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 .
DETAILED ACTION
Status of Claims
This First Office action is in reply to the application filed on 07 May 2025.
Claims 1-19 are currently pending and have been examined. The Information Disclosure Statement filed 08 September 2025 has been considered by the Examiner. A signed copy is enclosed with this Office Action.
Inventorship
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the Examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the Examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
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 1-19 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-18 are each focused to a statutory category of invention, namely “non-transitory computer-readable medium; system” sets. However independent “method” Claim 19 does not recite any computer architectural components to support the step limitations, meaning a user or person is manually/mentally performing the steps. Despite this failure to pass Step 1, the Examiner proceeds to the next steps of the analysis.
Step 2A: Prong One: Claims 1-18 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention as directed to an abstract idea without significantly more. The claims recite a series of steps (receiving data, generating time windows, and creating topological decompositions) that constitute a method of data manipulation and analysis as representative Claim 1:
“receiving information with temporal data, initial time, and a time unit, the temporal data including any information with time data over a duration;
generating historical time windows including a first set of historical time subsets each of a first length, and a second set of historical time subsets each of a second length, the second length being longer than the first length, the information contained in both the first set of historical time subsets being duplicated in the second set of historical time subsets, the first set of historical time subsets including a consecutive number of non-overlapping historical time subsets ending in the initial time, each of the first set of historical time subsets being of the first length equal to the time unit, the second set of historical time subsets including overlapping historical time subsets ending in the initial time, the first subset of the second set of historical time subsets ending at the initial time and the second subset of the second set of historical time subsets ending at the duration of a time unit before the initial time, the information contained in the first subset and the second subset of the second set of historical time subsets including at least one unit of duplicate information, the historical time windows including information being chronologically before the initial time;
generating future time windows including a first set of future time subsets each of the first length, the first set of future time subsets including a consecutive number of non- overlapping future time subsets beginning at the initial time, each of the first set of future time subsets being of the first length equal to the time unit, the first set of future time subsets including information being chronologically after the initial time;
creating past topological hierarchical decompositions for the first set of historical time subsets and the second set of historical time subsets;
creating future topological hierarchical decompositions for the first set of future time subsets;
creating a past directed graph adjacency array using weights derived from a distance as applied to embeddings from the past topological hierarchical decompositions, and creating a future directed graph adjacency array using weights derived from the distance as applied to embeddings from the future topological hierarchical decompositions;
generating a past window customer attention matrix identifying entity membership of groups across historical time subsets using the embeddings from the past topological hierarchical decompositions, and generating a future window customer attention matrix identifying the entity membership of groups across future time subsets using the embeddings from the future topological hierarchical decompositions;
performing matrix multiplication to multiply the past window customer attention matrix to the past directed graph adjacency array and a transpose of the past window customer attention matrix to create a past customer self-attention array;
performing the matrix multiplication to multiply the future window customer attention matrix to the future directed graph adjacency array and a transpose of the future window customer attention matrix to create a future customer self-attention array;
performing matrix multiplication of the past customer self-attention array to the future customer self-attention array; and
providing a dashboard forecasting demand after the initial time”
As detailed in the MPEP 2106 and commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, the 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter. The 35 U.S.C. 101 Step 2A, Prong One analysis focuses on whether a claim recites a judicial exception by evaluating if it falls into one of three specific groupings: mathematical concepts, mental processes, or certain methods of organizing human activity. Based on the provided steps above, the analysis for Step 2A Prong One is as follows:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. The claims specify mathematical and structural manipulations of time-series data and data parameters. It recites generating non-overlapping subsets of a "first length," generating overlapping subsets of a "second length," forcing "at least one unit of duplicate information," and creating "topological hierarchical decompositions. and time-window partitioning, creating a past directed graph adjacency array using weights, performing matrix multiplication to multiply the past window customer attention matrix to the past directed graph adjacency array and a transpose of the past window customer attention matrix to create a past customer self-attention array, etc.
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Data analysis, organization, and chronological sorting that can practically be performed in the human mind or via basic data processing. The claim fails to recite any physical architecture, specialized hardware, or computational constraints. Because these data manipulations, segmentations, and logic structures can be performed entirely via pen-and-paper or as a series of mental steps by a human analyzer tracking time windows, they constitute a mental process. The claim recites an abstract idea under 35 U.S.C. 101 Step 2A, Prong 1 because its steps collectively define a Mathematical Concept (algorithmic and topological structuring of time intervals) and a Mental Process (data steps that can be conceptually performed without specialized physical machinery).
See MPEP § 2106.04(a) III C.
Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. Independent “method” Claim 19 does not recite any computer architectural components to support the step limitations, meaning a user or person is manually/mentally performing the steps.
Prong Two: Claims 1-18: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1-18 recite additional elements directed to “non-transitory computer-readable medium; system” (e.g., see Applicants’ published Specification ¶’s 5-14). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h). Independent “method” Claim 19 does not recite any computer architectural components to support the step limitations, meaning a user or person is manually/mentally performing the steps.
Step 2B: As explained in MPEP § 2106.05, Claims 1-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of ‘non-transitory computer-readable medium; system”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. Independent “method” Claim 19 does not recite any computer architectural components to support the step limitations, meaning a user or person is manually/mentally performing the steps.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 5-14, 97-109) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “environment 200” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-19 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
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 1-19 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 pre-AIA the applicant regards as the invention.
The claims are narrative in form and replete with indefinite language. The structure which goes to make up the claim limitations must be clearly and positively specified. The structure must be organized and correlated in such a manner as to present a complete operative process. The claims must be in one sentence form only (see MPEP 2173.05(m) “Prolix”). Examiners should reject claims as prolix only when they contain such long recitations or unimportant details that the scope of the claimed invention is rendered indefinite thereby. Most of the claims are recited in paragraph format, particularly the independent claims. Claims 1-19 are rejected as prolix when they contain long recitations that the metes and bounds of the claimed subject matter cannot be determined.
The claims recite the following indefinite phrase limitations: “length; non-overlapping; unit of; using; distance; as applied to; attention; self-attention; any information; cover/coverings; leaf; branch/branch point”. Eash of these indefinite phrase limitations are not specifically defined within the overall claim scope and are interpreted as most broadly and reasonably possible. Additionally, the Examiner notes that the claimed contents of Claims 1-19 amount to non-functional descriptive material that do not functionally alter the claimed method. The recited method steps would be performed in the same manner regardless of what data is contained in “length; non-overlapping; unit of; using; distance; as applied to; attention; self-attention; any information; cover/coverings; leaf; branch/branch point. Thus, the prior art and the claimed invention have identical structure and the claimed descriptive material is insufficient to distinguish the claimed invention over the prior art. see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2106.
For examination purposes, the Examiner will interpret the claims as broadly and reasonably possible. Clarification is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ghosh et al. (Ghosh) (US 2014/0324532).
With regard to Claims 1, 10, and 19, Ghosh teaches a non-transitory computer-readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method/system/method (see at least paragraphs 8-11), the method comprising:
receiving information with temporal data, initial time, and a time unit, the temporal data including any information with time data over a duration ( Referring to FIG. 10, in accordance with an embodiment of the present invention, a system 1000 for the modeling of cyclical demand systems in the presence of dynamic controls or dynamic incentives, comprises a data module 1010, a modeling module 1020 connected with the data module 1010 and forecasting module 1030 connected with the modeling module 1020. In accordance with an embodiment of the present invention, the data module 1010 obtains historical data on one or more demand measurements over a plurality of demand cycles, historical data on incentive signals over the plurality of demand cycles, historical data on at least one covariate over the plurality of demand cycles, wherein the at least one covariate comprises at least one of weather, current events and unemployment, and/or calendar information over the plurality of demand cycles. The modeling module 1020 receives the historical data from the data module 1010, and uses the received historical data to construct a model. The modeling module comprises a specification module 1024 specifying a state-space model and variance parameters in the model, an estimation module 1026 estimating unknown variance parameters, and a conversion module 1022 converting time series data for the one or more demand measurements and the incentive signals expressed in terms of a predetermined time interval to time series data for the one or more demand measurements and the incentive signals expressed in terms of basis coefficients) (see at least paragraphs 8-11, 52, 110);
