DETAILED ACTION
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 .
Priority
Application 18/193,863 filed on 03/31/2023 claim priority to EUROPEAN PATENT OFFICE (EPO) 20200081.6 filed on 10/05/2020.
Response to Amendment
This office action is in response to a communication submitted on 04/30/2026 wherein claims 1-12 are pending and have been considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The recited “multiple second energy consumption scenarios of essentially the same quality measure are aggregated” is not described or recited in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
With respect to the claimed parts, the examiner was unable to find adequate structure (or material or acts) for performing the recited function and therefor fails the description required in 35 USC 112, first paragraph (see MPEP 2181).
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A method for evaluating an energy efficiency of a second energy consumption scenario of a site, the method comprising the steps of:
obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario;
obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario;
comparing the second time-series of energy consumption data to the first time-series of energy consumption data;
when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario; and
automatically controlling the site’s power consumption by adjusting device operations, based on the quality measure.
Claim 12 is copied below, with the limitations belonging to an abstract idea being underlined.
An artificial neural net (ANN), which is configured to:
in a first learning phase, obtaining a plurality of first energy consumption scenarios, which each comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario;
in a second learning phase, obtaining a plurality of second energy consumption scenarios, which each comprises a second time-series of energy consumption data of at least one device, and a similarity assessment of each second energy consumption scenario to each first energy consumption scenario;
in a third learning phase, analyzing the similarity assessments, by the ANN, in a productive phase, applying, by the ANN, the similarity assessments to a newly obtained second energy consumption scenario; and
when a similarity assessment of the newly obtained second energy consumption scenario is greater than a predefined value, outputting the quality measure for the energy efficiency of the scenario;
automatically controlling a site’s power consumption by adjusting device operations based on the quality measure.
Regarding the underlined limitation comparing the second time-series of energy consumption data to the first time-series of energy consumption data;
when the second time-series of energy consumption data is similar to the first time-series of energy consumption data. It is an abstract idea as it is programming routines and patterns for comparing data, therefore it is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation automatically controlling the site’s power consumption by adjusting device operations, based on the quality measure. It is an abstract idea as it is programming routines and patterns for automatically controlling therefore it is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation: analyzing the similarity assessments, by the ANN, in a productive phase, applying, by the ANN, the similarity assessments to a newly obtained second energy consumption scenario. It is an abstract idea at it is programming routines and patterns for comparing data therefore it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the learning phases: Learning phases are part of training a neural network and using the broadest reasonable interpretation, training the machine learning algorithm requires specific mathematical calculations and therefore encompasses mathematical concepts (see 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, example 47 claim 2). These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
In summary, the highlighted steps in the claims above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. The additional concepts of “obtaining,” “outputting,” and “controlling” equate to routine data gathering and extra solution data activity (see MPEP 2106.05(g)) and are not significantly more to provide an practical application.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1 and 12 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claim 2-11 are similarly ineligible. Dependent claims 2-11 further detail the abstract idea but do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself). Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
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 (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.
(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 12 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Matsuoka et al., hereinafter Matsuoka, U.S. Pub. No. 2014/0058567 A1.
Regarding Independent claim 12 Matsuoka teaches:
“An artificial neural net (ANN)” (Matsuoka, ¶ 0054, Matsuoka teaches “the design of thermostat management system 4606 and use of the thermostat management servers 4620 may be scaled to meet these demands on the system and efficiently track and organize the data from these multiple enclosures and thermostats for processing analysis, control and machine-learning purposes” (¶ 0167) where “machine-learning purposes” discloses a training phase or a learning phase. Moreover, Matsuoka teaches using “artificial neural networks” to carry out the “machine learning and mathematical optimization algorithms” (¶ 0054) therefore Matsuoka teaches using an “ANN” to carry out the “first learning phase,” “second learning phase,” and the analysis and application of the “third learning phase.”
“configured to: obtaining a plurality of first energy consumption scenarios, which each comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “historical or baseline performance is either calculated or retrieved (e.g. from a database with thermostat management system 4606 in Figs. 46A and 46B) (¶ 0186) where “historical performance” discloses “first energy consumption scenario” where the “energy saving performance is measured only by virtue of physical parameters that can be sensed or governed by the thermostat itself” (¶ 0186). Additionally Matsuoka teaches “calculate or retrieve historical energy saving performance using standalone performance metric” (fig 49 step 4912) where “historical energy saving performance” discloses “a quality measure of the first energy consumption scenario.” Moreover, the historical database contains both real time and non real time setpoint entries (fig 42A step 4212) disclosing the “energy consumption data” is “time-series” data and the thermostat controls an HVAC system disclosing “at least one device.” Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where each of the “others” has a device for which the “historical performance”(first energy consumption scenario) is “either calculated or retrieved” (¶ 0190) thereby disclosing a “plurality of first energy consumption scenarios”
“obtaining a plurality of second energy consumption scenarios, which each comprises a second time-series of energy consumption data of at least one device, and a similarity assessment of each second energy consumption scenario to each first energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0095, ¶ 0186-¶ 0190)Matsuoka teaches “calculate current energy saving performance using standalone performance metric (e.g. using information available from the thermostat itself and/or thermostat connected devices” (fig 49 step 4910) and “In step 4914 the current performance is compared with the historical performance” (00186) where “current performance” discloses “second energy consumption scenario.” Additionally, the data from the thermostat is both real-time and non-real-time data (¶ 0095) disclosing “time-series” data is used to calculate a “second energy consumption scenario.” Moreover, “In step 4914 the current performance is compared with the historical performance” (¶ 0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario” and where the comparison discloses a “similarity assessment.” Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where each of the “others” has a device for which the “current performance”(second energy consumption scenario) is “either calculated or retrieved” (¶ 0190) thereby disclosing a “plurality of second energy consumption scenarios.”)
“analyzing the similarity assessments, in a productive phase, applying the similarity assessments to a newly obtained second energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “In step 4914 the current performance is compared with the historical performance” (¶ 0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario” and where the comparison discloses a “similarity assessment.” If the result of the comparison is not greater than 1-5%, the system enters a loop where strategies for improving the performance, the “current performance,” are displayed. A new step 4914, the comparison, is performed using a different “current performance” thereby disclosing “applying the similarity assessments to a newly obtained second consumption scenario.”
“when a similarity assessment of the newly obtained second energy consumption scenario is greater than a predefined value, outputting the quality measure for the energy efficiency of the scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “In decision step 4920 (of fig 49) if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, (a predefined value) such as 1-5%) than the historical performance, control passes back up to the comparison step 4914” (¶ 0186) therefore if the performance is greater than 1-5% the “amount of energy and/or money saved is calculated and/or displayed to the user” (¶ 0186) disclosing “outputting the quality measure for the energy efficiency of the scenario.”)
“automatically controlling the site’s power consumption by adjusting device operations, based on the quality measure” (Matsuoka, ¶ 0040: Matsuoka teaches “the VSCU unit processes the learned and sensed information according to one or more advanced control algorithms, and then automatically adjusts its environmental control settings to optimize energy usage while at the same time maintaining the living space at optimal levels according to the learned occupancy patterns and comfort preferences of the user”(¶ 0040) where “automatically adjusts its environmental control settings” discloses “automatically controlling the site’s power consumption by adjusting device operations” and “optimize energy usage” discloses the adjustment is “based on the quality measure” of energy savings.
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 (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.
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 1-4 and 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al., hereinafter Matsuoka, U.S. Pub. No. 2014/0058567 A1 in view of Kamel et al., herein after Kamel, U.S. Pub. No. 2018/0299917 A1.
Regarding Independent claim 1 Matsuoka teaches:
“A method for evaluating an energy efficiency of a second energy consumption scenario of a site” (Matsuoka, ¶ 0031).
“the method comprising the steps of: obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario” (Matsuoka, fig 49, ¶ 0186: Matsuoka teaches “historical or baseline performance is either calculated or retrieved (e.g. from a database with thermostat management system 4606 in Figs. 46A and 46B) (¶ 0186) where “historical performance” discloses “first energy consumption scenario” where the “energy saving performance is measured only by virtue of physical parameters that can be sensed or governed by the thermostat itself” (¶ 0186). Additionally Matsuoka teaches “calculate or retrieve historical energy saving performance using standalone performance metric” (fig 49 step 4912) where “historical energy saving performance” discloses “a quality measure of the first energy consumption scenario.” Moreover, the historical database contains both real time and non real time setpoint entries (fig 42A step 4212) disclosing the “energy consumption data” is “time-series” data and the thermostat controls an HVAC system disclosing “at least one device.”
“obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario”
Matsuoka teaches “calculate current energy saving performance using standalone performance metric (e.g. using information available from the thermostat itself and/or thermostat connected devices” (fig 49 step 4910) and “In step 4914 the current performance is compared with the historical performance” (00186) where “current performance” discloses “second energy consumption scenario.” Additionally, the data from the thermostat is both real-time and non-real-time data (¶ 0095) disclosing “time-series” data is used to calculate a “second energy consumption scenario.” Moreover, the “current energy saving performance” discloses a “quality measure of the first energy consumption scenario.” Moreover, the compared historical data and current data have similar effective times (¶ 0119) where that time may be bi-weekly, semi-weekly, monthly, bi-monthly or seasonal (¶ 0091).)
“comparing the second time-series of energy consumption data to the first time-series of energy consumption data” (Matsuoka teaches “In step 4914 (of fig 49) the current performance is compared with the historical performance” (¶ 0186))
“when the second time-series of energy consumption data is similar to the first time-series of energy consumption data; (Matsuoka teaches “if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance” (¶ 0186) disclosing “the second time-series of energy consumption data is similar to the first time-series of energy consumption data.”)
“automatically controlling the site’s power consumption by adjusting device operations, based on the quality measure” (Matsuoka, ¶ 0040: Matsuoka teaches “the VSCU unit processes the learned and sensed information according to one or more advanced control algorithms, and then automatically adjusts its environmental control settings to optimize energy usage while at the same time maintaining the living space at optimal levels according to the learned occupancy patterns and comfort preferences of the user”(¶ 0040) where “automatically adjusts its environmental control settings” discloses “automatically controlling the site’s power consumption by adjusting device operations” and “optimize energy usage” discloses the adjustment is “based on the quality measure” of energy savings.
While Matsuoka teaches comparing current (second energy consumption scenario) and historical data (first energy consumption scenario) and displaying strategies for improving energy performance Matsuoka does not explicitly teach “outputting the quality measure of the first energy consumption scenario”
Kamel teaches:
“outputting the quality measure of the first energy consumption scenario” (Kamel, fig 9, fig 10, ¶ 0118-¶ 0119: Kamel teaches displaying a “monthly key performance indicator chart” where the chart “indicates the energy performance for a month” where the color of the bar indicates the energy performance where “red indicates a poor score, green indicates a good score, and yellow indicates a mediocre score that could be improved” (¶ 0118). Therefore the combination of Matsuoka and Kamel disclose the limitation “when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario.”
It would have been obvious for a person of ordinary skill in to art to have modified the system and method for improving energy efficiency of a device as taught by Matsuoka by including outputting the energy performance indicator as disclosed by Kamel in order to improve the user friendly system of Matsuoka. Matsuoka displays strategies to improve energy efficiency of a device and displaying the energy performance indicator would enhance the user’s understanding of why the energy efficiency of the device needs to be improved.
Regarding claim 2 Matsuoka as modified teaches:
“wherein the data being similar means a deviation of each data of the first time-series of energy consumption data to the data of the second time-series of energy consumption data of less than 40%” (Matsuoka, ¶ 0186: Matsuoka teaches “if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance” (¶ 0186) discloses a deviation of less than 40% between the “first time-series of energy consumption data” (historical performance data) and “the data of the second time-series of energy consumption data” (current performance data).
Regarding claim 3 Matsuoka as modified teaches:
“wherein the data being similar means that a trained artificial neural net, ANN, outputs the data of the second time-series of energy consumption data being part of the data of the first time-series of energy consumption data” Matsuoka teaches the compared historical data (first time-series of energy consumption data) and current data (second time-series of energy consumption data) have similar effective times (¶ 0119) where that time may be bi-weekly, semi-weekly, monthly, bi-monthly or seasonal (¶ 0091). A person of ordinary skill in the art would understand that the current data would be part of the historical data until a new time frame, where a new set of current data, would be collected.)
Regarding claim 4 Matsuoka as modified teaches:
“wherein the quality measure comprises an energy consumption class, a quality estimation and/or a measurement result of the energy consumed in this scenario” (Matsuoka, ¶ 0186-¶ 0188: Matsuoka teaches the “energy saving performance” (quality measure) is based on, in some instances, a “stand-alone performance metric (SPM)” (¶ 0186) where “an SPM is based only on the percentage of time that their HVAC system is cycled on (“on-time percentage” or “OTP”), wherein the performance metric is higher (better) when the on-time percentage is lower” (¶ 0187) where on-time percentage discloses “a measurement result of the energy consumed in this scenario.”)
Regarding claim 6 Matsuoka as modified teaches:
“wherein the quality measure is attributed with a comment or a hint” (Matsuoka, fig 49, ¶ 0186: The specification states “The quality measure may be attributed, for instance, with a comment, a recommendation, a hint, a statement, or the like” (PG PUB ¶ 0036). Matsuoka teaches “one or more strategies for helping or encouraging the user to improve performance is calculated and displayed to the user” (¶ 0186) where the strategies are with respect to the current energy saving performance (quality measure) (see fig. 49) additionally, “the amount of energy and/or money saved is calculated and/or displayed to the user” (¶ 0186) disclosing “the quality measure is attributed with a comment or a hint.”)
Regarding claim 7 Matsuoka as modified teaches:
“wherein the at least one device comprises a machine driven by electrical, mechanical, chemical, and/or further energy sources” (Matsuoka teaches a device as a HVAC system which provides “a source of heat using electricity or gas” (¶ 0058).)
Regarding claim 8 Matsuoka as modified teaches:
“wherein the first energy consumption scenario and the second energy consumption scenario comprise energy consumption data of at least two devices” (Matsuoka, fig 49, fig. 50, ¶ 00186-¶ 0190: Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where “the user’s energy-saving performance is measured using an SPM” (stand-alone performance metric) and “current energy performance (second energy consumption scenario) is either calculated or retrieved” (¶ 0190) where “others” form a “competition group” where a “ranking could be calculated in terms of current energy efficiency or some absolute or relative efficiency metric” (¶ 0190). The ranking “could be calculated in terms of most improved when compared with their own historical performance values” (first energy consumption scenario) (¶ 0190). Therefore Matsuoka discloses “the first energy consumption scenario and the second energy consumption scenario comprise energy consumption data of at least two devices” as each competitor would have their own data associated with their individual devices.)
Regarding claim 9 Matsuoka as modified teaches:
“wherein the first energy consumption scenario and the second energy consumption scenario comprise input-data” (Matsuoka, fig. 49, ¶ 0186: Matsuoka teaches “In step 4914 the current performance is compared with the historical performance” (0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario.” Because they are “compared,” they must be “input-data.”)
Regarding claim 10 Matsuoka as modified teaches:
“wherein the input-data comprise environment data, schedule data, production cycle data, and/or other data to influence at least one device of a scenario” (Matsuoka, ¶ 0057, ¶ 0187, Matsuoka teaches using “outside weather, such as the outside temperature” when determining the “User’s energy-saving performance” (¶ 0187) disclosing “environment data” as “input-data”).
Regarding claim 11 Matsuoka as modified teaches:
“when the second time-series of input-data is similar to more than one first time-series of input-data, namely to a primary and a secondary time-series of input-data of a primary and a secondary energy consumption scenario, outputting the quality measure of the primary and the secondary energy consumption scenario” (Matsuoka teaches “In decision step 4920 (of fig. 49), if the current performance (primary time-series of input-data) is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance (the second time-series of input-data), control passes back up to comparison step 4914 (of fig 49)” (¶ 0186) before which in step 4922 of fig 49 strategies for improving user’s current energy savings performance are “calculated and displayed” (fig. 49). After this the comparison (step 4914) is run again between the current performance and the historical performance disclosing a “secondary time-series of input data” as the current performance changes due to the strategies. Additionally, “the amount of energy and/or money saved is calculated and/or displayed to the user” (¶ 0186) disclosing outputting the quality measure of the primary and the secondary energy consumption scenario” as “the amount of energy saved” discloses the “quality measure.”)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka as modified by Kamel as applied to claim 4 above, and further in view of Venkatakrishnan et al., hereinafter Venkatakrishnan, U.S. Pub. 2012/0053740 A1.
Regarding claim 5 Matsuoka as modified does not teach:
“wherein multiple second energy consumption scenarios of essentially the same quality measure are aggregated.”
Venkatakrishnan teaches:
“wherein multiple second energy consumption scenarios of essentially the same quality measure are aggregated.” (Venkatakrishnan, ¶ 0033-¶ 0034: Venkatakrishnan teaches a “thermostat controller is coupled to the central controller” which provides “consumption data (second energy consumption scenarios) to the user via a user interface of a client application” and “individual home data is compiled into an aggregate home data base for multiple homes for a comparison to be made with the consumer’s individual home and other similar type homes” (¶ 0033) where “similar type homes” are based on “total power/energy consumption of the home” and “power/energy consumption of individual energy consuming devices at the home” (¶ 0034) disclosing “essentially the same quality measure” and where “consumption data” for “multiple homes” discloses “multiple second energy consumption scenarios.”)
Both Matsuoka and Venkatakrishnan are concerned with improving the energy efficiency of a system therefore it would have been obvious for a person of ordinary skill in to art to have modified the system and method for improving energy efficiency of a device as taught by Matsuoka by including by including the well known method of aggregating data as taught by Venkatakrishnan as aggregating data provides for enhanced identification of trends and patterns in order to provide an “increase efficiency” (Venkatakrishnan, ¶ 0034)
Response to Arguments
Applicant’s arguments (remarks), filed on 04/30/2026, have been fully considered.
Regarding B. The claim objections and § 112 indefiniteness rejections are obviated page 5-6 of Applicant’s remarks, due to Applicant’s arguments and changes made to the claims, the objections and § 112 indefiniteness rejections have been withdrawn.
Regarding C. The § 101 rejections are obviated page 6-8 of Applicant’s remarks, Applicant argues “Even if the claims recite an exception, this exception is integrated into a practical application because the claims include additional elements that are an improvement to technology
The claims have been amended to recite the site's power consumption is controlled automatically by adjusting device operations based on the quality measure. This is not a mental process or a way of conducting business; it drives an energy management system to optimize device operation.
As such, this is a concrete technological implementation. For example, upon recognizing that a current consumption pattern matches a known inefficient scenario, the system might automatically adjust certain machine settings or thermostats to reduce power usage. Such automatic control of industrial equipment in response to the analysis demonstrates a practical application of the identified pattern, ensuring the claims do not monopolize an abstract idea itself.
Moreover, the claims provide a technical improvement in the field of energy management. The Background in the specification notes that traditionally, analyzing a site's energy efficiency required complex manual modeling and was time-consuming. The claimed invention improves this by using historical example scenarios and pattern recognition (potentially via a trained ANN) to quickly evaluate new scenarios and control the site, without requiring a full physical model for each site.
This is an "integration of the judicial exception into a practical application" per the USPTO guidance - the algorithmic comparison is employed to solve a real-world technical problem (optimizing power consumption across devices) more efficiently. It is also analogous to an improvement in computer-related technology: the invention leverages an ANN-based pattern detector and an EMS control loop, which are decidedly technological tools, to achieve results that were not possible through mental processes or basic business practices” (remarks, pg 8).
Examiner respectfully disagrees. The claims language states “automatically controlling the site’s power consumption by adjusting device operations based on the quality measure.” This would amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(I) see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).)
Regarding D. The art-based rejections are traversed page 9 of Applicant’s remarks, Applicant argues “Matsuoka discusses providing suggestions or "strategies" to a user for improving HVAC performance, and Kamel shows visual indicators (red/green performance bars) to the user. These are user-centric outputs - there is no teaching of a closed-loop automated adjustment of equipment in response to the measured efficiency.
In contrast, the proposed amendments to claim 1 would require controlling the site's power consumption, based on the quality measure. This goes beyond the prior art's passive display of information. No combination of Matsuoka and Kamel suggests such an automated control-loop implementation. Even if one were to combine their teachings, at best the user would be alerted to take action (as in Matsuoka's strategy or Kamel's KPI chart), which is fundamentally different from the claimed system-driven control action. The claimed approach yields a more efficient solution (automatic response vs. human-in-the-loop)” (remarks, pg 9).
Examiner respectfully disagrees. Applicant’s arguments are mere allegations as Examiner can find no claim language reciting a “closed-loop automated adjustment” or a “control-loop implementation.” The MPEP states the Examiner should consider “whether, on balance, the applicant has met the burden of proof to show nonequivalence. However, under no circumstance Should an examiner accept as persuasive a bare statement or opinion that the element shown in the prior art is not an equivalent embraced by the claim limitation. Moreover, if an applicant argues that the means- (or step-) plus-function language in a claim is limited to certain specific structural or additional functional characteristics (as opposed to "equivalents" thereof) where the specification does not describe the invention as being only those specific characteristics, the claim should not be allowed until the claim is amended to recite those specific structural or additional functional characteristics” (MPEP 2184.II]). Additionally, MPEP 2111 states that “an examiner must construe claim terms in the broadest reasonable manner during prosecution as is reasonable allowed in an effort to establish a clear record of what applicant intends to claim” (4th paragraph). Examiner uses the broadest reasonable interpretation to interpret “automatically controlling the site’s power consumption by adjusting device operations, based on the quality measure.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Haghighat-Kashani et al., U.S. Pub. No 20180364666 A1, teaches managing and understanding power consumption in a premise of a user.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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