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 Claims
Pending
1-20
35 U.S.C. 112
1-20
35 U.S.C. 101
1-20
35 U.S.C. 102
1-5, 7-8, 10-11, 13-20
35 U.S.C. 103
6, 9, 12
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d), regarding Application No. JP 2024-016639, filed on 02/06/2024.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 02/03/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: a control apparatus, a target control amount calculator, an automatic operator, an input information obtainer, M control amount predictors, a weighted average calculator, an i-th control amount predictor in claim 1; a reliability level obtainer in claims 6, 9, 12; a steerer in claim 13; and a trainer in claims 14, 16, 18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The specification discloses the corresponding structure for a control apparatus, a target control amount calculator, an automatic operator, an input information obtainer, M control amount predictors, a weighted average calculator, an i-th control amount predictor in paragraphs [0053], [0103], FIG. 1; a reliability level obtainer in paragraphs [0052]-[0053] ; a steerer in paragraph [0042]; and a trainer in paragraph [0103]. Find the above paragraphs found in the PGPub US 2025/0249928 A1 of the instant application.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 19, 20 recite: “M (M is an integer greater than or equal to 2) different times during a prescribed amount of time before a current time, an i-th (i is an integer between 0 through M−1) control amount predictor calculates the predicted control amount by using a control amount prediction model that associates the input information as of an i amount of time before the current time with the control amount as of the current time”.
The term “M is an integer greater than or equal to 2” is a relative term which renders the claim indefinite. The term “M is an integer greater than or equal to 2” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “M is an integer greater than or equal to 2” can be an infinite value. M can be an infinite set of numbers, and there is no way to know what value is being used within the scope of the claim.
The term “a prescribed amount of time before a current time” is a relative term which renders the claim indefinite. The term “a prescribed amount of time before a current time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “a prescribed amount of time before a current time” can be an infinite value of time, and there is no way to know what time period is being used within the scope of the claim.
The term “i-th (i is an integer between 0 through M−1) control amount” is a relative term which renders the claim indefinite. The term “i-th (i is an integer between 0 through M−1) control amount” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “i-th (i is an integer between 0 through M−1) control amount” can be an infinite value. The value “i” is defined as an integer between 0 through M-1, and M is greater than or equal to 2. Therefore, M can be an infinite set of numbers, i can be a positive set of infinity-1 numbers, and there is no way to know what value is being used within the scope of the claim.
The term “an i amount of time before the current time” is a relative term which renders the claim indefinite. The term “an i amount of time before the current time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “an i amount of time before the current time” can be an infinite value of time. The value “i” is defined as an integer between 0 through M-1, and M is greater than or equal to 2. Therefore, M can be an infinite set of numbers, i can be a positive set of infinity-1 numbers, and there is no way to know what time period is being used within the scope of the claim.
Claims 4, 7, 10 recite: “a value greater than a j-th (j is an integer greater than i) weight”. The term “j is an integer greater than i” is a relative term which renders the claim indefinite. The term “j is an integer greater than i” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “j is an integer greater than i” can be an infinite value. The value “i” is defined as an integer between 0 through M-1, and M is greater than or equal to 2. Therefore, M can be an infinite set of numbers, i can be a positive set of infinity-1 numbers, j can be an infinite set of values that is greater than i, and there is no way to know what value is being used within the scope of the claim.
Claims 5, 8, 11 recite: “a value for a k-th (k is an integer between 0 through M−1) weight”. The term “k is an integer between 0 through M−1” is a relative term which renders the claim indefinite. The term “k is an integer between 0 through M−1” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “k is an integer between 0 through M−1” can be an infinite value. The value “k” is defined as an integer between 0 through M-1, and M is greater than or equal to 2. Therefore, M can be an infinite set of numbers, k can be a positive set of infinity-1 numbers, and there is no way to know what value is being used within the scope of the claim.
Claims 6, 9, 12 recite: “a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level”.