generating historical time windows (historic data) including a first set of historical time subsets (time period; time intervals) each of a first length, and a second set of historical time subsets each of a second length, the second length being longer than the first length (high or low over a given time period), the information contained in both the first set of historical time subsets being duplicated in the second set of historical time subsets, the first set of historical time subsets including a consecutive number of non-overlapping historical time subsets ending in the initial time, each of the first set of historical time subsets being of the first length equal to the time unit, the second set of historical time subsets including overlapping historical time subsets ending in the initial time, the first subset of the second set of historical time subsets ending at the initial time and the second subset of the second set of historical time subsets ending at the duration of a time unit before the initial time, the information contained in the first subset and the second subset of the second set of historical time subsets including at least one unit of duplicate information, the historical time windows including information being chronologically before the initial time (FIG. 1, steps for obtaining data sets required for creating a model of a cyclical demand system, according to an exemplary embodiment of the present invention, include obtaining historic data on demand measurements over several demand cycles 102, obtaining historic data on weather and other relevant covariates over the same time-period involving several demand cycles 104, obtaining historical data on dynamic price incentives over the same time period involving several demand cycles 106, and obtaining calendar information on day-of-week, holidays over the same time period involving several demand cycles 108. Historical data on demand measurements includes actual electricity usage at regular time intervals, e.g., every hour, 15 minutes, etc. over a given period (e.g., 3 months), of a group of consumers for whom forecasts are being made. The historic data is based on use patterns of consumers who have received the dynamic incentives. According to an embodiment of the present invention, the dynamic incentives include incentive signals supplied to the customer which include, for example, actual pricing and/or more general indications that the price will be high or low over a given time period. FIG. 4A provides a graphical illustration of historic demand data, illustrating daily load-curve profiles (according to hour and day) over a one-year period. More specifically, FIG. 4A illustrates the time series for the load curve for a group of residential electricity customers on the same residential sub-grid, which can be used for creating a model of a cyclical demand system according to an exemplary embodiment of the present invention, including peak usages) (see at least paragraphs 8-11, 52, 110);
generating future time windows (systems and methods for modeling the shape and evolution of a demand cycle in a cyclical demand system using historical data, and for using the evolution model for forecasting the future demand cycle, particularly under the use of dynamic controls such as dynamic price and price-like incentives. When consumers receive information from their supplier (e.g., electricity supplier) about current and/or future prices of the supplied product, consumers may change their consumption behavior or shift load patterns so that they are using more of the supplied product during the periods when it is cheaper to do so. Embodiments of the present invention provide methods and systems for generating demand forecasts that incorporate this type of load shifting behavior. In other words, embodiments of the present invention provide systems and methods for modeling and forecasting how the load shape will change in the wake of pricing information to which consumers are reacting) including a first set of future time subsets each of the first length, the first set of future time subsets including a consecutive number of non- overlapping future time subsets beginning at the initial time, each of the first set of future time subsets being of the first length equal to the time unit, the first set of future time subsets including information being chronologically after the initial time (FIG. 1, steps for obtaining data sets required for creating a model of a cyclical demand system, according to an exemplary embodiment of the present invention, include obtaining historic data on demand measurements over several demand cycles 102, obtaining historic data on weather and other relevant covariates over the same time-period involving several demand cycles 104, obtaining historical data on dynamic price incentives over the same time period involving several demand cycles 106, and obtaining calendar information on day-of-week, holidays over the same time period involving several demand cycles 108. Historical data on demand measurements includes actual electricity usage at regular time intervals, e.g., every hour, 15 minutes, etc. over a given period (e.g., 3 months), of a group of consumers for whom forecasts are being made. The historic data is based on use patterns of consumers who have received the dynamic incentives. According to an embodiment of the present invention, the dynamic incentives include incentive signals supplied to the customer which include, for example, actual pricing and/or more general indications that the price will be high or low over a given time period. FIG. 4A provides a graphical illustration of historic demand data, illustrating daily load-curve profiles (according to hour and day) over a one-year period. More specifically, FIG. 4A illustrates the time series for the load curve for a group of residential electricity customers on the same residential sub-grid, which can be used for creating a model of a cyclical demand system according to an exemplary embodiment of the present invention, including peak usages) (see at least paragraphs 8-11, 48-54, 110);
creating past topological (geographic; location; the residential consumers in the OlyPen project were located in a coastal region of the U.S. Pacific Northwest, where the summer temperatures are moderate so that the cooling load is small, and the winter temperatures are severe so that the heating load is significant. It is understood that residential consumers in other geographic locations may have a different local climate, and therefore different heating and cooling load profiles as well. In accordance with embodiments of the present invention, these climate and temperature dependencies can be broadly specified in proposed load curve models, and appropriate model parameters can be suitably estimated from time series of the residential demand data for a geographic region of interest) hierarchical decompositions (The model defined by (13)-(17) can be written as a hierarchical dynamic linear model, which in turn can be represented as a dynamic linear model (DLM) for which standard methods based on the Kalman filter recursions are available for model estimation and forecasting) for the first set of historical time subsets and the second set of historical time subsets (historic data is based on use patterns of consumers who have received the dynamic incentives) (see at least paragraphs 8-11, 48-54, 81-87, 110);
creating future topological hierarchical decompositions for the first set of future time subsets (Based on the historical data, a determination is made as to what time intervals are to be included in the model. These time intervals may range from, for example, 5 minutes to 1 hour, depending on the frequency at which the price incentive signals are transmitted, and the granularity of the metering and data collection in the smart grid implementation. The duration of these time intervals can vary through the day; for example, a smaller time interval may be used during the morning and evening peak periods, when there are significant variations in the demand, while a larger time interval may be used at night when there is little variation expected in the demand. The duration of these time intervals can also vary from one day to another; for example, during weekends, the morning and evening peaks are typically less pronounced and a larger time interval may be used. Nonuniform and varying time intervals for the observational data may be incorporated into the modeling methodology. For simplicity of exposition and not limitation, this disclosure uses fixed time intervals of equal duration for each day of the data) (see at least paragraphs 8-11, 48-54, 63, 81-95, 110);
creating a past directed graph adjacency array using weights (FIG. 5, graphs 500 and 520 respectively show basis functions with 6 and 9 unequally spaced knots. Each graph represents the magnitude of the basis functions (y-axis) across different hours of a day (x-axis). The pattern of demand over a time period is expressed in terms of the basis functions so that the eventual load curve is a weighted sum of contributions from demand shapes that follow the basis functions. The basis functions represent a simplification of overall load curve so that the overall load curve can be represented by a finite set of basis coefficients, which are the weight given to each of the basis functions) derived from a distance as applied to embeddings from the past topological hierarchical decompositions, and creating a future directed graph (graphs) adjacency array using weights derived from the distance as applied to embeddings from the future topological hierarchical decompositions (computations for block 304 involve "forward values", i.e., values of the covariates at future time points, these are first computed in cases when they are known (e.g., day-of-week effects) and obtained by forecasting in cases when they are not known (e.g., temperature, future price of electricity); these prior computations constitute step 302) (see at least paragraphs 72, 103-107);
generating a past window customer attention matrix identifying entity membership of groups across historical time subsets using the embeddings from the past topological hierarchical decompositions (The model in accordance with an embodiment of the present invention may be a starting point for demand forecasting with the smart grid, and can capture what may be the more important sources of predictable variability in demand, and is simple enough that the model parameters can be estimated from available historical data. It is contemplated that many variants and extensions of the model are possible, and that the invention is not necessarily limited to the model(s) disclosed herein. For example, permitting nonzero off diagonal elements, with an appropriate parsimonious parametrization, in the covariance matrices U and V may enable short-term forecasts to make better use of local patterns of intraday variation. Estimating an optimal number of basis functions and optimal positions of the knots from historical data, using, for example, a Bayesian approach that would use prior beliefs about plausible knot positions, may enable the model to better capture the load curve patterns specific to a particular location or customer set), and generating a future window customer attention matrix identifying the entity membership of groups across future time subsets using the embeddings from the future topological hierarchical decompositions (The model in accordance with an embodiment of the present invention may be a starting point for demand forecasting with the smart grid, and can capture what may be the more important sources of predictable variability in demand, and is simple enough that the model parameters can be estimated from available historical data. It is contemplated that many variants and extensions of the model are possible, and that the invention is not necessarily limited to the model(s) disclosed herein. For example, permitting nonzero off diagonal elements, with an appropriate parsimonious parametrization, in the covariance matrices U and V may enable short-term forecasts to make better use of local patterns of intraday variation. Estimating an optimal number of basis functions and optimal positions of the knots from historical data, using, for example, a Bayesian approach that would use prior beliefs about plausible knot positions, may enable the model to better capture the load curve patterns specific to a particular location or customer set) (see at least paragraphs 81-95);
performing matrix multiplication (covariance matrices/matrix) to multiply the past window customer attention matrix to the past directed graph adjacency array and a transpose of the past window customer attention matrix to create a past customer self-attention array (The model in accordance with an embodiment of the present invention may be a starting point for demand forecasting with the smart grid, and can capture what may be the more important sources of predictable variability in demand, and is simple enough that the model parameters can be estimated from available historical data. It is contemplated that many variants and extensions of the model are possible, and that the invention is not necessarily limited to the model(s) disclosed herein. For example, permitting nonzero off diagonal elements, with an appropriate parsimonious parametrization, in the covariance matrices U and V may enable short-term forecasts to make better use of local patterns of intraday variation. Estimating an optimal number of basis functions and optimal positions of the knots from historical data, using, for example, a Bayesian approach that would use prior beliefs about plausible knot positions, may enable the model to better capture the load curve patterns specific to a particular location or customer set) (see at least paragraphs 81-95);
performing the matrix multiplication to multiply the future window customer attention matrix to the future directed graph adjacency array and a transpose of the future window customer attention matrix to create a future customer self-attention array (The model in accordance with an embodiment of the present invention may be a starting point for demand forecasting with the smart grid, and can capture what may be the more important sources of predictable variability in demand, and is simple enough that the model parameters can be estimated from available historical data. It is contemplated that many variants and extensions of the model are possible, and that the invention is not necessarily limited to the model(s) disclosed herein. For example, permitting nonzero off diagonal elements, with an appropriate parsimonious parametrization, in the covariance matrices U and V may enable short-term forecasts to make better use of local patterns of intraday variation. Estimating an optimal number of basis functions and optimal positions of the knots from historical data, using, for example, a Bayesian approach that would use prior beliefs about plausible knot positions, may enable the model to better capture the load curve patterns specific to a particular location or customer set) (see at least paragraphs 81-95);
performing matrix multiplication of the past customer self-attention array to the future customer self-attention array (The model in accordance with an embodiment of the present invention may be a starting point for demand forecasting with the smart grid, and can capture what may be the more important sources of predictable variability in demand, and is simple enough that the model parameters can be estimated from available historical data. It is contemplated that many variants and extensions of the model are possible, and that the invention is not necessarily limited to the model(s) disclosed herein. For example, permitting nonzero off diagonal elements, with an appropriate parsimonious parametrization, in the covariance matrices U and V may enable short-term forecasts to make better use of local patterns of intraday variation. Estimating an optimal number of basis functions and optimal positions of the knots from historical data, using, for example, a Bayesian approach that would use prior beliefs about plausible knot positions, may enable the model to better capture the load curve patterns specific to a particular location or customer set) (see at least paragraphs 81-95, 110);
providing a dashboard forecasting demand after the initial time (Referring to FIG. 10, in accordance with an embodiment of the present invention, a system 1000 for the modeling of cyclical demand systems in the presence of dynamic controls or dynamic incentives, comprises a data module 1010, a modeling module 1020 connected with the data module 1010 and forecasting module 1030 connected with the modeling module 1020. In accordance with an embodiment of the present invention, the data module 1010 obtains historical data on one or more demand measurements over a plurality of demand cycles, historical data on incentive signals over the plurality of demand cycles, historical data on at least one covariate over the plurality of demand cycles, wherein the at least one covariate comprises at least one of weather, current events and unemployment, and/or calendar information over the plurality of demand cycles. The modeling module 1020 receives the historical data from the data module 1010, and uses the received historical data to construct a model. The modeling module comprises a specification module 1024 specifying a state-space model and variance parameters in the model, an estimation module 1026 estimating unknown variance parameters, and a conversion module 1022 converting time series data for the one or more demand measurements and the incentive signals expressed in terms of a predetermined time interval to time series data for the one or more demand measurements and the incentive signals expressed in terms of basis coefficients) (see at least paragraphs 81-95, 110).
With regard to Claims 2, 11,, Ghosh teaches wherein the information with temporal data includes a plurality of customers over time as well as customer purchasing of a plurality of products, the dashboard forecasting demand including a forecast of demand based on purchasing decisions over time before and after the initial time (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 3, 12, Ghosh teaches further comprising generating and providing an alert to a user when inventory is above a particular threshold or below a particular threshold based on forecasting to enable the user to purchase or not purchase one or more products based on forecasted demand such that inventory levels are not critically low or extremely high relative to demand (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 4, 13, Ghosh teaches: receiving incentive offers from a manufacturer of at least one product, the incentive offering a bonus for sales of the at least one product (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
generating and providing an alert to a user when forecasted demand for the at least one product is at or above an incentive threshold to enable the user to make changes to further increase sales of the at least one product (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 5, 14, Ghosh teaches: receiving a plurality of incentive offers from a plurality of manufacturer for volume sales of a plurality of products, each of the incentives of the plurality of incentive offers offering a bonus for sales of at least one product of the plurality of products, at least a subset of the plurality of incentive offers including different expiration dates after which a particular incentive is no longer available (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
for each of the plurality of incentive offers, identifying forecasted demand for an applicable product of the plurality of products (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
comparing forecasted demand for different products (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
generating and providing an alert to a user when forecasted demand for at least one of the plurality of products is higher than other products of the plurality of products before a particular expiration date expires (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 6, 15, Ghosh teaches:
receiving a plurality of incentive offers from a plurality of manufacturer for volume sales of a plurality of products, each of the incentives of the plurality of incentive offers offering a bonus for sales of at least one product of the plurality of products, at least a subset of the plurality of incentive offers including different expiration dates after which a particular incentive is no longer available (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
for each of the plurality of incentive offers, identifying forecasted demand for an applicable product of the plurality of products (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
comparing forecasted demand for different products (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
comparing overall incentive for a particular number of different products with high forecasted demand relative to forecasted demand of the different products (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
generating and providing an alert to a user when forecasted demand for at least one of the plurality of products is higher than other products of the plurality of products before a particular expiration date expires and when the overall incentive if above an incentive threshold (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 7, 16, Ghosh teaches:
projecting the information to a first embedding based on at least one metric (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
determining a first lowest cover resolution of the first embedding that identifies non- overlapping secondary coverings based on sets within one of the covers of the first embedding; identifying a branch point of a first connected-component network based on the non- overlapping secondary coverings; generating subsets from the branch point based on the non-overlapping secondary coverings (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
if a network generation threshold has not been met, then for each subset from the branch point, determining a second lowest cover resolution that identifies non-overlapping secondary coverings based on the sets within one of the covers of a particular subset to identify a new branch point and new subsets from that branch point of the first connected-component network (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
for each leaf of the connected-component network, identify embeddings of a feature space and generate a local object embedding space using a transposition of segmented features with related objects (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
adding coordinates of objects within each leaf of the local object embedding to a data array (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
projecting array data from the data array to a second embedding (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
determining a third lowest cover resolution of the second embedding that identifies non-overlapping secondary coverings based on sets within one of the covers of the second embedding (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
identifying a branch point of a second connected-component network based on the non- overlapping secondary coverings (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
generating subsets from the branch point based on the non-overlapping secondary coverings (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
if a network generation threshold has not been met, then for each subset from the branch point, determining a second lowest cover resolution that identifies non-overlapping secondary coverings based on the sets within one of the covers of a particular subset to identify a new branch point and new subsets from that branch point of the second connected-component network (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113);
generating at least one past topological hierarchical decomposition (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 8, 17, Ghosh teaches further comprising generating secondary coverings by determining, for each set that has data within the cover, a centroid and determining a radius based on the centroid that covers at least that particular set (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
With regard to Claims 9, 18, Ghosh teaches wherein the centroid for a particular set is determined based on the data within that particular set (see at least paragraphs 8-11, 48-54, 63, 81-95, 110-113).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Ozog (US 8706650)
Sustaeta et al (US 2009/0210081)
Chapados (US 11403573)
Shariff et al. (US 10032180)
Xue et al. (CN 117273599A)
Sharma (WO 2022106297 A1)
Mezzogori, Davide, and Francesco Zammori. "An entity embeddings deep learning approach for demand forecast of highly differentiated products." Procedia Manufacturing 39 (2019): 1793-1800.
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
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