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 .
Response to Arguments
The Remarks filed March 11, 2026 has been entered. No amendments to the claims were filed. Therefore all the claims are in original or previously presented form. Therefore, claims 1-22 are pending in the application.
The applicant states on page 4 of the Remarks that the specification has been amended to correct some typographical errors. The examiner accepts these amendments and withdraws the specification objection made in the last detailed action, which was the Non-Final Rejection dated January 7, 2026.
The applicant, beginning on page 9 of the Remarks, traverses the 35 USC 112(a) rejections made in the last detailed action.
After reviewing the applicant’s Remarks, the last detailed action, and the original disclosure, the examiner views Fig. 4 as important in interpretating the clause in claim 11 that recites “using an AI model to create a spread of possible trajectories with time”. This “spread of possible trajectories with time” is the dashed lines in Fig. 4, with particular attention required to the “S” spread in Fig. 4. As noted in the last detailed action, time tn is the present and time in the graph increases from left to right.
As the examiner wrote on pages 12 and 13 of the last detailed action:
In a broad reasonable interpretation, the sensors (not the AI system) recognize an object, as say, a motorcycle. Then the sensors input the environmental conditions (such as wet pavement), historical behavior data for other motorcycles in the same environment, and existing trajectory data into a model. The regression model will predict where the motorcycle might go a few steps into the future. But there may be some doubt. There may also be some error determined, apparently based on later ground truth data. If so the AI model will update some weights so that future regression analysis will make better predictions of trajectory spreads. So the AI model apparently just provides weights to a regression tool.
The examiner also wrote that:
According to paragraph 0098 the “AI models” differ in their “weights”. Rain, might have more of a weight for a motorcycle compared to the weight assigned for dry ground, paragraph 009 teaches. So as recited in paragraph 00101, “the selected AI model is used to create a spread of possible trajectories with time”. As far as the examiner can tell, the weights are the only thing supplied by the AI model when it comes to determining the spreads. So paragraph 00122 recites that if the model returns inaccurate results “the weights of the AI model” are “adjusted”.
This interpretation is supported by original claim 5 which recited that “regression analysis is initially used to predict the trajectory of each identification.” Here, regression analysis is used, not an AI model to predict the trajectory. It seems to the examiner that the dashed lines in Fig. 4 that do not include a spread are not necessarily created using an AI model. Since the data points for these trajectories line up apparently exactly, the R2 value indicates a good fit. Paragraph 0096, when discussing when AI is used states that “If the Goodness of Fit is not acceptable, a more advanced prediction may be made,” namely, using an AI model (emphasis added).
Fig. 7B teaches before the AI model gets involved that the system in step 735 can “predict a trajectory” for each identification, such as an identified pedestrian. This is be done using regression analysis, not an AI model. The “initial trajectory prediction” in claim 12 is found without using an AI model. Then an Fig. 7B, step 745 teaches that an AI model is used to predict trajectory “spreads”. This spread is different from a trajectory. Step 750 teaches that the AI model can “predict behaviors”. Behaviors in the specification and not synonymous with trajectories. An object can be classified as generally having an erratic behavior, which is different than its future trajectory being predicted. In step 755, the AI model can “select predicted trajectories and behaviors to send to each recipient”. These “predicted trajectories” are not described as found using the AI model, except in the sense that trajectories spreads might be included. The examiner withdraws the rejection of claim 12.
The applicant argues in the Remarks on page 10 that paragraph 101 of the specification supports claim 11, including the claim’s teaching of “using an AI model to create a spread of possible trajectories with time”. The examiner agrees that the disclosure supports the AI model creating a spread of possible trajectories with time. But the examiner believes that claim 11 needs to clearly state what is an input to the AI model. To merely state that the “AI model [is] trained to process” can reasonably be interpreted to mean that the AI model outputs these features. But in fact, the listed features in inputs to the AI model. Additional comments on claim 11 will be made in the response to the traversal of the 35 USC 112(b) rejections shortly in this “Response to Arguments” section.
The applicant, beginning on page 11 of the Remarks, traverses the 35 USC 112(b) rejections made in the last detailed action.
The applicant argues on page 11 of the Remarks beginning with “Page 6 of the present action…” that when claim 1 recites “predicting” it does provide antecedent basis for claims 7 and 11. The examiner will accept the argument for claim 7 and withdraws the rejection for that claim.
The applicant argues on page 11 of the Remarks beginning with “Page 7…” that claims 7 and 11 are definite. The examiner does not see why claim 11 should recite “predicting for an identification having a classification” when claim 1 already recited “at least one identification” and “for each identification, predicting a trajectory”. Why the formulation of “an identification” in claim 11? It seems to separate the “an identification” in claim 11 from those in claim 1 upon which claim 11 depends. It might be that there is a subset of each identification that includes only those “having a classification” but that still doesn’t seem to justify not reciting “each identification” here. The examiner respectfully does not withdraw the rejection for claim 11.
The applicant argues on page 11 of the Remarks beginning with “Page 8 of the present action…” that claim 1 is definite because identification and classification are not the same thing. The applicant states that claim 1. The examiner agrees with this argument.
The applicant argues on page 11 the Remarks in the second and last paragraph beginning with “Page 8 of the present action…” that “claim 9 does not recite a classifying step”. The examiner does not find this argument persuasive. Claim 9 recites that each “data structure further includes a classification of an identification; and wherein if the identification is classified as a motor vehicle…” The applicant emphasizes that the data structure is received from other motor vehicles, as made clear in claim 1. But that still doesn’t take away from the fact that claim 9 includes a classification step. Where claim 9 recites “the data structure” it should read “each data structure” to not have an antecedent basis issue with claim 1. Furthermore, if claim 9 wants to teach that the data structure might or might not have a classification, using the conditional “if” is fine, as recited in claim 9. Or stating that “each identification” may or might include a classification, but to introduce the new term of “an identification” when claim 1 already did not is an antecedent basis problem.
Claim 9 could be improved by reciting something like the following:
The method of claim 1, wherein
each] data structure [may] further include a classification of one or more of the each] identification; and
wherein if the [each] identification is classified as a motor vehicle having a model and make;
the predicting further includes:
looking up braking and handling capabilities of the motor vehicle according to its model and make;
determining whether velocity of the motor vehicle is unsafe in view of its braking and handling capabilities; and
raising an alert about the identification if the velocity is unsafe.
In terms of claim 11, the examiner also does not think that the claim should introduce a new term “predicting for an identification having a classification”. Some formulation similar to that recommended for claim 9 could be used for claim 11. But introducing a new term of “an identification” is an antecedent basis problem and separates claim 11 from claim 1. Alternatively, as suggested in the last detailed action, the input to the AI model would include “a classification” of each identification wherein a classification is provided by the received data structure.
The applicant argues on page 12 of the Remarks beginning with “Pages 9-12 allege…” that “claim 11 doesn’t recite that the AI model performs identification and classification, it recites that the AI model only takes classification into account.” The examiner is not sure he understands this argument correctly. The AI model of claim 11 clearly processes as input features various items, including a corresponding initial trajectory prediction, a goodness of fit, sensor data, and historical data. The claim seems to be aimed at stating that for each identification in which there is a classification, the AI model should process various input items to create a spread of possible trajectories. It just needs to be clear what the inputs are. Merely using the phrase “to process” does not make clear that the subsequent items are a list of inputs to the AI model.
The applicant argues on page 12 of the Remarks beginning with “Pages 12 and 16 of the present action…” that reciting “an identification” in claim 8 is not confusing. The examiner believes that introducing the term “an identification” in claim 8 is an antecedent basis problem for the same reasons as discussed above in reference to claim 11.
The applicant argues on page 12 of the Remarks beginning with “Pages 14-15 of the present action…” that the term “nearby” is defined in the specification. The examiner agrees and withdraws the rejection.
The applicant argues on page 12 of the Remarks beginning with “Pages 14-15 of the present action…” that the term “sufficiently” is not indefinite. The applicant points to paragraph 0075 of the disclosure. That recites a predetermined threshold prefaced with the term “e.g.”. The examiner accepts this definition and withdraws the rejection.
The applicant argues on page 12 of the Remarks beginning with “Pages 18-19 of the present action…” that claim 12 does not claim predicting a trajectory using an AI model. The examiner notes that, as discussed earlier in this section, “behavior” and “trajectory” are not the same. The examiner withdraws the rejection.
The applicant argues on pages 12-13 of the Remarks beginning with “Pages 19-20 of the present action…” that the last clause of claim 13 is not redundant. The applicant can leave the last clause in there. The rejection is withdrawn.
The applicant, beginning on page 4 of the Remarks, traverses the 35 USC 103 rejections made in the last detailed action.
The applicant argues that Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) do not teach at least claim 1.
The applicant on pages 4 and 5 of the Remarks summarizes present claim 1 on pages 4 and 5. The applicant notes on page 4 that in present claim 1 a plurality of motor vehicles can act as network edge devices. The applicant cites paragraphs 0084-0085 as teaching that edge computing involves devises that process data in real time near data sources, and a core device that receives the processed data from edge devices and performs further processing. Thus “some of the computational burden on the core device is offloaded to the edge devices.”
A big question regarding the present disclosure is what is the “network edge devices” and what is the “core computer”? This is answered in at least paragraph 0084 of the present disclosure which teaches that “the motor vehicles are used as network edge devices, and the server system 100 functions as a core computer.” The term “edge” server or “edge” device implies that the computer is on the “edge” of the road. But in the present disclosure, that is not the case. The “edge device” is on the motor vehicle. In other words, there really isn’t any edge device at all in its traditional definition.
What then is the “core computer”. That is a server. So the present disclosure doesn’t really teach edge servers at all in the traditional sense of the term. It teaches a plurality of vehicles, each having their own onboard computer and each connected to a server. That basic structure is taught in an extensive body of prior art.
The examiner notes that the present disclosure does not provide a figure illustrating what the structure the applicant has in mind looks like. But the examiner wonders why Oyama Figure 2 could not essentially provide this basic structure. In Oyama figure 2, item 70 is analogous to the “core computer” recited in the present disclosure’s paragraph 0084 and some of the present claims, while Oyama Fig. 2 item 10 is analogous to the “plurality of motor vehicles as network edge devices.” Although Oyama paragraph 0057 calls item 70 an edge server that doesn’t discount the fact that it performs the functions of what the present claim calls a core computer.
Oyama paragraph 0059 teaches that the transceiver 74 of item 70 “receives the road traffic detection information from the driving control apparatus 10 of each of the vehicles 5”. In other words, in the language of the present disclosure, the core computer receives data from the network edge devices, which are really not edge devices in the sense of the word understood by a person of ordinary skill in the vehicle control art.
The examiner further notes that, while present claim 1 recites receiving data structures with timestamped position data for “each identification,” such as another nearby vehicle, and then essentially generating a current trajectory based on that received data, this does not necessarily mean that the system obtains data on the same object from multiple different “network edge devices” that are onboard the vehicles. The examiner does not see that the claims nor specification states that. The present disclosure does not necessarily teach crowd sourcing sensor data on a single object and using data from multiple vehicles to piece together a single trajectory of a single object using that data from multiple vehicles. Rather, the core computer can receive sensor data from one vehicle and process it into an object trajectory for that one vehicle, and also receive sensor data from another vehicle and process that into a different object trajectory that that another vehicle.
Based on the above summary of the proposed invention, how does it relate to the cited art?
The applicant argues on page 6 of the Remarks that Oyama and Pan, alone or in combination, do not even “remotely suggest the method of claim 1.” Figs 2 and 12 of Oyama, as cited in the rejection of claim 1 in the last detailed action, show vehicles 5 with their vehicle computers 10 sending and receiving data to and from the server 70. Paragraphs 0035 teaches that each vehicle can gather “road traffic detection information” using sensors such as cameras, GNSS, etc. Paragraph 0153 teaches that vehicle 5 can have a lidar sensor. Paragraph 0036 teaches that the vehicles can send that information to the apparatus 70. Fig. 4 clearly states that the information includes a “date and time,” a “location” and a “vehicle ID”. Paragraph 0036 teaches that the sensor data sent to the apparatus 70 is that shown in Fig. 4. Oyama also teaches that the apparatus 70 can use that sensor data to generate control information for each vehicle. See paragraph 0039 for this including “target deceleration” information and see paragraph 0040 for this including “steering control of the vehicle 5 in case of an emergency.” This reasonably means that the apparatus 70 computes a current and future trajectory of the object that the vehicle 5 detected and then determines to avoid it. See paragraph 0076 for an obstacle having a high possibility of collision with the vehicle 5 being detected in the “traveling path of the vehicle 5”, and then “control information” being generated to avoid the collision. See paragraph 0085 for the apparatus 70 determining a time to collision, TTC.
Based on this, the examiner does not agree with the applicant’s statement on page 6 of the Remarks that “Oyama does not describe a single claim element of claim 1.” Rather, the examiner believes that Oyama teaches the limitations that were rejected with Oyama.
The applicant further argues in the Remarks starting on page 7 that Pan et al. (US2024/0288569) does not teach what the examiner cited Pan as teaching. Before getting into the arguments, the examiner notes that at the end of the rejection of claim 1 in the last detailed action, the examiner wrote:
Note the Oyama at least strongly teaches toward much of what Pan explicitly teaches. For example, Oyama, paragraph 0077, teaches that the system can identify “a pedestrian who is crossing the traveling path” of the host vehicle. In the context of the vehicle control art, this undoubtably means that the sensors of the system disclosed by Oyama is tracking the object over time. It seems to the examiner that this was so obvious to Oyama that it went without saying. But the examiner has added Pan anyway.
The applicant summarizes Pan on page 7 of the Remarks as teaching acquiring point cloud data, extracting features from that, and then generating an existing and predicted trajectory. On the face of it, the applicant’s summary seems to indicate that Pan is relevant art. That’s because the clauses in question of present claim 1 are:
aggregating a plurality of positions for each identification over time such that each identification has a corresponding time; and
for each identification, predicting a trajectory from the corresponding time series.
The examiner notes that in the Remarks on the last two lines of page 7 the applicant understands that when the examiner cited paragraph 0008 and particular sections, such as section 4 of Pan, the examiner actually meant to cite the paragraphs associated with those sections. The applicant argues that paragraphs 0008-0014, including sections (1) through (6) do not teach the above limitations, as the examiner argued. But to acquire point cloud data and process it, and then generate an “exiting trajectory” of an object, seems to the examiner to teach the present limitations. Paragraph 0073 teaches that the teachings of Pan can be applied to “tracking of a motor vehicle”. So Oyama teaches a computer 70 that receives sensor data and uses it to determine where an object is going. Pan teaches a computer that specifically receives sensor data and generates trajectory data. The combination seems obvious to the examiner.
The applicant further argues in the Remarks on page 8 that Oyama in view of Pan does not teach claim 18. The examiner respectfully does not find this argument persuasive. The various “modules” taught in the claim can reasonably be considered sections of code. Pan, paragraphs 0008-0014 teaches code performing those steps and therefore teaches those modules. The applicant further argues on page 9 of the Remarks that Pan does teach using a statistical model to predict trajectories. The examiner does not find this argument persuasive because cited paragraph 0020 of Pan recites a model for taking the mean of data points, which is a statistical method.
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 11 and 22 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 11 needs to clearly state what the term “process” means when referring to the AI model. To state that the “AI model is trained to process” without adding “features such as” could reasonably imply that the AI model is trained to process (interpreted as determine) and then output these features. That does not have written description. For examination purposes, the claim will be interpreted as if process meant: process features such as.
Claim 22 is rejected because the AI models do not predict trajectories. That is done using statistical methods in Fig. 7B, step 735. The AI can “predict trajectory spreads” in step 745. For examination purposes, claim 22 will be interpreted as:
The server system of claim 21, wherein
the core sever is further configured to use the data sets to train and update AI models
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 9 and 11 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 9 has antecedent basis issues. Where claim 9 recites “the data structure” it should read “each data structure” to not have an antecedent basis issue with claim 1. Furthermore, if claim 9 wants to teach that the data structure might or might not have a classification, using the conditional “if” is fine, as recited in claim 9. Or stating that “each identification” may or might include a classification, but to introduce the new term of “an identification” when claim 1 already did not is an antecedent basis problem. For examination purposes, the claim will be interpreted as if there were no antecedent basis issues.
Claim 11 has antecedent basis issues. The examiner also does not think that the claim should introduce a new term “predicting for an identification having a classification”. Some formulation similar to that recommended for claim 9 could be used for claim 11. But introducing a new term of “an identification” is an antecedent basis problem and separates claim 11 from claim 1. Alternatively, as suggested in the last detailed action, the input to the AI model would include “a classification” of each identification wherein a classification is provided by the received data structure. For examination purposes, the claim will be interpreted as if there were no antecedent basis issues.
Claims 1-4, 16-18, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569).
Regarding claim 1, Oyama teaches:
A computer-implemented method (see Fig. 6), comprising:
using a plurality of motor vehicles as network edge devices, including receiving data structures from the motor vehicles as the motor vehicles are on a road system (see Figs. 2 and 12),
each data structure including a timestamp, at least one identification of a nearby road object, and a position of each identification (see Figs. 4 and 5).
Yet Oyama does not explicitly further teach:
aggregating a plurality of positions for each identification over time such that each identification has a corresponding time; and
for each identification, predicting a trajectory from the corresponding time series.
However, Pan teaches:
aggregating a plurality of positions for each identification over time such that each identification has a corresponding time series (see paragraphs 0008-0014, especially section “(2)”.); and
for each identification, predicting a trajectory from the corresponding time series (see paragraphs 0008-0014, especially section “(4)”.).
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 Oyama, to add the additional features of aggregating a plurality of positions for each identification over time such that each identification has a corresponding time series; and for each identification, predicting a trajectory from the corresponding time series, as taught by Pan. The motivation for doing so would be to have a comprehensive precision tracking system with high reliability, as recognized by Pan (see paragraph 0005).
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.
Note the Oyama at least strongly teaches toward much of what Pan explicitly teaches. For example, Oyama, paragraph 0077, teaches that the system can identify “a pedestrian who is crossing the traveling path” of the host vehicle. In the context of the vehicle control art, this undoubtably means that the sensors of the system disclosed by Oyama is tracking the object over time. It seems to the examiner that this was so obvious to Oyama that it went without saying. But the examiner has added Pan anyway.
Regarding claim 2, Oyama and Pan teach the method of claim 1.
Yet Oyama does not further teach:
The method of claim 1, wherein the aggregating includes:
creating clusters of positions for each identification over time, whereby each identification has a corresponding time series of clusters; and
replacing the clusters with statistical models,
whereby each identification has corresponding time series of statistical models; and
wherein for each identification, the trajectory is predicted from the corresponding time series of statistical models.
However, Pan teaches:
creating clusters of positions for each identification over time, whereby each identification has a corresponding time series of clusters (in the present disclosure, paragraph 0060 recites “a time series of clusters for each identification.” The paragraph also states “Each cluster includes the position of an identification within a time slot.” In one broad reasonable interpretation, a vehicle could use a sensor such as lidar that creates multiple points of a single object at a single time slot. The lidar point cloud forms a cluster of data at a single time slot. When the data is strung together over multiple time slots it creates “a time series of clusters” for each identification, or, essentially, each nearby road object.
Based on this understanding, what does “replacing the clusters with statistical models” mean in a broad reasonable interpretation? Can it mean filtering out outliers using a Kalman filter, for example? According to the claim, there is a statistical model for each cluster. This model can be “a mean position” of the cluster. So a “time series of statical models” can be a time series of mean positions. Then a “trajectory is predicted” from the mean positions of time series data of a nearby object.
With all that in mind, see Pan, paragraph 0008 for obtaining point cloud data, and for “target feature extraction”. See paragraph 0029 for “T” being “the time interval between the neighboring two frames of radar point could”. A cloud is a cluster of an identification, an object. There is a cluster at each time.); and
replacing the clusters with statistical models,
whereby each identification has corresponding time series of statistical models (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.); and
wherein for each identification, the trajectory is predicted from the corresponding time series of statistical models (see paragraphs 0008-0014, item “(4)”. See also paragraphs 0029-0045 for generating a trajectory using the mean values.).
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 Oyama and Pan, to add the additional features of: creating clusters of positions for each identification over time, whereby each identification has a corresponding time series of clusters; and replacing the clusters with statistical models, whereby each identification has corresponding time series of statistical models; and wherein for each identification, the trajectory is predicted from the corresponding time series of statistical models, as taught by Pan. The motivation for doing so would be to have a comprehensive precision tracking system with high reliability, as recognized by Pan (see paragraph 0005).
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 3, Oyama and Pan teach the method of claim 2.
Yet Oyama does not further teach:
The method of claim 2, wherein
each statistical model includes a mean value of positions in the cluster it replaced.
However, Pan teaches:
each statistical model includes a mean value of positions in the cluster it replaced (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.).
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 Oyama and Pan, to add the additional features of: each statistical model includes a mean value of positions in the cluster it replaced, as taught by Pan. The motivation for doing so would be to have a comprehensive precision tracking system with high reliability, as recognized by Pan (see paragraph 0005).
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 4, Oyama and Pan teach the method of claim 1.
Oyama further teaches:
The method of claim 1, further comprising
transmitting at least one predicted trajectory to one or more recipients to enhance situational awareness of road objects on the road system (see Fig. 7, steps S204 and S206 and paragraph 0049. See paragraphs 0133-0135.).
Regarding claim 16, Oyama and Pan teach the method of claim 1.
Oyama further teaches:
The method of claim 1, further comprising
sending the identifications and the predicted trajectories to motor vehicles in a format that is usable by autonomous vehicle controls (see Fig. 7, steps S202 and S204 and S206).
Regarding claim 17, Oyama and Pan teach the method of claim 1.
Oyama further teaches:
The method of claim 1, further comprising
sending the predicted trajectories to motor vehicles on the road system, including sending a map of the road system (see Fig. 7, S206), wherein
the map is annotated with the predicted trajectories of the identifications (see Fig. 7, S206 and Fig. 8, S306).
Regarding claim 18, Oyama teaches:
A server system for edge computing, the server system comprising at least one server configured with (see Fig. 2, item 70):
a first module configured to receive data structures from a plurality of motor vehicles as the motor vehicles are on a road system,
each data structure including a timestamp, at least one identification of a nearby road object, and a position of each identification (see Fig. 4).
Yet Oyama does not explicitly further teach:
a second module configured to create clusters of positions for each identification over time, whereby each identification is associated with a time series of clusters;
a third module configured to replace the clusters with statistical models,
whereby each identification is associated with a time series of statistical models; and
a fourth module configured to, for each identification, predict a trajectory from the time series of statistical models.
However, Pan teaches:
a second module configured to create clusters of positions for each identification over time, whereby each identification is associated with a time series of clusters (see paragraphs 0008-0014, especially section “(2)”.);
a third module configured to replace the clusters with statistical models,
whereby each identification is associated with a time series of statistical models (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.); and
a fourth module configured to, for each identification, predict a trajectory from the time series of statistical models (see paragraphs 0008-0014, item “(4)”. See also paragraphs 0029-0045 for generating a trajectory using the mean values.).
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 Oyama, to add the additional features as taught by Pan of: a second module configured to create clusters of positions for each identification over time, whereby each identification is associated with a time series of clusters; a third module configured to replace the clusters with statistical models, whereby each identification is associated with a time series of statistical models; and a fourth module configured to, for each identification, predict a trajectory from the time series of statistical models.
The motivation for doing so would be to have a comprehensive precision tracking system with high reliability, as recognized by Pan (see paragraph 0005).
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 23, Oyama teaches:
An article comprising computer memory configured with machine-readable code that, when executed, causes a processor set to (see Fig. 2, item 70):
receive data structures from a plurality of motor vehicles as the motor vehicles are on a road system (see Figs. 2 and 4),
each data structure including a timestamp, at least one identification of a nearby road object, and a position of each identification (see Fig. 4).
Yet Oyama does not explicitly further teach:
create clusters of positions for each identification over time,
whereby each identification is associated with a time series of clusters;
replace the clusters with statistical models,
whereby each identification is associated with a time series of statistical models; and
for each identification, predict a trajectory from the time series of statistical models.
However, Pan teaches:
create clusters of positions for each identification over time,
whereby each identification is associated with a time series of clusters (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.);
replace the clusters with statistical models (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.),
whereby each identification is associated with a time series of statistical models (see paragraph 0017 for performing DBSCAN clustering process. As see in paragraph 0020 this involves taking the mean of not only velocity but also relative position.); and
for each identification, predict a trajectory from the time series of statistical models (see paragraphs 0008-0014, item “(4)”. See also paragraphs 0029-0045 for generating a trajectory using the mean values.).
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 Oyama, to add the additional features as taught by Pan of create clusters of positions for each identification over time, whereby each identification is associated with a time series of clusters; replace the clusters with statistical models, whereby each identification is associated with a time series of statistical models; and for each identification, predict a trajectory from the time series of statistical models. The motivation for doing so would be to have a comprehensive precision tracking system with high reliability, as recognized by Pan (see paragraph 0005).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Liu (US2023/0085296).
Regarding claim 5, Oyama and Pan teach the method of claim 1.
Yet Oyama and Pan do not further teach:
The method of claim 1, wherein
regression analysis is initially used to predict the trajectory of each identification.
However, Liu teaches:
regression analysis is initially used to predict the trajectory of each identification (see paragraph 0020).
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 Oyama and Pan, to add the additional features of: regression analysis is initially used to predict the trajectory of each identification, as taught by Liu. The motivation for doing so would be to have trajectories with increased accuracy, as recognized by Liu (see paragraph 0006).
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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Kundu et al. (US2020/0198783).
Regarding claim 8, Oyama and Pan teach the method of claim 1.
Yet Oyama and Pan do not further teach:
The method of claim 1, wherein
the data structures further include velocities of the [the at least one identification] identifications and sensor data about road conditions; and
wherein the predicting further includes determining whether each] identification is traveling at an unsafe velocity in view of the road conditions, and
raising an alert about the [each] identification if the velocity is.
However, Kundu teaches:
the data structures further include velocities of the [the at least one identification] identifications and sensor data about road conditions; and
wherein the predicting further includes determining whether each] identification is traveling at an unsafe velocity in view of the road conditions, and
raising an alert about the [each] identification if the velocity is unsafe (for all these bullets, see Kundu, claim 8).
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 Oyama and Pan, to add the additional features of the data structures further include velocities of the [the at least one identification] identifications and sensor data about road conditions; and wherein the predicting further includes determining whether each] identification is traveling at an unsafe velocity in view of the road conditions, and raising an alert about the [each] identification if the velocity is, as taught by Kundu. The motivation for doing so would be to suggest a safer speed and enhance safety, as recognized by Kundu (see paragraph 0032).
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.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Ohlarik et al. (US2021/0312811) in further view of Shalev-Shwartz et al. (US2021/0200235).
Regarding claim 9, Oyama and Pan teach the method of claim 1.
Yet Oyama and Pan do not further teach:
The method of claim 1, wherein
the [each] data structure further includes a classification of an [each] identification (see Fig. 3 for classifying the objects as a vehicle, scooter, etc.. See Fig. 6 for this information being transmitted in step 620.).
However, Ohlarik teaches:
the [each] data structure further includes a classification of an [each] identification (see Fig. 3 for classifying the objects as a vehicle, scooter, etc.. See Fig. 6 for this information being transmitted in step 620.).
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 Oyama and Pan, to add the additional features of [each] data structure further includes a classification of an [each] identification, as taught by Ohlarik. The motivation for doing so would be to send alerts to enhance safety, as recognized by Ohlarik (see Fig. 6, step 690).
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.
Yet Oyama, Pan, and Ohlarik do not further teach:
wherein if the [each] identification is classified as a motor vehicle having a model and make;
the predicting further includes:
looking up braking and handling capabilities of the motor vehicle according to its model and make;
determining whether velocity of the motor vehicle is unsafe in view of its braking and handling capabilities; and
raising an alert about the identification if the velocity is unsafe.
However, Shalev-Shwartz teaches:
wherein if the [each] identification is classified as a motor vehicle having a model and make;
the predicting further includes:
looking up braking and handling capabilities of the motor vehicle according to its model and make (see paragraph 0836 for determining the make and model of a target vehicle, and its “braking capabilities”. See paragraph 0859 for braking as related to weather condition. See paragraph 0875 for turn radius capability of a target vehicle. See paragraph 0897 for recognition of a vehicle using lidar or an imaging device.);
determining whether velocity of the motor vehicle is unsafe in view of its braking and handling capabilities (see paragraph 0859 for braking as related to weather condition.); and
raising an alert about the identification if the velocity is unsafe (see paragraph 0158 for alerts to the vehicle occupants. See paragraph 0823 for alerts when a distance is unsafe. This has to do with the braking capacity.).
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 Oyama, Pan, Ohlarik to add the additional features of [each] data structure further includes a classification of an [each] identification, as taught by Shalev-Shwartz. The motivation for doing so would be enhance safety, as recognized by Shalev-Shwartz (see paragraph 0823).
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.
Oyama and Ohlarik are highly compatible.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Liu (US2023/0085296).
Regarding claim 11, Oyama and Pan teach the method of claim 1.
Yet Oyama and Pan do not further teach:
The method of claim 1, wherein
the predicting for an [each] identification having a [each] classification includes using an AI model to create a spread of possible trajectories with time,
the AI model trained to process [input features comprising]
a corresponding initial trajectory prediction and
Goodness of Fit,
sensor data reported in the data structures,
[a classification of each identification,] and
historical data concerning the classification.
However, Liu teaches:
the predicting for an [each] identification having a [each] classification includes using an AI model to create a spread of possible trajectories with time (see Fig. 7 and paragraph 0049),
the AI model trained to process [input features comprising]
a corresponding initial trajectory prediction and
Goodness of Fit,
sensor data reported in the data structures,
[a classification of each identification,] and
historical data concerning the classification (for all these hollow bullets, see Fig. 3 which shows trajectory generation based in part on “vehicle history”. Furthermore, an initial trajectory can be used to generate future trajectories, which can feedback to adjust the initial trajectory prediction, implying the first two hollow bullets.).
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 Oyama and Pan to add the additional features as taught by Liu. The motivation for doing so would be increase accuracy of estaimtes, as recognized by Liu (see paragraph 0004).
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 12, Oyama and Pan teach the method of claim 1.
Yet Oyama and Pan do not further teach:
The method of claim 1, wherein
the predicting for an [each] identification having a classification [further] includes
using an AI model to predict behavior of each] identification based on
an initial trajectory prediction,
sensor data reported in the data structures, and
historical data about the classification.
However, Liu teaches:
the predicting for an [each] identification having a classification [further] includes
using an AI model to predict behavior of each] identification based on
an initial trajectory prediction,
sensor data reported in the data structures, and
historical data about the classification (for all these hollow bullets, see Fig. 3 which shows trajectory generation based in part on “vehicle history”. Furthermore, an initial trajectory can be used to generate future trajectories, which can feedback to adjust the initial trajectory prediction, implying the first two hollow bullets.).
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 Oyama and Pan to add the additional features as taught by Liu. The motivation for doing so would be increase accuracy of estaimtes, as recognized by Liu (see paragraph 0004).
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.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further of Liu (US2023/0085296) in further view of Shalev-Shwartz et al. (US2021/0200235).
Regarding claim 13, Oyama, Pan, and Liu teach the method of claim 12.
Yet Oyama, Pan, and Liu do not further teach:
The method of claim 12, wherein
the classification includes make/model of a vehicle; and
wherein characteristics of the make/model are
looked up and also
used by the AI model to predict the behavior. the classification includes make/model of a vehicle; and
However, Shalev-Shwartz further teaches:
the classification includes make/model of a vehicle (see paragraph 0836); and
wherein characteristics of the make/model are
looked up (see paragraph 0836 for determining the make and model of a target vehicle including its “braking capabilities”. See paragraph 0859 for braking as related to weather condition. See paragraph 0875 for turn radius capability of a target vehicle. See paragraph 0897 for recognition of a vehicle using lidar or an imaging device) and also
used by the AI model to predict the behavior (see paragraph 0178).
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 Oyama, Pan, Liu to add the additional features taught by Shalev-Shwartz. The motivation for doing so would be enhance safety, as recognized by Shalev-Shwartz (see paragraph 0823).
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.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view Liu (US2023/0085296) in further view of Ohlarik et al. (US2021/0312811 A1)
Regarding claim 14, Oyama, Pan, and Liu teach the method of claim 1.
Yet Oyama, Pan, and Liu do not teach:
The method of claim 12, wherein
the AI model used to predict the behavior is selected from a collection of AI models, where the selection is made according to classification.
However, Ohlarik teaches:
the AI model used to predict the behavior is selected from a collection of AI models, where the selection is made according to classification (see Fig. 3).
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 Oyama, Pan, and Liu to add the additional features of the AI model used to predict the behavior is selected from a collection of AI models, where the selection is made according to classification, as taught by Ohlarik. The motivation for doing so would be to send alerts to enhance safety, as recognized by Ohlarik (see Fig. 6, step 690).
Claims 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Suehiro et al. (US2023/0311941 A1).
Regarding claim 19, Oyama and Pan teach the server system of claim 18.
Yet Oyama and Pan do not further teach:
The server system of claim 18, wherein
the at least one server includes a core server and plurality of client servers assigned to a corresponding plurality of zones of the road system; and
wherein each client server includes first, second, third and fourth modules.
Yet Oyama and Pan do not further teach:
the at least one server includes a core server and plurality of client servers assigned to a corresponding plurality of zones of the road system; and
wherein each client server includes first, second, third and fourth modules.
However, Suehiro teaches:
the at least one server includes a core server and plurality of client servers assigned to a corresponding plurality of zones of the road system; and
wherein each client server includes first, second, third and fourth modules (see Fig. 1. Suehiro teaches that what is taught by Oyama and Pan can be replicated in the claimed architecture.).
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 Oyama and Pan to add the additional features of the at least one server includes a core server and plurality of client servers assigned to a corresponding plurality of zones of the road system, as taught by Suehiro. The motivation for doing so would be to reduce network congestion and provide for multiple support areas, as recognized by Suehiro (see paragraph 0005).
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 20, Oyama and Pan teach the server system of claim 19.
Yet Oyama and Pan do not further teach:
The server system of claim 19, wherein
each server is configured to receive data structures from motor vehicles in its corresponding zone.
However, Suehiro teaches:
each server is configured to receive data structures from motor vehicles in its corresponding zone (see Fig. 1 for vehicle 22 communicating with the base station 20-1 which in turn communicates with the edge server 30.).
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 Oyama, Pan, and Suehiro to add the additional features of each server is configured to receive data structures from motor vehicles in its corresponding zone, as taught by Suehiro. The motivation for doing so would be to reduce network congestion and provide for multiple support areas, as recognized by Suehiro (see paragraph 0005).
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 21, Oyama and Pan teach the server system of claim 19.
Yet Oyama and Pan do not further teach:
The server system of claim 19, wherein
the core server is configured to store data sets from each client server and make the data sets accessible to all of the client servers (see paragraph 0005 for a “core network”.).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Oyama et al. (US2024/0067210) in view of Pan et al. (US2024/0288569) in further view of Suehiro et al. (US2023/0311941 A1) in further view of Fowe (US2023/0204378).
Regarding claim 22, Oyama, Pan, and Suehiro teach the server system of claim 21.
Yet Oyama, Pan, and Suehiro do not further teach:
The server system of claim 21, wherein
the core sever is further configured to use the data sets to train and update AI models for predicting
However, Fowe teaches:
the core sever is further configured to use the data sets to train and update AI models for predicting see paragraphs 0072 and 0073).
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 Oyama, Pan, and Suehiro to add the additional features of the core sever is further configured to use the data sets to train and update AI models for predicting see Fig. 9, A250).
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.
Allowable Subject Matter
Claims 6, 7, and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and all other rejection can be resolved, such as 35 U.S.C. § 112 rejections.
Claim 6 is not taught by the prior art of record, alone or in combination. The claim recites:
The method of claim 5, wherein
the regression analysis produces the] predicted trajectory having a Goodness of Fit; and,
if the Goodness of Fit is not sufficiently accurate, a more advanced analysis is used to produce a more accurate predicted trajectory.
One close prior art is Pan et al (US2024/0288569). Pan teaches determining if the correlation was a success or failure in Fig. 1, but not, as in present claim 6, “if the Goodness of Fit is not sufficiently accurate, a more advanced analysis is used to produce a more accurate predicted trajectory.”
Another close prior art is Liu (US2023/0085296). Liu teaches using regression analysis in paragraph 0046 but not, as in present claim 6, “if the Goodness of Fit is not sufficiently accurate, a more advanced analysis is used to produce a more accurate predicted trajectory.”
Claim 7 is not taught by the prior art of record, alone or in combination. The claim recites:
The method of claim 1, wherein
the predicting for each identification includes creating a spread of possible trajectories with time as a function of corresponding Goodness of Fit.
The closest art is that cited in the discussion of claim 6 above but that art does not teach claim 7 either.
Claim 10 is not taught by the prior art of record, alone or in combination. The claim recites:
The method of claim 1, wherein
the predicting further includes computing a Fourier transform of frequency of velocity changes of that [each] identification and associating peaks of the Fourier transform to identify dangerous behavior.
One close art is Agata et al. (US2018/0204335), paragraph 0130. But Agata does not teach, as claim 10 does: the predicting further includes computing a Fourier transform of frequency of velocity changes of that [each] identification and associating peaks of the Fourier transform to identify dangerous behavior.
Additional Art
The prior art made of record here, though not relied upon, is considered pertinent to the present disclosure.
One close prior art is Herbach et al. (US 9,008,890). Herbach teaches a system in which an autonomous vehicle transmits sensor data to a server and the server generates trajectory data of objects and returns a safe trajectory of the own vehicle.
Another close prior art is Nister et al. (US2019/0243371). Nister teaches determining the spread or flare of a vehicle trajectory.
Another close prior art is Vemuri et al. (US2022/0386094). See paragraph 0062 for vehicle performing a search for a “potential host vehicle” with “MEC capabilities” defined as “having an onboard MEC module.”
Another close prior art is Hellgren et al. (US2024/0379000). See paragraph 0066 for “FIG. 2 shows, in accordance with a further embodiment, a computer system 200 for controlling a plurality of vehicles 299 sharing a set of resources. The computer system 200 may be fixedly installed or carried in a vehicle 299.”
Another close prior art is Di Francesco (US2023/0260334). See paragraph 0065 for “The Edge server 304 may be hardware onboard the vehicle or software running on a personal portable equipment such as a mobile phone. The Edge server 304 may contain an anti-tampering mechanism preventing fraudulent modification.”
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Another close prior art is Melen et al. (US2021/0291851). See paragraph 0004 for “Edge computing is beneficial to connected vehicles for various reasons, including, for example: (1) expanding the computational ability of vehicle's onboard computer systems by allowing them to access the computational resources of the edge server, cloud server, and/or other vehicles; (2) allowing vehicles to share important data with one another [e.g., sensor data, future vehicle decisions, any other important digital data which can be shared among vehicles]; and (3) allowing vehicles to participate in other vehicle cloudification.” Brackets in original.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at 571-272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL M. ROBERT/Primary Examiner, Art Unit 3665
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