DETAILED ACTION
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 8/14/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yu et al. [Yu] (US PGPub 2020/0074570).
As to claim 1
Yu discloses a processor implemented method of electricity consumption prediction, comprising:
receiving, via one or more hardware processors (joint utility predictor and controller (JUPAC) 180, see Fig. 1), a requirement data (input from the User 120; see paragraph 0076, lines 8-9) comprising a) a region of interest (specific area; see paragraph 0089, line 15), b) a target time period (specific time; see paragraph 0089, line 15) (see paragraph 0076, lines 1-10);
predicting, via the one or more hardware processors, one or more distributions of interest for a plurality of agents (consumers/users) from a population of the region of interest for the target time period, using a plurality of data models (multiple consumption/production models; see paragraph 0068, line 2), wherein the one or more distributions of interest comprises of a spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0075, lines 1-6; paragraph 0080, lines 3-14; and paragraph 0089, lines 1-15);
grouping, via the one or more hardware processors, each of the plurality of agents to one of a) an individual agent category, and b) a collective agent category, characterized by one or more activities being performed by each of the plurality of agents, by processing the spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0060, lines 1-2; paragraph 0065, lines 1-9; and paragraph 0089, lines 4-7);
predicting, via the one or more hardware processors, the electricity consumption for the target time period, for each of the plurality of agents in the individual agent category and the collective agent category, based on a) the one or more distributions of interest predicted for each of the plurality of agents, b) information on one or more electrical devices and associated parameter values for the one or more distributions of interest, and c) a historical data with respect to one or more factors affecting the electricity consumption for the one or more distributions of interest and for the associated parameter values for different combinations of the one or more electrical devices (see paragraph 0061; paragraph 0062; and paragraph 0089, lines 1-11); and
aggregating, via the one or more hardware processors, the predicted electricity consumption of the plurality of agents, to determine an electricity consumption at the region of interest (see paragraph 0089, lines 12-15).
As to claim 2
Yu discloses the processor-implemented method of claim 1, wherein one or more of the plurality of agents undergo transition between two or more agent categories during the target time period, wherein by following the transition between the two or more agent categories, associated one or more activities and one or more electrical devices are identified (see paragraph 0108, lines 1-4 and paragraph 0109, lines 1-4).
As to claim 3
Yu discloses the processor-implemented method of claim 1, wherein the plurality of data models comprises of a residential consumer model, a transport consumer model, an industrial consumer model, an institutional consumer model, an agricultural consumer model, and an environmental model, and wherein the plurality of data models are physics-driven, data-driven and hybrid models (see paragraph 0060, lines 1-6 and paragraph 0068, lines 1-3).
As to claim 4
Yu discloses the processor-implemented method of claim 3, wherein the plurality of data models are re-tuned if a measured accuracy of the electricity consumption prediction is below a threshold of accuracy (see paragraph 0077, lines 1-7 and paragraph 0093).
As to claim 5
Yu discloses the processor-implemented method of claim 1, wherein the one or more factors affecting the electricity consumption, forming the historical data, and associated current data, comprises a) a weather data, b) a demographic and economic data, c) an energy price data, d) a calendar data, e) a technology data, f) an administrative and policy decision data, g) a land use data, and h) a usage and behaviour data (see paragraphs 0061 and 0062).
As to claim 6
Yu discloses the processor-implemented method of claim 1, wherein the plurality of data models is trained using the historical data as training data, comprising:
pre-processing the historical data to obtain a pre-processed historical data;
dividing the historical data to a training data set and a testing dataset;
training each of the plurality of data models using the training data set to capture one or more distribution parameters associated with an energy consumption pattern of the historical data at each of a plurality of time instances; and
testing each of the plurality of data models using the testing dataset, to obtain an associated confidence score, wherein each of the plurality of data models is retuned till the associated confidence score is at least matching a threshold of confidence score (see paragraphs 0091 – 0093).
As to claim 7
Yu discloses the processor-implemented method of claim 1, wherein the current values in the spatial distribution of each of the plurality of agents are associated with one or more of a current location of the agent, a collective group, an agent role, the one or more activities, and one or more electrical device characteristics (see paragraph 0061, lines 11-16).
As to claim 8
Yu discloses the processor-implemented method of claim 1, wherein the distribution of interest comprises of a behavioral distribution of a plurality of roles associated with each of the plurality of agents, wherein each of the plurality of roles has an associated set of probable activities, and wherein the one or more electrical devices are associated with the set of probable activities (see paragraph 0061).
As to claim 9
Yu discloses a system (joint utility predictor and controller system) for electricity consumption prediction, comprising:
one or more hardware processors (joint utility predictor and controller (JUPAC) 180, see Fig. 1);
a communication interface (router 130, see Fig. 1); and
a memory (memory; see paragraph 0126, line 1) storing a plurality of instructions, wherein the plurality of instructions (instructions; see paragraph 0126, line 2) causes the one or more hardware processors to (see paragraph 0126, lines 1-4):
receive a requirement data (input from the User 120; see paragraph 0076, lines 8-9) comprising a) a region of interest (specific area; see paragraph 0089, line 15), b) a target time period (specific time; see paragraph 0089, line 15) (see paragraph 0076, lines 1-10);
predict one or more distributions of interest for a plurality of agents (consumers/users) from a population of the region of interest for the target time period, using a plurality of data models (multiple consumption/production models; see paragraph 0068, line 2), wherein the one or more distributions of interest comprises of a spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0075, lines 1-6; paragraph 0080, lines 3-14; and paragraph 0089, lines 1-15);
group each of the plurality of agents to one of a) an individual agent category, and b) a collective agent category, characterized by one or more activities, by processing the spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0060, lines 1-2; paragraph 0065, lines 1-9; and paragraph 0089, lines 4-7);
predict the electricity consumption for the target time period, for each of the plurality of agents in the individual agent category and the collective agent category, based on a) the one or more distributions of interest predicted for each of the plurality of agents, b) information on one or more electrical devices and associated parameter values for the one or more distributions of interest, and c) a historical data with respect to one or more factors affecting the electricity consumption for the one or more distributions of interest and for the associated parameter values for different combinations of the one or more electrical devices (see paragraph 0061; paragraph 0062; and paragraph 0089, lines 1-11); and
aggregate the predicted electricity consumption of the plurality of agents, to determine an electricity consumption at the region of interest (see paragraph 0089, lines 12-15).
As to claim 10
Yu discloses the system of claim 9, wherein one or more of the plurality of agents undergo transition between two or more agent categories during the target time period, and wherein the one or more hardware processors are configured to identify associated one or more activities and one or more electrical devices by following the transition between the two or more agent categories (see paragraph 0108, lines 1-4 and paragraph 0109, lines 1-4).
As to claim 11
Yu discloses the system of claim 9, wherein the plurality of data models comprises of a residential consumer model, a transport consumer model, an industrial consumer model, an institutional consumer model, an agricultural consumer model, and an environmental model, and wherein the plurality of data models are physics-driven, data-driven and hybrid models (see paragraph 0060, lines 1-6 and paragraph 0068, lines 1-3).
As to claim 12
Yu discloses the system of claim 11, wherein the one or more hardware processors are configured to re-tune the plurality of data models if a measured accuracy of the electricity consumption prediction is below a threshold of accuracy (see paragraph 0077, lines 1-7 and paragraph 0093).
As to claim 13
Yu discloses the system a claimed in claim 9, wherein the one or more factors affecting the electricity consumption, forming the historical data and associated current data, comprises a) a weather data, b) a demographic and economic data, c) an energy price data, d) a calendar data, e) a technology data, f) an administrative and policy decision data, g) a land use data, and h) a usage and behaviour data (see paragraphs 0061 and 0062).
As to claim 14
Yu discloses the system of claim 9, wherein the one or more hardware processors are configured to train the plurality of data models using the historical data as training data, by:
pre-processing the historical data to obtain a pre-processed historical data;
dividing the historical data to a training data set and a testing dataset;
training each of the plurality of data models using the training data set to capture one or more distribution parameters associated with an energy consumption pattern of the historical data at each of a plurality of time instances; and
testing each of the plurality of data models using the testing dataset, to obtain an associated confidence score, wherein each of the plurality of data models is retuned till the associated confidence score is at least matching a threshold of confidence score (see paragraphs 0091 – 0093).
As to claim 15
Yu discloses the system of claim 9, wherein the current values in the spatial distribution of each of the plurality of agents are associated with one or more of a current location of the agent, a collective group, an agent role, the one or more activities, and one or more electrical device characteristics (see paragraph 0061, lines 11-16).
As to claim 16
Yu discloses the system of claim 9, wherein the distribution of interest comprises of a behavioral distribution of a plurality of roles associated with each of the plurality of agents, wherein each of the plurality of roles has an associated set of probable activities, and wherein the one or more electrical devices are associated with the set of probable activities (see paragraph 0061).
As to claim 17
Yu discloses one or more non-transitory machine-readable information storage mediums (memory; see paragraph 0126, line 1) comprising one or more instructions (instructions; see paragraph 0126, line 2) which when executed by one or more hardware processors (joint utility predictor and controller (JUPAC) 180, see Fig. 1) (see paragraph 0126, lines 1-4) cause:
receiving a requirement data (input from the User 120; see paragraph 0076, lines 8-9) comprising a) a region of interest (specific area; see paragraph 0089, line 15), b) a target time period (specific time; see paragraph 0089, line 15) (see paragraph 0076, lines 1-10);
predicting one or more distributions of interest for a plurality of agents (consumers/users) from a population of the region of interest for the target time period, using a plurality of data models (multiple consumption/production models; see paragraph 0068, line 2), wherein the one or more distributions of interest comprises of a spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0075, lines 1-6; paragraph 0080, lines 3-14; and paragraph 0089, lines 1-15);
grouping each of the plurality of agents to one of a) an individual agent category, and b) a collective agent category, characterized by one or more activities being performed by each of the plurality of agents, by processing the spatial and temporal distribution of each of the plurality of agents and associated current values (see paragraph 0060, lines 1-2; paragraph 0065, lines 1-9; and paragraph 0089, lines 4-7);
predicting the electricity consumption for the target time period, for each of the plurality of agents in the individual agent category and the collective agent category, based on a) the one or more distributions of interest predicted for each of the plurality of agents, b) information on one or more electrical devices and associated parameter values for the one or more distributions of interest, and c) a historical data with respect to one or more factors affecting the electricity consumption for the one or more distributions of interest and for the associated parameter values for different combinations of the one or more electrical devices (see paragraph 0061; paragraph 0062; and paragraph 0089, lines 1-11); and
aggregating the predicted electricity consumption of the plurality of agents, to determine an electricity consumption at the region of interest (see paragraph 0089, lines 12-15).
Additional Related Art
Durst (US Patent No. 11,221,639) also teaches predicting energy usage based on region of interest and a target time period (see Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115
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