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 .
DETAILED ACTION
Status of the Application
1. The following is a Final Office Action in response to communication received on 8/6/2026. Claims 1-4, 6-17, and 19-20 are pending in this office action. The Information Disclosure Statements (IDSs) filed on behalf of this case on 8/6/2026 has been considered by the Examiner.
Response to Amendment
2. Applicant’s amendments to claims 1, 6, 8, 12, 14, and 19 are acknowledged. Applicant’s cancellation of claims 5 and 18 are acknowledged.
Response to Arguments
3. On Remarks pages 9-12, Applicant argues the 101 rejection. In particular Applicant argues Applicant’s amendments. The Examiner respectfully disagrees.
As per Applicant’s amendments filed 8/6/2026, these limitations further recite mental process and certain methods of organizing human activity steps of precalculating and aggregating calculated forecast values according to time, storing the forecast information by time, being able to receive an input relating to a forecast and displaying(rendering) the forecast information based on stored time information or more specifically as recited in the claims of precalculating forecast values, aggregating forecast values according to time intervals to produce summarized forecast values, storing the summarized forecast values according to time displayed in the visualization, summarized forecast values are segmented according to configurable input streams and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. This is part of the abstract idea.
The additional element that these are being performed by “at least one processor” merely results in apply it (see MPEP 2106.05(f)) or generally linking it to the field of computers (see MPEP 2106.05(h)). Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
As to Applicant’s arguments with respect to minimization of processing of data and cited paragraphs 0027-00238 and 0036 of Applicant’s specification for support of this argument, the Examiner respectfully disagrees, that the claims are eligible under 101. The Examiner has reviewed Applicant’s cited sections however these merely recite implementing a mental process or certain method of organizing human activity on a computer. Specifically, the claims and cited specification paragraphs (although it is noted the 101 analysis is based on Applicant’s claims, see MPEP 2106 III. SUMMARY OF ANALYSIS AND FLOWCHART) are claimed at such a broad level of recitation that they recite mental process and certain method of organizing human activities of precalculating forecast data and storing it according to a constraint like time, and then being able to display the precalculated data based on an input request. In the mental process and certain method of organizing human activity, even without a computer by for example pen and paper, this minimizes processing data and display time as a user merely has to pull the previously calculated stored data and display it to another user rather than have to first calculate the data and then display it. This is therefore part of the abstract idea.
The only additional element here in the claim is that this is being done by a software running a computer (processor) which merely results in apply it or generally linking it to the field of computers as discussed in the 101 rejection above.
Therefore the Examiner respectfully disagrees.
4. On Remarks pages 12—14, Applicant argues the prior art rejection. In particular Applicant argues Applicant’s amendments. While considered the arguments are moot in view of the newly cited prior art (and grounds of rejection) with respect to Montgomery, III et al. (United States Patent Application Publication Number: US 2017/0024751)
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-4, 6-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) determining baselines, generating forecasts and running different scenarios based on the baselines and forecasts where the information indicates carbon intensity, emissions, and carbon credits. Further the claims recite as amended on 8/6/2026 in the independent claims precalculating and storing simulation information by time prior to a request to later use it for display.
The claims are recited at such a high level of generality that they recite observations, evaluations, judgments, and opinions that could be performed in the human mind or with physical aid like pen and paper, accordingly, the claims recite a mental process (see MPEP 2106.04(a)).
Further the claims describe concepts relating to the economy or commerce accordingly the claims recite fundamental economic practices, which are certain methods of organizing human activities (see MPEP 2106.04(a)).
Mental processes and certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
Examiner notes that the Examiner has bolded and underlined the additional elements. Limitations not bolded and underlined are considered a part of the abstract idea.
1. A method comprising:
determining, via at least one processor, a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service;
generating, via the at least one processor, a forecast for the item based upon an assessment of the item during a life cycle;
and executing, via the at least one processor, different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast,wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item; and
generating, via the at least one processor, a visualization of the forecasts by:
prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values;
aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values;
storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization wherein the summarized forecast values are segmented according to respective configurable input streams;
and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets.
2. The method of claim 1, wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs.
3. The method of claim 1, further comprising:periodically updating, via the at least one processor, the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval.
4. The method of claim 1, wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval.
6. The method of claim 5, wherein the visualization includes a graph and a data table for the forecast for the item and actual values.
7. The method of claim 1, further comprising:updating, via the at least one processor, the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes.
