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 .
Claims 1-20 are presented for examination
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0076], line 4, “areas around” should read “areas around”.
In paragraph [0077], line 8, “action..” should read “action.”.
In paragraph [0092], lines 2 and 3, “general purpose” should read “general-purpose”.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites “A method for providing explanation of an artificial intelligence (AI) policy of behavior with causal reasoning, the method comprising”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
generating a data structure including states and actions to be executed at those states as determined by the AI policy of behavior: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate a data structure including states and actions to be executed at those states as determined by the AI policy of behavior.
determining… state factors associated with the state and responsibility scores for the state factors, each responsibility score indicating a causal impact for each of the actions associated with one of the states: These limitations recite a mentally performable process of observation to determine… state factors associated with the states and responsibility scores for the state factors, each responsibility score indicating a causal impact for each of the actions associated with one of the states.
identifying one or more of the state factors as a causal reason for an action resulting from the current state: These limitations recite a mentally performable process of observation to identify one or more of the state factors as a causal reason for an action resulting from the current state.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
with a first computing system that is offline… with a first computing system… machine learning (ML)… with a second computing system that is online… ML model: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
generating… a causal… model based on the state factors and the responsibility scores: These additional elements are mere instructions to implement the judicial exception with the mental process of generating operations because the additional elements only recite the idea of generation but fails to recite how generating… a causal… model based on the state factors and the responsibility scores is implemented, e.g. no details on how the state factors and the responsibility scores are utilized in the generation of a causal… model. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Further additional elements include a recitation of the words “apply it” (or an equivalent). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
further comprising reformulating the states and the actions into a table represented by indexes based on one or more criterion: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to reformulate the states and the actions into a table represented by indexes based on one or more criterion.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 3
Step 1: a process, as in claim 2.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 2.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the AI policy of behavior is an AI policy of behavior for an autonomous vehicle: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
the one or more criterion includes a defined number of sections each representing a different area adjacent to the autonomous vehicle: These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of the one or more criterion includes a defined number of sections each representing a different area adjacent to the autonomous vehicle which is well-understood, routine and conventional activity similar to receiving or transmitting data over a network. See MPEP 2106.05(d). Further additional elements include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Claim 4
Step 1: a process, as in claim 2.
Step 2A Prong 1: the claim recites, inter alia:
further comprising assigning values for the indexes based on a defined discretization formulation: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign values for the indexes based on a defined discretization formulation.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 5
Step 1: a process, as in claim 4.
Step 2A Prong 1: the claim recites, inter alia:
wherein determining… the state factors and the responsibility scores includes determining the state factors and the responsibility scores based on the values for the indexes: These limitations recite a mentally performable process of observation to determine… the state factors and the responsibility scores based on the values for the indexes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
with the first computing system that is offline: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the AI policy of behavior is an AI policy of behavior for an autonomous vehicle: These elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
the state factors are associated with a semantic abstraction: These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of the state factors are associated with a semantic abstraction which is well-understood, routine and conventional activity similar to receiving or transmitting data over a network. See MPEP 2106.05(d). Further additional elements include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Claim 7
Step 1: a process, as in claim 6.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 6.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the semantic abstraction includes one or more sections adjacent to the autonomous vehicle: These elements amount to insignificant extra-solution activity in the form or mere data gathering and output (see MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of wherein the semantic abstraction includes one or more sections adjacent to the autonomous vehicle which is well-understood, routine and conventional activity similar to receiving or transmitting data over a network. See MPEP 2106.05(d).
Claim 8
Step 1: a process, as in claim 7.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 7.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
further comprising displaying, on a display in the autonomous vehicle, a notification regarding the causal reason for the action resulting from the current state: These additional elements are recited at a high level of generality and merely recites insignificant extra-solution activity of generic output to a display in the autonomous vehicle of a notification regarding the causal reason for the action resulting from the current state. See MPEP 2106.05(g).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of further comprising displaying, on a display in the autonomous vehicle, a notification regarding the causal reason for the action resulting from the current state which is well-understood, routine and conventional activity similar to presenting offers and gathering statistics. See MPEP 2106.05(d)(II).
