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 1/31/2024. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are independent claims.
Claim Objections
Claims 16-20 are objected to because they recite “The non-transitory computer readable storage medium 14.” There is insufficient antecedent basis for this limitation. For the purposes of prior art and subject matter eligibility analyses Examiner assumes these claims depend from claim 15. Appropriate correction is required.
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 1-20 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 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 a first subset of the plurality of data fields corresponds to a plurality of measures and a second subset of the plurality of data fields corresponds to a plurality of dimensions” 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 “generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name; (ii) a measure; (iii) a time dimension; and (iv) an aggregation type” 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 “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 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 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 plurality of data fields includes additional contextual fields, including one or more related dimensions” 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).
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 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 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 plurality of data fields includes additional contextual fields, including time granularity” 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).
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 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 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 plurality of data fields includes additional contextual fields, including a favorability indicator” 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).
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 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 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 machine learning model is a generative artificial intelligence model” 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 machine learning model is a generative artificial intelligence model” 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 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 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 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).
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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 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 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 a first subset of the plurality of data fields corresponds to a plurality of measures and a second subset of the plurality of data fields corresponds to a plurality of dimensions” 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 “generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name; (ii) a measure; (iii) a time dimension; and (iv) an aggregation type” 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 computer system having one or more processors and memory, wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprise instructions for” 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).
No, the limitation “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 computer system having one or more processors and memory, wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprise instructions for” 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).
No, the limitation “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 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 plurality of data fields includes additional contextual fields, including one or more related dimensions” 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).
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 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 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 plurality of data fields includes additional contextual fields, including time granularity” 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).
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 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 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 plurality of data fields includes additional contextual fields, including a favorability indicator” 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).
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 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 machine learning model is a generative artificial intelligence model” 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 machine learning model is a generative artificial intelligence model” 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 13:
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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 14:
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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 “for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects” 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 15:
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 manufacture.
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 a first subset of the plurality of data fields corresponds to a plurality of measures and a second subset of the plurality of data fields corresponds to a plurality of dimensions” 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 “generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name; (ii) a measure; (iii) a time dimension; and (iv) an aggregation type” 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 non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and memory, the one or more programs comprising instructions for” 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).
No, the limitation “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and memory, the one or more programs comprising instructions for” 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).
No, the limitation “obtaining a plurality of data fields from a selected data source” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “prompting a machine learning model to generate a plurality of suggested metric objects” 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 “in response to prompting the machine learning model, [generating]” 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 “in response to prompting the machine learning model, [generating]” 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 16:
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 manufacture.
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 plurality of data fields includes additional contextual fields, including one or more related dimensions” 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).
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 17:
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 manufacture.
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 plurality of data fields includes additional contextual fields, including time granularity” 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).
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 18:
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 manufacture.
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 plurality of data fields includes additional contextual fields, including a favorability indicator” 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).
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 19:
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 manufacture.
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 machine learning model is a generative artificial intelligence model” 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 machine learning model is a generative artificial intelligence model” 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 20:
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 manufacture.
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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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 “validating metric definitions generated by the first machine learning model using a second machine learning 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.
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 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Khabiri et al. (US 2019/0205726 A1, hereinafter Khabiri).
As to independent claim 1, Khabiri discloses a method for automatically generating metric objects, including:
obtaining a plurality of data fields from a selected data source (“a user can enter, retrieve and/or view data stored, for example, in a relational database,” paragraph 0024 lines 3-5), wherein a first subset of the plurality of data fields corresponds to a plurality of measures (line graphs for paid visits versus owned visits, figure 9A part 907A) and a second subset of the plurality of data fields corresponds to a plurality of dimensions (“paid visits” versus “owned visits,” figure 9A part 907A);
prompting a machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5) to generate a plurality of suggested metric objects (“Via the interface 900, there is visually presented the answer(s) 905 to the original input question provided in natural language format, enhanced with additional windows 907A, 907B, . . . , 907N presenting the top-K additional insight data in a natural language format,” paragraph 0060 lines 3-8); and
in response to prompting the machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5), generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name (“paid visits” versus “owned visits,” figure 9A part 907A); (ii) a measure (line graphs for paid visits versus owned visits, figure 9A part 907A); (iii) a time dimension (“last 20 months,” figure 9A part 907A); and (iv) an aggregation type (“total,” figure 9A part 907A).
As to dependent claim 2, Khabiri further discloses a method wherein the plurality of data fields includes additional contextual fields, including one or more related dimensions (“owned” versus “paid” versus “earned,” figure 9A part 907B).
As to dependent claim 3, Khabiri further discloses a method wherein the plurality of data fields includes additional contextual fields, including time granularity (“months,” figure 9A part 907A).
As to dependent claim 4, Khabiri further discloses a method wherein the plurality of data fields includes additional contextual fields, including a favorability indicator (“visits were increasing for the last 1 months,” figure 9A part 907A).
As to dependent claim 5, Khabiri further discloses a method wherein the machine learning model is a generative artificial intelligence model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
As to independent claim 8, Khabiri discloses a computer system having one or more processors (figure 10 part 12) and memory (figure 10 part 16), wherein the memory stores one or more programs configured for execution (“the processor 12 may execute one or more modules 10 that are loaded from memory 16, where the program module(s) embody software (program instructions) that cause the processor to perform one or more method embodiments of the present invention,” paragraph 0069 lines 5-9) by the one or more processors, and the one or more programs comprise instructions for:
obtaining a plurality of data fields from a selected data source (“a user can enter, retrieve and/or view data stored, for example, in a relational database,” paragraph 0024 lines 3-5), wherein a first subset of the plurality of data fields corresponds to a plurality of measures (line graphs for paid visits versus owned visits, figure 9A part 907A) and a second subset of the plurality of data fields corresponds to a plurality of dimensions (“paid visits” versus “owned visits,” figure 9A part 907A);
prompting a machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5) to generate a plurality of suggested metric objects (“Via the interface 900, there is visually presented the answer(s) 905 to the original input question provided in natural language format, enhanced with additional windows 907A, 907B, . . . , 907N presenting the top-K additional insight data in a natural language format,” paragraph 0060 lines 3-8); and
in response to prompting the machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5), generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name (“paid visits” versus “owned visits,” figure 9A part 907A); (ii) a measure (line graphs for paid visits versus owned visits, figure 9A part 907A); (iii) a time dimension (“last 20 months,” figure 9A part 907A); and (iv) an aggregation type (“total,” figure 9A part 907A).
As to dependent claim 9, Khabiri further discloses a system wherein the plurality of data fields includes additional contextual fields, including one or more related dimensions (“owned” versus “paid” versus “earned,” figure 9A part 907B).
As to dependent claim 10, Khabiri further discloses a system wherein the plurality of data fields includes additional contextual fields, including time granularity (“months,” figure 9A part 907A).
As to dependent claim 11, Khabiri further discloses a system wherein the plurality of data fields includes additional contextual fields, including a favorability indicator (“visits were increasing for the last 1 months,” figure 9A part 907A).
As to dependent claim 12, Khabiri further discloses a system wherein the machine learning model is a generative artificial intelligence model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
As to independent claim 15, Khabiri discloses a non-transitory computer readable storage medium (figure 10 part 16) storing one or more programs configured for execution (“the processor 12 may execute one or more modules 10 that are loaded from memory 16, where the program module(s) embody software (program instructions) that cause the processor to perform one or more method embodiments of the present invention,” paragraph 0069 lines 5-9) by a computer system having one or more processors and memory, the one or more programs comprising instructions for:
obtaining a plurality of data fields from a selected data source (“a user can enter, retrieve and/or view data stored, for example, in a relational database,” paragraph 0024 lines 3-5), wherein a first subset of the plurality of data fields corresponds to a plurality of measures (line graphs for paid visits versus owned visits, figure 9A part 907A) and a second subset of the plurality of data fields corresponds to a plurality of dimensions (“paid visits” versus “owned visits,” figure 9A part 907A);
prompting a machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5) to generate a plurality of suggested metric objects (“Via the interface 900, there is visually presented the answer(s) 905 to the original input question provided in natural language format, enhanced with additional windows 907A, 907B, . . . , 907N presenting the top-K additional insight data in a natural language format,” paragraph 0060 lines 3-8); and
in response to prompting the machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5), generating a respective metric definition for each measure in the plurality of measures, wherein each generated respective metric definition includes a plurality of data fields, including: (i) a name (“paid visits” versus “owned visits,” figure 9A part 907A); (ii) a measure (line graphs for paid visits versus owned visits, figure 9A part 907A); (iii) a time dimension (“last 20 months,” figure 9A part 907A); and (iv) an aggregation type (“total,” figure 9A part 907A).
As to dependent claim 16, Khabiri further discloses a medium wherein the plurality of data fields includes additional contextual fields, including one or more related dimensions (“owned” versus “paid” versus “earned,” figure 9A part 907B).
As to dependent claim 17, Khabiri further discloses a medium wherein the plurality of data fields includes additional contextual fields, including time granularity (“months,” figure 9A part 907A).
As to dependent claim 18, Khabiri further discloses a medium wherein the plurality of data fields includes additional contextual fields, including a favorability indicator (“visits were increasing for the last 1 months,” figure 9A part 907A).
As to dependent claim 19, Khabiri further discloses a medium wherein the machine learning model is a generative artificial intelligence model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 6, 13, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Khabiri in view of Wubbels et al. (US 10,599,984 B1, hereinafter Wubbels).
