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 .
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 10 – 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically, an Abstract Idea without significantly more.
Step 1: Statutory Category
Claim 1 is directed to a method, which falls within the statutory categories of invention.
Step 2A: Prong 1: Judicial Exception
Claim 10 is directed to an Abstract Idea, specifically, mathematical concepts and mental processes. The claim recites the steps:
Generating a training set of environmental parameters and harvestable power over multiple episodes,
Training a model using the training set to provide schedules that minimize a difference between environmental parameters and sensor readings while conserving stored energy.
These steps involve mathematical calculations and mental processes, including collecting data, analyzing data, and training and optimization model.
The limitations can be performed with the human mind or with a pen and paper, including:
collecting data (monitoring environmental parameters),
analyzing data (reading environmental parameters), and
making a determination (creating a schedule).
Accordingly, the claim recites a judicial exception in the form of mathematical concepts
and mental processes.
Step 2A, Prong 2: Integration into a Practical Application
This judicial exception is not integrated into a practical application because the additional elements merely describe the environment in which the model will be used (i.e. a sensor system including sensors, energy harvester, energy store, power management circuit, and wireless transmitter). The recited sensor system is only described as a type of system and does not meaningfully limit how the training is performed. This additional element merely applies the Abstract Idea using generic components and does not impose any meaningful limit on the judicial exception. The active steps of the method are limited to generating a training set and training model. The claim does not recite deploying the model to control the sensor system or using the trained model to operate any physical device.
The claim:
does not improve the functioning of a sensor system,
does not recited a specific technical implementation for controlling a power management circuit, and
merely uses the calculated and predicted values to determine when to start or stop sensing.
Accordingly, the claim does not integrate the judicial exception into a practical application.
Step 2B: Additional elements
These elements are recited a high level of generality and represent well understood,
routine, conventional activities in the relevant field of sensing and generating data.
For example:
sensing environmental parameters are conventional,
reading environmental parameters is conventional, and
controlling a power management device based on calculated thresholds is conventional.
Taken individually and as an ordered combination, the additional elements do not add a specific limitation beyond the Abstract Idea that is not well understood, routine and conventional.
Hence, claim 10 is not patent eligible.
Dependent Claims 11-16 when analyzed as a whole, are held to be patent ineligible under 35
U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements, if any, in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with Claim 10.
Claim Rejections - 35 USC § 112
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 5, 6, 10 and 16 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 5 and 10, the limitation, “combined difference between the environmental parameters and sensor readings of the environmental parameters” is indefinite because it is unclear what mathematical operation constitutes the “combined difference.” Claims 1 and 10 disclose, “sensors measuring different environmental parameters,” and “read[ing] environmental parameters.” These two values of “environmental parameters” and “sensor readings of the environmental parameters” appear to be the same value, thus a “combined difference” can not be determined. For these reasons, claims 5 and 10 and indefinite.
Claims 6 and 10, the limitations, “conserving energy over the training set” is indefinite because it is unclear what degree of conservation is measured.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim ends with a semicolon (;) and not a period (.). MPEP § 608.01(m) states, “Each claim begins with a capital letter and ends with a period.” Appropriate correction is required.
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, 5,6, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Crescini (US 20170184560) in view of Wellig (US 20230393168).
Regarding claim 1, Crescini teaches a sensor system monitoring wildfire at a field location (figures 1 - 3 item 10 paragraph [0019] discloses sensor system defined as a multi-parametric environmental diagnostics and monitoring sensor node. Paragraph [0025] discloses wherein the system may monitor fires), comprising:
a sensor suite including multiple sensors measuring different environmental parameters and having different electrical energy demands (figures 1 -3 show a plurality of environmental sensors items 36a-36e monitoring different environmental parameters) ;
an energy harvester for extracting energy from the environment to provide electrical power (figure 9 paragraph [0038] discloses energy harvesting modules items 22, 16, 14, 34, and 18 extracting energy from the environment such as a photovoltaic cell)
an energy store communicating with the energy harvester for storing the provided electrical power (paragraph [0038] discloses wherein the energy harvesting modules store energy in supercapacitor item 52);
a power management circuit operating to read the environmental parameters (figure 3 item 47 a central processor. Paragraph [0020] discloses wherein the sensors are in communication with a processor); and
a wireless transmitter for communicating the environmental parameters to a remote fire assessment station (figure 2 item 42 shows an antenna for wirelessly communicating parameters).
Crescini does not explicitly teach wherein a power management circuit is configured to monitor energy in the energy storage to schedule the power consumption of each sensor from the energy store according to a schedule provided by a model trained with a training set of environmental parameters of wildfires and harvestable power over multiple predefined episodes.
Wellig teaches a power management circuit is configured to monitor energy in the energy storage to schedule the power consumption of each sensor from the energy store according to a schedule provided by a model trained with a training set of environmental parameters of wildfires and harvestable power over multiple predefined episodes (defined in paragraph [0040] wherein scheduling the power consumption of each sensor is interpreted as an activation schedule of the sensor based on past sensed parameters of the sensor 62 based on an AI model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low cost sensors are taught.
PNG
media_image1.png
515
517
media_image1.png
Greyscale
Crescini figure 3 shows a sensing device with a plurality of different sensors items 36a-f
PNG
media_image2.png
488
615
media_image2.png
Greyscale
Wellig discloses a sensor assembly system which uses artificial intelligence (AI) modeling to create an operating schedule based on sensor readings.
Regarding claim 2, Crescini teaches the sensor system of claim 1 wherein the sensors are selected from the group consisting of: humidity sensors, temperature sensors, cameras, and particles sensors (paragraph [0021] discloses wherein humidity and temperature sensor are used. Paragraph [0027] discloses monitoring particulate matter).
Regarding claim 3, Crescini teaches the sensor system of claim 1 but does not explicitly teach wherein the episode covers at least a year.
Wellig discloses wherein the episode covers at least a year (defined in paragraph [0040] wherein the episode covers at least a year).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low cost sensors are taught.
Regarding claim 5, Crescini teaches the sensor system of claim 1, but does not explicitly teach wherein the model is trained to provide schedules that minimize a combined difference between the environmental parameters and sensor readings of the environmental parameters over the training set.
Wellig teaches wherein the model is trained to provide schedules that minimize a combined difference between the environmental parameters and sensor readings of the environmental parameters over the training set (defined in paragraph [0040] wherein scheduling the power consumption of each sensor is interpreted as an activation schedule of the sensor based on past sensed parameters of the sensor 62 based on an AI model. A schedule based on the difference between threshold values of sensed parameters is determined).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low cost sensors are taught,.
Regarding claim 6, Crescini teaches the sensor system of claim 5 but does not explicitly teach wherein the model is trained to provide schedules that conserve the energy stored during the episode over the training set.
Wellig teaches wherein the model is trained to provide schedules that conserve the energy stored during the episode over the training set (defined in paragraph [0040] wherein scheduling the power consumption of each sensor is interpreted as an activation schedule of the sensor based on past sensed parameters of the sensor 62 based on an AI model. Paragraph [0043] discloses adjusting the activation schedule to conserve energy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low-cost sensors are taught.
Regarding claim 10, Crescini teaches a method of training a sensor system of a type (figures 1 - 3 item 10 paragraph [0019] discloses sensor system defined as a multi-parametric environmental diagnostics and monitoring sensor node. Paragraph [0025] discloses wherein the system may monitor fires) having:
an energy harvester for extracting energy from the environment to provide electrical power (figure 9 paragraph [0038] discloses energy harvesting modules items 22, 16, 14, 34, and 18 extracting energy from the environment such as a photovoltaic cell);
an energy store communicating with the energy harvester for storing the provided electrical power (paragraph [0038] discloses wherein the energy harvesting modules store energy in supercapacitor item 52);
a sensor suite including multiple sensors measuring different environmental parameters and having different electrical energy demands (figures 1 -3 show a plurality of environmental sensors items 36a-36e monitoring different environmental parameters);
a power management circuit operating to read the environmental parameters figure 3 item 47 a central processor. Paragraph [0020] discloses wherein the sensors are in communication with a processor)
a wireless transmitter for communicating the environmental parameters to a remote fire assessment station figure 2 item 42 shows an antenna for wirelessly communicating parameters).
Crescini does not explicitly teach a power management circuit configured to monitor the energy store to schedule the power consumption of each sensor from the energy store according to a schedule provided by a model; and a wireless transmitter for communicating the environmental parameters to a remote fire assessment station the method comprising: (a) generating a training set of environmental parameters of wildfires and harvestable power over multiple predefined episodes; and (b) training the model using the training set to provide schedules that minimize a combined difference between the environmental parameters and sensor readings of the environmental parameters over the training set comprised of different episodes while conserving the energy stored during the episode over the training set.
Wellig teaches a power management circuit configured to monitor the energy store to schedule the power consumption of each sensor from the energy store according to a schedule provided by a model (defined in paragraph [0040] wherein scheduling the power consumption of each sensor is interpreted as an activation schedule of the sensor based on past sensed parameters of the sensor 62 based on an AI model).
the method comprising: (a) generating a training set of environmental parameters of wildfires and harvestable power over multiple predefined episodes (paragraph [0020] discloses sensor assemblies monitoring environmental parameters such as temperature and humidity. Paragraphs [0021] and [0042] discloses generating or reporting the parameters over time) ; and
(b) training the model using the training set to provide schedules that minimize a combined difference between the environmental parameters and sensor readings of the environmental parameters over the training set comprised of different episodes while conserving the energy stored during the episode over the training set (defined in paragraph [0040] wherein scheduling the power consumption of each sensor is interpreted as an activation schedule of the sensor based on past sensed parameters of the sensor 62 based on an AI model. Paragraph [0043] discloses adjusting the activation schedule to conserve energy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low cost sensors are taught.
Regarding claim 11, Crescini teaches the method of claim 10 wherein the sensors are selected from the group consisting of: humidity sensors, temperature sensors, cameras, and particles sensors (paragraph [0021] discloses wherein humidity and temperature sensor are used. Paragraph [0027] discloses monitoring particulate matter).
Regarding claim 12, Crescini teaches the method of claim 10 but does not explicitly teach wherein the episode covers at least a year.
Wellig discloses wherein the episode covers at least a year (defined in paragraph [0040] wherein the episode covers at least a year).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of the Crescini reference with the teachings of sensor system of the Wellig reference so that the sensor are low powered and able to power themselves and to improve operational costs.
The suggestion/motivation for combination can be found in the Wellig reference in paragraph [0003] wherein low power and low cost sensors are taught.
Claims 4, 7-9 and 13 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Crescini (US 20170184560) in view of Wellig (US 20230393168) and in further view of Le (US 20220383102).
Regarding claim 4, Crescini in view Wellig teach the sensor system of claim 1, but do not explicitly teach wherein the model is trained using reinforcement learning.
Le teaches wherein the model is trained using reinforcement learning (paragraph [0142] discloses wherein a model is trained using reinforcement training to analyze wildfire data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 7, Crescini in view Wellig teach the sensor system of claim 1, but do not explicitly teach wherein the training set provides a simulation of a terrain of the field location.
Le teaches wherein the training set provides a simulation of a terrain of the field location (paragraphs [0079] – [0080] discloses using a simulation of landscape and terrain to determine a model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 8, Crescini in view Wellig teach sensor system of claim 7, but do not explicitly teach wherein the training set provides a simulation of a climate of the field location.
Le teaches wherein the training set provides a simulation of a climate of the field location (paragraphs [0079] – [0080] discloses using a simulation of climate to determine a model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 9, Crescini in view Wellig teach the sensor system of claim 1, but do not explicitly teach wherein the model is a simulation providing ground truth measurements and sensor measurements with probabilistically added noise.
Le teaches wherein the model is a simulation providing ground truth measurements and sensor measurements with probabilistically added noise (paragraph [0200] discloses wherein the simulation includes ground truth measurements, interpreted as daily temperature, wind, snow, precipitation, and other weather statistics measured. Paragraph [0200] discloses added noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 13, Crescini in view Wellig teach the method of claim 10, but do not explicitly teach wherein the model is trained using reinforcement learning.
Le teaches wherein the model is trained using reinforcement learning (paragraph [0142] discloses wherein a model is trained using reinforcement training to analyze wildfire data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 14, Crescini in view Wellig teach the method of claim 10, but do not explicitly teach wherein the training set provides a simulation of a terrain of a location of the sensor system.
Le teaches wherein the training set provides a simulation of a terrain of the field location (paragraphs [0079] – [0080] discloses using a simulation of landscape and terrain to determine a model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 15, Crescini in view Wellig teach the method of claim 14, but do not explicitly teach wherein the training set provides a simulation of a climate of the field location.
Le teaches wherein the training set provides a simulation of a climate of the field location (paragraphs [0079] – [0080] discloses using a simulation of climate to determine a model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Regarding claim 16, Crescini in view Wellig teach the method of claim 10, but do not explicitly teach wherein the model is a simulation providing ground truth measurements and sensor measurements with probabilistically added noise.
Le teaches wherein the model is a simulation providing ground truth measurements and sensor measurements with probabilistically added noise (paragraph [0200] discloses wherein the simulation includes ground truth measurements, interpreted as daily temperature, wind, snow, precipitation, and other weather statistics measured. Paragraph [0200] discloses added noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the Crescini and Welling reference with the teachings of the Le reference to provide a more accurate method of fire monitoring.
The suggestion/motivation for combination can be found in the Le reference in paragraph [0041] wherein a more accurate method of fire monitoring taught.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Us 20260180328 A1 Conserving Auxiliary Energy Arvanitis; Ioannis
Us 20180115170 A1 Scheduling Optimized Charging Bacarella; David J. Et Al.
Us 10175301 B1 Energy Managed Wireless Sensors Brubacher; Jonathan Quinn Et Al.
Us 20180358845 A1 Wireless Electricity Distribution Criswell; David R. Et Al.
Us 12676503 B2 Smart Energy Platforms Cardona; Alexander
Us 20160322835 A1 Charging Profiles Carlson; Eric Daniel Et Al.
Us 20260118837 A1 Ai-Based Energy Edge Platform Cella; Charles Howard Et Al.
Us 20260154091 A1 AI-Enabled Energy Storage Appliances Cook; Daniel Et Al.
Us 20180052505 A1 Electrical Power Management Cruickshank, Iii; Robert F.
Us 20210143659 A1 Power Management Scheme Franchitti; Julian Et Al.
Us 12632796 B2 Predict Characteristics Of Adverse Events Gupta; Akshina Et Al.
Us 20220344934 A1 Energy Demand Forecasting Jayan; Jinu Et Al.
Us 20190393723 A1 Intermittent Energy Management Pavlovski; Alexandre Et Al.
Us 20160330825 A1 Cloud Connected Motion Sensor Recker; Michael V. Et Al.
Us 20070247134 A1 Control Apparatus Ryan; Liam Anthony Et Al.
Us 20170235290 A1 Smart Home System And Method Weber; Zvika Et Al.
Us 20230216296 A1 Power Prediction Method Wang; Shuqian Et Al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS B PACHECO whose telephone number is (571)272-5979. The examiner can normally be reached M-F 9:00 - 5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman can be reached at 571-272-2147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
ALEXIS BOATENG PACHECO
Primary Examiner
Art Unit 2859
/ALEXIS B PACHECO/Primary Examiner, Art Unit 2859
Prosecution Insights