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 .
Status of Application
Claims 1-10 are pending.
Claims 1, 9, and 10 are independent.
This FINAL action is in response to “Amendments and Remarks” received on 17 December 2025.
Response to Amendment/Remarks
With respect to Applicant’s remarks filed 17 December 2025, Applicant’s “Amendments and Remarks” have been fully considered and were not wholly persuasive. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to claim rejections under 35 U.S.C. 102 and/or 35 U.S.C. 103, Applicant’s “Amendments and Remarks” have been fully considered and are persuasive. Therefore, the rejection is withdrawn. However, upon further consideration, there is a new ground(s) of rejection in view of newly found prior art.
Final Office Action
Claim Interpretation
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three-prong test is used to determine the scope of a means-plus-function limitation in a claim:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that the claims do not contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
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 and 5-10 are rejected under 35 U.S.C. 103 as being unpatentable over Moustafa et al. (US 20220126878 A1), hereinafter Moustafa, in view of Nickolaou (US 20150211780 A1).
Regarding claim 1, Moustafa et al. discloses:
A method for carrying out a partly automated driving function by a motor vehicle comprising the following steps (Abstract - autonomously control driving of the vehicle):
determining that an infrastructure element is located in an environment of the motor vehicle (Fig 1; Fig 6; [0045], observing portions of roadways and vehicles moving with the environment; [0061], LIDAR, cameras, radar, etc.; [0054], perception engine),
the infrastructure element being configured to determine an infrastructure assistance datum for an infrastructure-based, at least partly automated guidance of the motor vehicle ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks),
wherein the infrastructure element is part of an event chain for the at least partly automated guidance of the motor vehicle during a trip that is guided in an at least partly automated manner ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks);
carrying out the at least partly automated driving function based on the infrastructure assistance datum and depending on a result of the determination as to whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig 14; [0115], acceptable control signals to a vehicle actuation system; [0116], replace human driving with safer inputs).
However, Moustafa does not specifically state:
determining a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function;
determining which safety integrity level the event chain maximally fulfills;
determining, based on the minimum safety integrity level and the maximum safety level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element;
Nickolaou teaches:
determining a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function (Fig. 2; [0020], Turning now to FIG. 2, there is shown an embodiment of a driving enhancement method 100 that may utilize street level images provided by a network of stationary traffic can1eras to identify potential hazards or concerns located beyond the field of view of vehicle mounted sensors, and to provide an advanced warning or to take some other remedial action in response thereto; [0026], At this point, step 120 can evaluate the items identified in the street level images in order to classify any potential hazards or concerns that the present method may wish to address. Classification of such concerns can be carried out in any number of different ways. For example, potential concerns that are based on items extracted from the street level images in step 120 may be classified into one or more predetermined categories or groups, such as: construction concerns, traffic concerns, and weather concerns, to cite a few possibilities. These categories may not be entirely mutually exclusive, as one concern or event may be considered both a construction concern and a traffic concern, and the respective categorization may vary from that described below depending on the particular implementation of the present method. It should also be recognized that it is possible to categorize a concern or hazard in multiple categories and then continue with the method. This particular embodiment may allow for more lenient severity determinations, or no determination of severity at all, as will be described more fully below);
determining which safety integrity level the event chain maximally fulfills ([0030], As mentioned above, the various concerns or hazards that are detected from the street level images may be rated or weighted according to their potential severity and impact on vehicles traveling the particular road segment in question, like host vehicle 54. Before issuing a warning to the host vehicle 54 or taking some other remedial action, the method may first attempt to corroborate the weather concern 80 by evaluating the street level images and looking for other signs of ice. In this case, the street level images provided by traffic cameras 16, 18 show that vehicles Vl, V2, which are in the same area as the suspected ice patch 80, are operating their window wipers, which is indicative of precipitation that could lead to an ice patch; that is, corroboration or verification. According to this example, the ice patch 80 could be assigned a low severity rating before it is corroborated, a moderate severity rating once it is confirmed by the active window wipers, and potentially a high severity once it is confirmed and once the method recognizes that the ice patch is on or near a bridge B, which is more prone to ice than other road surfaces. If ice patch 80 appears to be quite large or if there are disabled vehicles near the ice patch, for example, these are indicia that are acquired from the street level images and may affect the severity level or rating of that particular weather concern);
determining, based on the minimum safety integrity level and the maximum safety level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig. 2, 180; [0004], According to one embodiment, a method of enhancing an automated driving mode of a host vehicle, comprising the steps of: comparing a host vehicle location to a geographic zone saved in a concern profile, and the host vehicle location corresponds to a current location of the host vehicle or an anticipated future location of the host vehicle and the concern profile is based on street level images gathered from a plurality of image sources; identifying a potential concern in response to the comparison of the host vehicle location to the saved geographic zone, and the potential concern is associated with the geographic zone in the concern profile; and performing a remedial action in response to the identification of the potential concern, wherein the remedial action is performed before the host vehicle encounters the potential concern and the remedial action affects the automated driving mode of the host vehicle);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nickolaou into the invention of Moustafa to include road condition severity determination as Nickolaou discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that uses additional information gleaned from street level images to allow for redundancy or corroboration to verify the presence and nature of a hazard or concern before implementing remedial measures (Nickolaou: [0010]). Additionally, the claimed invention is merely a combination of old, well-known elements of an autonomous vehicle that uses information from roadside sensors to inform driving decisions as disclosed by Moustafa and using data from roadside sensors to corroborate/verify information related to road conditions to inform an autonomous vehicle of remedial actions to be taken as taught by Nickolaou. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 2, Moustafa in view of Nickolaou teaches:
wherein the maximum safety integrity level of the event chain is determined based on the respective safety integrity levels of the parts of the event chain (Nickolaou: [0010], The additional information extracted from the street level images allows the present system and method to better recognize, identify, classify and/ or evaluate various hazards or concerns in upcoming road segments, including those that are far forward and beyond the field of view of vehicle mounted devices. Depending upon the type and severity of hazard or concern, the present system and method may develop remedial measures, such as adjusting an operating parameter of the automated driving mode, disabling the automated driving mode, or simply alerting the driver of the concern. Moreover, the additional information gleaned from the street level images can allow for better redundancy or corroboration to verify the presence and nature of the hazard or concern before implementing the remedial measures).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nickolaou into the invention of Moustafa to include road condition severity determination as Nickolaou discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that uses additional information gleaned from street level images to allow for redundancy or corroboration to verify the presence and nature of a hazard or concern before implementing remedial measures (Nickolaou: [0010]). Additionally, the claimed invention is merely a combination of old, well-known elements of an autonomous vehicle that uses information from roadside sensors to inform driving decisions as disclosed by Moustafa and using data from roadside sensors to corroborate/verify information related to road conditions to inform an autonomous vehicle of remedial actions to be taken as taught by Nickolaou. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 3, Moustafa in view of Nickolaou teaches:
wherein the infrastructure assistance datum is tested for correctness/plausibility based on the vehicle-generated environmental signals (Moustafa: [0117], (sensor) signal quality metric, traffic situation), wherein that at least partly automated driving function is carried out based on a result of the test for correctness/plausibility (Moustafa: [0123], based on signal quality metric some or all functions are automatic).
Regarding claim 5, Moustafa in view of Nickolaou teaches:
wherein, depending on a result determination as to whether the at least partly automated driving function may be carried out based in the infrastructure assistance datum on the infrastructure element (Moustafa: [0123], based on signal quality metric some or all driving functions are automatic; Fig 1; Fig 6; [0045], observing portions of roadways and vehicles moving with the environment, sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks; [0061], LIDAR, cameras, radar, etc.), the restricted or the unrestricted range of functions is selected so that the at least partly automated driving function is carried out according to the selected range of driving functions (Moustafa: [0123], based on signal quality metric some or all driving functions are automatic).
Regarding claim 6, Moustafa in view of Nickolaou teaches:
wherein the at least partly automated driving function is an element selected from the following group of driving functions: emergency braking function, electronic stability program (ESP) function, anti-lock braking system (ABS) function, automatic valet parking (AVP) function (Moustafa: Fig 2, 220; Fig 11, 1112; [0060], steering controls, acceleration/throttle controls, braking controls, signaling controls).
Regarding claim 7, Moustafa in view of Nickolaou teaches:
wherein the determination as to whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element is performed (Moustafa: [0123], based on signal quality metric some or all driving functions are automatic; Fig 1; Fig 6; [0045], observing portions of roadways and vehicles moving with the environment, sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks; [0061], LIDAR, cameras, radar, etc.) depending at least one of on a current situation, on current weather, on a current time , on a current date , on a vehicle type of the motor vehicle , on an infrastructure type of the infrastructure , on the driving function (Moustafa: Fig 28; [0117], traffic situation, weather conditions).
Regarding claim 8, Moustafa in view of Nickolaou:
wherein the infrastructure element is an element selected from the following group of infrastructure elements: traffic light system, electronic traffic sign, dynamic traffic sign (Moustafa: Fig 1, 130; [0045], roadside or overhead signage, roadside equipment or fixtures).
Regarding claim 9, Moustafa discloses:
A device configured to carry out a partly automated driving function by a motor vehicle, the device configured to ([0044], external sensor devices and other devices for autonomous driving infrastructure):
determine that an infrastructure element is located in an environment of the motor vehicle (Fig 1; Fig 6; [0045], observing portions of roadways and vehicles moving with the environment; [0061], LIDAR, cameras, radar, etc.; [0054], perception engine),
the infrastructure element being configured to determine an infrastructure assistance datum for an infrastructure-based, at least partly automated guidance of the motor vehicle ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks),
wherein the infrastructure element is part of an event chain for the at least partly automated guidance of the motor vehicle during a trip that is guided in an at least partly automated manner ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks);
carry out the at least partly automated driving function based on the infrastructure assistance datum and depending on a result of the determination as to whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig 14; [0115], acceptable control signals to a vehicle actuation system; [0116], replace human driving with safer inputs).
However, Moustafa does not specifically state:
determine a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function;
determine which safety integrity level the event chain maximally fulfills;
determine, based on the minimum safety integrity level and the maximum safety integrity level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element;
Nickolaou teaches:
determine a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function (Fig. 2; [0020], Turning now to FIG. 2, there is shown an embodiment of a driving enhancement method 100 that may utilize street level images provided by a network of stationary traffic can1eras to identify potential hazards or concerns located beyond the field of view of vehicle mounted sensors, and to provide an advanced warning or to take some other remedial action in response thereto; [0026], At this point, step 120 can evaluate the items identified in the street level images in order to classify any potential hazards or concerns that the present method may wish to address. Classification of such concerns can be carried out in any number of different ways. For example, potential concerns that are based on items extracted from the street level images in step 120 may be classified into one or more predetermined categories or groups, such as: construction concerns, traffic concerns, and weather concerns, to cite a few possibilities. These categories may not be entirely mutually exclusive, as one concern or event may be considered both a construction concern and a traffic concern, and the respective categorization may vary from that described below depending on the particular implementation of the present method. It should also be recognized that it is possible to categorize a concern or hazard in multiple categories and then continue with the method. This particular embodiment may allow for more lenient severity determinations, or no determination of severity at all, as will be described more fully below);
determine which safety integrity level the event chain maximally fulfills ([0030], As mentioned above, the various concerns or hazards that are detected from the street level images may be rated or weighted according to their potential severity and impact on vehicles traveling the particular road segment in question, like host vehicle 54. Before issuing a warning to the host vehicle 54 or taking some other remedial action, the method may first attempt to corroborate the weather concern 80 by evaluating the street level images and looking for other signs of ice. In this case, the street level images provided by traffic cameras 16, 18 show that vehicles V1, V2, which are in the same area as the suspected ice patch 80, are operating their window wipers, which is indicative of precipitation that could lead to an ice patch; that is, corroboration or verification. According to this example, the ice patch 80 could be assigned a low severity rating before it is corroborated, a moderate severity rating once it is confirmed by the active window wipers, and potentially a high severity once it is confirmed and once the method recognizes that the ice patch is on or near a bridge B, which is more prone to ice than other road surfaces. If ice patch 80 appears to be quite large or if there are disabled vehicles near the ice patch, for example, these are indicia that are acquired from the street level images and may affect the severity level or rating of that particular weather concern);
determine, based on the minimum safety integrity level and the maximum safety integrity level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig. 2, 180; [0004], According to one embodiment, a method of enhancing an automated driving mode of a host vehicle, comprising the steps of: comparing a host vehicle location to a geographic zone saved in a concern profile, and the host vehicle location corresponds to a current location of the host vehicle or an anticipated future location of the host vehicle and the concern profile is based on street level images gathered from a plurality of image sources; identifying a potential concern in response to the comparison of the host vehicle location to the saved geographic zone, and the potential concern is associated with the geographic zone in the concern profile; and performing a remedial action in response to the identification of the potential concern, wherein the remedial action is performed before the host vehicle encounters the potential concern and the remedial action affects the automated driving mode of the host vehicle);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nickolaou into the invention of Moustafa to include road condition severity determination as Nickolaou discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that uses additional information gleaned from street level images to allow for redundancy or corroboration to verify the presence and nature of a hazard or concern before implementing remedial measures (Nickolaou: [0010]). Additionally, the claimed invention is merely a combination of old, well-known elements of an autonomous vehicle that uses information from roadside sensors to inform driving decisions as disclosed by Moustafa and using data from roadside sensors to corroborate/verify information related to road conditions to inform an autonomous vehicle of remedial actions to be taken as taught by Nickolaou. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 10, Moustafa discloses:
A non-transitory machine-readable storage medium on which is stored a computer program for carrying out a partly automated driving function by a motor vehicle, the computer program, when executed by a computer, causing the computer to perform the following steps ([0048], non-transitory medium to store code adapted to be executed by micro-controller or processor):
determining that an infrastructure element is located in an environment of the motor vehicle (Fig 1; Fig 6; [0045], observing portions of roadways and vehicles moving with the environment; [0061], LIDAR, cameras, radar, etc.; [0054], perception engine),
the infrastructure element being configured to determine an infrastructure assistance datum for an infrastructure-based, at least partly automated guidance of the motor vehicle ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks),
wherein the infrastructure element is part of an event chain for the at least partly automated guidance of the motor vehicle during a trip that is guided in an at least partly automated manner ([0045], sensors attached to roadside equipment or fixtures, use sensor data to perform autonomous tasks);
carrying out the at least partly automated driving function based on the infrastructure assistance datum and depending on a result of the determination as to whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig 14; [0115], acceptable control signals to a vehicle actuation system; [0116], replace human driving with safer inputs).
However, Moustafa does not specifically state:
determining a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function;
determining which safety integrity level the event chain maximally fulfills;
determining, based on the minimum safety integrity level and the maximum safety integrity level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element;
Nickolaou teaches:
determining a minimum safety integrity level that the event chain must have for the infrastructure assistance datum of the infrastructure element to be used by the motor vehicle to carry out the at least partly automated driving function (Fig. 2; [0020], Turning now to FIG. 2, there is shown an embodiment of a driving enhancement method 100 that may utilize street level images provided by a network of stationary traffic can1eras to identify potential hazards or concerns located beyond the field of view of vehicle mounted sensors, and to provide an advanced warning or to take some other remedial action in response thereto; [0026], At this point, step 120 can evaluate the items identified in the street level images in order to classify any potential hazards or concerns that the present method may wish to address. Classification of such concerns can be carried out in any number of different ways. For example, potential concerns that are based on items extracted from the street level images in step 120 may be classified into one or more predetermined categories or groups, such as: construction concerns, traffic concerns, and weather concerns, to cite a few possibilities. These categories may not be entirely mutually exclusive, as one concern or event may be considered both a construction concern and a traffic concern, and the respective categorization may vary from that described below depending on the particular implementation of the present method. It should also be recognized that it is possible to categorize a concern or hazard in multiple categories and then continue with the method. This particular embodiment may allow for more lenient severity determinations, or no determination of severity at all, as will be described more fully below);
determining which safety integrity level the event chain maximally fulfills ([0030], As mentioned above, the various concerns or hazards that are detected from the street level images may be rated or weighted according to their potential severity and impact on vehicles traveling the particular road segment in question, like host vehicle 54. Before issuing a warning to the host vehicle 54 or taking some other remedial action, the method may first attempt to corroborate the weather concern 80 by evaluating the street level images and looking for other signs of ice. In this case, the street level images provided by traffic cameras 16, 18 show that vehicles V1, V2, which are in the same area as the suspected ice patch 80, are operating their window wipers, which is indicative of precipitation that could lead to an ice patch; that is, corroboration or verification. According to this example, the ice patch 80 could be assigned a low severity rating before it is corroborated, a moderate severity rating once it is confirmed by the active window wipers, and potentially a high severity once it is confirmed and once the method recognizes that the ice patch is on or near a bridge B, which is more prone to ice than other road surfaces. If ice patch 80 appears to be quite large or if there are disabled vehicles near the ice patch, for example, these are indicia that are acquired from the street level images and may affect the severity level or rating of that particular weather concern);
determining, based on the minimum safety integrity level and the maximum safety integrity level of the event chain, whether the at least partly automated driving function may be carried out based on the infrastructure assistance datum of the infrastructure element (Fig. 2, 180; [0004], According to one embodiment, a method of enhancing an automated driving mode of a host vehicle, comprising the steps of: comparing a host vehicle location to a geographic zone saved in a concern profile, and the host vehicle location corresponds to a current location of the host vehicle or an anticipated future location of the host vehicle and the concern profile is based on street level images gathered from a plurality of image sources; identifying a potential concern in response to the comparison of the host vehicle location to the saved geographic zone, and the potential concern is associated with the geographic zone in the concern profile; and performing a remedial action in response to the identification of the potential concern, wherein the remedial action is performed before the host vehicle encounters the potential concern and the remedial action affects the automated driving mode of the host vehicle);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nickolaou into the invention of Moustafa to include road condition severity determination as Nickolaou discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that uses additional information gleaned from street level images to allow for redundancy or corroboration to verify the presence and nature of a hazard or concern before implementing remedial measures (Nickolaou: [0010]). Additionally, the claimed invention is merely a combination of old, well-known elements of an autonomous vehicle that uses information from roadside sensors to inform driving decisions as disclosed by Moustafa and using data from roadside sensors to corroborate/verify information related to road conditions to inform an autonomous vehicle of remedial actions to be taken as taught by Nickolaou. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Moustafa in view of Nickolaou, and further in view of Rajski et al (US 20180252768 A1).
Regarding claim 4, Moustafa in view of Nickolaou does not disclose the safety integrity level includes a SIL and/or ASIL.
Rajski et al teaches adherence to safety standards such as ISO 26262 and Automotive Safety Integrity Level (ASIL) targets [Rajski et al [0006])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Rajski et al. into the invention of Moustafa in view of Nickolaou to include automotive safety integrity levels (ASIL) as outlined by ISO 26262 as Rajski et al. discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system with multiple safety redundancies to mitigate risk in autonomous vehicles. Additionally, the claimed invention is merely a combination of old, well-known elements of an autonomous vehicle as disclosed by Moustafa in view of Nickolaou and adherence to automotive standards as taught by Rajski et al. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Documents Considered but Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Barthowiak (US 20220076565 A1) teaches inter-vehicle (V2V) communication to determine roadway conditions for safe driving. Whitmire et al (US 20210088784 A1) teaches an eye tracking system to ensure driver awareness while driving. Schuller (US 20210078610 A1) teaches adjusting autonomous pathing depending on environment changes and infrastructure. Nordbruch (US 520210086766 A1) teaches using data from infrastructure to determine appropriate autonomous response that comply with functional safety requirements. Simon et al (US 10665109 B1) teaches mounting infrastructure capable of data transfer to one or more autonomous vehicles to construction equipment to better define dynamic road conditions that are different from a stored map. Schilling et al (US 20140365034 A1) teaches a radio remote control used to control vehicle functions relevant and not relevant for functional safety Nordbruch (US 20180240343 A1) teaches driverless/autonomous vehicle parking using onboard and external sensors. Song et al (US 20190143968 A1) teaches a driving assistance system to automatically brake and steer to avoid an object partially blocking the driving lane, recognize road signs, recognize traffic lights, and recognize possible collisions and perform avoidance actions when driver is not aware of risks.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IZCALLI ANDRE RIOS-AGUIRRE whose telephone number is (571)272-0790. The examiner can normally be reached Monday through Friday 8:30 - 17:00 EST.
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/I.A.R./ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666