DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-18 recite method claims. Claims 19 and 20 recite system claims. Therefore, claims 1-20 are directed to one of the four statutory categories of patentable subject matter.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites the step:
“formulating two or more insights from a data frame;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually formulate their own insights after looking at a data frame.)
“assigning a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually write down on paper scores that they determine from their own determinations of significance and confidence of the two or more insights.)
“and searching for an optimal insight among the two or more insights based on the respective score for each respective insight.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the optimal insight with consideration for the respective scores of the insights)
Thus, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 does not further recite any additional elements. Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 1 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites:
“The method of Claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 recites the additional elements:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites:
“The method of Claim 2,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 2.)
“wherein the significance of each respective insight is computed based on a p-value of each respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites:
“The method of Claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“further comprising computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites:
“The method of Claim 1, wherein:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“and the confidence of each respective is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 recites the additional elements:
“the data frame comprises two or more rows of data;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“the data frame comprises two or more rows of data;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites:
“The method of Claim 1, wherein searching for the optimal insight among the two or more insights based on the respective score for each respective insight comprises:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“selecting a first node for the tree based on one of the two or more insights based on a maximum of the respective score for each respective insight;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually select a node with consideration for the score.)
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually choose a branch with consideration for additional insights.)
“and selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine a node with consideration for the additional insights and their respective scores.)
Thus, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites:
“The method of Claim 1, wherein searching for the optimal insight among the two or more insights based on the respective score for each respective insight comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“utilizing a gradient based search.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements. Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 7 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 7 is subject-matter ineligible.
Regarding claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites:
“The method of Claim 1, further comprising” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 recites the additional elements:
“generating a human language representation of the optimal insight with a large language model.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“generating a human language representation of the optimal insight with a large language model.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites:
“The method of Claim 1, wherein the respective score for each respective insight of the two or more insights comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“a harmonic mean of the significance of each respective insight and the confidence in each respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 9 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 9 is subject-matter ineligible.
Regarding claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites:
“The method of Claim 1, wherein the respective score for each respective insight of the two or more insights comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“a geometric mean of the significance of each respective insight and the confidence in each respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 recites:
“formulating two or more insights from a data frame;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually formulate their own insights after looking at a data frame.)
“assigning a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually write down on paper scores that they determine from their own determinations of significance and confidence of the two or more insights.)
“searching for an optimal insight among the two or more insights based on the respective score for each respective insight, comprising growing a tree over the data frame utilizing a greedy binary search algorithm, comprising:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the optimal insight with consideration for the respective scores of the insights)
“selecting a first node for the tree based on one of the two or more insight based on a maximum of the respective score for each respective insight;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually select a node with consideration for the score.)
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node; and” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually choose a branch with consideration for additional insights.)
“selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine a node with consideration for the additional insights and their respective scores.)
Thus, claim 11 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 recites the additional elements:
“generating a human language representation of the optimal insight with a large language model.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 11 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 11 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“generating a human language representation of the optimal insight with a large language model.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 11 is subject-matter ineligible.
Regarding claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites:
“The method of Claim 11, wherein selecting the first node and selecting the second node are further based on” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
“utilizing a greedy binary search approach.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually perform a binary search approach by determining to take the best node.)
Thus, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 does not further recite any additional elements. Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 12 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 12 is subject-matter ineligible.
Regarding claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites:
“The method of Claim 11,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
Thus, claim 13 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 recites the additional elements:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 13 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 13 is subject-matter ineligible.
Regarding claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites:
“The method of Claim 13,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 13.)
“wherein the significance of each respective insight is computed based on a p-value of the respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 14 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 does not further recite any additional elements. Therefore, claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 14 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 14 is subject-matter ineligible.
Regarding claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites:
“The method of Claim 11, further comprising” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
“computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 does not further recite any additional elements. Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 15 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 15 is subject-matter ineligible
Regarding claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites:
“The method of Claim 11, wherein:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
“and the confidence of each respective insight is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 recites the additional elements:
“the data frame comprises two or more rows of data;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 16 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“the data frame comprises two or more rows of data;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 16 is subject-matter ineligible
Regarding claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 17 recites:
“The method of Claim 11, wherein the respective score for each respective insight of the two or more insights comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
“a harmonic mean of the significance of each respective insight and the confidence in each respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 17 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 17 does not further recite any additional elements. Therefore, claim 17 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 17 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 17 is subject-matter ineligible.
Regarding claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites:
“The method of Claim 11, wherein the respective score for each respective insight of the two or more insights comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 11.)
“a geometric mean of the significance of each respective insight and the confidence in each respective insight.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 18 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 18 does not further recite any additional elements. Therefore, claim 18 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 18 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 18 is subject-matter ineligible.
Regarding claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites:
“formulate two or more insights from a data frame;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually formulate their own insights after looking at a data frame.)
“assign a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually write down on paper scores that they determine from their own determinations of significance and confidence of the two or more insights.)
“and search for an optimal insight among the two or more insights based on the respective score for each respective insight.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the optimal insight with consideration for the respective scores of the insights)
Thus, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 recites the additional elements:
“a memory comprising computer-executable instructions;” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“and a processor configured to execute the computer-executable instructions and cause the processing system to” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“a memory comprising computer-executable instructions;” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“and a processor configured to execute the computer-executable instructions and cause the processing system to” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 19 is subject-matter ineligible
Regarding claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites:
“The processing system of Claim 19,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 19.)
“in order to search for the optimal insight among the two or more insights based on the respective score for each respective insight.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the optimal insight with respect to the score.)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 recites the additional elements:
“wherein the processor is further configured to cause the processing system to utilize a greedy binary search approach” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 20 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 20 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the processor is further configured to cause the processing system to utilize a greedy binary search approach” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 20 is subject-matter ineligible
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Harris et al. (US 20220244815 A1) (hereafter referred to as Harris).
Regarding claim 1, Harris teaches:
“A method, comprising: formulating two or more insights from a data frame;” (Harris ¶ [0013], “In this example, a data visualization system is configured to evaluate an input dataset to detect insights.” Harris ¶ [0029], “In some examples, the dataset 104 can be formatted as a table with N rows representing the N data entries and M columns representing the M data attributes.” Examiner notes that insights are plural. Examiner also notes the dataset of N rows and M columns teaches the data frame.)
“assigning a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (Harris ¶ [0038], “To generate the insight scores, each of the insight detection tools is configured to output an insight score for the detected insight indicating the significance of the detected insight or the relationship strength for the variables or data entries in the dataset. In some implementations, the insight score includes a score vector having the same size as the number of data entries N. In other words, the insight detection tool outputs a score for each data entry to indicate the relationship strength or the confidence of the attributes in that data entry having the corresponding feature. Using two-variable outliers as an example, the insight detection tool is configured to output a score for each of the N data entries to indicate the confidence of the two attributes in that data entry being an outlier. The insight scores output by different insight detection tools are aggregated to generate the insight score for an insight detected from the particular data attribute combination.”)
“and searching for an optimal insight among the two or more insights based on the respective score for each respective insight.” (Harris ¶ [0015], “For each selected insight type, the data visualization system ranks the insights according to their respective insight scores and selects the insights having the highest insight scores for visualization.”)
Regarding claim 19, Harris teaches:
“A processing system, comprising: a memory comprising computer-executable instructions; and a processor configured to execute the computer-executable instructions and cause the processing system to:” (Harris ¶ [0063], “The depicted example of a computing system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”)
“formulate two or more insights from a data frame;” (Harris ¶ [0013], “In this example, a data visualization system is configured to evaluate an input dataset to detect insights.” Harris ¶ [0029], “In some examples, the dataset 104 can be formatted as a table with N rows representing the N data entries and M columns representing the M data attributes.” Examiner notes that insights are plural. Examiner also notes the dataset of N rows and M columns teaches the data frame.)
“assign a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (Harris ¶ [0038], “To generate the insight scores, each of the insight detection tools is configured to output an insight score for the detected insight indicating the significance of the detected insight or the relationship strength for the variables or data entries in the dataset. In some implementations, the insight score includes a score vector having the same size as the number of data entries N. In other words, the insight detection tool outputs a score for each data entry to indicate the relationship strength or the confidence of the attributes in that data entry having the corresponding feature. Using two-variable outliers as an example, the insight detection tool is configured to output a score for each of the N data entries to indicate the confidence of the two attributes in that data entry being an outlier. The insight scores output by different insight detection tools are aggregated to generate the insight score for an insight detected from the particular data attribute combination.”)
“and search for an optimal insight among the two or more insights based on the respective score for each respective insight.” (Harris ¶ [0015], “For each selected insight type, the data visualization system ranks the insights according to their respective insight scores and selects the insights having the highest insight scores for visualization.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 CFR 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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Ronen et al. (US 11182441 B2) (hereafter referred to as Ronen).
Regarding claim 2, Harris teaches all the limitations of claim 1.
Harris does not distinctly disclose:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.”
However, Ronen teaches:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.” (Ronen column 5, lines 26-32, “Additionally or alternatively, the hypothesis may be used as a basis for an insight about an entity, even if the label is known. For example, a hypothesis as to whether a client will churn or not churn may be useful to understand the motivations of clients and allow an organization to improve its operation to reduce churn.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the hypothesis format of Ronen in order to utilize data that may be less explicit but still may be relevant to insights in the data (Ronen Column 4 line 66 – Column 5 line 2).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as modified by Ronen et al. (US 11182441 B2) (hereafter referred to as Ronen) as applied to claim 2, further in view of Katariya et al. (US 20170323329 A1) (hereafter referred to as Katariya).
Regarding claim 3, Harris as modified teaches all the limitations of claim 2.
Harris as modified does not distinctly disclose:
“wherein the significance of each respective insight is computed based on a p-value of each respective insight.”
However, Katariya teaches:
“wherein the significance of each respective insight is computed based on a p-value of each respective insight.” (Katariya ¶ [0039], “A p-value defines a strength of the evidence in performing the test, typically as a value between 0 and 1. For example, a small p-value indicates strong evidence against the null hypothesis, and thus is used to reject the null hypothesis. A large p-value, on the other hand, indicates weak evidence against the null hypothesis, and the null hypothesis is not rejected.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the use of a p-value of Katariya in order to determine how significant a hypothesis is (Katariya ¶[0039], last 5 lines)
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Katariya et al. (US 20170323329 A1) (hereafter referred to as Katariya).
Regarding claim 4, Harris teaches all the limitations of claim 1.
Harris does not distinctly teach:
“further comprising computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.”
However, Katariya teaches:
“further comprising computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.” (Katariya ¶[0007], “The inputs also include a power (i.e., statistical power) that defines a sensitivity in a hypothesis test that the test correctly rejects the null hypothesis, e.g., a false negative which may be defined “1−Type II error” which is equal to “1−β”. The inputs further include a baseline conversion rate (e.g., “μ.sub.A”) which is the statistic being tested in this example. A minimum detectable effect (MDE) is also entered as an input that defines a “lift” that can be detected with the specified power and defines a desirable degree of insensitivity as part of calculation of the confidence level.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the use of a minimum detectable effect and power of Katariya in order to determine the amount of sensitivity allowed for the confidence values(Katariya ¶[0007], lines 14 - 18).
Regarding claim 5, Harris teaches all the limitations of claim 1.
Harris further teaches:
“the data frame comprises two or more rows of data;” (Harris ¶[0013], “The input data include multiple data entries and each data entry has multiple data attributes which include numerical data attributes, categorical data attributes, or both. An insight includes relationships among variables or the data entries in the dataset.” Harris ¶[0029], “In some examples, the dataset 104 can be formatted as a table with N rows representing the N data entries and M columns representing the M data attributes.”)
Harris does not distinctly disclose:
“and the confidence of each respective is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.”
However, Katariya teaches:
“and the confidence of each respective is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.” (Katariya ¶[0007], “A common form of A/B testing is referred to as fixed-horizon hypothesis testing. In fixed-horizon hypothesis testing, inputs are provided manually by a user which are then “run” over a defined number of samples (i.e., the “horizon”) until the test is completed. These inputs include a confidence level that refers to a percentage of all possible samples that can be expected to include the true population parameter, e.g., “1−Type I error” which is equal to “1−α”. The inputs also include a power (i.e., statistical power) that defines a sensitivity in a hypothesis test that the test correctly rejects the null hypothesis” Examiner notes that with the insight being represented as a hypothesis, the testing of the hypothesis would refer to the rows satisfying or not satisfying the hypothesis.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the calculation of the confidence value as in Katariya in order to determine the amount of sensitivity allowed for the confidence values(Katariya ¶[0007], lines 14 - 18).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney).
Regarding claim 6, Harris teaches all the limitations of claim 1.
Harris does not distinctly disclose:
“growing a tree over the data frame utilizing a greedy binary search algorithm, comprising:”
“selecting a first node for the tree based on one of the two or more insights based on a maximum of the respective score for each respective insight;”
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node;”
“and selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights.”
However, Looney teaches:
“growing a tree over the data frame utilizing a greedy binary search algorithm, comprising:” (Looney Page 1, ¶2, “In this article, we will be looking at only one algorithm for fitting trees to data: the greedy recursive partitioning algorithm.”, Looney Page 2, ¶2, “We start by finding a single feature and a single split point which divides our data in two.” Examiner notes the splitting in exactly two makes this tree a binary tree.)
“selecting a first node for the tree based on one of the two or more insights based on a maximum of the respective score for each respective insight;” (Looney Page, ¶2, “We start by finding a single feature and a single split point which divides our data in two.” Looney Page 2, ¶4, “We aren’t choosing these features and split points randomly – rather, we choose them to maximize some condition” Examiner notes the maximized condition is the respective score for each respective insight.)
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node;” (Looney, Page 2, ¶5, “For a new data point, we can than start at the root node and trace a path down to a leaf node by taking the left fork when the condition is true, and the right fork when it is false.”)
“and selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights.” (Looney, Page 2, ¶3-4, “All of the training data for which this rule is true we place in the left subset; and everything else in the right subset. Dividing a set into non-overlapping subsets so that the union of the sets is the original set is called a partition. We then recursively apply the same algorithm to both the left and right subset. Hence, recursive partitioning. We aren’t choosing these features and split points randomly – rather, we choose them to maximize some condition”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the binary tree of Looney in order to easily deal with non-linear datasets (Looney Page 14, ¶2).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Aher (https://medium.com/@aher.darshs/gradient-descent-f872b47a275c) (hereafter referred to as Aher).
Regarding claim 7, Harris teaches all the limitations of claim 1.
Harris does not discretely disclose:
“wherein searching for the optimal insight among the two or more insights based on the respective score for each respective insight comprises utilizing a gradient based search.”
However, Aher teaches:
“wherein searching for the optimal insight among the two or more insights based on the respective score for each respective insight comprises utilizing a gradient based search.” (Aher Page 6, ¶1, “Batch Gradient Descent is one of the fundamental optimization algorithms used in machine learning to train models and find the optimal parameters that minimize (or maximize) a given objective function. In this variant, the entire training dataset is used to compute the gradient in each iteration.” Examiner notes the parameter to maximize in this case is the score of the insight.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the gradient search of Aher in order to achieve a smooth convergence on the desired maximum using the entire data frame (Aher, Page 6, ¶2).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn).
Regarding claim 8, Harris teaches all the limitations of claim 1.
Harris does not distinctly disclose:
“further comprising generating a human language representation of the optimal insight with a large language model.”
However, Hunn teaches:
“further comprising generating a human language representation of the optimal insight with a large language model.” (Hunn, ¶[0186], “In various embodiments, the insight manager 124 may generate an insight 1730 based on a formal deviation between the set of common parameters using a natural language generation (NLG) model, such as the NLG model 1732 of the model inferencer 208. NLG is an AI software process that produces a natural language output. Natural language is any language that evolved naturally in humans through use and repetition, such as speech and signing. A natural language is different from a constructed or formal language, such as those to program computers or to study logic. The NLG model 1732 produces information in a natural language as if it were generated by a human being, such as a written or spoken version of a human language, such as English, Spanish, French, Korean, and so forth.”, Hunn ¶[0187], “In various embodiments, the NLG model 1732 may comprise or be implemented as a large language model (LLM) to generate the natural language representation to describe the formal deviation.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the human language representation of Hunn in order to ensure that the insights are able to be understood by a human being (Hunn, ¶186, lines 14-17).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Kala et al. (US 20160019267 A1) (hereafter referred to as Kala).
Regarding claim 9, Harris teaches all the limitations of claim 1.
Harris does not distinctly disclose:
“wherein the respective score for each respective insight of the two or more insights comprises a harmonic mean of the significance of each respective insight and the confidence in each respective insight.”
However, Kala teaches:
“wherein the respective score for each respective insight of the two or more insights comprises a harmonic mean of the significance of each respective insight and the confidence in each respective insight.” (Kala ¶[0047], “Once the insights are generated, the data analysis engine 101 further prioritizes the insights using a suitable technique such as Harmonic Mean (HM), actionability, non-triviality and so on.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the harmonic mean of Kala in order to further prioritize the insights. (Kala, ¶[0047], lines 1-4).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 1, in view of Mushtaq et al. (US 20090265332 A1) (hereafter referred to as Mushtaq).
Regarding claim 10, Harris teaches all the limitations of claim 1.
Harris does not distinctly disclose:
“wherein the respective score for each respective insight of the two or more insights comprises a geometric mean of the significance of each respective insight and the confidence in each respective insight.”
However, Mushtaq teaches:
“wherein the respective score for each respective insight of the two or more insights comprises a geometric mean of the significance of each respective insight and the confidence in each respective insight.” (Mushtaq ¶[0098], “The opinion insight summarizer 826 includes hardware, software and/or firmware operative to receive the opinion scores generated by the sentiment rating engine 822 and the opinion groupings supplied by the information extractor 816, and to summarize the data received for presentation to a user of the system 800. In one embodiment, the opinion insight summarizer 826 is configured to serve as an aggregation engine, calculating weighted and unweighted advocacy scores by computing the arithmetic mean or weighted arithmetic mean of the opinion scores supplied by the sentiment rating engine 822 at the product family, brand, model, attribute, and/or feature levels. Other variables for aggregating scores, e.g., opinion source, date, date range, etc. are also possible. In alternative embodiments, the opinion insight summarizer 826 can be configured to calculate a weighted or unweighted advocacy score through other statistical measures, including by calculating the mode, median, or geometric mean of the opinion scores.” Examiner notes this aggregation into a geometric mean takes all values calculated for the insights. As the values calculated are confidence and significance, these would be the values used in the calculation.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the geometric mean of Mushtaq in order to calculate scores with consideration to all the aggregated information determined to be relevant to the insights (Mushtaq, ¶98).
Claim(s) 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn).
Regarding claim 11, Harris teaches:
“A method, comprising: formulating two or more insights from a data frame;” (Harris ¶ [0013], “In this example, a data visualization system is configured to evaluate an input dataset to detect insights.” Harris ¶ [0029], “In some examples, the dataset 104 can be formatted as a table with N rows representing the N data entries and M columns representing the M data attributes.” Examiner notes that insights are plural. Examiner also notes the dataset of N rows and M columns teaches the data frame.)
“assigning a respective score for each respective insight of the two or more insights based on a significance of each respective insight and a confidence in each respective insight;” (Harris ¶ [0038], “To generate the insight scores, each of the insight detection tools is configured to output an insight score for the detected insight indicating the significance of the detected insight or the relationship strength for the variables or data entries in the dataset. In some implementations, the insight score includes a score vector having the same size as the number of data entries N. In other words, the insight detection tool outputs a score for each data entry to indicate the relationship strength or the confidence of the attributes in that data entry having the corresponding feature. Using two-variable outliers as an example, the insight detection tool is configured to output a score for each of the N data entries to indicate the confidence of the two attributes in that data entry being an outlier. The insight scores output by different insight detection tools are aggregated to generate the insight score for an insight detected from the particular data attribute combination.”)
Harris does not distinctly disclose:
“searching for an optimal insight among the two or more insights based on the respective score for each respective insight, comprising growing a tree over the data frame utilizing a greedy binary search algorithm, comprising:”
“selecting a first node for the tree based on one of the two or more insight based on a maximum of the respective score for each respective insight;”
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node;”
“and selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights;”
“and generating a human language representation of the optimal insight with a large language model.”
However, Looney teaches:
“searching for an optimal insight among the two or more insights based on the respective score for each respective insight, comprising growing a tree over the data frame utilizing a greedy binary search algorithm, comprising:” (Looney Page 1, ¶2, “In this article, we will be looking at only one algorithm for fitting trees to data: the greedy recursive partitioning algorithm.”, Looney Page 2, ¶2, “We start by finding a single feature and a single split point which divides our data in two.” Examiner notes the splitting in exactly two makes this tree a binary tree.)
“selecting a first node for the tree based on one of the two or more insight based on a maximum of the respective score for each respective insight;” (Looney Page, ¶2, “We start by finding a single feature and a single split point which divides our data in two.” Looney Page 2, ¶4, “We aren’t choosing these features and split points randomly – rather, we choose them to maximize some condition” Examiner notes the maximized condition is the respective score for each respective insight.)
“determining a branch for the first node based on two or more additional insights from the data frame based on the first node;” (Looney, Page 2, ¶5, “For a new data point, we can than start at the root node and trace a path down to a leaf node by taking the left fork when the condition is true, and the right fork when it is false.”)
“and selecting a second node based on one of the two or more additional insights based on a maximum of a respective score for each respective insight of the two or more additional insights;” (Looney, Page 2, ¶3-4, “All of the training data for which this rule is true we place in the left subset; and everything else in the right subset. Dividing a set into non-overlapping subsets so that the union of the sets is the original set is called a partition. We then recursively apply the same algorithm to both the left and right subset. Hence, recursive partitioning. We aren’t choosing these features and split points randomly – rather, we choose them to maximize some condition”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the binary tree of Looney in order to easily deal with non-linear datasets (Looney Page 14, ¶2).
Harris as modified does not distinctly disclose:
“and generating a human language representation of the optimal insight with a large language model.”
However, Hunn teaches:
“and generating a human language representation of the optimal insight with a large language model.” (Hunn, ¶[0186], “In various embodiments, the insight manager 124 may generate an insight 1730 based on a formal deviation between the set of common parameters using a natural language generation (NLG) model, such as the NLG model 1732 of the model inferencer 208. NLG is an AI software process that produces a natural language output. Natural language is any language that evolved naturally in humans through use and repetition, such as speech and signing. A natural language is different from a constructed or formal language, such as those to program computers or to study logic. The NLG model 1732 produces information in a natural language as if it were generated by a human being, such as a written or spoken version of a human language, such as English, Spanish, French, Korean, and so forth.”, Hunn ¶[0187], “In various embodiments, the NLG model 1732 may comprise or be implemented as a large language model (LLM) to generate the natural language representation to describe the formal deviation.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the human language representation of Hunn in order to ensure that the insights are able to be understood by a human being (Hunn, ¶186, lines 14-17).
Regarding claim 12, Harris as modified teaches all limitations of claim 11.
Harris as modified further teaches:
“wherein selecting the first node and selecting the second node are further based on utilizing a greedy binary search approach.” (Looney Page 1, ¶2, “In this article, we will be looking at only one algorithm for fitting trees to data: the greedy recursive partitioning algorithm.”, Looney Page 2, ¶2, “We start by finding a single feature and a single split point which divides our data in two.”)
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn) as applied to claim 11 further in view of Ronen et al. (US 11182441 B2) (hereafter referred to as Ronen).
Regarding claim 13, Harris as modified teaches all the limitations of claim 11.
Harris as modified does not distinctly disclose:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.”
