DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 of US application 18/628,567 filed 4/5/24 were examined. Examiner filed a non-final rejection on 11/19/25.
Applicant filed remarks and amendments on 2/17/26. Claims 1, 3, 13-14 and 19-20 were amended. Claims 1-20 are presently pending and presented for examination.
Response to Arguments
Regarding the claim objections: applicant’s amendments failed to fully resolve the claim objections to claim 13. The objection is maintained in part. Please see the “Claim Objections” section for more details.
Regarding the claim rejections under 35 USC 112: applicant’s amendments have resolved the previously given 112(b) rejections. These previously given rejections are withdrawn. However, applicant’s amendments have introduced new 112 rejections. These new rejections may be found in the section of this office action titled “Claim Rejections – 35 USC § 112”.
Regarding the claim rejections under 35 USC 101: applicant’s amendments have obviated the 101 rejections by amending the claims to recite use of an actual accessory in conjunction with the data gathering pertaining to the accessory, which is something that a human cannot do mentally or manually because it requires the vehicle to be operated in order for the accessories to be used.
Regarding the claim rejections under 35 USC 102 and 103: Applicant's arguments filed 2/17/26 (hereinafter referred to as the “Remarks”) have been fully considered but they are not persuasive.
Regarding claims 1, 14, and 20, applicant suggests that applicant has amended the claims as discussed in the interview dated 2/17/26. However, this is not quite accurate. In the interview, examiner suggested that applicant amend the claim to include the limitation “wherein the respective vehicle accessory is at least one of a soft shackle, a snow chain, or a tow strap”. This is evidenced by the 413 interview summary mailed 2/20/26 summarizing the 2/17/26 interview. However, applicant instead amended the claim to include the limitation, “wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, tow straps, or equivalent attachable gear”. (emphasis added). This is a far broader limitation, since “equivalent attachable gear” is incredibly broad and not at all clear. Therefore, examiner will interpret “equivalent attachable gear” to be any equipment, component, or part that is attached to the vehicle, which essentially refers to any vehicle equipment, component, or part at all. The rationale for this can be found in the section of this office action titled “Claim Rejections – 35 USC § 112”.
Accordingly, all of the previously given prior art rejections are maintained.
Examiner’s suggestion to help applicant overcome all the rejections: applicant may overcome all of the rejections by amending the independent claims as follows:
“wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, or tow straps
However, further search and consideration will be required before it can be determined whether this amendment is allowable or not.
Claim Objections
Claim 13 is objected to because of the following informalities:
In claim 13, “by training a neural network to predict vehicle accessory” should be “by training a neural network to predict a vehicle accessory”
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 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.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed had possession of the claimed invention. Furthermore, claims 1-20 are further rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint regards as the invention.
Regarding claims 1, 14, and 20, these claims recite the limitation, “at least one of soft shackles, snow chains, tow straps, or equivalent attachable gear” (emphasis added).
However, it is unclear what is meant by “equivalent attachable gear”. Soft shackles, snow chains, and tow straps are not equivalent to each other, so it is unclear which of the three the “equivalent attachable gear” is supposed to be equivalent to. Moreover, it is unclear how the “equivalent attachable gear” is equivalent to any of these three. Is the “equivalent attachable gear” equivalent simply by virtue of being attachable? Or is it equivalent in function to one or more of the soft shackles, snow chains, and tow straps? The claims do not specify. And there is no basis in the specification for such clarification of “equivalent attachable gear” as well. So “equivalent” will be held to mean any object that is “attachable” to a vehicle.
