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 .
Information Disclosure Statement
The three information disclosure statements filed 06/25/2024, 01/02/2026, and 03/21/2026 have been considered.
Drawings
The drawings filed 06/25/2024 are considered acceptable for examination purposes.
Claim Interpretation
The “environmental factors” as seen throughout the claims will be interpreted to encompass anything related to the environment.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims recite "a computer-readable storage medium" without specifying "non-transitory" or otherwise excluding transitory signals/signals per se. Though it is recognized that the specification describes “non-transitory computer-readable storage medium(s)” as seen in Paragraph 39, it is not explicitly stated that a “computer-readable storage medium” is non-transitory.
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) 1-3, 5, 8-10, 12, 15-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240034180 A1 to Bhimani et al. (herein "Bhimani") in light of US 20110082598 A1 to Boretto et al. (hereiin "Boretto").
Regarding claim 1, Bhimani teaches a method (abstract “approaches, techniques”) comprising:
determining a future time to receive energy at a location based on historical energy consumed at the location over time (Paragraph 62, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”);
wherein the plurality of energy sources include an electricity provider (Paragraph 40 “The charging station is connected with a grid”), an electric vehicle (EV) battery (Paragraph 60 “can use the collected vehicle data…supplying electricity to homes”),
Bhimani does not specifically teach:
determining respective environmental factors of receiving energy from a plurality of energy sources at the location at the future time,
and an on-premises energy storage system of the location;
selecting an energy source from among the plurality of energy sources at the location based on the respective environmental factors at the future time; and
receiving energy from the selected energy source at the future time.
However, Boretto teaches determining respective environmental factors of receiving energy from a plurality of energy sources at the location
and an on-premises energy storage system of the location (Paragraph 6 “electrical energy storage device”);
selecting an energy source from among the plurality of energy sources at the location based on the respective environmental factors
receiving energy from the selected energy source
While Boretto does not specifically teach determining environmental factors of, selecting, and then receiving energy from energy sources at a future time, Bhimani already teaches an electrical system with predictive elements that performs determinations focusing on a future time.
Bhimani and Boretto are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Boretto in order to store power that might be lost when supply exceeds demand (Paragraph 27).
Regarding claim 2, the combination of Bhimani and Boretto teaches the method of claim 1.
Bhimani also teaches wherein the determining the respective environmental factors comprises predicting an anticipated demand of the electricity provider at the future time (Paragraph 68 “generate predictions/forecasts of electricity demands and emissions at grid and/or vehicle levels and/or home levels”) based on historical demand of the electricity provider (Paragraphs 53-54 “grid data”, “Additionally, optionally or alternatively…at the various levels”), and determining an environmental factor of the electricity provider at the future time based on the anticipated demand of the electricity provider at the future time (Paragraph 46 where the environmental factor of emissions increases when the grid is unable to meet demand with only green and/or renewable energy).
Regarding claim 3, the combination of Bhimani and Boretto teaches the method of claim 1.
Bhimani also teaches wherein the determining the respective environmental factors comprises predicting a source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”) based on historical charging data of the EV battery (Paragraph 58 “vehicle data”, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”) and determining an environmental factor of the EV battery at the future time based on the predicted source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”)
Regarding claim 5, the combination of Bhimani and Boretto teaches the method of claim 1.
Bhimani also teaches wherein the receiving comprises controlling, via a panel installed at the location (Paragraph 185), energy to be transferred to the location from at least one of the electricity provider and the EV battery, at the future time (Paragraph 50 “Hence, the production models…generate optimized electricity charging and/or transfer events”).
Boretto also teaches storing the energy within the on-premises energy storage until an energy storage threshold is reached (Paragraph 76 “maintain the battery or BSM 206 at a desired charge level”).
Regarding claim 8, Bhimani teaches a system (“mechanisms”) comprising:
At least one processor (Paragraph 181); and
A memory, wherein the at least one processor and the memory are communicably coupled (Paragraph 181), and wherein the at least one processor is configured to:
determine a future time to receive energy at a location based on historical energy consumed at the location over time (Paragraph 62, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”);
wherein the plurality of energy sources include an electricity provider (Paragraph 40 “The charging station is connected with a grid”), an electric vehicle (EV) battery (Paragraph 60 “can use the collected vehicle data…supplying electricity to homes”),
Bhimani does not specifically teach:
determine respective environmental factors of receiving energy from a plurality of energy sources at the location at the future time,
and an on-premises energy storage system of the location;
select an energy source from among the plurality of energy sources at the location based on the respective environmental factors at the future time; and
receive energy from the selected energy source at the future time.