The term “reliability level” and/or “i-th reliability level” is a relative term which renders the claim indefinite. The term “reliability level” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The term “i amount of time before the current time” is a relative term which renders the claim indefinite. The term “i amount of time before the current time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This use of “i amount of time before the current time” can be an infinite value of time. The value “i” is defined as an integer between 0 through M-1, and M is greater than or equal to 2. Therefore, M can be an infinite set of numbers, i can be a positive set of infinity-1 numbers, and there is no way to know what time period is being used within the scope of the claim.
Claims 2-18 are similarly rejected under 35 U.S.C. 112(b) as being indefinite by virtue of their dependency on claim 1.
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 20 is rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because its recites software per se. Claim 20 recites “A storage medium that stores a computer program for causing a computer to control a control amount defined for a control subject” which is a products that does not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations.
Claim(s) 1, 19, 20 is/are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
1.
A control apparatus that calculates, based on input information, a target control amount with respect to a control amount defined for a control subject, and operates the control subject based on the target control amount, the control apparatus comprising:
a target control amount calculator configured to calculate the target control amount based on time series data for the input information; and
an automatic operator configured to operate the control subject based on the target control amount, wherein
the target control amount calculator comprises
an input information obtainer configured to obtain the input information as of M (M is an integer greater than or equal to 2) different times during a prescribed amount of time before a current time,
M control amount predictors each configured to calculate a predicted control amount based on the input information obtained by the input information obtainer, and
a weighted average calculator configured to calculate, as the target control amount, a weighted average value of the M predicted control amounts calculated by the M control amount predictors, and
an i-th (i is an integer between 0 through M−1) control amount predictor calculates the predicted control amount by using a control amount prediction model that associates the input information as of an i amount of time before the current time with the control amount as of the current time.
19.
A control method that uses a computer to control a control amount defined for a control subject, the control method comprising:
obtaining input information as of M (M is an integer that is greater than or equal to 2) different times during a prescribed amount of time before a current time;
calculating M predicted control amounts based on the input information as of the M different times;
calculating, as a target control amount for the control amount, a weighted average value of the M predicted control amounts; and
operating the control subject based on the target control amount, wherein
the calculating the M predicted control amounts includes calculating an i-th (i is an integer between 0 through M−1) predicted control amount by using a control amount prediction model that associates the input information as of an i amount of time before the current time with the control amount as of the current time.
20.
A storage medium that stores a computer program for causing a computer to control a control amount defined for a control subject, wherein
the computer program causes the computer to perform operations that comprise:
obtaining input information as of M (M is an integer that is greater than or equal to 2) different times during a prescribed amount of time before a current time,
calculating M predicted control amounts based on the input information as of the M different times;
calculating, as a target control amount for the control amount, a weighted average value of the M predicted control amounts, and
operating the control subject based on the target control amount, and
the calculating the M predicted control amounts includes calculating an i-th (i is an integer between 0 through M−1) predicted control amount by using a control amount prediction model that associates the input information as of an i amount of time before the current time with the control amount as of the current time.
These limitations, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance of the mind, but for the recitation of the underlined and italicized limitations above. That is, other than reciting the underlined and italicized limitations, nothing in the claim elements preclude the steps from being performed in the mind. For example, a human can, in their mind, perform the bolded limitations recited above.
This judicial exception is not integrated into a practical application. The claim recites the additional elements underlined and italicized above. The italicized elements is/are recited at a high level of generality and merely link(s) the use of the abstract idea to a particular technological environment (see MPEP 2106.05(h)). The underlined elements is/are recited at a high level of generality and amounts to mere data gathering, manipulation, and transmission, which is a form of insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional italicized elements is/are no more than mere generic linking of the abstract idea to a technological environment, which cannot provide an inventive concept. The additional underlined elements is/are mere data gathering, manipulation, and transmission, and is a well-understood, routine, and conventional function (see MPEP 2106.05(d) and see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93), and thus is/are no more than insignificant extra-solution activity (see MPEP 2106.05(g) and see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Thus, the limitations do not provide an inventive concept, and the claim contains ineligible subject matter.
Claim(s) 2-18 recite(s) limitations that are no more that the abstract idea recited in claim(s) 1. See the table below for a detailed bolding/italicizing/underlining of the dependent claims.
