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 the Application
The following is a Final Office Action.
In response to Examiner's communication of 9/5/2025, Applicant responded on 1/16/2026. Amended claim 1, 2, 7, 15, 16, 17, 20. Cancelled claims 8, 14. Added claims 21, 22.
Claims 1-7, 9-13, 15-22 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 2, 7, 15, 16, 17, 20 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 2, 7, 15, 16, 17, 20 are sufficient to overcome the prior art rejections set forth in the previous action. The prior art rejections are hereby withdrawn.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…the claimed software achieves an improvement to machine learning technology…Thus, amended independent claim 1 recites a unique machine learning solution for predicting characteristics of a household from energy usage data. Indeed, the convolutional blocks of the first and second machine learning models, as recited in amended claim 1, are particularly effective at achieving household predictions from energy usage data. Thus, amended claim 1 recites an unconventional implementation for machine learning models that presents a unique and non-generic machine learning system. In other words, amended claim 1 achieves an improvement to machine learning technology, and thus is not directed to an abstract idea…Amended claim 1 recites a unique and non-conventional machine learning solution for processing energy usage data to generate household predictions. This processing of voluminous data requires sophisticated software techniques….Each of these examples relates to a function that is innately tied to computers and thus would not be practically performed in the human mind. Similarly, the recitations of claim 1 are also innately tied to computers, namely the processing of voluminous data via predictions models (e.g., the first and second trained machine learning models). This functionality, as a practical matter, cannot be performed entirely within the human mind…Amended claim 1 at least includes claim limitations that encompass artificial intelligence/ machine learning in a way that cannot be practically performed in the human mind, and thus amended claim 1 is not directed to a mental process…Amended claim 1 recites a unique and non-conventional machine learning solution for processing energy usage data to generate household predictions. As described in this submission, this unique and non-conventional machine learning solution achieves an improvement to machine learning technology. This is not an abstract idea that is similar to the ones identified by Alice or its successor cases, such as the examples disclosed at M.P.E.P § 2106.04(a)(2)(1) and M.P.E.P §2106.04(a)(2)(11). In fact, and as discussed herein, the recitations of amended claim 1 improve machine learning technology given the sophisticated data predictions achieved. Accordingly, the recitations of amended claim 1, and amended independent claims 16 and 20, which recite similar limitations, are not directed to mathematical concepts or certain methods of organizing human activity….the ordered combination of elements recited in amended claim 1 provides a technological solution to a technological problem. At least due to these specific improvements to technology, amended independent claim 1, and amended independent claims 16 and 20, which recite similar limitations, recite significantly more than any alleged abstract idea. The remaining claims depend from one of the above independent claims and these claims recite eligible subject matter for at least the same reasons as the independent claims….” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, the claims and the argued elements, are directed to, … predicting characteristics of a household from energy usage data…, is a problem directed to mental process(i.e. human observing and evaluating human household energy usage and using mathematical models to predict human household income and number of humans in a household in order to target energy usage advertisement campaigns to human households), organizing human activity (i.e. human observing and evaluating human household energy usage and using mathematical models to predict human household income and number of humans in a household in order to target energy usage advertisement campaigns to human households) and mathematical concepts(i.e. human observing and evaluating human household energy usage and using mathematical models to predict human household income and number of humans in a household in order to target energy usage advertisement campaigns to human households), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer and convolution neural network. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more in Step 2B.
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
However, Applicant’s amendments are sufficient to overcome the closest prior art, US Patent Publication to US20210158186A1 to MIMAROGLU et al., (hereinafter referred to as “MIMAROGLU”) in view of KR Patent Publication to KR20160105736A to JIN et al., (hereinafter referred to as “JIN”) in view of US Patent Publication to US20110023045A1 to Yates et al., (hereinafter referred to as “Yates”).
The prior art rejections are hereby withdrawn.
Claim Rejections - 35 USC § 112(b)
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.
Claim 9-10, 15, 22 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 9 recites “The method of claim 8”, Claim 8 is cancelled. It is unclear to which Claim 9 depends upon. Appropriate correction is required.
Claims 10 depend on claim 9 and do not cure the aforementioned deficiencies of claim 9, and thus, claims 10 is rejected for the reasons set forth above regarding claim 9 as a result.
