DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruffner et al. USPGPUB 20160373453 A1 (hereinafter “Ruffner”) in view of Crabtree et al. USPGPUB 20100332373 A1 (hereinafter “Crabtree”).
As to claim 1, Ruffner teaches a system (abstract “an energy management system”) comprising: networked production probes configured to capture production data from a plurality of energy providers (paragraph 0078-0082 and FIG. 1 “Figures, FIG. 1 depicts a system 100 for managing demand-response programs and events according to an embodiment. System 100 includes a plurality of electrical power generators 110A-110N, a utility provider computing system 120, an energy management system 130, a communication network 140, a plurality of energy consumer residences 150A-150N, and a power distribution network 160”); networked consumption probes configured to capture consumption data from a plurality of consumers of energy from a consumer energy distribution network (paragraph 0079-0084 “management includes ensuring the electricity is successfully communicated from the power generators 110A-110N to the residences 150A-150N, monitoring the amount of energy consumption at each of the residences 150A-150N, and collecting fees from occupants of the residences 150A-150N in accordance with the their respective monitored amount of energy consumption”); and a machine learning engine configured to receive and analyze the production data and the consumption data to detect patterns (paragraph 0376-0384 “smart meter 218 monitors some or all energy (electricity, gas, etc.) consumed by the devices in and around the structure 250, and may provide such raw data to the utility provider using some type of data communications link. However, likely due in significant part to the relatively uneven historical development and adoption patterns of smart meter”), including patterns of consumer and producer behavior (paragraph 0376-0392 “smart meter 218 and one or more of the intelligent, network-connected devices inside the smart home (e.g., thermostat 202), wherein the smart-home device then transmits the raw data to the utility provider through the smart home's high-speed broadband connection. In some embodiments, the alternative data communication channel can be provided by implementing a low-power RF connection (for example, using ZigBee, 6LowPAN, WiFi, NFC, etc.) between the smart meter 218 and one or more electronic devices of the smart home environment 200 (e.g., thermostat 202). In other embodiments, wired communications paths can be used. In such a case, real-time status updates may advantageously be communicated to the utility provider computing system 120 from the smarter meter 218 via another electronic device in the smart home environment 200 (e.g., thermostat 202), router 260, network 262, and remote server 264/energy management system 130” and “energy consumer indicates an increase in the amount of energy shifting resulting from participation in a first DR event, the DR implementation profile for a second, subsequent event may be made to more aggressively shift energy as compared to the DR implementation profile originally generated for the first event. FIG. 27 illustrates a particular process for learning the preferences of energy consumers during DR events” and 0329 “determining the DR implementation profile for a subsequent DR event profile. For example, as described with reference to operation 1602, past DR event behavior may be used when identifying the basis for the DR implementation profile. One particular process for using the stored deviations is described with reference to FIG. 28”).
Ruffner does not explicitly teach wherein machine learning output is used by an energy retail marketplace platform prediction engine to improve prediction of consumer and producer behavior.
However, Crabtree teaches wherein machine learning output is used by an energy retail marketplace platform prediction engine to improve prediction of consumer and producer behavior (paragraph 0163-0165, 0079-083, 0109-0110).
Ruffner and Crabtree are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management system.
Therefore at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above energy management system, as taught by Ruffner, and incorporating prediction engine to improve prediction of consumer and producer behavior, as taught by Crabtree.
One of ordinary skill in the art would have been motivated o systems for managing and optimizing the participation of human and machine participants in energy and externality markets, as suggested by Crabtree (paragraph 0003).
As to claim 2, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Ruffner further teaches wherein the patterns of user behavior include regional consumption patterns (paragraph 0180-0186, 0336-0353).
As to claim 3, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Ruffner further teaches wherein the patterns of user behavior include patterns of consumption based on consumer energy source selection patterns (paragraph 0336-0357).
As to claim 4, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of user behavior include patterns of consumption based on energy pricing differences (paragraph 0006-0013 and 0023-0027).
As to claim 5, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of user behavior include patterns of responsiveness to pricing alerts or other messages (paragraph 0127-0131).
As to claim 6, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of user behavior include consumer generated energy usage patterns (paragraph 0078-0083 and 0131).
As to claim 7, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of user behavior include consumer generated energy sell-back patterns (paragraph 0010-0025 and 0036).
As to claim 8, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of producer behavior include allocation of energy demand to providers who use different raw energy sources (paragraph 0154-0156).
As to claim 9, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the allocation of energy demand is based on relative price of energy from different raw energy sources (paragraph 0154-0165).
As to claim 10, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the allocation of energy demand is based on availability of energy from the different raw energy sources (paragraph 0012-0013 and 0074-0078).
As to claim 11, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the patterns of user behavior include gamification patterns detected from consumer interactions with an energy distribution network gamification engine (paragraph 0114-0118).
As to claim 12, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the gamification patterns include patterns of rewards provided to gamification engine users (paragraph 0114-0118).
As to claim 13, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine facilitates improving prediction of impact of factors on energy price, wherein the prediction of factors that have an impact on energy price include current and near-term usage (paragraph 0154-0158).
As to claim 14, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine facilitates improving prediction of impact of factors on energy price, wherein the prediction of factors that have an impact on energy price include current and near-term demand (paragraph 0006-0017).
As to claim 15, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine facilitates improving prediction of impact of factors on energy price, wherein the prediction of factors that have an impact on energy price include current usage (paragraph 0143-0152).
As to claim 16, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine uses a model type classifier performs classification based on similarity calculated with weights on attributes of patterns (paragraph 0078-0082).
As to claim 17, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine uses a model type classifier that facilitates detection and classification of similar users in similar homes experiencing similar weather with similar energy prices and energy mix (paragraph 0094-0102 and 0254).
As to claim 18, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine uses a hybrid of model type and neural network type classifiers, and/or a hybrid of cluster type and neural network type classifiers.
As to claim 19, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine uses a neural network is used to adjust presence of elements and/or weights on a model to improve the model (paragraph 0125 and 0010-0012).
As to claim 20, Ruffner and Crabtree teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the machine learning engine uses a neural network to adjust weights of clustering to arrive at better clusters (paragraph 0131-0135).
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and listed on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Crabtree et al. USPGPUB 20100218108 A1 teaches a system for presentation and management of energy-related information and securities, comprising a digital exchange, a client system comprising a plurality of display and input modalities, a communications interface software adapted to allow communications between the client system and the digital exchange, and a control interface within the client system adapted to drive the display and input modalities, wherein the control interface, on receiving input from a user, causes data from the digital exchange to be retrieved and displayed in one or more of the display modalities to the user, and upon receipt of a request from the user via an input modality of the client system after the user has retrieved and reviewed data from the digital exchange, an order to execute a transaction is transmitted to the digital exchange by the client system, and on receipt of an order to execute a transaction from a client system, the digital exchange combines the ordered transaction with other similar transactions from a plurality of users and thereby creates or modifies a marketable security visible to at least one other user via the digital exchange, is disclosed.
Hurri et al. USPGPUB 20120089523 A1 teaches a systems, devices, protocols, and processes for retrieving, accessing, and presenting information of energy usage using a distributed storage process and distributed logical services to provide a user with real-time energy usage information and visualization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL KARIM whose telephone number is (571)270-3279. The examiner can normally be reached on Monday-Thursday 8:00-4:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on 571 272 4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZIAUL KARIM/Primary Examiner, Art Unit 2119
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