DETAILED ACTION
This office action is in response to the amendment filed September 17, 2026.
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 Status
Claims 1, 12, and 19 are currently amended.
Claims 2, 13, and 16 are canceled.
Claims 3-11, 14-15, 17-18, and 20 are as previously presented.
Therefore, claims 1, 3-12, 14-15, and 17-20 are currently pending.
Claim Interpretation
Independent claims 1, 12, and 19 recite receiving “sensor data only from one or more sensors associated with one or more of: one or more vehicles, one or more infrastructure components, or one or more fixtures” in lines 2-4 of claims 1 and 12 and lines 5-6 of claim 19. The placement of the term “only” allows for two different interpretations. First, the claims can be interpreted as receiving only sensor data. Secondly, the claims can be interpreted as receiving data from only one or more of: one or more vehicles, one or more infrastructure components, or one or more fixtures. It is not clear what the intended interpretation is. Therefore, for purposes of examination, either interpretation can be given to the claim language presented in independent claims 1, 12, and 19.
The applicant’s representative and examiner briefly discussed this interpretation in the interview dated August 19, 2026. Specifically, the prosecution history was discussed with respect to the intended interpretation. However, no claim amendments or response in the applicant’s remarks addressed this interpretation in the claims. Therefore, either interpretation remains appropriate based on the claim language.
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.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-7, 9-12, 14-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hampiholi (US PG Pub #2015/0178578) in view of Tatourian et al. (Tatourian; US PG Pub #2016/0284212).
As to claim 1, Hampiholi teaches a method (Paragraph [0058] teaches a method), comprising:
receiving, by one or more processors of a server, sensor data only from one or more sensors associated with one or more of: one or more vehicles, one or more infrastructure components, or one or more fixtures (Paragraph [0025] teaches vehicles 202a, 202c, and 202d monitoring vehicle 202b and sending the image data from vehicles 202a, 202c, and 202d to a server; Paragraph [0058] teaches a server system receiving vehicle information from observing vehicles);
determining, by the one or more processors of the server, and based upon the sensor data, that a second vehicle, distinct from the one or more vehicles, is driving in an anomalous manner (Paragraphs [0058]-[0059] teach determining a verified erratic vehicle based on a threshold of erratic behaviors identified from information of observing vehicles) for one or more of a contextual condition or an environmental condition of an environment in which the second vehicle is operating (Paragraph [0052] teaches monitoring and analyzing an environment of the neighboring vehicles; Paragraph [0056] teaches changing a threshold for determining erratic behavior based on environmental factors and operating conditions with examples of small vs. large roadways or heavy vs. light traffic); and
in response to the one or more processors of the server determining that the second vehicle is driving in an anomalous manner, automatically communicating, by the one or more processors of the server, an indication of the second vehicle driving in the anomalous manner to an electronic device associated with a third vehicle for presentation via a user interface associated with the third vehicle, the third vehicle being distinct from the second vehicle (Paragraphs [0057] and [0059] teach that when erratic behavior is verified, an alert is sent to nearby vehicles within a threshold distance or vicinity for a visual and/or auditory alert presented on a display or via speakers of the vehicle).
However, Hampiholi does not explicitly teach the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian.
In the field of detecting traffic anomalies, Tatourian teaches the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian (Paragraph [0013] teaches environmental information including weather information and road conditions; Paragraph [0022] teaches a camera capturing image data including traffic conditions, and or characteristics of the road such as road conditions; Paragraph [0039] teaches detecting anomalies based on expected traffic behaviors based on density of vehicles; Paragraph [0049] teaches determining if an anomaly is attributed to weather conditions). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the density consideration of Tatourian because environmental considerations yield the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 3, depending from the method of claim 1, Hampiholi teaches wherein determining that the second vehicle is driving in the anomalous manner further comprises monitoring a set of current behaviors of one or more other vehicles, the one or more other vehicles including the second vehicle (Paragraph [0006] teaches monitoring each neighboring vehicle within a field of view of a camera, identifying a potential erratic vehicle of the neighboring vehicles, and transmitting information to a server; Paragraph [0052] teaches erratic behavior may be determined based on the behavior of the observing vehicle and/or one or more other neighboring vehicles compared to one another).
As to claim 4, depending from the method of claim 1, Hampiholi teaches wherein determining that the second vehicle is driving in the anomalous manner further comprises monitoring a set of current contextual conditions of a vehicle environment of the one or more vehicles (Paragraph [0056] teaches changing a threshold for determining erratic behavior based on environmental factors and operating conditions with examples of small vs. large roadways or heavy vs. light traffic).
As to claim 5, depending from the method of claim 1, Hampiholi does not explicitly teach wherein determining that the second vehicle is driving in the anomalous manner includes comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics by applying a model to the set of characteristics indicative of the one or more behaviors of the second vehicle, the model generated based upon a statistical analysis or a learning method performed on a set of historical vehicle behavior data, the set of historical vehicle behavior data based upon data obtained by a plurality of sensors while a plurality of drivers operated a plurality of vehicles.
In the field of detecting traffic anomalies, Tatourian teaches wherein determining that the second vehicle is driving in the anomalous manner includes comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics by applying a model to the set of characteristics indicative of the one or more behaviors of the second vehicle, the model generated based upon a statistical analysis or a learning method performed on a set of historical vehicle behavior data, the set of historical vehicle behavior data based upon data obtained by a plurality of sensors while a plurality of drivers operated a plurality of vehicles (Paragraphs [0038]-[0041] and [0048] teach determining historical traffic patterns to predict expected traffic patterns based on hysteresis and/or learning algorithms and compare the expected behavior with present behavior such that the expected pattern reads on the claimed model). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the historical patterns of Tatourian because using learned historical patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 6, depending from the method of claim 5, Hampiholi does not explicitly teach wherein determining the second vehicle is driving in the anomalous manner comprises determining the second vehicle is driving in the anomalous manner based upon an output generated from applying the model to the set of characteristics indicative of the one or more behaviors of the second vehicle.
In the field of detecting traffic anomalies, Tatourian teaches wherein determining the second vehicle is driving in the anomalous manner comprises determining the second vehicle is driving in the anomalous manner based upon an output generated from applying of the model to the set of characteristics indicative of the one or more behaviors of the second vehicle (Paragraphs [0016] and [0045] teach detecting an anomaly by comparing present behavior with historical patterns; Paragraphs [0050] and [0051] teach identifying vehicles associated with anomalies). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the historical patterns of Tatourian because using learned historical patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 7, depending from the method of claim 6, Hampiholi does not explicitly teach wherein:
the output generated from applying the model to the set of characteristics indicative of the one or more behaviors of the second vehicle includes an indication of one or more mitigation actions; and
mitigating an effect of the second vehicle driving in the anomalous manner by suggesting or performing the one or more mitigation actions.
In the field of detecting traffic anomalies, Tatourian teaches the output generated from applying the model to the set of characteristics indicative of the one or more behaviors of the second vehicle includes an indication of one or more mitigation actions; and
mitigating an effect of the second vehicle driving in the anomalous manner by suggesting or performing the one or more mitigation actions (Paragraphs [0016], [0043], and [0052]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the historical patterns of Tatourian because using learned historical patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 9, depending from the method of claim 1, Hampiholi teaches the method further comprising mitigating an effect of the second vehicle driving in the anomalous manner by at least one of:
providing an indication of the second vehicle driving in the anomalous manner for presentation via a user interface disposed in at least one vehicle of the one or more vehicles (Paragraph [0057] teaches a visual and/or auditory alert presented on a display or via speakers of the observing vehicle); or
providing an indication of a suggested modification to an operating behavior of the at least one vehicle for presentation via the user interface disposed in the at least one vehicle.
As to claim 10, depending from the method of claim 1, Hampiholi teaches the method further comprising mitigating an effect of the second vehicle driving in the anomalous manner by automatically notifying a public safety authority of the second vehicle driving in the anomalous manner (Paragraph [0050] teaches the server sending information or alerts to a law or traffic enforcement agency; Paragraph [0060] teaches notifying a law or traffic enforcement agency).
As to claim 11, depending from the method of claim 1, Hampiholi does not explicitly teach the method further comprising determining the set of anomalous vehicle behavior characteristics based upon a set of historical vehicle behavior data, the set of historical vehicle behavior data including data indicative of historical contextual conditions.
In the field of detecting traffic anomalies, Tatourian teaches the method further comprising determining a set of anomalous vehicle behavior characteristics based upon a set of historical vehicle behavior data, the set of historical vehicle behavior data including data indicative of historical contextual conditions (Paragraph [0076] teaches generating historical data including external influence factors; Paragraphs [0045] and [0046] teach examples of external influence factors). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the historical patterns of Tatourian because using learned historical patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 12, Hampiholi teaches a system (Paragraph [0058] teaches a server system), comprising:
one or more processors of a server configured to receive data from one or more sensors associated with one or more of: one or more vehicles, one or more infrastructure components, or one or more fixtures (Paragraph [0025] teaches vehicles 202a, 202c, and 202d monitoring vehicle 202b and sending the image data from vehicles 202a, 202c, and 202d to a server for processing; Paragraph [0058] teaches a server system receiving vehicle information from observing vehicles), the system to:
determine, based upon the data, that a second vehicle, distinct from the one or more vehicles, is driving in an anomalous manner (Paragraphs [0058]-[0059] teach determining a verified erratic vehicle based on a threshold of erratic behaviors identified from information of observing vehicles) for one or more of a contextual condition or an environmental condition of an environment in which the second vehicle is operating (Paragraph [0052] teaches monitoring and analyzing an environment of the neighboring vehicles; Paragraph [0056] teaches changing a threshold for determining erratic behavior based on environmental factors and operating conditions with examples of small vs. large roadways or heavy vs. light traffic); and
in response to the one or more processors of the server determining that the second vehicle is driving in an anomalous manner, automatically communicate an indication of the second vehicle driving in the anomalous manner to an electronic device associated with a third vehicle for presentation via a user interface associated with the third vehicle, the third vehicle being distinct from the second vehicle (Paragraphs [0057] and [0059] teach that when erratic behavior is verified, an alert is sent to nearby vehicles within a threshold distance or vicinity for a visual and/or auditory alert presented on a display or via speakers of the vehicle). Hampiholi does not explicitly teach the system comprising:
one or more tangible, non-transitory computer-readable media coupled to the one or more processors;
computer-executable instructions stored on the one or more tangible, non-transitory computer-readable media that, when executed by the one or more processors of the server cause the system to operate, and
the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian.
However, Hampiholi does teach one or more tangible, non-transitory computer-readable media coupled to one or more processors;
computer-executable instructions stored on the one or more tangible, non-transitory computer-readable media that are executed by the one or more processors (Paragraph [0030] teaches non-transitory storage devices storing instructions that are executed by a processor to control an in-vehicle computing system). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the server of Hampiholi with the in-vehicle computing architecture because the known use of non-transitory computer readable media and computer-executable instructions yields the predictable result of controlling a computer system to perform actions (Paragraph [0030]).
Hampiholi does not render obvious the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian.
In the field of detecting traffic anomalies, Tatourian teaches the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian (Paragraph [0013] teaches environmental information including weather information and road conditions; Paragraph [0022] teaches a camera capturing image data including traffic conditions, and or characteristics of the road such as road conditions; Paragraph [0039] teaches detecting anomalies based on expected traffic behaviors based on density of vehicles; Paragraph [0049] teaches determining if an anomaly is attributed to weather conditions). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the density consideration of Tatourian because environmental considerations yield the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 14, depending from the system of claim 12, Hampiholi does not explicitly teach wherein: the computer-executable instructions, when executed by the one or more processors, further cause the system to determine that the second vehicle is driving in the anomalous manner based on comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics.
In the field of detecting traffic anomalies, Tatourian teaches wherein: the computer-executable instructions, when executed by the one or more processors, further cause the system to determine that the second vehicle is driving in the anomalous manner based on comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics (Paragraph [0016] teaches the traffic analysis server detecting an anomaly based on expected traffic behavior or historical traffic patterns; Paragraph [0039] teaches detecting anomalies based on expected traffic behaviors where the expected traffic behaviors which are any type of behaviors exhibited by vehicles; Paragraph [0041] teaches detecting anomalies based on a comparison between expected behavior and current behavior; Paragraphs [0040] and [0044] teach the current behavior is any type of behavior exhibited by vehicles; Paragraph [0045] teaches detecting anomalies by comparing present behavior with historical behavior). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the anomaly detection of Tatourian because using historical or expected patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 15, depending from the system of claim 12, Hampiholi does not explicitly teach wherein the computer-executable instructions, when executed by the one or more processors, further cause the system to determine that the second vehicle is driving in the anomalous manner based on comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics; and wherein at least a subset of the set of anomalous vehicle behavior characteristics is generated from a set of historical vehicle behavior data by using at least one of a statistical analysis or a learning method, the set of historical vehicle behavior data corresponding to data obtained by a plurality of sensors while a plurality of drivers operated a plurality of vehicles.
In the field of detecting traffic anomalies, Tatourian teaches wherein the computer-executable instructions, when executed by the one or more processors, further cause the system to determine that the second vehicle is driving in the anomalous manner based on comparing a set of characteristics indicative of one or more behaviors of the second vehicle with a set of anomalous vehicle behavior characteristics; and wherein at least a subset of the set of anomalous vehicle behavior characteristics is generated from a set of historical vehicle behavior data by using at least one of a statistical analysis or a learning method, the set of historical vehicle behavior data corresponding to data obtained by a plurality of sensors while a plurality of drivers operated a plurality of vehicles (Paragraphs [0038]-[0041] and [0048] teach determining historical traffic patterns to predict expected traffic patterns based on hysteresis and/or learning algorithms and compare the expected behavior with present behavior such that the expected pattern reads on the claimed model). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the historical patterns of Tatourian because using learned historical patterns yields the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 18, depending from the system of claim 12, Hampiholi teaches wherein at least one of the one or more vehicles, and the third vehicle, are communicatively connected via one or more communication interfaces (Figure 2 shows vehicles 202a, 202c, and 202d connected to network 210).
As to claim 19, Hampiholi teaches a server (Paragraph [0058] teaches a server) to:
receive sensor data only from one or more sensors associated with one or more of: one or more vehicles, one or more infrastructure components, or one or more fixtures (Paragraph [0025] teaches vehicles 202a, 202c, and 202d monitoring vehicle 202b and sending the image data from vehicles 202a, 202c, and 202d to a server; Paragraph [0058] teaches a server system receiving vehicle information from observing vehicles);
determine, based upon the sensor data, that a second vehicle, distinct from the one or more vehicles, is driving in an anomalous manner (Paragraphs [0058]-[0059] teach determining a verified erratic vehicle based on a threshold of erratic behaviors identified from information of observing vehicles) for one or more of a contextual condition or an environmental condition of an environment in which the second vehicle is operating (Paragraph [0052] teaches monitoring and analyzing an environment of the neighboring vehicles; Paragraph [0056] teaches changing a threshold for determining erratic behavior based on environmental factors and operating conditions with examples of small vs. large roadways or heavy vs. light traffic); and
in response to the one or more processors of the server determining that the second vehicle is driving in an anomalous manner, automatically communicate an indication of the second vehicle driving in the anomalous manner to an electronic device associated with a third vehicle for presentation via a user interface associated with the third vehicle, the third vehicle being distinct from the second vehicle (Paragraphs [0057] and [0059] teach that when erratic behavior is verified, an alert is sent to nearby vehicles within a threshold distance or vicinity for a visual and/or auditory alert presented on a display or via speakers of the vehicle). However, Hampiholi does not explicitly teach one or more non-transitory computer-readable storage storing computer-readable instructions to be executed on one or more processors of the server, the computer-readable instructions, when executed by the one or more processors, cause the server to operate and the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian.
However, Hampiholi does teach one or more non-transitory computer-readable storage storing computer-readable instructions to be executed on one or more processors, the computer-readable instructions, when executed by the one or more processors, cause a device to operate (Paragraph [0030] teaches non-transitory storage devices storing instructions that are executed by a processor to control an in-vehicle computing system). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the server of Hampiholi with the in-vehicle computing architecture because the known use of non-transitory computer readable media and computer-executable instructions yields the predictable result of controlling a computer system to perform actions (Paragraph [0030]).
However, Hampiholi does not render obvious the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian.
In the field of detecting traffic anomalies, Tatourian teaches the one or more of the contextual condition or the environmental condition being one or more of: a weather condition, a road topography, a road condition, a traffic density, a traffic congestion, a presence or degree of traffic construction, or a presence of a pedestrian (Paragraph [0013] teaches environmental information including weather information and road conditions; Paragraph [0022] teaches a camera capturing image data including traffic conditions, and or characteristics of the road such as road conditions; Paragraph [0039] teaches detecting anomalies based on expected traffic behaviors based on density of vehicles; Paragraph [0049] teaches determining if an anomaly is attributed to weather conditions). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the condition detection of Hampiholi with the density consideration of Tatourian because environmental considerations yield the predictable result of successfully identifying anomalies to increase safety on the roadway.
As to claim 20, depending from the one or more non-transitory computer-readable storage media of claim 19, Hampiholi teaches causing the server to at least one of:
(a) provide a first instruction to automatically modify an operating behavior of at least one vehicle of the one or more vehicles, the first instruction based upon the second vehicle driving in the anomalous manner;
(b) provide a second instruction to automatically modify an operating behavior of the third vehicle, the second instruction based upon the second vehicle driving in the anomalous manner;
(c) provide an indication of the second vehicle driving in the anomalous manner for presentation via at least one of a user interface disposed in at least one vehicle of the one or more vehicles or a user interface disposed in the third vehicle operating (Paragraph [0057] teaches a visual and/or auditory alert presented on a display or via speakers of the observing vehicle); or
(d) provide an indication of a suggested modification to an operating behavior of at least one vehicle of the one or more vehicles for presentation via the user interface disposed in the at least one vehicle of the one or more vehicles. Hampiholi does not explicitly teach wherein the computer-readable instructions, when executed by the one or more processors, cause the server to operate.
However, Hampiholi does teach wherein the computer-readable instructions, when executed by the one or more processors, cause a device to operate (Paragraph [0030] teaches non-transitory storage devices storing instructions that are executed by a processor to control an in-vehicle computing system). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the server of Hampiholi with the in-vehicle computing architecture because the known use of non-transitory computer readable media and computer-executable instructions yields the predictable result of controlling a computer system to perform actions (Paragraph [0030]).
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hampiholi (US PG Pub #2015/0178578) in view of Tatourian et al. (Tatourian; US PG Pub #2016/0284212) as applied to claims 1 and 12 above, and further in view of Shiga et al. (Shiga; US PG Pub #2018/0049088).
As to claim 8, depending from the method of claim 1, Hampiholi does not explicitly teach the method further comprising mitigating an effect of the second vehicle driving in the anomalous manner by at least one of:
providing a first instruction to automatically modify an operating behavior of at least one vehicle of the one or more vehicles, the first instruction based upon the second vehicle driving in the anomalous manner; or
providing a second instruction to automatically modify an operating behavior of the third vehicle, the second instruction based upon the second vehicle driving in the anomalous manner.
In the field of vehicle communication systems, Shiga teaches the method further comprising mitigating an effect of the second vehicle driving in the anomalous manner by at least one of:
providing a first instruction to automatically modify an operating behavior of at least one vehicle of the one or more vehicles, the first instruction based upon the second vehicle driving in the anomalous manner; or
providing a second instruction to automatically modify an operating behavior of the third vehicle, the second instruction based upon the second vehicle driving in the anomalous manner (Paragraph [0037] teaches vehicle terminals used to receive a vehicle related service such as automatic driving and high-tech driving assistance; Paragraph [0040] teaches the server provides automatic driving and high-tech driving assistance; Paragraph [0041] teaches the server distributing a message indicating vehicle operation contents). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Hampiholi with that of Shiga such that operation of vehicles are automatically modified because this uses the information of a vehicle to prevent a collision of following vehicles (Paragraph [0096]).
As to claim 17, depending from the system of claim 12, Hampiholi does not explicitly teach wherein at least one of the one or more vehicles or the third vehicle is an autonomous vehicle.
In the field of vehicle communication systems, Shiga teaches wherein at least one of the one or more vehicles or the third vehicle is an autonomous vehicle (Paragraph [0037] teaches vehicle terminals used to receive a vehicle related service such as automatic driving; Paragraph [0040] teaches the server provides automatic driving). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Hampiholi with the automatic driving of Shiga because ensuring vehicles of all types operate within the system yields the predictable result of increasing the reliability of the system to increase safety on the road.
Response to Arguments
Applicant’s arguments with respect to independent claims 1, 12, 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
THIS ACTION IS MADE FINAL. 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN W SHERWIN whose telephone number is (571)270-7269. The examiner can normally be reached M-F, 9:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached at 571.270.1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN W SHERWIN/ Primary Examiner, Art Unit 2688
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