Furthermore, “attachable”, “attach”, and any variation thereof do not appear in the specification. So what is the meaning of “attachable” in the claims? Anyone of ordinary skill in the art will appreciate that all parts of a vehicle used to manufacture the vehicle may be regarded as “accessories” which are “attached” to the vehicle, whether by screws, bolts, wires, or some other means in order to make the vehicle function. A processor implementing an autonomous vehicle control system may be regarded as “equivalent attachable gear” under this interpretation. Or the various vehicle components (pedals, valves, brakes, etc.) controllable by the processor may be regarded as “equivalent attachable gear” under this interpretation. Because all of these are physical components that must be mounted somewhere in the vehicle for the vehicle to function. In the interview summary dated 2/20/26 summarizing the interview which took place on 2/17/26, examiner cautioned applicant that the word “attach” does not have basis in the specification, nor does the specification recite prompting an operator to perform such an attachment (See at least the PTO-413 or 413B Interview Summary Record filed 2/20/26). However, applicant failed to heed this warning.
Accordingly, claims 1, 14, and 20 and their dependents are indefinite under 35 USC 112(b).
Moreover, since the specification does not contain the phrase “equivalent attachable gear”, this limitation supporting written description in the specification, which means that the claims are also rejected under 35 U.S.C. 112(a).
For purposes of prior art rejection, examiner will apply examiner’s broadest reasonable interpretation so that “equivalent attachable gear” may refer to any vehicle accessory, part, or component at all, given the above discussion of how broad “equivalent” and “attachable” are.
Examiner’s suggestion to help applicant overcome all the rejections: applicant may overcome all of the rejections by amending the independent claims as follows:
“wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, or tow straps
However, further search and consideration will be required before it can be determined whether this amendment is allowable or not.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moustafa et al. (US 20220126864 A1) in view of Okajima et al. (US 20240043029 A1), hereinafter referred to as Moustafa and Okajima, respectively. Where applicable, for legibility, similar claims are grouped together and the language of the narrowest claim is mapped to the art. But it will be appreciated that all claims in such groups are rejected.
Regarding claims 1 and 14, Moustafa discloses A system (Moustafa discloses the autonomous vehicle [See at least Moustafa, 0251]) comprising:
a memory (Moustafa discloses the in-vehicle systems of the autonomous vehicles themselves [See at least Moustafa, 0251]); and
at least one processor coupled to the memory (Moustafa discloses the in-vehicle systems of the autonomous vehicles themselves [See at least Moustafa, 0251]) and configured to:
receive indication of a route projection of a vehicle (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]);
obtain information associated with the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]);
extract one or more features from the route projection and the information associated with the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]) using a trained machine learning model (Moustafa discloses that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]);
detect, using the trained machine learning model and based on the one or more features (Moustafa discloses that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]), a vehicle path condition in the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]);
generate, using the trained machine learning model and based on the vehicle path condition (Moustafa discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]), a plurality of predictions indicating first suggestions comprising a respective action to be executed by the vehicle along a vehicle path of the route projection (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions) and second suggestions comprising a respective vehicle accessory to be used by the vehicle along the vehicle path (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions that will require use of various vehicle components (“accessories”) in order to be executed), wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, tow straps, or equivalent attachable gear (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions that will require use of various vehicle components (“accessories”) in order to be executed. For example, remote valet and pull-over obviously both require control of accessories of the vehicle such as brakes and steering systems, which are attached to the vehicle. As discussed in the section of this office action titled “Claim Rejections – 35 USC § 112”, any of these pieces of hardware may be broadly regarded as “equivalent attachable gear”); and
provide for display, on a user interface, a notification indicating one or more of the first suggestions or the second suggestions (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]).
However, Moustafa does not explicitly teach the system wherein the processor is configured to cause the respective action to be executed by the vehicle based at least in part on a first received input indicating selection of at least one of the first suggestions or cause the respective vehicle accessory to be used by the vehicle based at least in part on a second received input indicating selection of at least one of the second suggestions.
