DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 2/15/2023. Claims 1-12 are pending in the case. Claims 1 and 7 are independent claims.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. § 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. § 112(f) 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) 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) 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) because the claim limitations use 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 limitations are: “a data augmentation device configured to acquire,” “an imitation learning device configured to perform,” and “a data augmentation model learning device configured to train” in claims 1 and 3.
Because these claim limitations are being interpreted under 35 U.S.C. § 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed functions, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. § 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. § 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. § 112(f).
Claim Rejections - 35 U.S.C. § 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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “infers behavioral data from input state data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “infers state data from input behavioral data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “[produce] behavioral data similar to the expert in a specific state using the plurality of demonstration data sets and the plurality of augmented data sets” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a data augmentation device configured to acquire a plurality of augmented data sets from a plurality of demonstration data sets corresponding to an expert's demonstration behavior trajectory” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “an inverse behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “an imitation learning device configured to perform imitation learning to derive a model that outputs [behavioral data]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the plurality of demonstration data sets and the plurality of augmented data sets each include a pair of corresponding state data and behavioral data” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a data augmentation device configured to acquire a plurality of augmented data sets from a plurality of demonstration data sets corresponding to an expert's demonstration behavior trajectory” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “an inverse behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “an imitation learning device configured to perform imitation learning to derive a model that outputs [behavioral data]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the plurality of demonstration data sets and the plurality of augmented data sets each include a pair of corresponding state data and behavioral data” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the data augmentation device inputs first state data included in each demonstration data set to the behavioral replication model and the behavioral replication model outputs first behavioral data inferred from the first state data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the data augmentation device inputs first state data included in each demonstration data set to the behavioral replication model and the behavioral replication model outputs first behavioral data inferred from the first state data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “the data augmentation device acquires augmented second state data by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the data augmentation device acquires augmented second state data by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “the data augmentation device … acquires augmented second behavioral data by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the data augmentation device … acquires augmented second behavioral data by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the data augmentation device inputs first state data included in each demonstration data set to the behavioral replication model and the behavioral replication model outputs first behavioral data inferred from the first state data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the data augmentation device inputs first state data included in each demonstration data set to the behavioral replication model and the behavioral replication model outputs first behavioral data inferred from the first state data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “the data augmentation device acquires augmented second state data by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the data augmentation device acquires augmented second state data by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “the data augmentation device … acquires augmented second behavioral data by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the data augmentation device … acquires augmented second behavioral data by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a data augmentation model learning device configured to train the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a data augmentation model learning device configured to train the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a data augmentation model learning device configured to train the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a data augmentation model learning device configured to train the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the data augmentation model learning device trains the behavioral replication model using a loss function value LBC of Equation 1 below …” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the data augmentation model learning device trains the inverse behavioral replication model using a loss function value LIBC of Equation 2 below …” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “infers behavioral data from input state data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “infers state data from input behavioral data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “[produce] behavioral data similar to an expert in a specific state using the plurality of demonstration data sets and the plurality of augmented data sets” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “constructing a behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “constructing … an inverse behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “acquiring a plurality of augmented data sets from a plurality of demonstration data sets corresponding to an expert's demonstration behavior trajectory using the behavioral replication model and the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “performing imitation learning to derive a model that outputs [behavioral data]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein each of the plurality of demonstration data sets and the plurality of augmented data sets includes a pair of corresponding state data and behavioral data” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “constructing a behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “constructing … an inverse behavioral replication model that [infers]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “acquiring a plurality of augmented data sets from a plurality of demonstration data sets corresponding to an expert's demonstration behavior trajectory using the behavioral replication model and the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “performing imitation learning to derive a model that outputs [behavioral data]” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein each of the plurality of demonstration data sets and the plurality of augmented data sets includes a pair of corresponding state data and behavioral data” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “acquiring first behavioral data from the behavioral replication model by inputting first state data included in each demonstration data set to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “acquiring first behavioral data from the behavioral replication model by inputting first state data included in each demonstration data set to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “acquiring augmented second state data from the inverse behavioral replication model by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “acquiring augmented second state data from the inverse behavioral replication model by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “acquiring augmented second behavioral data from the behavioral replication model by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “acquiring augmented second behavioral data from the behavioral replication model by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “acquiring first behavioral data from the behavioral replication model by inputting first state data included in each demonstration data set to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “acquiring first behavioral data from the behavioral replication model by inputting first state data included in each demonstration data set to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “acquiring augmented second state data from the inverse behavioral replication model by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “acquiring augmented second state data from the inverse behavioral replication model by inputting the first behavioral data to the inverse behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “acquiring augmented second behavioral data from the behavioral replication model by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “acquiring augmented second behavioral data from the behavioral replication model by inputting the second state data to the behavioral replication model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “training the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “training the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “training the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “training the behavioral replication model and the inverse behavioral replication model using the plurality of demonstration data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the training includes training the behavioral replication model using a loss function value LBC of Equation 1 below …” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the learning includes training the inverse behavioral replication model using a loss function value LIBC of Equation 2 below …” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
Claim Rejections - 35 U.S.C. § 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.