Claim 15 recites “The method of claim 14”, Claim 14 is cancelled. It is unclear to which Claim 15 depends upon. Appropriate correction is required.
Claim 22 recites “The method of claim 14”, Claim 14 is cancelled. It is unclear to which Claim 22 depends upon. Appropriate correction is required.
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 1-7, 9-13, 15-22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 16, 20) recite, “A method for selecting households using … predictions, the method comprising:
storing one or more trained … models, wherein a first … model is trained to predict household income using time-series energy usage data and a second … model is trained to predict a number of people within households using time-series energy usage data, and
wherein the first … model comprises one or more … and the second … model comprises one or more …, and
wherein the first … model and the second…model comprises a parallel architecture;
receiving input data comprising time-series energy usage data for a plurality of households;
predicting, via the first and second trained … models using the time-series energy usage data for a plurality of households, a household income per household and a number of people per household; and
selecting a subset of the households with a predicted household income and a predicted number of people that meet the one or more campaign criteria, wherein the selected subset of the households are targeted by an energy campaign that corresponds to the campaign criteria, the energy campaign comprising one or more actions to alter energy usage for the targeted households.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …storing one or more trained … models, wherein a first … model is trained to predict household income using time-series energy usage data and a second … model is trained to predict a number of people within households using time-series energy usage data, and wherein the first … model comprises one or more … and the second … model comprises one or more …, and wherein the first … model and the second…model comprises a parallel architecture; receiving input data comprising time-series energy usage data for a plurality of households; predicting, via the first and second trained … models using the time-series energy usage data for a plurality of households, a household income per household and a number of people per household; and selecting a subset of the households with a predicted household income and a predicted number of people that meet the one or more campaign criteria, wherein the selected subset of the households are targeted by an energy campaign that corresponds to the campaign criteria, the energy campaign comprising one or more actions to alter energy usage for the targeted households...., under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations; therefore, the claims are directed to a mental process.
Additionally, the limitations regarding, …storing one or more trained … models, wherein a first … model is trained to predict household income using time-series energy usage data and a second … model is trained to predict a number of people within households using time-series energy usage data, and wherein the first … model comprises one or more … and the second … model comprises one or more …, and wherein the first … model and the second…model comprises a parallel architecture; receiving input data comprising time-series energy usage data for a plurality of households; predicting, via the first and second trained … models using the time-series energy usage data for a plurality of households, a household income per household and a number of people per household; and selecting a subset of the households with a predicted household income and a predicted number of people that meet the one or more campaign criteria, wherein the selected subset of the households are targeted by an energy campaign that corresponds to the campaign criteria, the energy campaign comprising one or more actions to alter energy usage for the targeted households..., under the broadest reasonable interpretation, is managing human energy usage based on predicting human income and targeting humans with energy usage campaign, which are commercial interactions and managing human behaviors, therefore, the claims are directed to certain methods of organizing human activities.
Further, …storing one or more trained … models, wherein a first … model is trained to predict household income using time-series energy usage data and a second … model is trained to predict a number of people within households using time-series energy usage data, and wherein the first … model comprises one or more … and the second … model comprises one or more …, and wherein the first … model and the second…model comprises a parallel architecture; receiving input data comprising time-series energy usage data for a plurality of households; predicting, via the first and second trained … models using the time-series energy usage data for a plurality of households, a household income per household and a number of people per household; and selecting a subset of the households with a predicted household income and a predicted number of people that meet the one or more campaign criteria, wherein the selected subset of the households are targeted by an energy campaign that corresponds to the campaign criteria, the energy campaign comprising one or more actions to alter energy usage for the targeted households…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activities, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 16, 20: machine learning, A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to select households using machine learning predictions, wherein, when executed, the instructions cause the processor to, A system for selecting households using machine learning predictions, the system comprising: a processor; and a memory storing instructions for execution by the processor, the instructions configuring the processor to, convolutional neural network blocks
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receiving…”, “…actions to…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receiving …”, data output – “…actions to…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0028] For example, communication device 220 may include a network interface card that is configured to provide wireless network communications. A variety of wireless communication techniques may be used including infrared, radio, Bluetooth®, Wi-Fi, and/or cellular communications. Alternatively, communication device 220 may be configured to provide wired network connection(s), such as an Ethernet connection.
