812). The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
outputting one or more observed variables for monitoring an abnormal state of consumption of the energy with respect to a period in which the distribution difference is large. (Nayak: see the plant room dashboard 1700 illustrating energy consumption and deviation from baseline condition as illustrated in figure 17; see the power consumption baseline widget 1706 as illustrated in figure 23; “The widget may display the energy consumption 2301 of the chiller system against a reference energy consumption baseline 2302. This will help in understanding how much the chiller system has consumed with respect to the reference provided. The widget may also display deviation in form percentage 2303 from the baseline condition and the amount energy saved or exceeded 2304 as displayed in the right hand portion of the widget.” Paragraph 0163; “The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
One of ordinary skill in the art would have recognized that applying the known technique of Matsumaru, namely, calculating electricity cost per product manufactured for each cycle of operation for manufacturing a product, with the known techniques of Nayak, namely, fault detection using energy data for a building, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Matsumaru to determine energy usage with the teachings of Nayak to use energy usage to detect faults would have been recognized by those of ordinary skill in the art as resulting in an improved energy usage and fault detection system (i.e., the combination of the references provides for the collection and analysis of data to detect faults based on the teachings of collecting data for product production in Matsumaru and the teachings of analyzing energy data to detect faults in Nayak).
Claim 4:
Matsumaru does not explicitly describe data processing or outputting as described below. However, Nayak teaches the data processing and output as described below.
The cited prior art describes the method of claim 1, wherein the outputting of the one or more observed variables comprises:
determining one or more correlation coefficients for a linear relationship between the one or more observed variables; and (Nayak: “The plant room dashboard 1700 may also correlate energy performance of these equipment sets with their performance index in terms of operational efficiency and other parameters which have a direct correlation to energy. The different parameters in the plant room equipment can be compared, which provides invaluable insights for a building owner or a facility manager.” Paragraph 0151)
outputting the one or more correlation coefficients. (Nayak: see the plant room dashboard 1700 as illustrated in figures 17, 18 and as described in paragraph 0151)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 5:
Matsumaru does not explicitly describe outputting as described below. However, Nayak teaches the output as described below.
The cited prior art describes the method of claim 4, wherein the outputting of the one or more observed variables comprises:
outputting the one or more observed variables by sorting the one or more correlation coefficients in order of high correlation coefficient; or
outputting observed variables having correlation coefficients exceeding a predetermined value, among the one or more correlation coefficients. (Nayak: “FIG. 37 shows the rule editor 3700 being used to define a custom fault rule. Using this editor by selecting fault tab 3701, a customer can define his own fault rule from the available list of process points 3702. FIG. 38 shows the rule editor 3700 being used to define a custom diagnostic rule by selecting the diagnostics tab 3801. Using this editor a customer can define his own diagnostic rule from the available list of process points 3802. By default, customer-defined fault rules and diagnostic rules may be classified as internal rules. FIG. 39 illustrates a user interface 3900 for mapping newly-created diagnostic rules 3901 to existing global fault rules or internal fault rules 3902 using the map button 3903. FIG. 40 illustrates a user interface 4000 which allows a customer to define various conditions and thresholds which may be part of a rule.” Paragraph 0193)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 6:
The cited prior art describes the method of claim 1,
wherein the factory data comprises
information about products produced in the factory, (Matsumaru: see the information about one lot of products being manufactured S306 as illustrated in figure 3)
a quantity of the products produced, (Matsumaru: see the count of the number of products S205 as illustrated in figure 2)
the amount of the energy used, and (Matsumaru: see the start S202 and end of the measuring electric power S208 as illustrated in figure 2)
Matsumaru does not explicitly describe observed variables as described below. However, Nayak teaches the observed variables as described below.
the one or more observed variables. (Nayak: see the plant room dashboard 1700 illustrating energy consumption and deviation from baseline condition as illustrated in figure 17; see the power consumption baseline widget 1706 as illustrated in figure 23; “The widget may display the energy consumption 2301 of the chiller system against a reference energy consumption baseline 2302. This will help in understanding how much the chiller system has consumed with respect to the reference provided. The widget may also display deviation in form percentage 2303 from the baseline condition and the amount energy saved or exceeded 2304 as displayed in the right hand portion of the widget.” Paragraph 0163; “The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 7:
The cited prior art describes the method of claim 1, wherein the determining of the ESI comprises determining the ESI by dividing the amount of the energy used by a quantity of products produced. (Matsumaru: see the output electric energy / the number of products S209 as illustrated in figure 2; “On the basis of the power consumption measured by the power detection means 102, the output means 104 calculates the electric energy consumption of the whole apparatus during the processing operation, divides the electric energy consumption by the number of products counted by the count means 103 to obtain the average electric energy consumption per product, and outputs the average electric energy consumption to the display means 105 (S209).” Paragraph 0055)
Claim 8:
Matsumaru does not explicitly describe data processing as described below. However, Nayak teaches the data processing as described below.
