DETAILED ACTION
The following is a Non-Final, First Office Action on the Merits in response to communications filed May 15, 2023. Currently, claims 1–20 are pending.
Claim Objections
Claims 1, 7–13, and 16–18 are objected to because of the following informalities:
Claims 1, 11, and 16 recite “the at least one classified resource-related action” in the step for “performing”. Although the claim previously recites “classifying at least one resource-related action,” the claims do not recite “at least one classified resource-related action.” As a result, Examiner recommends amending the claims to recite “performing one or more automated actions based at least in part on the at least one
Claims 7–10 similarly recite “the at least one classified resource-related action” in the final line of each claim. Examiner recommends amending the claims to recite “the at least one
With respect to claims 12–13 and 17–18, claims 12 and 17 recite “wherein converting … comprises processing” and claims 13 and 18 recite “wherein generating … comprises segmenting … and implementing”. However, claims 11 and 16, from which claims 12–13 and 17–18 depend, do not recite any elements for “converting” or “generating”. Instead, claims 11 and 16 recite functions “to convert” and “to generate”. Examiner recommends amending the claims to recite, for example, “wherein the functionality to convert .
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2–10, 12–15, and 17–20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2, 12, and 17 recite “wherein converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast comprises”. However, claims 1, 11, and 16, from which claims 2, 12, and 17 depend, previously recite “at least a portion of the at least one resource-related forecast” and “at least one resource-related action forecast”. As a result, the scope of claims 2, 12, and 17 is indefinite because it is unclear whether Applicant intends for the recitations of claims 2, 12, and 17 to reference the previous recitations of claims 1, 11, and 16 or intends to introduce second, different elements.
For purposes of examination, claims 2, 12, and 17 are interpreted as reciting “wherein converting the at least a portion of the at least one resource-related forecast to the at least one resource-related action forecast comprises”.
In view of the above, claims 2, 12, and 17 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3, 13, and 18 recite “wherein generating at least one resource-related forecast comprises”. However, claims 1, 11, and 16, from which claims 3, 13, and 18 depend, previously recite “at least one resource-related forecast”. As a result, the scope of claims 3, 13, and 18 is indefinite because it is unclear whether Applicant intends for the recitation of claims 3, 13, and 18 to reference the previous recitation of claims 1, 11, and 16 or intends to introduce a second, different element.
For purposes of examination, claims 3, 13, and 18 are interpreted as reciting “wherein generating the at least one resource-related forecast comprises”.
In view of the above, claims 3, 13, and 18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4–5, 14, and 19, which depend from claims 3, 13, and 18, inherit the deficiencies described above. As a result, claims 4–5, 14, and 19 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4–5, 14, and 19 recite “wherein segmenting at least a portion of the resource-related data into multiple data segments comprises”. However, claims 3, 13, and 18, from which claims 4–5, 14, and 19 depend, previously recite “at least a portion of the resource-related data” and “multiple data segments”. As a result, the scope of claims 4–5, 14, and 19 is indefinite because it is unclear whether Applicant intends for the recitations of claims 4–5, 14, and 19 to reference the previous recitations of claims 3, 13, and 18 or intends to introduce second, different elements.
For purposes of examination, claims 4–5, 14, and 19 are interpreted as reciting “wherein segmenting the at least a portion of the resource-related data into the multiple data segments comprises”.
In view of the above, claims 4–5, 14, and 19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6, 15, and 20 recite “wherein processing at least a portion of the at least one resource-related action forecast using at least one classification model comprises”. However, claims 1, 11, and 16, from which claims 6, 15, and 20 depend, previously recite “at least a portion of the at least one resource-related action forecast” and “at least one classification model”. As a result, the scope of claims 6, 15, and 20 is indefinite because it is unclear whether Applicant intends for the recitation of claims 6, 15, and 20 to reference the previous recitations of claims 1, 11, and 16 or intends to introduce a second, different elements.
For purposes of examination, claims 6, 15, and 20 are interpreted as reciting “wherein processing the at least a portion of the at least one resource-related action forecast using the at least one classification model comprises”.
In view of the above, claims 6, 15, and 20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7–10 recite “wherein performing one or more automated actions comprises”. However, claim 1, from which claims 7–10 depend, previously recites “one or more automated actions”. As a result, the scope of claims 7–10 is indefinite because it is unclear whether Applicant intends for the elements of claims 7–10 to reference the element of claim 1 or intends to introduce a second, different element.
For purposes of examination, claims 7–10 are interpreted as reciting “wherein performing the one or more automated actions comprises”.
In view of the above, claims 7–10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements for “generating at least one resource-related forecast by processing, using at least one regression model, resource-related data within at least one predetermined temporal period”; “converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast in conjunction with one or more temporal lag values”; “classifying at least one resource-related action associated with at least a portion of the at least one predetermined temporal period by processing at least a portion of the at least one resource-related action forecast”; and “performing one or more automated actions based at least in part on the at least one classified resource-related action.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for fundamental economic principles or practices and/or commercial interactions because the elements describe a process for forecasting and managing monetary actions. Further, the elements recite mental processes because the elements embody observations or evaluations that can be practically performed in the mind or by a human using pen and paper. Finally, the element for “generating” recites mathematical concepts because regression techniques are statistical operations, such that the broadest reasonable interpretation of the step of “generating” recites mathematical calculations. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 11 and 16 include substantially similar limitations to those included with respect to claim 1. As a result, claims 11 and 16 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–10, 12–15, and 17–20 further describe the process for forecasting and managing monetary actions and further recite certain methods of organizing human activity, mental processes, and/or mathematical concepts for the same reasons as stated above. As a result, claims 2–10, 12–15, and 17–20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include at least one processing device comprising a processor coupled to a memory, using one or more machine learning techniques, and using at least one classification model. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 11 and 16 include substantially similar limitations to those included with respect to claim 1. Although claim 11 further includes a processor-readable storage medium, the additional element, when considered in view of the claim as a whole, does not integrate the abstract idea into a practical application because the additional computer element is a generic computing component that is merely used as a tool to perform the recited abstract idea. As a result, claims 11 and 16 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 8–10 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include elements for “training”. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 8–10 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–7, 12–15, and 17–20 do not include any additional elements beyond those included with respect to the claims from which claims 2–7, 12–15, and 17–20 depend. As a result, claims 2–7, 12–15, and 17–20 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include at least one processing device comprising a processor coupled to a memory, using one or more machine learning techniques, and using at least one classification model. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claims 11 and 16 include substantially similar limitations to those included with respect to claim 1. Although claim 11 further includes a processor-readable storage medium, the additional element does not amount to significantly more than the recited abstract idea because the additional computer element is a generic computing component that is merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 11 and 16 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 8–10 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include elements for “training”. The additional elements do not amount to significantly more than the recited abstract idea because the elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 8–10 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2–7, 12–15, and 17–20 do not include any additional elements beyond those included with respect to the claims from which claims 2–7, 12–15, and 17–20 depend. As a result, claims 2–7, 12–15, and 17–20 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1, 3–11, 13–16, and 18–20 are rejected under 35 U.S.C. 103 as being unpatentable over Mangharam et al. (U.S. 2017/0169344) in view of Bly et al. (U.S. 2023/0289698).
Claims 1, 11, and 16: Mangharam discloses a computer-implemented (See FIG. 14) method comprising:
generating at least one resource-related forecast by processing, using at least one regression model, resource-related data within at least one predetermined temporal period (See paragraphs 50–51, in view of FIG. 2 and paragraph 34, wherein a baseline prediction model predicts power consumption of a system using regression techniques; see also paragraph 52, wherein predictions are determined in the context of a prediction horizon);
converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast using one or more machine learning techniques in conjunction with one or more temporal lag values (See paragraph 52, in view of FIG. 3 and paragraphs 36, wherein autoregression techniques using lagged values are used to generate strategy evaluation regression trees to predict strategy effectiveness, and wherein the strategy evaluation regression trees measure curtailment with respect to the baseline forecast);
evaluating at least one resource-related action associated with at least a portion of the at least one predetermined temporal period by processing at least a portion of the at least one resource-related action forecast using at least one model (See paragraphs 53–54, wherein an autoregressive tree for each strategy is evaluated to determine which tree/strategy performs the best over the predicted horizon; see also paragraph 52, wherein a plurality of regression tree models are disclosed in the context of strategy evaluation); and
performing one or more automated actions based at least in part on the at least one resource-related action (See paragraphs 54–55, wherein control actions are implemented in response to strategy evaluation);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (See paragraphs 6–7). Mangharam does not expressly disclose the remaining claim elements.
Bly discloses classifying at least one action associated by processing at least a portion of the at least one action forecast using at least one classification model (See paragraphs 432–433, in the context of paragraph 50, wherein a classification model is used to make a decision/classification; see also paragraph 307, wherein the classification model employs lagged values); and
performing one or more actions based at least in part on the at least one classified resource-related action (See paragraphs 432–433, in the context of paragraph 50, wherein a classification model is used to make a decision/classification).
Mangharam discloses a system directed to predicting energy demand using regression forecasting and lag values. Bly discloses a system directed to monitoring metrics using machine learning and lag values. Each reference discloses a system directed to managing business operations by monitoring business metrics. The technique of using a classification model is applicable to the system of Mangharam as they both share characteristics and capabilities, namely, they are directed to managing business operations by monitoring business metrics.
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bly to the teachings of Mangharam would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate business management using metric monitoring into similar systems. Further, applying a classification model to Mangharam would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
With respect to claim 11, Mangharam further discloses a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform operations (See paragraphs 6–7).
Claims 3, 13, and 18: Mangharam discloses the computer-implemented method of claim 1, wherein generating at least one resource-related forecast comprises segmenting at least a portion of the resource-related data into multiple data segments, and implementing a respective regression model for each of the multiple data segments (See paragraphs 50–51, in view of paragraph 95, wherein a plurality of regression models are generated, including building-specific models).
Claims 4, 14, and 19: Although Mangharam discloses segmenting at least a portion of the resource-related data into multiple data segments (See citations above), Mangharam does not expressly disclose the remaining elements.
