DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 10, 2026 has been entered.
Response to Arguments
The amendment filed February 16, 2026 has been entered with the RCE filed March 10, 2026. Claims 1, 8, and 15 have been amended. Claims 5, 12, and 19 are presently canceled. The remaining claims are in original or previously presented form. Therefore, claims 1-4, 6-11, 13-18, and 20 are pending in the application. Claims 1, 8, and 15 are the independent claims.
The applicant’s Remarks, filed February 16, 2026, has been fully considered. The applicant argues, under the heading “Rejections under 35 U.S.C. §103,” that the independent claims 1, 8, and 15 have been amended to include the limitations of originally filed claim 5 and additional limitations relating to “driver movements” as supported by at least the originally filed claim 5 and paragraphs 0035, 0042, and 0045 of the specification. The applicant argues that claim 5 is not taught by the prior art. The examiner respectfully believes that Lin et al. (US2020/0126415) teaches these limitations and will cite Lin in the rejection of present claim 1. Overall, Kim et al. (US2019/0077402) is good on teaching a risk score. Lin teaches minimizing risk and also teaches a digital twin. Lin is a very strong reference. It teaches a lot about obtaining info via sensors and V2V of “other vehicles” in the environment of an intersection. It than determines to control the host vehicle “so that the flow of traffic through the intersection is maximized while a risk of collision within the intersection is minimized.” Lu et al. (US2021/0233406) is also a strong reference.
Despite these references, the examiner has found a better primary reference, and has used that in the rejections below. Due to applicant amendment, the grounds for rejection have changed. Please see the rejections below.
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 1-4, 6-11, 13-18, and 20 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 claims contain 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites:
A computer-implemented method for displaying a projection of driving risk to a vehicle from activity in a surrounding area, the computer-implemented method comprising:
acquiring a vehicle path from the vehicle;
capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area,
wherein the driving conditions include relevant data transmitted by the object and driver movements;
identifying a high-risk object in the surrounding area by:
determining intended movements of the object from the relevant data;
calculating a risk score for the object relative to the vehicle based on the vehicle path and the intended movements of the object;
creating a digital twin instance for the vehicle;
generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object,
wherein the digital twin simulation is based on the determining the intended movement of the object;
updating the risk score based on the digital twin simulation output; and
classifying, based on the updating, the object as the high-risk object when the risk score is above a risk threshold for the vehicle; and
generating, in response to the identifying the high-risk object, an augmented reality display of the surrounding area using an augmented reality device,
wherein the augmented reality display of the surrounding area indicates the high-risk object and the intended movements of the object.
Present claim 1, largely incorporates old claim 5, as the applicant acknowledges in the Remarks. But in paragraph 0045 of the present published disclosure (Fox – US2024/0391482) this discussion of “updating the risk score,” as recited in claim 1, is broadly and reasonably saying: the digital twin simulation system “may further continuously monitor the driving conditions…and update the calculating of risk score…Updating the risk score may also change whether a recognized object is classified as a high-risk object and therefore the augmented reality display may add or remove an indication of the object in the display.” So the “updating” is not a new step so much as a repeating of a previous step and a replacing of the previous risk value with a new risk value. “Updating,” broadly and reasonably, means that the computer constantly performs the method, which seems to the examiner to be pretty obvious. Most autonomous driving computers continuously execute their processing at the rate of the processor of the controller.
But the way claim 1 is worded makes it sound like the method first identifies a high-risk object by calculating a risk score using one system, and then after that, the system creates a digital twin, runs a simulation, and then updates the risk score. The implication in the claim based on the order of the statements is that one system calculates a risk score and another system—namely, the digital twin simulation—updates the risk score. But that isn’t quite how it works. That does not have written description, so far as the examiner can tell. According to the beginning of paragraph 0042 of the present published disclosure, it is the digital twin simulation itself that can perform the initial step of “calculating a risk score of the object.” Then the digital twin simulation repeats the process over and over again, updating the score each time, and re-determining what should be displayed in the AR. For examination purposes, that is how the claim will be interpreted.
Independent claims 8 and 15 are substantially similar, rejected for the same reasons, and will be interpreted in the same way.
Claim 1 is also rejected for reciting in part:
capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area,
wherein the driving conditions include relevant data transmitted by the object and driver movements
In the present published disclosure, paragraph 0035 recites “driver movements”. The entire phrase is “prediction of driver movements using learned behaviors that may be specific to the driver.” This is used, along with vehicle size, is used as “training data”. That training data is not “driving conditions” according to the disclosure. But the claim states that the “driving conditions include…driver movements.” That lacks written description. Rather, according to paragraph 0032, “driving conditions” are what are captured by sensors mounted on the vehicle regarding the surrounding environment of the host vehicle.
The specification discusses terms similar to “driver movements” and the examiner is not sure if those were intended or not. These include: “vehicle movements and driver actions,” in paragraph 0016. Fig. 2 also discusses capturing “driving conditions from the surrounding area” in step 204. Paragraph 0032 defines “driving conditions” as including objects…, human activity such as pedestrians…or any relevant aspect of the surrounding area.” Paragraph 003 defines driving conditions further as including “object presence, [and] relative location”. The disclosure also teaches determining the “intended movements of the recognized object,” as in paragraph 0041. This can be done, broadly and reasonably, by determining that most vehicles in a left turn lane historically turn left. Or, that a vehicle that is in the middle of turning left intends to continue to do so. That is a broad reasonable interpretation of determining intended movements.
For examination purposes the term “driver movements” will not be interpreted as “intended movements of the object,” with this phrase being interpreted as obtaining this intended movement either through trajectory forecasting based on previous trajectory in the immediate past, or a trajectory prediction based on historical data, or a trajectory prediction based on V2X transmission, or any combination of that. Instead, the amended phrase “and driver movements” will be ignored for examination purposes. That is because claim 1, a few clauses later, recites “determining intended movements of the object”. Therefore, the driver movements already get incorporated into the claim.
Independent claims 8 and 15 are substantially similar, rejected for the same reasons, and will be interpreted in the same way.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites in part:
generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object,
wherein the digital twin simulation is based on the determining the intended movement of the object;
The first clause states that the digital twin simulation output is generated in part by “simulating the intended movements of the object”. Then the second clause states that the simulation is “based on the determining the intended movement of the object”.
In the present published specification, paragraph 0042 teaches that a vehicle or object can produce sensor data which can be relayed to a processing system and applied to the digital copy. “Once informed with such data, the virtual model can be used to run simulations of incidents where the object and vehicle collide…”.
It seems to the examiner that the present clauses imply that the intended movement of the object is first determined, and then that intended movement serves as an input to the digital twin system, which simulates that intended movement of the object, but does not actually generate the intended movement from the received sensor data.
Paragraph 0040 teaches that a supervised machine learning model may be trained to predict the intended movements of a recognized object. Then paragraph 0041 teaches that “another supervised machine learning model, which may be separate from the above or combined, may be trained to calculate a risk score for the object.” Nothing about a digital twin simulation is mentioned in this paragraph.
Paragraph 0042 then states that “In a further embodiment, identifying a high-risk object in the surrounding area, including determining the intended movements of a recognized object, or calculating a risk score for the object, may be accomplished through a digital twin simulation”. This “or” seems to imply that the digital twin simulation can do either or both of “determining the intended movements of a recognized object, or calculating a risk score for the object.” Therefore, if the digital twin simulation only does that latter (i.e., the “calculating a risk score for the object”) than the “the intended movements of a recognized object” must come from somewhere else and serve as an input to the digital twin. The disclosure doesn’t say this explicitly, but it is reasonable that the intended movements of the recognized object could come from the system described in paragraph 0040, or some other method.
But even if such an interpretation has written description, which it barely does, it is still not clear what the second clause means when it recites “wherein the digital twin simulation is based on the determining the intended movement of the object”. What does this really mean? It seems possible that it could mean, or could be confused to mean, that the digital twin simulation determines the intended movement of the object and then based on that generates the digital twin simulation output. The simulation could still be “based on” determining the intended movements of the object even if it must perform that determination itself before generating its output.
Note that what the digital twin simulation output is is not explicitly stated in the claims. It seems that it is a “risk score” because claim 1 later recites “updating the risk score based on the digital twin simulation output” though that is not explicitly stated. But if that is true, then it seems that that would conflict with claim 6 which recites: “The computer-implemented method of claim 1, wherein the determining the risk score for the object uses a machine learning model that calculates a probability of an incident between the vehicle and the object based on prior detected movements of the object and the driving conditions.”
Overall, it might be helpful to state definitely what the output of the digital twin simulation is. Furthermore, it might be helpful to particularly point out whether or not the “determining the intended movement of the object” is performed by the digital twin simulation or something else.
For examination purposes, the second clause of “wherein the digital twin simulation is based on the determining the intended movement of the object;” will be interpreted as meaning that the intended movement of the object is generated by the digital twin simulation, but as the rejection of this clause in claim 1 shows, the cited reference teaches both interpretations.
Independent claims 8 and 15 are substantially similar, rejected for the same reasons, and will be interpreted in the same way.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6. 8-10, 13, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (US2019/0382003) in view of Kim et al. (US2019/0077402).
Regarding claim 1, Jiang teaches:
A computer-implemented method for displaying a projection of driving risk to a vehicle from activity in a surrounding area, the computer-implemented method comprising (see Figs. 1 and 3):
acquiring a vehicle path from the vehicle (see paragraph 0037, first sentence);
capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area (see paragraph 0037 under point “(2)”.),
wherein the driving conditions include relevant data transmitted by the object and driver movements (see paragraph 0048 for the “twin client of the ego vehicle uses V2X or V2V communication to share the digital behavior twin of the ego vehicle with the twin client of these remote vehicles.” The paragraph also teaches that “other relevant vehicles,” which the present claims call an “object,” can themselves be the ego vehicle. In other words, mutual exchange via V2V is taught. See paragraph 0028 for what a “twin client” is. See paragraph 0167 for “vehicles share their twin data with other vehicles via V2X or V2V” and “receive the twin data” as well.);
identifying a high-risk object in the surrounding area by (see Fig. 8 and paragraph 0189):
determining intended movements of the object from the relevant data (see paragraph 0135, section “(3)”. See paragraph 0176 for updating the twin model “for different vehicles based on the observed behavior of these vehicles using the sensor data that is generated by the sensor set of the ego vehicle.” See also paragraph 0170, first sentence);
calculating a risk score for the object relative to the vehicle based on the vehicle path and the intended movements of the object (see paragraph 0173);
creating a digital twin instance for the vehicle (see paragraph 0174);
generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object (see paragraph 0172 and 0173. The examiner notes here that what exactly is “output” is not specified in the claim.),
wherein the digital twin simulation is based on the determining the intended movement of the object (see also paragraph 0170, first sentence. See paragraph 0173.);
updating the risk score based on the digital twin simulation output (see paragraph 0173 for a first and second estimate of a risk. This second estimate is an updated estimate. See paragraph 0176 for updating the estimates.); and
classifying, based on the updating, the object as the high-risk object (see paragraph 0189 for “visually depicting a risk gradient”. It seems ); and
generating, in response to the identifying the high-risk object, an augmented reality display of the surrounding area using an augmented reality device (see Figs. 8 and 9 and paragraphs 0189-0190),
wherein the augmented reality display of the surrounding area indicates the high-risk object and the intended movements of the object (see Figs. 8 and 9 and paragraphs 0189-0190).
Yet Jiang does not explicitly further teach:
classifying, based on the updating, the object as the high-risk object
when the risk score is above a risk threshold for the vehicle.
However, Kim teaches:
classifying, based on the updating, the object as the high-risk object
when the risk score is above a risk threshold for the vehicle (see paragraph 0443 teaches identifying an erratically driven vehicle by its speed and the number of times it makes lane changes. Then the processor “may determine that the vehicle of interest has a high risk level”. Here it is the vehicle that is assigned a risk level, not a lane. Paragraph 0445 teaches that many surrounding vehicles will be assigned a risk level, because it teaches that one vehicle will end up having “the highest risk level.” See also paragraph 0365 for a “greater value may indicate a higher risk”. See paragraph 0409 for “the highest risk level” having a numerical value of 2. In terms of this being based on the updating, the examiner notes that that part is taught by the primary reference, but it would be unreasonable to interpret Kim as teaching a system that does not constantly update the risk scores because that is how computers in the art work. They constantly perform updates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Jiang, to add the additional features of classifying, based on the updating, the object as the high-risk object when the risk score is above a risk threshold for the vehicle, as taught by Kim. The motivation for doing so would be to provide an optimal path for a host vehicle, as recognized by Kim (see paragraph 0011).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
Regarding claim 2, Jiang and Kim teach the computer-implemented method of claim 1.
Jiang further teaches:
The computer-implemented method of claim 1, further comprising
transmitting a notification about the high-risk object to the vehicle, wherein the notification is a voice prompt warning (in the present disclosure, see paragraph 0044 for a notification including “a text message displayed on the augmented reality screen” or “a voice prompt”. With that in mind, see Jiang Figs. 8 and 9 and paragraph 0041 for HUD and “audio warnings”. See also paragraph 0145 for the “AR visualizations” that are “generated that visually depicts the likelihood of collision...based on the risk analysis.”).
Regarding claim 3, Jiang and Kim teach the computer-implemented method of claim 1.
Jiang further teaches:
The computer-implemented method of claim 1, wherein
the acquiring the vehicle path from the vehicle further comprises
the obtaining telemetry data from the vehicle (see paragraph 0135, section “(3)”. See paragraph 0176 for updating the twin model “for different vehicles based on the observed behavior of these vehicles using the sensor data that is generated by the sensor set of the ego vehicle.” See also paragraph 0170, first sentence.) and
determining the vehicle path from the telemetry data (see paragraph 0135. See also Fig. 6, step 603 and Fig. 7, step 705).
Regarding claim 6, Jiang and Kim teach the computer-implemented method of claim 1.
Jiang further teaches:
The computer-implemented method of claim 1, wherein
the determining the risk score for the object uses a machine learning model that calculates a probability of an incident between the vehicle and the object based on prior detected movements of the object and the driving conditions (see paragraph 0145 for a system that determines and then displays “the likelihood of collision”. See also Fig. 5, step 512, which is based on step 511.).
Regarding claim 8, Jiang teaches:
A computer system for displaying a projection of driving risk to a vehicle from activity in a surrounding area, the computer system comprising (see paragraph 0013):
one or more processors, one or more memories (see paragraph 0013), and
one or more computer-readable storage media (see paragraph 0131.);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to acquire a vehicle path from the vehicle (see paragraphs 0010 and 0131. For the remainder of the rejection, please see the analogous bullet points in the rejection for claim 1 which is substantially similar.);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to capture driving conditions from the surrounding area using a sensor and recognize an object in the surrounding area,
wherein the driving conditions include relevant data transmitted by the object and driver movements;
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to identify a high-risk object in the surrounding area by:
determining intended movements of the object from the relevant data;
calculating a risk score for the object relative to the vehicle based on the vehicle path and the intended movements of the object;
creating a digital twin instance for the vehicle;
generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object,
wherein the digital twin simulation is based on the determining the intended movement of the object;
updating the risk score based on the digital twin simulation output; and
classifying, based on the updating, the object as the high-risk object when the risk score is above a risk threshold for the vehicle; and
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to generate, in response to the identifying the high-risk object, an augmented reality display of the surrounding area using an augmented reality device,
wherein the augmented reality display of the surrounding area indicates the high-risk object and the intended movements of the object.
Regarding claims 9 and 16, see the rejection of claim 2 which is substantially similar.
Regarding claims 10 and 17, see the rejection of claim 3 which is substantially similar.
Regarding claims 13 and 20, see the rejection of claim 6 which is substantially similar.
Regarding claim 15, Jiang teaches:
A computer program product for displaying a projection of driving risk to a vehicle from activity in a surrounding area, the computer program product comprising (see paragraph 0007):
one or more computer-readable storage media (see paragraph 0131.);
program instructions, stored on at least one of the one or more computer-readable storage media, to acquire a vehicle path from the vehicle (see paragraphs 0010 and 0131. For the remainder of the rejection, please see the analogous bullet points in the rejection for claim 1 which is substantially similar.);
program instructions, stored on at least one of the one or more computer-readable storage media, to capture driving conditions from the surrounding area using a sensor and recognize an object in the surrounding area,
wherein the driving conditions include relevant data transmitted by the object and driver movements;
program instructions, stored on at least one of the one or more computer-readable storage media, to identify a high-risk object in the surrounding area by:
determining intended movements of the object from the relevant data;
calculating a risk score for the object relative to the vehicle based on the vehicle path and the intended movements of the object;
creating a digital twin instance for the vehicle;
generating a digital twin simulation output by simulating the vehicle path using the digital twin instance and simulating the intended movements of the object, wherein the digital twin simulation is based on the determining the intended movement of the object;
updating the risk score based on the digital twin simulation output; and
classifying, based on the updating, the object as the high-risk object when the risk score is above a risk threshold for the vehicle; and
program instructions, stored on at least one of the one or more computer-readable storage media, to generate, in response to the identifying the high-risk object, an augmented reality display of the surrounding area using an augmented reality device,
wherein the augmented reality display of the surrounding area indicates the high-risk object and the intended movements of the object.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Kim in further view of Rubin et al. (US2013/0281141 A1).
Regarding claim 4, Jiang and Kim teach the computer-implemented method of claim 1.
Yet Jiang and Kim do not further teach:
The computer-implemented method of claim 1, wherein the identifying the high-risk object further comprises:
determining that a recognized object is not transmitting the relevant data; and classifying the recognized object as the high-risk object.
However, Rubin teaches:
The computer-implemented method of claim 1, wherein the identifying the high-risk object further comprises:
determining that a recognized object is not transmitting the relevant data; and classifying the recognized object as the high-risk object (see Rubin paragraph 0570 for determining if a vehicle has a “failed V2V transmitter” and that information being recorded and “the risk broadcast”.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Jiang and Kim to add the additional features of determining that a recognized object is not transmitting the relevant data; and classifying the recognized object as the high-risk object, as taught by Rubin. The motivation for doing so would be to spread the alarm and detect a high-risk situation early, as recognized by Rubin (see paragraphs 0572 and 0628).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
Regarding claims 11 and 18, see the rejection of claim 4 which is substantially similar.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Kim in further view of Kentley et al. (U.S. 9,517,767 B1).
Regarding claim 7, Jiang and Kim teach the computer-implemented method of claim 1.
Yet Jiang and Kim do not further teach:
The computer-implemented method of claim 1, wherein
the risk threshold for the vehicle is determined using a machine learning model that predicts vehicle risk based on [[a]] historical vehicle data
However, Kentley teaches:
The computer-implemented method of claim 1, wherein
the risk threshold for the vehicle is determined using a machine learning model that predicts vehicle risk based on [[a]] historical vehicle data in a broad reasonable interpretation, there are really two parts to this clause. The first is teaching that the machine learning model can determine a “risk threshold for the vehicle”. The second is teaching that the machine learning model can predict vehicle risk based on historical vehicle data. Regarding the first teaching, a “risk threshold” is, for example, a threshold to determine whether the risk is high or low. The present claim says not that the risk is determined using a machine learning model, but the “risk threshold” itself is determined. To understand what this means it is necessary to go back to claim 1. Claim 1 recites determining an object trajectory, and giving it a risk score. Then the object can be classified as a “high-risk object when the risk score is above a risk threshold for the vehicle”. So in the present claim, claim 7, it recites that this risk threshold is determined using a machine learning model. In one reasonable interpretation, this means that an object may be moving toward a vehicle. A collision is predicted. But when should the host vehicle be warned? Right away even though the potential collision is a good ways off? Probably not. There must be some threshold to trigger the warning. But how can that threshold itself be determined? It could be determined using a machine learned model, how exactly, the present disclosure does not say. The claim says that the machine learned model is also one “that predicts vehicle risk based on historical vehicle data,” which can include a vehicle size, and the “driving conditions,” which can be the driving trajectories or time of day. But again that is regarding predicting “risk” not a ”risk threshold.” Kentley, col. 8, line 57 – col. 9, line 16 teach a system in which “subsets of thresholds…may be calculated based on the probabilities of impact”. What this means is that the system calculates that the closer an object gets the higher the risk to the vehicle. In response to “the safety system may be activated” based on the calculated thresholds. In a broad reasonable interpretation, a pedestrian might have a different set of calculated thresholds compared to a car. This, in fact, reasonably relates to the historical data on the objects. A pedestrian could be nearby a vehicle and no alarm is generated by a car that is further away could generate an alarm. It is important that Kentley teaches that the thresholds themselves are “calculated”. They are calculated, implemented, and then they end up triggering safety systems within the vehicle, which could reasonably be a HUD warning.
Regarding the second teaching in the present claim, Kentley also teaches a machine learning model to determine “risk based on [[a]] historical vehicle data ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Jiang and Kim, to add the additional features of the risk threshold for the vehicle is determined using a machine learning model that predicts vehicle risk based on [[a]] historical vehicle data see col. 9, lines 8-16).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
Regarding Claim 14, see the rejection of claim 7 which is substantially similar.
Additional Art
The prior art made of record here, though not relied upon, is considered pertinent to the present disclosure.
Shiraishi et al. (US2019/0384870) a Toyota disclosure entitled “Digital Twin for Vehicle Risk Evaluation,” teaches determining collision risks using a digital twin.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL M. ROBERT whose telephone number is (571)270-5841. The examiner can normally be reached M-F 7:30-4:30 EST.
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/DANIEL M. ROBERT/Primary Examiner, Art Unit 3665
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