Claims 1-4 and 7-10 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Wei et al. (“Learning to Simulate on Sparse Trajectory Data,” 22 March 2021, https://arxiv.org/abs/2103.11845, hereinafter Wei).
As to independent claim 1, Wei discloses a system for imitation learning, comprising:
a data augmentation device configured to acquire a plurality of augmented data sets from a plurality of demonstration data sets (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2) corresponding to an expert's demonstration behavior trajectory using a behavioral replication model that infers behavioral data from input state data (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8) and an inverse behavioral replication model that infers state data from input behavioral data (“While learning to differentiate the expert trajectories from generated trajectories, the discriminator in ImIn-GAIL also learns to interpolate a sparse trajectory to a dense trajectory,” page 6 section “3.2 Imitation with Interpolation” paragraph 4 line 2 to page 7 line 1); and
an imitation learning device configured to perform imitation learning to derive a model that outputs behavioral data similar to the expert in a specific state using the plurality of demonstration data sets and the plurality of augmented data sets (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8),
wherein the plurality of demonstration data sets and the plurality of augmented data sets each include a pair of corresponding state data and behavioral data (“Each driving point includes a driving state and an action of the vehicle at the observed time step,” page 2 figure 1 caption lines 3-4).
As to dependent claim 2, Wei further discloses a system wherein, when the data augmentation device inputs first state data included in each demonstration data set to the behavioral replication model and the behavioral replication model outputs first behavioral data inferred from the first state data, the data augmentation device acquires augmented second state data by inputting the first behavioral data to the inverse behavioral replication model (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2) and acquires augmented second behavioral data by inputting the second state data to the behavioral replication model (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8).
As to dependent claim 3, Wei further discloses a system comprising a data augmentation model learning device configured to train the behavioral replication model and the inverse behavioral replication model (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8) using the plurality of demonstration data sets (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8).
As to dependent claim 4, Wei further discloses a system wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models (“neural nets,” page 1 paragraph 2 line 5).
As to independent claim 7, Wei discloses an imitation learning method of an imitation learning device, the method comprising:
constructing a behavioral replication model that infers behavioral data from input state data (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8) and an inverse behavioral replication model that infers state data from input behavioral data (“While learning to differentiate the expert trajectories from generated trajectories, the discriminator in ImIn-GAIL also learns to interpolate a sparse trajectory to a dense trajectory,” page 6 section “3.2 Imitation with Interpolation” paragraph 4 line 2 to page 7 line 1);
acquiring a plurality of augmented data sets from a plurality of demonstration data sets (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2) corresponding to an expert's demonstration behavior trajectory using the behavioral replication model (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8) and the inverse behavioral replication model (“While learning to differentiate the expert trajectories from generated trajectories, the discriminator in ImIn-GAIL also learns to interpolate a sparse trajectory to a dense trajectory,” page 6 section “3.2 Imitation with Interpolation” paragraph 4 line 2 to page 7 line 1); and
performing imitation learning to derive a model that outputs behavioral data similar to an expert in a specific state using the plurality of demonstration data sets and the plurality of augmented data sets (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8),
wherein each of the plurality of demonstration data sets and the plurality of augmented data sets includes a pair of corresponding state data and behavioral data (“Each driving point includes a driving state and an action of the vehicle at the observed time step,” page 2 figure 1 caption lines 3-4).
As to dependent claim 8, Wei further discloses a method wherein the acquiring includes:
acquiring first behavioral data from the behavioral replication model by inputting first state data included in each demonstration data set to the behavioral replication model (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8);
acquiring augmented second state data from the inverse behavioral replication model by inputting the first behavioral data to the inverse behavioral replication model (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2); and
acquiring augmented second behavioral data from the behavioral replication model by inputting the second state data to the behavioral replication model (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8).
As to dependent claim 9, Wei further discloses a method comprising training the behavioral replication model and the inverse behavioral replication model (“The output of the discriminator Dψ(s,a) can then be used as a surrogate reward function whose value grows larger as actions sampled from 𝜋θ look similar to those chosen by experts,” page 7 lines 6-8) using the plurality of demonstration data sets (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8).
As to dependent claim 10, Wei further discloses a method wherein the behavioral replication model and the inverse behavioral replication model are artificial neural network-based models (“neural nets,” page 1 paragraph 2 line 5).