However, Okajima does explicitly teach the vehicle wherein the processor is configured to cause the respective action to be executed by the vehicle based at least in part on a first received input indicating selection of at least one of the first suggestions or prompt an operator of the vehicle to use the respective vehicle accessory based at least in part on a second received input indicating selection of at least one of the second suggestions (See at least Fig. 6 in Okajima: Okajima teaches that a button for selecting switching to manual driving may be displayed by the display unit 86 [See at least Okajima, 0064]. Okajima further teaches that This example shows an example in which the display unit 86 is integrated with the input receiving unit 87 to be implemented by a touch panel or the like [See at least Okajima, 0064]). Both Okajima and Moustafa teach methods for displaying one or more driving options involving driving mode to a user and allowing users to select an option. However, only Okajima explicitly teaches where the autonomous vehicle executes the selected driving mode.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the driving mode selection method of Moustafa to also execute the driving mode selected by the user, as in Okajima. Anyone of ordinary skill in the art will appreciate that an autonomous vehicle should execute the mode selected by the user, or else there is no point in having the user select the mode in the first place.
Examiner’s suggestion to help applicant overcome all the rejections: applicant may overcome all of the rejections by amending the independent claims as follows:
“wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, or tow straps
However, further search and consideration will be required before it can be determined whether this amendment is allowable or not.
Regarding claims 2 and 18, Moustafa in view of Okajima teaches The system of claim 14, wherein the at least one processor is further configured to generate a first tutorial that outlines steps on how to cause one or more actions associated with the first suggestions to be executed by the vehicle along the vehicle path and display, on the user interface, the first tutorial (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. The displaying of the options may be regarded as displaying steps that the user can take to cause the respective options to be executed by the vehicle, since the displaying of an option for selection by the user may broadly be regarded as a tutorial or step).
Regarding claims 3 and 19, Moustafa in view of Okajima teaches The system of claim 14, wherein the at least one processor is further configured to generate a second tutorial that outlines steps on how to apply the respective vehicle accessory associated with the second suggestions to use along the vehicle path and display, on the user interface, the second tutorial (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. The displaying of the options may be regarded as displaying steps that the user can take to cause the respective options to be executed by the vehicle, since the displaying of an option for selection by the user may broadly be regarded as a tutorial or step).
Regarding claim 4, Moustafa in view of Okajima teaches The method of claim 1, further comprising detecting, by the one or more processors, based on the one or more features, a vehicle path condition in the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]).
Regarding claims 5-6 and 15, Moustafa in view of Okajima teaches The system of claim 14, wherein the at least one processor configured to detect the vehicle path condition is further configured to detect a type of terrain along the vehicle path condition in the route projection (Moustafa discloses that the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further discloses that this metadata can be additional environment information indicating environmental contexts such as terrain information (e.g., “hilly” or “flat”) [See at least Moustafa, 0315]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]), and wherein the at least one processor is further configured to generate the plurality of predictions based at least in part on the detected type of terrain along the vehicle path condition in the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that recommendation system may be utilized to generate alerts for presentation on the vehicle's audio and/or graphic displays, such as to alert a driver of potential areas of concerns, prepare one or more passengers for a handover or pullover event, warn passengers of the likelihood of such events, warn passengers of potential downgrades in the autonomous driving level (e.g., from L5 to L4, L4 to L3, etc.) based on driving conditions detected ahead (and also, in some implementations, user preference information (identifying the user's risk and manual driving tolerances)), among other examples [See at least Moustafa, 0212]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]).
Regarding claims 7-9 and 16-17, Moustafa in view of Okajima teaches The system of claim 14, wherein the at least one processor configured to detect the vehicle path condition is further configured to detect a type of hindrance along the vehicle path condition in the route projection (Moustafa discloses that the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further discloses that this metadata can be additional environment information indicating environmental contexts such as terrain information (e.g., “hilly” or “flat”) [See at least Moustafa, 0315]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]. Moustafa further discloses that vehicle decision-making may be controlled by a machine learning model using data collected by the vehicle itself or collected or received from other vehicles [See at least Moustafa, 0200]), and wherein the at least one processor is further configured to generate the plurality of predictions based at least in part on the detected type of hindrance along the vehicle path condition in the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that recommendation system may be utilized to generate alerts for presentation on the vehicle's audio and/or graphic displays, such as to alert a driver of potential areas of concerns, prepare one or more passengers for a handover or pullover event, warn passengers of the likelihood of such events, warn passengers of potential downgrades in the autonomous driving level (e.g., from L5 to L4, L4 to L3, etc.) based on driving conditions detected ahead (and also, in some implementations, user preference information (identifying the user's risk and manual driving tolerances)), among other examples [See at least Moustafa, 0212]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]. Moustafa further discloses that this decision-making may be controlled by a machine learning model using data collected by the vehicle itself or collected or received from other vehicles [See at least Moustafa, 0200]).
