CTNF 19/123,072 CTNF 79297 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment Examiner acknowledges the preliminary amendment filed April 22, 2025. Claims 1 and 4-9 are pending and have been examined on the merits set forth below. Information Disclosure Statement The information disclosure statement (IDS) submitted on 4/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim(s) 1 and 4-9 are rejected under 35 U.S.C. 101 because the claimed invention is not eligible for patenting. There are two criteria for determining subject matter eligibility: (a) first, a claimed invention must fall within one of the four statutory categories of invention set forth in 35 U.S.C. 101, i.e., process, machine, manufacture, or composition of matter ( Step 1 ); and (b) second, a claimed invention must be directed to patent-eligible subject matter and not a judicial exception (unless the claim as a whole includes additional limitations amounting to significantly more than the exception) ( Step 2 ). Step 1: Claim(s) 1 and 4-9 are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test. Step 2: As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes. More specifically, independent claims 1 and 9 recite: acquiring a past data set and a predicted value of a load item that affects the energy demand, the past data set including a past value of the load item and a past value of a demand item indicating the energy demand; determining a degree of similarity between the predicted value of the load item and the past value of the load item and extracting the past data set based on the degree of similarity; dividing the past data set into a plurality of sections with reference to time based on an operating status of the consumer; deriving a regression formula for prediction of the energy demand that is made based on the extracted past data set, for each of the plurality of sections; and calculating a predicted value of the demand item by applying the predicted value of the load item to the regression formula. The claims recite data analysis steps to gather data, perform analysis and generate a predicted demand value. The steps are considered mental processes as they recite observation and evaluations that can be done in the mind or with pen and paper. The nominal recitation of a computer elements in claim 1 does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A. Regarding Prong Two of Step 2A , a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018. In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. Independent claim 1 recites a device comprising a memory and a processor. This amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application. Independent claim 9 does not recite any additional elements that would integrate the abstract idea into a practical application. The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application. Dependent claims 2 and 3 recite details of the first and second ranges comprising confidence intervals to which the process parameter is compared. As in claim 1, this is mental process. There are no additional elements that integrate the abstract idea into a practical application. Dependent claims 4-7 recite details of additional steps of the abstract idea identified in claim 1. These claims can be practically performed by pen and paper or in the mind and is therefore mental process. Any computer implementation amounts to using a computer as a tool and does not integrate the abstract idea into a practical application. Dependent claim 8 recites a storage device and a data acquisition device which amount to using a computer as a tool to perform the storage and gathering of data. There is no integration into a practical application. The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system/software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. Lastly and in accordance with Step 2B , the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, and when considered individually and in combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (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. 07-15-aia AIA Claim(s) 1 and 4-9 is/are rejected under 35 U.S.C. 102 (a)(1) and 35 U.S.C. 102 (a)(2) as being anticipated by Sawa et al . As per claim 1, Sawa et al discloses a demand prediction device that predicts an energy demand of a consumer at a prediction target date and time, the demand prediction device comprising: a memory [0031-0032]; and a processor [0031], wherein the processor is configured to: acquire a past data set and a predicted value of a load item that affects the energy demand, the past data set including a past value of the load item and a past value of a demand item indicating the energy demand ([0008, 64] – weather record from past and weather forecast data are gathered to generate electric power demand data used to predict electric power demand); determine a degree of similarity between the predicted value of the load item and the past value of the load item, and extract the past data set based on the degree of similarity ([0064] – similarity is calculated by clustering weather forecast group in current year and actual weather group last year); divide the past data set into a plurality of sections with reference to time based on an operating status of the consumer ([0064-0067] – determine number of days in weather groups and wherein days of week including weekends/holidays when demand is low (understood as low or no operating status causing less demand)); derive a regression formula for prediction of the energy demand that is made based on the extracted past data set, for each of the plurality of sections ([0067] – regression analysis to generate demand forecast model); and calculate a predicted value of the demand item by applying the predicted value of the load item to the regression formula ([0067] – demand forecast is calculated based on predicted weather data). Claims 2. - 3. (Canceled) As per claim 4, Sawa et al discloses the demand prediction device of claim 1,wherein the processor is configured to divide the past data set into the plurality of sections based on an operation state of equipment provided in the consumer ([0035, 0067] - determine number of days in weather groups and wherein days of week including weekends/holidays when demand is low (understood as low or no operating status causing less demand). As per claim 5, Sawa et al discloses the demand prediction device of claim 1, wherein the processor is configured to divide the past data set into the plurality of sections such that a correlation coefficient between the demand item and the load item in each of the plurality of sections is higher than or equal to a predetermined value ([0047, 0067] – weather groups are set based on similarity of weather data; [0069] – coefficients for a weather variable corresponds to the sensitivity of demand with respect to each variable. For the flag, when this is 1, the flag indicates a variation in the base amount of demand relative to when it is 0. That is, the demand of a forecast target day increases from the previous year by the amount of the regression coefficient of this flag). As per claim 6, Sawa et al discloses the demand prediction device of claim 1, further comprising: wherein the processor is configured to: acquire a learning data set including the load item and the demand item, and produce, using the learning data set, a learned model for inference of the load item for use in the determination of the degree of similarity from the load items that are applied as candidate load items ([0071] – demand forecast