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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed has been considered.
Drawings
The drawings are objected to because Figures 4-6 are illegible; the shaded areas cannot be read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 2 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. On page 2, claim 3 discloses “the elements generating energy from renewable energy sources are photovoltaic panels, or wind farms, or heat pumps”. However, heat pumps are generally understood in the art as part of a system for heating and/or cooling. It is not clear how a heat pump could serve as a source of renewable energy in the same way as a photovoltaic panel or wind farm.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a process.
Regarding claim 1:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“A method for maximising an yield of energy generated from renewable energy sources in a given area, using a minimum number of energy-generating elements, and using an artificial intelligence module connected to a server, comprising the steps of:”
“Determining, by the artificial intelligence module, a vector field of solar irradiation intensity at a given time on every day in the year, for a given geometry of the area and objects located thereon, based on the data acquired in step b);”
“d) Determining, by the artificial intelligence module, a vector field of the direction and strength of the wind at a given time on every day in the year, for a given geometry of the area and objects located thereon, based on the data acquired in step b);”
“e) Determining, by the artificial intelligence module, a vector field of the temperature and moisture of ground at a given time on every day in the year, for a given geometry of the area and objects located thereon, based on the data acquired in step b);”
“f) Determining, by the artificial intelligence module, preferable and unpreferable zones for a given type of elements generating energy from renewable energy sources on the area, based on the data determined in steps d), e), and f);”
“g) Calculating a possible maximum yield output of the energy generated by elements generating energy from renewable energy sources in a given area;”
“h) Selection the type of elements generating energy from renewable energy sources, and planning their spatial distribution in the given area, so as to maximise the yield of the energy generated by these elements;”
The limitations of maximising an yield of energy generated from renewable energy sources in a given area using a minimum number of energy-generating elements, determining a vector field of solar irradiation, determining a vector field of the direction and strength of the wind, determining a vector field of the temperature and moisture of the ground, determining preferable and unpreferable zones, calculating a possible maximum yield output, selecting the types of elements and planning their spatial distribution can be reasonably performed using the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“a) Transmitting to a server historical weather data for a given area, types of elements generating energy from renewable energy sources fulfilling area requirements, as well as area geometry data, including data on: location of the area, terrain, shape and location of buildings in the area;”
“b) Transmitting the data from step a) to the artificial intelligence module;”
“Installing elements generating energy from renewable energy sources according to the plan developed in step h), and connecting them to a power grid by through a shared energy coupling;”
“j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid;”
“k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system; characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).”
The limitations of transmitting to a server historical weather data for a given area, types of elements of generating energy from renewable energy sources, and area geometry data including location of the area, terrain, shape and location of buildings in the area falls under both data gathering and transmitting data and is thus insignificant extra-solution activity (MPEP 2106.05(f) and 2106.05(g)). The limitation of transmitting the data from step a) to the artificial intelligence module falls under transmitting data and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitations of installing elements generating energy from renewable energy sources, activating the energy coupling and transmitting the energy, as well as disconnecting the energy coupling if the instantaneous yield of the generated energy exceeds 100% of the maximum yield in order to avoid an overload are post-solution activities and thus also fall under insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of transmitting to a server historical weather data for a given area, types of elements of generating energy from renewable energy sources, and area geometry data including location of the area, terrain, shape and location of buildings in the area, transmitting the data to the artificial intelligence module, installing elements generating energy, activating the energy coupling and transmitting the energy, as well as disconnecting the energy coupling in order to avoid an overload are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “a) Transmitting to a server historical weather data for a given area, types of elements generating energy from renewable energy sources fulfilling area requirements, as well as area geometry data, including data on: location of the area, terrain, shape and location of buildings in the area;”, WO 2021063461 A1 to Qvist et al. (“Qvist”) describes “obtaining, for at least one geographical area, correlated sets of historical meteorological data and terrain data, relating to the respective geographical area(s)” (page 4 lines 16-18), and “Thus, according to the first aspect, the invention provides a method for planning a layout of a renewable energy site...substations, power electronics, grid connections, etc.” (page 5 lines 8-14). US 20180365352 A1 to Bieganek et al. (“Bieganek”) describes “As an example, the geographic map data 14 can define boundary or property lines…such as buildings, rock formations, bodies of water” (Paragraph 23), “the geographic map data 14 can include data associated with climate data, such as can be indicative of sunlight exposure to the geographic region” (Paragraph 23), and “A solar panel block…and each virtual solar panel block type includes predetermined dimensions and a predefined output power rating” (abstract). US 6098893 A to Berglund et al. (“Berglund”) describes “The invention includes structure for receiving weather forecast data, structure for combining the data with a group of external-building characteristics” (page 4 col. 2 lines 11-14) and “external-building characteristics include at least one of the following parameters: i. the height of the building; ii. the cross-sectional profile of the building from each of a plurality of directions relative to the building; iii. the exterior cross-sectional shape of the building;” (page 4 col. 2 lines 22-29).