Regarding claim 10, Moustafa in view of Okajima teaches The method of claim 1, further comprising receiving, via the user interface, user input indicating a selection of at least one of the first suggestions or the second suggestions (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]).
Regarding claim 11, Moustafa in view of Okajima teaches The method of claim 1, further comprising receiving vehicle data information associated with the vehicle (Moustafa discloses that the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further discloses that this metadata can be additional environment information indicating environmental contexts such as terrain information (e.g., “hilly” or “flat”) [See at least Moustafa, 0315]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]. Moustafa further discloses that vehicle decision-making may be controlled by a machine learning model using data collected by the vehicle itself or collected or received from other vehicles [See at least Moustafa, 0200]. All of the data collected by the vehicle may be regarded as being “associated with the vehicle”) and generating the one or more predictions based at least in part on a detected vehicle path condition and the vehicle data information (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that recommendation system may be utilized to generate alerts for presentation on the vehicle's audio and/or graphic displays, such as to alert a driver of potential areas of concerns, prepare one or more passengers for a handover or pullover event, warn passengers of the likelihood of such events, warn passengers of potential downgrades in the autonomous driving level (e.g., from L5 to L4, L4 to L3, etc.) based on driving conditions detected ahead (and also, in some implementations, user preference information (identifying the user's risk and manual driving tolerances)), among other examples [See at least Moustafa, 0212]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]. Moustafa further discloses that vehicle decision-making may be controlled by a machine learning model using data collected by the vehicle itself or collected or received from other vehicles [See at least Moustafa, 0200]).
Regarding claim 12, Moustafa in view of Okajima teaches The method of claim 1, wherein receiving the route projection of the vehicle and the information associated with the route projection comprises receiving one or more of navigational mapping data associated with the route projection, location data of the vehicle, environment information, or road condition information (Moustafa discloses that the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further discloses that this metadata can be additional environment information indicating environmental contexts such as terrain information (e.g., “hilly” or “flat”) [See at least Moustafa, 0315]. Moustafa further discloses that the recommendation system 620 may leverage information collected from sensors primarily utilized in the core autonomous driving pipeline as well as additional sensor external and/or internal to the vehicle to collect information concerning conditions, which may impact passenger experience [See at least Moustafa, 0202]. Moustafa further discloses that For instance, the sense phase 605 of the pipeline may be expanded to have information from the external sensors of the vehicle on external environment conditions that can impact passengers experience such as weather conditions, allergen levels, external temperatures, road surface conditions (e.g., wet, dusty, clear, salted, etc.), road characteristics (e.g., curviness, embankments, grade, etc.), elevation, humidity, darkness, angle of the sun, light conditions, among other examples [See at least Moustafa, 0202]. Moustafa further discloses that vehicle decision-making may be controlled by a machine learning model using data collected by the vehicle itself or collected or received from other vehicles [See at least Moustafa, 0200]).
Regarding claim 13, Moustafa in view of Okajima teaches The method of claim 1, further comprising producing the trained machine learning model by training (Moustafa discloses that cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further discloses machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]) a neural network (Moustafa discloses that any of the machine learning models discussed herein may utilize one or more neural networks [See at least Moustafa, 0193]) to predict vehicle accessory to be used on the vehicle and vehicle actions to be executed by the vehicle along the vehicle path in the route projection (Moustafa discloses that cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further discloses machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]. Obviously, this hand-off and the previously discussed displaying and user selections of [Moustafa, 0251] are going to utilize components or “accessories” of the vehicle).