[0029] Processor 222 may include one or more general or specific purpose processors to perform computation and control functions of system 200.Processor 222 may include a single integrated circuit, such as a micro-processing device, or may include multiple integrated circuit devices and/or circuit boards working in cooperation to accomplish the functions of processor 222. In addition, processor 222 may execute computer programs, such as operating system 215,prediction tool 216, and other applications 218, stored within memory 214.
[0030] System 200 may include memory 214 for storing information and instructions for execution by processor 222.Memory 214 may contain various components for retrieving, presenting, modifying, and storing data. For example, memory 214 may store software modules that provide functionality when executed by processor 222. The modules may include an operating system 215 that provides operating system functionality for system 200. The modules can include an operating system 215, a prediction tool 216 that implements the household prediction functionality disclosed herein, as well as other applications modules 218. Operating system 215 provides operating system functionality for system 200. In some instances, prediction tool 216 may be implemented as an in-memory configuration. In some implementations, when system 200 executes the functionality of prediction tool 216, it implements a non- conventional specialized computer system that performs the functionality disclosed herein.
[0031] Non-transitory memory 214 may include a variety of computer-readable medium that may be accessed by processor 222. For example, memory 214 may include any combination of random access memory ("RAM"), dynamic RAM ("DRAM"), static RAM ("SRAM"), read only memory ("ROM"), flash memory, cache memory, and/or any other type of non-transitory computer-readable medium. Processor 222 is further coupled via bus 212 to a display 224, such as a Liquid Crystal Display ("LCD"). A keyboard 226 and a cursor control device 228, such as a computer mouse, are further coupled to communication device 212 to enable a user to interface with system 200.
[0032] In some embodiments, system 200 can be part of a larger system. Therefore, system 200 can include one or more additional functional modules 218 to include the additional functionality. Other applications modules 218 may include various modules of Oracle® Utilities Customer Cloud Service, Oracle® Cloud Infrastructure, Oracle® Cloud Platform, Oracle® Cloud Applications, for example. Prediction tool 216, other applications module 218, and any other suitable component of system 200 can include various modules of Oracle® Data Science Cloud Service, Oracle® Data Integration Service, or other suitable Oracle® products or services.
[0033] A database 217 is coupled to bus 212 to provide centralized storage for modules 216 and 218 and to store, for example, data received by prediction tool 216 or other data sources. Database 217 can store data in an integrated collection of logically related records or files. Database 217 can be an operational database, an analytical database, a data warehouse, a distributed database, an end-user database, an external database, a navigational database, an in-memory database, a document-oriented database, a real-time database, a relational database, an object-oriented database, a non-relational database, a NoSQL database, Hadoop®distributed file system ("HEDS"), or any other database known in the art.
[0034] Although shown as a single system, the functionality of system 200 may be implemented as a distributed system. For example, memory 214 and processor 222 may be distributed across multiple different computers that collectively represent system 200. In one embodiment, system 200 may be part of a device (e.g., smartphone, tablet, computer, etc.). In an embodiment, system 200 may be separate from the device, and may remotely provide the disclosed functionality for the device. Further, one or more components of system 200 may not be included. For example, for functionality as a user or consumer device, system 200 may be a smartphone or other wireless device that includes a processor, memory, and a display, does not include one or more of the other components shown in Fig. 2, and includes additional components not shown in Fig. 2, such as an antenna, transceiver, or any other suitable wireless device component.
[0039] In some embodiments, the design of prediction module 306 can include any suitable machine learning model components (e.g., a neural network, support vector machine, specialized regression model, and the like). For example, a neural network can be implemented along with a given cost function (e.g., for training/gradient calculation). The neural network can include any number of hidden layers (e.g., 0, 1, 2, 3, or many more), and can include feed forward neural networks, recurrent neural networks, convolution neural networks, modular neural networks, and any other suitable type.
[0067] Fig. 5 illustrates a flow diagram for selecting a subset of households using machine learning according to an example embodiment. In some embodiments, the functionality of Fig. 5 can be implemented by software stored in memory or other computer-readable or tangible medium, and executed by a processor. In other embodiments, each functionality may be performed by hardware (e.g., through the use of an application specific integrated circuit ("ASIC"), a programmable gate array ("PGA"), a field programmable gate array ("FPGA"), etc.), or any combination of hardware and software. In embodiments, the functionality of Fig. 5 can be performed by one or more elements of system 200 of Fig. 2.
[0096] Some embodiments include a convolutional neural network (CNN). In practice, many applications of CNNs are designed to recognize visual patterns (e.g., directly from images for classification). On the other hand, embodiments use a CNN architecture for predicting household information using time-series household energy usage data of the first type (e.g., metered electricity usage). For example, the CNN can be designed to have a number of convolutional layers with various kernel sizes and shapes. This design can be used to learn trends and other aspects of the metered energy usage data. In some embodiments, the deep learning framework includes multiple architectures, such as a recurrent neural network (RNN), convolutional neural network (CNN), one or more blocks of known neural networks (e.g., LeNet, AlexNet, ZFNet, GoogleNet/Inception, VGGNet, ResNet, etc.). Any other suitable neural network architecture or machine learning architecture can be implemented.
[0097] In some embodiments, the machine learning model(s) can be configured to generate income prediction(s), number of people prediction(s), age category prediction(s), and/or second type of energy usage prediction(s) using time-series energy usage data of the first type for a plurality of households over a defined period of time, such as weeks, a month, multiple months, a quarter, a year, multiple years, and the like. For example, the time-series data input to generate the prediction(s) can be processed such that it covers the defined period of time. Other input data (e.g., weather data) can be similarly processed to cover the period of time. In some implementations, the period of time can be adjusted, for example during training, testing, retraining, and/or tuning, to achieve a desired performance for the machine learning model(s).
[0098] Some embodiments utilize multiple trained learning models, such as an ensemble approach that combines outputs from multiple trained models. Embodiments that implement the ensemble approach can train or configure individual machine learning models for specific prediction tasks, such as predicting household income, predicting a number of people for a household, predicting an age category for people at the household, and/or predicting second type of energy usage values for the household.
[0099] In some implementations, the multiple trained models of the ensemble approach operate in parallel (rather than in sequence). For example, two, three, or more individual machine learning models can receive, as input, the first type of energy usage (e.g., metered electricity usage) for a given household along with other suitable features for the given household and each can generate an individual component prediction (e.g., income prediction, number of people prediction, age category prediction, or second type of energy usage prediction). In some implementations, the other features for the given household received by each individual model can comprise similar features, different features, or any other suitable set of other household features. For example, each individual model can receive a set of other household features, where some features are shared among the sets and some features are only provided to one or more of the individual models. One or more of the model predictions can be used to select a subset of households/customer profiles that meet a campaign qualification.
[00100] In some implementations, the campaign qualification can relate to one or more campaigns that reduce the energy burden on qualifying households. The qualification for such campaigns can include meeting income criteria based on a number of people at a given household. In some examples, the age of the people at a given household can also impact qualification for a campaign. Different campaigns can reduce energy burden in different ways, such as through cost saving incentives, supporting device upgrades (e.g., credits for upgrading heating and cooling systems, household appliances, household insulation, and other devices), providing credits for low-income households, credits for insulation repairs/upgrades, and the like. Implementations of these campaigns can improve the overall performance of an energy grid, such as by achieving improved efficiencies for energy consuming devices that consume energy from the power grid, increasing the efficiency of heating or cooling a household via improved insulation, or through other suitable improvements.
[00101] The features, structures, or characteristics of the disclosure described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, the usage of "one embodiment,""some embodiments,""certain embodiment,""certain embodiments," or other similar language, throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present disclosure. Thus, appearances of the phrases "one embodiment,""some embodiments,""a certain embodiment,""certain embodiments," or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[00102] One having ordinary skill in the art will readily understand that the embodiments as discussed above may be practiced with steps in a different order, and/or with elements in configurations that are different than those which are disclosed. Therefore, although this disclosure considers the outlined embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of this disclosure. In order to determine the metes and bounds of the disclosure, therefore, reference should be made to the appended claims.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-7, 9-13, 15-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN MAX LEE whose telephone number is (571)272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
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/PO HAN LEE/Primary Examiner, Art Unit 3623