With regards to “b) Transmitting the data from step a) to the artificial intelligence module;” Qvist describes “feeding meteorological data and terrain data related to the renewable energy site to the trained data model” (page 4 lines 24-26). US 20190137134 A1 to Koop describes “Server 100 can further communicate with one or more data sources, such as data source 120, for either download/uploading third party data from one or more servers. For example, this data can include but is not limited to: geographic data, climate data, weather data, environmental data, building data, location data…” (Paragraph 81). Berglund describes “sends weather forecast data over the Internet to a building management provider” (abstract) and “At the provider's reception station, data on the external-building characteristics of all the buildings are compiled with the received data” (abstract).
With regards to “Installing elements generating energy from renewable energy sources according to the plan developed in step h), and connecting them to a power grid by through a shared energy coupling;”, Qvist describes “The method may further comprise the step of constructing the renewable energy site in accordance with the layout of the renewable energy site. Thus, renewable energy generating units of the kind or kinds specified by the layout are in fact erected at the positions within the site, which are specified by the layout.” (page 14 lines 13-16). Bieganek describes “The optimized algorithm 402 can thus be stored as the solar farm design 22 in the memory system 12. Accordingly, the solar farm design 22 can be accessed from the memory system 12 for installation (e.g., building) a resultant solar farm on the geographic region associated with the geographic map data 14.” (Paragraph 66). EP 3823125 A1 to Mendizabal et al. (“Mendizabal”) describes “All energy generating units as well as the units of the energy storage system may be connected at a common busbar” (Paragraph 8) and “Between the busbar at which all the energy generating units are connected… allowing to disconnect the power plant from the utility grid in case of a failure or the like” (Paragraph 9).
WO 2020123799 A1 to Burra et al. (“Burra”) describes “the electrical power generated by one or more of these power sources may be supplied to a power grid 108 and/or one or more electrical loads via a point of interconnect (POI) 110” (Paragraph 26).
With regards to “j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid;”, Mendizabal describes “All energy generating units as well as the units of the energy storage system may be connected at a common busbar” (Paragraph 8) and “Between the busbar at which all the energy generating units are connected… allowing to disconnect the power plant from the utility grid in case of a failure or the like” (Paragraph 9).
Burra describes “the electrical power generated by one or more of these power sources may be supplied to a power grid 108 and/or one or more electrical loads via a point of interconnect (POI) 110” (Paragraph 26).
With regards to “k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system; characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).”, Mendizabal describes “All energy generating units as well as the units of the energy storage system may be connected at a common busbar” (Paragraph 8), “Between the busbar at which all the energy generating units are connected… allowing to disconnect the power plant from the utility grid in case of a failure or the like” (Paragraph 9), and “If, plant power output is above a first threshold but below a second threshold…a relay at the input of the plant transformer will disconnect the plant from the grid in case of overcurrent.” (Paragraph 32). US 20190280640 A1 to Ganireddy et al. (“Ganireddy”) describes “The switch 206 is operated to selectively connect or disconnect the respective hybrid power generation system 100. In some embodiments, the switch 206 may be electronically controllable by the hybrid controller 110 of the respective hybrid power generation system 100” (paragraph 32) and “In some embodiments…a maximum apparent power limit of the hybrid-level transformer 204” (Paragraph 42). Qvist describes “The deep learning algorithm may comprise convolutional network models, recurrent neural networks, generative adversarial network models and/or feed forward models. Thus, it may be possible to use a deep learning algorithm comprising a variety of models.” (page 19 lines 4-7). Performance improvement of a photovoltaic system with a radial basis function network based on particle swarms optimization by Hichem et al. (“Hichem”) describes “a RBF neural network based on particle swarm optimization (PSO) algorithm” (abstract). Knowledge Distillation : Simplified by Ganesh describes “teaching a smaller network, step by step, exactly what to do using a bigger already trained network” (page 1 What is Knowledge Distillation?). Distilling the Knowledge in a Neural Network by Hinton et al. (“Hinton”) describes “distilling the knowledge from a large model into a small one” (page 2).
Regarding claim 2, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of maximizing the energy output according to claim 1, characterized in that the elements generating energy from renewable energy sources are photovoltaic panels, or wind farms, or heat pumps falls under mere instructions to apply an exception (MPEP 2106.06(f))
Regarding claim 3, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of maximizing the energy output according to claim 1, characterised in that historical data on the prices of electrical energy in a given area are additionally transmitted to the server falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 4, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations of maximizing the energy output according to claim 1, characterised in that the data on the type of buildings and their function are additionally transmitted to the server in step a), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well falls under data gathering and is thus insignificant extra-solution activity. (MPEP 2106.05(g))
Regarding claim 5, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of maximizing the energy output according to claim 2, characterised in that historical data on the prices of electrical energy in a given area are additionally transmitted to the server falls under data gathering and is thus insignificant extra-solution activity. (MPEP 2106.05(g))
Regarding claim 6, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations of maximizing the energy output according to claim 2, characterised in that the data on the type of buildings and their function are additionally transmitted to the server in step a), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well falls under data gathering and is thus insignificant extra-solution activity. (MPEP 2106.05(g))
Regarding claim 7, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations of maximizing the energy output according to claim 3, characterised in that the data on the type of buildings and their function are additionally transmitted to the server in step a), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well falls under data gathering and is thus insignificant extra-solution activity. (MPEP 2106.05(g))
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.
Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qvist et al. (WO 2021063461 A1) in light of Shen et al. (US 20160246271 A1), further in light of Koop (US 20190137134 A1), further in light of Mendizabal et al. (EP 3823125 A1), further in light of Hichem et al. (Performance improvement of a photovoltaic system....particle swarms optimization).
Regarding claim 1, Qvist teaches a method for maximising an yield of energy generated from renewable energy sources in a given area (abstract), using a minimum number of energy-generating elements, and using an artificial intelligence module connected to a server (abstract “data model”), comprising the steps of:
a) Transmitting to a server historical weather data for a given area (page 4 lines 16-18 “meteorological data”, lines 24-26), types of elements generating energy from renewable energy sources fulfilling area requirements (page 3 lines 8-14), as well as area geometry data, including data on: location of the area, terrain (page 4 lines 16-18 “terrain data”);
b) Transmitting the data from step a) to the artificial intelligence module (page 4 lines 26-28);
c) Determining, by the artificial intelligence module, a vector field of solar irradiation intensity at a given time on every day in the year, for a given geometry of the area and objects located thereon, based on the data acquired in step b) (page 4 lines 3-6, page 11 lines 2-4 “Thus, the historical meteorological data may be a series of values of a quantity obtained at successive times, such as approximately every 30 minutes, 1 hour, 1 day, etc”);
d) Determining, by the artificial intelligence module, a vector field of the direction and strength of the wind at a given time on every day in the year, for a given geometry of the area and objects located thereon, based on the data acquired in step b) (page 4 lines 3-6, page 13 lines 2-4 “Thus, the historical meteorological data may be a series of values of a quantity obtained at successive times, such as approximately every 30 minutes, 1 hour, 1 day, etc”);
f) Determining, by the artificial intelligence module, preferable and unpreferable zones for a given type of elements generating energy from renewable energy sources on the area, based on the data determined in steps d), e), and f) (page 10 lines 6-19 where the preferable zones are the optimal locations, and the unpreferable zones are all other locations in the area);
g) Calculating a possible maximum yield output of the energy generated by elements generating energy from renewable energy sources in a given area (page 12 lines 7-9);
h) Selection the type of elements generating energy from renewable energy sources, and planning their spatial distribution in the given area, so as to maximise the yield of the energy generated by these elements (page 10 lines 6-19);
Installing elements generating energy from renewable energy sources according to the plan developed in step h) (page 14 lines 13-16).
Qvist does not specifically teach:
the area geometry data including shape and location of buildings in the area;
e) Determining, by the artificial intelligence module, a vector field of the temperature and moisture of ground at a given time on every day in the year, for a given geometry of the area and objects located thereon.
Qvist also does not teach:
and connecting them to a power grid by through a shared energy coupling;
j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid;
k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system; characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).
However, Shen teaches collecting data on the shape and location of buildings (Paragraph 72 “shape of the building”, “geographic location of the building”).
Both Qvist and Shen are analogous to the claimed invention because both are in the field of efficient power management. It would have been obvious of one of ordinary skill in the art to incorporate the method of Qvist with the data of Shen. Such a combination would merely be combining prior art elements (gathering terrain data for an area, gathering data on buildings) according to known methods to yield predictable results, where the predictable result would be gathering terrain data for an area factoring in the shape and location of buildings within the area.
The combination of Qvist and Shen does not specifically teach:
e) Determining, by the artificial intelligence module, a vector field of the temperature and moisture of ground at a given time on every day in the year, for a given geometry of the area and objects located thereon.
The combination of Qvist and Shen also does not teach:
and connecting them to a power grid by through a shared energy coupling;
j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid;
k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system; characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).
However, Koop teaches a vector field of the temperature and moisture of ground at a given time on every day in the year (Paragraph 84 “undisturbed earth temperature”, “ground moisture”).
Both Qvist and Koop are analogous to the claimed invention because both are in the field of power management. It would be obvious to incorporate the method of Qvist with the temperature and moisture data of Koop. Such a combination would merely be combining prior art elements (gathering terrain data for an area, gathering data on temperature and moisture of the ground) according to known methods to yield predictable results, where the predictable result would be gathering terrain data for an area factoring in the temperature and moisture of the ground in the area.
The combination of Qvist, Shen, and Koop does not teach:
and connecting them to a power grid by through a shared energy coupling;
j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid;
k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system; characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).
However, Mendizabal teaches and connecting them to a power grid by through a shared energy coupling (Paragraph 8 “common busbar”, “which may be connected at the secondary side to a utility grid”);
j) Activating the energy coupling, and transmitting the energy produced by the installation made in step i) to the power grid (Paragraph 8 “common busbar”, “which may be connected at the secondary side to a utility grid”);
k) Disconnecting the energy coupling, and interrupting the transmission of energy to the power grid, if the instantaneous yield of the generated energy exceeds 100% of the maximum yield of the generated energy calculated in step g), in order to avoid an overload of the system (Paragraph 32 “a relay at the input of the plant transformer will dis-connect the plant from the grid in case of overcurrent”);
Both Qvist and Mendizabal are analogous to the claimed invention because they are in the field of renewable power plants. It would be obvious to one of ordinary skill in the art to incorporate the method of Qvist with the method of Mendizabal to protect components of the power grid (Paragraph 33).
The combination of Qvist, Shen, Koop, and Mendizabal does not teach:
characterised in that the steps from c) to h) are carried out automatically by means of the artificial intelligence module comprising two neural networks, the first one of them being an RBF type (Radial Basis Function), and the second one being a PSO type (Particle Swarm Optimisation), wherein first network (RBF) has a negative feedback loop comprising the second network (PSO) and aggregating the data generated by the second network (PSO).
However, Hichem et al. teaches an artificial intelligence module comprising an artificial intelligence module comprising two neural networks, the first one of them being an RBF type, and the second one being a PSO type (abstract “a RBF neural network based on particle swarm optimization (PSO) algorithm”) wherein first network has a negative feedback loop comprising the second network and aggregating the data generated by the second network (abstract “The PSO algorithm is used to optimize the parameters of the RBFNN”, “A modified PSO algorithm is used to determine the centers, widths, and connection weights of RBF neural network to ensure a good follow-up of the MPPT”, page 4 “reduce the number of neurons and error value between target and real output”).
Both Qvist and Hichem are analogous to the claimed invention because they are in the field of control using neural networks. It would be obvious to one of ordinary skill in the art to incorporate the method of Qvist with the RBF neural network of Mendizabal for increased training speed and accuracy (abstract).
Regarding claim 2, Qvist also teaches the method for maximising the energy output according to claim 1, characterised in that the elements generating energy from renewable energy sources are photovoltaic panels (page 5 line 11 “photovoltaic cells”), or wind farms (page 5 line 11 “wind turbines”), or heat pumps.
Regarding claim 3, the combination of Qvist, Shen, Koop, Mendizabal, and Hichem,
Shen also teaches characterised in that historical data on the prices of electrical energy in a given area are additionally transmitted to the server in step a). (Paragraph 83 “energy prices”)
Regarding claim 4, the combination of Qvist, Shen, Koop, Mendizabal, and Hichem teaches the method for maximising the energy output according to claim 1.
Shen also teaches characterised in that the data on the type of buildings and their function are additionally transmitted to the server in step a) (Paragraph 72 “building type”), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well (Paragraph 74 “occupancy information”).
Regarding claim 5, the combination of Qvist, Shen, Koop, Mendizabal, and Hichem teaches the method for maximising the energy output according to claim 2.
Shen also teaches characterised in that historical data on the prices of electrical energy in a given area are additionally transmitted to the server in step a). (Paragraph 83 “energy prices”)
Regarding claim 6, the combination of Qvist, Shen, Koop, Mendizabal, and Hichem teaches the method for maximising the energy output according to claim 2.
Shen also teaches characterised in that the data on the type of buildings and their function are additionally transmitted to the server Preliminary Amendment Page -4- in step a) (Paragraph 72 “building type”), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well (Paragraph 74 “occupancy information”).
Regarding claim 7, the combination of Qvist, Shen, Koop, Mendizabal, and Hichem teaches the method for maximising the energy output according to claim 3.
Shen also teaches characterised in that the data on the type of buildings and their function are additionally transmitted to the server in step a) (Paragraph 72 “building type”), and in the case of residential buildings, the data on the number of people inhabiting them are transmitted as well (Paragraph 74 “occupancy information”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM XIANG ZHANG whose telephone number is (571)272-1276. The examiner can normally be reached M-F (8:30 AM - 5 PM).
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/W.X.Z./ Examiner, Art Unit 2117
/ROBERT E FENNEMA/ Supervisory Patent Examiner, Art Unit 2117
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