model prepared by causing a neural network to learn using actual weather and actual demand of a forecast weather group and actual weather and actual demand of an actual weather group) As per claim 7, Sawa et al discloses the demand prediction device of claim 1, wherein the processor is configured to: acquire a kind of the load item that is applied as a candidate load item for use in the determination of the degree of similarity to the demand item, and select the load item for use in the determination of the degree of similarity from the load items that are applied as candidate load items, based on the kind of the load item, by using the learned model for inference of the load item for use in the determination of the degree of similarity ([0047, 0067] – setting conditions for selecting a similar period of time considers forecast weather group and actual weather group; start and end days are set to screen the periods in the past to some extent. As an actual weather group, the target for calculating similarity is not limited to the previous year, but by the setting here, the similarity may be calculated back to several years ago, such as the year prior to the previous year. This is an example of setting a total number of days of the forecast weather group to be the same as the number of days of the actual weather group). As per claim 8, Sawa et al discloses a demand prediction system comprising: the demand prediction device of claim 1 ; a storage device configured to store the past data set and a predicted value of the load item ([0031-0034, 0064] – storing past and forecast weather data); and a data acquisition device configured to acquire the past data set and the predicted value of the load item, and store the past data set and the predicted value of the load item in the storage device ([0031-0036, 0064] – acquires past and forecast weather data). As per claim 9, Sawa et al discloses a demand prediction method of predicting an energy demand of a consumer at a prediction target date and time, the demand prediction method comprising: acquiring a past data set and a predicted value of a load item that affects the energy demand, the past data set including a past value of the load item and a past value of a demand item indicating the energy demand ([0008, 64] – weather record from past and weather forecast data are gathered to generate electric power demand data used to predict electric power demand); determining a degree of similarity between the predicted value of the load item and the past value of the load item and extracting the past data set based on the degree of similarity ([0064] – similarity is calculated by clustering weather forecast group in current year and actual weather group last year); dividing the past data set into a plurality of sections with reference to time based on an operating status of the consumer ([0064-0067] – determine number of days in weather groups and wherein days of week including weekends/holidays when demand is low (understood as low or no operating status causing less demand)); deriving a regression formula for prediction of the energy demand that is made based on the extracted past data set, for each of the plurality of sections ([0067] – regression analysis to generate demand forecast model); and calculating a predicted value of the demand item by applying the predicted value of the load item to the regression formula ([0067] – demand forecast is calculated based on predicted weather data) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20210358058 – da Mata Cecilio et al - DISTRIBUTED ENERGY RESOURCE SYSTEM DESIGN AND OPERATION - certain external inputs may be used by the top-level designer, including results from one or more forecast models (e.g., fossil fuel energy price forecast model, climate change forecast model, etc.). Such results may include forecasted energy consumption, forecasted energy generation potential, forecasted energy prices, energy demand, energy generation, environmental factors, energy storage capacities, and so forth. Such results may be accompanied by corresponding uncertainties. US 20180128863 – Utsumi et al - Energy Demand Predicting System And Energy Demand Predicting Method - demand granularity and a time granularity are generated on the basis of actual energy demand information for a default historical period; a demand type is generated for each set of demand pattern generation data; an energy demand at a default historical date and time is calculated as a predicted value for evaluation, for each demand type; a contracted demand granularity and a time granularity are determined on the basis of the predicted value for evaluation for each demand type and the actual energy demand information, in such a way as to minimize an error between an estimated value or a predicted value of energy demand at a historical date and time, and the actual observed value at said date and time; and the energy demand value at an arbitrarily defined date and time is estimated or predicted on the basis of the determined results US 20200133220 – Anichkov et al - METHOD AND SYSTEM FOR MANAGING MICROGRID ASSETS - producing a load forecast error probability distribution for the energy load from a historic load forecast and one or more measurements; generating random energy load inputs from at least one of historic load data, the historic load forecast, and the load forecast error probability distribution; using the energy load inputs, further calculating the microgrid performance value using the microgrid performance model US 20140229026 – Cabrini - PREDICTION OF FUTURE ENERGY AND DEMAND USAGE USING HISTORICAL ENERGY AND DEMAND USAGE - computer assisted prediction of energy and demand usage by energy consuming facilities based in part on historical energy and demand usage, and, more particularly, provides a prediction of energy and demand usage for a given consumer facility for a future time interval based on previous energy and demand usage by that facility and associated parameters US 20140058572 – Stein et al - SYSTEMS AND METHODS FOR ENERGY CONSUMPTION AND ENERGY DEMAND MANAGEMENT - interval energy data of a specific building may be collected with a fixed time interval and paired with local historical weather data and other forms of operational data, as well as financial data including historical utility bills, utility rate structures and billing cycle dates. Paired energy interval data and the local historical weather data may be analyzed according to one or more analytic algorithms Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNA LOFTIS whose telephone number is (571)272-6736. The examiner can normally be reached M-F 7:00am-3:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. JOHNNA LOFTIS Primary Examiner Art Unit 3625 /JOHNNA R LOFTIS/ Primary Examiner, Art Unit 3625 Application/Control Number: 19/123,072 Page 2 Art Unit: 3625 Application/Control Number: 19/123,072 Page 3 Art Unit: 3625 Application/Control Number: 19/123,072 Page 4 Art Unit: 3625 Application/Control Number: 19/123,072 Page 5 Art Unit: 3625 Application/Control Number: 19/123,072 Page 6 Art Unit: 3625 Application/Control Number: 19/123,072 Page 7 Art Unit: 3625 Application/Control Number: 19/123,072 Page 8 Art Unit: 3625 Application/Control Number: 19/123,072 Page 9 Art Unit: 3625 Application/Control Number: 19/123,072 Page 10 Art Unit: 3625 Application/Control Number: 19/123,072 Page 11 Art Unit: 3625 Application/Control Number: 19/123,072 Page 12 Art Unit: 3625 Application/Control Number: 19/123,072 Page 13 Art Unit: 3625
Prosecution Insights