8. A system comprising:
one or more memories;
and at least one processor coupled to the one or more memories, and configured to:
determine a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service;
generate a forecast for the item based upon an assessment of the item during a life cycle;
execute different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast, wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item, and:
generate a visualization of the forecasts by:
prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values;
aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values;
storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams;
and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets
9. The system of claim 8, wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs.
10. The system of claim 8, wherein the at least one processor is further configured to: periodically update the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval.
11. The system of claim 8, wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval.
12. The system of claim 8, wherein the visualization includes a graph and a data table for the forecast for the item and actual values.
13. The system of claim 8, wherein the at least one processor is further configured to: update the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes.
14. A computer program product comprising one or more non-transitory computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to:
determine a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service;
generate a forecast for the item based upon an assessment of the item during a life cycle;
execute different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast, wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item; and
generate a visualization of the forecast by:
prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values;
aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values;
storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and
rending the visualization by retrieving the summarized forecast values from the chronological time buckets.
15. The computer program product of claim 14, wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs.
16. The computer program product of claim 14, wherein the instructions further cause the at least one processor to: periodically update the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval.
17. The computer program product of claim 14, wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval.
19. The computer program product of claim 18, wherein the visualization includes a graph and a data table for the forecast for the item and actual values.
20. The computer program product of claim 14, wherein the instructions further cause the at least one processor to: update the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes.
As per claim 1, the claims recite mental process and certain methods of organizing human activity steps of determining a baseline, generating a forecast, and executing different scenarios to compare forecasts to baseline where these indicate carbon intensity, emissions, and carbon credits, where the different scenarios are based on changes to factors. This is part of the abstract idea. The additional element that these are being performed by at least “one processor” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further these limitations of by at least one “processor” merely result in generally linking it to the field of computers.
As per Applicant’s amendments filed 8/6/2026, these limitations further recite mental process and certain methods of organizing human activity steps of precalculating and aggregating calculated forecast values according to time, storing the forecast information by time, being able to receive an input relating to a forecast and displaying the forecast information based on stored time information or more specifically as recited in the claims of precalculating forecast values, aggregating forecast values to time intervals to produce summarized forecast values, storing the summarized forecast values according to time displayed in the visualization, summarized forecast values are segmented according to configurable input streams and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. This is part of the abstract idea.
The additional element that these are being performed by “at least one processor” merely results in apply it or generally linking it to the field of computers as previously discussed in this claim above.
As per claim 2, the claims recite mental process and certain methods of organizing human activity steps of factors include one or more of material inputs, electricity mix, processes, equipment, and outputs. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 3, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval. This is part of the abstract idea. The additional element of being done by at “one processor” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 4, the claims recite mental process and certain methods of organizing human activity steps of baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 6, the claims recite mental process and certain methods of organizing human activity steps of wherein the visualization includes a graph and a data table for the forecast for the item and actual values. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 7, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios as data changes. This is part of the abstract idea. The additional element of being done by “at least one processor” merely results in apply it or generally linking it to the field of computers as discussed above. The additional element of this being done in “real time” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further these limitations of this being done in “real time” merely result in generally linking it to the field of computers.
As per claim 8, the claims recite mental process and certain methods of organizing human activity steps of determining a baseline, generating a forecast, and executing different scenarios to compare forecasts to baseline where these indicate carbon intensity, emissions, and carbon credits, where the different scenarios are based on changes to factors. This is part of the abstract idea. The additional element that these are being performed by at least a computer with a memory “one or more memories; and at least one processor coupled to the one or more memories, and configured to” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further these limitations of by at least one “one or more memories; and at least one processor coupled to the one or more memories, and configured to” merely result in generally linking it to the field of computers.
As per Applicant’s amendments filed 8/6/2026, these limitations further recite mental process and certain methods of organizing human activity steps of precalculating and aggregating calculated forecast values according to time, storing the forecast information by time, being able to receive an input relating to a forecast and displaying the forecast information based on stored time information or more specifically as recited in the claims of precalculating forecast values, aggregating forecast values to time intervals to produce summarized forecast values, storing the summarized forecast values according to time displayed in the visualization, summarized forecast values are segmented according to configurable input streams and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. This is part of the abstract idea.
The additional element that these are being performed by “one or more memories; and at least one processor coupled to the one or more memories, and configured to” merely results in apply it or generally linking it to the field of computers as previously discussed in this claim above.
As per claim 9, the claims recite mental process and certain methods of organizing human activity steps of factors include one or more of material inputs, electricity mix, processes, equipment, and outputs. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 10, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval. This is part of the abstract idea. The additional element of being done by at least “one processor” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 11, the claims recite mental process and certain methods of organizing human activity steps of baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 12, the claims recite mental process and certain methods of organizing human activity steps of generating a visualization of the forecast for the item against actual values. This is part of the abstract idea. The additional element of being done by “at least one processor” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 13, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios as data changes. This is part of the abstract idea. The additional element of being done by “at least one processor” and being done in real time results in apply it or generally linking it to the field of computers as discussed above.
As per claim 14, the claims recite mental process and certain methods of organizing human activity steps of determining a baseline, generating a forecast, and executing different scenarios to compare forecasts to baseline where these indicate carbon intensity, emissions, and carbon credits, where the different scenarios are based on changes to factors. This is part of the abstract idea. The additional element that these are being performed by at least a software running on a computer “a computer program product comprising one or more non-transitory computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further these limitations of being performed by at least a software running on a computer “a computer program product comprising one or more non-transitory computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to” merely result in generally linking it to the field of computers.
As per Applicant’s amendments filed 8/6/2026, these limitations further recite mental process and certain methods of organizing human activity steps of precalculating and aggregating calculated forecast values according to time, storing the forecast information by time, being able to receive an input relating to a forecast and displaying the forecast information based on stored time information or more specifically as recited in the claims of precalculating forecast values, aggregating forecast values to time intervals to produce summarized forecast values, storing the summarized forecast values according to time displayed in the visualization, summarized forecast values are segmented according to configurable input streams and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. This is part of the abstract idea.
The additional element that these are being performed by at least a software running on a computer “a computer program product comprising one or more non-transitory computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to” merely results in apply it or generally linking it to the field of computers as previously discussed in this claim above.
As per claim 15, the claims recite mental process and certain methods of organizing human activity steps of factors include one or more of material inputs, electricity mix, processes, equipment, and outputs. This is part of the abstract idea. The additional element of a “computer program product” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 16, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval. This is part of the abstract idea. The additional element of being done by at least “one processor” and a “computer program product” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 17, the claims recite mental process and certain methods of organizing human activity steps of baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. This is part of the abstract idea. The additional element of “computer program product” merely result in apply it or generally linking it to the field of computers as discussed above.
As per claim 19, the claims recite mental process and certain methods of organizing human activity steps of wherein the visualization includes a graph and a data table for the forecast for the item and actual values. This is part of the abstract idea. The additional element of “computer program product” merely result in apply it or generally linking it to the field of computers as discussed above.
As per claim 20, the claims recite mental process and certain methods of organizing human activity steps of updating the baseline forecast and the forecasts for the item and different scenarios as data changes. This is part of the abstract idea. The additional elements of being done by “at least one processor”, being performed by a “computer program product”, and being done in “real time” results in apply it or generally linking it to the field of computers as discussed above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above under the practical application step.
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
9. Claim(s) 1-4, 6-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Narasimhan et al. (United States Patent Application Publication Number: US 2022/0108252) further in view of Montgomery, III et al. (United States Patent Application Publication Number: US 2017/0024751).
As per claim 1, Narasimhan et al. teaches A method comprising: (see abstract, Examiner’s note: method for generating substantiality insights and recommendations).
determining, via at least one processor, (see claims 18 and paragraph 0110, Examiner’s note: software running on a computer performing functions).
a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service; generating, via the at least one processor, a forecast for the item based upon an assessment of the item during a life cycle; and executing, via the at least one processor, different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast,[AltContent: rect]wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item (see Figures 3-5 and paragraphs 0061, 0068, Examiner’s note: teaches historical and predicted projections, for example 2021 is projected predictions as they occur in the future. Further Figures teach carbon emissions, carbon intensity, and emissions saved which as broadly recited here are interpreted as carbon credits).
Narasimhan et al. does not expressly teach precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims generating, via the at least one processor, a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chorological time buckets.
However, Montgomery, III et al. which is in the art of forecasting future demand (see abstract) teaches precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims generating, via the at least one processor, a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chorological time buckets (see paragraphs 0017, 0034, 0038-0040, Examiner’s note: teaches an interface for users to navigate to future demand (see paragraph 0017) where demand may be weekly, hourly or daily (see paragraphs 0034 and 0038) and further teaches information may be precalculated or calculated in response to a user request (see paragraphs 0039-0040)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Narasimhan et al. with the aforementioned teachings from Montgomery, III et al. with the motivation of providing another known way to calculate forecast information (see Montgomery, III et al. paragraphs 0039-0040) so that information can be quickly retrieved upon request by not having to wait for it to be calculated, when calculating future predictions of information in simulations is known (see Narasimhan et al. Figures 3-5 and paragraphs 0061, 0068).
As per claim 2, Narasimhan et al. teaches
wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs (see Figures 3-4, Examiner’s note: related to emissions for customer cloud usage (or server farms). It is noted only one is required by the claims but this reads on at least the broad alternatives of material inputs, processes, equipment, and outputs).
As per claim 3, Narasimhan et al. teaches
further comprising: periodically updating, via the at least one processor, the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval (see paragraphs 0083, 0086, 0100, Examiner’s note: teaches updating information including predictions based on updates input into the system. It is noted updating information in a time interval is interpreted to read on “configurable time interval”).
As per claim 4, Narasimhan et al. teaches
wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. (see paragraphs 0083, 0086, 0100, Examiner’s note: teaches updating information including predictions (which can be in the future as discussed in claim 1 above) based on updates input into the system. It is noted updating information in a time interval is interpreted to read on “configurable time interval”).
As per claim 6, Narasimhan et al. teaches
wherein the visualization includes a graph and a data table for the forecast for the item and actual values. (see Figures 3-5, Examiner’s note: reference character 342 (Figure 3), reference character 420 (Figure 4), and reference character 518 (Figure 5), show actual vs projected forecasts. It is noted that any of the above references can be interpreted as either a graph or a table, as the meaning of the term table is a concise list, which all of these reference characters show. Further all the reference characters graph the differences between actual and projected).
As per claim 7, Narasimhan et al. teaches
further comprising :updating, via the at least one processor, the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes. (see paragraph 0083, Examiner’s note: teaches using data in real time to reflect modifications).
As per claim 8, Narasimhan et al. teaches A system comprising: (see abstract, Examiner’s note: system for generating substantiality insights and recommendations).
one or more memories; and at least one processor coupled to the one or more memories, and configured to: (see claims 18 and paragraph 0110, Examiner’s note: software running on a computer performing functions).
determine a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service; generate a forecast for the item based upon an assessment of the item during a life cycle; execute different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast, wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item. (see Figures 3-5 and paragraphs 0061 and 0068, Examiner’s note: teaches historical and predicted projections, for example 2021 is projected predictions as they occur in the future. Further Figures teach carbon emissions, carbon intensity, and emissions saved which as broadly recited here are interpreted as carbon credits).
Narasimhan et al. does not expressly teach precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims and generate a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets.
However, Montgomery, III et al. which is in the art of forecasting future demand (see abstract) teaches precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims and generate a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. (see paragraphs 0017, 0034, 0038-0040, Examiner’s note: teaches an interface for users to navigate to future demand (see paragraph 0017) where demand may be weekly, hourly or daily (see paragraphs 0034 and 0038) and further teaches information may be precalculated or calculated in response to a user request (see paragraphs 0039-0040)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Narasimhan et al. with the aforementioned teachings from Montgomery, III et al. with the motivation of providing another known way to calculate forecast information (see Montgomery, III et al. paragraphs 0039-0040) so that information can be quickly retrieved upon request by not having to wait for it to be calculated, when calculating future predictions of information in simulations is known (see Narasimhan et al. Figures 3-5 and paragraphs 0061, 0068).
As per claim 9, Narasimhan et al. teaches
wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs. (see Figures 3-4, Examiner’s note: related to emissions for customer cloud usage (or server farms). It is noted only one is required by the claims but this could read on at least the alternatives of material inputs, processes, equipment, and outputs).
As per claim 10, Narasimhan et al. teaches
wherein the at least one processor is further configured to: periodically update the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval. (see paragraphs 0083, 0086, and 0100, Examiner’s note: teaches updating information including predictions based on updates input into the system. It is noted updating information in a time interval is interpreted to read on “configurable time interval”).
As per claim 11, Narasimhan et al. teaches
wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. (see paragraphs 0083, 0086, and 0100, Examiner’s note: teaches updating information including predictions (which can be in the future as discussed in claim 1 above) based on updates input into the system. It is noted updating information in a time interval is interpreted to read on “configurable time interval”).
As per claim 12, Narasimhan et al. teaches
wherein the visualization includes a graph and a data table for the forecast for the item and actual values. (see Figures 3-5, Examiner’s note: reference character 342 (Figure 3), reference character 420 (Figure 4), and reference character 518 (Figure 5), show actual vs projected forecasts) It is noted that any of the above references can be interpreted as either a graph or a table, as the meaning of the term table is a concise list, which all of these reference character show. Further the reference characters graph actual compared to projected data).
As per claim 13, Narasimhan et al. teaches
wherein the at least one processor is further configured to: update the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes. (see paragraph 0083, Examiner’s note: teaches using data in real time to reflect modifications).
As per claim 14, Narasimhan et al. teaches A non-transitory computer program product comprising one or more computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to: (see claims 18 and paragraphs 0110 and 0113, Examiner’s note: software running on a computer performing functions (see claim 18 and paragraph 0110) and paragraph 0113 teaches this can be a disk).
determine a baseline forecast for an item based on operational data for the item, wherein the item includes one of a product, process, and service; generate a forecast for the item based upon an assessment of the item during a life cycle; and execute different scenarios relative to the assessment to compare forecasts for the item and different scenarios to the baseline forecast, wherein the baseline forecast and the forecasts for the item and different scenarios indicate carbon intensity, emissions, and carbon credits, and wherein the different scenarios are based upon changes to factors affecting the forecast for the item. (see Figures 3-5 and paragraphs 0061 and 0068, Examiner’s note: teaches historical and predicted projections, for example 2021 is projected predictions as they occur in the future. Further Figures teach carbon emissions, carbon intensity, and emissions saved which as broadly recited here are interpreted as carbon credits).
Narasimhan et al. does not expressly teach precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims and generate a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets.
However, Montgomery, III et al. which is in the art of forecasting future demand (see abstract) teaches precalculating simulations and storing them related to time to then be used in display of forecast information or more specifically as recited in the claims and generate a visualization of the forecasts by: prior to rendering the visualization, pre-calculating forecast values to produce calculated forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; aggregating the calculated forecast values according to respective configured time intervals to produce summarized forecast values; storing the summarized forecast values in chronological time buckets corresponding to respective time increments displayed in the visualization, wherein the summarized forecast values are segmented according to respective configurable input streams; and rendering the visualization by retrieving the summarized forecast values from the chronological time buckets. (see paragraphs 0017, 0034, and 0038-0040, Examiner’s note: teaches an interface for users to navigate to future demand (see paragraph 0017) where demand may be weekly, hourly or daily (see paragraphs 0034 and 0038) and further teaches information may be precalculated or calculated in response to a user request (see paragraphs 0039-0040)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Narasimhan et al. with the aforementioned teachings from Montgomery, III et al. with the motivation of providing another known way to calculate forecast information (see Montgomery, III et al. paragraphs 0039-0040) so that information can be quickly retrieved upon request by not having to wait for it to be calculated, when calculating future predictions of information in simulations is known (see Narasimhan et al. Figures 3-5 and paragraphs 0061, 0068).
As per claim 15, Narasimhan et al. teaches
wherein the factors include one or more from a group of material inputs, electricity mix, processes, equipment, and outputs. (see Figures 3-4, Examiner’s note: related to emissions for customer cloud usage (or server farms). It is noted only one is required by the claims but this could read on at least the alternatives of material inputs, processes, equipment, and outputs).
As per claim 16, Narasimhan et al. teaches
wherein the instructions further cause the at least one processor to: periodically update the baseline forecast and the forecasts for the item and different scenarios at a configurable time interval. (see paragraph 0083, Examiner’s note: updating data and changing based on modifications, this is periodically based on updates to information).
As per claim 17, Narasimhan et al. teaches
wherein the baseline forecast and the forecasts for the item and different scenarios encompass a configurable future time interval. (see paragraphs 0083, 0086, and 0100, Examiner’s note: teaches updating information including predictions (which can be in the future as discussed in claim 1 above) based on updates input into the system. It is noted updating information in a time interval is interpreted to read on “configurable time interval”).
As per claim 19, Narasimhan et al. teaches
wherein the visualization includes a graph and a data table for the forecast for the item and actual values. (see Figures 3-5, Examiner’s note: reference character 342 (Figure 3), reference character 420 (Figure 4), and reference character 518 (Figure 5), show actual vs projected forecasts. It is noted that any of the above references can be interpreted as either a graph or a table, as the meaning of the term table is a concise list, which all of these reference character show. Further the reference characters show a graph of actual compared to predicted values or data).
As per claim 20, Narasimhan et al. teaches
wherein the instructions further cause the at least one processor to: update the baseline forecast and the forecasts for the item and different scenarios in real-time as data changes. (see paragraph 0083, Examiner’s note: teaches using data in real time to reflect modifications).
Conclusion
10. 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.
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a. Sidhu et al. (United States Patent Application Publication Number: US 2023/0059038) teaches a system for determining carbon emissions for a streaming service including forecasting futures (see abstract, paragraph 0069, and Figures 8-9)
b. Feickert et al. (United States Patent Application Publication Number: US 2023/0061787) teaches system for calculating emission factors for activity data (see abstract and Figure 3)
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/KIERSTEN V SUMMERS/ Primary Examiner, Art Unit 3626
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