Claim Rejections - 35 U.S.C. § 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 of this title, 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 5-6 and 11-12 are rejected under 35 U.S.C. § 103 as being unpatentable over Wei in view of Chen et al. (US 2021/0287116 A1, hereinafter Chen).
As to dependent claim 5, the rejection of claim 3 is incorporated. Wei further teaches a system wherein the data augmentation model learning device trains the behavioral replication model using a loss function [including] behavioral data included in an expert's demonstration data set (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8), [] behavioral data inferred through the behavioral replication model (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2), and [] an action space that is a set of all possible actions (“each driving point is embedded to a 10-dimensional latent space,” page 7 section “3.3 Training and Implementation” paragraph 1 lines 9-10).
Wei does not appear to expressly teach a system wherein the loss function is calculated using a summation of squared Euclidean norms.
Chen teaches a system wherein the loss function is calculated using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” claim 15 lines 1-4, 12).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the loss function of Wei to comprise the summation of squared Euclidean norms of Chen. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calculating the loss function using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” Chen claim 15 lines 1-4, 12). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 6, the rejection of claim 3 is incorporated. Wei further teaches a system wherein the data augmentation model learning device trains the inverse behavioral replication model using a loss function [including] state data included in an expert's demonstration data set (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8), [] state data inferred through the inverse behavioral replication model (“Each driving point includes a driving state and an action of the vehicle at the observed time step,” page 2 figure 1 caption lines 3-4), and [] a state space that is a set of all possible states (“each driving point is embedded to a 10-dimensional latent space,” page 7 section “3.3 Training and Implementation” paragraph 1 lines 9-10).
Wei does not appear to expressly teach a system wherein the loss function is calculated using a summation of squared Euclidean norms.
Chen teaches a system wherein the loss function is calculated using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” claim 15 lines 1-4, 12).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the loss function of Wei to comprise the summation of squared Euclidean norms of Chen. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calculating the loss function using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” Chen claim 15 lines 1-4, 12). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 11, the rejection of claim 9 is incorporated. Wei further teaches a method wherein the data augmentation model learning device trains the behavioral replication model using a loss function [including] behavioral data included in the expert's demonstration data set (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8), [] behavioral data inferred through the behavioral replication model (“Given an initialized driving policy 𝜋θ, the dense trajectories TGD of vehicles can be generated in the simulator,” page 5 section “3.2 Imitation with Interpolation” paragraph 2 lines 1-2), and [] an action space that is a set of all possible actions (“each driving point is embedded to a 10-dimensional latent space,” page 7 section “3.3 Training and Implementation” paragraph 1 lines 9-10).
Wei does not appear to expressly teach a method wherein the loss function is calculated using a summation of squared Euclidean norms.
Chen teaches a method wherein the loss function is calculated using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” claim 15 lines 1-4, 12).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the loss function of Wei to comprise the summation of squared Euclidean norms of Chen. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calculating the loss function using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” Chen claim 15 lines 1-4, 12). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 12, the rejection of claim 9 is incorporated. Wei further teaches a method wherein the data augmentation model learning device trains the inverse behavioral replication model using a loss function [including] state data included in the expert's demonstration data set (“we can only observe a set of sparse trajectories TE generated by expert policy 𝜋E as expert trajectory … Our goal is to learn a parameterized policy 𝜋θ that imitates the expert policy 𝜋E,” page 4 lines 4-8), [] state data inferred through the inverse behavioral replication model (“Each driving point includes a driving state and an action of the vehicle at the observed time step,” page 2 figure 1 caption lines 3-4), and [] a state space that is a set of all possible states (“each driving point is embedded to a 10-dimensional latent space,” page 7 section “3.3 Training and Implementation” paragraph 1 lines 9-10).
Wei does not appear to expressly teach a method wherein the loss function is calculated using a summation of squared Euclidean norms.
Chen teaches a method wherein the loss function is calculated using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” claim 15 lines 1-4, 12).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the loss function of Wei to comprise the summation of squared Euclidean norms of Chen. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calculating the loss function using a summation of squared Euclidean norms (“the first loss value is computed using J1(α)=Σk=1c[ek∥Vmrαk−yk∥22+λ1Σi=1m((Σiir)0.5|αi,k|)+λ2Σi=1m((Σiir)0.5αi,k)2], where … ∥ ∥2 indicates a squared Euclidean norm,” Chen claim 15 lines 1-4, 12). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Sadat et al. (“Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles,” 10 October 2019, https://arxiv.org/abs/1910.04586) disclosing a behavior planner and a trajectory planner
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Michelle Bechtold can be reached at 571 431 0762. The fax phone number for the organization where this application or proceeding is assigned is 571 273 8300.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148