However, Ronen teaches:
“further comprising representing each respective insight of the two or more insights as a conditional test of hypothesis.” (Ronen column 5, lines 26-32, “Additionally or alternatively, the hypothesis may be used as a basis for an insight about an entity, even if the label is known. For example, a hypothesis as to whether a client will churn or not churn may be useful to understand the motivations of clients and allow an organization to improve its operation to reduce churn.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the hypothesis format of Ronen in order to utilize data that may be less explicit but still may be relevant to insights in the data (Ronen Column 4 line 66 – Column 5 line 2).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn) further in view of Ronen et al. (US 11182441 B2) (hereafter referred to as Ronen) as applied to claim 13, further in view of Katariya et al. (US 20170323329 A1) (hereafter referred to as Katariya).
Regarding claim 14, Harris as modified teaches all the limitations of claim 13.
Harris does not distinctly disclose:
“wherein the significance of each respective insight is computed based on a p-value of the respective insight.”
However, Katariya teaches:
“wherein the significance of each respective insight is computed based on a p-value of the respective insight.” (Katariya ¶ [0039], “A p-value defines a strength of the evidence in performing the test, typically as a value between 0 and 1. For example, a small p-value indicates strong evidence against the null hypothesis, and thus is used to reject the null hypothesis. A large p-value, on the other hand, indicates weak evidence against the null hypothesis, and the null hypothesis is not rejected.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the use of a p-value of Katariya in order to determine how significant a hypothesis is (Katariya ¶[0039], last 5 lines)
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn). as applied to claim 11, further in view of Katariya et al. (US 20170323329 A1) (hereafter referred to as Katariya).
Regarding claim 15, Harris as modified teaches all the limitations of claim 11.
Harris as modified does not distinctly disclose:
“further comprising computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.”
However, Katariya teaches:
“further comprising computing the confidence in each respective insight based on a power of the respective insight at a minimum detectable effect.” (Katariya ¶[0007], “The inputs also include a power (i.e., statistical power) that defines a sensitivity in a hypothesis test that the test correctly rejects the null hypothesis, e.g., a false negative which may be defined “1−Type II error” which is equal to “1−β”. The inputs further include a baseline conversion rate (e.g., “μ.sub.A”) which is the statistic being tested in this example. A minimum detectable effect (MDE) is also entered as an input that defines a “lift” that can be detected with the specified power and defines a desirable degree of insensitivity as part of calculation of the confidence level.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the use of a minimum detectable effect and power of Katariya in order to determine the amount of sensitivity allowed for the confidence values(Katariya ¶[0007], lines 14 - 18).
Regarding claim 16, Harris as modified teaches all the limitations of claim 11.
Harris as modified further teaches:
“the data frame comprises two or more rows of data;” (Harris ¶[0013], “The input data include multiple data entries and each data entry has multiple data attributes which include numerical data attributes, categorical data attributes, or both. An insight includes relationships among variables or the data entries in the dataset.” Harris ¶[0029], “In some examples, the dataset 104 can be formatted as a table with N rows representing the N data entries and M columns representing the M data attributes.”)
Harris as modified does not distinctly disclose:
“and the confidence of each respective insight is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.”
However, Katariya teaches:
“and the confidence of each respective insight is computed based on a proportion of rows of the data frame satisfying the respective insight to rows of the data frame not satisfying the respective insight.” (Katariya ¶[0007], “A common form of A/B testing is referred to as fixed-horizon hypothesis testing. In fixed-horizon hypothesis testing, inputs are provided manually by a user which are then “run” over a defined number of samples (i.e., the “horizon”) until the test is completed. These inputs include a confidence level that refers to a percentage of all possible samples that can be expected to include the true population parameter, e.g., “1−Type I error” which is equal to “1−α”. The inputs also include a power (i.e., statistical power) that defines a sensitivity in a hypothesis test that the test correctly rejects the null hypothesis” Examiner notes that with the insight being represented as a hypothesis, the testing of the hypothesis would refer to the rows satisfying or not satisfying the hypothesis.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the calculation of the confidence value as in Katariya in order to determine the amount of sensitivity allowed for the confidence values(Katariya ¶[0007], lines 14 - 18).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn). as applied to claim 11, further in view of Kala et al. (US 20160019267 A1) (hereafter referred to as Kala).
