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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/05/2025 has been entered.
Status of Application
Claims 1-29 are pending. Claims 1, 8 and 17 are the independent claims. Claims 1, 8 and 17 have been currently amended. Claims 28 and 29 have been newly added. This office action is in response to the Amendments received on 12/02/2026.
Response to Arguments
With respect to Applicant’s remarks filed on 12/05/2025, “Applicant Arguments/Remarks Made in an Amendment” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant's arguments according to the Applicant’s Remarks filed on 12/05/2025, see pages 12-13 “Rejections under 35 U.S.C § 101”, with respect to the rejection of claims 1-27 as being directed to an abstract idea, has been fully considered and is persuasive. The applicant argues that the claims as a whole is considered to integrate the judicial exception (abstract idea) into a practical application of the exception under Step 2A because the claims direct an improvement in the technical field of estimating traffic volume without requiring installation of permanent counting equipment or access to private data (containing probe identifiers). The argument is persuasive and accordingly, the rejection of claims 1-27 under 35 U.S.C § 101 (judicial exception) has been withdrawn. Also, applicant’s argument regarding further rejection of claims 17-27 under 35 U.S.C § 101 as being directed toward an ineligible subject matter has been considered but is not, respectfully, persuasive. According to MPEP 2111.01 II, it is improper to import claim limitations from the specification. This is the office stance that the claims should be interpreted in light of the specification is a quite different thing from ‘reading limitations of the specification into a claim’. See MPEP 2111 [R-10.2019]. Examiner note: If the applicant amends the claim to encompass term non-transitory, it would overcome the rejection.
Applicant’s arguments, on pages 15-16 “Rejection under 35 U.S.C § 102”, with respect to claims 17-27 and on pages “16-18”, with respect to claims 1-16, have been fully considered but they are not, respectfully, persuasive.
Applicant amended claim 17 to encompass in part “wherein the true number of probes is unknown” and argued that Chapman fails to teach or suggest “wherein the true number of probes is unknown” rather, Chapman’s system uses a count-based approach where distinct mobile data sources are known. First, the number of probes in the context of the claim, render the claim indefinite (See the rejection of claims under 35 U.S.C § 112b in office action below). Second, according to at least paragraph [0034], Chapman’s system detects unreliable/missing data samples which suggest that they don’t know the number of probes that will provide valuable data. Further, according to, for example, paragraphs [0054], and [0082]-[0085], it is disclosed that they have to use those data samples to identify the number of vehicles in the road mentioned. Therefore, the number of probes considering the missing/unreliable data samples is not known in Chapman. It is understood that Chapman’s data sample provided by mobile data source may include a source identifier as well to identify the vehicle acting as a mobile data source (at least according to [0039]), however, due to the missing and unreliable data as discussed above with reference to the cited paragraphs of Chapman, it still covers the number of probes being unknown as recited in the claim. Applicant arguments regarding the independent claims 1 and 8 and their dependent claims have been considered but not persuasive for the same reason discussed for claim 17.
Office Note: Due to applicant’s amendments, further claim rejections appear on the record as stated in the below Office Action.
It is the Office’ stance that all of applicant arguments have been considered.
Drawings
The drawings are objected to because in the legend of Fig. 1, the black circular dot is numbered as 102 and black square sign in numbered as 104, while the associated numbers shown in the figure is reverse.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 8 and 17, claims encompass the limitation of “wherein the true number of probes is unknown, wherein the concentration of the area of interest includes a summation of moving speeds obtained from the data associated with the area of interest, and wherein the concentration for the area of interest is used for estimating the number of probes and thereby in place of the true number of probes in determining the estimated traffic volume;”. The newly added limitations render the claim indefinite as the metes and bound of the claim is not clear to the office. In particular, the term “number of probes” as recited in the claims is not clear to the office. It is understood that probes are the devices for transferring data (or sample data sources). However, it the context of the claim, it is unclear whether the number of probes estimated would be indicative of the transmitting sources or the total vehicles on the road. Without the true number of probes being connected to the true number of vehicles on the road, the estimated traffic volume cannot be determined. For the purposes of this examination, it will be interpreted that the true number of probes is equivalent to the true number of vehicles required to determine the estimated traffic volume."
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 17-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because:
Regarding Claim 17, the claim recites “A computer-readable storage medium having instructions stored thereon…”
Step 1: Statutory category- No
Claim 17 is directed toward a computer readable storage medium. The claim does not recite that the computer readable medium is limited to non-transitory embodiments. A claim encompassing both transitory and non-transitory embodiments, such as applicant’s claimed computer readable medium, does not fall within one of the four categories of patent eligible subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (“A transitory, propagating signal like Nuitjen’s is not a process, machine, manufacture, or composition of matter.’ … Thus, such a signal cannot be patentable subject matter.”).
Therefore Claim 17 is further ineligible under 35 U.S.C. 101 because it is not directed toward a statutory category.
Further, Dependent claims 18-27 are also directed toward a computer readable storage medium in claim 17 and, therefore, do not fall within one of the four categories of patent eligible subject matter. Therefore, claims 18-27 are further rejected under 35 USC §101 as being directed toward ineligible subject matter.
Examiner Note: The claims may be amended to avoid a rejection under 35 U.S.C. 101 by adding the limitation “non-transitory” to the claim. Such an amendment would not raise the issue of new matter because the specification supports a claim drawn to at least one non-transitory embodiment.
Rejections - 35 USC § 102
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.
Claims 17-19, 21 and 25-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chapman et al., US 20080071465 A1, hereinafter, Chapman.
Regarding Claim 17, Chapman teaches a computer-readable storage medium having instructions stored thereon (Paragraphs [0106], and [0121], “Some or all of the system components or data structures may also be stored (e.g., as software instructions or structured data) on a computer-readable medium,” ) that, when executed by a processing system, perform a method (Page 54, Claim 91, Paragraph [0106], “The computing system 300 includes a central processing unit ("CPU") 335”), comprising: obtaining, via a data resource, data associated with an area of interest regarding an estimated traffic volume (Paragraph [0004], “techniques for determining road traffic conditions based on data obtained from various data sources, such as by inferring current traffic-related information and/or predicting future traffic-related information for roads of interest based in part on data samples that reflect actual travel on those roads.”), the data comprising at least spatiotemporal data and geographic information data; (Paragraph [0111], “The road traffic sensors 386 include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as for one or more geographic areas.”, Paragraph [0112], “Such data sources include map services and/or databases that provide information regarding road networks, such as the connectivity of various roads to one another as well as traffic control information related to such roads (e.g., the existence and location of traffic control signals and/or speed zones).”) determining a concentration for the area of interest based on the data; (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”, __determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration according to para [0048] of instant specification, “the sum of recorded speeds within an area of interest (i.e.,concentration)”__), wherein the concentration of the area of interest includes a summation of moving speeds obtained from the data associated with the area of interest (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”, __determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration), Paragraph [0073], and Fig. 2C, “Column 221b contains the reported speed of each of the data samples, measured in miles per hour. Column 221c lists the other data samples in the group against which the data sample of a given row will be compared, and column 221d lists the approximate average speed of the group of data samples indicated by column 221c.”, __ the average speed of the data samples was calculated which includes summation of speeds and reads on the claim) and is a number that is proportional to an estimated number of probes, which asymptotically approaches a true number of probes ( __according to at least the cited portions of Chapman (i.e. [0052]-[0053], [0072], [0076]) in mapping previous limitation of concentration being the summation of speed, the average of the data samples were calculated and the data samples were each from different sources, therefore the summation would be proportional to the number of sources__) wherein the true number of probes is unknown (__according to [0034] of Chapman, it is disclosed that their invention detects unreliable/missing data samples which suggest that they don’t know the number of probes that will provide valuable data. Further according to, for example, paragraphs [0054], and [0082]-[0085], it is disclosed that they have to use those data samples to identify the number of vehicles in the road mentioned. Therefore, the number of probes considering the missing/unreliable data samples is not known in Chapman___), obtaining, via the data resource, one or more known volume-to-concentration ratios; (Paragraph [0088], __according to the examiner broadest reasonable interpretation of the term known volume-to-concentration ratio, known penetration factor that is given via a data source reads on known volume-to-concentration ratio __) wherein each known volume-to-concentration ratio represents a relationship between a known traffic volume at a location and a known concentration of probe data determined for that location ([0088], “a known percentage q of total vehicles on the road that are mobile data sources, also referred to as the "penetration factor", ”, __the number of mobile data sources (reads on concentration according to the claimed invention) divided by the total number of vehicles (reads on traffic volume) make the ratio which reads on concentration-to-volume ratio__) the concentration for the area of interest being used for estimating the number of probes and thereby in place of the true number of probes in determining the estimated traffic volume ([0054], “the Sensor Data Aggregator component 110 may provide information that is complementary to assessed traffic condition information provided by components such as the Data Sample Speed Assessor component 107 and/or the Data Sample Flow Assessor component 108, or may instead be used if data samples from mobile data sources are not available at all or in sufficient quantity of reliable data samples to allow other components such as the Data Sample Speed Assessor component 107 and Data Sample Flow Assessor component 108 to provide accurate assessed road traffic condition information.”, __according to at least this cited paragraph and for example paragraphs [0082]-[0085], [0089], and [0091], the number of probes are not necessarily known in Chapman due to missing/unreliable data samples) and determining the estimated traffic volume for the area of interest based on the concentration of the area of interest and the one or more known volume-to-concentration ratios. (Paragraph [0085], “Given a number of distinct mobile data sources observed to be traveling over a given road segment during a given time window, and a known or expected percentage of total vehicles that are mobile data sources, it is possible to infer a total traffic volume-”,__ a number of distinct mobile data source observed to be traveling over a given road segment during a given time reads on concentration (i.e. distribution of concentration based on specification) and a known expected percentage of total vehicles that are mobile data source reads on the volume-to-concentration ratio that is interpreted by examiner as penetration rate__).
Regarding Claim 18, Chapman teaches the computer-readable storage medium of claim 17, wherein the instructions further comprise instructions for identifying the area of interest (Paragraph [0157], “receives an indication of a road segment,”) in which to determine the estimated traffic volume. (Abstract, “predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy”, Paragraph [0157], “estimate the indicated type of traffic flow information based on one or more related road segments,”)
Regarding Claim 19, Chapman teaches the computer-readable storage medium of claim 18 (See Rejection of Claim 18), wherein the instructions for identifying the area of interest comprise instructions for receiving at least one of a user selection of the area of interest on a digital map and an algorithmic selection of an uninterrupted road segment. (Paragraph [0237])
Regarding Claim 21, Chapman teaches the computer-readable storage medium of claim 17, wherein the data further comprises vehicle occupancy data. (Paragraph [0091], “additional types of traffic flow information may be reported by traffic sensors (e.g., traffic volume and occupancy),”)
Regarding Claim 25, Chapman teaches the computer-readable storage medium of claim 17, wherein the instructions further comprise instructions for: collecting, via a data collection system (Paragraphs [0153]-[0154] and Fig. 13, __steps 1315 and 1320__), the data associated with the area of interest from a plurality of probes; (Paragraph [0031], “obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from road traffic sensors (e.g., physical sensors that are embedded in or otherwise near to the roads).”__mobile traffic data and sensors on the roads read on plurality of probes) and storing, in the data resource, the data associated with the area of interest. (Paragraph [0151], “the determined traffic sensor data reading may be stored (e.g., in a database or file system) for later use by other components”)
Regarding Claim 26, Chapman teaches the computer-readable storage medium of claim 17, wherein the instructions for determining the concentration for the area of interest based on the data comprise instructions for determining a summation of speeds (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”, __determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration), Paragraph [0073], and Fig. 2C, “Column 221b contains the reported speed of each of the data samples, measured in miles per hour. Column 221c lists the other data samples in the group against which the data sample of a given row will be compared, and column 221d lists the approximate average speed of the group of data samples indicated by column 221c.”, __ the average speed of the data samples was calculated which includes summation of speeds and reads on the claim) recorded by a plurality of probes within the area of interest based on the data. (e.g., Paragraph [0032], “in some embodiments obtained road traffic condition information data may include multiple data samples, including data samples provided by mobile data sources (e.g., vehicles), data readings from road-based traffic sensors” __mobile data or traffic sensors reads on plurality of probes, and traffic condition information includes speeds of the vehicles, e.g., (Paragraph [0036]- [0037] __), wherein the summation of speeds from each recorded data captured within the area of interest in a time period corresponds to the concentration and is proportional to the true number of probes (Paragraph [0070], “particular time windows during the time period being represented”, “ distribution of speeds”).
Regarding Claim 27, Chapman teaches the computer-readable storage medium of claim 17, wherein the instructions for determining the concentration for the area of interest based on the data comprise instructions for determining a speed distribution (Fig 10B, and Paragraph [0070], “ modeling a distinct distribution (e.g., a normal or Gaussian distribution) for the observed speeds of each group.”) from moving speed data (Paragraph [0031], “mobile data source”) recorded by a plurality of probes within the area of interest based on the data. (e.g., Paragraph [0032], “in some embodiments obtained road traffic condition information data may include multiple data samples, including data samples provided by mobile data sources (e.g., vehicles), data readings from road-based traffic sensors” __mobile data or traffic sensors reads on plurality of probes, and traffic condition information includes speeds of the vehicles, e.g., (Paragraph [0036]- [0037] __), wherein the speed distribution from the moving speed data from each recorded data captured within the area of interest in a time period corresponds to the concentration and is proportional to the true number of probes (Paragraph [0032], “Assessing obtained data may in at least some embodiments include determining traffic conditions (e.g., average traffic speed or other measurements of traffic flow”, Paragraph [0038]), “Moreover, 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 sensor data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of 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.
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, 3-8 and 10-16, 22-24, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Chapman et al., US 20080071465 A1, hereinafter, Chapman, in view of Fei et al., US 20120109506 A1, hereinafter, Fei.
Regarding Claims 1 and 8, Chapman teaches A system and method comprising: a processing system; (Paragraph [0106]) a storage system; (Paragraph [0106]) and instructions stored on the storage system that when executed by the processing system direct the processing system (Paragraph [0121], “system components or data structures may also be stored (e.g., as software instructions or structured data) on a computer-readable medium,” and Page 54, Claim 91]) to at least: identify an area of interest (Paragraph [0157], “receives an indication of a road segment,”) in which to determine an estimated traffic volume; (Abstract, “ predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy”, Paragraph [0157], “ estimate the indicated type of traffic flow information based on one or more related road segments,”) obtain, via a data resource, data associated with the area of interest (Paragraph [0004], “techniques for determining road traffic conditions based on data obtained from various data sources, such as by inferring current traffic-related information and/or predicting future traffic-related information for roads of interest based in part on data samples that reflect actual travel on those roads.”), the data comprising at least spatiotemporal data (Paragraph [0039], “data samples provided by mobile data sources may include one or more of a source identifier, a speed indication, an indication of a heading or direction, an indication of a location, a timestamp, and a status identifier.”, Paragraph [0059], “data samples 205a-k reported by multiple mobile data sources (e.g., vehicles, not shown) traveling in the area 200 during a particular time interval or other time period (e.g. 1 minute, 5 minutes, 10 minutes, 15 minutes, etc.)”, __data samples are taken in a particular space and time which reads on spatiotemporal data__ ) and geographic information data; (Paragraph [0111], “The road traffic sensors 386 include multiple sensors that are installed in, at, or near various streets, highways, or other roads, such as for one or more geographic areas.”, Paragraph [0112], “Such data sources include map services and/or databases that provide information regarding road networks, such as the connectivity of various roads to one another as well as traffic control information related to such roads (e.g., the existence and location of traffic control signals and/or speed zones).”) determine a concentration of the area of interest based on the data; (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”__, determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration), wherein the concentration of the area of interest is a number that is proportional to an estimated number of probes, which asymptotically approaches a true number of probes ( __according to at least the cited portions of Chapman (i.e. [0052]-[0053], [0072], [0076]) in mapping previous limitation of concentration being the summation of speed, the average of the data samples were calculated and the data samples were each from different sources, therefore the summation would be proportional to the number of sources__), wherein the true number of probes is unknown (__according to [0034] of Chapman, it is disclosed that their invention detects unreliable/missing data samples which suggest that they don’t know the number of probes that will provide valuable data. Further according to, for example, paragraphs [0054], and [0082]-[0085], it is disclosed that they have to use those data samples to identify the number of vehicles in the road mentioned. Therefore, the number of probes considering the missing/unreliable data samples is not known in Chapman___), wherein the concentration of the area of interest includes a summation of moving speeds obtained from the data associated with the area of interest (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”, __determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration), Paragraph [0073], and Fig. 2C, “Column 221b contains the reported speed of each of the data samples, measured in miles per hour. Column 221c lists the other data samples in the group against which the data sample of a given row will be compared, and column 221d lists the approximate average speed of the group of data samples indicated by column 221c.”, __ the average speed of the data samples was calculated which includes summation of speeds and reads on the claim), and wherein the concentration for the area of interest is used for estimating the number of probes and thereby in place of the true number of probes in determining the estimated traffic volume ([0054], “the Sensor Data Aggregator component 110 may provide information that is complementary to assessed traffic condition information provided by components such as the Data Sample Speed Assessor component 107 and/or the Data Sample Flow Assessor component 108, or may instead be used if data samples from mobile data sources are not available at all or in sufficient quantity of reliable data samples to allow other components such as the Data Sample Speed Assessor component 107 and Data Sample Flow Assessor component 108 to provide accurate assessed road traffic condition information.”, __according to at least this cited paragraph and for example paragraphs [0082]-[0085], [0089], and [0091], the number of probes are not necessarily known in Chapman due to missing/unreliable data samples); obtain, via the data resource, one or more known volume-to-concentration ratios; (Paragraph [0088], __according to the examiner broadest reasonable interpretation of the term known volume-to-concentration ratio, known penetration factor that is given via a data source reads on known volume-to-concentration ratio __) wherein each known volume-to-concentration ratio represents a relationship between a known traffic volume at a location and a known concentration of probe data determined for that location ([0088], “a known percentage q of total vehicles on the road that are mobile data sources, also referred to as the "penetration factor", ”, __the number of mobile data sources (reads on concentration according to the claimed invention) divided by the total number of vehicles (reads on traffic volume) make the ratio which reads on concentration-to-volume ratio__) ; determine the estimated traffic volume for the area of interest based the concentration of the area of interest and the one or more known volume-to-concentration ratios; (Paragraph [0085], “Given a number of distinct mobile data sources observed to be traveling over a given road segment during a given time window, and a known or expected percentage of total vehicles that are mobile data sources, it is possible to infer a total traffic volume-”,__ a number of distinct mobile data source observed to be traveling over a given road segment during a given time reads on concentration (i.e. distribution of concentration based on specification) and a known expected percentage of total vehicles that are mobile data source reads on the volume-to-concentration ratio that is interpreted by examiner as penetration rate__)
However, Chapman doesn’t teach determine at least one traffic element recommendation for the area of interest based on the estimated traffic volume; and provide the at least one traffic element recommendation for the area of interest to an end user.
Nevertheless, Fei teaches determine at least one traffic element recommendation for the area of interest based on the estimated traffic volume; (Paragraph [0055], “If the expected traffic volume is larger than the average traffic volume, the real-time prediction system 100 recommends an alternative traffic management strategy that can decrease the expected traffic volume, for example, increasing a fare of the traffic link.” and Page 8, Claims 6 and 7, “the alternative traffic management strategy includes one or more of: detouring traffic in the traffic link through another traffic link, adjusting a traffic signal in the traffic link, adjusting a speed limit in the traffic link, and adjusting a fare of the traffic link.”) and provide the at least one traffic element recommendation for the area of interest to an end user. (Paragraph [0055], “the traffic microsimulation tool 160 recommends to a user an alternative traffic management strategy based on the estimated traffic volume 150 and/or the estimated traffic speed. The alternative traffic management strategy includes, but is not limited to: detouring traffic in the traffic link through another traffic link, adjusting a timing or length of a traffic signal in the traffic link, adjusting a speed limit in the traffic link, and adjusting a fare of the traffic link.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman to include the step of determining a traffic element recommendation base on the traffic volume estimation and providing it to an end user as taught by Fei in order to improve the road safety, reduce congestion and enhance travel efficiency.
Regarding Claim 3, and 10, Chapman in view of Fei teaches the system of claim 1 (See rejection for claim 1), the method of claim 8 (See rejection for claim 8), wherein the instructions directing the processing system to identify the area of interest comprise instructions that direct the processing system to receive at least one of a user selection of the area of interest on a digital map and an algorithmic selection of an uninterrupted road segment. (Paragraph [0237], “User-selectable display option controls”, “map”)
Regarding Claim 4, 13 and 24, Chapman in view of Fei teaches the system of claim 1 (See rejection for claim 1), the method of claim 8 (See rejection for claim 8), and the computer readable storage medium of claim 22 (See rejection for claim 22), however, Chapman in view of Fei doesn’t teach at least one traffic element recommendation is at least one of a transportation engineering solution and a zoning recommendation.
Nevertheless, Fei teaches at least one traffic element recommendation is at least one of a transportation engineering solution and a zoning recommendation. (Paragraph [0055], “The alternative traffic management strategy includes, but is not limited to: detouring traffic in the traffic link through another traffic link, adjusting a timing or length of a traffic signal in the traffic link, adjusting a speed limit in the traffic link, and adjusting a fare of the traffic link. For example, upon receiving the signal 175 indicating an occurrence of an incident (e.g., a long-term road construction, etc.), the real-time prediction system 100 may compare the expected traffic volume to an average traffic volume, which may be stored in a database (not shown). If the expected traffic volume is larger than the average traffic volume, the real-time prediction system 100 recommends an alternative traffic management strategy that can decrease the expected traffic volume, for example, increasing a fare of the traffic link.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman to include the step of determining a traffic element recommendation base on the traffic volume estimation such as determining a transportation solution like detouring traffic or zoning recommendation like adjusting speed limit as taught by Fei in order to improve the road safety, reduce congestion and enhance travel efficiency.
Regarding Claim 5 and 14, Chapman in view of Fei teaches the system of claim 1 (See rejection for claim 1) and method of claim 8 (See rejection for claim 8), and Chapman teaches wherein the instructions further direct the processing system to: collect, via a data collection system, (Paragraph [0153]-[0154] and Fig. 13, __steps 1315 and 1320__) the data associated with the area of interest from a plurality of probes; (Paragraph [0031], “obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from road traffic sensors (e.g., physical sensors that are embedded in or otherwise near to the roads).”__mobile traffic data and sensors on the roads read on plurality of probes) and store, in the data resource, the data associated with the area of interest. (Paragraph [0151], “the determined traffic sensor data reading may be stored (e.g., in a database or file system) for later use by other components”)
Regarding Claim 6 and 15, Chapman in view of Fei teaches the system of claim 1 and method of claim 8, and Chapman teaches wherein the instructions directing the processing system to determine the concentration for the area of interest based on the data comprise instructions that direct the processing system to determine a summation of speeds (Paragraph [0053], “The Data Sample Flow Assessor component 108 assesses traffic flow information for road segments of interest for at least one time period of interest, such as to assess traffic volume (e.g., expressed as a total or average number of vehicles…”, “The assessment of the traffic flow information in the illustrated embodiment is based at least in part on traffic speed-related information provided by the Data Sample Speed Assessor component 107 and the Data Sample Outlier Eliminator component 106”, Paragraph [0052], “the assessed speed(s) may include an average of the speeds for multiple of the data samples”, __determining the average speed of the samples, for a road segment of interest, which includes the step of summation of speeds reads on determining concentration), Paragraph [0073], and Fig. 2C, “Column 221b contains the reported speed of each of the data samples, measured in miles per hour. Column 221c lists the other data samples in the group against which the data sample of a given row will be compared, and column 221d lists the approximate average speed of the group of data samples indicated by column 221c.”, __ the average speeds of the samples were calculated which includes summation of speeds and reads on the claim) recorded by a plurality of probes within the area of interest based on the data. (e.g., Paragraph [0032], “in some embodiments obtained road traffic condition information data may include multiple data samples, including data samples provided by mobile data sources (e.g., vehicles), data readings from road-based traffic sensors” __mobile data or traffic sensors reads on plurality of probes, and traffic condition information includes speeds of the vehicles, e.g., (Paragraph [0036]- [0037] __), wherein the summation of speeds from each recorded data captured within the area of interest in a time period corresponds to the concentration and is proportional to the true number of probes (Paragraph [0032], “Assessing obtained data may in at least some embodiments include determining traffic conditions (e.g., average traffic speed or other measurements of traffic flow”, Paragraph [0038]), “Moreover, 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 sensor data readings, such as in absolute terms (e.g., average speed; volume of traffic for an indicated period of time;”)
Regarding Claim 7 and 16, Chapman teaches the system of claim 1 and method of claim 8, wherein the instructions directing the processing system to determine the concentration for the area of interest based on the data comprise instructions that direct the processing system to determine a speed distribution (Fig 10B, and Paragraph [0070], “ modeling a distinct distribution (e.g., a normal or Gaussian distribution) for the observed speeds of each group.”) from moving speed data (Paragraph [0031], “”mobile data source) recorded by a plurality of probes within the area of interest based on the data, (e.g., Paragraph [0032], “in some embodiments obtained road traffic condition information data may include multiple data samples, including data samples provided by mobile data sources (e.g., vehicles), data readings from road-based traffic sensors” __mobile data or traffic sensors reads on plurality of probes, and traffic condition information includes speeds of the vehicles, e.g., (Paragraphs [0036]- [0037] __), wherein the speed distribution from the moving speed data from each recorded data captured within the area of interest in a time period corresponds to the concentration and is proportional to the true number of probes (Paragraph [0070], “particular time windows during the time period being represented”, “ distribution of speeds”).
Regarding Claims 11, Chapman in view of Fei teaches the method of claim 8 and Chapman teaches wherein the data further comprises vehicle occupancy data. (Paragraph [0091], “additional types of traffic flow information may be reported by traffic sensors (e.g., traffic volume and occupancy),”)
Regarding Claim 22, Chapman teaches the computer-readable storage medium of claim 17 (See rejection for Claim 17), however, Chapman doesn’t teach wherein the instructions further comprise instructions for determining at least one traffic element recommendation for the area of interest based on the estimated traffic volume.
Nevertheless, Fei teaches the instructions for determining at least one traffic element recommendation for the area of interest based on the estimated traffic volume; (Paragraph [0055], “If the expected traffic volume is larger than the average traffic volume, the real-time prediction system 100 recommends an alternative traffic management strategy that can decrease the expected traffic volume, for example, increasing a fare of the traffic link.” and Page 8, Claims 6 and 7, “the alternative traffic management strategy includes one or more of: detouring traffic in the traffic link through another traffic link, adjusting a traffic signal in the traffic link, adjusting a speed limit in the traffic link, and adjusting a fare of the traffic link.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman to include the step of determining a traffic element recommendation base on the traffic volume estimation as taught by Fei in order to improve the road safety, reduce congestion and enhance travel efficiency.
Regarding Claim 23, Chapman teaches the computer-readable storage medium of claim 22, however, Chapman doesn’t teach the instructions further comprise instructions for providing the at least one traffic element recommendation for the area of interest based on the estimated traffic volume to an end user.
Nevertheless, Fei teaches instructions for providing the at least one traffic element recommendation for the area of interest to an end user. (Paragraph [0055], “the traffic microsimulation tool 160 recommends to a user an alternative traffic management strategy based on the estimated traffic volume 150 and/or the estimated traffic speed. The alternative traffic management strategy includes, but is not limited to: detouring traffic in the traffic link through another traffic link, adjusting a timing or length of a traffic signal in the traffic link, adjusting a speed limit in the traffic link, and adjusting a fare of the traffic link.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman to include the step of determining a traffic element recommendation base on the traffic volume estimation and providing it to an end user as taught by Fei in order to improve the road safety, reduce congestion and enhance travel efficiency.
Regarding claim 29, Modified Chapman teaches the method of claim 1, and Chapman teaches wherein the data resource comprises a plurality of data resources ([0004], “various data sources”), and wherein the spatiotemporal data is obtained at a specific interval via transmissions from at least one of the plurality of data resources ([0042], [0048], “at least some vehicle-based data sources may each provide data samples that include only a source identifier and a geographic location, and if so groups of multiple distinct data samples provided periodically over a particular time interval or other time period can thereby be associated with one another as having been provided by a particular vehicle.”).
Claims 2, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Chapman in view of Fei, further in view of Zhang et al., CN 111524367 A, hereinafter Zhang.
Regarding Claim 2, and 9, Chapman in view of Fei teaches, the system of claim 1 (See rejection for claim 1), the method of claim 8 (See rejection for claim 1), and Chapman in view of Fei teaches wherein the instructions directing the processing system to determine the concentration of the area of interest based on the data comprise instructions that direct the processing system to: determine, from the data associated with the area of interest, at least one of a variance, a standard deviation, a variance-to-mean ratio and a coefficient of variation ; (Paragraphs [0072]-[0074], “determining the deviation of the speed of each data sample”, “The deviation of each data sample may be measured, for example, in terms of the number of standard deviations difference from the average speed of the other data samples in the group,”).
However, Chapman in view of Fei doesn’t teach determine an optimal cordon length within the area of interest based on a minimized value of the at least one of the variance, the standard deviation, the variance-to-mean ratio and the coefficient of variation.
Nevertheless, Zhang teaches determine an optimal cordon length within the area of interest based on a minimized value of the at least one of the variance, the standard deviation, the variance-to-mean ratio and the coefficient of variation. (Abstract, “obtaining the distance information collected by each distance measuring sensor at the same time; calculating the estimation value of the measurement variance according to the distance information; calculating the optimal weighting factor of each ranging sensor according to the measured variance estimation value”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman in view of Fei to include determining the optimal length for the area of interest based on the estimation value of the measurement variance as taught by Zhang in order to ensure a reliable basis for defining the optimal length of the segment that represents a consistent traffic pattern which is necessary to have an accurate data analysis for estimating the traffic volume.
Claims 20 is rejected under 35 U.S.C. 103 as being unpatentable over Chapman in view of Zhang.
Regarding Claim 20, Chapman teaches the computer-readable storage medium of claim 17, wherein the instructions for determining the concentration for the area of interest based on the data comprise instructions for: determining, from the data associated with the area of interest, at least one of a variance, a standard deviation , a variance-to-mean ratio and a coefficient of variation; (Paragraphs [0072]-[0074], “determining the deviation of the speed of each data sample”, “The deviation of each data sample may be measured, for example, in terms of the number of standard deviations difference from the average speed of the other data samples in the group,”).
However, Chapman in view of Fei doesn’t teach determining an optimal cordon length within the area of interest based on a minimized value of the at least one of the variance, the standard deviation, the variance-to-mean ratio and the coefficient of variation.
Nevertheless, Zhang teaches determining an optimal cordon length within the area of interest based on a minimized value of the at least one of the variance, the standard deviation, the variance-to-mean ratio and the coefficient of variation. (Abstract, “obtaining the distance information collected by each distance measuring sensor at the same time; calculating the estimation value of the measurement variance according to the distance information; calculating the optimal weighting factor of each ranging sensor according to the measured variance estimation value”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for assessing road traffic such as determining traffic volume as taught by Chapman in view of Fei to include determining the optimal length for the area of interest based on the estimation value of the measurement variance as taught by Zhang in order to ensure a reliable basis for defining the optimal length of the segment that represents a consistent traffic pattern which is necessary to have an accurate data analysis for estimating the traffic volume.
Allowable Subject Matter
Claim 28 is objected to as being dependent upon a rejected base claim 1, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: None of the prior art of record, taken alone or in combination, teach the specific limitations of “wherein the true number of probes is unknown because pseudonyms associated with each of the probes are not available” as stated in claim 28.
The prior art of record, Chapman is the most relevant prior art and is representative of the current state of the art. However, Chapman, neither alone nor in combination, doesn’t teach or suggest the true number of probes is unknown because pseudonyms associated with each of the probes are not available, and wherein the spatiotemporal data is anonymized. In fact, the first part of the recited limitation wherein the true number of probes is unknown is still be covered by Chapman, according to at least paragraph [0034], when chapman suggested missing/unreliable sample data which results in number of probes being unknown. However, the second part of the claimed limitation recites because pseudonyms associated with each of the probes are not available, which is not taught or suggested by Chapman. In Chapman, each probe has an identifier and therefore each sample data is associated with a probe, so if the data from a probe is not available, the pseudonym of the probe is still known/available. Therefore, Chapman doesn’t teach or suggest the estimated traffic volume wherein the true number of probes is unknown because pseudonyms associated with each of the probes are not available, and wherein the spatiotemporal data is anonymized.
No other prior reference either alone, or in combination, has been found to teach the aforementioned limitation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAJAR HASSANIARDEKANI whose telephone number is (571)272-1448. The examiner can normally be reached Monday thru Friday 8 am-5 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Piateski can be reached at 5712707429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.H./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669