DETAILED ACTION
Notice of 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 .
Priority
The present application’s priority under Provisional US Application Number 63/304,243 is acknowledged.
Status of Claims
Claims 1-9, 11-18, 20-22 are pending.
Claims 1 and 15 have been amended.
Claims 10 and 19 were previously canceled.
Claim 22 has been added.
Response to Amendment
Rejections Under 35 U.S.C. §101: The amendments to claims 1 and 15 do not overcome the rejection. The rejection to claims 1-9, 11-18, and 20-21 is maintained.
Rejections Under 35 U.S.C. §103:
Claims 1 and 15 have been amended to change the scope of the claimed invention. Specifically, amended claim 1 recites “using output from one sensor of the plurality of sensors to assess the output of an other sensor of the plurality of sensors and, in real time, omitting the output of the other sensor if the other sensor id determined to be faulty”, which changes the scope of the claimed invention.
Response to Arguments
Rejections Under 35 U.S.C. §101:
Applicant's arguments included in Remarks filed 04/01/2026 have been fully considered but they are not persuasive. Applicant argues on pg 10 of Remarks that “The human mind is not equipped to perform sensor orchestration”. However, the method recites “receiving … sensor data”, which is an additional element and extra-solution activity. The method does not positively recite a separate step of collecting data by sensor orchestration.
Applicant further argues on pg 10 of Remarks that “The human mind is not equipped to … assess in real time the output of one sensor using the output of another sensor and omit the output of the other sensor if the other sensor is determined to be faulty”. However, examiner maintains that a person is capable of looking out live outputs of multiple sensors of the same type of information and ignoring one output that is significantly different from the other outputs.
Applicant further argues that, citing paragraph [0126] of the specification, “the recited steps provide an improvement to the functioning of the computer … as well as improving battery life, which is a tangible physical effect.” However, examiner maintains that the limitation cited by applicant as an additional element that provides an improvement is instead part of the abstract idea, and therefore is not eligible to provide an improvement to the technology.
Applicant further argues that “the recited steps are not merely “receiving or transmitting data over a network” … but provide a coordinated orchestration of multiple sensors that improves computer technology”. Examiner maintains that the method does not positively recite a step of coordinating sensor collection. Instead, the method only recites a step of receiving sensor data whose origin included sensor orchestration. Also, the alleged improvements to computer technology and battery life are tied to a step in the abstract idea, not an additional element, and therefore do not integrate the abstract idea into a practical application. Applicant cites Thales to argue that the instant claims “utilize sensors in a non-conventional manner to reduce errors”. However, examiner maintains that the method positively recites a step of receiving sensor data, not orchestrating sensors data collection.
Rejections Under 35 U.S.C. §103: Applicant’s amendments have necessitated new grounds of rejection presented in this Office action. Accordingly, Applicant’s arguments with respect to claims 1 and 15 have been considered but are moot because the arguments do not apply to the current rejection. Examiner relies on new reference Gangumalla et al. (US 20230110372 A1) to teach collecting sensor data from at least two of an accelerometer, a gyroscope, a magnetometer, a barometer, and a location sensor (see Gangumalla paragraph [0043]) and omitting sensor output based on output from other sensors (see Gangumalla paragraph [0043]).
Claim Objections
Claims 1 and 15 are objected to because of the following informalities: “an other sensor” should be changed to “another sensor”. Appropriate correction is required.
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 an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim fall within one of the statutory categories? Yes. The claim is directed toward a Process which falls within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Example: iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
See claim 1 language below:
A computer-implemented method comprising:
receiving, via a user device, user input identifying a baseline mode of transport, the baseline mode of transport comprising a vehicle;
acquiring an initial odometer reading for the vehicle at an initial date;
acquiring a subsequent odometer reading at a subsequent date;
determining a user consumption baseline based on a difference between the subsequent odometer reading and the initial odometer reading over the period between the subsequent date and the initial date;
determining an emissions baseline over time using the user consumption baseline and a stored emission profile for the baseline mode of transport in a database;
receiving, from the user device:
an indication of a start point at which a user has started a trip, the indication including a mode of transport;
location data collected during the trip; and
sensor data collected by orchestration of a plurality of sensors of the user device simultaneously during the trip, the plurality of sensors comprising at least two of: an accelerometer, a gyroscope, a magnetometer, a barometer, and a location sensor;
using output from one sensor of the plurality of sensors to assess the output of an other sensor of the plurality of sensors and, in real time, omitting the output of the other sensor if the other sensor is determined to be faulty;
verifying the mode of transport in real time by determining whether the sensor data corresponds to stored sensor data for the mode of transport in a database at discrete points during the trip;
retrieving an emissions profile for the mode of transport from a database;
receiving, from the user device or a remote server, data input comprising at least one of: temperature, humidity, weather conditions, or the geographical area associated with the location data;
dynamically adjusting, in real time, the emissions profile for at least one of the mode of transport and the baseline mode of transport based on the data input;
determining, using the sensor data, an end point at which the user stops using the mode of transport;
determining a trip distance between the start point and the end point of the trip using the location data; and
determining an emission savings value for the trip based on a difference between an emission estimate for the trip using the actual mode of transport and a baseline emission estimate for the trip distance, the baseline emission estimate being estimated for the trip distance using the emissions baseline over time.
The Process in claim 1, specifically the limitations bolded above, is a mental process that can be practicably performed in the human mind with the aid of a pen and paper and, therefore, an abstract idea. It merely consists of determining a user consumption baseline, determining an emissions baseline over time, verifying a mode of transportation, adjusting an emission profile, determining an end point, determining a trip distance, and determining an emission savings value. This is equivalent to (1) comparing two odometer readings to determine a mileage per time baseline value, (2) determining a emissions value using the mileage per time value and an emission profile for a vehicle, such as a car (example calculation: the user travels 5 miles per day, and the vehicle gets 15 miles per gallon, so the user is using 1/3 gallons per day), (3) comparing live sensor data from multiple sensors of the same type and ignoring an output that is most dissimilar to the other outputs, (4) comparing maximum speed data collected during the trip with maximum speeds associated with different modes of transportation to infer a likely mode of transportation for the trip, (5) mentally adjusting an emission profile for the current mode of transportation based on if currently traveling up a mountain (example: adjust the mpg of the car from 15 to 7.5), (6) determine that the user has stopped using a mode of transportation based on velocity data changing significantly, (7) calculating the distance between the start and end locations of the trip, which could be carried out using latitude and longitude coordinate data, (8) and calculating an emission savings value by comparing the likely emissions for the mode of transportation for the trip and a baseline vehicle. In an instance where the mode of transportation for the trip is the same as the mode of transportation of the baseline vehicle, the emissions savings value would be zero.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements underlined above that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The steps of “receiving, via a user device, user input identifying…”, “acquiring an initial odometer reading…”, “acquiring a subsequent odometer reading…”, “receiving, from a user device: an indication of a start point… location data … and sensor data…”, “retrieving an emissions profile for the mode of transport…”, and “receiving, from the user device or a remote server, data input…” are recited at a high level of generality and amount to mere data gathering steps, which is a form of insignificant extra solution activity.
The “computer-implemented” designation merely describes how to generally “apply” the otherwise mental judgments in a generic or general-purpose computing environment. The computer is recited at a high level of generality and merely automate the process steps.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well- understood, routine, conventional (WURC) activity in the field.
Mere data communication steps that can be performed entirely on any one or more generic computer/-s have also been previously identified by the courts as an abstract idea (i.e. a judicial exception): (A) Receiving and/or transmitting data is considered to be well-understood, routine, or conventional at least as evidenced by MPEP § 2106.05(d)(II)(i) "Receiving or transmitting data over a network", and (iv) "Storing and retrieving information in memory" and (B) Comparing the received data to other data is considered to be well-understood, routine or conventional at least as evidenced by MPEP§ 2106.05(d)(II)(ii) "Performing repetitive calculations".
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claim 1 is directed towards non-statutory subject matter.
Additionally, Claims 2-9, 11-14, 16-18, and 20-21:
fall within one of the statutory categories (Claims 2-9, 11-14, 16-18, and 20-21: Process)
directed toward an abstract idea (Mental Process),
do not recite additional elements that integrate the judicial exception into a practical application, and
do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, it is clear that Claims 2-20 are directed towards non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7, and 11 rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), and Oshiro et al. (US 20140277971 A1).
Regarding claim 1, Rudow teach A computer-implemented method comprising: receiving, via a user device, user input identifying a baseline mode of transport, the baseline mode of transport (see at least [0068]: “the registration process establishes an association between a particular vehicle and user, so that carbon output from that vehicle can be tracked and associated with the user”) comprising a vehicle;
determining a user consumption baseline (see at least [0075]: “calculating the vehicle's … change of altitude from the position information and associated time stamp”)
determining an emissions baseline over time (see at least [0075]: “calculate an estimated carbon output value”) using the user consumption baseline (see at least [0075]: “by calculating the vehicle's … change of altitude from the position information and associated time stamp”) and a stored emission profile (see at least [0075]: “estimating the carbon output based on vehicle characteristics”; [0083]: “the server may maintain and/or have access to a database of characteristics for many different vehicles, and the server might search such a database to identify the relevant characteristics for the current vehicle. Relevant characteristics can include, without limitation, vehicle mileage (e.g., gallons of fuel consumed per mile driven), fuel type (e.g., unleaded gasoline, diesel, etc.), vehicle engine type, gear ratios”) for the baseline mode of transport in a database;
receiving, from the user device (see at least FIG. 2A: Cell Phone/PDA 205):
an indication of a start point ([0170]: “information about the trip, such as … a path of the trip (e.g. starting and stopping locations)”) at which a user has started a trip, the indication including a mode of transport (see at least [0140]: “the user information might include identification of a vehicle to be tracked”);
location data (see at least FIG. 13 step 1330: “ascertain operating parameters”; [0146]: “ascertaining a set of one or more operating parameters of the vehicle; “exemplary operating parameters … can include … vehicle position”; [0147]: “location information from a GNSS receiver”) collected during the trip; and
sensor data (see at least [0149]: “operating parameters can comprise … sensing data”) collected by orchestration (see at least [0062]: “Together, these sensors … can collect data about one or more monitored elements 230, including the vehicle's operating parameters”) of a plurality of sensors (see at least [0062]: “specialized sensors”) of the user device ([0108]: “GNSS data received by a mobile tracking device”) simultaneously (see at least [0074]: “Periodically (e.g., every 10 seconds, every minute, every five minutes, etc.), the application 300 collects and/or stores measurements, such as information about the vehicle's current position (block 364) … the application 300 might collect and/or store other measurements of vehicle operating parameters”) during the trip, the plurality of sensors comprising (see at least [0063]: “GNSS receiver 250”);
verifying the mode of transport in real time ([0113]: “the mode selection process may be repeated … upon detection of a material difference in location (e.g., relative to a particular road) and/or speed”) by determining (see at least FIG. 1A, [0052]: “the inference manager 175 will receive operating data from a vehicle (e.g., via the vehicle data collector 115), and from that data, infer a mode of transport currently employed by the user.”) whether the sensor data corresponds to stored sensor data for the mode of transport in a database (see at least FIG. 1A, [0053]: “inference manager 175 might be integrated with … the database management system 110.”) at discrete points ([0108]: “HIM inference method 700, which can infer a mode of conveyance (and/or vehicle identity) based on … speed information, which can be obtained, for example, from GNSS data received by a mobile tracking device”; [0111]: “the HIM might also factor velocity information into the analysis. Hence, the method 700 can further include determining an average and/or peak speed (block 730) over a given period of time …. It should be apparent that such calculations can be performed using one or more position/time data pairs as input.”) during the trip;
retrieving an emissions profile (see at least [0075]: “estimating the carbon output based on vehicle characteristics”; [0083]: “the server may maintain and/or have access to a database of characteristics for many different vehicles, and the server might search such a database to identify the relevant characteristics for the current vehicle. Relevant characteristics can include, without limitation, vehicle mileage (e.g., gallons of fuel consumed per mile driven), fuel type (e.g., unleaded gasoline, diesel, etc.), vehicle engine type, gear ratios”) for the mode of transport from a database;
receiving, from the user device or a remote server ([0084]: “weather information … is available via numerous web sites”), data input comprising at least one of:
weather ([0084]: “weather information might be useful, and such information can be obtained from public sources (based on current vehicle location)”; “the system can provide to the server any operating parameters available from the vehicle that can be used to calculate performance statistics, including without limitation atmospheric carbon output”) conditions associated with the location ([0084]: “… (based on current vehicle location)”) data;
determining, using the sensor data, an end point ([0170]: “information about the trip, such as … a path of the trip (e.g. starting and stopping locations)”) at which the user stops using the mode of transport;
determining a trip (see at least [0147]: “a mobile device might receive location information from a GNSS receiver … . From this position information (and associated time information), vehicle direction and velocity … can be calculated.”) distance ([0075]: “the server can calculate an estimated carbon output value (e.g., by calculating the vehicle's … change of altitude from the position information and associated time stamp and estimating the carbon output based on vehicle characteristics”) between the start point and the end point ([0170]: “information about the trip, such as … a path of the trip (e.g. starting and stopping locations)”) of the trip using the location data; and
determining an emission savings value (see at least FIG. 13 step 1365: “Generate Carbon Emission Value”) for the trip based on a difference between (see at least [0158]: “a carbon emission value of 1.5 might indicate that the user's carbon output rate is 50% greater than the baseline rate (… for another vehicle to which the user seeks comparison, etc.) over the course of a … trip”) an emission estimate for the trip distance using the actual mode of transport (see at least [0075]: “calculate an estimated carbon output value”) and a baseline emission estimate for the trip (see at least [0168]: “the report might compare the user's carbon output over one interval with the user's carbon output over another interval, e.g. to provide a historical comparison. (In an aspect, a vehicle's carbon output over one interval of time can be considered a baseline carbon output rate that can be compared to output over another interval of time and/or from another vehicle.)”), the baseline emission estimate being estimated for the trip distance using the emissions baseline over time. *Examiner’s interpretation: for the case described in Rudow where a historical carbon output is used as a baseline for comparison with a current carbon output, the baseline emission estimate for the trip would be the same as the emissions baseline over time.*
However, Rudow does not explicitly teach acquiring an initial odometer reading for the vehicle at an initial date;
acquiring a subsequent odometer reading at a subsequent date;
based on a difference between the subsequent odometer reading and the initial odometer reading over the period between the subsequent date and the initial date;
at least two of: an accelerometer, a gyroscope, a magnetometer, a barometer;
using output from one sensor of the plurality of sensors to assess the output of an other sensor of the plurality of sensors and, in real time, omitting the output of the other sensor of the other sensor is determined to be faulty;
dynamically adjusting, in real time, the emissions profile for at least one of the mode of transport and the baseline mode of transport based on the data input.
Mitchell teach acquiring an initial odometer reading (see at least [0028]: “image … files captured by the electronic devices 12 and stored in the repository 16”; ([0059]: “time-stamped images of a vehicle's odometer”)) for the vehicle at an initial date *Examiner’s interpretation: Merriam Webster defines timestamp as “an indication of the date and time as part of a digital signal or file (such as an … digital photograph …) indicating the time of creation, transmission…”. Therefore, a timestamped image of an odometer will include date information.*;
acquiring a subsequent odometer reading (see at least [0044]: “the analysis application may utilize image recognition techniques to determine a value of a vehicle's mileage from analyzing an image of the vehicle's odometer”) at a subsequent date
determining a user consumption baseline (see at least [0059]: “the analysis application may utilize image recognition techniques to determine a value of a vehicle's mileage from analyzing an image of the vehicle's odometer. For instance, period time-stamped images of a vehicle's odometer may be used … to verify an amount of miles that a vehicle is driven by an operator.”) based on a difference between the subsequent odometer reading and the initial odometer reading over the period between the subsequent date and the initial date;
It would have been obvious to one of ordinary skill in the art before the effective filing date of Mitchell claimed invention to have modified Rudow to incorporate the teachings of Mitchell to determine mileage based on odometer image analysis. Doing so would make data necessary for vehicle claims processing more up-to-date and accessible, as recognized by Mitchell in paragraphs [0003]-[0004].
Gangumalla teaches the plurality of sensors comprising at least two of: an accelerometer, a gyroscope, a magnetometer, a barometer (see at least [0043]: “Internal sensor arrangement 112 may be a plurality of one or more sensors of the same type for measuring motion, position, and/or orientation of device 100 in space, such as an accelerometer, a gyroscope, a magnetometer, a pressure sensor or others.”), and a location sensor (see at least [0031]: “GPS”);
using output from one sensor of the plurality of sensors to assess the output of an other sensor of the plurality of sensors and, in real time, omitting (see at least [0043]: “the N−K sensors are monitored for anomaly. For example, the output of one sensor can be compared to the others and if there is a deviation of more than a threshold, it may be determined the sensor is not operating correctly. When anomalous behavior is detected, the affected sensor is removed and considered to be in an inactive state so that its measurements are not used with the outputs from the remaining active sensors.”) the output of the other sensor of the other sensor is determined to be faulty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Gangumalla to omit data from faulty sensors. Doing so would “improve accuracy”, as recognized by Gangumalla in paragraph [0042].
Oshiro teach dynamically adjusting ([0062]: “The fuel economy data store 120 may then be accessed by vehicle simulation tools, advanced driver assistance systems, a GPS display, a data analysis tool, or any other system that consumes drive cycle profile information.”), in real time ([0036]: “current driving behavior based on a fuel economy estimate … retrieved from the fuel economy data store 120.”; FIG. 6: MPG), the emissions profile ([0006]: “generate one or more vehicle fuel economy estimates by obtaining vehicle information and a path of travel; obtaining road information for segments of the path of travel; generating a drive cycle profile based on the path of travel, the road condition information, and the vehicle configuration information; and generating a fuel economy estimates based on the drive cycle profile.”; [0062]: “stores the one or more fuel economy estimates in the fuel economy data store 120”) for at least one of the mode of transport and the baseline mode of transport based on the data input ([0001]: “a drive cycle profile may include a vehicle’s … location over the course of a typical trip”; [0055]: “ambient conditions included in the drive cycle profile, such as the weather conditions”; [Abstract]: “One or more drive cycle profiles are used to generate a fuel economy estimate”) *Examiner’s interpretation: drive cycle profiles and fuel economy estimates are generated based on road segments, and in FIG. 6 of Oshiro, a reference to an ideal MPG estimate based on the current road segment / vehicle location is displayed. When the current road segment for the vehicle changes, MPG estimates generated for that segment to reflect things like weather conditions are called up for use in an application.*;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Oshiro to adjust an emissions profile. “The ambient conditions included in the drive cycle profile, such as the weather conditions … , are not easily captured in traditional methods, and provide at least one benefit to using the current system instead of traditional methods”, as recognized by Oshiro in paragraph [0055], and “Embodiments of the present disclosure provide advantages in these roles, as the generated drive cycle profiles to no require profile data recorded from an actual vehicle while traveling along the planned route”, as recognized by Oshiro in paragraph [0031]. Furthermore, doing so can be used “to optimize the performance of the vehicle”, as recognized by Oshiro in paragraph [0074].
Regarding claim 2, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Rudow further teach wherein the sensor data comprises at least one of: photographic data, video data, accelerometer data, vibration data, sound signature data, and altitude data (see at least [0145]: “the position may be identified in three dimensions (e.g., to allow tracking of vehicle altitude changes”).
Regarding claim 3, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Rudow further teach wherein the location data comprises at least one of GPS (see at least [0048]: “the activity might be tracked by a tracking system that employs an application on a mobile device, such as a … GPS receiver”; [0063]: “positional data from the GNSS receiver 240”) logs, cell phone pings and cell tower triangulation (see at least [0103]: “obtain position/time data (e.g., … from cellular triangulation”), cell signal strength data, mapping software data (see at least [0109]: “From the position information, the HIM determines the position of the user, typically by reference to a map”), and compass data (see at least FIG. 2A, [0062]: “data about one or more monitored elements 230, including the vehicle's operating parameters (… movement direction”).
Regarding claim 7, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Rudow further teach wherein attempting to verify the mode of transport further comprises identifying (see at least [0052]: “the inference manager 175 is a predictive modeling tool that functions to predict what mode of transport (e.g., a particular vehicle”) a vehicle via a connection between (see at least FIG. 1A, [0048]: “individual user's vehicle activity 135a, the activity might be tracked by a tracking system that employs an application on a mobile device … in communication with) a vehicle diagnostic system”) the vehicle and the user device.
Regarding claim 11, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Rudow further teach further comprising attempting to verify (see at least [0068]: “the registration process establishes an association between a particular vehicle and user, so that carbon output from that vehicle can be tracked and associated with the user. After the registration process has been completed, it may be verified”) the baseline (see at least [0168]: “the report might compare the user's carbon output over one interval with the user's carbon output over another interval, e.g. to provide a historical comparison. (In an aspect, a vehicle's carbon output over one interval of time can be considered a baseline carbon output rate that can be compared to output over another interval of time and/or from another vehicle.)”) mode of transport.
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Oliver Gomila et al. (US 20200284600 A1).
Regarding claim 4, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Rudow further teach location data from a start point (see at least FIG. 6 step 610: “Note Start Time”; step 630: “Begin Tracking/Reporting”; [0103]: “obtain position/time data”) and an end point (see at least [0089]: “upon detecting that a trip has ended”) of the trip.
Oliver Gomila teach wherein determining the trip distance comprises determining displacement of the user between a start location and an end location (see at least [0075]: “distance travelled for the trip … could be ascertained using available means of identifying a user at a particular location (typically, at a point of entry or exit) … . This information could be used to identify the start point of the … trip and the end point of the … trip, and to calculate the distance travelled between those two points.”) using location data from a start point and an end point of the trip.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Gomila to determine a trip distance using start and end locations of the trip. Doing so would help to “incentivize more environmentally-sustainable transportation choices”, as recognized by Oliver Gomila in paragraph [0004].
Regarding claim 8, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
Oliver Gomila teach wherein determining the trip distance comprises determining an actual distance travelled by the user using the location data (see at least [0052]: “the modal shift application tracks and records the user's physical movements, including distance traveled for each mode of transport … for the user's trip.”) from various points during the trip (see at least [0041]: “trip between the start point and end point”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Gomila to determine a trip distance. Doing so would help to “incentivize more environmentally-sustainable transportation choices”, as recognized by Oliver Gomila in paragraph [0004].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and daCosta (US 9172738 B1).
Regarding claim 5, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
However, the combination of Rudow, Mitchell, Gangumalla, and Oshiro does not explicitly teach wherein attempting to verify the mode of transport further comprises:
determining a predicted travel time for the trip based on historical data for the mode of transport;
receiving an actual travel time for the trip from the user device; and
determining whether the actual travel time corresponds to the predicted travel time.
daCosta teach wherein attempting to verify the mode of transport further comprises:
determining a predicted travel time for the trip (see at least (153) Column 21 lines 43-48: “In the absence of knowing a priori when customer will arrive at the store, we need to predict Likely “Arrival” time based on ETA estimates provided by the mobile device software, which is aware of multiple modes of transport (driving/walking) and has the ability to correctly infer the current mode.”) based on historical data (see at least (56) Column 12 lines 30-32: “Adjust ETA … based on analysis of recent historical data collected”) for the mode of transport;
receiving an actual travel time for the trip from the user device (see at least (140) Column 21 line 51: “based on historical actual arrival time”); and
determining whether the actual travel time corresponds to the predicted travel time (see at least (151) Column 7 lines 63-66: “ETA errors from actual and previously predicted from previous samples. The mobile device is sampled periodically to provide this information. The error and its trends are computed.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of daCosta to verify mode of transportation based on predicted and actual travel times. Doing so would help to “estimate, to a high level of accuracy, the arrival time”, as recognized by daCosta in Column 1 lines 46-55.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), daCosta (US 9172738 B1), and Oe et al. (US 20190108752 A1).
Regarding claim 6, the combination of Rudow, Mitchell, Gangumalla, Oshiro, and daCosta teach The method of claim 5.
Rudow further teaches wherein attempting to verify the mode of transport further comprises:
determining a travel speed (see at least [0111]: “determining an average and/or peak speed (block 730) over a given period of time (for example, the length of the trip”) based on the actual travel time; and
determining whether the travel speed is possible (see at least FIG. 10: Possible Mode for Access Highway Path Match is Auto) for the mode of transport.
However, the combination of Rudow, Mitchell, Gangumalla, Oshiro, and daCosta does not explicitly teach and the trip distance.
Oe teach determining a travel speed (see at least [0063]: “calculate the average speed of the vehicle 5 during traveling based on an elapsed time while the vehicle 5 is traveling in one trip of the vehicle 5 and a moving distance while the vehicle 5 is traveling.”) based on the actual travel time and the trip distance.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Oe to determine speed based on travel time and trip distance. Doing so would help to improve vehicle safety, as recognized by Oe in paragraph [0214].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Hager (US 20070250258 A1).
Regarding claim 9, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 8.
Rudow further teach using the distance to determine the emission savings value (([0075]: “the server can calculate an estimated carbon output value (e.g., by calculating the vehicle's … change of altitude from the position information and associated time stamp and estimating the carbon output based on vehicle characteristics”)) for the trip.
However, the combination of Rudow, Mitchell, and Oshiro does not explicitly teach determining a shortest reasonable distance for the trip using a mapping data source;
determining whether the actual distance is longer than the shortest reasonable distance; and
upon determining that the actual distance is longer than the shortest reasonable distance, using the shortest reasonable distance to determine for the trip
Hager teach determining a shortest reasonable distance (see at least [0055]: “calculating the mileage of the shortest distance for the route segments”) for the trip using a mapping data source;
determining whether the actual distance is longer (see at least [0055]: “This could be an indication that the trip included personal side trips that do not qualify for the original business purposes of the main trip”) than the shortest reasonable distance; and
upon determining that the actual distance is longer than the shortest reasonable distance (see at least [0055]: “If the mileage data recorded by the GPS hardware is in line with the mileage data calculated by the router 110 and mileage calculation module 112, the mileage recorded by the GPS hardware will be accepted by the system and forwarded to the Summary calculation module 122 to calculate the route's expense. Otherwise, the system may reject the GPS mileage data and use the validated mileage data calculated by the router 110”), using the shortest reasonable distance (see at least [0055]: “the system will calculate the shortest distance for a given trip and will accept that distance as the validated distance to use for actual calculations”) to determine for the trip.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Hager to use a shortest distance for mileage calculation. Doing so would help to “accurately and automatically reconstruct … mileage”, as recognized by Hager in paragraph [0012].
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Pearce (US 20200219198 A1).
Regarding claim 12, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 11.
Rudow further teach wherein attempting to verify the baseline mode of transport comprises at least one of:
determining if the vehicle is registered (see at least [0068]: “verification … could involve performing a check with an appropriate regulatory body to determine whether the identified VIN … is indeed registered to the user that provided it.”) to the user; and
determining vehicle insurance (see at least [0141]: “the system might search … a database operated by … an insurance company database … to find a vehicle associated with the user and matching any identifying information provided by the user.”).
However, the combination of Rudow, Mitchell, Gangumalla, and Oshiro does not explicitly teach determining if the vehicle is covered by an active insurance policy for at least part of the trip.
Pearce teach determining if the vehicle is covered (see at least [0062]: “determines whether the insurance for the automobile is presently in an active status”) by an active insurance policy for at least part of the trip (see at least [0061]: “the identifier of the automobile … is acquired by the insurance application program … by, for example, the automobile being started up”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Pearce to verify if a vehicle is covered by an active insurance policy. Doing so would help to gather information that can be used to calculate more competitive discounts for drivers, as recognized by Pearce in paragraph [0003].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), Pearce (US 20200219198 A1), and Oliver Gomila et al. (US 20200284600 A1).
Regarding claim 13, the combination of Rudow, Mitchell, Gangumalla, Oshiro, and Pearce teach The method of claim 12.
However, the combination of Rudow, Mitchell, Gangumalla, Oshiro, and Pearce does not explicitly teach wherein attempting to verify the baseline mode of transport further comprises comparing an emissions reduction claimed by the user with a per vehicle normalized average emissions reduction for a geographical area.
Oliver Gomila teach wherein attempting to verify the baseline mode of transport further comprises comparing an emissions reduction claimed by the user with a per vehicle normalized average emissions reduction (see at least [0102]: “Emissions calculations … may be based on an average of … the emissions factors across the geographic regions over which the travel takes place.”; FIG. 13A, [0081]: “emission factor for the baseline mode of transport for the applicable geographic region”) for a geographical area (see at least [0094]: “The baseline is assessed for each geographic region based on the particularities of that region”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Oliver Gomila to verify the baseline vehicles using geographic region emission factors. Doing so would help to “incentivize more environmentally-sustainable transportation choices”, as recognized by Oliver Gomila in paragraph [0004].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Papineau et al. (US 20190228425 A1).
Regarding claim 14, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
However, the combination of Rudow, Mitchell, Gangumalla, and Oshiro does not explicitly teach further comprising: receiving, from the user device, user input indicating at least one additional passenger using the same mode of transport;
attempting to pair the user device with at least one passenger device; and
upon successful pairing, allocating the emission savings value between the user and the at least one additional passenger.
Papineau teach further comprising: receiving, from the user device, user input indicating at least one additional passenger using the same mode of transport (see at least [0048]: “send a photo of vehicle occupants … . Uploading the time-stamped photo to the server permits the photo verification of the number of occupants in a vehicle”);
attempting to pair (see at least [0044]: “During matchmaking 100, driver's device 102 and rider's device 104 are paired”) the user device (see at least FIG. 1: driver’s device 102) with at least one passenger device (see at least FIG. 1: rider’s device 104); and
upon successful pairing (see at least FIG. 1: step 110 occurs after step 105 (match made)), allocating the emission savings value between the user (see at least FIG. 1 step 110: “Transmitting rewards to Validated Driver”) and the at least one additional passenger (see at least [0051]: “the reward will be provided to a person associated with the vehicle, where the person associated with the vehicle may include a driver, a rider”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Papineau to allocate emissions savings values to drivers and riders. Doing so would protect against cheating by drivers trying to gain rewards, as recognized by Papineau in paragraph [0029].
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Pearce (US 20200219198 A1), Mitchell et al. (US 20220101445 A1), and Gangumalla et al. (US 20230110372 A1).
Regarding claim 15, Rudow teach A computer-implemented method comprising:
receiving (see at least [0094]: “a request to register a vehicle (block 505). In many cases, the request may be received from a mobile device operated by user”), from a user device (see at least FIG. 2A: Cell Phone/PDA 205), user input identifying (see at least [0068]: “the registration process establishes an association between a particular vehicle and user, so that carbon output from that vehicle can be tracked and associated with the user.”) a baseline (see at least [0168]: “the report might compare the user's carbon output over one interval with the user's carbon output over another interval, e.g. to provide a historical comparison. (In an aspect, a vehicle's carbon output over one interval of time can be considered a baseline carbon output rate that can be compared to output over another interval of time and/or from another vehicle.)”) vehicle, the user input including at least one of a user name, a vehicle identification number (VIN) (see at least [0095]: “The user may provide the registration information as user input”; [0096]: “the registration information included a VIN number”), and an insurance policy number;
attempting to verify the baseline vehicle by at least one of:
determining if the VIN corresponds with a stored VIN associated with the user in a vehicle registration database (see at least [0068]: “the verification could involve checking the VIN vial validation algorithm that is configured to … verify[] that the identified VIN corresponds to the equipment specified during the registration process.”);
transmitting a request for confirmation of registration to an external vehicle registry, the request for confirmation indicating at least one of the user name and the VIN (see at least [0068]: “verification … could involve performing a check with an appropriate regulatory body to determine whether the identified VIN … is indeed registered to the user that provided it.”);
determining if the insurance policy number corresponds with a stored active insurance policy number associated with the user in an insurance policy database; and
transmitting a request (see at least [0141]: “the system might search … a database operated by … an insurance company database … to find a vehicle associated with the user and matching any identifying information provided by the user.”),
upon successful verification (see at least [0068]: “After the registration process has been completed, it may be verified”)”) of the baseline (see at least [0168]: “a vehicle's carbon output over one interval of time can be considered a baseline carbon output rate that can be compared to output over another interval of time and/or from another vehicle”) vehicle, determining an emissions baseline over time (see at least FIG. 13: step 1315 “Discover VIN” [Wingdings font/0xE0] [Wingdings font/0xE0] step 1350 “Estimate Carbon Output Rate”; [0141]: “the system may discover the VIN by looking up all vehicles registered with the system by that user”) by:
receiving, via a user device, user input identifying a baseline mode of transport (see at least [0068]: “the registration process establishes an association between a particular vehicle and user, so that carbon output from that vehicle can be tracked and associated with the user”), the baseline mode of transport comprising a vehicle;
determining a user consumption baseline (see at least [0075]: “calculating the vehicle's … change of altitude from the position information and associated time stamp”)
determining the emissions baseline over time (see at least [0075]: “calculate an estimated carbon output value”) using the user consumption baseline (see at least [0075]: “by calculating the vehicle's … change of altitude from the position information and associated time stamp”) and a stored emission profile (see at least [0075]: “estimating the carbon output based on vehicle characteristics”) for the baseline mode of transport in a database;
receiving, from the user device a request for verification of a trip (see at least FIG. 1A, [0052]: “the inference manager 175 will receive operating data from a vehicle (e.g., via the vehicle data collector 115), and from that data, infer a mode of transport currently employed by the user.”), the request for verification indicating a mode of transport; and
receiving, from the user device, sensor data ([0108]: “location and/ speed information, which can be obtained, for example, from GNSS data received by a mobile tracking device”; [0111]: “determining an average and/or peak speed (block 730) over a given period of time (for example, the length of the trip, some specified interval, the length of time for which the user's location has corresponded to a particular road, etc.). It should be apparent that such calculations can be performed using one or more position/time data pairs as input.”) collected by orchestration (see at least [0062]: “Together, these sensors … can collect data about one or more monitored elements 230, including the vehicle's operating parameters”) of a plurality of sensors of the user device simultaneously (see at least [0074]: “Periodically (e.g., every 10 seconds, every minute, every five minutes, etc.), the application 300 collects and/or stores measurements, such as information about the vehicle's current position (block 364) … the application 300 might collect and/or store other measurements of vehicle operating parameters … . The frequency of reporting may be adjusted”) during the trip, the plurality of sensors comprising (see at least [0063]: “GNSS receiver 250”);
;
verifying the mode of transport ([0111]: “the HIM might also factor velocity information into the analysis.”) by determining whether the sensor data corresponds to stored sensor data for the mode of transport in a database ([0111]-[0112]: “The HIM then can select a list of possible options … based on the speed information (block 735). From this list of possible options, the HIM selects a vehicle/mode of conveyance as being the most likely current mode of operation for the user (block 740)… additional information may be used to make the selection from the list of possible options identified by the HIM. … the user's historical activities might be used to inform the selection”); and
determining an emission savings value (see at least FIG. 13 step 1365: “Generate Carbon Emission Value”) based on a difference in estimated emissions between (see at least [0158]: “a carbon emission value of 1.5 might indicate that the user's carbon output rate is 50% greater than the baseline rate (… for another vehicle to which the user seeks comparison, etc.) over the course of a … trip”) the mode of transport (see at least [0075]: “calculate an estimated carbon output value”) and the baseline vehicle for the trip (see at least [0168]: “the report might compare the user's carbon output over one interval with the user's carbon output over another interval, e.g. to provide a historical comparison. (In an aspect, a vehicle's carbon output over one interval of time can be considered a baseline carbon output rate that can be compared to output over another interval of time and/or from another vehicle.)”), the estimated emissions of the baseline vehicle being estimated for the trip using the emissions baseline over time *Examiner’s interpretation: for the case described in Rudow where a historical carbon output is used as a baseline for comparison with a current carbon output, the baseline emission estimate for the trip would be the same as the emissions baseline over time*.
However, Rudow does not explicitly teach for confirmation of an active insurance policy;
the request indicating at least one of the user name, the VIN, and the insurance policy number;
acquiring an initial odometer reading for the vehicle at an initial date, the initial odometer reading being acquired by
a) capturing, via the user device, an image of a bill of sale or insurance policy; or
b) requesting the initial odometer reading from an external data source;
acquiring a subsequent odometer reading, the subsequent odometer reading being acquired by capturing, via the user device, a digital photograph of the vehicle odometer, and
detecting a time stamp of the digital photograph;
based on a difference between the subsequent odometer reading and the initial odometer reading over the period between the time stamp of the digital photograph and the initial date;
at least two of: an accelerometer, a gyroscope, a magnetometer, a barometer, and a location sensor;
using output from one sensor of the plurality of sensor to assess the output of an other sensor of the plurality of sensors and, in real time, omitting the output of the other sensor if the other sensor is determined to be faulty.
Pearce teach transmitting a request (see at least FIG. 10 step 1010: “GET INSURANCE STATUS”; [0049]: “The mobile device, under control of the dynamic insurance application program code, can connect to a web services data center 119 that includes an insurance server 120. The insurance server 120 relays … information to a backend 122, which includes a database. … there can be a policy record 124 for an insured entity (e.g. the owner of mobile device 108).”) for confirmation of an active insurance policy (see at least [0062]: “determines whether the insurance for the automobile is presently in an active status”) to an external insurance provider (see at least [0058]: “the identifier can be sent to a remote processor such as the insurance web service data center”), the request indicating at least one of the user name, the VIN (see at least [0021]: “the identifier provided by the telematics unit is a vehicle identification number of the specific automobile”), and the insurance policy number.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Pearce to verify if a vehicle is covered by an active insurance policy. Doing so would help to gather information that can be used to calculate more competitive discounts for drivers, as recognized by Pearce in paragraph [0003].
Mitchell teach acquiring an initial odometer reading for the vehicle at an initial date, the initial odometer reading being acquired by
a) capturing, via the user device, an image of a bill of sale or insurance policy (see at least [0050]: “the analysis results file may include the value of a vehicle's mileage derived from … data determined from a scanned document image”; [0018]: “The scanned document files may include scans or images of documents such as insurance policies, … receipts”); or
b) requesting the initial odometer reading from an external data source (see at least [0028]: “image … files captured by the electronic devices 12 and stored in the repository 16”; ([0059]: “time-stamped images of a vehicle's odometer”));
acquiring a subsequent odometer reading (see at least [0044]: “ the analysis application may utilize image recognition techniques to determine a value of a vehicle's mileage from analyzing an image of the vehicle's odometer”), the subsequent odometer reading being acquired by capturing, via the user device, a digital photograph (see at least [0032]: “a picture of a vehicle's odometer may be taken”) of the vehicle odometer, and detecting a time stamp (see at least [0059]: “time-stamped images of a vehicle's odometer”) of the digital photograph;
determining a user consumption baseline (see at least [0059]: “the analysis application may utilize image recognition techniques to determine a value of a vehicle's mileage from analyzing an image of the vehicle's odometer. For instance, period time-stamped images of a vehicle's odometer may be used … to verify an amount of miles that a vehicle is driven by an operator.”) based on a difference between the subsequent odometer reading and the initial odometer reading over the period between the time stamp of the digital photograph and the initial date.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Mitchell claimed invention to have modified Rudow to incorporate the teachings of Mitchell to determine mileage based on odometer image analysis. Doing so would make data necessary for vehicle claims processing more up-to-date and accessible, as recognized by Mitchell in paragraphs [0003]-[0004].
Gangumalla teaches the plurality of sensors comprising at least two of: an accelerometer, a gyroscope, a magnetometer, a barometer (see at least [0043]: “Internal sensor arrangement 112 may be a plurality of one or more sensors of the same type for measuring motion, position, and/or orientation of device 100 in space, such as an accelerometer, a gyroscope, a magnetometer, a pressure sensor or others.”), and a location sensor (see at least [0031]: “GPS”);
using output from one sensor of the plurality of sensors to assess the output of an other sensor of the plurality of sensors and, in real time, omitting (see at least [0043]: “the N−K sensors are monitored for anomaly. For example, the output of one sensor can be compared to the others and if there is a deviation of more than a threshold, it may be determined the sensor is not operating correctly. When anomalous behavior is detected, the affected sensor is removed and considered to be in an inactive state so that its measurements are not used with the outputs from the remaining active sensors.”) the output of the other sensor of the other sensor is determined to be faulty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Gangumalla to omit data from faulty sensors. Doing so would “improve accuracy”, as recognized by Gangumalla in paragraph [0042].
Regarding claim 16, the combination of Rudow, Pearce, Mitchell, and Gangumalla teaches The method of claim 15.
Rudow further teach wherein attempting to verify the baseline vehicle
comprises transmitting the request for confirmation to the external insurance provider (see at least [0141]: “the system might search an external system (such as a database operated by … an insurance company database, and/or the like) to find a vehicle associated with the user and matching any identifying information provided by the user.”)
Pearce further teach transmitting the request (see at least FIG. 10 step 1010: “GET INSURANCE STATUS”; [0049]: “The mobile device, under control of the dynamic insurance application program code, can connect to a web services data center 119 that includes an insurance server 120. The insurance server 120 relays … information to a backend 122, which includes a database. … there can be a policy record 124 for an insured entity (e.g. the owner of mobile device 108).”) for confirmation of the active insurance policy (see at least [0062]: “determines whether the insurance for the automobile is presently in an active status”) to the external insurance provider (see at least [0058]: “the identifier can be sent to a remote processor such as the insurance web service data center”), and
further comprises receiving a response (see at least [0062]: “the automobile insurance status is presently active”) from the external insurance provider, the response including the VIN (see at least [0009]: “the identifier … as a vehicle identification number”) of the baseline vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Pearce to verify if a vehicle is covered by an active insurance policy. Doing so would help to gather information that can be used to calculate more competitive discounts for drivers, as recognized by Pearce in paragraph [0003].
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Pearce (US 20200219198 A1), Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), and Oliver Gomila et al. (US 20200284600 A1).
Regarding claim 17, the combination of Rudow, Pearce, Mitchell, and Gangumalla teach The method of claim 15.
However, the combination of Rudow, Pearce, Mitchell, and Gangumalla does not explicitly teach further comprising comparing an emissions reduction claimed by the user with a per vehicle normalized average emissions reduction for a geographical area.
Oliver Gomila teaches further comprising comparing an emissions reduction claimed by the user with a per vehicle normalized average emissions reduction (see at least [0102]: “Emissions calculations … may be based on an average of … the emissions factors across the geographic regions over which the travel takes place.”; FIG. 13A, [0081]: “emission factor for the baseline mode of transport for the applicable geographic region”) for a geographical area (see at least [0094]: “The baseline is assessed for each geographic region based on the particularities of that region”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Oliver Gomila to verify the baseline vehicles using geographic region emission factors. Doing so would help to “incentivize more environmentally-sustainable transportation choices”, as recognized by Oliver Gomila in paragraph [0004].
Regarding claim 18, the combination of Rudow, Pearce, Mitchell, and Gangumalla teach The method of claim 15.
Rudow further teaches further comprising: receiving, from the user device, location data (see at least FIG. 13 step 1330: “ascertain operating parameters”; [0146]: “ascertaining a set of one or more operating parameters of the vehicle; “exemplary operating parameters … can include … vehicle position”; [0147]: “location information from a GNSS receiver”) collected during the trip; and
determining a trip (see at least [0145]: “the method 1300 can comprise capturing a first position of the vehicle at a first time, and capturing a second position of the vehicle at a second, subsequent time.”; [0147]: “a mobile device might receive location information from a GNSS receiver … . From this position information (and associated time information), vehicle direction and velocity … can be calculated.”) using the location data.
However, the combination of Rudow, Pearce, Mitchell, and Gangumalla does not explicitly teach distance.
Oliver Gomila teaches determining a trip distance (see at least [0052]: “the modal shift application tracks and records the user's physical movements, including distance traveled for each mode of transport … for the user's trip.”) using the location data (see at least [0052]: “participating users need to enable their device's geolocation function for the duration of their journey.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Oliver Gomila to determine a trip distance. Doing so would help to “incentivize more environmentally-sustainable transportation choices”, as recognized by Oliver Gomila in paragraph [0004].
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Pearce (US 20200219198 A1), Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), and Papineau et al. (US 20190228425 A1).
Regarding claim 20, the combination of Rudow, Pearce, Mitchell, and Gangumalla teach The method of claim 15.
However, the combination of Rudow, Pearce, Mitchell, and Gangumalla does not explicitly teach further comprising:
receiving, from the user device, user input indicating at least one additional passenger using the same mode of transport;
attempting to pair the user device with at least one passenger device; and
upon successful pairing, allocating the emission savings value between the user and the at least one additional passenger.
Papineau teach further comprising:
receiving, from the user device, user input indicating at least one additional passenger using the same mode of transport (see at least [0048]: “send a photo of vehicle occupants … . Uploading the time-stamped photo to the server permits the photo verification of the number of occupants in a vehicle”);
attempting to pair (see at least [0044]: “During matchmaking 100, driver's device 102 and rider's device 104 are paired”) the user device (see at least FIG. 1: driver’s device 102) with at least one passenger device (see at least FIG. 1: rider’s device 104); and
upon successful pairing (see at least FIG. 1: step 110 occurs after step 105 (match made)), allocating the emission savings value between the user (see at least FIG. 1 step 110: “Transmitting rewards to Validated Driver”) and the at least one additional passenger (see at least [0051]: “the reward will be provided to a person associated with the vehicle, where the person associated with the vehicle may include a driver, a rider”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Papineau to allocate emissions savings values to drivers and riders. Doing so would protect against cheating by drivers trying to gain rewards, as recognized by Papineau in paragraph [0029].
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Slavin et al. (US 20100289644 A1).
Regarding claim 21, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
However, the combination of Rudow, Mitchell, Gangumalla, and Oshiro does not explicitly teach further comprising: generating a pre-set proximity around a location associated with the user;
initiating the collection of the sensor data via the user device when the location data indicates that the user is outside of the pre-set proximity.
Slavin teach further comprising: generating a pre-set proximity around a location (see at least [0054]: “the native monitoring application 142 enables users to create and modify allowed geographic areas (geofences) through the native monitoring application 142.”; [0057]: “The size and location of geofences … may change based on time of day (e.g., a moving asset may be allowed to move further from home during the daytime than during the nighttime).”) associated with the user;
initiating the collection of the sensor data via the user device when the location data indicates that the user is outside of the pre-set proximity (see at least [0059]: “when the native monitoring application 142 receives data indicating that a person (e.g., … teenager, Nanny with the kids, etc.) has been detected leaving the house, the native monitoring application 142 starts tracking where the moving asset 170 (i.e., car) is going.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Slavin to collect sensor data when a device exits a geofence. Doing so would be helpful for parents to ensure their children’s safety, as recognized by Slavin in paragraph [0100].
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Rudow et al. (US 20110184784 A1) in view of Mitchell et al. (US 20220101445 A1), Gangumalla et al. (US 20230110372 A1), Oshiro et al. (US 20140277971 A1), and Tulpule (US 20200276974 A1).
Regarding claim 22, the combination of Rudow, Mitchell, Gangumalla, and Oshiro teach The method of claim 1.
However, the combination of Rudow, Mitchell, Gangumalla, and Oshiro does not explicitly teach wherein the data input used to dynamically adjust the emissions profile comprises temperature data and/or humidity data collected by the user device during the trip.
Tulpule teach wherein the data input used to dynamically (see at least [0015]: “The internal information may include information about components of the vehicle and information about the vehicle's immediate surroundings that changes with time and is only available substantially instantaneously.”) adjust the emissions profile (see at least [Abstract]: “vehicle loss coefficients”; [0021]: “determination of changes to … coefficients based on advanced sensors (internal information) and external static and dynamic information. As indicated above, the information used may include the proximity of other vehicles including a front vehicle, the size and shape of surrounding vehicles, the relative velocity of surrounding vehicles, weather information such as rain, snow, and sun light, wind speed and direction, relative humidity, air temperature, air density, road surface information (e.g., amount of ice and/or water on the road), tire pressure, etc.”) comprises temperature data and/or humidity data (see at least FIG. 2: vehicle loss coefficients = f (relative humidity, temp)) collected by the user device during the trip.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rudow to incorporate the teachings of Tulpule to adjust an emission profile based on temperature and humidity data. Doing so would “accurately predict[] vehicle losses to determine how best to control vehicle/engine parameters … to improve fuel economy”, as recognized by Tulpule in paragraph [0018].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Previously disclosed prior art:
Delk (US 20080154671 A1) teaches a system that shares emission credits with multiple participants of a ridesharing application (see paragraph [0046]).
Zimmerman (US 7457758 B2) teaches a system that defines a baseline vehicle “as an average of a larger community, rather than for … a single entity” (see Column 3 lines 46-49).
Edholm et al. (US 20090210295 A1) teaches a system that identifies a baseline vehicle as an average vehicle representative of typical vehicles in a market (see paragraph [0035]).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE ALCORN whose telephone number is (571) 270-3763. The examiner can normally be reached M-F, 9:30 am – 6:30 pm est.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571) 270-3415. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE A ALCORN III/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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