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 12/02/2024, 01/07/2025, 05/14/2026 are being considered by the examiner.
Drawings
The drawings filed on: 08/16/2024 are accepted.
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.
With regards to claim 1, it is a method claim and thus directed to a statutory category.
101 – Analysis Step 2A, Prong 1:
Claim 1 recites the following limitations (of which bolded limitations constitute a ‘mental process’ that covers performance of the limitations in the human mind).
A computer-implemented method comprising: determining a location of a road attribute in a road network; retrieving probe data collected using one or more location sensors of one or more vehicles in the road network; determining an analysis distance before and after the location of the road attribute in the road network, the analysis distance being greater than a stopping distance of the one or more vehicles, the stopping distance of the one or more vehicles being determined based on the retrieved probe data; sampling the probe data collected inside the analysis distance before and after the location of the road attribute in the road network; processing the sampled probe data using a machine learning model to determine one or more machine-learned speed profiles of one or more road segments of the road network within the analysis distance; and providing the one or more machine-learned speed profiles as an output.
As a note, steps that fall within the mental process groupings of abstract ideas because
they cover concepts performed in the human mind, including: observation, evaluation,
judgement and opinion (See MPEP 2106.04(a)(2), subsection III).
With respect to the particular limitations (that were bolded above), these steps can be
practically performed in the human mind using observation, evaluation, judgement
and/or opinion. For example the particular limitations encompass: 1) making a judgement to determine a location of a road attribute, 2) making a judgement to determine an analysis distance that is before and after the location, 3) evaluating probe data collected inside the analysis distance, and 4) evaluating the analysis distance and making a judgment on one or more speed profiles.
101 Analysis Step 2A, Prong Two
The claim recites the following additional elements:
“A computer-implemented … ”, “processing the sampled probe data using a machine learning model to ...”, “machine-learned” – these additional elements are considered merely reciting the words ‘apply it’ with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The courts have identified these types of elements/limitations as insufficient to integrate the judicial exception into a practical application.
“retrieving probe data collected using one or more location sensors of one or more vehicles in the road network”, “and providing the one or more machine-learned speed profiles as an output” - these additional elements are considered adding insignificant extra solution activity for mere data gathering. See 2106.05(g) i.e. “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). The courts have identified these types of elements/limitations are insufficient to integrate the judicial exception into a practical application.
101 Analysis Step 2B:
The claim recites the following additional elements:
• “A computer-implemented … ”, “processing the sampled probe data using a machine learning model to ...”, “machine-learned” – As discussed above, these additional elements are considered merely reciting the words ‘apply it’ with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (discussed in MPEP § 2106.05(f)). The courts have identified these types of elements to be insufficient to qualify as ‘significantly more’ when recited in a claim with a judicial exception.
• “retrieving probe data collected using one or more location sensors of one or more vehicles in the road network”, “and providing the one or more machine-learned speed profiles as an output” – As discussed above, these additional elements are considered adding insignificant extra solution activity for mere data gathering. See 2106.05(g) i.e. “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). The courts have identified these types of elements as insignificant extra solution activity and the courts have identified these types of limitations/elements to be insufficient to qualify as ‘significantly more’ when recited in a claim with a judicial exception.
101 Analysis for claims 2-13:
Dependent claims 2-13, do not recite any further limitations that cause the
claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed
toward additional aspects of the judicial exception. Additional elements such as the
following do not integrate the judicial exception into a practical application, nor do they amount to significantly more when recited in a claim with a judicial exception: “receiving a request ..”, “processes the prober data to determine …”, “wherein the machine learning model is trained …”, “filtering …”, “machine learning model” as they are either directed to insignificant extra solution activity of data collection/manipulation/selection or applying the exception using a computer as a tool to perform an abstract idea.
101 Analysis claim 14
With regards to claim 14, it is rejected under similar rationale as claim 1. It is noted that it additionally recites additional elements of “an apparatus comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform …”. These elements are interpreted as merely reciting the words ‘apply it’ with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (discussed in MPEP § 2106.05(f)). The courts have identified these types of elements to be insufficient to integrate the judicial exception into a practical application and also the courts have deemed these additional elements to be insufficient to qualify as ‘significantly more’ when recited in a claim with a judicial exception.
101 Analysis for claims 15-17
Dependent claims 2-13, do not recite any further limitations that cause the
claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed
toward additional aspects of the judicial exception. Additional elements such as the
following do not integrate the judicial exception into a practical application, nor do they amount to significantly more when recited in a claim with a judicial exception: “receiving a request ..”, “processes the prober data to determine …” as they are either directed to insignificant extra solution activity of data collection/manipulation/selection or applying the exception using a computer as a tool to perform an abstract idea.
101 Analysis for claims 18:
With regards to claim 18, it is rejected under similar rationale as claim 1. It is noted that it additionally recites additional elements of “A non-transitory computer-readable storage medium, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform …”. These elements are interpreted as merely reciting the words ‘apply it’ with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (discussed in MPEP § 2106.05(f)). The courts have identified these types of elements to be insufficient to integrate the judicial exception into a practical application and also the courts have deemed these additional elements to be insufficient to qualify as ‘significantly more’ when recited in a claim with a judicial exception.
101 Analysis for claims 19-20
Dependent claims 19-20, do not recite any further limitations that cause the
claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed
toward additional aspects of the judicial exception. Additional elements such as the
following do not integrate the judicial exception into a practical application, nor do they amount to significantly more when recited in a claim with a judicial exception: “receiving a request ..”, as it is directed to insignificant extra solution activity of data collection/manipulation/selection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014).
With regards to claim 1, Dorum teaches a computer-implemented method comprising: determining a location of a road attribute in a road network (Fig. 1: column 2, lines 38-45, column 6, lines 65-67, column 7, lines 1-13: a computer implemented method incorporating memor(ies) and processor(s) is implemented, and a road attribute includes a curve in a located on a road/segment);
retrieving probe data collected using one or more location sensors of one or more vehicles in the road network (column 2, lines 21-47: probe data of vehicles is retrieved);
determining an analysis distance [based on] the location of the road attribute in the road network, the analysis distance being … a stopping distance of the one or more vehicles, the stopping distance of the one or more vehicles being determined based on the retrieved probe data ( column 6, lines 10-15, Fig 4: data analyzed includes point data of a curve that includes traffic flow data of acceleration/deceleration data of vehicles before entering a curve (attribute) and speed/acceleration data of vehicles after a curve );
sampling the probe data collected inside the analysis distance [based on] the location of the road attribute in the road network (column 10, lines 1-27: the probe data is sampled for the location of the curve in the road network );
processing the sampled probe data using a machine learning model to determine one or more machine-learned speed profiles of one or more road segments of the road network within the analysis distance (column 10, lines 1-27: speed profiles could be determined based upon averages present within the curve); and providing the one or more machine-learned speed profiles as an output (a speed tolerance profile for a curve is determined and compared to current speed of a driver to generate a warning).
However Dorum et al does not expressly teach determining an analysis distance before and after the location, …the analysis profile being greater than a stopping distance of the one or more vehicles … ; sampling the probe data collected inside the analysis distance before and after the location of the road attribute …
Yet Kristinsson et al teaches determining an analysis distance before and after the location, …the analysis profile being greater than a stopping distance of the one or more vehicles … ; sampling the probe data collected inside the analysis distance before and after the location of the road attribute … (Fig 6: stop sign location is associated at a location/longitude having zero speed value and data is recorded in a window/’area’ before and after the stop sign location. The examiner points out velocity is distance with respect to time and the graph depicts speed collected over time before and after the stop sign, thus data is analyzed over a distance before and after the stop sign. The examiner further points out that the claim recites ‘stopping distance’ , which can be interpreted as any deceleration distance associated with stopping, and the claim does not require a ‘maximum’ stopping distance, nor how a stopping distance would be calculated).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum et al’s ability to assess a range of probe data with respect to an attribute location to determine a speed profile, such that the type of attribute could include a stop sign attribute of a road for the collected probe data, in order to generate the speed profile, as taught by Kristinsson et al. The combination would have allowed determination of a speed profile that would have optimized energy consumption.
With regards to claim 6. The method of claim 1, Dorum teaches wherein the analysis distance is determined based on a speed limit, a traffic flow speed, or a combination thereof with proximity of the location of the road attribute (column 6, lines 10-15, Fig 4: data analyzed includes point data of a curve that includes traffic flow data of acceleration/deceleration data of vehicles before entering a curve (attribute) and speed/acceleration data of vehicles after a curve).
With regards to claim 14, the combination of Dorum and Kristinsson et al teaches an apparatus comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine a location of a road attribute in a road network; retrieve probe data collected using one or more location sensors of one or more vehicles in the road network; determine an analysis distance before and after the location of the road attribute in the road network, the analysis distance being greater than a stopping distance of the one or more vehicles, the stopping distance of the one or more vehicles being determined based on the retrieved probe data; sample the probe data collected inside the analysis distance before and after the location of the road attribute in the road network; process the sampled probe data using a machine learning model to determine one or more machine-learned speed profiles of one or more road segments of the road network within the analysis distance; and provide the one or more machine-learned speed profiles as an output, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 18, the combination of Dorum and Kristinsson et al teaches a non-transitory computer-readable storage medium, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform: determining a location of a road attribute in a road network; retrieving probe data collected using one or more location sensors of one or more vehicles in the road network; determining an analysis distance before and after the location of the road attribute in the road network, the analysis distance being greater than a stopping distance of the one or more vehicles, the stopping distance of the one or more vehicles being determined based on the retrieved probe data; sampling the probe data collected within the analysis distance before and after the location of the road attribute in the road network; processing the sampled probe data using a machine learning model to determine one or more machine-learned speed profiles of one or more road segments of the road network within the analysis distance; and providing the one or more machine-learned speed profiles as an output, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
Claim(s) 2, 3, 7, 8, 13, 15, 16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of Laraki et al (US Application: US 20220215749, published: Jul. 7, 2022, filed: May 18, 2020).
With regards to claim 2. The method of claim 1, the combination of Dorum and Kristinsson et al teaches… the one or more machine-learned speed profiles associated with the road attribute, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly … further comprising: receiving a request specifying a navigation route; and determining a route speed profile for the navigation route based, at least in part, on the one or more machine-learned speed profiles associated with the road attribute based on identifying the road attribute in one or more road segments of the navigation route.
Yet Laraki et al teaches … further comprising: receiving a request specifying a navigation route; and determining a route speed profile for the navigation route based, at least in part, on the one or more machine-learned speed profiles associated with the road attribute based on identifying the road attribute in one or more road segments of the navigation route (Fig 7, Fig 8, paragraphs 0094, 0040, 0072, 0089, 0106, 0132, 0146, 0157, claim 23 of Laraki et al: an analysis distance is the route to be traveled for a requested navigation route (the route containing road segments). The determined navigation route data includes annotated classes for specific colored lights (such as green-state or red-state/classes), for which the states to decelerate include a change to red light in Fig. 7. It is further noted that speed profiles based on attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street) are predicted using K means classification upon the segments for the span/distance between origin and destination. In other words, a navigation route is processed based on road attributes that can include a traffic light and other road attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum and Kristinsson et al’s ability to determine a speed profile with a learning algorithm, such that the learning algorithm could encompass states (such as traffic light states) as part of the learning algorithm in order to select speed profiles of one or more road, as taught by Laraki et al. The combination would have allowed Dorum and Kristinsson et al to have determined routes for vehicle navigation with more precise predictions of vehicle speed by taking into account road and traffic conditions in terms of travel time (Laraki et al, paragraph 0009).
With regards to claim 3, The method of claim 2, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the navigation route includes the road attribute and one or more other road attribute types, and wherein the route speed profile is further based on one or more other machine-learned speed profiles associated with the one or more other road attribute types, as similarly explained in the rejection of claim 2 (Fig 7, Fig 8, paragraphs 0094, 0040, 0072, 0089, 0106, 0132, 0146, 0157, claim 23 of Laraki et al: an analysis distance is the route to be traveled for a requested navigation route (the route containing road segments). The determined navigation route data includes annotated classes for specific colored lights (such as green-state or red-state/classes), for which the states to decelerate include a change to red light in Fig. 7. It is further noted that speed profiles based on attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street) are predicted using K means classification upon the segments for the span/distance between origin and destination. In other words, a navigation route is processed based on road attributes that can include a traffic light and other road attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street), and is rejected under similar rationale.
With regards to claim 7. The method of claim 1, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the road attribute is a traffic light, the method further comprising: annotating one or more points of the probe data based on one or more traffic light states, wherein the machine learning model is trained using the one or more annotated points to classify the one or more machine-learned speed profiles with respect to the one or more traffic light states, as similarly explained in the rejection of claim 2 (Fig 7, Fig 8, paragraphs 0094, 0040, 0072, 0089, 0106, 0132, 0146, 0157, claim 23 of Laraki et al: an analysis distance is the route to be traveled for a requested navigation route (the route containing road segments). The determined navigation route data includes annotated classes for specific colored lights (such as green-state or red-state/classes), for which the states to decelerate include a change to red light in Fig. 7. It is further noted that speed profiles based on attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street) are predicted using K means classification upon the segments for the span/distance between origin and destination. In other words, a navigation route is processed based on road attributes that can include a traffic light and other road attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street), and is rejected under similar rationale.
With regards to claim 8. The method of claim 7, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the one or more traffic light states include: one or more decelerating states comprising a red-light state, a green-light-to-red-light state, or a combination thereof; and one or more accelerating states comprising a green-light state, a red-light-to-green-light state, or a combination thereof, as similarly explained in the rejection of claim 2 (Fig 7, Fig 8, paragraphs 0094, 0040, 0072, 0089, 0106, 0132, 0146, 0157, claim 23 of Laraki et al: an analysis distance is the route to be traveled for a requested navigation route (the route containing road segments). The determined navigation route data includes annotated classes for specific colored lights (such as green-state or red-state/classes), for which the states to decelerate include a change to red light in Fig. 7. It is further noted that speed profiles based on attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street) are predicted using K means classification upon the segments for the span/distance between origin and destination. In other words, a navigation route is processed based on road attributes that can include a traffic light and other road attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street), and is rejected under similar rationale.
With regards to claim 13. The method of claim 1, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the machine learning model is an unsupervised model that uses K-means clustering to determine the one or more machine-learned speed profiles, as similarly explained in the rejection of claim 2 (Fig 7, Fig 8, paragraphs 0094, 0040, 0072, 0089, 0106, 0132, 0146, 0157, claim 23 of Laraki et al: an analysis distance is the route to be traveled for a requested navigation route (the route containing road segments). The determined navigation route data includes annotated classes for specific colored lights (such as green-state or red-state/classes), for which the states to decelerate include a change to red light in Fig. 7. It is further noted that speed profiles based on attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street) are predicted using K means classification upon the segments for the span/distance between origin and destination. In other words, a navigation route is processed based on road attributes that can include a traffic light and other road attribute types such as slope, signs, infrastructure, or road-type (such as highway or side-street), and is rejected under similar rationale.
With regards to claim 15. The apparatus of claim 14, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the apparatus is further caused to: receive a request specifying a navigation route; and determining a route speed profile for the navigation route based, at least in part, on the one or more machine-learned speed profiles associated with the road attribute based on identifying the road attribute in one or more road segments of the navigation route, as similarly explained in the rejection of claim 2, and is rejected under similar rationale.
With regards to claim 16. The apparatus of claim 15, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the navigation route includes the road attribute and one or more other road attribute types, and wherein the route speed profile is further based on one or more other machine-learned speed profiles associated with the one or more other road attribute types, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
With regards to claim 19. non-transitory computer-readable storage medium of claim 18, the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the apparatus is caused to further perform: receiving a request specifying a navigation route; and determining a route speed profile for the navigation route based, at least in part, on the one or more machine-learned speed profiles associated with the road attribute based on identifying the road attribute in one or more road segments of the navigation route, as similarly explained in the rejection of claim 2, and is rejected under similar rationale.
With regards to claim 20. The non-transitory computer-readable storage medium of claim 19 , the combination of Dorum, Kristinsson et al and Laraki et al teaches wherein the navigation route includes the road attribute and one or more other road attribute types, and wherein the route speed profile is further based on one or more other machine-learned speed profiles associated with the one or more other road attribute types, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
Claim(s) 4 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of Kumar et al (US Patent: 10785604, issued: Sep. 22, 2020, filed: Jul. 26, 2019).
With regards to claim 4, The method of claim 1, Dorum and Kristinsson teaches wherein the machine learning model further processes the probe data, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach … to determine a stopping time period associated with the road attribute based on a time that the one or more vehicles is traveling below a threshold speed value.
Yet Kumar et al teaches … to determine a stopping time period associated with the road attribute based on a time that the one or more vehicles is traveling below a threshold speed value (column 3, lines 58-67a stoppage point instance/period/moment in time is recognized when a speed is below a threshold and also when the speed is stopped for a period of time (zero speed)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum and Kristinsson et al’s ability to use a learning algorithm to identify features/attributes for a road to include stop recognition via processing of probe data, such that the stop recognition could have identified by checking if a vehicle is below a threshold speed, as taught by Kumar et al. The combination would have allowed the combination to have accurately determined/evaluate driving behaviors (Kumar et al, column 1, lines 25-29).
With regards to claim 17. The apparatus of claim 14, the combination of Dorum, Kristinsson and Kumar et al et al teaches wherein the machine learning model further processes the probe data to determine a stopping time period associated with the road attribute based on a time that the one or more vehicles is traveling below a threshold speed value, as similarly explained in the rejection of claim 4, and is rejected under similar rationale.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of Kumar et al (US Patent: 10785604, issued: Sep. 22, 2020, filed: Jul. 26, 2019) in view of Vervaet et al (US Application: 2012/0197839, published: Aug. 2, 2012, filed: Dec. 31, 2009).
With regards to claim 5. The method of claim 1, the combination of Dorum, Kristinsson and Kumar et al et al teaches wherein the machine learning model further processes the probe data … associated with the road attribute … on one or more instances of the one or more vehicles traveling below a threshold speed value for more than a threshold period of time, as similarly explained in the rejection of claim 4, and is rejected under similar rationale.
However the combination does not expressly teach does not expressly teach … a probability of stopping associated with the road attribute based on a percentage of one or more instances of the one or more vehicles…
Yet Vervaet et al teaches … a probability of [a road attribute] associated with the road attribute based on a percentage of one or more instances of the one or more vehicles… (paragraphs 0040-0041 (and table/example referenced by the paragraphs): a probability/percentage of chance of a road attribute occurring with respect to one or more vehicles is calculated).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum and Kristinsson et al and Kumar et al’s ability to identify stopping occurring as a road attribute for vehicles based upon speed being below a threshold amount, such that the occurrence recognition is represented in terms of probability of the occurrence of the road attribute, as also taught by Vervaet et al. The combination would have ensured digital map data is accurate and complete by implementing an improved map updating process to reflect changes of attributes (Vervaet et al, paragraph 0010 and 0011).
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of in view of T’Siobbel et al (US Application: US 2012/0283942, published: Nov. 8, 2012, filed: Nov. 12, 2009).
With regards to claim 9. The method of claim 1, the combination of Dorum and Kristinsson teaches … wherein the one or more machine learned speed profiles are determined based on … [probe data] …, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach .. further comprising: filtering the probe data according to at least one filtering category, wherein the one or more machine-learned speed profiles are determined based on the at least one filtering category.
Yet T’Siobbel et al teaches further comprising: filtering the probe data according to at least one filtering category, … based on the at least one filtering category (paragraphs 0037 and 0038, speed profiles determined are based upon data selected for “specific time in a day, day in a week, season, considering holiday periods, etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum and Kristinsson’s ability to process probe data to determine speed profile(s), such that the determined speed profile(s) could have been based upon filtering based on data for a specific time of data or day of week, as taught by T’Siobbel et al. The combination would have allowed speed behavior to be contextually determined, given context data such as traffic flows for specific routes and time dependent data (T’Siobbel et al, paragraphs 0008 and 0037).
With regards to claim 10. The method of claim 9, the combination of Dorum, Kristinsson and T’Siobbel et al teaches wherein the at least one filtering category includes at least one of a time of day, a day of week, a season, before Covid, low sampling frequency probes, or a combination thereof, as similarly explained in the rejection of claim 9, and is rejected under similar rationale.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of T’Siobbel et al (US Application: US 2012/0283942, published: Nov. 8, 2012, filed: Nov. 12, 2009) in view of Hiestermann et al (US Application: US 2011/0307166, published: Dec. 15, 2011, filed: Jan. 13, 2010) ..
With regards to claim 11. The method of claim 9, the combination of Dorum, Kristinsson and Hiestermann et al teaches wherein the at least one filtering category, as similarly explained in the rejection of claim 9, and is rejected under similar rationale.
However the combination does not expressly teach … includes a vehicle type, a vehicle characteristic, or a combination thereof.
Yet Hiestermann et al teaches least one filtering category includes a vehicle type, a vehicle characteristic, or a combination thereof (paragraphs 0016, 0040, 0041, 0044, 0048 0055, and 0066: route speed profiles can be based upon attributes that include speed profiles, road type/gradient, number of lanes, etc. The road segments having the attributes are selected based on optimization of energy efficiency for available road segments between origin and destination and the efficiency is also further based on a plurality of factors that include data selected/filtered based on vehicle type/category (such as light or heavy truck) or vehicle mass).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum, Kristinsson and Hiestermann et al’s ability to have used a learning algorithm to determine speed profiles (with respect to road attribute(s)), such that road segments between origin and destination is based on a plurality of factors that include data selected/filtered based on vehicle type/category, as taught by Hiestermann et al. The combination would have allowed determined a route that is the most efficient between two locations with respect to a map via monitoring dynamic traffic elements (Hiestermann et al, paragraph 0013).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorum (US Patent: 9387860, issued: Jul. 12, 2016, filed: May 8, 2015) in view of Kristinsson (US Patent: 9663111, issued: May 30, 2017, filed: May 30, 2014) in view of King et al (US Patent: 11900689, issued: Feb. 13, 2024, filed: Sep. 30, 2020).
With regards to claim 12. The method of claim 1, the combination of Dorum and Kristinsson teaches wherein the machine learning model… to determine the one or more machine-learned speed profiles, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach … the machine learning model is a supervised K-nearest neighbors (KNN) algorithm to determine the one or more machine-learned speed profiles.
Yet King et al teaches … the machine learning model is a supervised K-nearest neighbors (KNN) algorithm to determine the one or more machine-learned speed profiles (abstract, fig 5, column 23, lines 15-35, column 26, lines 25-40, column 27, lines 39-59: a supervised (KNN) model is used to determine and a driving context for the road segment having traffic lights having multiple configurations/states and bulb-patterns. An autonomous vehicle can be controlled based on the determined driving context derived from the states/patterns).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Dorum and Kristinsson et al’s ability to use a machine learning model to process probe data, such that the machine learning model could have also been a supervised machine learning model to process probe data that is multi-state to determine the driving context (occurring within the analysis distance of a route being traveled) for controlling an autonomous vehicle, as taught by King et al. The combination would have increased reliability for interpreting an environment within which the vehicle is operating (King et al , column 1, lines 35-41).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shalev-Shwartz et al (US Application: US 20190291728): This reference teaches navigating a vehicle by detecting obstacle(s) in an environment through collected sensor data.
Seegmiller (US Application: 20220250641): This reference teaches topological planning for autonomous driving using constraint based analysis.
Golding et al (US Patent: 7512487): This reference teaches personalizing a navigation system through model based analysis of different driving /road – attributes.
Fowe et al (US Application: US 20180286220): This reference teaches collecting probe data from a plurality of vehicles to determine vehicle traffic states.
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/WILSON W TSUI/Primary Examiner, Art Unit 2172
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