Bolded limitations can reasonably be performed in the human mind.
Italicized elements are recited at a high level of generality to generically link the use of the abstract idea in a particular technological environment.
Underlined elements is/are mere data gathering, manipulation, and transmission, and is/are a well-understood, routine, and conventional function, and thus is/are no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Thus, the claim(s) contain(s) ineligible subject matter.
2.
wherein the control amount prediction model is constructed using machine learning in which input sample data that is time series data for the input information and ideal output data that is time series data for an ideal control amount with respect to the input sample data are employed as teaching data.
3.
wherein an i-th control amount prediction model is constructed using the teaching data, in which the input sample data and the ideal output data resulting from advancing time with respect to the input sample data by the i amount of time are employed as a set.
4, 7, 10.
wherein the weighted average calculator sets an i-th weight for the predicted control amount calculated by the i-th control amount predictor to a value greater than a j-th (j is an integer greater than i) weight for the predicted control amount calculated by a j-th control amount predictor.
5, 8, 11.
wherein the weighted average calculator sets a value for a k-th (k is an integer between 0 through M−1) weight such that the k-th weight exponentially decreases with respect to a value of k.
6, 9, 12.
wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer.
13.
wherein the control subject is a steerer in a vehicle, the control amount is a steering angle that is in accordance with the steerer, and the input information includes external information pertaining to a periphery of the vehicle.
14, 16, 18.
further comprising: a trainer configured to train the control amount prediction model based on time series data for the input information and the control amount as of a time of manual driving in which a driver of the vehicle is an agent who operates the control subject.
15.
wherein the control subject is a travel driver in a vehicle, the control amount is a travel drive force that is in accordance with the travel driver, and the input information includes external information pertaining to a periphery of the vehicle.
17.
wherein the control subject is a brake in a vehicle, the control amount is a braking force in accordance with the brake, and the input information includes external information pertaining to a periphery of the vehicle.
Examiner Note - Prior Art
Examiner has cited particular paragraphs, columns, lines, or figures in the references as applied to the claims for the convenience of Applicant. Although the specified citations are representative of the teachings in the art and are applied to limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from Applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. See MPEP 2141.02 [R-01.2024] VI. and MPEP §2123.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-8, 10-11, 13-20 is/are rejected under 35 U.S.C. 102(a)(1)as being anticipated by Kim (US 20220063614 A1, “Kim”).
Regarding claim 1: Kim teaches: A control apparatus that calculates, based on input information, a target control amount with respect to a control amount defined for a control subject, and operates the control subject based on the target control amount, the control apparatus comprising: ([0002] The present invention relates to a driving control method and system of a vehicle, and more particularly, to stable driving control of an autonomous vehicle. [0003] The auto industry of the present time proceeds to implementation of autonomous driving in which involvement of a driver in vehicle driving is minimized. An autonomous vehicle refers to a vehicle which autonomously determines a driving path by recognizing environments around the vehicle through external information sensing and processing functions during driving, and independently drives using its own power supply. [0040] Referring to FIGS. 1 and 2, a system for controlling driving of a vehicle configured to travel based on driving information according to one embodiment of the present invention includes a target calculator 10 configured to calculate a target control value regarding the behavior of the vehicle based on the driving information with which the vehicle is travelling, a correction calculator 20 configured to calculate a corrected control value regarding the behavior of the vehicle based on the driving information and behavior data of the vehicle depending on the driving information, and a controller 30 configured to control driving of the vehicle based on the target control value calculated by the target calculator 10 and the corrected control value calculated by the correction calculator 20)
a target control amount calculator configured to calculate the target control amount based on time series data for the input information; and ([0047] The correction calculator 20 may calculate the corrected control value regarding the behavior of the vehicle based on the driving information input to the correction calculator 20. Concretely, the correction calculator 20 may calculate a corrected control value depending on newly input driving information using previously input driving information and behavior data of the vehicle depending on the previously input driving information)
an automatic operator configured to operate the control subject based on the target control amount, wherein the target control amount calculator comprises ([0042] The target calculator 10 may calculate the target control value regarding the behavior of the vehicle based on the driving information of the vehicle input to the target calculator 10. Here, the driving information of the vehicle may he information regarding expected driving of the vehicle through autonomous driving or an advanced driver assistance system (ADAS). [0044] The target calculator 10 may calculate the target control value regarding the behavior of the vehicle. Here, the target control value may be a target value configured to control an apparatus, such as a driving apparatus, a braking apparatus, a steering apparatus or suspension apparatuses, included in the vehicle so as to change the behavior of the vehicle)
an input information obtainer configured to obtain the input information as of M (M is an integer greater than or equal to 2) different times during a prescribed amount of time before a current time, ([0047] The correction calculator 20 may calculate the corrected control value regarding the behavior of the vehicle based on the driving information input to the correction calculator 20. Concretely, the correction calculator 20 may calculate a corrected control value depending on newly input driving information using previously input driving information and behavior data of the vehicle depending on the previously input driving information. [0057] Concretely, the correction calculator 20 may include a deep neural network (DNN) trained based on previously input driving information and previously input behavior data of the vehicle depending on the corresponding driving information)
M control amount predictors each configured to calculate a predicted control amount based on the input information obtained by the input information obtainer, and ([0043] For example, the driving information of the vehicle may be generated based on road information sensed by sensors, for example, camera sensors, radar sensors, ultrasonic sensors, lidars, or the like, which are mounted in the vehicle, and a set path of the vehicle. That is, the driving information of the vehicle with which the vehicle is expected to drive may be set depending on a path along which the vehicle is expected to drive and the road information)
a weighted average calculator configured to calculate, as the target control amount, a weighted average value of the M predicted control amounts calculated by the M control amount predictors, and ([0057]-[0069] Concretely, the correction calculator 20 may include a deep neural network (DNN) trained based on previously input driving information and previously input behavior data of the vehicle depending on the corresponding driving information. The deep neural network (DNN) includes an input layer, an output layer, and hidden layers between the input layer and the output layer. When specific input data is given, a weight between the input data and output data (i.e., a label) is adjusted. A connection link between nodes includes the weight. The structure of the deep neural network (DNN) will be expressed as the following equation. Here, x is an input, W is a weight, and b is a bias value. In an activation function, the calculated value of v is regarded as an output y. The activation function may employ a sigmoid function, a SoftMax function, a rectified linear unit (ReLU) function or the like. A vanishing gradient, which is a problem of a backpropagation algorithm, may be solved by applying the ReLU function. In the ReLU function, the node output of a neural network may exceed 1, thereby solving the vanishing gradient. Further, overfitting, which is another problem of the backpropagation algorithm, means a situation that corresponds too closely or exactly to an input value. One of solutions to overfitting is dropout which is a regularization technique. In dropout, the entirety of a neural network is not trained, and some nodes are randomly selected and trained. In general, the dropout rate of hidden layers is 50%, and the dropout rate of input nodes is 25%. The key to the backpropagation algorithm is to update a weight of a neural network by reflecting the error of a previous result. That is, the weight is adjusted by repeatedly executing a learning rule so as to reduce the error. Concretely, this will be expressed as the following equation. Here, w is a weight, η is a learning rate (in the range of 0 to 1), e is an error (d-y, d being a label and y being an output), and x is an input. When the sigmoid function is used, δ=ø′(v) will be expressed as follows)
an i-th (i is an integer between 0 through M−1) control amount predictor calculates the predicted control amount by using a control amount prediction model that associates the input information as of an i amount of time before the current time with the control amount as of the current time ([0055] The predictor 40 may predict the behavior of the vehicle based on the driving information of the vehicle, and concretely, may predict the behavior of the vehicle, such as the position, rotational angle (in the roll, pitch or yaw direction), and speed of the vehicle, using the driving path and the speed profile of the vehicle. [0057] Concretely, the correction calculator 20 may include a deep neural network (DNN) trained based on previously input driving information and previously input behavior data of the vehicle depending on the corresponding driving information. [0060] Here, x is an input, W is a weight, and b is a bias value).
Regarding claim 2: Kim further teaches: The control apparatus according to claim 1, wherein the control amount prediction model is constructed using machine learning in which input sample data that is time series data for the input information and ideal output data that is time series data for an ideal control amount with respect to the input sample data are employed as teaching data ([0057]-[0069]).
Regarding claim 3: Kim further teaches: The control apparatus according to claim 2, wherein an i-th control amount prediction model is constructed using the teaching data, in which the input sample data and the ideal output data resulting from advancing time with respect to the input sample data by the i amount of time are employed as a set ([0057]-[0069]).
Regarding claim 4: Kim further teaches: The control apparatus according to claim 1, wherein the weighted average calculator sets an i-th weight for the predicted control amount calculated by the i-th control amount predictor to a value greater than a j-th (j is an integer greater than i) weight for the predicted control amount calculated by a j-th control amount predictor ([0057]-[0069]).
Regarding claim 5: Kim further teaches: The control apparatus according to claim 4, wherein the weighted average calculator sets a value for a k-th (k is an integer between 0 through M−1) weight such that the k-th weight exponentially decreases with respect to a value of k ([0057]-[0069]).
Regarding claim 7: Kim further teaches: The control apparatus according to claim 2, wherein the weighted average calculator sets an i-th weight for the predicted control amount calculated by the i-th control amount predictor to a value greater than a j-th (j is an integer greater than i) weight for the predicted control amount calculated by a j-th control amount predictor ([0057]-[0069]).
Regarding claim 8: Kim further teaches: The control apparatus according to claim 7, wherein the weighted average calculator sets a value for a k-th (k is an integer between 0 through M−1) weight such that the k-th weight exponentially decreases with respect to a value of k ([0057]-[0069]).
Regarding claim 10: Kim further teaches: The control apparatus according to claim 3, wherein the weighted average calculator sets an i-th weight for the predicted control amount calculated by the i-th control amount predictor to a value greater than a j-th (j is an integer greater than i) weight for the predicted control amount calculated by a j-th control amount predictor ([0057]-[0069]).
Regarding claim 11: Kim further teaches: The control apparatus according to claim 10, wherein the weighted average calculator sets a value for a k-th (k is an integer between 0 through M−1) weight such that the k-th weight exponentially decreases with respect to a value of k ([0057]-[0069]).
Regarding claim 13: Kim further teaches: The control apparatus according to claim 1, wherein the control subject is a steerer in a vehicle, the control amount is a steering angle that is in accordance with the steerer, and the input information includes external information pertaining to a periphery of the vehicle ([0043]. [0044]. [0052] The target calculator 10 may calculate the target value of a steering angle, a steering angular velocity, an acceleration of the vehicle or a braking of the vehicle as the target control value regarding the behavior of the vehicle).
Regarding claim 14: Kim further teaches: The control apparatus according to claim 13, further comprising: a trainer configured to train the control amount prediction model based on time series data for the input information and the control amount as of a time of manual driving in which a driver of the vehicle is an agent who operates the control subject ([0057]-[0069]. [0042]).
Regarding claim 15: Kim further teaches: The control apparatus according to claim 1, wherein the control subject is a travel driver in a vehicle, the control amount is a travel drive force that is in accordance with the travel driver, and the input information includes external information pertaining to a periphery of the vehicle ([0044]. [0052]).
Regarding claim 16: Kim further teaches: The control apparatus according to claim 15, further comprising: a trainer configured to train the control amount prediction model based on time series data for the input information and the control amount as of a time of manual driving in which a driver of the vehicle is an agent who operates the control subject ([0057]-[0069]. [0042]).
Regarding claim 17: Kim further teaches: The control apparatus according to claim 1, wherein the control subject is a brake in a vehicle, the control amount is a braking force in accordance with the brake, and the input information includes external information pertaining to a periphery of the vehicle ([0044]. [0052]).
Regarding claim 18: Kim further teaches: The control apparatus according to claim 17, further comprising: a trainer configured to train the control amount prediction model based on time series data for the input information and the control amount as of a time of manual driving in which a driver of the vehicle is an agent who operates the control subject ([0057]-[0069]. [0042]).
Regarding claim 19: Claim 19 corresponds in scope to claim 1 and is similarly rejected. Claim 19 additionally recites and Kim teaches: A control method that uses a computer to control a control amount defined for a control subject, the control method comprising ([0040]. [0041]. [0095]).
Regarding claim 20: Claim 20 corresponds in scope to claim 1 and is similarly rejected. Claim 20 additionally recites and Kim teaches: A storage medium that stores a computer program for causing a computer to control a control amount defined for a control subject, wherein the computer program causes the computer to perform operations that comprise ([0040]. [0041]).
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.
Claim(s) 6, 9, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220063614 A1, “Kim”) and further in view of Torii et al. (US 2004/0162644 A1, “Torii”).
Regarding claim 6: Kim further teaches: The control apparatus according to claim 5. However, Kim does not explicitly teach: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer.
Torii teaches: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer ([0034] FIG. 2 is a diagram showing the structure of the RNN which functions as the estimating module. Here, the RNN constituting the estimating module 21 for the yaw rate is used for description of the RNN. In this case, the operator determines the structure of the network so that each layer has the optimum node number in consideration of the conflicting relationship between the enhancement in reliability of the solution achieved by increasing the number of the nodes Nn and enhancement of processing speed achieved by reducing the number of the nodes Nn. [0057] Thus, the state variable P is preferably output from the estimating module 21 for the yaw rate (in short, the reliability of the state variable P is enhanced). [0062] method of determining the optimum solution of the coupling weight coefficient and the threshold value. steering angle, steering angular velocity, steering angular acceleration, steering reaction force, vehicle speed and vehicle acceleration are input to selected nodes. The next, any one of the nodes different from nodes used for input is selected, and output is acquired from the node thus selected. Based on this output and the vehicle parameters described, the adaptive value A is calculated by using estimation function shown by equation 3. selection, elimination and generation of the next generation population are repeated until the adaptive value A becomes equal to or smaller than the judgment adaptive value Aerror. Consequently, the optimum genetic type is determined, whereby the optimum solution of the coupling weight coefficient and threshold value in the RNN is determined).
Kim and Torii are analogous art to the claimed invention since they are from the similar field of vehicle controls using predictive modelling and machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Kim with the aspects of Torii to create, with a reasonable expectation for success, a control apparatus wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer. The motivation for modification would have been to generate a vehicle motion model which represents the motion state of an actual vehicle more accurately than the feed-forward type neural network, that is, has excellent reproducibility of the motion state of a vehicle (Torii, [0054]).
Regarding claim 9: Kim further teaches: The control apparatus according to claim 8. However, Kim does not explicitly teach: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer.
Torii teaches: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer ([0034]. [0057]. [0062]).
Kim and Torii are analogous art to the claimed invention since they are from the similar field of vehicle controls using predictive modelling and machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Kim with the aspects of Torii to create, with a reasonable expectation for success, a control apparatus wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer. The motivation for modification would have been to generate a vehicle motion model which represents the motion state of an actual vehicle more accurately than the feed-forward type neural network, that is, has excellent reproducibility of the motion state of a vehicle (Torii, [0054]).
Regarding claim 12: Kim further teaches: The control apparatus according to claim 11. However, Kim does not explicitly teach: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer.
Torii teaches: wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer ([0034]. [0057]. [0062]).
Kim and Torii are analogous art to the claimed invention since they are from the similar field of vehicle controls using predictive modelling and machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Kim with the aspects of Torii to create, with a reasonable expectation for success, a control apparatus wherein the target control amount calculator further comprises a reliability level obtainer configured to obtain a reliability level of the input information as of the i amount of time before the current time, and the weighted average calculator sets the value of the i-th weight based on an i-th reliability level obtained by the reliability level obtainer. The motivation for modification would have been to generate a vehicle motion model which represents the motion state of an actual vehicle more accurately than the feed-forward type neural network, that is, has excellent reproducibility of the motion state of a vehicle (Torii, [0054]).
Conclusion
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/MADISON B EMMETT/Examiner, Art Unit 3658
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