The cited prior art describes the method of claim 1, wherein the generating of the ESID comprises generating a probability density function with respect to the ESI determined for the preset time period. (Nayak: “The widget “Consumption By Commodity” 1303 may breakdown the entire building's consumption by the different types of commodity being used within the building. The widget “Energy Density By Space” 1304 may be derived from an automated calculation of kWh/unit area/day for all the subspaces within the building. The subspaces within the widget may be automatically arranged in a descending fashion by highlighting the spaces which have the highest energy density within the building. The “Consumption By Space” widget 1305 may arrange the subspaces in a descending fashion by highlighting the spaces which register the highest consumption on the top of the list.” Paragraph 0139)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 9:
Matsumaru does not explicitly describe data processing or outputting as described below. However, Nayak teaches the data processing and output as described below.
The cited prior art describes the method of claim 1, wherein the receiving, the determining, the generating, and the outputting are performed iteratively for an inspection period predetermined for monitoring the abnormal state. (Matsumaru: see the production of the next product as illustrated in figures 3, 4, 5) (Nayak: see the plant room dashboard 1700 illustrating energy consumption and deviation from baseline condition over time and up to the current time as illustrated in figure 17; see the power consumption baseline widget 1706 as illustrated in figure 23; “The widget may display the energy consumption 2301 of the chiller system against a reference energy consumption baseline 2302. This will help in understanding how much the chiller system has consumed with respect to the reference provided. The widget may also display deviation in form percentage 2303 from the baseline condition and the amount energy saved or exceeded 2304 as displayed in the right hand portion of the widget.” Paragraph 0163; “The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 10:
Matsumaru does not explicitly describe data processing or outputting as described below. However, Nayak teaches the data processing and output as described below.
The cited prior art describes the method of claim 1, wherein the predetermined reference distribution is an ESID determined to be ideal in a process of iterating the receiving, the determining, the generating, and the outputting. (Matsumaru: see the production of the next product as illustrated in figures 3, 4, 5) (Nayak: see the plant room dashboard 1700 illustrating energy consumption and deviation from baseline condition over time and up to the current time as illustrated in figure 17; see the power consumption baseline widget 1706 as illustrated in figure 23; “The widget may display the energy consumption 2301 of the chiller system against a reference energy consumption baseline 2302. This will help in understanding how much the chiller system has consumed with respect to the reference provided. The widget may also display deviation in form percentage 2303 from the baseline condition and the amount energy saved or exceeded 2304 as displayed in the right hand portion of the widget.” Paragraph 0163; “The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
Matsumaru and Nayak are combinable for the same rationale as set forth above with respect to claim 1.
Claim 11:
Claim 11 is substantially similar to claim 1 and is rejected based on the same reasons and rationale.
11. An electronic device comprising:
a processor configured to:
receive factory data related to product production of a factory and energy used in the factory,
determine an energy stability indicator (ESI) indicating an amount of the energy used for the product production based on the factory data,
generate a normalized energy stability indicator distribution (ESID) with respect to a preset time period based on the ESI,
determine a distribution difference by comparing the generated ESID and a reference distribution predetermined for energy consumption of the factory, and
output one or more observed variables for monitoring an abnormal state of consumption of the energy with respect to a period in which the distribution difference is large.
Claim 14:
Claim 14 is substantially similar to claim 4 and is rejected based on the same reasons and rationale.
14. The electronic device of claim 11, wherein the processor is further configured to:
determine one or more correlation coefficients for a linear relationship between the one or more observed variables, and
output the one or more correlation coefficients.
Claim 15:
Claim 15 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
15. The electronic device of claim 14, wherein the processor is further configured to:
output the one or more observed variables by sorting the one or more correlation coefficients in order of high correlation coefficient, or
output observed variables having correlation coefficients exceeding a predetermined value, among the one or more correlation coefficients.
Claim 16:
Claim 16 is substantially similar to claim 6 and is rejected based on the same reasons and rationale.
16. The electronic device of claim 11,
wherein the factory data comprises
information about products produced in the factory,
a quantity of the products produced,
the amount of the energy used, and
the one or more observed variables.
Claim 17:
Claim 17 is substantially similar to claim 7 and is rejected based on the same reasons and rationale.
17. The electronic device of claim 11, wherein the processor is further configured to determine the ESI by dividing the amount of the energy used by a quantity of products produced.
Claim 18:
Claim 18 is substantially similar to claim 8 and is rejected based on the same reasons and rationale.
18. The electronic device of claim 11, wherein the processor is further configured to generate a probability density function with respect to the ESI determined for the preset time period.
Claim 19:
Claim 19 is substantially similar to claim 9 and is rejected based on the same reasons and rationale.
19. The electronic device of claim 11, wherein the processor is further configured to iteratively perform the receiving, the determining, the generating, and the outputting for an inspection period predetermined for monitoring the abnormal state.
Claim 20:
Claim 20 is substantially similar to claim 10 and is rejected based on the same reasons and rationale.
20. The electronic device of claim 11, wherein the predetermined reference distribution is an ESID determined to be ideal in a process of iterating the receiving, the determining, the generating, and the outputting.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2013/0204417 (Matsumaru) in view of
U.S. Patent Application Publication No. 2019/0034309 (Nayak) and further in view of
Hamadouche, Anis, Abdelmalek Kouadri, and Abderazak Bensmail. "Kernelized relative entropy for direct fault detection in industrial rotary kilns." International Journal of Adaptive Control and Signal Processing 32.7 (2018): 967-979 (Hamadouche).
Claim 2:
Matsumaru and Nayak do not explicitly describe relative entropy as described below. However, Hamadouche teaches the relative entropy as described below.
The cited prior art describes the method of claim 1, wherein the determining of the distribution difference is based on relative entropy using information entropy with respect to the predetermined reference distribution and the generated ESID. (Hamadouche: see the use of relative entropy for fault detection as described on pages 970, 973) (Nayak: “The second dimension may be calculated similarly, using consumption and NLA information for the same timeline the previous year (step 812). The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
One of ordinary skill in the art would have recognized that applying the known technique of Matsumaru, namely, calculating electricity cost per product manufactured for each cycle of operation for manufacturing a product, with the known techniques of Nayak, namely, fault detection using energy data for a building, and the known techniques of Hamadouche, namely, using relative entropy to detect faults in industrial systems, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Matsumaru to determine energy usage with the teachings of Nayak to use energy usage to detect faults and the teachings of Hamadouche to use relative entropy to detect faults would have been recognized by those of ordinary skill in the art as resulting in an improved energy usage and fault detection system (i.e., the combination of the references provides for the collection and analysis of data to detect faults using various data analysis mechanisms based on the teachings of collecting data for product production in Matsumaru and the teachings of analyzing energy data to detect faults in Nayak and the teachings of analyzing data using relative entropy in Hamadouche).
Claim 12:
Claim 12 is substantially similar to claim 2 and is rejected based on the same reasons and rationale.
12. The electronic device of claim 11, wherein the processor is further configured to determine the distribution difference based on relative entropy using information entropy with respect to the predetermined reference distribution and the generated ESID.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2013/0204417 (Matsumaru) in view of
U.S. Patent Application Publication No. 2019/0034309 (Nayak) and further in view of
Z. Huo, M. Martínez-García, Y. Zhang, R. Yan and L. Shu, "Entropy Measures in Machine Fault Diagnosis: Insights and Applications," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 6, pp. 2607-2620, June 2020 (Huo).
Claim 3:
Matsumaru and Nayak do not explicitly describe cross entropy as described below. However, Huo teaches the cross entropy as described below.
The cited prior art describes the method of claim 1, wherein the determining of the distribution difference comprises determining the distribution difference to be a difference between cross entropy between the generated ESID and the predetermined reference distribution and entropy of the predetermined reference distribution. (Huo: see the use of cross entropy for machine fault analysis as described in sections II.A and III.C) (Nayak: “The second dimension may be calculated similarly, using consumption and NLA information for the same timeline the previous year (step 812). The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).” Paragraph 0127)
One of ordinary skill in the art would have recognized that applying the known technique of Matsumaru, namely, calculating electricity cost per product manufactured for each cycle of operation for manufacturing a product, with the known techniques of Nayak, namely, fault detection using energy data for a building, and the known techniques of Huo, namely, using entropy measures to detect faults in machines, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Matsumaru to determine energy usage with the teachings of Nayak to use energy usage to detect faults and the teachings of Huo to use entropy to detect faults would have been recognized by those of ordinary skill in the art as resulting in an improved energy usage and fault detection system (i.e., the combination of the references provides for the collection and analysis of data to detect faults using various data analysis mechanisms based on the teachings of collecting data for product production in Matsumaru and the teachings of analyzing energy data to detect faults in Nayak and the teachings of analyzing data using cross entropy in Huo).
Claim 13:
Claim 13 is substantially similar to claim 3 and is rejected based on the same reasons and rationale.
13. The electronic device of claim 11, wherein the processor is further configured to determine the distribution difference to be a difference between cross entropy between the generated ESID and the predetermined reference distribution and entropy of the predetermined reference distribution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2014/0142904 describes generating an energy use model for a building.
U.S. Patent Application Publication No. 2021/0240167 describes detecting abnormality of manufacturing facility.
U.S. Patent Application Publication No. 2019/0391573 describes deep learning based fault detection in building automation systems.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117