Regarding claims 20, Moustafa discloses A vehicle (Moustafa discloses the autonomous vehicle [See at least Moustafa, 0251]), comprising:
a user interface (Moustafa discloses displays of the autonomous vehicle [See at least Moustafa, 0251]); and
a processor (Moustafa discloses the in-vehicle systems of the autonomous vehicles themselves [See at least Moustafa, 0251]) configured to:
provide a route projection of the vehicle and information associated with the route projection (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]) to a trained machine learning model (Moustafa discloses that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]) configured to extract one or more features of the route projection and the information associated with the route projection and detect a vehicle path condition in the route projection based on the one or more features (Moustafa discloses that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]);
generate, using the trained machine learning model and based on the detected vehicle path condition (Moustafa discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]), first suggestions comprising a respective action to be executed by the vehicle along a vehicle path of the route projection (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions);
generate, using the trained machine learning model and based on the detected vehicle path condition (Moustafa discloses that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]), second suggestions comprising a respective vehicle accessory to be used by the vehicle along the vehicle path (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions that will require use of various vehicle components (“accessories”) in order to be executed), wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, tow straps, or equivalent attachable gear (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]. Notice that there are multiple options, include some actions that will require use of various vehicle components (“accessories”) in order to be executed. For example, remote valet and pull-over obviously both require control of accessories of the vehicle such as brakes and steering systems, which are attached to the vehicle. As discussed in the section of this office action titled “Claim Rejections – 35 USC § 112”, any of these pieces of hardware may be broadly regarded as “equivalent attachable gear”);
provide for display, on the user interface, a notification indicating the first suggestions and the second suggestions (Moustafa discloses that displays of the autonomous vehicle may present warnings or instructions to in-vehicle passengers regarding an upcoming, predicted issue and the possibility of a pull-over and/or remote valet handover [See at least Moustafa, 0251]. Moustafa further discloses that In some cases, this information may be presented in an interactive display through which a passenger may register their preference for handling the upcoming trip segment either through a handover to the passenger, handover to a remote valet service, selection of alternative route, or a pull-over event [See at least Moustafa, 0251]).
However, Moustafa does not explicitly teach the vehicle wherein the processor is configured to cause the respective action to be executed by the vehicle based at least in part on a first received input indicating selection of at least one of the first suggestions or cause the respective vehicle accessory to be used by the vehicle based at least in part on a second received input indicating selection of at least one of the second suggestions.
However, Okajima does explicitly teach the vehicle wherein the processor is configured to cause the respective action to be executed by the vehicle based at least in part on a first received input indicating selection of at least one of the first suggestions or prompt an operator of the vehicle to use the respective vehicle accessory based at least in part on a second received input indicating selection of at least one of the second suggestions (See at least Fig. 6 in Okajima: Okajima teaches that a button for selecting switching to manual driving may be displayed by the display unit 86 [See at least Okajima, 0064]. Okajima further teaches that This example shows an example in which the display unit 86 is integrated with the input receiving unit 87 to be implemented by a touch panel or the like [See at least Okajima, 0064]). Both Okajima and Moustafa teach methods for displaying one or more driving options involving driving mode to a user and allowing users to select an option. However, only Okajima explicitly teaches where the autonomous vehicle executes the selected driving mode.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the driving mode selection method of Moustafa to also execute the driving mode selected by the user, as in Okajima. Anyone of ordinary skill in the art will appreciate that an autonomous vehicle should execute the mode selected by the user, or else there is no point in having the user select the mode in the first place.
Examiner’s Suggestion to Help Applicant Overcome All the Rejections
Applicant may overcome all of the rejections by amending the independent claims as follows:
“wherein the respective vehicle accessory comprises at least one of soft shackles, snow chains, or tow straps
However, further search and consideration will be required before it can be determined whether this amendment is allowable or not.
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 NAEEM T ALAM whose telephone number is (571)272-5901. The examiner can normally be reached M-F, 9am-5pm.
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/NAEEM TASLIM ALAM/Examiner, Art Unit 3668