DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/21/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1-19, and 21 have been examined.
Claims 20 have been cancelled.
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 1-19 and 21 si/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 11-19, and 21 is/are recite a method/process, therefore claims 11-19 and 21 are within at least one of the four statutory categories.
Claims 1-10 is/are recite an apparatus/machine, therefore claims 1-10 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mental processes and/or mathematical concepts (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A signal analysis apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to:
estimate a speed of a vehicle traveling on a road at each of times at each of positions on the road using a signal obtained by measuring the road; and
detect an event occurring on the road based on at least one of a corrected speed obtained by performing a smoothing process on an estimated speed that has been estimated and an inaccuracy degree indicating a degree of inaccuracy of the estimated speed.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process and/or mathematical concept. That is, nothing in the claim elements preclude the steps from practically being performed as mathematical concepts. For example, " estimate a speed of a vehicle …" and " detect an event occurring...", encompass subject matter that a human can reasonably perform in the human mind with or without paper and pencil. For example, “estimate” in the context of this claim encompasses a person (driver/operator/user/human, etc.,) looking at data collected (from sensors, LIDAR, cameras) and forming a simple judgement in this case estimating a speed of a vehicle traveling on a road. " detect an event occurring..." involves a mathematical equation and is a mathematical concept, such as performing a smoothing process on an estimated speed, although this step could also be considered a mental process as well. Thus, the claim recites at least a mathematical concept and a mental process.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
A signal analysis apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to:
estimate a speed of a vehicle traveling on a road at each of times at each of positions on the road using a signal obtained by measuring the road; and
detect an event occurring on the road based on at least one of a corrected speed obtained by performing a smoothing process on an estimated speed that has been estimated and an inaccuracy degree indicating a degree of inaccuracy of the estimated speed.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of "at least one processor…" and “at least one memory…” the components are merely generic components to perform a function using computer code. The generic components are recited at a high level of generality (i.e. a generic processor and memory) such that it amounts to no more than mere instructions to apply the exception using generic computer components. The examiner submits that these limitations are merely applying the above-noted abstract idea by merely using a general controller to perform the process (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 12 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of "at least one processor…" and “at least one memory…” amounts to nothing more than applying the exception using a generic computer component. Mere instructions cannot provide an inventive concept. Hence, the claim is not patent eligible.
Claims 1 recites analogous limitations to that of claim 11, and 21 are therefore rejected by the same premise.
Dependent claims 2-10, and 12-19 specify limitations that elaborate on the abstract idea of claims 1, 11 and 21, and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to “significantly more” for similar reasons.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 11-19 and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Chapman (US20080071465A1).
Claim.1 Chapman discloses a signal analysis apparatus (see at least fig.1, data sample manger system 100) comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to (see at least fig.3, p106, the computing system 300 includes a central processing unit (“CPU”) 335, various input/output (“I/O”) components 305, storage 340, and memory 345, p217, the system components or data structures may also be stored (e.g., as software instructions or structured data) on a computer-readable medium, such as a hard disk, a memory, a network, or a portable media article to be ready by an appropriate drive or via an appropriate connection): estimate a speed of a vehicle traveling on a road at each of times at each of positions on the road using a signal obtained by measuring the road (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links); and detect an event occurring on the road based on at least one of a corrected speed obtained by performing a smoothing process on an estimated speed that has been estimated and an inaccuracy degree indicating a degree of inaccuracy of the estimated speed (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.2 Chapman discloses wherein the at least one processor is further configured to execute the instructions to detect an event when the corrected speed is equal to or less than a predetermined first threshold(see at least fig.1,12, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p149, where it determines a corrected data reading for the unhealthy traffic sensor based on data readings from other healthy traffic ).
Claim.3 Chapman discloses wherein the at least one processor is further configured to execute the instructions to detect an event when the corrected speed is equal to or less than the first threshold and the inaccuracy degree is equal to or less than a predetermined second threshold (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p72, a predetermined threshold, p74, deviation is below the threshold of 1.5 standard deviations, deviation is above the threshold of 1.5 standard deviations).
Claim.4 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate a corrected speed related to a first estimated speed at a first position by performing the smoothing process on the first estimated speed at the first position at a first time using at least one of an estimated speed at a position near the first position at the first time and an estimated speed at the first position at a time near the first time (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p48, data samples may then be further processed in order to determine other travel-related information, such as a heading for each data sample, (e.g., by calculating the angle between the position of a data sample and the position of a prior and/or subsequent data sample) and/or a speed for each data sample (e.g., by calculating the distance between the position of a data sample and the position of a prior and/or subsequent data sample, and by dividing the distance by the corresponding time)).
Claim.5 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate a corrected speed related to the first estimated speed by performing the smoothing process using an estimated speed at the first position at a time earlier than the first time (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.6 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate the inaccuracy degree at a first estimated speed of a first position at a first time using at least one of an estimated speed at a position near the first position at the first time and an estimated speed at the first position at a time near the first time (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.7 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate the inaccuracy degree using an estimated speed at the first position at a time earlier than the first time (see at least fig.6-8, p131, traffic flow estimator routine (described with reference to FIG. 14) in order to obtain estimated average traffic speed for the road segment for the period of time, In step 620, the routine then provides an indication of the estimated average speed, In step 625, the routine selects ,the next time interval or window for which an average speed is to be assessed, beginning with the first time interval, in step 630, the routine then calculates a weighted average traffic speed for the data samples within the time interval, with the weighting of the data samples being based on one or more factors).
Claim.8 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate an index representing a variation between the first estimated speed and at least one of the estimated speed at the position near the first position at the first time and the estimated speed at the first position at a time near the first time as the inaccuracy degree (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.9 Chapman discloses wherein the at least one processor is further configured to execute the instructions to calculate the inaccuracy degree so that the inaccuracy degree of the first estimated speed increases as variations in a first estimated speed, the estimated speed at the position near the first position at the first time, and the estimated speed at the first position at the time near the first-time increase (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.11 Chapman discloses a signal analysis method (see at least fig.1, data sample manger system 100) comprising: estimating a speed of a vehicle traveling on a road at each of times at each of positions on the road using a signal obtained by measuring the road (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links); and detecting an event occurring on the road based on at least one of a corrected speed obtained by performing a smoothing process on an estimated speed that has been estimated and an inaccuracy degree indicating a degree of inaccuracy of the estimated speed (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.12 Chapman discloses further comprising detecting an event when the corrected speed is equal to or less than a predetermined first threshold (see at least fig.1,12, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p149, where it determines a corrected data reading for the unhealthy traffic sensor based on data readings from other healthy traffic ).
Claim.13 Chapman discloses further comprising detecting an event when the corrected speed is equal to or less than the first threshold and the inaccuracy degree is equal to or less than a predetermined second threshold (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p72, a predetermined threshold, p74, deviation is below the threshold of 1.5 standard deviations, deviation is above the threshold of 1.5 standard deviations).
Claim.14 Chapman discloses further comprising calculating a corrected speed related to a first estimated speed at a first position by performing the smoothing process on the first estimated speed at the first position at a first time using at least one of an estimated speed at a position near the first position at the first time and an estimated speed at the first position at a time near the first time (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links, p48, data samples may then be further processed in order to determine other travel-related information, such as a heading for each data sample, (e.g., by calculating the angle between the position of a data sample and the position of a prior and/or subsequent data sample) and/or a speed for each data sample (e.g., by calculating the distance between the position of a data sample and the position of a prior and/or subsequent data sample, and by dividing the distance by the corresponding time)).
Claim.15 Chapman discloses further comprising calculating a corrected speed related to the first estimated speed by performing the smoothing process using an estimated speed at the first position at a time earlier than the first time (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.16 Chapman discloses further comprising calculating the inaccuracy degree at a first estimated speed of a first position at a first time using at least one of an estimated speed at a position near the first position at the first time and an estimated speed at the first position at a time near the first time (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.17 Chapman discloses further comprising calculating the inaccuracy degree using an estimated speed at the first position at a time earlier than the first time (see at least fig.6-8, p131, traffic flow estimator routine (described with reference to FIG. 14) in order to obtain estimated average traffic speed for the road segment for the period of time, In step 620, the routine then provides an indication of the estimated average speed, In step 625, the routine selects ,the next time interval or window for which an average speed is to be assessed, beginning with the first time interval, in step 630, the routine then calculates a weighted average traffic speed for the data samples within the time interval, with the weighting of the data samples being based on one or more factors).
Claim.18 Chapman discloses further comprising calculating an index representing a variation between the first estimated speed and at least one of the estimated speed at the position near the first position at the first time and the estimated speed at the first position at a time near the first time as the inaccuracy degree (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.19 Chapman discloses further comprising calculating the inaccuracy degree so that the inaccuracy degree of the first estimated speed increases as variations in the first estimated speed, the estimated speed at the position near the first position at the first time, and the estimated speed at the first position at the time near the first time increase (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
Claim.21 Chapman discloses a non-transitory computer-readable medium storing a program for causing a computer (see at least fig.3, p106, the computing system 300 includes a central processing unit (“CPU”) 335, various input/output (“I/O”) components 305, storage 340, and memory 345, p217, the system components or data structures may also be stored (e.g., as software instructions or structured data) on a computer-readable medium, such as a hard disk, a memory, a network, or a portable media article to be ready by an appropriate drive or via an appropriate connection) to execute: a step of estimating a speed of a vehicle traveling on a road at each of times at each of positions on the road using a signal obtained by measuring the road (see at least fig.1, p47, road traffic sensors 103, and other data sources 102, Road traffic sensors 102 may include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as loop sensors embedded in the pavement that are capable of measuring the number of vehicles passing above the sensor per unit time, vehicle speed, and/or other data related to traffic flow, data may similarly be obtained from the road traffic sensors 102 via wire-based on wireless-based data links); and a step of detecting an event occurring on the road based on at least one of a corrected speed obtained by performing a smoothing process on an estimated speed that has been estimated and an inaccuracy degree indicating a degree of inaccuracy of the estimated speed (see at least fig.1-2C, abstract, the road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples, p42, types of input data may include the following: information about current and past amounts of traffic for various target road segments of interest in a geographic area, such as for a network of selected roads in the geographic area, information about current, past and expected future weather conditions (e.g., precipitation, temperature, wind direction, wind speed, etc.); information about at least some current, past and future scheduled events (e.g., type of event, expected start and end times of the event, and/or a venue or other location of the event, etc., such as for all events, events of indicated types, events that are sufficiently large, such as to have expected attendance above an indicated threshold (for example, 1000 or 5000 expected attendees), etc.), input data may be known and represented with varying degrees of certainty (e.g., expected weather), and additional information may be generated to represent degrees of confidence in and/or other metadata for the generated predictions,p33-34, the conditioning of obtained data samples may include rectifying erroneous data samples, such as by detecting and/or correcting errors present in the data in various ways (e.g., for data readings received from road traffic sensors), p38, road traffic conditions may be measured and represented in one or more of a variety of ways, whether based on data samples from mobile data sources and/or from traffic sensors data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time), p244, displayed with a degree of predicted traffic congestion level at a particular currently selected time).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chapman (US20080071465A1) as applied to claims 1 above, and further in view of Wu (US20160275788A1).
Claim.10 Chapman does not disclose wherein the at least one processor is further configured to execute the instructions to estimate the speed of the vehicle using a signal detected using an optical fiber provided along the road.
However, Wu discloses wherein the at least one processor is further configured to execute the instructions to estimate the speed of the vehicle using a signal detected using an optical fiber provided along the road (see at least fig.1-2, abstract, a phase-sensitive optical time domain reflectometry and its monitoring method are related to a field of intelligent transportation and an application of distributed fiber sensing, p7, sensing fiber cables buried along a road, p51, to detect and locate the vibration caused by the moving vehicles, moving speeds, moving directions, locations of the vehicles, and a traffic volume are all obtained in real time from the vibration temporal-spatial response curves and vehicle moving trajectories).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to modify Chapman to include wherein the at least one processor is further configured to execute the instructions to estimate the speed of the vehicle using a signal detected using an optical fiber provided along the road by Wu in order to solve traffic congestion problem and informing drivers of real-time traffic volume, and contributes to realize an intelligent city traffic regulation (see Wu’s abstract).
Conclusion
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/SHARDUL D PATEL/Primary Examiner, Art Unit 3664