DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 9 May 2025. This communication is the first action on merits. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-11 are original / previously presented.
Claims 1-11 are currently pending and have been examined.
Priority
The application 19/203,248 filed on 9 May 2025 claims priority from Republic of Korea application KR10-2024-0066721 filed on 22 May 2024.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 9 May 2025 has been acknowledged by the Office.
Claim Objections
Claim 7 is objected to because of the following informalities. Appropriate correction is required.
Claim 7:
Claim 7 includes the word ‘analyzie’ in line 9 which is likely a misspelling of the word ‘analyze’. The Office recommends amending to correct the misspelling for clarity.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-6:
Claim 1 recites the limitation "the next day power demand estimation data" in line 7. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 2-6. It is unclear whether this is referring to next day power demand estimation data of a target area (claim 1 line 4) or next day power demand estimation data of a non-target area (claim 1 line 6). For the purpose of examination, this will be interpreted as referring to next day power demand estimation data of the non-target area.
Claim 1 recites the limitation "the date data" in line 7. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 2-6. It is unclear whether this is referring to the first instance of date data (claim 1 line 3) or the second instance of date data (claim 1 line 5). For the purpose of examination, this will be interpreted as referring to date data of the non-target area.
Claim 1 recites the limitation "the meteorological data" in lines 7-8. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 2-6. It is unclear whether this is referring to the first instance of meteorological data (claim 1 line 3) or the second instance of meteorological data (claim 1 line 5). For the purpose of examination, this will be interpreted as referring to meteorological data of the non-target area.
Claim 1 recites the limitation "the past power demand data" in line 8. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 2-6. It is unclear whether this is referring to the first instance of past power demand data (claim 1 line 3) or the second instance of past power demand data (claim 1 line 5). For the purpose of examination, this will be interpreted as referring to past power demand data of the non-target area.
Claims 7-11:
Claim 7 recites the limitation "the next day power demand estimation data" in line 9. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 8-11. It is unclear whether this is referring to next day power demand estimation data of a target area (claim 7 line 6) or next day power demand estimation data of a non-target area (claim 7 line 8). For the purpose of examination, this will be interpreted as referring to next day power demand estimation data of the non-target area.
Claim 7 recites the limitation "the date data" in line 9. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 8-11. It is unclear whether this is referring to the first instance of date data (claim 7 line 5) or the second instance of date data (claim 7 line 7). For the purpose of examination, this will be interpreted as referring to date data of the non-target area.
Claim 7 recites the limitation "the meteorological data" in lines 9-10. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 8-11. It is unclear whether this is referring to the first instance of meteorological data (claim 7 line 5) or the second instance of meteorological data (claim 7 line 7). For the purpose of examination, this will be interpreted as referring to meteorological data of the non-target area.
Claim 7 recites the limitation "the past power demand data" in line 10. There is insufficient antecedent basis for this limitation in the claim, and the dependent claims 8-11. It is unclear whether this is referring to the first instance of past power demand data (claim 7 line 5) or the second instance of past power demand data (claim 7 line 7). For the purpose of examination, this will be interpreted as referring to past power demand data of the non-target area.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-11:
Step 1:
Claims 1-5 recite a method; and claims 7-11 recite a device. Since the claims recite either a process, machine, manufacture, or composition of matter, the claims satisfy Step 1 of the Subject Matter Eligibility Framework in MPEP 2106 and the 2019 Patent Examination Guidelines (PEG).
Claim 6 recites a computer readable recording medium. The claims do not recite a process, machine, manufacture, or composition of matter, and do not satisfy Step 1 of the Subject Matter Eligibility Framework in MPEP 2106 and the 2019 Patent Examination Guidelines (PEG). However, the claim can be amended to fall within a statutory category. See MPEP 2106.04(II). Analysis proceeds to Step 2A prong one.
Step 2A – Prong One:
Claim(s) 1-11 recite an abstract idea. Independent claims 1 and 7 recite analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a target area; analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a non-target area; analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data, and past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area; analyzing the next day power demand estimation data of the target area, generator characteristics data, and system constraint data to generate next day power generation planning estimation data of the target area; and analyzing the next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area to calculate next day SMP data of the target area. The claims as a whole recite certain methods of organizing human activities, and individual limitations recite mathematical concepts.
First, the limitations of analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a target area; analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a non-target area; analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data, and past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area; analyzing the next day power demand estimation data of the target area, generator characteristics data, and system constraint data to generate next day power generation planning estimation data of the target area; and analyzing the next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area to calculate next day SMP data of the target area are certain methods of organizing human activities. For instance, these limitations represent the sub-groupings of managing personal behavior, and following rules or instructions. For example,
managing personal behavior includes analyzing date / meteorological / past power demand data…, generating next day power demand estimation of target area…, analyzing date / meteorological / past power demand data…, generating next day power demand estimation data of non-target area…, analyzing next day power estimation / date / meteorological / past power demand / past SMP data…, generating next day SMP estimation data of non-target area…, analyzing next day power demand estimation data of target area / generator characteristics / system constraint data…, generating next day power generation estimation data of the target area…, analyzing next day power generation planning estimation data of target area / system constraint / generator characteristics / next day SMP estimation data of non-target area…, calculating next day SMP data of the target area; and following rules or instructions includes analyzing date / meteorological / past power demand data…, generating next day power demand estimation of target area…, analyzing date / meteorological / past power demand data…, generating next day power demand estimation data of non-target area…, analyzing next day power estimation / date / meteorological / past power demand / past SMP data…, generating next day SMP estimation data of non-target area…, analyzing next day power demand estimation data of target area / generator characteristics / system constraint data…, generating next day power generation estimation data of the target area…, analyzing next day power generation planning estimation data of target area / system constraint / generator characteristics / next day SMP estimation data of non-target area…, calculating next day SMP data of the target area. The presence of generic computer components such as a power market price calculation device, processor, memory does not preclude the steps from reciting certain methods of organizing human activities, since the number of people involved in the activities is not dispositive as to whether a claim limitation falls within this grouping and instead it is based on whether an activity itself falls within one of the sub-groupings. If a claim limitation, under its broadest reasonable interpretation, covers certain methods of organizing human activity (e.g. managing personal behavior or relationships or interactions between people, following rules or instructions) regardless of the recitation of generic computer components or other machinery in its ordinary capacity, then it falls within the ‘Certain Methods of Organizing Human Activity’ grouping of abstract ideas.
Second, the limitations of analyzing the next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area to calculate next day SMP data of the target area recite a mathematical formula or calculation that is used to calculate next day system marginal price of the target area. Thus, the claim recites a mathematical concept. Note that in this claim, this limitation is determined to recite a mathematical concept because the claim explicitly recites calculating with at least four parameters (e.g. next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area). If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts (e.g. mathematical calculations) but for the recitation of generic computer components, then it falls within the ‘Mathematical Concepts’ grouping of abstract ideas.
Accordingly, the claim(s) recite an abstract idea. Analysis proceeds to Step 2A Prong Two.
Step 2A – Prong Two:
This judicial exception is not integrated into a practical application. First, claims 1-11 as a whole merely describes how to generally ‘apply’ the concept of certain methods of organizing human activities in a computer environment. The claimed computer components (i.e. power market price calculation device, processor, memory) are recited at a high-level of generality and are merely invoked as tools to perform an existing manual process. Simply implementing the abstract idea on a generic / general purpose computer is not a practical application of the abstract idea. See MPEP 2106.04(d) and 2016.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The combination of these additional elements is no more than mere instructions to apply the exception using generic computers / general computer components (power market price calculation device, processor, memory). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Hence, the claim is directed to an abstract idea. Analysis proceeds to Step 2B.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the additional element of using power market price calculation device, processor, memory to perform analyzing date / meteorological / past power demand data…, generating next day power demand estimation of target area…, analyzing date / meteorological / past power demand data…, generating next day power demand estimation data of non-target area…, analyzing next day power estimation / date / meteorological / past power demand / past SMP data…, generating next day SMP estimation data of non-target area…, analyzing next day power demand estimation data of target area / generator characteristics / system constraint data…, generating next day power generation estimation data of the target area…, analyzing next day power generation planning estimation data of target area / system constraint / generator characteristics / next day SMP estimation data of non-target area…, calculating next day SMP data of the target area; and following rules or instructions includes analyzing date / meteorological / past power demand data…, generating next day power demand estimation of target area…, analyzing date / meteorological / past power demand data…, generating next day power demand estimation data of non-target area…, analyzing next day power estimation / date / meteorological / past power demand / past SMP data…, generating next day SMP estimation data of non-target area…, analyzing next day power demand estimation data of target area / generator characteristics / system constraint data…, generating next day power generation estimation data of the target area…, analyzing next day power generation planning estimation data of target area / system constraint / generator characteristics / next day SMP estimation data of non-target area…, calculating next day SMP data of the target area amounts to no more than mere instructions to ‘apply’ the exception using generic computers. The same analysis applies here in Step 2B, i.e. mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f). Furthermore, see the Applicant’s specification Fig 4, ¶[0060-64] describing the additional element of the power market price calculation device (with processor and memory) at such a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe the particulars to satisfy 35 USC 112(a). Hence, these features do not provide an inventive concept / significantly more.
The claims do not improve another technology or technical field. Instead the claims represent a generic implementation of organizing human activities ‘applied’ by generic / general purpose computers. The claims do not provide meaningful limitations beyond generally linking the user of an abstract idea to a particular technological environment. At best, the claims are more directed towards solving a business / entrepreneurial problem (i.e. how to calculate a power market price / system marginal price of a target area based on data available), that is tangentially associated with a technology element (e.g. computers), rather than solving a technology based problem. See MPEP 2106.05(a). The claims do not improve the functioning of a computer itself. The claims are more directed towards improving a business / entrepreneurial process rather than improving a computer outside of a business use, i.e. using computers a tool. The claims do not apply the judicial exception with or by use of a particular machine. The claims do not effect a transformation or reduction to a particular article to a different state or thing. The claims do not add a specific limitation other than what is well understood, routine, and conventional in a way that confines the claim to a particular useful application.
Viewing the claim limitations as an ordered combination does not add anything further than looking at each of the claim limitations individually, both with respect to the independent claims 1 and 7, and further considering the addition of dependent claims 2-6, 8-11. Note that the combination of limitations and claim elements add nothing that is not already present when the steps are considered separately, simply reciting implementation as performed by using generic computers / general computer components, see Alice (2014), and does not provide a non-conventional and non-generic arrangement of various computer components to achieve a technical improvement, see BASCOM Global Internet v. AT&T Mobility LLC (2016). Hence, the ordered combination of elements does not provide significantly more. With respect to the dependent claims:
Dependent claims 2 and 8: First, the limitation generating next day power demand data of the target area corresponding to the date data, the meteorological data, and the past power demand data using a first machine learning model which generates the next day power demand estimation data of the target area based on the date data, the meteorological data, and the past power demand data is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. The recitation of a processor and first machine learning model are recited at a high level of generality and amounts to (1) generally linking use of the judicial exception to a particular technological environment (i.e. machine learning) and (2) ‘applying’ the abstract idea on a generic computer, which is not a practical application or significantly more. Second, the limitation of the first machine learning model comprises a model trained by a supervised learning method using first training data having the date data, the meteorological data of the target area, the past power demand data of the target area, and area data as an input and the next day power demand data of the target area as a label is an additional element, that also generally links the use of the judicial exception to a particular technical environment (i.e. supervised machine learning), with high level training required of any machine learning model (e.g. inputs, labels). See Recentive Analytics, Inc. v. Fox Corp (Fed. Cir. 2025) “Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning”. There are no particular technical details regarding the algorithm steps involved to train the machine learning model, or in the machine learning algorithm itself, amounting to no more than using computers as a tool to perform the judicial exception (i.e. generating next day power demand estimation data of the target area), generally linking it to a supervised machine learning environment, which is not a practical application or significantly more. See the Applicant’s specification ¶[0033-34] describing the additional element of the using first machine learning model / training the first machine learning model at such a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe the particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claims 3 and 9: First, the limitation wherein the generating the next day SMP estimation data of the non-target area comprises generating the next day SMP estimation data of the non-target area corresponding to the next day power demand estimation data of the non-target area, the date data, the meteorological data, the past power demand data, and the past SMP data using a second machine learning model which generates the next day SMP estimation data of the non-target area based on the next day power demand estimation data of the non-target area, the date data, the meteorological data, the past power demand data, and the past SMP data is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. The recitation of a processor and second machine learning model are recited at a high level of generality and amounts to (1) generally linking use of the judicial exception to a particular technological environment (i.e. machine learning) and (2) ‘applying’ the abstract idea on a generic computer, which is not a practical application or significantly more. Second, the limitation of the second machine learning model comprises a model trained by a supervised learning method using second training data having the next day power demand estimation data of the non-target area, the date data, the meteorological data of the non-target area, the past power demand data of the non-target area, and the past SMP data of the non-target area as an input and the next day SMP estimation data of the non-target area as a label is an additional element, that also generally links the use of the judicial exception to a particular technical environment (i.e. supervised machine learning), with high level training required of any machine learning model (e.g. inputs, labels). See Recentive Analytics, Inc. v. Fox Corp (Fed. Cir. 2025) “Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning”. There are no particular technical details regarding the algorithm steps involved to train the machine learning model, or in the machine learning algorithm itself, amounting to no more than using computers as a tool to perform the judicial exception (i.e. generating next day SMP estimation data of the non-target area), generally linking it to a supervised machine learning environment, which is not a practical application or significantly more. See the Applicant’s specification ¶[0040-41] describing the additional element of the using a second machine learning model / training the second machine learning model at such a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe the particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claims 4 and 10: First, the limitation wherein the generating the next day power generation planning estimation data of the target area comprises generating the next day power generation planning estimation data of the target area corresponding to the next day power demand estimation data of the target area, the generator characteristics data, and the system constraint data using a third machine learning model which generates the next day power generation planning estimation data of the target area based on the next day power demand estimation data of the target area, the generator characteristics data, and the system constraint data is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. The recitation of a processor and third machine learning model are recited at a high level of generality and amounts to (1) generally linking use of the judicial exception to a particular technological environment (i.e. machine learning) and (2) ‘applying’ the abstract idea on a generic computer, which is not a practical application or significantly more. Second, the limitation of the third machine learning model comprises a model trained by a supervised learning method using third training data having the next day power demand estimation data of the target area, the generator characteristics data of the target area, and the system constraint data of the target area as an input and power generation planning data of the target area, comprising next day power generation amounts of generators disposed in the target area and next day shutdown results of the generators, as a label is an additional element, that also generally links the use of the judicial exception to a particular technical environment (i.e. supervised machine learning), with high level training required of any machine learning model (e.g. inputs, labels). See Recentive Analytics, Inc. v. Fox Corp (Fed. Cir. 2025) “Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning”. There are no particular technical details regarding the algorithm steps involved to train the machine learning model, or in the machine learning algorithm itself, amounting to no more than using computers as a tool to perform the judicial exception (i.e. generating next day power generation planning estimation data of the target area), generally linking it to a supervised machine learning environment, which is not a practical application or significantly more. See the Applicant’s specification ¶[0045-46] describing the additional element of the using a third machine learning model / training the third machine learning model at such a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe the particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claims 5 and 11: The limitations wherein the calculating the next day SMP data of the target area comprises: determining respective generation prices of a plurality of generators included in a generator group based on the next day power generation planning estimation data of the target area, the system constraint data, and the generator characteristics data; determining one or more generation prices which satisfy pricing conditions among the respective generation prices of the plurality of generators to be one or more system prices; and determining a highest value of the one or more system prices to be the next day SMP data of the target area are further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 6: The limitation of a computer-readable recording medium on which a computer program for executing the method of claim 1 using a computer is stored represents an additional element (i.e. mere instructions to implement an abstract idea on a computer or merely use a computer as a tool) that is not indicative of a practical application or significantly more. For the reasons described above with respect to the independent claims, this judicial exception is not meaningfully integrated into a practical application, and is not significantly more than the abstract idea.
Therefore claims 1 and 7, and the dependent claims 2-6, 8-11 and all limitations taken both individually and as an ordered combination, do not integrate the judicial exception into a practical application, nor do they include additional elements that are sufficient to amount to significantly more than the judicial exception. Accordingly, claims 1-11 are ineligible.
Claims 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 7:
Claim 7 recites a computer readable recording medium, which stores a computer program. The specification only sets forth exemplary embodiments of computer readable storage media (see e.g. Applicant Specification ¶[0076]), and therefore, in view of the ordinary and customary meaning of computer readable media and in accordance with the broadest reasonable interpretation of the claim, said medium could be directed towards a transitory propagating signal per se and considered to be non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. 101, Aug 24, 2009, p. 2. Claims that recite nothing but the physical characteristics of a form of energy, such as a frequency, voltage, or the strength of a magnetic field, define energy or magnetism, per se, and as such are non-statutory natural phenomena. O'Reilly, 56 U.S. (15 How.) at 112-14. Moreover, it does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in §101. Please refer to MPEP 2111.01 and the USPTO’s “Subject Matter Eligibility of Computer Readable Media” memorandum dated January 26, 2010, http://www.uspto.gov/patents/law/notices/101_crm_20100127.pdf.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-7, 11 are rejected under 35 U.S.C. 103 as being unpatentable over US patent application publication 2014/0222228 A1 to Sawa et al. in view of “What is System Marginal Price (SMP)? <https://www.lnrg.technology/2023/03/31/what-is-system-marginal-price-smp/> (<https://web.archive.org/web/20240421112825/https://www.lnrg.technology/2023/03/31/what-is-system-marginal-price-smp/> captured on 21 April 2024 using Wayback Machine) to LNRG in view of Republic of Korea patent application publication 20160022614 A1 to Lee et al. in view of “Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction” (Engineering Application of Artificial Intelligence 126 (2023) 106817) to Kim et al.
Claim 1:
Sawa, as shown, teaches the following:
A method of calculating a power market price performed by a process of a power market price calculation device, the method comprising:
analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a target area (Sawa ¶[0003], ¶[0034] details predicting electric power demand for a region using weather data with past and actual demand records, forecasting the power demand for the next day, capturing the actual electric power demand data for each region under the weather and regional conditions when requested, i.e. a target area and non-target area);
analyzing date data, meteorological data, and past power demand data to generate next day power demand estimation data of a non-target area (Sawa ¶[0003], ¶[0034] details predicting electric power demand for a region using weather data with past and actual demand records, forecasting the power demand for the next day, capturing the actual electric power demand data for each region under the weather and regional conditions when requested, i.e. a target area and non-target area);
With respect to the following:
analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data, and past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area;
Sawa, as shown in ¶[0034-35], ¶[0075] details analyzing the next day power demand forecast data based on past and actual power demand with the weather data, and requesting demand forecasts for different weather / regional conditions (i.e. non-target area); but does not explicitly state (1) analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data… to generate next day SMP estimation data of the non-target area; and (2) analyzing past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area.
Regarding (1) “analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data… to generate next day SMP estimation data of the non-target area” LNRG teaches this limitation, determining a system marginal price (SMP) for a specific location (i.e. non-target area) to meet the demand for a given moment, SMP is influenced by constraints, weather conditions, demand fluctuations; and the algorithms for forecasting SMP involve demand patterns, weather conditions, availability of generation resources; and SMP is predicted from several hours or days ahead (LNRG ¶1-3 beginning “System Marginal Price (SMP) refers to the electricity market clearing price…”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data… to generate next day SMP estimation data of the non-target area as taught by LNRG with the teachings of Sawa, with the motivation of “efficient and reliable operation of the electricity grid, as it allows grid operators to anticipate and respond to changes in electricity supply and demand in a timely and effective manner” (LNRG ¶4 beginning “Accurate SMP forecasting can help market participants…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing the next day power demand estimation data, the date data, the meteorological data, the past power demand data… to generate next day SMP estimation data of the non-target area as taught by LNRG in the system of Sawa, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Regarding (2) analyzing past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area, Lee teaches this limitation such that the system marginal price (SMP) can be predicted based on historical information including the previous system marginal price and power supply information and a 12 month seasonality model, and predicts long-term grid marginal prices (Lee pg. 4 ln 50 through pg. 5 ln 5, pg. 7 ln 16-31). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area as taught by Lee with the teachings of Sawa in view of LNRG, with the motivation to predict pricing that “responds to electricity supply history and events” (Lee pg. 7 ln 24-31). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing past system marginal price (SMP) data to generate next day SMP estimation data of the non-target area as taught by Lee in the system of Sawa in view of LNRG, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
With respect to the following:
analyzing the next day power demand estimation data of the target area, generator characteristics data, and system constraint data to generate next day power generation planning estimation data of the target area; and
Sawa, as shown in ¶[0033-34], ¶[0045-46] details generating the next day power demand estimation data of the regions (i.e. target area / non target area), preparing an operation plan, and performing the planning and/or controlling for operating the equipment of the electric power system (e.g. operation of a power plant) based on the reported demand forecast value; but does not explicitly state analyzing the generator characteristics data and system constraint data to generate the next day power generation planning estimation data of the target area. However, Kim teaches this limitation predicting day-ahead peak electricity demand and SMP for Jeju island (i.e. target area), which also considers additional key factors including fuel cost (generator characteristics) and national holiday information of South Korea and number of tourists (system constraint data), in order to maintain a proper reserve margin level and distributed power generation and the cost of limited power generation in the power market, i.e. planning estimation data of the target area (Kim pg. 2 col 1 ¶1, pg. 4 col 1 ¶1-2, pg. 5 col 1 including table 2, pg. 10 col 1 ¶4 through col 2 ¶1 including Fig 16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing the next day power demand estimation data of the target area, generator characteristics data, and system constraint data to generate next day power generation planning estimation data of the target area as taught by Kim with the teachings of Sawa in view of LNRG in view of Lee, with the motivation of “requiring more accurate electricity demand forecasts” and “it is necessary to accurately predict peak demand in advance for a stable power supply. Forecasting electricity demand is an important issue directly connected to facility investment” (Kim Abstract, pg. 3 col 2 ¶5). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing the next day power demand estimation data of the target area, generator characteristics data, and system constraint data to generate next day power generation planning estimation data of the target area as taught by Kim in the system of Sawa in view of LNRG in view of Lee, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Kim (of Sawa in view of LNRG in view of Lee in view of Kim) also teaches the following:
analyzing the next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area to calculate next day SMP data of the target area (Kim pg. 2 col 1 ¶1, pg. 4 col 1 ¶1-2, pg. 5 col 1 including table 2, pg. 10 col 1 ¶4 through col 2 ¶1 including Fig 16 details calculating the day ahead SMP for Jeju island (i.e. target area) to maintain the proper level of reserve margin for a stable power supply (i.e. analyzing next day power generation planning estimation data of the target area), and calculating SMP for Jeju island separately from the calculated SMP for the Korean mainland (i.e. non-target area) adding additional factors because the power generation needs differ regarding fuel cost (generator characteristics data) and number of tourists since Jeju island is the most popular tourist spot in Korea (system constraint data) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include analyzing the next day power generation planning estimation data of the target area, the system constraint data, the generator characteristics data, and the next day SMP estimation data of the non-target area to calculate next day SMP data of the target area as taught by Kim in the system of Sawa in view of LNRG in view of Lee (in view of Kim), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Claim 5:
Sawa in view of LNRG in view of Lee in view of Kim, as shown above, teach the limitations of claim 1. LNRG also teaches the following:
wherein the calculating the next day SMP data of the target area comprises:
determining respective generation prices of a plurality of generators included in a generator group based on the next day power generation planning estimation data of the target area, the system constraint data, and the generator characteristics data (LNRG ¶1-2 beginning “System Marginal Price (SMP) refers to the electricity…”, ¶3 beginning “SMP forecasting typically involves the use of mathematical models…”, ¶6 bullet 2 details SMP is forecasted based on identifying the most expensive generator needed to meet the demand at any given moment and the availability of generation resources (system constraints, generator characteristics), and retail electricity providers use the SMP forecasting to develop pricing strategies that are responsive to changes in market conditions);
determining one or more generation prices which satisfy pricing conditions among the respective generation prices of the plurality of generators to be one or more system prices (LNRG ¶1-3 beginning “System Marginal Price (SMP) refers to the electricity…” details the SMP is a market clearing price determined by the most expensive generator needed to meet the demand at any given moment); and
determining a highest value of the one or more system prices to be the next day SMP data of the target area (LNRG ¶1-2 beginning “System Marginal Price (SMP) refers to the electricity…” details the SMP is determined by the marginal cost of the most expensive generator that is needed to meet the demand at any given moment, and the System Marginal Price represents the real-time cost of electricity in a particular location for a future time period days ahead).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the calculating the next day SMP data of the target area comprises: determining respective generation prices of a plurality of generators included in a generator group based on the next day power generation planning estimation data of the target area, the system constraint data, and the generator characteristics data; determining one or more generation prices which satisfy pricing conditions among the respective generation prices of the plurality of generators to be one or more system prices; and determining a highest value of the one or more system prices to be the next day SMP data of the target area as taught by LNRG with the teachings of Sawa (in view of LNRG in view of Lee in view of Kim), with the motivation of “efficient and reliable operation of the electricity grid, as it allows grid operators to anticipate and respond to changes in electricity supply and demand in a timely and effective manner” (LNRG ¶4 beginning “Accurate SMP forecasting can help market participants…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the calculating the next day SMP data of the target area comprises: determining respective generation prices of a plurality of generators included in a generator group based on the next day power generation planning estimation data of the target area, the system constraint data, and the generator characteristics data; determining one or more generation prices which satisfy pricing conditions among the respective generation prices of the plurality of generators to be one or more system prices; and determining a highest value of the one or more system prices to be the next day SMP data of the target area as taught by LNRG in the system of Sawa (in view of LNRG in view of Lee in view of Kim), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Claim 6:
Sawa in view of LNRG in view of Lee in view of Kim, as shown above, teach the limitations of claim 1. Sawa also teaches the following:
A computer-readable recording medium on which a computer program for executing the method of claim 1 using a computer is stored (Sawa ¶[0031] details a storage medium that stores a program executed by the CPU / processor).
Claim 7:
Claim 7 recites substantially similar limitations as claim 1 and therefore claim 7 is rejected under the same rationale and reasoning presented above for claim 1.
Claim 11:
Claim 11 recites substantially similar limitations as claim 5 and therefore claim 11 is rejected under the same rationale and reasoning presented above for claim 5.
Novelty / Non-Obviousness
Claims 2-4, 8-10 are not rejected under 35 USC 102 or 35 USC 103. The Examiner knows of no art which teaches or suggests the features as recited in claim 2 (including all claimed features in the claims which claim 2 is dependent on), and similarly recited in claim 8; and the features as recited in claim 3 (including all claimed features in the claims which claim 3 is dependent on), and similarly recited in claim 9; and the features as recited in claim 4 (including all claimed features in the claims which claim 4 is dependent on), and similarly recited in claim 10. The following reference(s) teach the additional individual features in the limitations of claims 2-4 and 8-10, however the Examiner has determined it would not have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to combine these references with the previously applied references (e.g. claims 1 and 7: Sawa in view of LNRG in view of Lee in view of Kim) to render claims 2-4 and 8-10 obvious.
Claims 2 and 8:
wherein the generating the next day power demand estimation data of the target area comprises generating next day power demand data of the target area corresponding to the date data, the meteorological data, and the past power demand data using a first machine learning model which generates the next day power demand estimation data of the target area based on the date data, the meteorological data, and the past power demand data, and
the first machine learning model comprises a model trained by a supervised learning method using first training data having the date data, the meteorological data of the target area, the past power demand data of the target area, and area data as an input and the next day power demand data of the target area as a label.
The prior art of Guo et al. (US 2022/0407310 A1) details forecasting future electricity demand using past electricity demand data with a trained machine learning model; the inputted past electricity demand data includes one or more of weather, temperature, humidity, atmospheric pressure, month / time / dates / day of the week; the training data includes data representing weather, temperature, humidity, atmospheric pressure, months of a year, time of day, dates, days of the week, whether or not the day is a holiday, and past electricity usage, identifying trends with the electrical usage over time periods in the training data set (Guo ¶[0022-23], ¶[0026], ¶[0077]).
The prior art of Kinoshita et al. (US 2022/0057768 A1) details power generation planning, and a machine learning model to predict power generation demand using temperature, cloud cover, climate, pressure, humidity, precipitation, demand record, photovoltaic power generation record as model inputs; predicts the power generation outputs and demand; and calculates the supply power required in the electric generator as the generation planning target (Kinoshita ¶[0042], ¶[0083-88]).
Claims 3 and 9:
wherein the generating the next day SMP estimation data of the non-target area comprises generating the next day SMP estimation data of the non-target area corresponding to the next day power demand estimation data of the non-target area, the date data, the meteorological data, the past power demand data, and the past SMP data using a second machine learning model which generates the next day SMP estimation data of the non-target area based on the next day power demand estimation data of the non-target area, the date data, the meteorological data, the past power demand data, and the past SMP data, and
the second machine learning model comprises a model trained by a supervised learning method using second training data having the next day power demand estimation data of the non-target area, the date data, the meteorological data of the non-target area, the past power demand data of the non- target area, and the past SMP data of the non-target area as an input and the next day SMP estimation data of the non-target area as a label.
The prior art of Shihidehpour et al. (US 2003/0182250 A1) details forecasting the market pricing of electricity, using a supervised artificial neural network trained on historical time and load information to forecast locational marginal pricing (LMP) for specific zones over a 24 hour period; training the model with the previous four weeks of historic data considering time, temperature, and transmission congestion (Shihidehpour ¶[0001], ¶[0008], ¶[0043], ¶[0066], ¶[0075], claim 1).
Claims 4 and 10:
wherein the generating the next day power generation planning estimation data of the target area comprises generating the next day power generation planning estimation data of the target area corresponding to the next day power demand estimation data of the target area, the generator characteristics data, and the system constraint data using a third machine learning model which generates the next day power generation planning estimation data of the target area based on the next day power demand estimation data of the target area, the generator characteristics data, and the system constraint data, and
the third machine learning model comprises a model trained by a supervised learning method using third training data having the next day power demand estimation data of the target area, the generator characteristics data of the target area, and the system constraint data of the target area as an input and power generation planning data of the target area, comprising next day power generation amounts of generators disposed in the target area and next day shutdown results of the generators, as a label.
The prior art of Shi et al. (US 2023/0198258 A1) details a power flow machine learning model that generates a power flow allocation for node production of electricity based on predicted consumption by power consumers (e.g. increasing production during peak consuming times; reducing production based on low voltage consumption); and the power flow machine learning model is trained using a power flow training dataset; training data includes market data, weather data, holiday data, seasons; and training data may correlate to any output data (i.e. increasing / reducing production) (Shi ¶[0028], ¶[0038], ¶[0042]).
The prior art of Richmond et al. (US 2025/0118962 A1) details predictive load compensation and real time power conditioning, with a neural network that adjusts power conditioning parameters based on learned correlations (i.e. training data) between historical load requirements and predicted downstream responses (e.g. increasing output voltage, activating harmonic filters) (Richmond ¶[0306-307]).
Conclusion
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BRIAN TALLMAN
Examiner
Art Unit 3628
/BRIAN A TALLMAN/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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