Claim 9
Step 1: a process, as in claim 8.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 8.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the notification includes a graphical representation highlighting an area adjacent to the autonomous vehicle in which at least one section is located: These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of wherein the notification includes a graphical representation highlighting an area adjacent to the autonomous vehicle in which at least one section is located which is well-understood, routine, and conventional activity similar to receiving or transmitting data over a network. See MPEP 2106.05(d).
Claim 10
Step 1: a process, as in claim 9.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the notification includes a description of the area adjacent to the autonomous vehicle in which the at least one section is located: These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of wherein the notification includes a description of the area adjacent to the autonomous vehicle in which the at least one section is located which is well-understood, routine, and conventional activity similar to receiving and or transmitting data over a network. See MPEP 2106.05(d).
Claim 11
Step 1: a process, as in claim 9.
Step 2A Prong 1: the claim recites, inter alia:
wherein a size of the area adjacent to the autonomous vehicle is adjustable based on a parameter of the autonomous vehicle and/or a traffic distribution density near the autonomous vehicle: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to adjust based on a parameter of the autonomous vehicle and/or a traffic distribution density near the autonomous vehicle.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 12
Step 1: a process, as in claim 9.
Step 2A Prong 1: the claim recites, inter alia:
wherein a color of the highlighted area is adjustable based on a confidence values associated with the at least one section: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to adjust based on a confidence value associated with the at least one section.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 13
Step 1: The claim recites “A method for providing explanation of an artificial intelligence (AI) policy of behavior with causal reasoning, the method comprising”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: the claim recites, inter alia:
determining, based on the causal… state factors associated with a current state: These limitations recite a mentally performable process of observation to determine, based on the causal… state factors associated with a current state.
identifying one or more of the state factors as a causal reason for an action resulting from the current state: These limitations recite a mentally performable process of observation to identify one or more of the state factors as a causal reason for an action resulting from the current state.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
receiving a causal machine learning (ML) model: These additional elements are recited at a high level of generality and merely recites insignificant extra-solution activity of receiving a causal machine learning (ML) model. See MPEP 2106.05(g).
ML model: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
displaying a notification regarding the causal reason for the action resulting from the current state: These additional elements are recited at a high level of generality and merely recites insignificant extra-solution activity of generic output to a display of a notification regarding the causal reason for the action resulting from the current state. See MPEP 2106.05(g).
Step 2B: The additional elements from Step 2A prong 2 include insignificant extra-solution activity of receiving a causal machine learning (ML) model and displaying a notification regarding the causal reason for the action resulting from the current state which is well-understood, routing and conventional activity similar to presenting offers and gathering statistics. See MPEP 2106.05(d)(II). Further additional elements include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claims 14-20
Step 1: a process, as in claim 13.
Step 2A Prong 1: Claims 14-20 recite substantially the same abstract ideas as in claims 6-12, respectively.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application.
Step 2B: These claims do not contain significantly more than the judicial exception. The analysis at this step is substantially the same as that of claims 6-12, respectively.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 6, 13, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goldman-Shenhar (US 20220230081 A1, filed 01/20/2021), hereinafter Goldman.
Regarding claim 1, Goldman teaches A method for providing explanation of an artificial intelligence (AI) policy of behavior with causal reasoning, the method comprising: (Goldman; [0114], Briefly described, a policy of explanations explaining the causal reasoning from the action taken by the AI policy):
generating a data structure including states and actions to be executed at those states
as determined by the AI policy of behavior: (Goldman; [0072], Briefly described, a data structure in the form of a planner including states and actions with the actions to be executed at the given context of states; [0119], Briefly described, an automated control system generating a plan to execute actions, with an explanation to be provided at the user’s request; [0123], Briefly described, the automated control system generates a plan and executes actions):
determining, with a first computing system that is offline, state factors associated with
the states and responsibility scores for the state factors, each responsibility score indicating a causal impact for each of the actions associated with one of the states: (Goldman; [0093], Briefly described, an indicated causal impact for an action performed; [0101], Briefly described, a computing system that is computed offline, including all of the possible explanations of the causal impact for all possible actions; [0105], Briefly described, explanations for each assortment of states and actions ton be considered as responsible to the user; [0107], Briefly described, an understandability score associated with the action taken at a certain state to mitigate the user’s concern on a particular action chosen over others considered):
generating, with the first computing system, a causal machine learning (ML) model
based on the state factors and the responsibility score: (Goldman; [0087], Briefly described, a user model generated offline):
determining, with a second computing system that is online based on the generated
causal ML model, state factors associated with a current state: (Goldman; [0087], Briefly described, the user model functioning online through updates on different states of the user like agitation and distraction; [0093], Briefly described, state factor explanations associated with their respective states; [0101], Briefly described, executing the online computing system to provide an explanation for a stored state to be presented when the vehicle is at that current state):
identifying one or more of the state factors as a causal reason for an action resulting
from the current state: (Goldman; [0093], Briefly described, state factor explanations providing the causal reason for an action from the current state; [0113], Briefly described, actions associated with states to be chosen from semantic categories following identification).