Bly discloses wherein segmenting comprises segmenting the at least a portion of the resource-related data into multiple time-based data segments (See paragraph 195, wherein data is aggregated according to time period).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 5: Mangharam discloses the computer-implemented method of claim 3, wherein segmenting at least a portion of the resource-related data into multiple data segments comprises segmenting the at least a portion of the resource-related data into multiple data type-based segments (See paragraphs 50–51, in view of paragraph 95, wherein a plurality of regression models are generated, including building-specific models, and see paragraphs 47–49, wherein data is segmented into weather, schedule, and building data).
Claims 6, 15, and 20: Mangharam discloses the computer-implemented method of claim 1, wherein processing at least a portion of the at least one resource-related action forecast using at least one model comprises processing the at least a portion of the at least one resource-related action forecast and one or more items of additional resource-related data (See FIG. 6 and paragraphs 53–54, wherein an autoregressive tree for each strategy is evaluated to determine which tree/strategy performs the best over the predicted horizon, and wherein the models utilize strategy-specific set-points). Mangharam does not expressly disclose the remaining claim elements.
Bly discloses a classification model using one or more time series forecasting models (See paragraphs 432–433, in the context of paragraph 224, wherein a classification model is used to make a decision/classification, and wherein model performance evaluation relies on time series analysis; see also paragraph 307, wherein the classification model employs lagged values).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 7: Although Mangharam discloses performing one or more automated actions (See citations above), Mangharam does not expressly disclose the remaining claim elements.
Bly discloses wherein performing one or more automated actions comprises automatically generating at least one communication to at least one user based at least in part on the at least one classified resource-related action (See paragraphs 267–268, in view of paragraph 307, wherein alert status notifications are generated based on the classification model).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 8: Although Mangharam discloses performing one or more automated actions (See citations above) and training at least a portion of the at least one regression model (See paragraph 46, wherein the regression trees are trained on historical data), Mangharam does not expressly disclose the remaining claim elements.
Bly discloses wherein performing one or more automated actions comprises automatically training at least a portion of the at least one model using feedback related to the at least one classified resource-related action (See paragraph 239, in view of paragraphs 307 and 432–433, wherein continuous learning is based on updating metrics and correlations).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 9: Although Mangharam discloses performing one or more automated actions (See citations above) and training at least a portion of the one or more machine learning techniques (See paragraph 52, wherein the auto-regressive tree learning models are trained), Mangharam does not expressly disclose the remaining claim elements.
Bly discloses performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to the at least one classified resource-related action (See paragraph 239, in view of paragraphs 307 and 432–433, wherein continuous learning is based on updating metrics and correlations).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 10: Although Mangharam discloses performing one or more automated actions (See citations above), Mangharam does not expressly disclose the remaining claim elements.
Bly discloses performing one or more automated actions comprises automatically training at least a portion of the at least one classification model using feedback related to the at least one classified resource-related action (See paragraph 239, in view of paragraphs 307 and 432–433, wherein continuous learning is based on updating metrics and correlations).
One of ordinary skill in the art would have recognized that applying the known technique of Bly would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 2, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mangharam et al. (U.S. 2017/0169344) in view of Bly et al. (U.S. 2023/0289698), and in further view of Garg et al. (U.S. 2022/0076848).
Claims 2, 12, and 17: As disclosed above, Mangharam and Bly disclose the elements of claim 1.
Mangharam further discloses the computer-implemented method of claim 1, wherein converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast (See citations above) comprises processing the at least one resource-related forecast and historical resource-related data using the one or more machine learning techniques in conjunction with multiple temporal lag values (See paragraph 52, in view of FIG. 3 and paragraphs 36, wherein autoregression techniques using lagged values are used to generate strategy evaluation regression trees to predict strategy effectiveness, and wherein the strategy evaluation regression trees measure curtailment with respect to the baseline forecast; see also paragraphs 28 and 33, wherein strategy models use historical data). Mangharam and Bly do not expressly disclose the remaining claim elements.
Garg discloses wherein the multiple temporal lag values comprise one or more temporal lag values associated with each one of different temporal periods within the historical resource-related data (See paragraph 31, wherein covariate values are associated with differing temporal lag periods; see also paragraph 30).
As disclosed above, Mangharam discloses a system directed to predicting energy demand using regression forecasting and lag values, and Bly discloses a system directed to monitoring metrics using machine learning and lag values. Garg discloses a system directed to generating seasonally adjusted predictions using lag values. Each reference discloses a system directed to performing statistical analysis. The technique of using lag values for different periods is applicable to the systems of Mangharam and Bly as they each share characteristics and capabilities; namely, they are directed to performing statistical analysis.
One of ordinary skill in the art would have recognized that applying the known technique of Garg would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Garg to the teachings of Mangharam and Bly would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate statistical analysis into similar systems. Further, applying period-specific lag values to Mangharam and Bly would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Kajino (U.S. 2020/0118024) discloses a system directed to using autoregression techniques in multi-step ahead forecasting.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623