However, Boretto teaches determine respective environmental factors of receiving energy from a plurality of energy sources at the location
and an on-premises energy storage system of the location (Paragraph 6 “electrical energy storage device”);
select an energy source from among the plurality of energy sources at the location based on the respective environmental factors
receive energy from the selected energy source
While Boretto does not specifically teach determining environmental factors of, selecting, and then receiving energy from energy sources at a future time, Bhimani already teaches an electrical system with predictive elements that performs determinations focusing on a future time.
Bhimani and Boretto are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Boretto in order to store power that might be lost when supply exceeds demand (Paragraph 27).
Regarding claim 9, the combination of Bhimani and Boretto teaches the system of claim 8.
Bhimani also teaches wherein the processor is configured to predict an anticipated demand of the electricity provider at the future time (Paragraph 68 “generate predictions/forecasts of electricity demands and emissions at grid and/or vehicle levels and/or home levels”) based on historical demand of the electricity provider (Paragraphs 53-54 “grid data”, “Additionally, optionally or alternatively…at the various levels”), and determine an environmental factor of the electricity provider at the future time based on the anticipated demand of the electricity provider at the future time (Paragraph 46 where the environmental factor of emissions increases when the grid is unable to meet demand with only green and/or renewable energy).
Regarding claim 10, the combination of Bhimani and Boretto teaches the system of claim 8.
Bhimani also teaches wherein the processor is configured to predict a source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”) based on historical charging data of the EV battery (Paragraph 58 “vehicle data”, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”) and determine an environmental factor of the EV battery at the future time based on the predicted source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”)
Regarding claim 12, the combination of Bhimani and Boretto teaches the system of claim 8.
Bhimani also teaches wherein the processor is configured to control, via a panel installed at the location (Paragraph 185), energy to be transferred to the location from at least one of the electricity provider and the EV battery, at the future time (Paragraph 50 “Hence, the production models…generate optimized electricity charging and/or transfer events”).
Boretto also teaches store the energy within the on-premises energy storage until an energy storage threshold is reached (Paragraph 76 “maintain the battery or BSM 206 at a desired charge level”).
Regarding claim 15, Bhimani teaches a computer-readable storage medium (Paragraph 41) comprising instructions, that when read by a processor (Paragraph 42 “instructions”), cause the processor to perform:
determining a future time to receive energy at a location based on historical energy consumed at the location over time (Paragraph 62, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”);
wherein the plurality of energy sources include an electricity provider (Paragraph 40 “The charging station is connected with a grid”), an electric vehicle (EV) battery (Paragraph 60 “can use the collected vehicle data…supplying electricity to homes”),
Bhimani does not specifically teach:
determining respective environmental factors of receiving energy from a plurality of energy sources at the location at the future time,
and an on-premises energy storage system of the location;
selecting an energy source from among the plurality of energy sources at the location based on the respective environmental factors at the future time; and
receiving energy from the selected energy source at the future time.
However, Boretto teaches determining respective environmental factors of receiving energy from a plurality of energy sources at the location
and an on-premises energy storage system of the location (Paragraph 6 “electrical energy storage device”);
selecting an energy source from among the plurality of energy sources at the location based on the respective environmental factors
receiving energy from the selected energy source
While Boretto does not specifically teach determining environmental factors of, selecting, and then receiving energy from energy sources at a future time, Bhimani already teaches an electrical system with predictive elements that performs determinations focusing on a future time.
Bhimani and Boretto are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Boretto in order to store power that might be lost when supply exceeds demand (Paragraph 27).
Regarding claim 16, the combination of Bhimani and Boretto teaches the computer-readable storage medium of claim 15.
Bhimani also teaches wherein the determining the respective environmental factors comprises predicting an anticipated demand of the electricity provider at the future time (Paragraph 68 “generate predictions/forecasts of electricity demands and emissions at grid and/or vehicle levels and/or home levels”) based on historical demand of the electricity provider (Paragraphs 53-54 “grid data”, “Additionally, optionally or alternatively…at the various levels”), and determining an environmental factor of the electricity provider at the future time based on the anticipated demand of the electricity provider at the future time (Paragraph 46 where the environmental factor of emissions increases when the grid is unable to meet demand with only green and/or renewable energy).
Regarding claim 17, the combination of Bhimani and Boretto teaches the computer-readable storage medium of claim 15.
Bhimani also teaches wherein the determining the respective environmental factors comprises predicting a source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”) based on historical charging data of the EV battery (Paragraph 58 “vehicle data”, Paragraph 97 “Additionally, optionally or alternatively…at the various levels”) and determining an environmental factor of the EV battery at the future time based on the predicted source of electricity used to charge the EV battery at the future time (Paragraph 54 “the grid data may include….source types of energy supply”, Paragraph 83 “The process flow or algorithm…associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”)
Regarding claim 19, the combination of Bhimani and Boretto teaches the computer-readable storage medium of claim 15.
Bhimani also teaches wherein the receiving comprises controlling, via a panel installed at the location (Paragraph 185), energy to be transferred to the location from at least one of the electricity provider and the EV battery, at the future time (Paragraph 50 “Hence, the production models…generate optimized electricity charging and/or transfer events”).
Boretto also teaches storing the energy within the on-premises energy storage until an energy storage threshold is reached (Paragraph 76 “maintain the battery or BSM 206 at a desired charge level”).
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhimani in light of Boretto, further in light of US 20230139514 A1 to Tennant.
Regarding claim 4, the combination of Bhimani and Boretto teaches the method of claim 1.
The combination of Bhimani and Boretto does not teach wherein the determining the respective environmental factors comprises predicting a state of charge of the on-premises energy storage based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location, and determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
However, Tennant teaches predicting a state of charge of the on-premises energy storage (Paragraph 122 “The amount of grid-derived energy required may be determined by predicting a state of charge of the energy storage devices”) based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location (Paragraph 23 “historical renewable energy data”, “wind charge dataset and solar charge dataset”, Paragraph 109 “the generation 220 of the renewable energy availability prediction 225 is performed by a model based on at least the weather forecast data 205”).
Tennant does not specifically teach determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage. However, Bhimani already teaches determining an environmental factor of an EV battery based on historical energy transfers from power sources including in part renewable power (see claim 3 analysis and Paragraph 45 of Bhimani), where the EV battery is also a kind of energy storage. Bhimani only does not teach an on-premises energy storage or the historical energy transfer data specifically for said on-premises energy storage. However, Boretto already teaches the on-premises energy storage (see claim 1 analysis). Since Tennat teaches the historical energy transfers, the combination of Bhimani, Boretto, and Tennat teaches determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
Bhimani and Tennant are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Tennant in order to further minimize costs and environmental impact by reducing the amount of non-renewable energy used to charge the energy storage (Paragraph 63).
Regarding claim 11, the combination of Bhimani and Boretto teaches the system of claim 8.
The combination of Bhimani and Boretto does not teach wherein the processor is configured to predict a state of charge of the on-premises energy storage based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location, and determine an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
However, Tennant teaches wherein the processor is configured to predict a state of charge of the on-premises energy storage (Paragraph 122 “The amount of grid-derived energy required may be determined by predicting a state of charge of the energy storage devices”) based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location (Paragraph 23 “historical renewable energy data”, “wind charge dataset and solar charge dataset”, Paragraph 109 “the generation 220 of the renewable energy availability prediction 225 is performed by a model based on at least the weather forecast data 205”).
Tennant does not specifically teach determine an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage. However, Bhimani already teaches determining an environmental factor of an EV battery based on historical energy transfers from power sources including in part renewable power (see claim 10 analysis and Paragraph 45 of Bhimani), where the EV battery is also a kind of energy storage. Bhimani only does not teach an on-premises energy storage or the historical energy transfer data specifically for said on-premises energy storage. However, Boretto already teaches the on-premises energy storage (see claim 1 analysis). Since Tennat teaches the historical energy transfers, the combination of Bhimani, Boretto, and Tennat teaches determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
Bhimani and Tennant are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Tennant in order to further minimize costs and environmental impact by reducing the amount of non-renewable energy used to charge the energy storage (Paragraph 63).
Regarding claim 18, the combination of Bhimani and Boretto teaches the computer-readable storage medium of claim 15.
The combination of Bhimani and Boretto does not teach wherein the determining the respective environmental factors comprises predicting a state of charge of the on-premises energy storage based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location, and determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
However, Tennant teaches predicting a state of charge of the on-premises energy storage (Paragraph 122 “The amount of grid-derived energy required may be determined by predicting a state of charge of the energy storage devices”) based on historical energy transfers to the on-premises energy storage from one or more renewable energy sources at the location (Paragraph 23 “historical renewable energy data”, “wind charge dataset and solar charge dataset”, Paragraph 109 “the generation 220 of the renewable energy availability prediction 225 is performed by a model based on at least the weather forecast data 205”).
Tennant does not specifically teach determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage. However, Bhimani already teaches determining an environmental factor of an EV battery based on historical energy transfers from power sources including in part renewable power (see claim 17 analysis and Paragraph 45 of Bhimani), where the EV battery is also a kind of energy storage. Bhimani only does not teach an on-premises energy storage or the historical energy transfer data specifically for said on-premises energy storage. However, Boretto already teaches the on-premises energy storage (see claim 1 analysis). Since Tennat teaches the historical energy transfers, the combination of Bhimani, Boretto, and Tennat teaches determining an environmental factor of the on-premises energy storage at the future time based on the historical energy transfers to the on-premises energy storage.
Bhimani and Tennant are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Tennant in order to further minimize costs and environmental impact by reducing the amount of non-renewable energy used to charge the energy storage (Paragraph 63).
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhimani in light of Boretto, further in light of US 20230020417 A1 to Elbsat et al. (herein "Elbsat).
Regarding claim 6, the combination of Bhimani and Boretto teaches the method of claim 1.
The combination of Bhimani and Boretto does not specifically teach comprising restricting energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location.
However, Elbsat teaches teach comprising restricting energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location (Paragraph 126 “Central plant controller 506 may determine on/off configurations….weather data”).
Both Bhimani and Elbsat are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Elbsat in order to minimize energy costs (Paragraph 91).
Regarding claim 13, the combination of Bhimani and Boretto teaches the system of claim 8.
The combination of Bhimani and Boretto does not specifically teach wherein the processor is further configured to restrict energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location.
However, Elbsat teaches teach comprising restricting energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location (Paragraph 126 “Central plant controller 506 may determine on/off configurations….weather data”).
Both Bhimani and Elbsat are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Elbsat in order to minimize energy costs (Paragraph 91).
Regarding claim 20, the combination of Bhimani and Boretto teaches the computer-readable storage medium of claim 15.
The combination of Bhimani and Boretto does not specifically teach wherein the processor is further configured to perform restricting energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location.
However, Elbsat teaches teach comprising restricting energy use within the location at the future time based on the respective environmental factors of the selected energy source, wherein the restricting comprises at least one of preventing and reducing operation of one or more energy consuming systems within the location (Paragraph 126 “Central plant controller 506 may determine on/off configurations…weather data”).
Both Bhimani and Elbsat are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Elbsat in order to minimize energy costs (Paragraph 91).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhimani in light of Boretto, further in light of US 20220284519 A1 to Pancholi et al. (herein "Pancholi").
Regarding claim 7, the combination of Bhimani and Boretto teaches the method of claim 1.
Bhimani also teaches the method of claim 1, comprising training an artificial intelligence (AI) model to predict clean energy scores
While Bhimani does not specifically teach training and executing an AI model to predict clean energy scores of the plurality of energy sources (an electricity provider, an EV battery, and an on-premises energy storage system), the combination of Bhimani and Boretto teaches the plurality of energy sources (see claim 1 analysis).
Boretto also teaches the selecting comprises selecting the energy source from among the plurality of energy sources based on the environmental impact (Paragraph 5 “receiving a first demand…determined power provision arrangement”, Paragraph 10 “local or global environmental impact per unit of electrical power…”).
While Boretto does not teach specifically selecting based on the clean energy scores, Pancholi teaches wherein carbon emissions are an example of one type of environmental impact (Paragraph 195 “sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.)”).
Bhimani and Pancholi are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Pancholi so that other types of environmental impact can be measured and factored into the clean energy score (Paragraph 210 “Although carbon emissions is provided as one example of a sustainability metric… can be replaced with any other sustainability control objective or sustainability metric”).
Regarding claim 14, the combination of Bhimani and Boretto teaches the system of claim 8.
Bhimani also teaches the system of claim 8, wherein the processor is further configured to train an artificial intelligence (AI) model to predict clean energy scores
While Bhimani does not specifically teach training and executing an AI model to predict clean energy scores of the plurality of energy sources (an electricity provider, an EV battery, and an on-premises energy storage system), the combination of Bhimani and Boretto teaches the plurality of energy sources (see claim 8 analysis).
Boretto also teaches select the energy source from among the plurality of energy sources based on the environmental impact (Paragraph 5 “receiving a first demand…determined power provision arrangement”, Paragraph 10 “local or global environmental impact per unit of electrical power…”).
While Boretto does not teach specifically selecting based on the clean energy scores, Pancholi teaches wherein carbon emissions are an example of one type of environmental impact (Paragraph 195 “sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.)”).
Bhimani and Pancholi are analogous to the claimed invention because both are in the field of managing electrical power. It would be obvious to one of ordinary skill in the art to incorporate the system of Bhimani with the system of Pancholi so that other types of environmental impact can be measured and factored into the clean energy score (Paragraph 210 “Although carbon emissions is provided as one example of a sustainability metric… can be replaced with any other sustainability control objective or sustainability metric”).
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 5712722748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.X.Z./ Examiner, Art Unit 2117
/ROBERT E FENNEMA/ Supervisory Patent Examiner, Art Unit 2117
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