DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgement is made of Applicant’s claim of priority from Foreign Application No. CN202311754128.2, filed December 19, 2023.
Claim Objections
Claims 1, 5-7, 11-13 and 17-18 are objected to because of the following informalities: claims 1, 11 and 17 recite “lane line formation”. Examiner believes this was meant to recite “lane line information”. Claims 5-6, 11-12 and 17-18 recite “the element coding information, the timing coding information, and the topology coding information”. Examiner believes this was meant to recite “the element encoding information, the timing encoding information, and the topology encoding information”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a device, method, and non-transitory computer-readable medium for traffic light prediction. Consider method claim 1:
Step 1:
With regard to Step 1, the instant claim is directed to a method or a process; and therefore, the claim is directed to one of the statutory categories of invention.
Step 2A, Prong One:
With regard to 2A, Prong One, the limitations “determining, based on current position information of a vehicle, lane line information of a lane where the vehicle is located and information of a target traffic light corresponding to the lane, and recording the lane line formation and the information of the target traffic light as element information”, “recognizing an obstacle in an image acquired by the vehicle to obtain obstacle information; associating the element information with the obstacle information to generate topology information, wherein the topology information is used to characterize a binding relationship among the target traffic light, the lane line, and the obstacle” and “generating a prediction result of the target traffic light based on the element information, the obstacle information, and the topology information” as drafted, recite an abstract idea, such as a process that, under its broadest reasonable interpretation, covers performance of the limitations manually and in the mind of a person. That is, a user or person skilled in the art may determine a lane where a vehicle is located from the position of the vehicle, determine a traffic light corresponding to that lane, recognize an obstacle such as a pedestrian in an image, associate the obstacle, traffic light, and lane with each other, and predict the color of the traffic light based on the information of the lane, the light and the object (e.g., determine the traffic light in the lane is red when a pedestrian is walking across the crosswalk). This is the concept that falls under the grouping of abstract ideas mental processes, i.e., a concept performed in the human mind, evaluation, judgement, and/or opinion of the user.
Step 2A, Prong Two:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional step or a combination of additional steps in the claim to apply, rely on, or use the judicial exception. In addition, with respect to the device and computer-readable medium claims of claims 7-12 and 13-18, the mere recitation of a generic processor, memory, or storage medium to perform/store programming instructions of the recited/identified abstract idea does not integrate the identified abstract idea into a practical application. Accordingly, the above-mentioned additional elements/limitations do not integrate the abstract idea into a practical application; and therefore, the independent claims recite an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B. The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/limitations to perform the recited steps, amount to no more than insignificant extra-solution activity. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Therefore, independent claims 1, 7 and 13 are not patent eligible. In addition, claims 2-6, 8-12 and 14-18 of the instant application provide limitations that both individually or in combination do not integrate the identified abstract idea into a practical application or provide significantly more than the identified abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 12,456,307 B1, filed November 30, 2022) in view of Hutchinson et al. (US 2024/0371178 A1, filed May 3, 2023).
Regarding claim 1, Das teaches a traffic light prediction method, comprising:
determining, based on current position information of a vehicle, lane line information of a lane where the vehicle is located and information of a target traffic light corresponding to the lane, and recording the lane line formation and the information of the target traffic light as element information (Das, Col. 18 line 50 – Col. 19 line 11, the vehicle computing system may determine one or more pixels represented in an image that may be associated with one or more lanes of traffic in the environment (i.e., determining lane line information of a lane where the vehicle is located). The vehicle computing system may also, or instead, determine one or more pixels represented in an image that may be associated with one or more traffic lights and/or traffic signs in the environment (i.e., information of a target traffic light corresponding to the lane). Col. 19, lines 52-65, the vehicle computer system may determine whether one or more of the probabilities of potential light/lane associations for individual pixels in one or more images meet or exceed a threshold probability. For those probabilities that meet or exceed such a threshold, the vehicle computing system may assign a corresponding light/lane association label to that pixel (i.e., recording the lane line information and the information of the target traffic light as element information));
recognizing an obstacle in an image acquired by the vehicle to obtain obstacle information (Das, Col. 27, lines 33-48, the perception component can include functionality to perform object detection, segmentation, and/or classification, in addition to, or instead of, traffic light and lane association detection and labeling and machine-learned model training operations as described herein. The perception component can provide processed sensor data that indicates a presence of an entity that is proximate to the vehicle and/or a classification of the entity as an entity type (e.g., car, pedestrian, cyclist, animal, building, tree, road surface, curb, sidewalk, traffic signal, traffic light, car light, brake light, solid object, impeding object, non-impeding object, small, dynamic, non-impeding object, unknown) (i.e., recognizing an obstacle in an image acquired by the vehicle to obtain obstacle information).
Although Das teaches detecting entities proximate to the vehicle (Das, Col. 27, lines 33-48), Das does not explicitly teach “associating the element information with the obstacle information to generate topology information, wherein the topology information is used to characterize a binding relationship among the target traffic light, the lane line, and the obstacle” and “generating a prediction result of the target traffic light based on the element information, the obstacle information, and the topology information”. However, in an analogous field of endeavor, Hutchinson teaches the state detection engine (or other software component of the autonomy system) references additional types of information indicating features of the roadways. For example, the state detection engine determines or references a pedestrian traffic light, the presence of pedestrians in front of the automated vehicle, and the state of adjacent traffic lights of adjacent lanes of travel, among other types of information. In some cases, the state detection engine (or other software component of the autonomy system) detects and references the accelerations and velocities of all traffic around the intersection to help confirm the traffic light state detection. For instance, if all of the vehicles ahead of and before the intersection are decelerating to a stop, the state detection engine may be more likely to determine the state of the traffic light in the direction of travel is yellow or red (i.e., associating the element information with the obstacle information to generate topology information, wherein the topology information is used to characterize a binding relationship among the target traffic light, the lane line, and the obstacle and generating a prediction result of the target traffic light based on the element information, the obstacle information, and the topology information). In some cases, the state detection engine (or other software component of the autonomy system) detects and references brake lights of vehicles ahead of the ego vehicle to improve estimates of vehicle accelerations and/or confirm the traffic light state detection as red (Hutchinson, Para. [0102]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Das with the teachings of Hutchinson by including associating the lane line and traffic light information with information on surrounding obstacles (i.e., pedestrian traffic lights or surrounding vehicles) to generate topology information and generate a traffic light state detection based on the element information, obstacle information and topology information. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for automatically detecting and identifying the state of traffic lights, as recognized by Hutchinson. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 7 recites a device with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das and Hutchinson references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Das and Hutchinson references discloses a processor and a memory (Das, Col. 25, lines 14-34, vehicle computing system that may include one or more processors and a memory).
Claim 13 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das and Hutchinson references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Das and Hutchinson references discloses a computer readable storage medium (Das, Col. 33, lines 5-20, the memories are examples of non-transitory computer-readable media).
Claims 2, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 12,456,307 B1, filed November 30, 2022) in view of Hutchinson et al. (US 2024/0371178 A1, filed May 3, 2023), as applied to claims 1, 7 and 13 above, and further in view of Sakakura et al. (US 2022/0402492 A1).
Regarding claim 2, Das in view of Hutchinson teaches the method according to claim 1, as described above.
Although Das in view of Hutchinson teaches determining lane line information and traffic light information (Das, Col. 18 line 50 – Col. 19 line 11), they do not explicitly teach “determining the lane where the vehicle is located from a high-precision map based on the current position information, and acquiring the lane line information of the lane” and “determining the information of the target traffic light corresponding to the lane based on the lane line information”. However, in an analogous field of endeavor, Sakakura teaches the high-precision map includes, as lane information, information of lane nodes that indicate reference points on a lane reference line (for example, a central line in a lane) and information of lane links that indicate forms of lane sections between lane nodes (i.e., determining the lane from a high-precision map based on the current position information and acquiring the lane line information of the lane) (Sakakura, Para. [0028]). The high-precision map further includes information of a traffic light that exists on a lane or in the vicinity of the lane. Information of traffic lights included in the high-precision map data is sometimes referred to as “traffic light information” (i.e., determining the information of the target traffic light corresponding to the lane based on the lane line information) (Sakakura, Para. [0031]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Das in view of Hutchinson with the teachings of Sakakura by including determining lane information from a high-precision map and determining the traffic light information from the lane line information. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for autonomous driving based on recognition of a traffic light, as recognized by Sakakura. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 8 recites a device with elements corresponding to the steps recited in Claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson and Sakakura references, presented in rejection of Claim 2, apply to this claim. Finally, the combination of the Das, Hutchinson and Sakakura references discloses a processor and a memory (Das, Col. 25, lines 14-34, vehicle computing system that may include one or more processors and a memory).
Claim 14 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 2. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson and Sakakura references, presented in rejection of Claim 2, apply to this claim. Finally, the combination of the Das, Hutchinson and Sakakura references discloses a computer readable storage medium (Das, Col. 33, lines 5-20, the memories are examples of non-transitory computer-readable media).
Claims 3-4, 9-10 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 12,456,307 B1, filed November 30, 2022) in view of Hutchinson et al. (US 2024/0371178 A1, filed May 3, 2023) further in view of Sakakura et al. (US 2022/0402492 A1), as applied to claims 2, 8 and 14 above, and further in view of Huberman et al. (US 12,620,240 B2, filed August 10, 2022).
Regarding claim 3, Das in view of Hutchinson further in view of Sakakura teaches the method according to claim 2, recognizing the obstacle in the image acquired by the vehicle to obtain obstacle information includes:
detecting an obstacle in the image and determining the category information of the obstacle by using the image detection method (Das, Col. 27, lines 33-48, the perception component can include functionality to perform object detection, segmentation, and/or classification, in addition to, or instead of, traffic light and lane association detection and labeling and machine-learned model training operations as described herein. The perception component can provide processed sensor data that indicates a presence of an entity that is proximate to the vehicle and/or a classification of the entity as an entity type (e.g., car, pedestrian, cyclist, animal, building, tree, road surface, curb, sidewalk, traffic signal, traffic light, car light, brake light, solid object, impeding object, non-impeding object, small, dynamic, non-impeding object, unknown).
Although Das in view of Hutchinson further in view of Sakakura teaches determining category information for an obstacle (Das, Col. 27, lines 33-48), they do not explicitly teach “wherein the obstacle information includes category information of the obstacle and historical observation information of the obstacle” and “tracking the obstacle by using a Kalman filtering method to obtain the historical observation information of the obstacle”. However, in an analogous field of endeavor, Huberman teaches a processing unit may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit may construct the measurements based on estimation techniques using a series of time-based observations (i.e., historical observation information) such as Kalman filters (i.e., tracking the obstacle by using Kalman filtering to obtain the historical observation information) or linear quadratic estimation (LQE), and/or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). Processing unit may identify vehicles and pedestrians (i.e., category information) appearing within the set of captured images and derive information (e.g., position, speed, size) (i.e., historical information) associated with the vehicles and pedestrians. (Huberman, Col. 26, lines 47-67).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Das in view of Hutchinson further in view of Sakakura with the teachings of Huberman by including determining historical observation information (i.e., time-based observations) for the object by using Kalman filtering. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for performing navigational response in a vehicle based on detected object information, as recognized by Huberman. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 4, Das in view of Hutchinson further in view of Sakakura and Huberman teaches the method according to claim 3, wherein the method further comprises:
determining timing information of a roadside traffic light corresponding to the obstacle based on the obstacle information (Hutchinson, Para. [0107], using various types of information, the autonomy system may generate an estimate of when the traffic light will next change state. For instance, if the pedestrian traffic light (i.e., roadside traffic light) for a parallel crosswalk changes from a “walk” indication to a “don't walk” indication, or starts counting down to a future “don't walk” indication (i.e., timing information), then the autonomy system may infer that there is a higher probability that the vehicle traffic lights governing vehicle travel lanes travelling parallel to the crosswalk will soon turn from green to red); and
associating the element information with the obstacle information to generate the topology information includes:
associating the roadside traffic light with the target traffic light by using a calibration external parameter of the vehicle to generate a binding relationship between the target traffic light and the lane line (Hutchinson, Para. [0107], using various types of information, the autonomy system may generate an estimate of when the traffic light will next change state. For instance, if the pedestrian traffic light for a parallel crosswalk changes from a “walk” indication to a “don't walk” indication, or starts counting down to a future “don't walk” indication, then the autonomy system may infer that there is a higher probability that the vehicle traffic lights governing vehicle travel lanes travelling parallel to the crosswalk will soon turn from green to red).
The proposed combination as well as the motivation for combining the Das, Hutchinson, Sakakura and Huberman references presented in the rejection of Claim 3, apply to Claim 4 and are incorporated herein by reference. Thus, the method recited in Claim 4 is met by Das in view of Hutchinson further in view of Sakakura and Huberman.
Claims 9-10 recite devices with elements corresponding to the steps recited in Claims 3-4, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura and Huberman references, presented in rejection of Claim 3, apply to these claims. Finally, the combination of the Das, Hutchinson, Sakakura and Huberman references discloses a processor and a memory (Das, Col. 25, lines 14-34, vehicle computing system that may include one or more processors and a memory).
Claims 15-16 recite computer-readable storage mediums storing a program with instructions corresponding to the steps recited in Claims 3-4, respectively. Therefore, the recited programming instructions of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura and Huberman references, presented in rejection of Claim 3, apply to these claims. Finally, the combination of the Das, Hutchinson, Sakakura and Huberman references discloses a computer readable storage medium (Das, Col. 33, lines 5-20, the memories are examples of non-transitory computer-readable media).
Claims 5, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 12,456,307 B1, filed November 30, 2022) in view of Hutchinson et al. (US 2024/0371178 A1, filed May 3, 2023) further in view of Sakakura et al. (US 2022/0402492 A1) and Huberman et al. (US 12,620,240 B2, filed August 10, 2022) as applied to claims 3-4, 9-10 and 15-16 above, and further in view of Pan et al. (US 2021/0001843 A1).
Regarding claim 5, Das in view of Hutchinson further in view of Sakakura and Huberman teaches the method according to claim 4, wherein generating the prediction result of the target traffic light based on the element information, the obstacle information, and the topology information comprises:
generating a prediction result of the target traffic light based on the element coding information, the timing coding information, and the topology coding information (Hutchinson, Para. [0107], using various types of information, the autonomy system may generate an estimate of when the traffic light will next change state. For instance, if the pedestrian traffic light for a parallel crosswalk changes from a “walk” indication to a “don't walk” indication, or starts counting down to a future “don't walk” indication, then the autonomy system may infer that there is a higher probability that the vehicle traffic lights governing vehicle travel lanes travelling parallel to the crosswalk will soon turn from green to red).
The proposed combination as well as the motivation for combining the Das, Hutchinson, Sakakura and Huberman references presented in the rejection of Claim 3, apply to Claim 5 and are incorporated herein by reference.
Although Das in view of Hutchinson further in view of Sakakura and Huberman teaches one or more wheel encoders (Das, Col. 31, lines 29-50), they do not explicitly teach “encoding the element information by using an element encoder to obtain element encoding information”, “encoding historical observation information of the obstacle and timing information of the roadside traffic light by using a timing encoder to obtain timing encoding information”, “encoding the topology information by using a topology encoder to obtain topology encoding information”. However, in an analogous field of endeavor, Pan teaches a lane selection prediction module that includes one or more lane feature encoders (i.e., element encoder), an obstacle feature encoder (i.e., timing encoder), an environment feature encoder (i.e., topology encoder) and a lane selection predictor (Pan, Para. [0046]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Das in view of Hutchinson further in view of Sakakura and Huberman with the teachings of Pan by including encoders for encoding the different information types used for prediction. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for operating a vehicle in an autonomous mode, as recognized by Pan. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 11 recites a device with elements corresponding to the steps recited in Claim 5. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura, Huberman and Pan references, presented in rejection of Claim 5, apply to this claim. Finally, the combination of the Das, Hutchinson, Sakakura, Huberman and Pan references discloses a processor and a memory (Das, Col. 25, lines 14-34, vehicle computing system that may include one or more processors and a memory).
