DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-6, 9-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application because the generic computer elements listed are not sufficiently more and it merely indicates a field of use. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited computer elements are generic, a memory that stores instructions, a processor that clusters and processes data, and a probe/sensor that collects data are all common uses of the generic computer elements. The “controlling of an autonomous vehicle” is a general linking to a field of use that does so at a very high level which renders it as not significantly more.
In regards to claim 1, receiving, in real time, at least one road sign observation associated with a location wherein the road sign observation comprises at least a road sign type (A person can observe a road sign at a location and determine the type of road sign or their location in real time, so an observation and judgement of the observation so a mental process); triggering, in real time, a collection of sensor data associated with the at least one road sign observation, based on the road sign type (Is a mental process as it would be collecting data that is described at a high level of generality with no specificity at what triggering the collection of sensor data would entail so it is data-gathering), wherein the sensor data comprises at least road boundary data and probe data, wherein the probe data indicates a plurality of vehicle paths traversing a distance after the location (A person of ordinary skill in the art can receive date, probe or no probe, that indicates a plurality of vehicle paths by simple observation, as a plurality only requires 2 vehicle paths although this can entail more than two and be within the scope of a mental process), and wherein the probe data is collected using one or more sensors of one or more vehicles (Similar to the choice above, there would only have to be 1 sensor of one vehicle in the BRI, and a human being can attain data of the path of one car visually); calculating, in real time, a first number of lanes based on a lateral offset data and a longitudinal offset data of the road boundary data calculating, in real time (This is mere data gathering, a person of ordinary skill can reasonably ascertain a relative distance or offset by simple approximation from observing the distance between the lines that correspond to the boundary data and the centerline of the road), a second number of lanes by aggregating the plurality of vehicle paths indicated by the probe data and by performing one or more clustering operations on the aggregated plurality of vehicle paths to determine a number of physical lanes traversed by the one or more vehicles (A person of ordinary skill in the art can perform clustering or aggregation, particularly if it is disclosed at such a high level, on at least 1 or 2 or more vehicle paths as entailed by the BRI of 1 or more or plurality); validating, in real time, the road sign based on the first number of lanes or the second number of lanes, wherein the road sign is validated as a correct road sign when the first number of lanes or the second number of lanes is less than the number of lanes obtained from map data associated with the location (one could judge the road sign as valid based on this criteria, this is a judgement which is a mental process); and controlling, in real time, an autonomous guidance of a vehicle based on the validated road sign (Under the BRI of “controlling an autonomous guidance of a vehicle” this would include such things like a person choosing a particular route which is a judgement and thus a mental process or even the controlling of a non-autonomous vehicle would be analogous to this process).
In regards to claim 2, further comprises validating that the road sign is a false positive road sign when the first number of lanes is equal to the number of lanes obtained from the map data associated with the location (One could further judge a road sign as a false positive if number of lanes differed from map data, this is a judgement modified by a generic tool such as a map).
In regards to claim 3, wherein the road boundary data comprises one or more of: a position offset data and a timestamp data (One could easily get position data of the road boundary, such as lines marking edges and this would be observations of the location performed with the help of a generic computer element, a sensor, so it is a mental process further modified by a generic computer element and are mere data gathering, the time stamp data could be analogously gotten through a method of determining time from a clock or even the sun’s position).
In regards to claim 4, wherein the road sign type comprises at least a road narrows sign wherein the road narrows sign comprises at least one of: the road narrows on the right, the road narrows on the left, or the road narrows on the right and left (One of ordinary skill in the art can identify traffic signs, one without skill in the art can identify most traffic signs or signals, thus the further limiting of the road sign type to these specific types is merely a form of observation or mental process).
In regards to claim 5, wherein the road narrows sign further comprises at least of a road works (This is a form of observation, any person of ordinary skill in the art or not, could receive sensor data of some form of a road narrows sign, and determine if there was roadwork nearby. As this is merely an observation, it is a mental process).
In regards to claim 6, wherein the road sign observation further comprises at least a background color, the background color further comprising at least one of a color indicating a temporary change in the first number of lanes (A person could easily look at a road sign and determine the background color of said sign along with whether it was indicative of a temporary change in the lanes, this is an observation coupled with a judgement which are both mental processes).
In regards to claim 9, further comprising validating that the road sign is correct or not based on the first number of lanes and second number of lanes (This claim is being interpreted as if “correct road” was “correct road sign”. One could compare and contrast the two sensor readings to see if the road sign was the correct road sign, this would be judgement of two observations, and thus a mental process).
The rationale for claim 1 applies to claims 10 and 19 too.
The rationale for claim 2 applies to claims 11 and 20 too.
The rationale for claim 3 applies to claim 12.
The rationale for claim 4 applies to claim 13.
The rationale for claim 5 applies to claim 14.
The rationale for claim 6 applies to claim 15.
The rationale for claim 9 applies to claim 18.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1,3, 9-10, 12, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 20200372262 A1), hereinafter referred to as Ma in view of Seo et al. (“Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles”) and in view of Fowe et al. (US 20190026591 A1), hereinafter referred to as Fowe.
In regards to claim 1, Ma discloses a method for validating a road sign, the method comprising: receiving at least one road sign observation associated with a location wherein the road sign observation comprises at least a road sign type (Paragraph 10, the paragraph identifies the use of a sensor of some sort that can observe a road object which includes signs and various types of the aforementioned signs); triggering a collection of sensor data associated with the at least one road sign observation, based on the road sign type (Paragraph 10, the paragraph identifies the use of a sensor of some sort that can observe a road object which includes signs and various types of the aforementioned signs, usage of the sensor would include triggering a collection of sensor data); calculating a first number of lanes based on a lateral offset data the road boundary data (Paragraph 12 and paragraph 33, calculates the number of lanes both closed and open along with the total number with paragraph 33’s “width of the roadway” covering lateral offset data); validating the road sign based on the first number of lanes or the second number of lanes, wherein the road sign is validated as a correct road sign when the first number of lanes or the second number of lanes is less than a number of lanes obtained from map data associated with the location (Paragraph 27, 28, and 17, Paragraph 27 and 28 disclose using the map data to check the area to see if there are lanes that are open or closed and the number associated there. Paragraph 17 discloses updating the maps stored to indicate lane closure is a validation of the road sign as the detected road sign would need to be correct for the map to update); and controlling an autonomous guidance of a vehicle based on the validated road sign (Paragraph 14, describes that the statues such as object detections and construction signage).
Ma does not explicitly disclose performing this in real time and wherein the sensor data comprises probe data indicating a plurality of vehicle paths traversing a distance after the location, and wherein at least probe data, wherein the probe data is collected using one or more sensors of one or more vehicles; by aggregating the plurality of vehicle paths indicated by the probe data and by performing one or more clustering operations on the aggregated plurality of vehicle paths to determine a number of physical lanes traversed by the one or more vehicles and calculating, in real time, a first number of lanes based on a longitudinal offset data.
However, Seo does disclose performing this in real time (Abstract, where the inventive concept is directed to overcoming changes in real time which implies that the actions are performed in real time) and wherein the sensor data comprises probe data indicating a plurality of vehicle paths traversing a distance after the location, and wherein at least road boundary data and probe data, wherein the probe data is collected using one or more sensors of one or more vehicles (Page 10, this page discusses the frequent clustering and uncoupling of nodes that represent vehicles, when a node is removed, it would happen during and after first observing a road narrowing event as the cars or nodes would be removed and added on to the cluster of the narrowed lanes as they merge with the road boundary data already being disclosed by Ma).
It would have been prima facie obvious to combine the teaching of Seo and Ma as this would predictably allow for greater accuracy in road narrowing events as they occur as being able to perform these actions in real time allows for greater ability to perform in real time. This in turn, would make it very easy and accurate to determine closed lanes as they occur. Therefore, it would have been prima facie obvious to combine the disclosures of both arts.
Seo does not disclose calculating a first number of lanes based on a longitudinal offset data and calculating a second number of lanes by aggregating the plurality of vehicle paths indicated by the probe data and by performing one or more clustering operations on the aggregated plurality of vehicle paths to determine a number of physical lanes traversed by the one or more vehicles.
