DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/19/2023 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
112b Rejection Arguments
Applicant asserts:
Applicant argues, on page 2, “the above recited variables that are included in the amended claims are clearly defined as shown below in Table A.”
Examiner response:
Examiner respectfully disagrees. The combinations of variables “ab”, “CabC”, “Cab” represent ie coordinates, vectors, or distance value. In addition, Matrix of “S1” is represented unclearly as a division of values or separate values; For example [1/(ab)/CabC] or [1/(ab), CabC]. Also, S1 is the simplex, but it is not clear which simplex that is referring to. The claims does not show how “λa”, “λb”, “λc” are obtained or represent. The table introduced does not aid in making the variables clear and therefore does not help in defining the recited variables.
101 Rejection Arguments
Applicant asserts:
Applicant argues, on page 3-4, that “it is infeasible to perform the claimed elements within the human mind. For example, the claim terms identify, from the inverted coactivation matrix, two or more coordinates for each end point of a simplex, generate a pie chart encoder and display a pie chart using the generated pie chart encoder.”
Examiner response:
Examiner respectfully disagrees. Examiner interprets the “identifying, from the inverted coactivation matrix, two or more coordinates for each end point of a simplex” and “generating a pie chart encoder to display data” as an abstract idea. The claims do not describe these steps in a way such that human could not identify two or more coordinates for each end point of a simplex by analyzing the inverted coactivation matrix and create a mental pie chart encoder to display that is based on the simplex. The additional limitation “display a pie chart using the generated pie chart encoder” is interpreted as extra solution activity.
In addition, MPEP 2106.05 states “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:... Adding insignificant extra-solution activity to the judicial exception”
103 Rejection Arguments
Applicant asserts:
Applicant argues, on page 4-5, that “Applicant respectfully notes that each of the cited references are in a different technical realm and therefore there is no motivation to combine the references… Therefore, applicant respectfully submits that that the wide range of diverse references are clearly use of impermissible hindsight.”
Examiner response:
Examiner respectfully disagrees. In response to applicant' s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. Kaspersky teaches threats on the web and preventions against them. With their motivation to combine within Scikit Paragraph 3, Preindl Paragraph 0104, Zhao Paragraph 0004, and Kaspersky Paragraph 9.
Applicant asserts:
Applicant argues, on page 4-5, that “the independent claims recite generate the simplex from the two or three coordinates identified for each end point of the simplex. The Office Action on page 34 states that Preindl shows/suggests identifying two or three coordinates from each endpoint of a simplex. The portion of Preindl cited by the Examiner stated “the transformation matrix may identify the n+1 points that create the simplex.” Preindl does not contemplate two or three coordinates for each end point of the simplex, rather, Preindl discusses points that create a simplex, not coordinates of endpoints and surely not two or three coordinates for each endpoint.”
Examiner response:
Examiner respectfully disagrees. Using BRI, Examiner interprets the claims that the two or three coordinates are identified as each end point of the simplex. Preindl is used to teach the limitation of identifying coordinate points that form a simplex.
Applicant asserts:
Applicant argues, on page 5, that the prior art does not teach “identify, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity.”
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112a
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation “generating a pie chart encoder to display data, said data being generated based on the simplex;” and “displaying, using the pie chart encoder, a pie chart”. While applicant’s original disclosure discusses in paragraph 0009 the artificially intelligent environment may be able to receive neurons encoded as line segments and generate a pie chart encoder, Applicant’s original disclosure does not support using the pie chart encoder to display a pie chart. For purposes of examination, Examiner is interpreting that pie chart encoder is a neural network used to interpret data into a pie chart.
Claim 9 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 9 recites the limitation “y=yabcT-1S1 (x)”. While applicant’s original disclosure discusses ŷ=yabcT-1S1 (x), Applicant’s original disclosure does not support y=yabcT-1S1 (x). For purposes of examination, Examiner is interpreting that y is a quantity y within a space of the two-dimension simplex.
Claim Rejections - 35 USC § 112b
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites variables “ab”, “CabC”, “Cab”, “λa”, “λb”, “λc”. Variables “λa”, “λb”, “λc” is not defined and how they are obtained. Matrix of “S1” is represented unclearly as a division of values or separate values. In addition, S1 is referred to as the simplex, but it is unclear if it is referring to the first, second, third, or fourth simplex or the two dimensional simplex. “ab”, “CabC”, and “Cab” is unclear as to what they represent and how they are obtained. The metes and bounds of the claim is unclear. For examination purposes, Examiner will interpret them as any set of values needed to perform operations to obtain a simplex and S1 is interpreted as the two dimensional simplex.
Claim 9 recites “yabc”. The claims do not define what yabc represent or how it is obtained. The metes and bounds of the claim is unclear
In reference to dependent claims 7 and 10, claims 7 and 10 do not cure the deficiencies noted in the rejection of dependent claims 6 and 9. Therefore, these claims are rejected under the same rationale as claims 6 and 9.
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 1-7, and 9-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“projecting the new data point on the plurality of neurons;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(I)(c)).
“generating a reconstruction error value between the new data point and the plurality of neurons, the reconstruction error value quantifying the new data point;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a reconstruction error value between the new data point and the plurality of neurons.
“creating a coactivation matrix for each of the neurons included in the plurality of neurons and the reconstruction error value;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could create a coactivation matrix for each of the neurons.
“inverting the coactivation matrix;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(a)).
“identifying, from the inverted coactivation matrix, two or three coordinates for each end point of a simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify two or three coordinates from the inverted coactivation matrix.
“generating the simplex from the two or three coordinates identified for each end point of the simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate the simplex within their mind from the two or three coordinates for each end point of the simplex.
“generating a pie chart encoder to display data, said data being generated based on the simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a mental pie chart encoder to display/represent data as a pie chart.
“plotting the data structure corresponding to the entity within the simplex to determine whether the entity is of malicious intent;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could plot the data structure within the simplex.