Regarding claim 17, Harris as modified teaches all the limitations of claim 11.
Harris as modified does not distinctly disclose:
“wherein the respective score for each respective insight of the two or more insights comprises a harmonic mean of the significance of each respective insight and the confidence in each respective insight.”
However, Kala teaches:
“wherein the respective score for each respective insight of the two or more insights comprises a harmonic mean of the significance of each respective insight and the confidence in each respective insight.” (Kala ¶[0047], “Once the insights are generated, the data analysis engine 101 further prioritizes the insights using a suitable technique such as Harmonic Mean (HM), actionability, non-triviality and so on.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the harmonic mean of Kala in order to further prioritize the insights. (Kala, ¶[0047], lines 1-4).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris), in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney), further in view of Hunn et al. (US 20240330605 A1) (hereafter referred to as Hunn). as applied to claim 11, further in view of Kala et al. (US 20160019267 A1) (hereafter referred to as Kala) further in view of Mushtaq et al. (US 20090265332 A1) (hereafter referred to as Mushtaq).
Regarding claim 18, Harris as modified teaches all the limitations of claim 11.
Harris as modified does not distinctly disclose:
“wherein the respective score for each respective insight of the two or more insights comprises a geometric mean of the significance of each respective insight and the confidence in each respective insight.”
However, Mushtaq teaches:
“wherein the respective score for each respective insight of the two or more insights comprises a geometric mean of the significance of each respective insight and the confidence in each respective insight.” (Mushtaq ¶[0098], “The opinion insight summarizer 826 includes hardware, software and/or firmware operative to receive the opinion scores generated by the sentiment rating engine 822 and the opinion groupings supplied by the information extractor 816, and to summarize the data received for presentation to a user of the system 800. In one embodiment, the opinion insight summarizer 826 is configured to serve as an aggregation engine, calculating weighted and unweighted advocacy scores by computing the arithmetic mean or weighted arithmetic mean of the opinion scores supplied by the sentiment rating engine 822 at the product family, brand, model, attribute, and/or feature levels. Other variables for aggregating scores, e.g., opinion source, date, date range, etc. are also possible. In alternative embodiments, the opinion insight summarizer 826 can be configured to calculate a weighted or unweighted advocacy score through other statistical measures, including by calculating the mode, median, or geometric mean of the opinion scores.” Examiner notes this aggregation into a geometric mean takes all values calculated for the insights. As the values calculated are confidence and significance, these would be the values used in the calculation.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris as modified with the geometric mean of Mushtaq in order to calculate scores with consideration to all the aggregated information determined to be relevant to the insights (Mushtaq, ¶98).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (US 20220244815 A1) (hereafter referred to as Harris) as applied to claim 19, in view of Looney (https://www.oranlooney.com/post/ml-from-scratch-part-4-decision-tree/) (hereafter referred to as Looney).
Regarding claim 20, Harris teaches all the limitations of claim 19.
Harris does not distinctly disclose:
“wherein the processor is further configured to cause the processing system to utilize a greedy binary search approach in order to search for the optimal insight among the two or more insights based on the respective score for each respective insight.”
However, Looney teaches:
“wherein the processor is further configured to cause the processing system to utilize a greedy binary search approach in order to search for the optimal insight among the two or more insights based on the respective score for each respective insight.” (Looney Page 1, ¶2, “In this article, we will be looking at only one algorithm for fitting trees to data: the greedy recursive partitioning algorithm.”, Looney Page 2, ¶2, “We start by finding a single feature and a single split point which divides our data in two.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating insights of Harris with the binary tree of Looney in order to easily deal with non-linear datasets (Looney Page 14, ¶2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Automatic Insights for Multi-dimensional data (US 2018/0357276 A1) also discloses generation of insights for multi-dimensional data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter T Annis whose telephone number is (571)270-1059. The examiner can normally be reached M-F, 7:30am to 5pm ET.
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/PETER THOMAS ANNIS/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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