However, Fowe does disclose calculating a first number of lanes based on a longitudinal offset data (Abstract and paragraph 39, these disclose that longitudinal and latitudinal position can be communicated by the probe data with the abstract showing that such data is used in the determination of the number of lanes) and calculating a second number of lanes by aggregating the plurality of vehicle paths indicated by the probe data and by performing one or more clustering operations on the aggregated plurality of vehicle paths to determine a number of physical lanes traversed by the one or more vehicles (Paragraphs 6, 9, 12, 15, and 63, these paragraphs all disclose the calculation of the number of road lanes used in regards to a clustering algorithm).
It would be prima facie obvious to combine these arts as it would lead to a predictable increase in accuracy as combining the longitudinal data presented by Fowe along with the clustering operation would allow for more accurate predictions. Ma discloses lateral offset data, but does not appear to disclose longitudinal offset data. Fowe’s addition of an additional data point allows for more accurate approximations as on some roads, the offset may not be reflected in just the lateral data. Likewise, while Seo does disclose a clustering operation that would by implication contain the number of lanes, it does not explicitly disclose that it ever derives the number of lanes from this clustering operation. Fowe more directly provides a way to calculate the total number of lanes from their clusters. As such, it would be prima facie obvious to combine.
In regards to claim 3, Ma discloses wherein the road boundary data comprises one or more of: a position offset data and a timestamp data (Paragraph 33, “position offset data” is covered by the accounting of the number of “closed lanes” and the number of “open lanes”).
In regards to claim 9, Ma discloses further comprising validating that the road sign is correct or not based on the first number of lanes and second number of lanes (Paragraph 52, Ma describes a situation where another vehicle with sensors is used to validate the lane information, as number of lanes is already covered by Paragraph 12, a second car with sensors performing the same process and validating the results from the first car covers this. This claim is being interpreted as correct road being a typo for “correct road sign”).
The rationale for claim 1 applies to claims 10 and 19 too.
The rationale for claim 3 applies to claim 12.
The rationale for claim 7 applies to claim 16.
The rationale for claim 9 applies to claim 18.
Claims 2, 6, 11, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 20200372262 A1), hereinafter referred to as Ma in view of Seo et al. (“Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles”) and in view of Fowe et al. (US 20190026591 A1), hereinafter referred to as Fowe, as applied to claims 1,3, 9-10, 12, and 18-19 above, and further in view of Fairfield et al. (US 20160092755 A1), hereinafter referred to as Fairfield.
In regards to claim 2, Ma does not disclose further comprises validating that the road sign is a false positive road sign when the first number of lanes is equal to the number of lanes obtained from the map data associated with the location
However, Fairfield discloses further comprises validating that the road sign is a false positive road sign when the first number of lanes is equal to the number of lanes obtained from the map data associated with the location (Paragraph 107, Fairfield establishes the usage of a positive or negative indication of the existence of a construction zone which the negative is the identification of a “false positive”. Fairfield discloses the idea of a “false positive” since it uses the detection of road work signs. Ma discloses the calculation of lanes and checking it with map data).
It would have been prima facie obvious to combine the teachings of Fairfield with the teachings of Ma. Ma discloses the calculation of lanes and cross referencing that with a map, and Fairfield includes the determination of false positives. Thus, the combination of the two arts would have led to a predictable result, namely an increase in accuracy for self-driving cars as they would not create artificial slowdowns by being able to differentiate between a “false positive” and a “true positive”.
In regards to claim 6, Ma does not explicitly disclose wherein the road sign observation further comprises at least a background color, the background color further comprising at least one of a color indicating a temporary change in the first number of lanes.
However, Fairfield does disclose wherein the road sign observation further comprises at least a background color, the background color further comprising at least one of a color indicating a temporary change in the first number of lanes (Paragraphs 129 and 130, Fairfield describes how signs have rules for standard background colors and reflective backgrounds in 129, and covers that the device detects these standards in the sign in 130).
It would have been prima facie obvious to combine the teachings of Ma with the teachings of Fairfield. Fairfield’s teachings allow for a system to detect the various standardized features of road signs. As such, combining that teaching with the teachings of Ma would have led to a predictable increase in accuracy and efficiency as being able to quickly discern these standardized features would allow for more accurate self-driving capabilities but allow the system to be able to respond to such signage more quickly.
The rationale for claim 2 applies to claims 11 and 20 too.
The rationale for claim 6 applies to claim 15.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 20200372262 A1), hereinafter referred to as Ma in view of Seo et al. (“Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles”) and in view of Fowe et al. (US 20190026591 A1), hereinafter referred to as Fowe, as applied to claims 1,3, 8-10, 12, and 17-19 above, and further in view of Mai et al. (“Recognition of Vietnamese Warning Traffic Signs Using Scale Invariant Feature Transform” provided by Applicant with the IDS of 12/6/2022), hereinafter referred to as Mai.
In regards to claim 4, Ma does not explicitly disclose wherein the road sign type comprises at least a road narrows sign wherein the road narrows sign comprises at least one of: the road narrows on the right, the road narrows on the left, or the road narrows on the right and left.
However, Mai does disclose wherein the road sign type comprises at least a road narrows sign wherein the road narrows sign comprises at least one of: the road narrows on the right, the road narrows on the left, or the road narrows on the right and left (Page 1, Mai describes the types of signs that they identify, “Dangerous Turn Left (201a), Dangerous Turn Right (201b), Dangerous Multiple Turns (202), Narrow Road on the Left (203b), Narrow Road on the Right (203c) and Narrow Road on Both Sides (203a).” Narrow Road on the Left, Narrow Road on the Right, and Narrow Road on Both Sides or right and left).
It would have been prima facie obvious to combine the teachings of Ma and Mai. As combining the two teachings would have led to a predictable outcome. Mai teaches categorizing the road narrowing signs by specific types, this would allow for a self-driving system to quickly be able to determine whether it needs to go towards the right, left, or center more quickly as it would merely have to assign it that category before determining where to head.
The rationale for claim 4 applies to claim 13.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 20200372262 A1), hereinafter referred to as Ma in view of Seo et al. (“Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles”) and in view of Fowe et al. (US 20190026591 A1), hereinafter referred to as Fowe, and in view of Mai et al. (“Recognition of Vietnamese Warning Traffic Signs Using Scale Invariant Feature Transform” provided by Applicant with the IDS of 12/6/2022), hereinafter referred to as Mai, as applied to claims 4 and 13 above, and further in view of Fairfield et al. (US 20160092755 A1), hereinafter referred to as Fairfield.
In regards to claim 5, Ma and Mai fail to disclose wherein the road narrows sign further comprises at least of a road works.
However, Fairfield does disclose wherein the road narrows sign further comprises at least of a road works (Paragraph 107, Fairfield establishes the usage of a positive or negative indication of the existence of a construction zone which the negative is the identification of a “false positive”).
It would have been prima facie obvious to combine the teachings of Fairfield with the teachings of Ma and Mai. The combination of the teachings would have resulted in a predictable increase in the effectiveness of the self-driving mechanism. As, the positive identification of road work would have allowed for the car to be able to distinguish between “false positives” and “false negatives” for road narrows signs specifically which are frequently used with road works. Thus, it would have been prima facie obvious to combine the teachings.
The rationale for claim 5 applies to claim 14.
Response to Amendment
The amendment entered 3/12/2026 has been considered in full. It overcame the previous grounds of rejection under 35 U.S.C. 103, but upon further search, additional prior art was located that established a prima facie obvious case to reject the claims under 35 U.S.C. 103.
Response to Arguments
Applicant's arguments filed 3/12/2026 have been fully considered but they are not persuasive. The arguments made against the 101 rejections are not persuasive. Arguments assert that a human being cannot mentally process lateral and longitudinal offsets in real time, but this is not persuasive. Human beings in the course of everyday activities deal with distance offsets consistently. A person of ordinary skill in the art can tell roughly how much distance is offset if a lane is closed while driving. Nothing about this aspect of the process appears to be more than simple human observation followed by a determination or judgement by a person. The argument against aggregation and clustering processes has been covered in prior responses, but to reiterate the prior argument, the clustering required is simply that of a plurality. A human being can cluster a group of five, ten, or more or less vehicles to determine whether a lane is closed or not. Nothing about the process appears to be more than observation and determination for a human being. As such, the arguments are not persuasive.
Applicant’s arguments with respect to claims 1, 10, and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stenneth et al. (US 20220277163 A1) is pertinent prior art due it reciting both terms lateral offset data and longitudinal offset data for a related issue. The terms are ultimately used for a different intended purpose than in this application, hence it is not relied upon as previously cited Fowe while not using that specific terminology still reads upon those terms.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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