“identifying, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify a correspondence value between the data structure and the end point.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method for generating a simplex from a plurality of neurons, the method comprising: receiving a plurality of neurons, each neuron, included in the plurality of neurons, encoding a data point;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving a new data point;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving a data structure corresponding to an entity;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“displaying, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.” (insignificant extra-solution activity transmitting data MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method for generating a simplex from a plurality of neurons, the method comprising: receiving a plurality of neurons, each neuron, included in the plurality of neurons, encoding a data point;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving a new data point;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving a data structure;” (well-understood, routine, conventional MPEP 2106.05(d))
“and displaying a pie chart, for the data structure, that includes identifiers for each of the end points and the correspondence values.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Claim 2 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“assigning each data point, included in the plurality of data points, a zero value;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could assign each data point a zero value.
“identify that a first data point, included in the plurality of data points, is not a second data point, included in the plurality of data points;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify a first data point that is not a second data point.
“encode a line segment between the first data point and the second data point;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could encode a line segment between the first data point and the second data point.
“reconstruct each of the plurality of data points by projecting each of the plurality of data points onto the line segment;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could project each of the plurality of data point onto the line segment.
“identify that a third data point, included in the plurality of data points, includes a component that is orthogonal to: a first simplex that encodes the first data point; a second simplex that encodes the second data point; a third simplex that encodes the line segment between the first data point and the second data point; and a fourth simplex that encodes zero;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify that a third data point has a component that is orthogonal to a first, second, third, and fourth simplex.
“use the line segment, a second line segment that encodes (the third data point, the first line segment and the third data point) the first simplex, the second simplex, the third simplex and the fourth simplex to form a reconstruction error value;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could use the line segment, a second line segment, the first, second, third, and fourth simplex to form a reconstruction error value.
“and use the reconstruction error value, the line segment, the second line segment, the third data point, the first simplex, the second simplex, the third simplex and the fourth simplex to generate a two-dimensional simplex.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could use the reconstruction error value, the line segment, the second line segment, the third data point, the first, second, third, and fourth simplex to generate a two-dimensional simplex.
“identify two or three coordinates for each end point of a simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify two or three coordinates from the inverted coactivation matrix.
“generate a pie chart encoder to display data, said data being generated based on the simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a mental pie chart encoder to display/represent data as a pie chart.
“plot the data structure corresponding to the entity within the simplex to determine whether the entity is of malicious intent;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could plot the data structure within the simplex.
“identify, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify a correspondence value between the data structure and the end point.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method for naive tessellation of a topologically continuous subspace, the method comprising: receiving a plurality of data points;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receive a data structure corresponding to an entity;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.” (insignificant extra-solution activity transmitting data MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method for naive tessellation of a topologically continuous subspace, the method comprising: receiving a plurality of data points;” (well-understood, routine, conventional MPEP 2106.05(d))
“receive a data structure corresponding to an entity;” (well-understood, routine, conventional MPEP 2106.05(d))
“display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 3 wherein each data point, included in the plurality of data points, in a Cartesian two-dimensional space, project onto a reconstruction.” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 5:
Claim 5 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 3 where each of the corners of the two-dimensional simplex are calculated by T^-1 S1 , where S1 is the simplex, T is a coactivation matrix, a is the first data point, b is the second data point and c is the third data point:
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” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(c)).
where λ = T-1 S1 calculates a set of barycentric coordinates from the first simplex, the second simplex, the third simplex and the fourth simplex.” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 6 further comprising gradient boosting the first simplex, the second simplex, the third simplex and the fourth simplex.” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations and further adds onto the abstract idea of claim 6. (MPEP 2106.04(a)(2)(1)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“where the fifth data point is plotted within the two-dimensional simplex, y = YabcT-1 S1 (x) identifies a quantity y within a space of the two-dimensional simplex.” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(b)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 3 further comprising receiving a fifth data point assigned the variable name x,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 3 further comprising receiving a fifth data point assigned the variable name x,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Claim 10 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“projecting the new data point on the plurality of neurons;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(I)(c)).
“generating a reconstruction error value between the new data point and the plurality of neurons, the reconstruction error value quantifying the new data point;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a reconstruction error value between the new data point and the plurality of neurons.
“creating a coactivation matrix for each of the neurons included in the plurality of neurons and the reconstruction error value;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could create a coactivation matrix for each of the neurons.
“inverting the coactivation matrix;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(1)(a)).
“identifying, from the inverted coactivation matrix, two or three coordinates for each end point of a simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify two or three coordinates from the inverted coactivation matrix.
“generate the simplex from the two or three coordinates identified for each end point of the simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate the simplex within their mind from the two or three coordinates for each end point of the simplex
“generate a pie chart encoder to display data, said data being generated based on the simplex;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a mental pie chart encoder to display/represent data as a pie chart.
“plot the data structure corresponding to the entity within the simplex to determine whether the entity is of malicious intent;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could plot the data structure within the simplex.
“identify, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could identify a correspondence value between the data structure and the end point.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A system for generating a two-dimensional simplex from a plurality of neurons, the system comprising: a receiver operable to receive:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“a plurality of neurons, each neuron, included in the plurality of neurons, encoding a data point;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“a new data point;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“a hardware processor operable to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a data structure corresponding to an entity;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.” (insignificant extra-solution activity transmitting data MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A system for generating a two-dimensional simplex from a plurality of neurons, the system comprising: a receiver operable to receive:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“a plurality of neurons, each neuron, included in the plurality of neurons, encoding a data point;” (well-understood, routine, conventional MPEP 2106.05(d))
“a new data point;” (well-understood, routine, conventional MPEP 2106.05(d))
“a hardware processor operable to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a data structure corresponding to an entity;” (well-understood, routine, conventional MPEP 2106.05(d))
“display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 12:
Claim 12 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 13:
Claim 13 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 14:
Claim 14 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 15:
Claim 15 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2 , and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chandan Gope et al; US 20240351472 A1 filed on Apr 12, 2023 (hereinafter “Gope”) in view of Scikit; “Sparse inverse covariance estimation” publicly available on Apr 20, 2021 (hereinafter “Scikit”) in further view of Matthias Preindl et al; US 20250096709 A1 filed on Jan 27, 2023 (hereinafter “Preindl”) in further view of Lifei Zhao et al; US 20240265595 A1 filed on Dec 28, 2021 (hereinafter “Zhao”) in further view of Eugene Kaspersky; “The Dangers of Exploits and Zero-Days, and Their Prevention” available Apr 02, 2023 (hereinafter “Kaspersky”).
Regarding claim 1, Gope teaches A method for generating a simplex from a plurality of neurons, the method comprising: receiving a plurality of neurons, each neuron, included in the plurality of neurons, encoding a data point; (Gope Paragraph 0008; “the one or more outlier detection machine learning models include a plurality of neurons arranged in a plurality of layers in a neural network. The plurality of neurons may include an input layer of neurons corresponding with a battery node diagnostic data portion” Gope Paragraph 0009; “machine learning models may include a variational autoencoder encoding the input layer of neurons into a latent space layer of neurons” Examiner notes that a plurality of neurons is received from outlier detection machine learning models, each neuron encodes a data point (battery node diagnostic data))
receiving a new data point; (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902. In some implementations, the request may be generated when a battery pack is placed into a diagnostic mode. When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that new data point (data related to battery nodes) is received)
projecting the new data point on the plurality of neurons; (Gope Paragraph 0218; “Input feature data for each of the battery nodes is determined at 906. According to various embodiments, determining the input feature data may involve performing feature selection on the available data. Any of a variety of feature selection techniques may be used, which may include, but are not limited to: principal component analysis” Examiner notes that principal component analysis is used to project the new data point on the plurality of neurons)
generating a reconstruction error value between the new data point and the plurality of neurons, the reconstruction error value quantifying the new data point; (Gope Paragraph 0132; “outlier values for battery nodes are determined based on the application of one or more machine learning models to battery node diagnostic data. In some embodiments, an outlier value may indicate a degree to which a node or group of nodes deviates from its peers along one or more dimensions. An outlier value may be determined by providing to the trained model input data corresponding to a battery node.” Gope Paragraph 0134; “an outlier value may be a reconstruction error value... The output values may be compared with the input values to determine the reconstruction error, which may be used as the outlier value.” Examiner notes that the reconstruction error value (outlier value) is generated/determined between the new data point (input values) and the plurality of neurons (set of output values produced by plurality of neurons in machine learning model), reconstruction error value quantifying the new data point (quantifies a degree to which the new data point deviates))
creating [a coactivation matrix for each of the neurons included in the plurality of neurons and] the reconstruction error value; (Gope Paragraph 0134; “an outlier value may be a reconstruction error value... The output values may be compared with the input values to determine the reconstruction error, which may be used as the outlier value.” Examiner notes that reconstruction error value (outlier value) is created/determined)
Gope does not teach creating a coactivation matrix for each of the neurons included in the plurality of neurons [and the reconstruction error value;]
inverting the coactivation matrix;
However, Scikit does teach creating a coactivation matrix for each of the neurons included in the plurality of neurons [and the reconstruction error value;] (Scikit Code “Estimate the covariance” shows creating a coactivation matrix (covariance matrix) for each of the neurons included in the plurality of neurons)
inverting the coactivation matrix; (Scikit Paragraph 2; “estimating the precision matrix, that is the inverse covariance matrix” Examiner notes that the coactivation matrix (covariance matrix) is inverted)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope and Scikit. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. One of ordinary skill would have motivation to combine Gope and Scikit to ensure that there is not too much correlation and that now small coefficients cannot be recovered “we ensure that the data is not too much correlated (limiting the largest coefficient of the precision matrix) and that there a no small coefficients in the precision matrix that cannot be recovered.” (Scikit Paragraph 3).
Gope in view of Scikit does not teach identifying, from the inverted coactivation matrix, two or three coordinates for each end point of a simplex;
generating the simplex from the two or three coordinates identified for each end point of the simplex;
receiving a data structure corresponding to an entity;
plotting the data structure corresponding to an entity within the simplex to determine whether the entity is of malicious intent;
However, Preindl does teach identifying, from the inverted coactivation matrix, two or three coordinates for each end point of a simplex; (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex” Examiner notes that the two or three coordinates (n+1 points) for each end point of a simplex from the inverted coactivation matrix (transformation matrix))
generating the simplex from the two or three coordinates identified for each end point of the simplex; (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex, where one selected point (p.sub.0) creates the origin of the simplex, and each of the other points (p.sub.1 to p.sub.n) in the simplex can be expressed as one of N vectors p.sub.x0 relative to the origin (where N=n, and for x=1 to N).” Examiner notes that the simplex is generated from the two or three coordinates)
receiving a data structure corresponding to an entity; (Preindl Paragraph 0126; “the simplex generation process includes the electronic controller 400 (1) obtaining a data set of current-flux linkage pairs for operational points of the electric motor;” Examiner notes that a data structure (data set of current-flux linkage pairs) is received/obtained corresponding to an entity (electric motor))
plotting the data structure corresponding to an entity within the simplex to determine whether the entity is of malicious intent; (Preindl Paragraph 0073; “the motor controller 120 may iteratively check simplices (e.g., one by one) to determine whether the current value resides thereon, and continue to do so until the simplex having the current value is identified. Mathematically, this check may be described as projecting the coordinate(s) of current(s) into the local coordinate system of the simplex as a local coordinate vector and, when the components of the local coordinate vector are all be between 0 and 1, a point is considered contained within a simplex.” Preindl Paragraph 0096; “a first plot 500 of each of the current simplices generated by the Delaunay triangulation algorithm executed on the example data set is illustrated.” Examiner notes that data structure (data set) corresponding to an entity (electric motor) is plotted within the simplex using the Delaunay triangulation algorithm to determine whether the entity is of malicious intent (iteratively checking simplices can determine whether value resides within the simplex/if entity of malicious intent))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope , Scikit, and Preindl. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. Preindl teaches a method for utilizing simplices to calculate flux linkage. One of ordinary skill would have motivation to combine Gope, Scikit, and Preindl to apply aspects of the simplices to improve accuracy “Generally, the more operational points selected in the first step, the more simplices generated in the second step, and the larger and/or more complex the PWA map. As the PWA map increases in size and/or complexity, the accuracy of the PWA map may improve until reaching an approximate peak.” (Preindl Paragraph 0104).
Gope in view of Scikit in further view of Preindl does not teach generating a pie chart encoder to display data, [said data being generated based on the simplex];
identifying, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, [each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;]
and displaying, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.
However, Zhao does teach generating a pie chart encoder to display data, [said data being generated based on the simplex]; (Zhao Paragraph 0087; “ in response to a graph drawing command from a user, determining a graph type corresponding to the graph drawing command; and in response to the graph drawing command from the user, drawing a graph of the graph type corresponding to the graph drawing command according to the data recognition result, and displaying the drawn graph in the display area.” Zhao Paragraph 0470; “the recognition of the writing trajectory information (including digits, graphics, titles of various coordinate axes, etc.) in the embodiments may be realized by using a neural network such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc., such as long short-term memory (LSTM) network model, etc.” Examiner notes that a pie chart encoder (neural networks) is generated/realized to display data (recognize of the writing trajectory information to determine graph and display graph))
identifying, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, [each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;] (Zhao Paragraph 0272; “a proportion of the slice with the title of February is 10%, a proportion of the slice with the title of March is 20%, a proportion of the slice with the title of April is 30%, and a proportion of the slice with the title of May is 40%. The pie chart is drawn according to the titles and the proportions of the respective slices.” Zhao Paragraph 0331; “If it is a pie chart, the determined data groups contains the data that corresponds to each object in a one-to-one correspondence” Examiner notes that a correspondence value (proportion of the slice) is identified between the data structure (data groups) and the end point (each object), where the correspondence values for each of the end points add up to 100% (10% + 20% + 40% + 30% = 100%))
and displaying, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points [identifying the malicious activity] and the correspondence values associated therewith. (Zhao Paragraph 0272; “a proportion of the slice with the title of February is 10%, a proportion of the slice with the title of March is 20%, a proportion of the slice with the title of April is 30%, and a proportion of the slice with the title of May is 40%. The pie chart is drawn according to the titles and the proportions of the respective slices.” Zhao Paragraph 0470; “the recognition of the writing trajectory information (including digits, graphics, titles of various coordinate axes, etc.) in the embodiments may be realized by using a neural network such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc., such as long short-term memory (LSTM) network model, etc.” Zhao Paragraph 0543; “a guide template corresponding to the pie chart is displayed in a display area.” Examiner notes that a pie chart, using the pie chart encoder (neural network), is displayed, for the data structure, that includes identifiers (titles) for each of the end points and the correspondence values)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Scikit, Preindl, and Zhao. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. One of ordinary skill would have motivation to combine Gope, Scikit, Preindl, and Zhao to improve interactive experience during conference “ At present, one method is to use a hand-drawn graph, which is not only inefficient but also difficult to accurately show the change rules of data group, causing unnecessary deviations for later analysis; the other is to draw a graph on the computer and project the graph on the electronic whiteboard, which is cumbersome to operate and not suitable for use in conference interaction, resulting in poor interactive experience during conference.” (Zhao Paragraph 0004).
Gope in view Scikit in further view of Preindl in further view of Zhao does not teach plotting the data structure corresponding to an entity [within the simplex [to determine whether the entity is of malicious intent];
each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity
However, Kaspersky does teach each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity (Kaspersky Figure referenced below shows each endpoint identifying a malicious activity (different threats) and each correspondence value defining a numerical correspondence (percentage of that threat type) between the entity (the web) and the malicious activity (threat))
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It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Scikit, Preindl, Zhao, and Kaspersky. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. Kaspersky teaches threats on the web and preventions against them. One of ordinary skill would have motivation to combine Gope, Scikit, Preindl, Zhao, and Kaspersky to detect and prevent malicious activities or threats “Our products have a dedicated database containing signatures of known exploits, malicious URLs via which infections are loaded, and also special heuristic and proactive technologies for detecting the future challenges of this type of threat. If this kind of malicious code is found, the program is blocked, the user and sysadmin are warned, and the actions of the attack are rolled back. We also provide a very useful feature for on-demand scanning for the presence of vulnerabilities:” (Kaspersky Paragraph 9).
Regarding claim 2, Gope teaches The method of claim 1, wherein the data point corresponds to an experience. (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902... When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that data point (data related to battery nodes) corresponds to an experience (evidence/knowledge related to battery nodes); Examiner interprets experience as knowledge/evidence)
Regarding claim 11, Gope teaches A system for generating a two-dimensional simplex from a plurality of neurons, the system comprising: a receiver operable to receive: a plurality of neurons, each neuron included in the plurality of neurons encoding a data point; (Gope Paragraph 0008; “the one or more outlier detection machine learning models include a plurality of neurons arranged in a plurality of layers in a neural network. The plurality of neurons may include an input layer of neurons corresponding with a battery node diagnostic data portion” Gope Paragraph 0009; “machine learning models may include a variational autoencoder encoding the input layer of neurons into a latent space layer of neurons” Examiner notes that a plurality of neurons is received from outlier detection machine learning models, each neuron encodes a data point (battery node diagnostic data); input layer is receiver)
a new data point; (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902. In some implementations, the request may be generated when a battery pack is placed into a diagnostic mode. When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that new data point (data related to battery nodes) is received)
a hardware processor operable to: project the new data point on the plurality of neurons; (Gope Paragraph 0218; “Input feature data for each of the battery nodes is determined at 906. According to various embodiments, determining the input feature data may involve performing feature selection on the available data. Any of a variety of feature selection techniques may be used, which may include, but are not limited to: principal component analysis” Gope Paragraph 0288; “a system uses a processor in a variety of contexts but can use multiple processors while remaining within the scope of the present disclosure unless otherwise noted.” Examiner notes that principal component analysis is used to project the new data point on the plurality of neurons)
Claim 11 is a system claim of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
Regarding claim 12, Gope does not teach The system of claim 11 wherein the hardware processor is further operable to display a pie chart, for the data structure, that includes identifiers for each of the end points and the correspondence values.
However, Zhao does teach The system of claim 11 wherein the hardware processor is further operable to display a pie chart, for the data structure, that includes identifiers for each of the end points and the correspondence values. (Zhao Paragraph 0272; “a proportion of the slice with the title of February is 10%, a proportion of the slice with the title of March is 20%, a proportion of the slice with the title of April is 30%, and a proportion of the slice with the title of May is 40%. The pie chart is drawn according to the titles and the proportions of the respective slices.” Zhao Paragraph 0543; “a guide template corresponding to the pie chart is displayed in a display area.” Examiner notes that a pie chart is displayed, for the data structure, that includes identifiers (titles) for each of the end points and the correspondence values)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope , Scikit, Preindl, and Zhao. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Scikit teaches inversing a covariance matrix. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. One of ordinary skill would have motivation to combine Gope, Scikit, Preindl, and Zhao to improve interactive experience during conference “ At present, one method is to use a hand-drawn graph, which is not only inefficient but also difficult to accurately show the change rules of data group, causing unnecessary deviations for later analysis; the other is to draw a graph on the computer and project the graph on the electronic whiteboard, which is cumbersome to operate and not suitable for use in conference interaction, resulting in poor interactive experience during conference.” (Zhao Paragraph 0004).
Regarding claim 13, Gope teaches The system of claim 11, wherein the neurons are received from an artificial intelligence neural network. (Gope Paragraph 0008; “In some embodiments, the one or more outlier detection machine learning models include a plurality of neurons arranged in a plurality of layers in a neural network.”)
Regarding claim 14, Gope teaches The system of claim 11, wherein each of the neurons included in the plurality of neurons correspond to an experience. (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902... When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that data point (data related to battery nodes) corresponds to an experience (evidence/knowledge related to battery nodes); Examiner interprets experience as knowledge/evidence)
Regarding claim 15, Gope teaches The system of claim 11 wherein the new data point corresponds to a new experience. (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902... When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that data point (data related to battery nodes) corresponds to an experience (evidence/knowledge related to battery nodes); Examiner interprets experience as knowledge/evidence); collected data is new data)
Claim(s) 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Chandan Gope et al; US 20240351472 A1 filed on Apr 12, 2023 (hereinafter “Gope”) in view of Ziyue Xu et al; US 20240303504 A1 filed on Mar 22, 2024 (hereinafter “Xu”) in further view of Sunshine; “(Projected) Point on Line (2D) Algorithm” publicly available on Oct 29, 2022 (hereinafter “Sunshine”) in further view of Matthias Preindl et al; US 20250096709 A1 filed on Jan 27, 2023 (hereinafter “Preindl”) in further view of Eugene Kaspersky; “The Dangers of Exploits and Zero-Days, and Their Prevention” available Apr 02, 2023 (hereinafter “Kaspersky”).
Regarding claim 3, Gope teaches A method for naive tessellation of a topologically continuous subspace, the method comprising: receiving a plurality of data points; (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902. In some implementations, the request may be generated when a battery pack is placed into a diagnostic mode. When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that new data points (data related to battery nodes) is received)
reconstruct each of the plurality of data points by projecting each of the
plurality of data points onto the line segment; (Gope Paragraph 0218; “Input feature data for each of the battery nodes is determined at 906. According to various embodiments, determining the input feature data may involve performing feature selection on the available data. Any of a variety of feature selection techniques may be used, which may include, but are not limited to: principal component analysis” Examiner notes that principal component analysis is used to reconstruct each of the plurality of data points (input feature data) by projecting each of the plurality of data points onto the line segment (Principal component analysis can be used to project data points onto a lower-dimensional subspace, such as a line))
Gope does not teach assigning each data point, included in the plurality of data points, a zero value;
However, Xu does teach assigning each data point, included in the plurality of data points, a zero value; (Xu Paragraph 0064; “global update 152 represents a model generated using different initializing techniques (e.g., zero initialization” Examiner notes that assigning each data point a zero value is zero initialization)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope and Xu. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. One of ordinary skill would have motivation to combine Gope and Xu to improve neural network training and inferencing “techniques and systems described and suggested herein provide various advantages to optimize neural network training and inferencing” (Xu Paragraph 107).
Gope in view of Xu does not teach identify that a first data point, included in the plurality of data points, is not a second data point, included in the plurality of data points;
encode a line segment between the first data point and the second data point;
identify that a third data point, included in the plurality of data points,
However, Sunshine does teach identify that a first data point, included in the plurality of data points, is not a
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second data point, included in the plurality of data points; (Sunshine Graph and Introduction; “we have following input data: two points v1 and v2 which define the line to against. In particular, this implies that both points lie on this line of course.” Examiner notes that first data point (V1) is not a second data point (V2), included in the plurality of data points is identified (having different x and y coordinates))
encode a line segment between the first data point and the second data point; (Examiner refers to previous mapping to show that a line segment (line) is encoded/defined between the first data point and the second data point (points v1, and v2))
identify that a third data point, included in the plurality of data points, includes a component that is orthogonal to: [a first simplex that encodes] the first data point; [a second simplex that encodes] the second data point; [a third simplex that encodes] the line segment between the first data point and the second data point; and [a fourth simplex that encodes] zero; (Sunshine Section 4; “In this chapter an algorithm is presented to test if the projected point p' of the point p onto the line e1 lies on inside the closed line segment.The projected point p' is the nearest point to p that lies on the given line.”
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Examiner notes that section 4 shows how to identify that a third data point (point p), included in the plurality of data points, includes a component (line e1) that is orthogonal to the first data point (v1), the second data point (v2), the line segment between the first data point and the second data point (close line segment), zero (zero vector is always present in vector space; a zero vector is orthogonal to itself and all other vectors))
use the line segment, a second line segment that encodes (the third data
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point, the first line segment and the third data point) the first simplex, the second simplex, the third simplex and the fourth simplex to form a reconstruction error value; (Sunshine Section 5 Graph and “At first let's calculate angle α using the dot product. We know that DP(e1, e2) = |e1| * |e2| * cos(α).”; shows line segment (e1), a second line segment (blue line in figure) that encodes the third data point (p), the first line segment (e1) and the projected third data point (p’), the first simplex (v1), the second simplex (v2), the third simplex (p) and the fourth simplex (zero vector is always present in vector space); one could perform various trigonometric operations to obtain length of blue line as the reconstruction error value)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, and Sunshine. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. One of ordinary skill would have motivation to combine Gope, Xu, and Sunshine to overcome the disadvantages of only using the x-intercept form and to better understand the relation of the components “The most significant disadvantage: The x-intercept form cannot represent all lines. It is not possible to define a vertical line because it's slope would be infinite. This case would have to be handled in a special which makes this approach quite unattractive (note however that this case can be easily identified: the x-coordinate of both points are equal for vertical lines.) Another point to think about is the precision of floating point numbers. bt would never be perfectly equal to b, so an epsilon value must be used when testing for equality. However, finding such a epsilon is not easy and depends e.g. on the slope because the calculation error increase with a larger slope, especially if the line converges to the vertical case.” (Sunshine Section 1).
Gope in view of Xu in further view of Sunshine does not teach includes a component that is orthogonal to: a first simplex that encodes the first data point;
a second simplex that encodes the second data point;
a third simplex that encodes the line segment between the first data point and the second data point;
and a fourth simplex that encodes zero;
and use the reconstruction error value, the line segment, the second line segment,
the third data point, the first simplex, the second simplex, the third simplex and the fourth simplex to generate a two-dimensional simplex.
Identify two or three coordinates for each end point of a simplex;
receive a data structure corresponding to an entity;
plot the data structure corresponding to an entity within the simplex to determine whether the entity is of malicious intent;
However, Preindl does teach includes a component that is orthogonal to:
a first simplex that encodes the first data point; (Preindl Paragraph 0073; “the input current does reside on the simplex. Here, the transformation matrix may identify the n+1 points that create the simplex” Examiner notes that a first simplex encodes the first data point (input))
a second simplex that encodes the second data point; (Examiner refers to previous mapping to show that a second simplex encodes the first data point (input))
a third simplex that encodes the line segment between the first data point and the second data point; (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex, where one selected point (p.sub.0) creates the origin of the simplex, and each of the other points (p.sub.1 to p.sub.n) in the simplex can be expressed as one of N vectors p.sub.x0 relative to the origin (where N=n, and for x=1 to N)” Examiner notes that a third simplex encodes the line segment (vector) between the first data point (selected point) and the second data point (other point))
and a fourth simplex that encodes zero; (Preindl Paragraph 0073; “the input current does reside on the simplex. Here, the transformation matrix may identify the n+1 points that create the simplex” Examiner notes that a fourth simplex encodes zero (input))
and use the reconstruction error value, the line segment, the second line segment,
the third data point, the first simplex, the second simplex, the third simplex and the fourth simplex to generate a two-dimensional simplex. (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex, where one selected point (p.sub.0) creates the origin of the simplex, and each of the other points (p.sub.1 to p.sub.n) in the simplex can be expressed as one of N vectors p.sub.x0 relative to the origin (where N=n, and for x=1 to N).” Examiner notes that the third data point, the first simplex, the second simplex, the third simplex and the fourth simplex are identified points that create the simplex; the line segment and the second line segment that encodes reconstruction error value are the vectors used to express the simplex)
identify two or three coordinates for each end point of a simplex; (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex” Examiner notes that the two or three coordinates (n+1 points) for each end point of a simplex from the inverted coactivation matrix (transformation matrix))
receive a data structure corresponding to an entity; (Preindl Paragraph 0126; “the simplex generation process includes the electronic controller 400 (1) obtaining a data set of current-flux linkage pairs for operational points of the electric motor;” Examiner notes that a data structure (data set of current-flux linkage pairs) is received/obtained corresponding to an entity (electric motor))
plot the data structure corresponding to an entity within the simplex to determine whether the entity is of malicious intent; (Preindl Paragraph 0073; “the motor controller 120 may iteratively check simplices (e.g., one by one) to determine whether the current value resides thereon, and continue to do so until the simplex having the current value is identified. Mathematically, this check may be described as projecting the coordinate(s) of current(s) into the local coordinate system of the simplex as a local coordinate vector and, when the components of the local coordinate vector are all be between 0 and 1, a point is considered contained within a simplex.” Preindl Paragraph 0096; “a first plot 500 of each of the current simplices generated by the Delaunay triangulation algorithm executed on the example data set is illustrated.” Examiner notes that data structure (data set) corresponding to an entity (electric motor) is plotted within the simplex using the Delaunay triangulation algorithm to determine whether the entity is of malicious intent (iteratively checking simplices can determine whether value resides within the simplex/if entity of malicious intent))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, and Preindl. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, and Preindl to apply aspects of the simplices to improve accuracy “Generally, the more operational points selected in the first step, the more simplices generated in the second step, and the larger and/or more complex the PWA map. As the PWA map increases in size and/or complexity, the accuracy of the PWA map may improve until reaching an approximate peak.” (Preindl Paragraph 0104).
Gope in view of Xu in further view of Sunshine in further view of Preindl does not teach generate a pie chart encoder to display data, [said data being generated based on the simplex];
identify, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, [each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;]
and display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points identifying the malicious activity and the correspondence values associated therewith.