Regarding claim 2, Goldman teaches the method of claim 1 wherein reformulating the states and the actions in a table represented by indexes based on one or more criterion: (Goldman; [0084], Briefly described, a lookup table for each explanation comprising a state and an action taken at that state by the vehicle, pairs selected based on a contextual if-then rule system of one or more criterion; [0088], Briefly described, one or more criterion for selection of state and action pairs to be executed and explained by the explanation engine, these computed explanations are output to a storage location like a look up table).
Regarding claim 6, Goldman teaches the method of claim 1 wherein the AI policy of behavior is an AI policy of behavior for an autonomous vehicle: (Goldman; [0034], Briefly described, the explanation system presents explanations of behavior to the automated system which is part of an autonomous vehicle; [0075], Briefly described, a policy of behavior is generated during the autonomous vehicle operation):
the state factors are associated with a semantic abstraction: (Goldman; [0093], Briefly
described, a semantic explanation provided for the state factors the autonomous vehicle is situated in; [0111], Briefly described, semantic explanations generated or selected relevant to the action chosen based on the state the vehicle was in; [0112], Briefly described, an explainable semantic formula where explanations are generated based on the action and state belonging to the relevant semantic category).
Regarding claim 13, Goldman teaches A method for providing explanation of an artificial intelligence (AI) policy of behavior with causal reasoning, the method comprising: (Goldman; [0114], Briefly described, a policy of explanations explaining the causal reasoning from the action taken by the AI policy):
receiving a causal machine learning (ML) model: (Goldman; [0087], Briefly described,
the user model is updated during execution, being received after generation):
determining, based on the causal ML model, state factors associated with a current
state: (Goldman; [0087], Briefly described, the user model functioning online through updates on different states of the user like agitation and distraction; [0093], Briefly described, state factor explanations associated with their respective states; [0101], Briefly described, executing the computing system based on the ML model to provide an explanation for a stored state to be presented when the vehicle is at that current state):
identifying one or more of the state factors as a causal reason for an action resulting
from the current state: (Goldman; [0093], Briefly described, state factor explanations providing the causal reason for an action from the current state; [0113], Briefly described, actions associated with states to be chosen from semantic categories following identification)
displaying a notification regarding the causal reason for the action resulting from the
current state: (Goldman; [0044], the vehicle includes different display or displays for the output of the notification; [0063], Briefly described, the explanation notification can be displayed to the user).
Regarding claim 14, it is a method claim that corresponds to method claim 6. Therefore, it is rejected for the same reason as claim 6 above.
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 3-5, 7, 8, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Goldman-Shenhar (US 20220230081 A1, filed 01/20/2021), hereinafter Goldman, and in view of 샬리스워츠 et al. (KR 102479471 B1, filed on 03/20/2019), hereinafter KR 102479471 B1.
Regarding claim 3, Goldman teaches the invention of claim 2 wherein the AI policy of behavior is an AI policy of behavior for an autonomous vehicle: (Goldman; [0034], Briefly described, the explanation system presents explanations of behavior to the automated system which is part of an autonomous vehicle; [0075], Briefly described, a policy of behavior is generated during the autonomous vehicle operation):
However, Goldman fails to expressly teach – the one or more criteria includes a defined number of sections each representing a different area adjacent to the autonomous vehicle.
In the same field of endeavor, KR 102479471 B1 teaches:
the one or more criterion includes a defined number of sections each representing a
different area adjacent to the autonomous vehicle: (KR 102479471 B1; Page 27 Paragraph 1, Briefly described, different sections captured by image capture devices to gather different fields of view adjacent to the autonomous vehicle and to the other sections captured).
