DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 Information Disclosure Statements, filed 28 September 2023 and 20 October 2023 have been fully considered by the examiner. Signed copies are attached.
Claims 1-20 are pending.
Claims 1-20 are rejected, grounds follow.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8 and 12-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaufman et al., US Pg-Pub 2010/00274377 (hereafter Kaufman’377).
Regarding Claim 1, 12 and 18, Kaufman’377 discloses:
A system (see figs. 1, 6, 7, 10), comprising: one or more automation devices; (see [0028] “the energy manager or processing component 140 is typically a server or computer system such as a batch server for industrial control systems. This can include processing components of a recipe that are subsequently executed by the processing or manager component 140, where the recipe identifies what aspects of a process are employed to produce a given recipe. In one example, an S88 standard provides models that define equipment control, procedure control, and activity.”)
and a computing system (ibid. energy manager or processing component 140 is typically a server or computer) configured to: receive motion data ([0022] “energy monitors [X, Y, and Z] … monitor Z is tied to a conveyor”) associated with a transit of a plurality of products; ([0045] “The various bottling or other discrete processes (e.g., bottle capping) can be monitored for energy usage. This includes monitoring motors that move the lines and conveyors”)
associate a first portion of the motion data to a first product of the plurality of products; ([0038] “the monitored factor data of 510 is tagged to indicate which portion of a production process or product that the sustainability factor is associated with or linked to.”)
determine motion energy consumption data for the first product based on the first portion of the motion data; ([0022] “an energy metric application… link the time stamped data to a batch” [0024] “after tagging, the data is associated with… a bill of material (BOM)” [0031] “the product itself may be associated with a factor as illustrated in FIG. 3, where various products outlined in an example bill of material 300 in a left column 310 are associated with example sustainability factors such as electrical usage 320”)
receive machine data associated with one or more operations performed on the plurality of products at a location; ([0022] “For instance, energy monitor X is tied to a drive on a mixer, monitor Y is tied to an oven”)
associate a second portion of the machine data to the first product; ([0038] “the monitored factor data of 510 is tagged to indicate which portion of a production process or product that the sustainability factor is associated with or linked to.”)
determine machine energy consumption data for the first product based on the second portion of the machine data; ([0022] “an energy metric application… link the time stamped data to a batch” [0024] “after tagging, the data is associated with… a bill of material (BOM)” [0031] “the product itself may be associated with a factor as illustrated in FIG. 3, where various products outlined in an example bill of material 300 in a left column 310 are associated with example sustainability factors such as electrical usage 320”)
determine energy consumption data based on the motion energy consumption data and the machine energy consumption data; ([0033] “[0033] Energy monitoring on the production floor can be tied to an energy tracking software package and correlate production output to the energy consumed. Energy could be metered and the empirical results could be added to the production Bill of Material (BOM). This allows the use of standard production simulation and forecasting tools, as well as, lean six sigma tools to optimize production against additional variables such as energy, rate schedules, and emissions. FIG. 3 shows an example of electricity data at 310 and 320 extracted from the BOM 300.”)
and send one or more control signals (such as automated demand response, see [0040] “ An automated process is usually chosen since it can continuously monitor the situation and react quickly without intervention” and [0043] “Automated Demand Response enables users of the supplied energy to react to the available supply in real time. … modulating the production process also keeps workers active, at a reduced rate, instead of idling them when equipment is shut down.”) to the one or more automation devices based on the energy consumption data. ([0041] “It is now possible to construct a mathematical model that includes cost of raw materials, amortization of capital equipment, floor space, labor, prioritized production requirements, and energy. The output of the model allows control choices to be made that manage output requirements and energy usage while also optimizing the economic return to the company.”)
Regarding Claims 12 and 18, these claims recite substantively the same subject matter as discussed above with respect to claim 1, except embodied as a non-transitory computer readable medium and a method, respectively; mutatis mutandis, these claims are likewise anticipated by Kaufman’377 for the same reasons discussed with respect to claim 1.
Regarding Claims 2, 13, and 20 Kaufman’377 discloses all of the limitations of parent claims 1, 12 and 18, respectively;
Kaufman’377 further discloses:
(claim 2 representative) wherein the computing system is configured to store energy consumption data in a storage component ([0022] “raw data [nb. from the monitors X, Y, Z] can be evaluated or manipulated when stored according to time stamping procedures.” [0039] “The system 600 can include a historian component 620 for archiving process data”, see also Kaufman’377 claim 13) configured to be accessed by one or more additional computing systems within an industrial system comprising the one or more automation devices. (see fig. 7, additional systems such as the production simulation software, the pavilion decision engine, the automated demand response engine, etc. which may be a plurality of computing devices, see [0020] reciting that components may be distributed across multiple controllers; and [0026] reciting that controllers may be distributed across multiple computer hardware communicating over a network.)
Regarding Claims 3 and 14, Kaufman’377 discloses all of the limitations of parent claims 1 and 12, respectively;
Kaufman’377 further discloses:
(claim 3 representative) wherein the motion data is associated with a travel path of the plurality of products to the location. (with reference to fig. 10, [0045] “The various bottling or other discrete processes (e.g., bottle capping) can be monitored for energy usage. This includes monitoring motors that move the lines and conveyors, valves, robots, pick and place equipment”)
Regarding Claims 4 and 15, Kaufman’377 discloses all of the limitations of parent claims 1 and 12 respectively,
Kaufman’377 further discloses:
(claim 4 representative) wherein the first product is associated with an identifier, (see fig. 3, 310 and [0031] Product identifiers such as (for example) “XY123” in a bill of material (BOM))
and wherein the computing system is configured to associate one or more data points of the motion data to the identifier. ([0038] “the monitored factor data of 510 is tagged to indicate which portion of a production process or product that the sustainability factor is associated with or linked to.”)
Regarding Claim 5, Kaufman’377 discloses all of the limitations of parent claim 1,
Kaufman’377 further discloses:
(claim 5 representative) wherein the motion data comprises a distance traveled from an additional location to the location, a weight of the first product, energy consumed during travel, (Motor usage, see [0038], [0022] “monitor Z is tied to a conveyor”, [0045] “monitored for energy usage. This includes monitoring motors that move the lines and conveyors”) a dwell time at the location, a settling time at the location, a start time for transportation of the first product, an end time for the transportation of the first product, an amount of transportation time between the additional location to the location, or any combination thereof. (interpreted as a Markush claim where each element is recited in the alternative, see MPEP 2117).
Regarding Claims 6 and 16, Kaufman’377 discloses all of the limitations of parent claims 1 and 12, respectively;
Kaufman’377 further discloses:
(claim 6 representative) wherein the computing system is configured to update the energy consumption data (data may be recorded in raw form with timestamps, see [0022] “time stamping methodology such as IEEE 1588”) at each of a plurality of locations ([0045] “a plurality of locations and/or components can be monitored across the factory 1000”.) within an industrial system (e.g. bottling facility, see fig. 10) comprising the one or more automation devices. ([0045] “The various bottling or other discrete processes (e.g., bottle capping) can be monitored for energy usage. This includes monitoring motors that move the lines and conveyors, valves, robots, pick and place equipment, assembly equipment, drilling equipment, labeling equipment, and so forth.”)
Regarding Claim 7, Kaufman’377 discloses all of the limitations of parent claim 1,
Kaufman’377 further discloses:
wherein the machine data is received from the one or more automation devices. ([0024] “Automated monitors 110 can receive data from a plurality of sustainable sources 120 that are distributed across an industrial process.” [0045] “The various bottling or other discrete processes (e.g., bottle capping) can be monitored for energy usage. This includes monitoring motors that move the lines and conveyors, valves, robots, pick and place equipment, assembly equipment, drilling equipment, labeling equipment, and so forth.”)
Regarding Claims 8, 17, and 19 Kaufman’377 discloses all of the limitations of parent claims 1, 12, and 18, respectively;
Kaufman’377 further discloses:
(Claim 8 representative) wherein the energy consumption data comprises a sum (see e.g., [0038] aggregated data, nb. which results in a final single total per product, see fig. 3, 320) of the motion energy consumption data (e.g. tagged data from monitor Z, see [0022]) and the machine energy consumption data. (e.g. tagged data from monitors X and Y, see [0022]) (nb. [0038] “At 520, the monitored factor data of 510 is tagged to indicate which portion of a production process or product that the sustainability factor is associated with or linked to. Such tagging can include data labels or memory metadata assignments that indicate which portion of the process (discrete or batch) that the sustainability factor is tied to. At 530, the tagged sustainability data is aggregated. Such aggregation can be performed by a processor or energy manager that collects the data from across the plant or across various facilities via a network connection.”)
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) 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaufman’377 in view of Kaufman et al., US Pg-Pub 2016/0147242 (hereafter Kaufman’242).
Regarding Claim 9, Kaufman’377 teaches all of the limitations of parent claim 1,
Kaufman’377 differs from the claimed invention in that:
Kaufman’377 does not appear to clearly articulate the one or more control signals are configured to control a traffic flow of each of the plurality of products.
However, Kaufman’242 teaches an industrial automation system (see e.g. figs. 1, 2, 4) which uses determined energy consumption information of different areas of the plant (see [0055] “multiple utility meters 80 may be included to monitor the energy usage of a particular area 16 (e.g., area 1 or area 2) or cell 18 (e.g., cells 1-6). For example a first utility meter may measure the utility usage of area 1 and a second utility meter may measure the utility usage of cell 5.”) and uses that information to reduce the energy consumed by a plant by controlling a traffic flow of products ([0057] “For example, the control system 22 may instruct the power device 78 to slow down the conveyer section 54 in order to reduce energy usage. Thus, the control system 22 may determine operational parameters for each component 20, each cell 18, each area 16, and each factory 14….”) including schedules and routing (ibid. [0057] “…More specifically, the operational parameters may include energy usage/consumption, product mixes, product recipes, operating setpoints (e.g., motor speeds, tensions, oven temperature, and conveyor speeds), production run rates, production schedules, product routing, and control algorithms.)
Kaufman’242 is analogous art because it is from the same field of endeavor as the claimed invention and other references of industrial automation systems.
One of ordinary skill in the art before the effective filing date of the application could have modified the teachings of Kaufman’377 to incorporate modifying the operational parameters of the production facility regarding scheduling, routing and conveyor speed of products as suggested by Kaufman’242 into the automated demand response of Kaufman’377.
One of ordinary skill in the art before the effective filing date of the application could have been motivated to make this modification in order to reduce the total energy required to operate the system, as suggested by Kaufman’242 ([0036] “Additionally, since the expected energy usage of the components in a system or process may be determined by a control system, the affects[sic] of adjusting operation of the components may be better quantified by the control system. In other words, as will be described in more detail below, various operating plans for the system or process may be evaluated taking into account energy usage costs. More specifically, in some embodiments, an operating strategy (e.g., plan) may be selected based in part on the expected energy usage cost, the value added to a product, and any additional costs associated with the operation plan, such as energy usage allotments (e.g., caps), energy usage premiums, and maintenance costs.”)
Regarding Claim 10, Kaufman’377 in view of Kaufman’242 teaches all of the limitations of parent claim 9,
Kaufman’242 further teaches
Wherein the computing system is configured to determine the traffic flow based on one or more simulations ([0135] “For example, the control system 22 may identify relationships between the various operational parameters, such as a product being produced, a time of day, operators on duty, environmental conditions, materials being used, product mix, operating conditions, production run rates, production schedules, product routing, control algorithms, and the like. In general, the energy usage under a particular set of operational parameters is expected to similar to energy usage in a previous instance under a similar set of operational parameters.”) of the traffic flow of the plurality of the plurality of products and the energy consumption data. ([0093] “To facilitate determining the energy usage by the production process 136, a process model may be developed that describes the power usage at each stage. … In other words, the process model may simulate operation of a process stage to describe the relationship between operational parameters of the process stage and the energy or power usage.”)
Regarding Claim 11, Kaufman’377 teaches all of the limitations of parent claim 1,
Kaufman’377 further teaches:
wherein the computing system is configured to: receive water consumption data, carbon emissions data, waste data, or any combination thereof associated with the first product; ([0032] “As used herein, "sustainability factors" are intended to broadly relate to energy consumption or water consumption, and can include, e.g., energy, water, emissions, an energy source or provider, an energy type, raw materials, carbon footprint of materials, waste”)
aggregate the water consumption data, the carbon emissions data, the waste data, or any combination thereof; (see fig. 3, column 310 “carbon dioxide (kg)” and [0032] “Moreover, it should be appreciated that values for emissions or other sustainability factors (e.g., columns 310, 312, etc.) can be obtained directly from energy consumption data 104 or processed and/or retrieved by analysis component 116 as processed data 118.”)
Kaufman’377 differs from the claimed invention in that:
Kaufman’377 does not appear to clearly articulate generat[ing] the one or more control signals based on the aggregated water consumption data, the aggregated carbon emissions data, the aggregated waste data, or any combination thereof.
However, Kaufman’242 teaches an industrial automation system (see e.g. figs. 1, 2, 4) which uses determined energy consumption information of different areas of the plant (see [0055] “multiple utility meters 80 may be included to monitor the energy usage of a particular area 16 (e.g., area 1 or area 2) or cell 18 (e.g., cells 1-6). For example a first utility meter may measure the utility usage of area 1 and a second utility meter may measure the utility usage of cell 5.”) and uses that information to generate operational control commands intended to reduce the carbon footprint of the operational process (see fig. 18, and [0186] “In addition to implementing an operating strategy based on economic analysis, an operating strategy may be selected and implemented based on other criteria, such as a carbon footprint. To help illustrate, one embodiment of a process 248 for determining an operating strategy for one or more components based on carbon footprint is described in FIG. 18. Generally, the process 248 includes determining multiple operating strategies (process block 250), determining expected carbon costs for each operating strategy (process block 252), determining value added for each operating strategy (process block 254), and selecting and implementing one of the operating strategies (process block 256).”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Copperthite, et al., US Pg-Pub 2024/0201675 – automation process control system with logic to minimize carbon emissions based on data aggregation of energy consumptions (whole disclosure, but see figs. 3, 5, 6, 8, particularly.)
Kaufman, et al., US Pg-Pub 2010/0274602 – particularly paragraphs [0051]-[0054] describing the use of machine learning classifiers for aggregating energy consumption data in industrial automation processes
Tan, Yee Shee, Yen Ting Ng, and Jonathan Sze Choong Low. "Internet-of-things enabled real-time monitoring of energy efficiency on manufacturing shop floors." Procedia CIRP 61 (2017): 376-381. – describing a location based monitoring system for a manufacturing facility using wireless sensors for energy consumption monitoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA T SANDERS whose telephone number is (571)272-5591. The examiner can normally be reached Generally Monday through Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.T.S./Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights