Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/26/2025 was filed before the first action on the merits of the application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant states that this application is a continuation or divisional application of the prior-filed application. A continuation or divisional application cannot include new matter. Applicant is required to delete the benefit claim or change the relationship (continuation or divisional application) to continuation-in-part because this application contains the following matter not disclosed in the prior-filed application:
Regarding Claim 14, the originally filed specification of 18/173150 was not found to have any reference towards generating an alert of potential sensor degradation or environmental interference impacting perception accuracy.
Regarding Claim 16, the originally filed specification of 18/173150 was not found to have any reference towards the “importance” of an object in navigational safety, object proximity, or data reliability being used as recited in claim 16. As such claim 16 recites subject matter not supported within the original disclosure of 18/173150.
Regarding Claim 17, the originally filed specification of 18/173150 was not found to have reference to storing historical detection parameters and analysis results; and identifying patterns in the detection performance overtime in order to predict when sensor maintenance is needed.
Regarding Claim 18, the originally filed specification of 18/173150 was not found to have reference towards obtaining of the baseline detection parameters from a fleet operating in similar environments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14 and 16-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As noted in the priority section above these claims were found to not have reference in the originally filed application 18/173150, CON applications cannot have new matter introduced in the claims, these claims recite new matter not disclosed in the specification.
Regarding Claim 14, the originally filed specification of 18/173150 was not found to have any reference towards generating potential sensor degradation or environmental interference impacting perception accuracy.
Regarding Claim 16, the originally filed specification of 18/173150 was not found to have any reference towards the “importance” of an object in navigational safety, object proximity, or data reliability. As such claim 16 recites subject matter not supported within the original disclosure of 18/173150.
Regarding Claim 17, the originally filed specification of 18/173150 was not found to have reference to storing historical detection parameters and analysis results; and identifying patterns in the detection performance overtime in order to predict when sensor maintenance is needed.
Regarding Claim 18, the originally filed specification of 18/173150 was not found to have reference towards obtaining of the baseline detection parameter operating in similar environments.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 17, the limitation “to predict when sensor maintenance is needed“ renders the scope of protection unclear for claim 17 in that it is uncertain if infringement would occur when a pattern (of performance degradation) is identified or if it also requires that this pattern is then used to determine how long/when maintenance would be needed. E.g. if a hypothetical analysis of detection parameters and analysis results is performed and various patterns of degradation are identified, but there is no further analysis performed to determine if/how long till sensor maintenance needs to be performed would that infringe on the claim?
Regarding Claim 18, the limitations “operating in similar environmental conditions” renders the scope of protection unclear in that “similar” is functioning as a relative term and neither the claims nor the specification makes clear how to determine if/at what point two environments and their respective conditions would constitute “similar” environments. As such “similar” is a subjective determination left only to the reader’s interpretation and the bounds of protection thus would vary from reader to reader and are indefinite.
Claim 18 then further goes on to recite “and wherein the baseline parameters are shared among….to improve collective perception performance assessment” this limitation renders the scope of protection unclear in that it is unclear if infringement only requires the sharing or if it requires both the sharing between vehicles and then using that shared parameters “to improve collective perception performance assessment”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
On January 7, 2019, the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 10 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. The claim is directed toward a method.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 1 is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. “detecting” (i.e. an observation) of objects and then analyzing (evaluation/judgement/opinion) a parameter of the current observation to a baseline and then “modifying a control strategy or configuration of a sensor”, “modifying a control strategy” at the level of generality currently claimed includes/is equivalent to purely mental judgments. This is equivalent to a person recognizing when they are first able to see or read a street sign on a road and compare that recognition time/distance to what they consider normal and then based on determining/thinking that their recognition range/sightlines are reduced deciding that they show have a lower top speed for driving a vehicle.
Notably, the claim does not positively recite any limitations which require a navigation response (slowing down, changing lanes, etc) by the vehicle.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. While the claim does recite that the method comprises ”receiving, at the computing system, sensor data… surroundings of the vehicle”, at the level of generality recited this is insignificant pre-solution activity of data gathering.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Receiving and performing determinations on data are fundamental, i.e. WURC, activities performed by computers, such as the computing system, in claim 1.
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claim 1 is directed towards non-statutory subject matter.
Regarding Claim 2, it recites that the baseline parameter is specifically a detection distance for a class of objects determined based on past observations; this is still a mental process in that a person can recognize that they should be/usually (based on past observations) recognize street signs as X seconds/Y distance before the vehicle reaches them and then compare that to their current observations.
Regarding Claim 3, it recites the detection time between recognizing that there is an object and the subsequent detection/determination of the objects classification; this is a mental determination in the human mind in that a person can first recognize that there is a roadside object but they cannot tell what the object is; (e.g. due to fog they can see a rough shape/outline but tell specifically what a sign on the road is) and then as they get closer recognizing when they can read/classify what the sign is and then determining the time difference in their mind.
Regarding Claim 4 is recites that the detection parameter comprises localization accuracy and is determined over multiple past observations. At the level of generality claimed towards this calculation of confidence it can be performed mentally and/or with the aid of pen and paper using standard statistical analysis (e.g. determining a standard deviation based on received data values/a list of numbers). As such claim 4 still recites a mental determination reasonably performed in the human mind.
Regarding Claim 5, it recites determining a classification confidence value for an object and then determining the detection distance based when this confidence value exceeds a threshold. This is equivalent to a person recognizing a specific type of sign (confidence value exceeds threshold) and then mentally counting how long it takes the vehicle to pass the sign (and from that time estimate the distance) or using some known reference points to estimate the distance when they recognize the sign. As such claim 5 is directed to a mental determination reasonably performed in the human mind.
Regarding Claim 6, it recites determining a difference between a recognized parameter and a baseline parameter, this is a mental process which can be reasonably performed in the human mind.
Regarding Claim 7, it recites that the classification groups – baseline detection distance are determined from a lookup table with the different classification groups having corresponding baseline detection distances, this is equivalent to a person having a mental list of object types – baseline detection distance pairings.
Regarding Claim 8, it recites that the baseline detection parameter is generated using a machine learning (ML) model based on the object classification group and environmental conditions and that the ML model is trained to generate the baseline based on inputed classification groups and environmental conditions. The underlying determiatnion performed by the ML model is a mental determination reasonably performable in the human mind. As such the addition limitations of using a ML model is merely using a ML model as a tool to perform an mental determination which, similar to implementing an abstract idea on a computer, does not integrate the abstract idea into practical application.
Regarding Claim 10, it recites that “modifying the configuration” of a sensor includes at least one of:… or an adjustment of sensor parameters. The second option (triggering a calibration process) is equivalent to a person (who is wearing contacts) recognizing that their vision is impaired and causing thus moving their eyes to attempt to adjust/correct their contacts (i.e. triggering a calibration process) and a final option (adjustment of parameters) is/includes a mental determination reasonably performed in the human mind in that a person can mentally recognize that they should adjust where they should be looking for objects (i.e. adjusting the parameters (field of view) of the sensor (their eyes));
Regarding Claim 11, it recites determining environmental conditions (this is mental determination which can be performed in the human mind in that a person can recognize the surrounding weather) and then adjusting the baseline detection parameter is selected or adjusted based on these detected condition(s). This is a mental determination in that a person can have a mental list of corresponding object-detection baselines for different given weather conditions (i.e. under clear sight conditions object should be viewable from X distance, under rain viewable from Y distance, under low light conditions viewable from Z distance).
Regarding Claim 12, it recites that the sensor is multiple sensors including at least two of a camera, lidar, radar, or ultrasonic sensor and that the baseline detection distance is based on the fusion of these two sensor types. The recited sensors are all well understood, routine, and conventional types of sensors in the automotive field and sensor fusion at the level of generality recited is also WURC and conventional, as such the limitations of claim 12 represent only a general linking to a field of use (vehicles) of the abstract idea of the independent claim.
Regarding Claim 13, it recites that the baseline detection parameter is updated periodically based on recent detections/performance this is mental determination reasonably performed in the human mind in that a person can keep a mental tally of their recent performance an update their expectations/baseline parameters overtime.
Regarding Claim 14, it recites determining a degraded performance detection and then outputting of an alert. This is equivalent to a person recognizing that they are having trouble seeing. And outputting of an alert is/includes in significant post-solution activity of displaying the result of a mental determination.
Regarding Claim 15, it recites that an object includes multiple objects of different classification groups with each group having its own respective baseline detection parameters. This is equivalent to a person recognizing a road sign and that it has multiple sub-components/text (e.g. a sign can have road names and directional arrow(s) to indicate lane directions/merging/splitting and an exit number ) and each of these individual components (classifications) can have their own respective baseline detection distance
Regarding Claim 16, it recites weighting of the detection parameter based on the importance for navigation, proximity, or reliability of the sensor data used for detection. This is a mental determination that can be performed in the human mind, in that a person can recognize that a certain object is unimportant for navigation and then ignore it (weight = 0)
Regarding Claim 17, it recites storing of historical detection parameters and analysis results, this is a mental process reasonably performed in the human mind, and identifying patterns in performance degradation overtime to predict when sensor maintenance is needed. This is a mental determination reasonably performed in the human mind in that a person can recognize that they get slower at identifying objects under low light conditions (recognizing pattern of degradation) and used that to predict that they should see an eye doctor to test their vision/get an updated prescription (for sensor maintenance)
Regarding claim 18, it recites that the baseline is obtained from a fleet of vehicles (i.e. pre-solution activity of data gathering) at a high level of generality and then recites a descriptive clause (i.e. stating why) that the fleet shares the baseline.
Regarding Claims 19-20 they are system and non-transitory equivalents to method claim 1 and have the same overall 101 analysis.
Claim 9 is not rejected under U.S.C 101 as it recites active vehicle control response steps which provide specific machine integration.
Claim 10, while rejected under U.S.C 101 (as “adjustment of sensor parameters” and triggering of a calibration process) at the level of generality recited currently is/includes mental determinations reasonably performed in the human mind), one of the options of the “at least one” list are not abstract in that they recite triggering a cleaning operation which is not an abstract determination.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 6, 9-12, 14, 16 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20230147535 A1, “VEHICLE POSITION ESTIMATION DEVICE AND TRAVELING CONTROL DEVICE”, Terazawa.
Regarding Claim 1, Terazawa teaches “ A method comprising: receiving, at a computing system, sensor data from at least one sensor associated with a vehicle, the sensor data representing surroundings of the vehicle; detecting, based on the sensor data, one or more objects in the surroundings; determining at least one detection parameter associated with at least one object; analyzing the at least one detection parameter relative to at least one baseline detection parameter, wherein the at least one baseline detection parameter is associated with at least one characteristic of the at least one object; ”( [0081] For example, the adverse environment determination unit F6 may determine whether the surrounding environment corresponds to the adverse environment based on the effective recognition distance calculated by the recognition accuracy evaluation unit F5. For example, the adverse environment determination unit F6 may determine that the environment is adverse when the effective recognition distance is smaller than a predetermined threshold (hereinafter referred to as distance threshold). A specific value of the distance threshold may be set to, for example, within a range of 20 meters to 30 meters. As an example, the distance threshold is set to 25 meters. The distance threshold may be determined according to the designed recognition limit distance of the system or device” Here the vehicle (based on sensor data) determines the actual recognition limit (detection parameter) for landmarks (objects) surrounding the vehicle and compares it to the designed recognition limit distance of the system (baseline detection distance) + [0086]);” and modifying, based on the analysis, a control strategy for the vehicle or a configuration of the at least one sensor.”( [0100] When the adverse environment determination unit F6 determines the adverse environment, the deceleration request unit F8 outputs a predetermined deceleration request signal to the driving assist ECU 30. For example, the deceleration request signal may be a signal that requests a decrease of the set value of the target speed by a predetermined amount from the current value. The deceleration amount requested by the deceleration request signal may be a fixed amount, such as 3 km/h, 5 km/h, or 10 km/h. Alternatively, the deceleration amount may be a value correlated to the target speed, such as 10% of the target speed. Alternatively, the deceleration request signal may request deceleration of the current speed to a preset restrict speed. The restrict speed may be set according to the road type. For example, the restrict speed may be set to 60 km/h for expressways, and may be set to 40 km/h for general roads.” Here teaches when the adverse environment is detected (Based on the comparison of the current detection limit to the designed detection limit) the vehicle slows down (changes a control strategy))
Regarding Claim 6, Terazawa teaches “The method of claim 1, wherein analyzing the at least one detection parameter comprises: determining a difference between the at least one detection parameter and the at least one baseline detection parameter, and determining whether the difference exceeds a threshold difference value.”( [0089] As shown in FIG. 12, the adverse environment level determination unit F62 may evaluate a level of the adverse environment, in other words, a level of deterioration in object recognition performance executed based on the image frame. For example, the adverse environment level may be classified in four level 0 to level 3. A higher level indicates a highly adverse environment. The level of adverse environment can be evaluated based on the effective recognition distance. For example, the adverse environment level determination unit F62 may determine the adverse environment as level 1 when the effective recognition distance is less than a predetermined first distance and equal to or greater than a second distance. The adverse environment level determination unit F62 may determine the adverse environment as level 2 when the effective recognition distance is less than the predetermined second distance and equal to or greater than a third distance. When the effective recognition distance is less than the third distance, the level of adverse environment may be determined as level 3. When the effective recognition distance is equal to or greater than the first distance, the level of adverse environment is determined as level 0, that is, the environment is not adverse.” Here teaches comparing the current detection limit to various thresholds; based on this it exceeding certain thresholds )
Regarding Claim 9, Terazawa teaches “The method of claim 1, wherein modifying the control strategy comprises at least one of: adjusting a speed of the vehicle, modifying a following distance to other objects, or changing a navigation path based on the analysis.”( [0100] When the adverse environment determination unit F6 determines the adverse environment, the deceleration request unit F8 outputs a predetermined deceleration request signal to the driving assist ECU 30. For example, the deceleration request signal may be a signal that requests a decrease of the set value of the target speed by a predetermined amount from the current value. The deceleration amount requested by the deceleration request signal may be a fixed amount, such as 3 km/h, 5 km/h, or 10 km/h. Alternatively, the deceleration amount may be a value correlated to the target speed, such as 10% of the target speed. Alternatively, the deceleration request signal may request deceleration of the current speed to a preset restrict speed. The restrict speed may be set according to the road type. For example, the restrict speed may be set to 60 km/h for expressways, and may be set to 40 km/h for general roads.
[0101] The deceleration request signal may request a decrease of current set value of the target speed as described above, or may request a decrease of ACC upper limit speed. When the current target speed reaches the ACC upper limit speed, the vehicle speed can be indirectly suppressed by lowering the ACC upper limit speed.)
Regarding Claim 10, Terazawa teaches “wherein modifying the configuration of the at least one sensor comprises triggering at least one of: a cleaning process for the at least one sensor, a calibration process for the at least one sensor, or an adjustment of sensor parameters.”([0162] For example, when the adverse environment determination unit F6 determines that the environment is adverse, the weight of the radar detection result used in calculation of the landmark position may be increased compared to the weight used in normal environment. Since the millimeter wave radar 12 is not easily affected by rain, fog, or the like, it is possible to maintain the estimation accuracy of the distance to landmark at a high level by increasing the weight of radar detection result. Alternatively, when the position of the landmark is calculated by the sensor fusion of the front camera 11 and the millimeter wave radar 12, the weight of the image recognition result may be decreased (for example, set to zero). For a predetermined time period after the successful sensor fusion, as long as the millimeter wave radar 12 can track the landmark, the position of the landmark is calculated and updated based on the radar detection result. In adverse environment, such as rainy weather condition, the recognition ability of front camera 11 is deteriorated. According to above configuration, the occurrence frequency of an event in which the position of landmark cannot be specified can be suppressed. As a result, it is possible to suppress deterioration in localization process accuracy.” Based on the detected adverse environment the sensor weighting is changed for localization (adjustment of sensor parameters))
Regarding Claim 11, Terazawa teaches “1, further comprising determining environmental conditions of the surroundings, wherein the at least one baseline detection parameter is selected or adjusted based on the environmental conditions, the environmental conditions including at least one of weather conditions, lighting conditions, or road condition”( [0077] The effective recognition distance of the landmark may be reduced by other factors, such as weather, occlusion by a preceding vehicle. Thus, when a preceding vehicle is present within the predetermined distance, the calculation of the effective recognition distance may be omitted. Alternatively, when a preceding vehicle is present, the effective recognition distance may be provided to the adverse environment determination unit F6 by adding data indicating the presence of the preceding vehicle (for example, a preceding vehicle flag). When the road ahead the subject vehicle is not a straight road, that is, when the road ahead is a curved road, the effective recognition distance may also be decreased. Therefore, when the road ahead is the curved road, calculation of the effective recognition distance may be omitted. Further, when the road ahead is the curved road, the effective recognition distance may be provided to the adverse environment determination unit F6 in association with data (for example, a curve flag) indicating that the road ahead is curved. A road having a curvature equal to or greater than a threshold value may be determined as the curved road.” Here teaches that based on road conditions (road shape) is used to adjusted the expected effective recognition distance (for determining if there is an adverse environment) for adverse environment detection )
Regarding Claim 12, Terazawa teaches “. The method of claim 1, wherein the at least one sensor comprises multiple sensor types including at least two of: a camera, a lidar sensor, a radar sensor, or an ultrasonic sensor, and wherein the at least one detection parameter is determined based on sensor fusion of data from the multiple sensor types.”( [0160] According to the above configuration, the localization unit F0 executes the localization process without using the detection result of the millimeter wave radar 12 when the environment is determined to be not adverse (that is, normal environment). Thus, processing load in the processing unit 21 can be reduced. Under adverse environment, localization process is performed using the observation coordinates of landmarks calculated by the sensor fusion, that is, combination of the detection results of millimeter wave radar and camera. Thus, it is possible to suppress the deterioration in position estimation accuracy caused by rainfall or the like.” Here teaches detection (of landmark/distance) includes fusion of both a camera and radar)
Regarding Claim 14, Terazawa teaches “The method of claim 1, further comprising: determining that the analysis indicates degraded detection performance;”( [0089] As shown in FIG. 12, the adverse environment level determination unit F62 may evaluate a level of the adverse environment, in other words, a level of deterioration in object recognition performance executed based on the image frame. For example, the adverse environment level may be classified in four level 0 to level 3. A higher level indicates a highly adverse environment. The level of adverse environment can be evaluated based on the effective recognition distance.);” and generating an alert or notification to indicate potential sensor degradation or environmental interference impacting perception accuracy.”( [0103] As shown in FIG. 4, the deceleration notification image may include a deceleration amount, a deceleration reason, and an estimated period for maintaining a deceleration state. By including the deceleration amount or the like in the deceleration notification image, it is possible to suppress the user from feeling discomfort for the deceleration of vehicle. A configuration in which the user is notified of the reason for deceleration due to heavy rain, afternoon sun, or the like can further enhance the user’s sense of satisfaction and improve user experience. The deceleration notification image may include a text message as shown in FIG. 4. The deceleration notification image may be an icon that does not include any text information as shown in FIG. 5A. The deceleration notification image may be an icon that includes text information indicating speed after change, such as the deceleration amount as shown in FIG. 5B. In the examples shown in FIG. 5A and FIG. 5B, an additional icon image indicating an adverse environment type, such as heavy rain or afternoon sun may be added to the icon image.” In response to an adverse environment (deterioration of sensor performance) an alert including the environmental interference reason (rain, sun, etc) is provided to the user )
Regarding Claim 16, Terazawa teaches “The method of claim 1, wherein analyzing the at least one detection parameter comprises weighting the analysis based on at least one of: object importance for navigation safety, proximity of the object to the vehicle, or reliability of the sensor data used for detection.”([0161]-[0162] in these paragraphs the weighting of sensor data (for determining the effective recognition distance) is changed based on the detected environment with lower reliability sensor data being weight less compared to higher reliability sensor data)
Claims 19-20 are system and non-transitory computer readable medium equivalents to the method claim 1, it has the same grounds of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa as applied to claim 1 above, and further in view of US 20240092391 A1, “METHOD FOR IMPROVING SAFETY PRECAUTIONS FOR VEHICLES MOVING IN AN AT LEAST PARTIALLY AUTOMATED MANNER”, Ghanbari Milani et al
Regarding Claim 3, Terazawa while teaching a calculation/determination of an adverse environment; it does not specifically teach recognizing/utilizing recognizing such based on the time duration between an initial detection of an object and the determination of the object’s classification.
Ghanbari Milani et al teaches a vehicle perception system which includes determining of a safety metric which indicate how the vehicle is currently performing compared to a given threshold/value for a certain scenario/environment ([0006] in which the safety metric includes detecting of a object classification delays ([0039] and table 1; from classification delay (the time between detecting an object and classifying it (i.e. point of time where confidence in a classification exceeds a threshold level) it exceeded (is delayed) is compared to an expected time; from the term “delay” it is implicit that there is an expected (i.e. baseline) speed/time which a classification should occur at/within).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Terazawa to include the recognition of object classification delays as another input for determining an adverse environment. One would be motivated to implement such in order to improve the safety of operation of the vehicle by increasing the ways it can recognize adverse environments/dangerous operating conditions and/or to identify if there is a fault in the vehicle’s systems. This motivation is taught in Terazawa (0005] In particular, the present invention relates to improving (or increasing) safety precautions for vehicles moving in an at least partially automated manner, viz, based on trigger events and safety metrics…. [0006] A safety metric can define a first metric that, in a vehicle moving in an at least partially automated manner, during operation of the vehicle, determines values for one or more vehicle and/or environmental parameters (or measures them; this can be the same and/or different or further vehicle and/or environmental parameters than for the scenario) and compares them to one or more threshold values (e.g., upper and/or lower threshold value). The safety metric is deemed to be fulfilled if at least one of the values of the first metric exceeds or falls below the one or at least one of the plurality of threshold values. In this context, the safety metrics are also referred to as so-called continuous safety metrics
+
[0025] However, it may now be the case that scenario information and safety metric information will still be transmitted. According to an example embodiment of the present invention, in this respect, it is expedient to check, e.g., at the evaluation site, whether a trigger event has already been defined for the scenario present at that time point. If this is the case, and the information should therefore not have actually been obtained at all, the information, i.e., e.g., the scenario information and safety metric information, obtained from the respective vehicle can be provided for checking the vehicle. It can then be checked, for example, whether there is a fault in the vehicle.
[0026] It can also be provided that the faulty vehicle is automatically brought into a safe state or that a safe state is established (i.e., for example, the vehicle can be stopped in an automated manner). Moreover, a user or driver can be informed of the fault or that the vehicle is faulty. Further sensor and system self-tests may likewise be initiated, for example.)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa as applied to claim 1 above, and further in view of ADAPTIVE POSITIONING SYSTEM, US 20170023659 A1, Bruemmer et al
Regarding Claim 4, Terazawa while teaching a determination of adverse environments based in part on sensor information it does not specifically teach that the detected parameters (For adverse environment detection) includes the determining the accuracy of an objects position relative to the vehicles based on multiple sensors overtime.
Bautista teaches an autonomous vehicle control system which includes determining localization accuracy of objects based on sensor data from multiple sensors over time in order to determine sensor faults/degredation (An Adaptive Positioning System provides a method for directing and tracking position, motion and orientation of mobile vehicles, people and other entities using multiple complementary positioning components to provide seamless positioning and behavior across a spectrum of indoor and outdoor environments. The Adaptive Positioning System (APS) provides for complementary use of peer to peer ranging together with map matching to alleviate the need for active tags throughout an environment. Moreover, the APS evaluates the validity and improves the effective accuracy of each sensor by comparing each sensor to a collaborative model of the positional environment. The APS is applicable for use with multiple sensors on a single entity (i.e. a single robot) or across multiple entities (i.e. multiple robots) and even types of entities (i.e. robots, humans, cell phones, cars, trucks, drones, etc.). + [0093] “0IGS. 4A and 4B provides a simple rendition of the multimodal estimator's ability to continually assess and resolve sensor failure. FIG. 4 shows historical path of an object 410. The object's position is identified in 4 discrete positions by a plurality of sensors. Initially the position of the object 410 is estimated by the unimodal sensors to be within the depicted circle 415. Along the path exists landmarks or other features that enable one or more sensors to determine the object's position.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Terazawa to include the sensor object location accuracy assessment as taught by Bautista in order to allow for individualized sensor failure detection and subsequent adjusting/correcting of object position determination by filtering out the failed sensor data from the position estimation thereby improving localization accuracy. Buatista teaches this improvement in ([0117] Responsive to a determination of the failure of a particular sensor 1060 the Adaptive Positioning System of the present invention filters out that positional determination and eliminates its contribution to the overall assessment as to the object's estimated position. With the failed sensor removed from consideration the process begins anew and gain makes a determination as to whether this or other positional sensors have failed.)
Claim(s) 13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa et al as applied to claim 1 above, and further in view of US 20180211176 A1, “Blended IoT Device Health Index”, Khurshudov et al.
Regarding Claim 13, Terazwa does not suggest updating a baseline detection parameter by replacing older baselines with new baselines to maintain relevance to current operating conditions.
Khurshudov et al teaches a sensor health monitoring system in which an expected sensor parameter baseline is established and then updated (i.e. older baseline is replaced) with a new baseline in order to improve the ability to detect sensor degredation ([0007] The present invention is directed to solving disadvantages of the prior art. In accordance with embodiments of the present invention, a method is provided for a device not having an available history of either failures or degraded performance. The method includes establishing, by a computer coupled to the device, an initial baseline of sensor data from the device, receiving new sensor data after establishing the initial baseline, creating an updated baseline based on the new sensor data, evaluating, by the computer, the new sensor data compared to the updated baseline based on a plurality of different time scales, and determining whether the device is indicating an increased probability of failure or degraded performance based on the evaluated sensor data.)
It would have been obvious to one of ordinary skill in the art to substitute the static (designed) baseline for performance with the updating baseline for sensing as taught by Khurshudov et al in order to allow for a system which can determine current performance trends and predict future failure of the sensors. Khurshudov et al teaches this in ([0007]… evaluating, by the computer, the new sensor data compared to the updated baseline based on a plurality of different time scales, and determining whether the device is indicating an increased probability of failure or degraded performance based on the evaluated sensor data.”)
Regarding Claim 17, Terazawa does not suggest storing historical detection parameters and analysis results in order to identify patterns in degradation overtime to predict when sensor maintenance is needed.
Khurshudov et al teaches a adaptive sensor performance threshold in which the historic sensor data is analyized overtime to detect degredation in performance to predict sensor failure (i.e. when maintenance is needed) ([0053] An example of the result of an anomaly detection algorithm such as may be performed by one or more applications 416 of an IoT health evaluation device 112 is illustrated in FIGS. 8A and 8B. An anomaly detector uses and stores historical sensor data, which is then used in the processing and detection of anomalies. For each of the time scales selected in the configuration phase (block 916 of FIG. 9), the number of anomalies that result from processing the raw data with any anomaly detector is accumulated for each of the time scales, for example a rolling window 832 in FIG. 8A. At least three anomaly data values (i.e., anomaly counts from three time periods for a particular time scale), and preferably more, are required in order to calculate an initial baseline of anomaly counts for each time scale. More data values 504 may be required to calculate the initial baseline in other embodiments as described previously. Preferably, the initial baseline is at least comparable to the time between typical failures, if that is known. However, if the time between typical failures is a long period of time, for example a year, it may not be practical to gather data for an initial baseline for that long of time. Once the initial baseline has been calculated, the next data point becomes a first operational phase data point 536, the data point after that becomes a second operational phase data point 540, and so on.” + [0028] teaches various examples parameters/information for [0029]” All of the above information could be used to perform an assessment of device's health and there are two general ways of doing that: with the knowledge of the device, its reliability and failure mechanisms, design, and operational requirements, and without that knowledge. The first approach relies on the knowledge of how device works and how it fails. For example, if it is known that exceeding a specific temperature or pressure leads to the device failure, it is possible to monitor IoT sensor data from device's temperature or pressure sensors and, if the pre-specified limit is exceeded, report the “failure condition” or alert the owner/operator of the device of the impending failure. The same approach could be implemented in some combination with, for example, mathematical or empirical models of this device when the sensor data is fed into the model which, in turn, makes predictions about device's remaining life or its failure risks.”)
It would have been obvious to one of oridnary skill in the art, before the effective filing date of the application to implement the remaining lifespan determination for sensors/components based on detected parameters/analysis in order to more accurately determine if a failure is a temporary anomaly or if the sensor itself on the vehicle needs to be replaced. Improving safety of operation.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1-10, 12, and 19-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 7, 11-12, 14, 17, 19-20 of U.S. Patent No. 12528482. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims represent a more specific implementation (i.e. species) equivalent to the generalized (genus) claims of the application. The claiming of a species renders obvious the claiming of its equivalent genus.
More specifically:
Regarding Application claim 1, 482 claims “A method comprising: receiving, at a computing system, sensor data from at least one sensor associated with a vehicle, the sensor data representing surroundings of the vehicle; detecting, based on the sensor data, one or more objects in the surroundings; determining at least one detection parameter associated with at least one object;”(Claim 1 “A method comprising: receiving, at a computing system and from a sensor coupled to a vehicle, sensor data representing an environment of the vehicle as the vehicle navigates a path; detecting, based on the sensor data, an object in the environment; responsive to detecting the object, determining a detection distance between the object and the sensor” determining a detection distance is a specific form of at least one detection parameter associated with the object );” analyzing the at least one detection parameter relative to at least one baseline detection parameter, wherein the at least one baseline detection parameter is associated with at least one characteristic of the at least one object;”( claim 1” performing, by the computing system, a comparison between the detection distance and a baseline detection distance, wherein the baseline detection distance depends on one or more prior detections of given objects that are in a classification group comprising the object;” here comparing detection distance to baseline detection distance is a species equivalent to comparing a detection parameter to a baseline parameter);”and modifying, based on the analysis, a control strategy for the vehicle or a configuration of the at least one sensor.”(Claim 1 “and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.” Here is a more specific implementation of a control strategy/configuration of at least one sensor)
Regarding claim 2, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a detection distance between the at least one object and the at least one sensor, and wherein the at least one baseline detection parameter comprises a baseline detection distance based on prior detections of objects in a same classification group as the at least one object.”( claim 1” performing, by the computing system, a comparison between the detection distance and a baseline detection distance, wherein the baseline detection distance depends on one or more prior detections of given objects that are in a classification group comprising the object;”)
Regarding claim 3, 482 claims “The method of claim 1, wherein determining the at least one detection parameter comprises determining a detection time representing a duration between an initial detection of the at least one object and a subsequent detection when a classification confidence exceeds a threshold confidence level.”(Claim 11, ‘responsive to detecting the object, determining a detection time associated with detecting the object, wherein the detection time represents a duration between an initial detection of the object and a subsequent detection of the object when a classification confidence for the object exceeds a threshold confidence;…”)
Regarding Claim 4, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a localization accuracy representing how accurately a position of the at least one object is determined relative to the vehicle, and wherein the localization accuracy is determined using sensor data from multiple sensors over a duration of time.”(Claim 12, “further comprising: responsive to detecting the object, determining a localization accuracy associated with detecting the object, wherein the localization accuracy depends on at least one of: (i) subsequent sensor data from the sensor or (ii) sensor data from a second sensor coupled to the vehicle; and performing a second comparison between the localization accuracy associated with detecting the object and a baseline localization accuracy, wherein the baseline localization accuracy depends on one or more prior localization accuracies associated with detecting given objects in the classification group comprising the object.”)
Regarding claim 5, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a classification confidence representing a confidence level assigned to a classification of the at least one object, and wherein the at least one baseline detection parameter comprises a baseline classification confidence based on prior classifications of objects in a same classification group.”(Claim 5, “T he method of claim 1, further comprising: based on detecting the object, determining a detection confidence for the object exceeds a threshold confidence; and based on determining the detection confidence for the object exceeds the threshold confidence, determining the detection distance between the object and the sensor.”)
Regarding Claim 6, 482 claims “The method of claim 1, wherein analyzing the at least one detection parameter comprises: determining a difference between the at least one detection parameter and the at least one baseline detection parameter, and determining whether the difference exceeds a threshold difference value.”(Claims 13 “determining, based on the comparison, a difference between the detection distance and the baseline detection distance; and based on the difference exceeding a threshold difference, causing the vehicle to decrease speed.”+14 “determining, based on the comparison, a difference between the detection distance and the baseline detection distance; and based on the difference exceeding a threshold difference, triggering at least a cleaning process or a calibration process for the sensor.”)
Regarding Claim 7, 482 claims “The method of claim 1, wherein the at least one baseline detection parameter is obtained from a reference table storing baseline parameters for multiple classification groups, each classification group corresponding to different types of objects encountered during vehicle navigation.”( The method of claim 6, wherein identifying the baseline detection distance comprises: obtaining the baseline detection distance from a reference table, wherein the reference table comprises at least a first baseline detection distance corresponding to a first classification group and a second baseline detection distance corresponding to a second classification group, and wherein objects in the first classification group differ from objects in the second classification group.)
Regarding Claim 8, 482 claims “The method of claim 1, wherein the at least one baseline detection parameter is generated using a machine learning model trained on sensor data from the vehicle or other vehicles, the machine learning model configured to output baseline parameters based on object classification groups and environmental conditions.”(Claim 18 “The method of claim 1, further comprising: receiving sensor data from a plurality of sensors coupled to the vehicle, wherein the plurality of sensors includes the sensor; generating a machine learning model based on the sensor data, wherein the machine learning model uses the sensor data to group objects according to classification groups and generates a baseline detection distance corresponding to each classification group; and wherein identifying the baseline detection distance comprises: obtaining the baseline detection distance based on the machine learning model.”)
Regarding Claim 9, 482 claims “wherein modifying the control strategy comprises at least one of: adjusting a speed of the vehicle, modifying a following distance to other objects, or changing a navigation path based on the analysis”(Claim 1 “…and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.”)
Regarding Claim 10, 482 claims “wherein modifying the configuration of the at least one sensor comprises triggering at least one of: a cleaning process for the at least one sensor, a calibration process for the at least one sensor, or an adjustment of sensor parameters” (Claim 1 “…and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.”)
Regarding Claim 12, 482 claims “The method of claim 1, wherein the at least one sensor comprises multiple sensor types including at least two of: a camera, a lidar sensor, a radar sensor, or an ultrasonic sensor, and wherein the at least one detection parameter is determined based on sensor fusion of data from the multiple sensor types.”(Claim 1The method of claim 1, further comprising: receiving sensor data from a plurality of sensors coupled to the vehicle, wherein the plurality of sensors includes the sensor; detecting a plurality of objects based on the sensor data from the plurality of sensors; determining, for each detected object, a given detection distance between the detected object and the vehicle; and generating a plurality of baseline detection distances based on respective detection distances associated with detecting the plurality of objects, wherein the plurality of baseline detection distances comprises at least a first baseline detection distance corresponding to a first classification group and a second baseline detection distance corresponding to a second classification group, and wherein objects in the first classification group differs from objects in the second classification group.” Here claims using a plurality of sensors based on their data to detect objects/their associated detection distances (i.e. the fusion of multiple sensors) while this claim does not recite the specific type of sensors as recited in the applications claims, official notice is taken that cameras, lidar, radar, and ultrasonic are all WURC sensor in the automotive field as such one of ordinary skill in the art would understand the “sensors” of 482’s claim to include these recited types of sensors)
Regarding Claim 19-20, 482’s claims 19-20 recite system and non-transitory computer readable medium equivalents to the method claim 1, they have the same overall double patenting analysis as claim 1’s double patenting above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20210276577 A1; US 20220121210 A1; US 20220129362 A1;
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/KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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