DETAILED ACTION
Status of the Application
Claims 1-9 have been examined in this application. This communication is the first action on the merits. The information disclosure statement (IDS) submitted on 01/07/2025, ; was filed with this application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
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 .
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
This action is a Non-Final Action on the merits in response to the application filed on 01/07/2025.
Claims 1-9remain pending in this application.
Foreign Priority
The Examiner/office does acknowledges that the applicant claims foreign priority to the date 07/19/2022 but will not honor it because the Applicant provided documents are not translated in English. As such, the provided documents from the Applicant could possibly be different content from the present claims and specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are directed towards a system and claim 9 directed towards a method, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-9, the independent claims (claims 1 and 9) are directed to managing of consumption/emission data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, An analysis system comprising:
at least one processor to execute a program; and
at least one memory to store the program which, when it is executed by the processor, performs processes of:
measuring at least one of the amount of consumed energy, the amount of emitted drainage, and the amount of exhausted air as a consumption/emission amount in a step including manufacture of a target product; and
extracting the step corresponding to the consumption/emission amount that is equal to or greater than a first threshold by analyzing the consumption/emission amount,
wherein information determining the configuration of data to be measured or a method of measuring the data in a changeable manner is defined as a profile, and
the measuring includes setting a measurement unit of the consumption/emission amount or a method of measuring the consumption/emission amount on the basis of the profile.
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction includes business relations; managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2), subsection II).
Regarding steps of:
an analysis system comprising:
at least one processor to execute a program; and
at least one memory to store the program which, when it is executed by the processor, performs processes of:
measuring at least one of the amount of consumed energy, the amount of emitted drainage, and the amount of exhausted air as a consumption/emission amount in a step including manufacture of a target product; and
extracting the step corresponding to the consumption/emission amount that is equal to or greater than a first threshold by analyzing the consumption/emission amount,
wherein information determining the configuration of data to be measured or a method of measuring the data in a changeable manner is defined as a profile, and
the measuring includes setting a measurement unit of the consumption/emission amount or a method of measuring the consumption/emission amount on the basis of the profile.
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the 2019 PEG, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of processor, memory, machine learning, model. The claims recite the steps are performed by the processor, memory, machine learning, model.
The limitations of
an analysis system comprising:
at least one processor to execute a program; and
at least one memory to store the program which, when it is executed by the processor, performs processes of:
measuring at least one of the amount of consumed energy, the amount of emitted drainage, and the amount of exhausted air as a consumption/emission amount in a step including manufacture of a target product; and
extracting the step corresponding to the consumption/emission amount that is equal to or greater than a first threshold by analyzing the consumption/emission amount,
wherein information determining the configuration of data to be measured or a method of measuring the data in a changeable manner is defined as a profile, and
the measuring includes setting a measurement unit of the consumption/emission amount or a method of measuring the consumption/emission amount on the basis of the profile.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by m processor, memory, machine learning, model. The processor, memory, machine learning, model are recited at a high level of generality. In limitation (a), the machine learning model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the processor, memory, machine learning, model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0068 “The consumption/emission amount learning unit 213 performs machine learning on the basis of a consumption/emission amount 220 stored in the storage device 212B and generates a consumption/emission model 221 for making an inference.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
an analysis system comprising:
at least one processor to execute a program; and
at least one memory to store the program which, when it is executed by the processor, performs processes of:
measuring at least one of the amount of consumed energy, the amount of emitted drainage, and the amount of exhausted air as a consumption/emission amount in a step including manufacture of a target product; and
extracting the step corresponding to the consumption/emission amount that is equal to or greater than a first threshold by analyzing the consumption/emission amount,
wherein information determining the configuration of data to be measured or a method of measuring the data in a changeable manner is defined as a profile, and
the measuring includes setting a measurement unit of the consumption/emission amount or a method of measuring the consumption/emission amount on the basis of the profile.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor, memory, machine learning, model to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-8 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 5 and 6 recite machine learning to generate and extract data. The dependent claims 2-8 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-8 recites processor, memory, machine learning, model which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-8 recites processor, memory, machine learning, model, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-8 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1 and 9. Therefore claims 2-8 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Pub. US 20250069098, Kobayashi, et al. to hereinafter Kobayashi in view of United States Pub. US 20250342483, Cole.
Referring to Claim 1, Kobayashi teaches an analysis system comprising:
at least one processor to execute a program (
Kobayashi : Sec. 0134, The controller 11 includes one or more processors. In one embodiment, “processor” can be a general purpose processor, or a dedicated processor specialized for particular processing, but is not limited to this.
Kobayashi: Sec. 0137, The functions of the first server apparatus 10 can be realized by executing a computer program (program) according to the present embodiment by a processor included in the controller 11. In other words, the functions of the first server apparatus 10 can be realized by software. );and
at least one memory to store the program which, when it is executed by the processor, performs processes of (
Kobayashi: Sec. 0134, The controller 11 includes one or more processors. In one embodiment, “processor” can be a general purpose processor, or a dedicated processor specialized for particular processing, but is not limited to this.
Kobayashi: Sec. 0135, The memory 12 includes, for example, any memory module such as a hard disk drive (HDD), solid state drive (SSD), read-only memory (ROM), and random access memory (RAM). The memory 12 may function as, for example, a main memory, an auxiliary memory, or a cache memory. The memory 12 stores any information used in the operations of the first server apparatus 10. For example, the memory 12 may store system programs, application programs, and various information received by the communication interface 13. The memory 12 is not limited to one built in the first server apparatus 10, but may be an external database or external memory module):
measuring at least one of the amount of consumed energy, the amount of emitted drainage, and the amount of exhausted air as a consumption/emission amount in a step including manufacture of a target product (
Kobayashi: Sec. 0077, Thus, in the information processing method, the amount of greenhouse gas emissions is acquired by estimating the measurement value of the physical quantity at the location at which the physical quantity has not been measured by the sensor. Therefore, the information processing method can provide information on the amount of greenhouse gas emissions even when sensors are not exhaustively located or not all the sensors work properly.
Kobayashi: Sec. 0130, The first server apparatus 10 displays the energy consumption at the plant so that the amount of energy consumed through use and the amount of energy consumed through loss (amount of energy discarded) can be distinguished.
Kobayashi: Sec. 0206, The height of the image 229 indicates the amount of transmission loss incurred when draining from the fourth area. An image 232 indicates the amount of drainage loss due to drainage. As illustrated in FIG. 16 , the first server apparatus 10 may display the breakdown of the energy consumed at each area of the steam flow path. This allows the user to check the distribution of loss at each area and choose appropriate measures for decarbonization.);and
extracting the step corresponding to the consumption/emission amount that is equal to or greater than a first threshold by analyzing the consumption/emission amount (
Kobayashi: Sec. 0070, (3) In the information processing method according to (1) or (2), the controller may be configured to notify an alarm when the predicted future trend in the amount of the greenhouse gas emissions becomes greater than the reference value.
Kobayashi: Sec. 0071, Thus, the user can know in advance that the future trend in the amount of greenhouse gas emissions is likely to become greater than the reference value, and can take necessary measures in advance.),
wherein information determining the configuration of data to be measured or a method of measuring the data in a changeable manner is defined as a profile (See Cole) (
Kobayashi: Sec. 0164, The measurement value of the physical quantity in the flow path where no measurement has been performed may be estimated by a method other than that described above, depending on the type of physical quantity to be measured. ), and
the measuring includes setting a measurement unit of the consumption/emission amount or a method of measuring the consumption/emission amount on the basis of the profile (See Cole) (
Kobayashi: Sec. 0023, controller configured to:
Kobayashi: Sec. 0024, acquire a measurement value of a physical quantity on greenhouse gas emissions, the measurement value being measured by a sensor at a plant for process manufacturing;
Kobayashi: Sec. 0025, acquire, based on the acquired measurement value, an amount of the greenhouse gas emissions integrated in a predetermined period;
Kobayashi: Sec. 0074, In the information processing method according to any one of (1) to (4), the controller may be configured to:
Kobayashi: Sec. 0075, estimate, using the measurement value of the physical quantity measured by the sensor,
Kobayashi: Sec. 0192, FIG. 15 illustrates an example of a screen 211 for defining the variable line function. In the screen 211, an input area 212 is an area for setting a name to be applied to the line graph. An input area 213 is an area for selecting the function of the line graph. An input area 214 is an area for selecting tags to be set to the X-axis, Y-axis, and the like. An input area 215 is an area for setting a display scale, display period, calculation/display intervals, threshold value, and upper/lower limits. An area 216 is an area for setting coordinate values of the line function. A display 217 is an area for displaying a preview screen of the graph of the variable line function.).
Kobayashi does not explicitly teach a profile.
However, Cole teaches a profile (
Cole: Sec. 0016, The method can be performed by one or more processors, coupled with memory. The method can include the one or more processors receiving, from a payroll processing system, data for each of a plurality of profiles linked with one or more locations of an entity. The data can be indicative of energy consumption associated with the one or more locations of the entity.
Cole: Sec. 0031, The technical solution can utilize machine learning or artificial intelligence models (e.g., neural networks, deep learning, support vector machines, or reinforcement learning using Q-learning or policy gradient methods) to analyze the data to quantify the environmental impact of different job roles, considering factors such energy usage, transportation, and resource consumption. The technical solution can generate an action to reduce or otherwise manage the carbon emissions, such as by adjusting profiles associated with a role in a manner that reflects the environmental impact of the role, thereby reducing carbon footprint or emissions of the entity
Cole: Sec. 0101, the data processing system 105 can apply a power consumption profile to the server that may be consuming excessive energy.)
Kobayashi and Cole are both directed to the analysis of the measurement of consumed energy data (See Kobayashi at 0065-0069; Cole at 0004, 0006-0009). Kobayashi discloses that additional elements, such as the use of sensor at a plant can be considered (See Kobayashi at 0024). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobayashi, which teaches detecting and repairing information technology problems in view of Cole, to efficiently apply analysis of the measurement of consumed energy data to enhancing the capability to modify and train the data in a machine learning model. (See Cole at 0065, 0069, 0082, 0104, 0122).
Referring to Claim 2, Kobayashi teaches the analysis system according to claim 1, wherein the consumed energy includes at least one of the energy consumed by the target product itself or the energy consumed in the step (
Kobayashi: Sec. 0130, The first server apparatus 10 displays the energy consumption at the plant so that the amount of energy consumed through use and the amount of energy consumed through loss (amount of energy discarded) can be distinguished. Thus, the user can easily and accurately grasp the status of energy use at the plant and can take necessary measures for more efficient energy use.).
Referring to Claim 4, Kobayashi teaches the analysis system according to claim 1, wherein the measuring includes measuring the consumption/emission amount with respect to each item or each order of the target product (
Kobayashi: Sec. 0069, Thus, in the information processing method, the energy consumption at the plant is calculated for each production lot, production line, or device in the plant, and the amount of greenhouse gas emissions is acquired based on the energy consumption. Therefore, the information processing method can calculate the amount of greenhouse gas emissions with high accuracy.
Kobayashi: Sec. 0112, a data collection step of collecting various operation data on the plants from a data storage device in each plant to the computer in the management center by a communication means;
Kobayashi: Sec. 0132, The first server apparatus 10 also organizes the information on the amount of greenhouse gas emissions not only for each energy flow in the plant but also for each product supply chain, and provides the organized information to the client apparatus 50
Kobayashi: Sec. 0153, Specifically, the controller 11 converts each measurement value of the physical quantity into the energy consumption using a conversion expression predetermined according to the type of the physical quantity. The controller 11 may calculate the energy consumption so that the amount of energy actually consumed by operations of a device constituting the plant and the amount (loss) of energy lost due to exhaust or other reasons can be distinguished.).
Referring to Claim 5, Kobayashi teaches the analysis system according to claim 1,further comprising:
generating first prediction data that is data about a predicted amount of the consumption/emission amount on the basis of the first consumption/emission model (See Cole) (
Kobayashi: Sec. 0027, transmit, by a communication interface to the client apparatus, an image representing the past trend and the predicted future trend in the amount of the greenhouse gas emissions), wherein
the extracting includes extracting the step corresponding to the consumption/emission amount that is equal to or greater than the first threshold by analyzing the predicted amount of the consumption/emission amount based on the first prediction data (
Kobayashi: Sec. 0070, (3) In the information processing method according to (1) or (2), the controller may be configured to notify an alarm when the predicted future trend in the amount of the greenhouse gas emissions becomes greater than the reference value.
Kobayashi: Sec. 0071, Thus, the user can know in advance that the future trend in the amount of greenhouse gas emissions is likely to become greater than the reference value, and can take necessary measures in advance.).
Kobayashi does not explicitly teach performing machine learning using the consumption/emission amount and generating a first consumption/emission model.
However, Cole teaches performing machine learning using the consumption/emission amount and generating a first consumption/emission model (
Cole: Sec. 0031, the technical solution can use machine learning to analyze various factors associated with a location of entity, including, for example, energy consumption, resource utilization on a per-role basis, and job responsibilities. The technical solution can determine, compute, estimate, or otherwise predict or identify a carbon footprint attributed to the role based on these factors. Thus, the technical solution can integrate payroll, job role, and environmental impact data to generate a holistic metric of the carbon footprint of each role. The technical solution can utilize machine learning or artificial intelligence models (e.g., neural networks, deep learning, support vector machines, or reinforcement learning using Q-learning or policy gradient methods) to analyze the data to quantify the environmental impact of different job roles, considering factors such energy usage, transportation, and resource consumption. The technical solution can generate an action to reduce or otherwise manage the carbon emissions, such as by adjusting profiles associated with a role in a manner that reflects the environmental impact of the role, thereby reducing carbon footprint or emissions of the entity.)
Kobayashi and Cole are both directed to the analysis of the measurement of consumed energy data (See Kobayashi at 0065-0069; Cole at 0004, 0006-0009). Kobayashi discloses that additional elements, such as the use of sensor at a plant can be considered (See Kobayashi at 0024). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobayashi, which teaches detecting and repairing information technology problems in view of Cole, to efficiently apply analysis of the measurement of consumed energy data to enhancing the capability to modify and train the data in a machine learning model. (See Cole at 0065, 0069, 0082, 0104, 0122).
Referring to Claim 6, Kobayashi teaches the analysis system according to claim 5, wherein the generating the first consumption/emission model (See Cole) includes performing additional learning (See Cole) using the consumption/emission amount and generating a second consumption/emission model,
the analysis system further comprises:
generating second prediction data that is data about a predicted amount of the consumption/emission amount on the basis of the second consumption/emission model (
Kobayashi: Sec. 0165, Return to the explanation in FIG. 7 . In step S21, the controller 11 predicts future trends in the amount of greenhouse gas emissions, based on past trends. The future trends in the amount of greenhouse gas emissions can be estimated based on any method. For example, the controller 11 may perform regression analysis on the past trends of the amount of greenhouse gas emissions and predict the future trends based on a regression curve.),
Kobayashi describes determining multiple trends and user various tools to update the trends, in which the Examiner is interpreting as using additional learning to the generate a second consumption/emission model
and the extracting includes extracting the step corresponding to the consumption/emission amount that is equal to or greater than the first threshold by selecting and analyzing the first prediction data or the second prediction data relating to a particular time period, the selected and analyzed prediction data representing the predicted amount of the consumption/emission amount having a smaller difference from an actual value corresponding to the time period (
Kobayashi: Sec. 0175, Next, examples of the diagnostic KPIs will be described with reference to FIGS. 10A to 13 . FIGS. 10A and 10B are diagrams illustrating an example of a graph on rise/drop detection. FIGS. 10A and 10B illustrate an example of notifying the user of an alarm when a measurement value of an object to be monitored becomes smaller than a lower limit or larger than an upper limit.
Kobayashi: Sec. 0209, The images 242 to 253 each illustrate the relationship between lower and upper limits for a measurement value of the steam consumption or loss and the measurement value. As described with reference to FIG. 10B, the first server apparatus 10 may notify the user of an alarm when the measurement value of the steam consumption or loss becomes smaller than the lower limit or greater than the upper limit.).
Kobayashi does not explicitly teach model includes performing additional learning.
However, Cole teaches model includes performing additional learning (
Cole: Sec. 0031, the technical solution can use machine learning to analyze various factors associated with a location of entity, including, for example, energy consumption, resource utilization on a per-role basis, and job responsibilities. The technical solution can determine, compute, estimate, or otherwise predict or identify a carbon footprint attributed to the role based on these factors. Thus, the technical solution can integrate payroll, job role, and environmental impact data to generate a holistic metric of the carbon footprint of each role. The technical solution can utilize machine learning or artificial intelligence models (e.g., neural networks, deep learning, support vector machines, or reinforcement learning using Q-learning or policy gradient methods) to analyze the data to quantify the environmental impact of different job roles, considering factors such energy usage, transportation, and resource consumption. The technical solution can generate an action to reduce or otherwise manage the carbon emissions, such as by adjusting profiles associated with a role in a manner that reflects the environmental impact of the role, thereby reducing carbon footprint or emissions of the entity.
Cole: Sec. 0127, If the output is satisfactory, then the data processing system can proceed to ACT 330 to add the machine learning model to the data repository or otherwise activate the model for inference or use by the data processing system. If, however, the data processing system determines the output is not valid, then the data processing system can proceed to ACT 325 to receive additional training data, and return to ACT 310 to re-train, tune, or otherwise update the model using machine learning.)
Kobayashi and Cole are both directed to the analysis of the measurement of consumed energy data (See Kobayashi at 0065-0069; Cole at 0004, 0006-0009). Kobayashi discloses that additional elements, such as the use of sensor at a plant can be considered (See Kobayashi at 0024). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobayashi, which teaches detecting and repairing information technology problems in view of Cole, to efficiently apply analysis of the measurement of consumed energy data to enhancing the capability to modify and train the data in a machine learning model. (See Cole at 0065, 0069, 0082, 0104, 0122).
Referring to Claim 7, Kobayashi teaches the analysis system according to claim 6, further comprising: replacing the first consumption/emission model with the second consumption/emission model if the extracting selects the second prediction data (
Kobayashi: Sec. 0154, The controller 11 may update the integrated value by retaining the energy consumption and adding the energy consumption calculated in step S12 to the integrated value. When updating the integrated value results in the integrated value representing the integrated value of the energy consumption for the first period, the controller 11 may proceed to step S14, and then initialize the integrated value of the energy consumption to a value 0.
Kobayashi: Sec. 0156, Similar to the calculation of the integrated value of the energy consumption for each first period, the controller 11 may retain an integrated value of the amount of greenhouse gas emissions and add the amount of greenhouse gas emissions acquired in step S14 to the integrated value to update the integrated value. When updating the integrated value results in the integrated value representing the integrated value of the amount of greenhouse gas emissions for the second period, the controller 11 may proceed to step S16, and then initialize the integrated value of the amount of greenhouse gas emissions to a value 0. This allows the controller 11 to calculate the integrated value of the amount of greenhouse gas emissions for each second period. The controller 11 may perform the process of step S16 every second period.).
Kobayashi describes updating, in which the Examiner is interpreting updating as replacing.
Referring to Claim 8, Kobayashi teaches the analysis system according to claim 1, further comprising:
updating the profile (See Cole) if time-series change in the consumption/emission amount is equal to or greater than a second threshold (
Kobayashi: Sec. 0154, The controller 11 may update the integrated value by retaining the energy consumption and adding the energy consumption calculated in step S12 to the integrated value. When updating the integrated value results in the integrated value representing the integrated value of the energy consumption for the first period, the controller 11 may proceed to step S14, and then initialize the integrated value of the energy consumption to a value 0.
Kobayashi: Sec. 0156, Similar to the calculation of the integrated value of the energy consumption for each first period, the controller 11 may retain an integrated value of the amount of greenhouse gas emissions and add the amount of greenhouse gas emissions acquired in step S14 to the integrated value to update the integrated value. When updating the integrated value results in the integrated value representing the integrated value of the amount of greenhouse gas emissions for the second period, the controller 11 may proceed to step S16, and then initialize the integrated value of the amount of greenhouse gas emissions to a value 0. This allows the controller 11 to calculate the integrated value of the amount of greenhouse gas emissions for each second period. The controller 11 may perform the process of step S16 every second period
Kobayashi: Sec. 0209, As described with reference to FIG. 10B, the first server apparatus 10 may notify the user of an alarm when the measurement value of the steam consumption or loss becomes smaller than the lower limit or greater than the upper limit. Instead of displaying the measurement value of the steam consumption or loss so as to be comparable with both the upper and lower limits as illustrated in FIG. 17 , the first server apparatus 10 may display the measurement value of the steam consumption or loss so as to be comparable with the upper limit as illustrated in FIGS. 9A and 9B.
Kobayashi: Sec. 0070, (3) In the information processing method according to (1) or (2), the controller may be configured to notify an alarm when the predicted future trend in the amount of the greenhouse gas emissions becomes greater than the reference value.
Kobayashi: Sec. 0071, Thus, the user can know in advance that the future trend in the amount of greenhouse gas emissions is likely to become greater than the reference value, and can take necessary measures in advance. ).
Kobayashi does not explicitly teach the profile.
However, Cole teaches the profile (
Cole: Sec. 0016, The method can be performed by one or more processors, coupled with memory. The method can include the one or more processors receiving, from a payroll processing system, data for each of a plurality of profiles linked with one or more locations of an entity. The data can be indicative of energy consumption associated with the one or more locations of the entity.
Cole: Sec. 0031, The technical solution can utilize machine learning or artificial intelligence models (e.g., neural networks, deep learning, support vector machines, or reinforcement learning using Q-learning or policy gradient methods) to analyze the data to quantify the environmental impact of different job roles, considering factors such energy usage, transportation, and resource consumption. The technical solution can generate an action to reduce or otherwise manage the carbon emissions, such as by adjusting profiles associated with a role in a manner that reflects the environmental impact of the role, thereby reducing carbon footprint or emissions of the entity
Cole: Sec. 0101, the data processing system 105 can apply a power consumption profile to the server that may be consuming excessive energy.)
Kobayashi and Cole are both directed to the analysis of the measurement of consumed energy data (See Kobayashi at 0065-0069; Cole at 0004, 0006-0009). Kobayashi discloses that additional elements, such as the use of sensor at a plant can be considered (See Kobayashi at 0024). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobayashi, which teaches detecting and repairing information technology problems in view of Cole, to efficiently apply analysis of the measurement of consumed energy data to enhancing the capability to modify and train the data in a machine learning model. (See Cole at 0065, 0069, 0082, 0104, 0122).
Claim 9 recite limitations that stand rejected via the art citations and rationale applied to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Quigley et al., W.O. Pub. 2022120046, (discussing the measuring and controlling of greenhouse gas emissions ).
Al-Aomar et al., A Data-Driven Predictive Maintenance Model For Hospital HVAC System With Machine Learning, https://doi.org/10.1080/09613218.2023.2206989, Building Research & Information, 2024 (discussing the measuring the usage of machinery with the use of machine learning).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UCHE BYRD whose telephone number is (571)272-3113. The examiner can normally be reached Mon.-Fri..
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/UCHE BYRD/Examiner, Art Unit 3624