Claim 17 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 5. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura, Huberman and Pan references, presented in rejection of Claim 5, apply to this claim. Finally, the combination of the Das, Hutchinson, Sakakura, Huberman and Pan references discloses a computer readable storage medium (Das, Col. 33, lines 5-20, the memories are examples of non-transitory computer-readable media).
Claims 6, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 12,456,307 B1, filed November 30, 2022) in view of Hutchinson et al. (US 2024/0371178 A1, filed May 3, 2023) further in view of Sakakura et al. (US 2022/0402492 A1), Huberman et al. (US 12,620,240 B2, filed August 10, 2022) and Pan et al. (US 2021/0001843 A1), as applied to claims 5, 11 and 17 above, and further in view of Chen et al. (US 2023/0351773 A1, filed April 28, 2022).
Regarding claim 6, Das in view of Hutchinson further in view of Sakakura, Huberman and Pan teaches the method according to claim 5, wherein generating the prediction result of the target traffic light based on the element coding information, the timing coding information, and the topology coding information comprises:
connecting the element coding information, the timing coding information, and the topology coding information by using a map attention neural network to obtain target feature information (Pan, Para. [0015], Once the obstacle features are encoded, it can be used as an input to the lane-encoder to provide attention score and let the lane-encoder focus on the important lane-points. Once we have all the forward and backward encodings, they can be passed through an aggregating module to form an aggregated encoding (i.e., target feature information)).
The proposed combination as well as the motivation for combining the Das, Hutchinson, Sakakura, Huberman and Pan references presented in the rejection of Claim 5, apply to Claim 6and are incorporated herein by reference.
Although Das in view of Hutchinson further in view of Sakakura, Huberman and Pan teaches encoders for encoding relevant information (Pan, Para. [0046]), they do not explicitly teach “decoding the target feature information to generate the prediction result of the target traffic light”. However, in an analogous field of endeavor, Chen teaches the output of the encoder is fed to multiple heads in parallel that decode the features or attributes for forming predictions (Chen, Para. [0027]). The 3D detection head decodes the features from the encoder to detect traffic lights. The recognition head decodes the output of the encoder and outputs red, green, yellow, symbol, and so on information (Chen, Para. [0028]).
Therefore, it would have been obvious to one having ordinary skill in the art to modify the method of Das in view of Hutchinson further in view of Sakakura, Huberman and Pan with the teachings of Chen by including decoding the target feature information (i.e., output of the encoder) to generate the prediction result of the target traffic light. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for improved detection of traffic lights corresponding to driving lanes, as recognized by Chen. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 12 recites a device with elements corresponding to the steps recited in Claim 6. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura, Huberman, Pan and Chen references, presented in rejection of Claim 6, apply to this claim. Finally, the combination of the Das, Hutchinson, Sakakura, Huberman, Pan and Chen references discloses a processor and a memory (Das, Col. 25, lines 14-34, vehicle computing system that may include one or more processors and a memory).
Claim 18 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 6. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Das, Hutchinson, Sakakura, Huberman, Pan and Chen references, presented in rejection of Claim 6, apply to this claim. Finally, the combination of the Das, Hutchinson, Sakakura, Huberman, Pan and Chen references discloses a computer readable storage medium (Das, Col. 33, lines 5-20, the memories are examples of non-transitory computer-readable media).
Conclusion
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/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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