As to dependent claim 6, the rejection of claim 1 is incorporated. Khabiri further teaches a method wherein the machine learning model is a first machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
Khabiri does not appear to expressly teach a method comprising validating metric definitions generated by the first machine learning model using a second machine learning model.
Wubbels teaches a method comprising validating metric definitions generated by the first machine learning model using a second machine learning model (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” column 6 lines 16-19).
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 generating of Khabiri to comprise the validating of Wubbels. (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 validating the generated content (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” Wubbels column 6 lines 16-19). 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 13, the rejection of claim 8 is incorporated. Khabiri further teaches a system wherein the machine learning model is a first machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
Khabiri does not appear to expressly teach a system wherein the one or more programs comprise instructions for validating metric definitions generated by the first machine learning model using a second machine learning model.
Wubbels teaches a system wherein the one or more programs comprise instructions for validating metric definitions generated by the first machine learning model using a second machine learning model (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” column 6 lines 16-19).
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 generating of Khabiri to comprise the validating of Wubbels. (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 validating the generated content (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” Wubbels column 6 lines 16-19). 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 20, the rejection of claim 15 is incorporated. Khabiri further teaches a medium wherein the machine learning model is a first machine learning model (“a recursive machine learning (not shown) algorithm may be employed in system 100 for use as a prediction tool to generate a new related question that is most relevant given the user’s history of questions that the user (or multiple users) has asked,” paragraph 0040 lines 1-5).
Khabiri does not appear to expressly teach a medium wherein the one or more programs comprise instructions for validating metric definitions generated by the first machine learning model using a second machine learning model.
Wubbels teaches a medium wherein the one or more programs comprise instructions for validating metric definitions generated by the first machine learning model using a second machine learning model (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” column 6 lines 16-19).
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 generating of Khabiri to comprise the validating of Wubbels. (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 validating the generated content (“the validator module 400 can compare an accuracy and a latency of the machine learning model 420 in generating the inference to that of a second machine learning model,” Wubbels column 6 lines 16-19). 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).
Claims 7 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Khabiri in view of Barros (US 2006/0174209 A1).
As to dependent claim 7, the rejection of claim 1 is incorporated. Khabiri further teaches a method comprising displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning model (“Via the interface 900, there is visually presented the answer(s) 905 to the original input question provided in natural language format, enhanced with additional windows 907A, 907B, . . . , 907N presenting the top-K additional insight data in a natural language format,” paragraph 0060 lines 3-8).
Khabiri does not appear to expressly teach a method comprising for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects.
Barros teaches a method comprising for each of the one or more suggested [] objects, displaying, in the user interface, an option to select a respective suggested [] object to be saved in a [] database that includes other [] objects (“each page contains several items; each item may have its own image 1, name 2, and description 3. A “Save in My Collection” checkmark 4 is included for each item, thereby turning the document into a control panel or key,” paragraph 0143 lines 8-12).
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 suggested objects of Khabiri to comprise the individual save options of Barros. (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 displaying individual save options (“each page contains several items; each item may have its own image 1, name 2, and description 3. A “Save in My Collection” checkmark 4 is included for each item, thereby turning the document into a control panel or key,” Barros paragraph 0143 lines 8-12). 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 14, the rejection of claim 8 is incorporated. Khabiri further teaches a system wherein the one or more programs comprise instructions for displaying, in a user interface, one or more suggested metric objects, each based on a respective metric definition generated by the machine learning model (“Via the interface 900, there is visually presented the answer(s) 905 to the original input question provided in natural language format, enhanced with additional windows 907A, 907B, . . . , 907N presenting the top-K additional insight data in a natural language format,” paragraph 0060 lines 3-8).
Khabiri does not appear to expressly teach a system wherein the one or more programs comprise instructions for: for each of the one or more suggested metric objects, displaying, in the user interface, an option to select a respective suggested metric object to be saved in a metrics database that includes other metric objects.
Barros teaches a wherein the one or more programs comprise instructions for: for each of the one or more suggested [] objects, displaying, in the user interface, an option to select a respective suggested [] object to be saved in a [] database that includes other [] objects (“each page contains several items; each item may have its own image 1, name 2, and description 3. A “Save in My Collection” checkmark 4 is included for each item, thereby turning the document into a control panel or key,” paragraph 0143 lines 8-12).
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 suggested objects of Khabiri to comprise the individual save options of Barros. (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 displaying individual save options (“each page contains several items; each item may have its own image 1, name 2, and description 3. A “Save in My Collection” checkmark 4 is included for each item, thereby turning the document into a control panel or key,” Barros paragraph 0143 lines 8-12). 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:
Business Computer Skills (“The Ultimate Guide To Excel Charts and Graphs,” 16 August 2021, https://web.archive.org/web/20210816082428/https://www.businesscomputerskills.com/tutorials/excel/the-ultimate-guide-to-excel-charts-and-graphs.php) disclosing metric objects with additional contextual fields
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)).
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights