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 1-13 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) 1-6, 9 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. USPGPUB 2010/0332373 (hereinafter “Crabtree”) in view of Dölle et al. USPGPUB 2023/0040893 (hereinafter “Dölle”).
As to claim 1, Crabtree teaches a method for classifying decentralized energy resources within a power grid for control of the power grid (paragraph 0005 “Power system reliability and cost will be based on many unique users' ability to interact with highly decentralized markets” and FIG. 21), the method comprising: determining an affiliation of a respective decentralized energy resource to one of a plurality of classes using an artificial neural network (paragraph 0094-0096 “gateway iNode 310 may connect to home computer 510 only via the Internet (often through the use of a remote website operated by another entity for the purpose of allowing homeowners and small business operators to manage their energy management networks. This approach would be common where, for example, local network 302 is a specialized wireless network based on a standard such as 802.15 or Zigbee.TM.; desktop computers are typically not equipped to interface with such networks. In other embodiments, users may interact with their home energy management networks from remote locations using laptop or handheld computers 512 and communicating over external data network 301 (for example, the Internet); in other embodiments, users may interact using mobile devices connected over communications network 500 (typically a wireless network with data capabilities, as are common in the art today)” and paragraph 0189-0195, 0228).
Crabtree does not explicitly teach wherein the artificial neural network is trained to use a load series as input data and determine the assignment of the respective load series to one of the plurality of classes as output data; wherein multiple specified classes are associated with a technical type of the respective decentralized energy resource; carrying out the affiliation by determining an affiliation of the respective energy resource to one of the classes; wherein each of the energy resources is provided with a load series associated with the respective energy resource.
Dölle teaches wherein the artificial neural network is trained to use a load series as input data and determine the assignment of the respective load series to one of the plurality of classes as output data (paragraph 0037-0039 “determined and associated with the individual energy installations by way of the disaggregation method. Known classification methods, for example non-intrusive load monitoring, can also be used for the association” and FIG.1); wherein multiple specified classes are associated with a technical type of the respective decentralized energy resource (paragraph 0038-0039 “energy installation of the energy system has been ascertained by the test unit by means of the disaggregation, the amount of energy ascertained or, in the case of a plurality of energy installations, the amounts of energy ascertained are certified. In other words, the test unit issues a certificate for the amount of energy ascertained”); carrying out the affiliation by determining an affiliation of the respective energy resource to one of the classes (paragraph 0037-0039 “test unit attests to which amount of energy was generated or consumed by which energy installation”, “a certified, that is to say attested or tested, amount of energy generated from renewable sources”); wherein each of the energy resources is provided with a load series associated with the respective energy resource (paragraph 0048-0051 “measured signal is transmitted and/or the certificate is received by a calibrated measuring device with a communication interface, in particular by a smart meter. Smart meters already have a communication interface (smart meter gateway) that can be used for communication, that is to say for exchanging data with the test unit” and paragraph 0037-0039).
Crabtree and Dölle 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 Crabtree, and incorporating determining an affiliation of the respective energy resource to one of the classes, as taught by Dölle.
One of ordinary skill in the art would have been motivated to improve monitoring, controlling or enable real-time and forward balancing of electrical systems through a diverse set of contractual arrangements capable of managing the financial and physical risks in human-machine systems, as suggested by Crabtree (paragraph 0005).
As to claim 2, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Dölle further teaches wherein the class defines a set of flexibly controllable assets (paragraph 0055-0057 “set of available energy installations (A) (assets), for example a photovoltaic installation, cogeneration plant and/or others”).
As to claim 3, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the class defines technical types including: battery stores, heat pumps, charging stations, and photovoltaic installations (paragraph 0089 “include solar panels or arrays, wind turbines, or small internal combustion generators. Electrical sources or generators feed power into the home power system and, if it generates more electricity than is used in the home, they can actually cause electricity to flow back to electricity grid 300. Source iNode 322 is an iNode similar to those iNodes 210 described above” and paragraph 0096 “coordinate the starting of heat pumps”, “distributed battery storage systems” and paragraph 0095 “charging stations”).
As to claim 4, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein one or more of the load series are provided by smart meter data of the respective energy resource (paragraph 0079 “electricity grid 300 to electrical loads 331, again usually through a power distribution panel and often via a electricity usage meter (both not shown for simplicity). Electrical loads 331 can include any electrical devices that consumer electric power, such as heat pumps and air conditioners, lights or common lighting circuits, hot tubs, computers, ovens, ranges, refrigerators and other kitchen appliances, and any number of other electrical devices common in the art. One or more electrical loads 331 are coupled with load iNodes 321, for example of the type shown in FIG. 2”).
As to claim 5, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein one or more of the load series include a residual load series (paragraph 0015-0016 “residential, industrial, institutional, and commercial consumers of energy) is "smart metering". Smart meters are a natural extension of the well-established electricity meters that today measure electricity usage at virtually all consumer locations” and FIG. 2-5).
As to claim 6, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the artificial neural network is trained by load series of known types of decentralized energy resources (paragraph 0145-0160).
As to claim 9, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the artificial neural network includes: an input layer for the input data, an output layer for the output data, and at least two hidden layers (paragraph 0096-0097 “several distinct layers beyond the three layers shown in FIG. 7 are possible”).
As to claim 11, Crabtree teaches a method for controlling a power grid including one or more decentralized energy resources connected via a network node of the power grid(paragraph 0005 “Power system reliability and cost will be based on many unique users' ability to interact with highly decentralized markets” and FIG. 21), the method comprising: controlling electrical power to the respective network node based at least in part on the technical type of the respective energy resources connected to the respective network node (paragraph 0154-0156 “control of one or more energy resources may choose to participate only in real-time transactions, in essence using their energy assets (e.g. storage capability, distributed generation capability or demand reduction capability) as a means to execute arbitrage strategies” and paragraph 0177-0179).
Crabtree does not explicitly teach identifying a technical type of one or more of the energy resources.
However, Dölle teaches identifying a technical type of one or more of the energy resources (paragraph 0005 “energy generated from renewable sources, identifying and verifying the amount of energy generated”).
Crabtree and Dölle 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 Crabtree, and incorporating determining an affiliation of the respective energy resource to one of the classes, as taught by Dölle.
One of ordinary skill in the art would have been motivated to improve monitoring, controlling or enable real-time and forward balancing of electrical systems through a diverse set of contractual arrangements capable of managing the financial and physical risks in human-machine systems, as suggested by Crabtree (paragraph 0005).
As to claim 12, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Dölle further teaches wherein the decentralized energy resources comprise flexibly controllable assets (paragraph 0055-0057 “set of available energy installations (A) (assets), for example a photovoltaic installation, cogeneration plant and/or others”).
As to claim 13, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
Crabtree further teaches wherein the decentralized energy resources include: battery stores, heat pumps, charging stations, and/or photovoltaic installations (paragraph 0089 “include solar panels or arrays, wind turbines, or small internal combustion generators. Electrical sources or generators feed power into the home power system and, if it generates more electricity than is used in the home, they can actually cause electricity to flow back to electricity grid 300. Source iNode 322 is an iNode similar to those iNodes 210 described above” and paragraph 0096 “coordinate the starting of heat pumps”, “distributed battery storage systems” and paragraph 0095 “charging stations”).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. USPGPUB 2010/0332373 (hereinafter “Crabtree”) in view of Dölle et al. USPGPUB 2023/0040893 (hereinafter “Dölle”) further in view of Davies et al. USPGPUB 2020/0225060 (hereinafter “Davies”).
As to claim 7, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
But the combination of Crabtree and Dölle does not explicitly teach wherein the artificial neural network is trained by using a binary cross-entropy as a loss function.
However, Davies teaches wherein the artificial neural network is trained by using a binary cross-entropy as a loss function (paragraph 0250 “loss function may also be a cross-entropy loss function”).
Crabtree, Dölle and Davies 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 Crabtree, and incorporating binary cross-entropy as a loss function, as taught by Davies.
One of ordinary skill in the art would have been motivated to improve monitoring, controlling or enable real-time and forward balancing of electrical systems through a diverse set of contractual arrangements capable of managing the financial and physical risks in human-machine systems, as suggested by Crabtree (paragraph 0005).
As to claim 8, the combination of Crabtree, Dölle and Davies teaches all the limitations of the base claims as outlined above.
Crabtree further wherein the artificial neural network is trained by using a statistical gradient method as an optimizer (paragraph 0021-0024 “statistics techniques with simulation and optimization techniques employing a variety of methods, which can generate data to guide decision-making via automation or human decision support”).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. USPGPUB 2010/0332373 (hereinafter “Crabtree”) in view of Dölle et al. USPGPUB 2023/0040893 (hereinafter “Dölle”) further in view of ISAO et al. WO20222601642020/0225060 (hereinafter “ISAO”).
As to claim 10, the combination of Crabtree and Dölle teaches all the limitations of the base claims as outlined above.
But the combination of Crabtree and Dölle does not explicitly teach wherein the artificial neural network has the function ReLU as inner activation function and the function Sigmoid as outer activation function.
However, ISAO teaches wherein the artificial neural network has the function ReLU as inner activation function and the function Sigmoid as outer activation function (paragraph 0267).
Crabtree, Dölle and ISAO 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 Crabtree, and incorporating ReLU as inner activation function and the function Sigmoid as outer activation function , as taught by ISAO.
One of ordinary skill in the art would have been motivated to improve monitoring, controlling or enable real-time and forward balancing of electrical systems through a diverse set of contractual arrangements capable of managing the financial and physical risks in human-machine systems, as suggested by Crabtree (paragraph 0005).
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.
Tran USPGPUB 20160035052 A1 a systems and methods are disclosed for improving energy efficiency by determining a home composite load signature and energy saving based on predetermined weather factors and a prior success history of the message with predetermined group members; capturing feedback on messages to consumers to see if at least a change has occurred since a communication, wherein the feedback includes a user action taken with respect to the message; and presenting to the user cost savings between the user's existing appliance and a substitute appliance.
Cherian et al. USPGPUB 20120029720 A1 teaches a distributive and decentralized power grid control system passes aggregate information to and from hierarchal nodes. A particular node can operates without knowing anything about which specific assets are available for control below it in the hierarchy or the individual capabilities of those assets. Moreover the objective function is distributed in that parent nodes may or may not have access to all local goals of its children nodes. The computational burden for building a control solution is spread among many computational nodes within the system.
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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