However, Zhao does teach generate a pie chart encoder to display data, [said data being generated based on the simplex]; (Zhao Paragraph 0087; “ in response to a graph drawing command from a user, determining a graph type corresponding to the graph drawing command; and in response to the graph drawing command from the user, drawing a graph of the graph type corresponding to the graph drawing command according to the data recognition result, and displaying the drawn graph in the display area.” Zhao Paragraph 0470; “the recognition of the writing trajectory information (including digits, graphics, titles of various coordinate axes, etc.) in the embodiments may be realized by using a neural network such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc., such as long short-term memory (LSTM) network model, etc.” Examiner notes that a pie chart encoder (neural networks) is generated/realized to display data (recognize of the writing trajectory information to determine graph and display graph))
identify, for each end point, a correspondence value between the data structure and the end point, where the correspondence values for each of the end points add up to 100%, [each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity;] (Zhao Paragraph 0272; “a proportion of the slice with the title of February is 10%, a proportion of the slice with the title of March is 20%, a proportion of the slice with the title of April is 30%, and a proportion of the slice with the title of May is 40%. The pie chart is drawn according to the titles and the proportions of the respective slices.” Zhao Paragraph 0331; “If it is a pie chart, the determined data groups contains the data that corresponds to each object in a one-to-one correspondence” Examiner notes that a correspondence value (proportion of the slice) is identified between the data structure (data groups) and the end point (each object), where the correspondence values for each of the end points add up to 100% (10% + 20% + 40% + 30% = 100%))
and display, using the pie chart encoder, a pie chart, for the data structure, that includes identifiers for each of the end points [identifying the malicious activity] and the correspondence values associated therewith. (Zhao Paragraph 0272; “a proportion of the slice with the title of February is 10%, a proportion of the slice with the title of March is 20%, a proportion of the slice with the title of April is 30%, and a proportion of the slice with the title of May is 40%. The pie chart is drawn according to the titles and the proportions of the respective slices.” Zhao Paragraph 0470; “the recognition of the writing trajectory information (including digits, graphics, titles of various coordinate axes, etc.) in the embodiments may be realized by using a neural network such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc., such as long short-term memory (LSTM) network model, etc.” Zhao Paragraph 0543; “a guide template corresponding to the pie chart is displayed in a display area.” Examiner notes that a pie chart, using the pie chart encoder (neural network), is displayed, for the data structure, that includes identifiers (titles) for each of the end points and the correspondence values)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, Preindl, and Zhao. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, Preindl, and Zhao to improve interactive experience during conference “ At present, one method is to use a hand-drawn graph, which is not only inefficient but also difficult to accurately show the change rules of data group, causing unnecessary deviations for later analysis; the other is to draw a graph on the computer and project the graph on the electronic whiteboard, which is cumbersome to operate and not suitable for use in conference interaction, resulting in poor interactive experience during conference.” (Zhao Paragraph 0004).
Gope in view Xu in further view of Sunshine in further view of Preindl in further view of Zhao does not teach plotting the data structure corresponding to an entity [within the simplex [to determine whether the entity is of malicious intent];
each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity
However, Kaspersky does teach each end point identifying a malicious activity, each correspondence value defining a numerical correspondence between the entity and the malicious activity (Kaspersky Figure referenced below shows each endpoint identifying a malicious activity (different threats) and each correspondence value defining a numerical correspondence (percentage of that threat type) between the entity (the web) and the malicious activity (threat))
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It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, Preindl, Zhao, and Kaspersky. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. Zhao teaches a method for displaying a chart. Kaspersky teaches threats on the web and preventions against them. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, Preindl, Zhao, and Kaspersky to detect and prevent malicious activities or threats “Our products have a dedicated database containing signatures of known exploits, malicious URLs via which infections are loaded, and also special heuristic and proactive technologies for detecting the future challenges of this type of threat. If this kind of malicious code is found, the program is blocked, the user and sysadmin are warned, and the actions of the attack are rolled back. We also provide a very useful feature for on-demand scanning for the presence of vulnerabilities:” (Zhao Paragraph 0004).
Regarding claim 4, Gope teaches The method of claim 3 wherein each data point, included in the plurality of data points, in a Cartesian two-dimensional space, project onto a reconstruction. (Gope Paragraph 0218; “Input feature data for each of the battery nodes is determined at 906. According to various embodiments, determining the input feature data may involve performing feature selection on the available data. Any of a variety of feature selection techniques may be used, which may include, but are not limited to: principal component analysis” Examiner notes that principal component analysis is used to project each of the plurality of data points (input feature data) onto a reconstruction (available data/prior experience))
Regarding claim 5, Gope does not teach The method of claim 3 wherein the corners of the two-dimensional simplex are the first data point, the second data point and the third data point.
However, Preindl does teach The method of claim 3 wherein the corners of the two-dimensional simplex are the first data point, the second data point and the third data point. (Preindl Paragraph 0073; “Here, the transformation matrix may identify the n+1 points that create the simplex, where one selected point (p.sub.0) creates the origin of the simplex, and each of the other points (p.sub.1 to p.sub.n) in the simplex can be expressed as one of N vectors p.sub.x0 relative to the origin (where N=n, and for x=1 to N).” Examiner notes that the first, second, and third data points (identified points) are the corners that form the two-dimensional simplex)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, and Preindl. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, and Preindl to apply aspects of the simplices to improve accuracy “Generally, the more operational points selected in the first step, the more simplices generated in the second step, and the larger and/or more complex the PWA map. As the PWA map increases in size and/or complexity, the accuracy of the PWA map may improve until reaching an approximate peak.” (Preindl Paragraph 0104).
Claim(s) 6, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Chandan Gope et al; US 20240351472 A1 filed on Apr 12, 2023 (hereinafter “Gope”) in view of Ziyue Xu et al; US 20240303504 A1 filed on Mar 22, 2024 (hereinafter “Xu”) in further view of Sunshine; “(Projected) Point on Line (2D) Algorithm” publicly available on Oct 29, 2022 (hereinafter “Sunshine”) in further view of Matthias Preindl et al; US 20250096709 A1 filed on Jan 27, 2023 (hereinafter “Preindl”) in further view of Eugene Kaspersky; “The Dangers of Exploits and Zero-Days, and Their Prevention” available Apr 02, 2023 (hereinafter “Kaspersky”) in further view of UCIC; “Cartesian and Barycentric Coordinates” publicly available on Jan 30, 2023 (hereinafter “UCIC”).
Regarding claim 6, Gope in view of Xu in further view of Sunshine in further view of Preindl does not teach The method of claim 3 where each of the corners of the two-dimensional simplex are calculated by T^-1 S1 , where S1 is the simplex, T is a coactivation matrix, a is the first data point, b is the second data point and c is the third data point:
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where λ = T-1 S1 calculates a set of barycentric coordinates from the first simplex, the second simplex, the third simplex and the fourth simplex.
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However, UIUC does teach The method of claim 3 where each of the corners of the two-dimensional simplex are calculated by T^-1 S1 , where S is the simplex, T is a coactivation matrix, a is the first data point, b is the second data point and c is the third data point:
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(UIUC Section “Barycentric Coordinates” Subsection “Comment”; “Let A=(a1,a2) ,B=(b1,b2) ,C=(c1,c2) be three given distinct and non-collinear points in the Cartesian plane and P=(p1,p2) an arbitrary point. Then the 3 linear equations above can be written in one matrix equation” Examiner notes that person having ordinary skill in the art would substitute aspects of the matrix equation to fit the simplex, coactivation matrix, and data points a, b, and c; further perform matrix operations to obtain “λ”/corners of the two-dimensional simplex)
where λ = T-1 S1 calculates a set of barycentric coordinates from the first simplex, the second simplex, the third simplex and the fourth simplex. (UIUC Section “Barycentric Coordinates” Subsection “Comment”; “Let A=(a1,a2) ,B=(b1,b2) ,C=(c1,c2) be three given distinct and non-collinear points in the Cartesian plane and P=(p1,p2) an arbitrary point. Then the 3 linear equations above can be written in one matrix equation” Examiner notes that matrix equation can be manipulated using matrix operations to obtain set of barycentric coordinates from the first simplex, the second simplex, the third simplex and the fourth simplex)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, Preindl, and UIUC. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. UIUC teaches a formula relating Barycentric coordinates to Cartesian coordinates. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, Preindl, and UIUC to use Barycentric coordinates based on motivated use case “Barycentric coordinates are motivated by the problem of finding the center of gravity” (UIUC Section “Barycentric Coordinates”).
Regarding claim 9, Gope teaches The method of claim 3 further comprising receiving a fifth data point assigned the variable name x, (Gope Paragraph 0216; “A request to determine outlier values for a set of battery nodes is received at 902. In some implementations, the request may be generated when a battery pack is placed into a diagnostic mode. When in the diagnostic mode, a variety of data related to battery nodes within the battery pack may be collected.” Examiner notes that new data points (data related to battery nodes) is received)
Gope does not teach where the fifth data point is plotted within the two-dimensional simplex, y = YabcT-1 S1 (x) identifies a quantity y within a space of the two-dimensional simplex.
However, UIUC does teach where the fifth data point is plotted within the two-dimensional simplex, y = YabcT-1 S1 (x) identifies a quantity y within a space of the two-dimensional simplex. (UIUC Section “Barycentric Coordinates” Subsection “Comment”; “Let A=(a1,a2) ,B=(b1,b2) ,C=(c1,c2) be three given distinct and non-collinear points in the Cartesian plane and P=(p1,p2) an arbitrary point. Then the 3 linear equations above can be written in one matrix equation” Examiner notes that person having ordinary skill in the art would substitute aspects of the matrix equation to plot the fifth data point and identify a quantity y within a space of the two-dimension simplex)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, Preindl, and UIUC. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. UIUC teaches a formula relating Barycentric coordinates to Cartesian coordinates. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, Preindl, and UIUC to use Barycentric coordinates based on motivated use case “Barycentric coordinates are motivated by the problem of finding the center of gravity” (UIUC Section “Barycentric Coordinates”).
Regarding claim 10, Gope does not teach The method of claim 9 wherein the quantity y is a low dimensional solution of a high dimensional problem.
However, Preindl does teach The method of claim 9 wherein the quantity y is a low dimensional solution of a high dimensional problem. (Preindl Paragraph 0268; “First, the motor controller 120 identifies on which simplex (or domain) of the PWA map the current value resides. Second, the motor controller 120 uses the particular affine function associated with the identified simplex to calculate the flux linkage.” Examiner notes that the quantity y (current value) is a low dimensional solution (current value is projected on the simplex) of a high dimensional problem (calculating the flux linkage))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, and Preindl. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, and Preindl to apply aspects of the simplices to improve accuracy “Generally, the more operational points selected in the first step, the more simplices generated in the second step, and the larger and/or more complex the PWA map. As the PWA map increases in size and/or complexity, the accuracy of the PWA map may improve until reaching an approximate peak.” (Preindl Paragraph 0104).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chandan Gope et al; US 20240351472 A1 filed on Apr 12, 2023 (hereinafter “Gope”) in view of Ziyue Xu et al; US 20240303504 A1 filed on Mar 22, 2024 (hereinafter “Xu”) in further view of Sunshine; “(Projected) Point on Line (2D) Algorithm” publicly available on Oct 29, 2022 (hereinafter “Sunshine”) in further view of Matthias Preindl et al; US 20250096709 A1 filed on Jan 27, 2023 (hereinafter “Preindl”) in further view of Eugene Kaspersky; “The Dangers of Exploits and Zero-Days, and Their Prevention” available Apr 02, 2023 (hereinafter “Kaspersky”) in further view of UCIC; “Cartesian and Barycentric Coordinates” publicly available on Jan 30, 2023 (hereinafter “UCIC”) in further view of Sarkhan Badirli et al; “Gradient Boosting Neural Networks: GrowNet” publicly on Jun 14, 2020 (hereinafter “Badirli”).
Regarding claim 7, Gope in view of Xu in further view of Sunshine in further view of Preindl in further view of UIUC does not teach The method of claim 6 further comprising gradient boosting the first simplex, the second simplex, the third simplex and the fourth simplex.
However, Badirli does teach The method of claim 6 further comprising gradient boosting the first simplex, the second simplex, the third simplex and the fourth simplex. (Badirli Page 3 Paragraph 4; “The key idea in gradient boosting is to take simple, lower-order models as weak learners and use them as fundamental building blocks to build a powerful, higher-order model by sequential boosting using first or second order gradient statistics.” Examiner notes that first, second, third, and fourth simplices are lower-order models that are gradient boosted together)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gope, Xu, Sunshine, Preindl, UIUC, and Badirli. Gope teaches a method of using one or more outlier detection machine learning models for battery diagnostic data. Xu teaches techniques to train/use one or more neural networks. Sunshine teaches different algorithms about the relation of a given point to a given line in the two-dimension case. Preindl teaches a method for utilizing simplices to calculate flux linkage. UIUC teaches a formula relating Barycentric coordinates to Cartesian coordinates. Badirli teaches a novel gradient boosting framework. One of ordinary skill would have motivation to combine Gope, Xu, Sunshine, Preindl, UIUC, and Badirli to use build a powerful, higher-order model “The key idea in gradient boosting is to take simple, lower-order models as weak learners and use them as fundamental building blocks to build a powerful, higher-order model by sequential boosting using first or second order gradient statistics.” (Badirli Page 3 Paragraph 4).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.D.T./Examiner, Art Unit 2147
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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