DETAILED ACTION
Claims 1-2, 4, 6-7, 10-11, 14, 16 and 18-28 are presented for examination. Claims 1, 4, 6-7, 10-11, 14, 16, 18, 20-21, 24 and 27-28 are currently amended. This office action is response to the submission on 5/18/2026.
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 information disclosure statement (IDS) submitted on 5/12/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1, 27, and 28 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-2, 4, 6-7, 10-11, 16, 18, 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Tinio et al. (US20200387981A1), in view of Amri (US20220067830A1).
Claim 1:
Tinio teaches “A system for connecting an electrical appliance to an electrical grid, the system comprising: a switch module configured to electrically connect the electrical appliance and the electrical grid;” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it is connected to a grid in Tinio [0007] " system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."), and
“and a processing system configured to: (Tinio teaches a method wherein a real time price threshold is set based on an average forecasted price and a weighing factor i.e. it determines whether to purchase electricity based on an average price and a settable weighing factor in Tinio [0057-0062] "FIG. 4A illustrates a sum of the last five hours, starting at x=0 (HE24) and then ending in x=xmax (HE0) FIG. 4B illustrates the sum of the last five hours minus a given interval: FIG. 4C illustrates collecting all five hour range sums in a vector: =R i ,R i+τ ,R i+1*τ . . . R i+M*τ :M=24/τ FIG. 4D illustrates Rmax=argmax and correlating the sected range to the time range in the forecast to get the average Threshold=½∫x=0 5Rmax(x) This algorithm can be continuously enhanced and can be replicated at any peak-load shifting stored energy dispatch 136. By combining current and forecasted real-time prices, the present invention provides power to the grid when power is in demand and power is scare." and in Tinio [0009] "Next each average in the series of averages of the forecasted price data set for the settable time window is analyzed to identify a maximum forecasted price data subset of the forecasted price data set within the settable time period. A real-time price threshold based on a settable weighing factor applied to the maximum forecasted price data subset is determined.").
Tinio does not appear to explicitly teach “and a processing system configured to: determine a price index computed based on a current However, Amri does teach this claim limitation (Amri teaches calculating a price change divided by a historical average price i.e. a price index. The concept of calculating a price change divided by a historical average price to determine whether to purchase something may be applied to purchasing electricity from the grid of Tinio in Amri [0047] "At step 402, method 400 involves calculating a current weight of a bought-in material over a current period, where the bought-in material is procured by an enterprise from an external supplier. At step 404, method 400 involves calculating a current weighted unit price of the bought-in material. At step 406, method 400 involves calculating a historical weighted average price of the bought-in material during a historical period. At step 408, method 400 involves calculating a difference between the current weighted unit price and the historical weighted average price. At step 410, method 400 involves calculating a material price percentage change by dividing the difference by the historical weighted average price. At step 412, method 400 involves multiplying the material price percentage change by the current weight to obtain a weighted average percentage change for the bought-in material. At step 414, method 400 involves performing one or more actions in connection with the weighted average percentage change."; Amri teaches calculating an average price in a historical period i.e. a rolling average in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years."; Amri teaches sending an offer to purchase based on the weighted average percentage change i.e. based on the percentage change exceeding a threshold in Amri [0054] "In some implementations, performing one or more actions in connection with the weighted average percentage change involves: generating, based on the weighted average percentage change, an offer for a purchase of the material; and submitting the offer to a system of an external vendor for the material."
Tinio and Amri are analogous art because they are from the same field of endeavor of purchasing based on an average price. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Tinio and Amri before him/her, to modify the teachings of Regulating charging and discharging of an energy storage device as part of an electrical power distribution network of Tinio to include the determination of a price change divided by historical average price to determine whether to purchase of Amri because adding the Automated inventory management of Amri would establish procurement strategies which can be used to purchase agreements to avoid increased costs as described in Amri [0014] “The subject matter described in this specification can be implemented to realize one or more of the following advantages. The disclosed IMS applies a weighted impact when generating price change trends, which improves the accuracy and utility of the data. The IMS also has the ability to track prices on an overall enterprise level or drill down to a commodity and material level. Further, the IMS can perform actions in connection with the generated price change trends. For example, the IMS can generate alerts that indicate a threshold change in price. Additionally, the IMS can generate a procurement strategy for frequently procured materials with specific prices and specific vendors to establish long term agreements in order to avoid price uncertainty. Also, the system can use the generated price change trends to predict future prices of materials. Furthermore, the IMS provides visibility on high spend materials and determines a cost impact of those materials. The cost impact can be used to show savings or added costs. As such, procurement strategies can be established based on the data provided by the IMS, which in turn, can be used to generate purchase agreements for high impact materials in order to avoid increased costs.”
Claim 2:
Tinio in view of Amri teaches “The system of claim 1, wherein the processing system is configured to operate the switch module to (1) either allow or impede the transfer of electricity between the electrical grid and the electrical appliance” (Tinio teaches discharging the energy storage device until real time price falls below a threshold i.e. it impedes transfer of electricity from the grid to the storage device in Tinio [0037] "FIG. 2 is an example graph 200 of the overall concept of using real-time and forecasted data to regulate charge and discharge of an energy storage device, e.g. BESS or battery. The Y-axis represents power in Mega Watts (MW) and includes a zero crossing line 202. The X-axis represents time. The real-time price 214 is shown plotted with the zero crossing line 202. The signal 212 is a representation of the process flow in FIG. 3 in which both the forecasted price and the real-time price is used with set the threshold of when to charge/discharge the energy storage device. Below the zero crossing line 202 the energy storage device is charging."), and
“and/or (2) increase or decrease the transfer of electricity between the electrical grid and the electrical appliance.” (Tinio teaches discharging the energy storage device until real time price falls below a threshold i.e. if the price drops below the threshold, it will go from charging the storage device to discharging the storage device and vice versa, increasing and decreasing the transfer of electricity in Tinio [0037] "FIG. 2 is an example graph 200 of the overall concept of using real-time and forecasted data to regulate charge and discharge of an energy storage device, e.g. BESS or battery. The Y-axis represents power in Mega Watts (MW) and includes a zero crossing line 202. The X-axis represents time. The real-time price 214 is shown plotted with the zero crossing line 202. The signal 212 is a representation of the process flow in FIG. 3 in which both the forecasted price and the real-time price is used with set the threshold of when to charge/discharge the energy storage device. Below the zero crossing line 202 the energy storage device is charging.").
Claim 4:
Tinio in view of Amri teaches “The system of claim 1, wherein, if the price index is less than the price index threshold, the processing system is configured to operate the switch module to allow or increase a supply of electricity by the electrical grid to the electrical appliance for powering the electrical appliance, or wherein, if the price index is greater than the price index threshold, the processing system is configured to operate the switch module to impede or reduce a supply of electricity by the electrical grid to the electrical appliance.” (Tinio teaches discharging the energy storage device until real time price falls below a threshold i.e. if the price drops below the threshold, it will go from charging the storage device to discharging the storage device and vice versa, increasing and decreasing the transfer of electricity in Tinio [0037] "FIG. 2 is an example graph 200 of the overall concept of using real-time and forecasted data to regulate charge and discharge of an energy storage device, e.g. BESS or battery. The Y-axis represents power in Mega Watts (MW) and includes a zero crossing line 202. The X-axis represents time. The real-time price 214 is shown plotted with the zero crossing line 202. The signal 212 is a representation of the process flow in FIG. 3 in which both the forecasted price and the real-time price is used with set the threshold of when to charge/discharge the energy storage device. Below the zero crossing line 202 the energy storage device is charging”).
Claim 6:
Tinio in view of Amri teaches “The system of claim 1, wherein the price index threshold is computed by: determining a difference between the rolling average of the electricity prices and the current electricity price; and dividing the difference between the rolling average of the electricity prices and the current electricity price by the rolling average of the electricity prices.” (Amri teaches calculating a price change divided by a historical average price i.e. the concept of calculating a price change to determine whether to purchase something may be applied to purchasing electricity from the grid of Tinio in Amri [0047] "At step 402, method 400 involves calculating a current weight of a bought-in material over a current period, where the bought-in material is procured by an enterprise from an external supplier. At step 404, method 400 involves calculating a current weighted unit price of the bought-in material. At step 406, method 400 involves calculating a historical weighted average price of the bought-in material during a historical period. At step 408, method 400 involves calculating a difference between the current weighted unit price and the historical weighted average price. At step 410, method 400 involves calculating a material price percentage change by dividing the difference by the historical weighted average price. At step 412, method 400 involves multiplying the material price percentage change by the current weight to obtain a weighted average percentage change for the bought-in material. At step 414, method 400 involves performing one or more actions in connection with the weighted average percentage change.").
Claim 7:
Tinio in view of Amri teaches “The system of claim 1, wherein the period immediately precedes or immediately follows the time in which the current electricity price applies.” (Amri teaches calculating an average price in a historical period including the current time period i.e. the period immediately precedes the current price in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years.").
Claim 10:
Tinio in view of Amri teaches “The system of claim 1, wherein, the period does not immediately precede or immediately follow the time in which the current electricity price applies.” (Amri teaches calculating an average price in a historical period not including the current time period i.e. the period does not immediately precede the current price in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years.").
Claim 11:
Tinio in view of Amri teaches “The system of claim 1, wherein, the price index threshold is a static threshold.” (Tinio teaches a method wherein a real time price threshold is set based on an average forecasted price and a weighing factor i.e. the weighing factor may be static in Tinio [0057-0062] ""FIG. 4A illustrates a sum of the last five hours, starting at x=0 (HE24) and then ending in x=xmax (HE0) FIG. 4B illustrates the sum of the last five hours minus a given interval: FIG. 4C illustrates collecting all five hour range sums in a vector: =R i ,R i+τ ,R i+1*τ . . . R i+M*τ :M=24/τ FIG. 4D illustrates Rmax=argmax and correlating the sected range to the time range in the forecast to get the average Threshold=½∫x=0 5Rmax(x) This algorithm can be continuously enhanced and can be replicated at any peak-load shifting stored energy dispatch 136. By combining current and forecasted real-time prices, the present invention provides power to the grid when power is in demand and power is scare."" and in Tinio [0009] ""Next each average in the series of averages of the forecasted price data set for the settable time window is analyzed to identify a maximum forecasted price data subset of the forecasted price data set within the settable time period. A real-time price threshold based on a settable weighing factor applied to the maximum forecasted price data subset is determined."; Amri teaches sending an offer to purchase based on the weighted average percentage change i.e. based on the percentage change exceeding a static threshold in Amri [0054] "In some implementations, performing one or more actions in connection with the weighted average percentage change involves: generating, based on the weighted average percentage change, an offer for a purchase of the material; and submitting the offer to a system of an external vendor for the material.").
Claim 16:
Tinio in view of Amri teaches “The system of claim 1, wherein the statistical quantity is determined from (1) past electricity prices (Amri teaches calculating an average price in a historical period in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years.").
Claim 18:
Tinio in view of Amri teaches “The system of claim 1, wherein the price index threshold is computed by dividing the current electricity price by the rolling average of the electricity prices.” (Amri teaches calculating a price change divided by a historical average price i.e. the concept of dividing the price by the historical average price may be used to determine whether to purchase something may be applied to purchasing electricity from the grid of Tinio in Amri [0047] "At step 402, method 400 involves calculating a current weight of a bought-in material over a current period, where the bought-in material is procured by an enterprise from an external supplier. At step 404, method 400 involves calculating a current weighted unit price of the bought-in material. At step 406, method 400 involves calculating a historical weighted average price of the bought-in material during a historical period. At step 408, method 400 involves calculating a difference between the current weighted unit price and the historical weighted average price. At step 410, method 400 involves calculating a material price percentage change by dividing the difference by the historical weighted average price. At step 412, method 400 involves multiplying the material price percentage change by the current weight to obtain a weighted average percentage change for the bought-in material. At step 414, method 400 involves performing one or more actions in connection with the weighted average percentage change.").
Claim 26:
Tinio in view of Amri teaches “The system of claim 1, wherein the electrical appliance comprises an energy storage device.” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. a battery is an energy storage device in Tinio [0007] "system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high.").
Claim 27:
Tinio teaches “A method for connecting an electrical appliance to an electrical grid, the method comprising: providing a switch module configured to electrically connect the electrical appliance and the electrical grid;” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it is connected to a grid in Tinio [0007] " system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."),
“(Tinio teaches a method wherein a real time price threshold is set based on an average forecasted price and a weighing factor i.e. it determines whether to purchase electricity based on an average price and a settable weighing factor in Tinio [0057-0062] "FIG. 4A illustrates a sum of the last five hours, starting at x=0 (HE24) and then ending in x=xmax (HE0) FIG. 4B illustrates the sum of the last five hours minus a given interval: FIG. 4C illustrates collecting all five hour range sums in a vector: =R i ,R i+τ ,R i+1*τ . . . R i+M*τ :M=24/τ FIG. 4D illustrates Rmax=argmax and correlating the sected range to the time range in the forecast to get the average Threshold=½∫x=0 5Rmax(x) This algorithm can be continuously enhanced and can be replicated at any peak-load shifting stored energy dispatch 136. By combining current and forecasted real-time prices, the present invention provides power to the grid when power is in demand and power is scare." and in Tinio [0009] "Next each average in the series of averages of the forecasted price data set for the settable time window is analyzed to identify a maximum forecasted price data subset of the forecasted price data set within the settable time period. A real-time price threshold based on a settable weighing factor applied to the maximum forecasted price data subset is determined."), and
“wherein the electricity price at a given time comprises the price set by an operator of the electrical grid to purchase from one or more electrical generators enough electricity to meet an expected demand for electricity from the electrical grid at the given time.” (Tinio teaches the present invention takes into account real time pricing from a power system i.e. the electricity is purchased from an electric grid with electrical generators in Tinio [0041] "FIG. 3 is an example overview of the process flow for regulating charging and discharging 300. The present invention is unlike prior art system, that simply follows a daily 24 hour charge/discharge schedule. Simply following a 24 hour charge/discharge schedule would not take advantage of large swings in real-time pricing (RTP) spikes that occur, such as from IESO (Independent Electricity System Operator) of Ontario's power system) that could occur at any time of the day.").
Tinio does not appear to explicitly teach “determining a price index computed based on a current However, Amri does teach this claim limitation (Amri teaches calculating a price change divided by a historical average price i.e. a price index. The concept of calculating a price change divided by a historical average price to determine whether to purchase something may be applied to purchasing electricity from the grid of Tinio in Amri [0047] "At step 402, method 400 involves calculating a current weight of a bought-in material over a current period, where the bought-in material is procured by an enterprise from an external supplier. At step 404, method 400 involves calculating a current weighted unit price of the bought-in material. At step 406, method 400 involves calculating a historical weighted average price of the bought-in material during a historical period. At step 408, method 400 involves calculating a difference between the current weighted unit price and the historical weighted average price. At step 410, method 400 involves calculating a material price percentage change by dividing the difference by the historical weighted average price. At step 412, method 400 involves multiplying the material price percentage change by the current weight to obtain a weighted average percentage change for the bought-in material. At step 414, method 400 involves performing one or more actions in connection with the weighted average percentage change."; Amri teaches calculating an average price in a historical period i.e. a rolling average in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years."; Amri teaches sending an offer to purchase based on the weighted average percentage change i.e. based on the percentage change exceeding a threshold in Amri [0054] "In some implementations, performing one or more actions in connection with the weighted average percentage change involves: generating, based on the weighted average percentage change, an offer for a purchase of the material; and submitting the offer to a system of an external vendor for the material."
Tinio and Amri are analogous art because they are from the same field of endeavor of purchasing based on an average price. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Tinio and Amri before him/her, to modify the teachings of Regulating charging and discharging of an energy storage device as part of an electrical power distribution network of Tinio to include the determination of a price change divided by historical average price to determine whether to purchase of Amri because adding the Automated inventory management of Amri would establish procurement strategies which can be used to purchase agreements to avoid increased costs as described in Amri [0014] “The subject matter described in this specification can be implemented to realize one or more of the following advantages. The disclosed IMS applies a weighted impact when generating price change trends, which improves the accuracy and utility of the data. The IMS also has the ability to track prices on an overall enterprise level or drill down to a commodity and material level. Further, the IMS can perform actions in connection with the generated price change trends. For example, the IMS can generate alerts that indicate a threshold change in price. Additionally, the IMS can generate a procurement strategy for frequently procured materials with specific prices and specific vendors to establish long term agreements in order to avoid price uncertainty. Also, the system can use the generated price change trends to predict future prices of materials. Furthermore, the IMS provides visibility on high spend materials and determines a cost impact of those materials. The cost impact can be used to show savings or added costs. As such, procurement strategies can be established based on the data provided by the IMS, which in turn, can be used to generate purchase agreements for high impact materials in order to avoid increased costs.”
Claim 28:
Tinio teaches “A switch module for electrically connecting an electrical appliance to an electrical grid,” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it is connected to a grid in Tinio [0007] " system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."),
“the switch module comprising a processing system configured to: determine (Tinio teaches a method wherein a real time price threshold is set based on an average forecasted price and a weighing factor i.e. it determines whether to purchase electricity based on an average price and a settable weighing factor in Tinio [0057-0062] "FIG. 4A illustrates a sum of the last five hours, starting at x=0 (HE24) and then ending in x=xmax (HE0) FIG. 4B illustrates the sum of the last five hours minus a given interval: FIG. 4C illustrates collecting all five hour range sums in a vector: =R i ,R i+τ ,R i+1*τ . . . R i+M*τ :M=24/τ FIG. 4D illustrates Rmax=argmax and correlating the sected range to the time range in the forecast to get the average Threshold=½∫x=0 5Rmax(x) This algorithm can be continuously enhanced and can be replicated at any peak-load shifting stored energy dispatch 136. By combining current and forecasted real-time prices, the present invention provides power to the grid when power is in demand and power is scare." and in Tinio [0009] "Next each average in the series of averages of the forecasted price data set for the settable time window is analyzed to identify a maximum forecasted price data subset of the forecasted price data set within the settable time period. A real-time price threshold based on a settable weighing factor applied to the maximum forecasted price data subset is determined."), and
“wherein the electricity price at a given time comprises the price set by an operator of the electrical grid to purchase from one or more electrical generators enough electricity to meet an expected demand for electricity from the electrical grid at the given time.” (Tinio teaches the present invention takes into account real time pricing from a power system i.e. the electricity is purchased from an electric grid with electrical generators in Tinio [0041] "FIG. 3 is an example overview of the process flow for regulating charging and discharging 300. The present invention is unlike prior art system, that simply follows a daily 24 hour charge/discharge schedule. Simply following a 24 hour charge/discharge schedule would not take advantage of large swings in real-time pricing (RTP) spikes that occur, such as from IESO (Independent Electricity System Operator) of Ontario's power system) that could occur at any time of the day.").
Tinio does not appear to explicitly teach “the switch module comprising a processing system configured to: determine a price index computed based on a current and the price index threshold, However, Amri does teach this claim limitation (Amri teaches calculating a price change divided by a historical average price i.e. a price index. The concept of calculating a price change divided by a historical average price to determine whether to purchase something may be applied to purchasing electricity from the grid of Tinio in Amri [0047] "At step 402, method 400 involves calculating a current weight of a bought-in material over a current period, where the bought-in material is procured by an enterprise from an external supplier. At step 404, method 400 involves calculating a current weighted unit price of the bought-in material. At step 406, method 400 involves calculating a historical weighted average price of the bought-in material during a historical period. At step 408, method 400 involves calculating a difference between the current weighted unit price and the historical weighted average price. At step 410, method 400 involves calculating a material price percentage change by dividing the difference by the historical weighted average price. At step 412, method 400 involves multiplying the material price percentage change by the current weight to obtain a weighted average percentage change for the bought-in material. At step 414, method 400 involves performing one or more actions in connection with the weighted average percentage change."; Amri teaches calculating an average price in a historical period i.e. a rolling average in Amri [0030] "At step 208, the price trend module 102 calculates a weighted average price of the material over a historical period. The weighted average price of the material is an average of all values of the weighted unit price of the material that were calculated during the historical period. For example, if the weighted unit price of the material is calculated on a quarterly basis, the weighted average price of the material is the average of the values of the weighted unit price that were calculated on a quarterly basis in the historical period. The historical period may be on the order of days, weeks, months, or years. In an example, the historical period precedes the current period such that the historical period does not overlap with the current period. In another example, a start date of the historical period may precede the current period, but an end date of the historical period may overlap with the current period. In one example, the historical period is two years."; Amri teaches sending an offer to purchase based on the weighted average percentage change i.e. based on the percentage change exceeding a threshold in Amri [0054] "In some implementations, performing one or more actions in connection with the weighted average percentage change involves: generating, based on the weighted average percentage change, an offer for a purchase of the material; and submitting the offer to a system of an external vendor for the material."
Tinio and Amri are analogous art because they are from the same field of endeavor of purchasing based on an average price. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Tinio and Amri before him/her, to modify the teachings of Regulating charging and discharging of an energy storage device as part of an electrical power distribution network of Tinio to include the determination of a price change divided by historical average price to determine whether to purchase of Amri because adding the Automated inventory management of Amri would establish procurement strategies which can be used to purchase agreements to avoid increased costs as described in Amri [0014] “The subject matter described in this specification can be implemented to realize one or more of the following advantages. The disclosed IMS applies a weighted impact when generating price change trends, which improves the accuracy and utility of the data. The IMS also has the ability to track prices on an overall enterprise level or drill down to a commodity and material level. Further, the IMS can perform actions in connection with the generated price change trends. For example, the IMS can generate alerts that indicate a threshold change in price. Additionally, the IMS can generate a procurement strategy for frequently procured materials with specific prices and specific vendors to establish long term agreements in order to avoid price uncertainty. Also, the system can use the generated price change trends to predict future prices of materials. Furthermore, the IMS provides visibility on high spend materials and determines a cost impact of those materials. The cost impact can be used to show savings or added costs. As such, procurement strategies can be established based on the data provided by the IMS, which in turn, can be used to generate purchase agreements for high impact materials in order to avoid increased costs.”
Claims 14 and 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Tinio et al. (US20200387981A1) in view of Amri (US20220067830A1), further in view of Langton et al. (US20200406778A1).
Claim 14:
Tinio in view of Amri teaches “The system of claim 1,” as described above. Neither Tinio or Amri appear to explicitly teach “wherein the price index threshold is a dynamic threshold, and wherein the processing system is configured to set the index threshold based on an urgency for the electrical appliance to consume electricity from the electrical grid.” However, Langton does teach this claim limitation (Langton teaches determining a minimum temperature for a water heater i.e. a water heater may be the appliance, meaning the appliance will draw electricity regardless of price when the temperature is below the threshold i.e. the threshold may be dynamic based on the water temperature or an urgency to consume electricity in Langton [0051] "The system server 500 may determine a temperature minimum profile, which correlates minimum temperatures of the stored water in the water heater (or air in the HVAC) that are sufficient for anticipated uses of the water heater (or HVAC), for each point-in-time over the time period. The temperature minimum profile may be determined based on the utility related data, the appliance related data, and/or the vehicle related data.").
Tinio, Amri, and Langton are analogous art because they are from the same field of endeavor of supplying power to appliances based on price. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Timio, Amri, and Langton before him/her, to modify the teachings of Regulating charging and discharging of an energy storage device as part of an electrical power distribution network of Timio, modified to include the determination of a price change divided by historical average price to determine whether to purchase of Amri, to include control of a water heater including providing power based on the temperature of the water regardless of price of Langton because adding the System for Integrating On-Premises Electric Appliances with Vehicle-To-Grid Electric Vehicles of Langton would allow for improved energy arbitrage and management as described in Langton [0038] “The system server 500 may perform various tasks based on the processing of data, information and/or commands that may have been received by the system server 500. In particular, the system server 500 may control one or more of: the electric appliance 200, the electric vehicle 100 and the local power grid 300, based on the data, information and/or commands received, so as to facilitate the improved energy arbitrage and management.”
Claim 19:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 1, wherein the electrical appliance is electrically connected to an auxiliary electricity source configured to supply electricity to the electrical appliance and to the electrical grid,” (Tinio teaches that the energy storage element may be charged by the grid or renewable energy resources i.e. an auxiliary electrical source in Tinio [0052] "In step 322 the energy storage element is switch to charge from the power being generated by the renewal energy sources on charge by the stored energy dispatch 136. In another example, the energy to charge the stored energy element may come from the grid or a combination of the grid and the renewable energy resources. The charging of the energy storage element is also controlled by a charge schedule (not show). The purpose of the charge schedule is to meet daily capacity commitments and power purchase agreements (PPA) allowing the energy storage element to charge. The process continues to step 324."), and
“and wherein the processing system is further configured to alter an amount of electricity supplied by the auxiliary electricity source to the electrical grid by operating the electrical appliance to control its electrical energy consumption.” (Langton teaches an electrical vehicle 100 including an energy store 110 which may provide power to an electrical appliance 200 which may be a water heater, and that the system server 500 may power the water heater with electricity from the grid 420 or energy store 110 in order to optimize costs in Langton [0052] "The server system 500 may, based on one or more of the characteristic profiles and boundary conditions, determine, for each of one or more operating periods Δtn, whether it is overall more desirable to power the water heater with electricity drawn from the municipal power grid 420 or from the energy store 110 of the electric vehicle. The system server 500 may, in response to the determination, control the vehicle to discharge electricity to the local power grid 300 for use by the water heater in operating at the energy usage state during the respective operating period, or may otherwise cause the vehicle not to so discharge and the water heater to draw electricity from the municipal power grid 420 instead. As discussed above, this determination may take into account one or more of: cost optimization, renewable energy optimization and carbon emissions optimization, in addition to other desirable boundary conditions that may affect the desirability of one source over another, including local electricity distribution infrastructure conditions.").
Claim 20:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 19, wherein, if the price index is lower than the price index threshold, the processing system is configured to operate the electrical appliance to increase or allow electrical energy consumption by the electrical appliance.” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it increases energy consumption when price is low in Tinio [0007] "system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."; Tinio may be modified as described above in claim 1 to determine whether a price index is lower than a threshold.).
Claim 21:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 19, wherein, if the price index is greater than the price index threshold, the processing system is configured to operate the electrical appliance to reduce or stop electrical energy consumption by the electrical appliance.” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it decreases energy consumption when price is high in Tinio [0007] "system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."; Tinio may be modified as described above in claim 1 to determine whether a price index is higher than a threshold.).
Claim 22:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 19, wherein the processing system is further configured to: receive auxiliary supply data indicative of an amount of electricity supplied by the auxiliary electricity source to the electrical appliance;” (Langton teaches local grid 300 in addition to the utility service provider 400 may allocate electricity to appliances in Langton [0033-0034] "The system server 500 may receive and transmit data, information and/or commands to and from the electric appliance 200 and/or the electric vehicle 100. In some embodiments, the local power grid 300 may be a smart local power grid 300 in which the allocation and supply of electricity within the local power grid 300 to on-premises appliances is controlled by a central control unit 302, which may be a standard processor, such as a central processing unit (CPU), or may be a dedicated processor, such as an application-specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The central control unit 302 may be coupled to a local memory 304, which may be hardware capable of storing information accessible by the central control unit 302, such as a ROM, RAM, hard-drive, CD-ROM, DVD, write-capable, read-only, etc. The local memory 304 may further store a set of instructions included in software that can be implemented by the central control unit 302 to perform the various tasks, either individually or in connection with other components of the local power grid 300, in accordance with the principles discussed herein."), and
“and operate the switch module to control an amount of electricity supplied by the electrical grid to the electrical appliance based on the auxiliary supply data.” (Langton teaches an electrical vehicle 100 including an energy store 110 which may provide power to an electrical appliance 200 which may be a water heater, and that the system server 500 may power the water heater with electricity from the grid 420 or energy store 110 in order based on local distribution conditions i.e. it controls the amount of electricity supplied by the grid to the appliance based on auxiliary supply data in Langton [0052] "The server system 500 may, based on one or more of the characteristic profiles and boundary conditions, determine, for each of one or more operating periods Δtn, whether it is overall more desirable to power the water heater with electricity drawn from the municipal power grid 420 or from the energy store 110 of the electric vehicle. The system server 500 may, in response to the determination, control the vehicle to discharge electricity to the local power grid 300 for use by the water heater in operating at the energy usage state during the respective operating period, or may otherwise cause the vehicle not to so discharge and the water heater to draw electricity from the municipal power grid 420 instead. As discussed above, this determination may take into account one or more of: cost optimization, renewable energy optimization and carbon emissions optimization, in addition to other desirable boundary conditions that may affect the desirability of one source over another, including local electricity distribution infrastructure conditions.”).
Claim 23:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 1, wherein the electrical appliance comprises a water heater.” (Langton teaches the electrical appliance may be a water heater in Langton [0025] "In some embodiments, the electric appliance 200 may be a smart water heater whose first state, heats the stored water to a first target temperature, and whose second state maintains the stored water at or near the first target temperature, or heats the stored water to a second target temperature lower than the first target temperature. The principles of the invention are particularly applicable to smart water heaters because smart water heaters can maintain the stored water at or near the target water temperature for an extended period of time with minimal electricity usage, whereas heating the water to the target water temperature requires significantly more electricity usage.").
Claim 24:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 23, wherein the processing system is configured to operate the switch module based on: the comparison of the price index and the price index threshold;” (Tinio teaches a bi-directional switch which charges a battery when prices are low and discharges the battery when prices are high i.e. it adjusts a bi-directional switch in Tinio [0007] "system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high."; Tinio may be modified as described above in claim 1 to determine whether a price index is lower than a threshold.), and
“and an urgency for the water heater to heat water.” (Langton teaches determining a minimum temperature for a water heater meaning the appliance will draw electricity regardless of price when the temperature is below the threshold in Langton [0051] "The system server 500 may determine a temperature minimum profile, which correlates minimum temperatures of the stored water in the water heater (or air in the HVAC) that are sufficient for anticipated uses of the water heater (or HVAC), for each point-in-time over the time period. The temperature minimum profile may be determined based on the utility related data, the appliance related data, and/or the vehicle related data.").
Claim 25:
Tinio in view of Amri, further in view of Langton teaches “The system of claim 24, wherein the urgency depends on the temperature of water stored in the water heater,” (Langton teaches the server 500 determines a temperature minimum profile i.e. if the water temperature is below the minimum, it will enable heating in Langton [0051] "The system server 500 may determine a temperature minimum profile, which correlates minimum temperatures of the stored water in the water heater (or air in the HVAC) that are sufficient for anticipated uses of the water heater (or HVAC), for each point-in-time over the time period. The temperature minimum profile may be determined based on the utility related data, the appliance related data, and/or the vehicle related data."),
“and wherein the processing system is further configured to: receive temperature data indicative of the temperature of water stored in the water heater;” (Langton teaches heating water to the minimum temperature, which would require receiving data indicative of the water temperature in Langton [0055] "The determination of one or more operating periods Δtn may be subject to one or more constraints based on one or more of the characteristic profiles. The determination may be constrained to ensure that the state-of-charge of the vehicle energy store during an operating period is sufficient to operate the water heater to heat water at least to the minimum temperature corresponding to the operating period. The determination may be constrained to ensure that the state-of-charge of the vehicle energy store after discharging to the local power grid 300 is at or above the corresponding minimum state-of-charge. The determination may be constrained by preferred energy performances of the water heater and/or the electric vehicle 100 over the operating periods."), and
“and based on the temperature data, operate the switch module to control the transfer of electricity between the electrical grid and the water heater.” (Langton teaches providing power to an electrical appliance 200 which may be a water heater, and that the system server 500 may power the water heater with electricity from the grid 420 based on characteristic profiles which may include the temperature minimum profile in Langton [0051-0052] "The system server 500 may determine a temperature minimum profile, which correlates minimum temperatures of the stored water in the water heater (or air in the HVAC) that are sufficient for anticipated uses of the water heater (or HVAC), for each point-in-time over the time period. The temperature minimum profile may be determined based on the utility related data, the appliance related data, and/or the vehicle related data. The server system 500 may, based on one or more of the characteristic profiles and boundary conditions, determine, for each of one or more operating periods Δtn, whether it is overall more desirable to power the water heater with electricity drawn from the municipal power grid 420 or from the energy store 110 of the electric vehicle. The system server 500 may, in response to the determination, control the vehicle to discharge electricity to the local power grid 300 for use by the water heater in operating at the energy usage state during the respective operating period, or may otherwise cause the vehicle not to so discharge and the water heater to draw electricity from the municipal power grid 420 instead. As discussed above, this determination may take into account one or more of: cost optimization, renewable energy optimization and carbon emissions optimization, in addition to other desirable boundary conditions that may affect the desirability of one source over another, including local electricity distribution infrastructure conditions.").
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Z.A.C./ Examiner, Art Unit 2116
/KENNETH M LO/ Supervisory Patent Examiner, Art Unit 2116
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