It would have been obvious to one of ordinary skill in the art before the publishing date of the invention to have incorporated – the one or more criterion includes a defined number of sections each representing a different area adjacent to the autonomous vehicle as suggested by Goldman and KR 102479471 B1. Doing so would be desirable because an AI policy of behavior can utilize the image capturing devices to identify the state the autonomous vehicle is in and provide explanations for the action taken by the autonomous vehicle based on the sections captured by the image capturing devices. The database in which the autonomous vehicle with capturing field of view devices can store explanations from the combination of Goldman and KR 102479471 B1, so the lookup tables in the database may incorporate the image capturing devices to gather adjacent sections of the fields of view of the autonomous vehicle. This provides the user with an explanation for an action based on the captured adjacent section of the autonomous vehicle.
Regarding claim 4, Goldman teaches the invention of claim 2.
However, Goldman fails to expressly teach – assigning values for the indexes based on a defined discretization formulation.
In the same field of endeavor, KR 102479471 B1 teaches:
assigning values for the indexes based on a defined discretization formulation: (KR
102479471 B1; Page 81 Paragraph 2, Briefly described, state and action sets assigned values through discretization based on the sensing state parameters of the vehicle).
It would have been obvious to one of ordinary skill in the art before the publishing date of the invention to have incorporated – assigning values for the indexes based on a defined discretization formulation as suggested by Goldman and KR 102479471 B1. Doing so would be desirable because the state and action sets that are paired together and given indexes based on one or more criterion can be formulated through the discretization based on sensing state parameters of the vehicle. A look up table that can store these state and action sets can sort them through indexes assigned values. The look up table in which the state and action sets are stored can store more sets based on the sensing state parameters of the vehicle from the combination of Goldman and KR 102479471 B1, so the assigned values to each set is based on the discretization formulation and are stored in the look up table. This provides the user with coherent reasoning as to why a value associated to a certain state determined the vehicle’s action.
Regarding claim 5, the combination of Goldman and KR 102479471 B1 teaches the invention of claim 4 wherein determining, with the first computing system that is offline, the state factors and the responsibility scores includes determining the state factors and the responsibility scores: (Goldman; [0093], Briefly described, an indicated causal impact for an action performed; [0101], Briefly described, a computing system that is computed offline, including all of the possible explanations for all possible actions based on their respective state factors; [0105], Briefly described, explanations for each assortment of states and actions ton be considered as responsible to the user; [0107], Briefly described, an understandability score associated with the action taken at a certain state to mitigate the user’s concern on a particular action chosen over others considered):
based on the values for the indexes: (KR 102479471 B1; Page 81 Paragraph 2, Briefly
described, state and action sets assigned values through discretization based on the sensing state parameters of the vehicle).
Regarding claim 7, Goldman teaches the invention of claim 6.
However, Goldman fails to expressly teach – wherein the semantic abstraction includes one or more sections adjacent to the autonomous vehicle.
In the same field of endeavor, KR 102479471 B1 teaches:
wherein the semantic abstraction includes one or more sections adjacent to the
autonomous vehicle: (KR 102479471 B1; Page 42 Paragraph 4, Briefly described, semantic meanings for an autonomous vehicle to assign to a target section based on the targeting adjacent section’s state whether it be, for example, to an adjacent slowing target vehicle or to an adjacent lane.
It would have been obvious to one of ordinary skill in the art before the publishing date of the invention to have incorporated – wherein the semantic abstraction includes one or more sections adjacent to the autonomous vehicle as suggested by Goldman and KR 102479471 B1. Doing so would be desirable because the explanations from the AI policy of behavior for an autonomous vehicle can explain the actions taken based on the state of the sections adjacent to the autonomous vehicle. These states are acted upon through a semantic abstraction, so the vehicle has clear guidelines as to what action it takes based on the adjacent sections. The semantic abstractions in which the autonomous vehicle acts upon can direct the behavior of the autonomous vehicle on what action to take based on the adjacent sections from the combination of Goldman and KR 102479471 B1, so as to categorize state factors. This provides a user with an understanding of why the explanation derives from categorical semantic abstractions.
Regarding claim 8, the combination of Goldman and KR 102479471 B1 teaches the invention of claim 7 wherein further comprising displaying, on a display in the autonomous vehicle, a notification regarding the causal reason for the action resulting from the current state: (Goldman; [0044], Briefly described, a display in the autonomous vehicle to present explanations or causal reasonings to a user; [0063[, Briefly described, the explanation taking the form of an action that was performed or an action that was not performed but considered when the vehicle performs a certain action at a certain state, and the explanation displayed to the user).
Regarding claims 15 and 16, they are method claims that correspond to method claims 7 and 8. Therefore, they are rejected for the same reasons as claims 7 and 8 above.
Claims 9-12 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goldman in view of KR 102479471 B1, as applied in the rejection of claims 8 and 16 above, further in view of Leary et al. (US 20210087866 A1, filed 09/30/2019), hereinafter Leary.
Regarding claim 9, the combination of Goldman and KR 102479471 B1 teaches the invention of claim 8.
However, the combination of Goldman and KR 102479471 B1 fail to expressly teach – wherein the notification includes a graphical representation highlighting an area adjacent to the autonomous vehicle in which at least one section is located.
In the same field of endeavor, Leary teaches:
wherein the notification includes a graphical representation highlighting an area
adjacent to the autonomous vehicle in which at least one section is located: (Leary; [0113], Briefly described, a highlighted graphical representation of an area adjacent to the autonomous vehicle for a user to notify the user of the section the vehicle is located).
It would have been obvious to one of ordinary skill in the art before the publishing date of the invention to have incorporated – wherein the notification includes a graphical representation highlighting an area adjacent to the autonomous vehicle in which at least one section is located as suggest by Goldman, KR 102479471 B1, and Leary. Doing so would be desirable because the display in the vehicle can display the notification including the graphical representation highlighting the adjacent area to the autonomous vehicle in which at least one section is located. The combined method would provide for illustrative explanation to the user of the highlighted area in which the vehicle bases its action on from the combination of Goldman, KR 102479471 B1, and Leary. This provides a user with a graphical display as to adjacent area a vehicle provides an explanation for.
Regarding claim 10, the combination of Goldman, KR 102479471 B1, and Leary teaches the invention of claim 9 wherein the notification includes a description of the area adjacent to the autonomous vehicle in which the at least one section is located: (Leary; [0013], Briefly described, an area adjacent to the autonomous vehicle indicating a located section; [0115], Briefly described, a description of the area indicating an environment or objects near the autonomous vehicle).
Regarding claim 11, the combination of Goldman, KR 102479471 B1, and Leary teaches the invention of claim 9 wherein a size of the area adjacent to the autonomous vehicle is adjustable based on a parameter of the autonomous vehicle and/or traffic distribution density near the autonomous vehicle: (Leary; [0113], Briefly described, an area adjacent to the autonomous vehicle; [0115], Briefly described, the area adjacent to the autonomous vehicle with an adjustable size based on objects near the autonomous vehicle and the distance between the vehicle and a target in a section).
Regarding claim 12, the combination of Goldman, KR 102479471 B1, and Leary teaches the invention of claim 9 wherein a color of the highlighted area is adjustable based on a confidence value associated with the at least one section: (Leary; [0113], Briefly described, an area adjacent to the autonomous vehicle; [0115], Briefly described, the area adjacent to the autonomous vehicle with an adjustable color based on the distance between the vehicle and a target in a section; [0123], Briefly described, a color effect applied to an area adjacent to the autonomous vehicle to indicate a target within a field-of-view of the autonomous vehicle to ensure confidence of an area of focus examined in a graphical effect).
Regarding claims 17-20, they are method claims that correspond to method claims 9-12. Therefore, they are rejected for the same reasons as claims 9-12 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Li et al. (CN 113460042 B) teaches in Page 23 Paragraph 5 and in Figure 8 adjacent areas to the autonomous vehicle taking the form of lanes with a section on the left lane and the right lane of the autonomous vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROLANDO PATRICK VIRREIRA whose telephone number is (571)270-1570. The examiner can normally be reached Monday – Friday, 8:30AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROLANDO PATRICK VIRREIRA/
Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights