DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the arguments filed 07/17/2025. Claims 1,3-5, 7-8, 10-12 and 14-15 and 17-22 are pending in the application and have been considered below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are highlighted below with the generic place holder in bold and the functional language italicize:
Claim 8:
a mapping system configured to determine…
an asset management system configured to determine...
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitations “mapping system” and “asset management system” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 10-12 and 14 inherited the deficiencies of the parent claim. Therefore, they are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim 10 recites the limitation “the mapping manager.” There is insufficient antecedent basis for this limitation in the claims.
Claims 14 and 22 recite the limitation “the asset manager.” There is insufficient antecedent basis for this limitation in the claims.
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 1,3-5, 7-8, 10-12 and 14-15 and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
For Step 1, the claim is a method, so it does recite a statutory category of invention.
For Step 2, Prong 1:
The claim recites the limitation of “monitor a device.” The “monitor” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the monitor step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of "[training a first classification model by] determining patterns for each of the plurality of properties within data values of sample sensor data.” The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes determining step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “[training a second classification model] based on] a comparison of variables to property types of the sample sensor data.” The comparison of variables” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the “comparison of variables” step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “comparing the data values to pairs of threshold values to identify patterns for each of the plurality of properties, wherein each of the properties is associated with a respective one of the pairs of threshold values, and each of the patterns corresponds to a different one of the plurality of properties.” The “comparing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the comparing step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of "identify associations between the variables and the
plurality of properties." The "identify" limitation, as drafted, is a process that, under its
broadest reasonable interpretation, covers performance of the limitation in the mind but
for the recitation of generic computer components. That is nothing in the claim
precludes the identify step from practically being performed in the human mind. This
limitation is a mental process.
The claim recites the limitation of “generating...a mapping from the first classification model and the second classification model, wherein the mapping associates a respective one of the patterns and a respective one of the associations with a respective property of the plurality of properties.” The “generating” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generating step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “determining first confidence scores by comparing values of the sensor data with the patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties.” The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determining second confidence scores by comparing variable data of the sensor data with the variables of the second classification model.” The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determining that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores.” The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determining associations between subsets of the sensor data with the plurality of properties by processing the sensor data the sensor data with combinations of the first confidence scores and the second confidence scores.” The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of "determining a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties." The "determining" limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
For Step 2, Prong 2, the claim recites additional elements: receiving sensor data, computing system, sensor, first classification model and second classification model.
The “receiving sensor data” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The additional element of “sensor “is generally linked to the judicial exception and does not amount to an improvement in another technology.
The training a first classification model is” a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05(f).
The training a second classification model is” a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05(f).
DOUBLE CHECK WITH VIKER ABOUT training first and second classification model under 2106(f).
The “computer system, first classification model and second classification model” are generic computer components to apply an abstract idea under 2106.05(f).
Step 2B
The additional elements of first classification model and second classification model” do not amount to significantly more for the reasons set forth in step 2A above.
Under the Subject Matter Eligibility (SME), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receiving sensor data from a sensor” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer system,” first classification model and second classification model” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3:
Claim 3, which incorporates the rejection of claim 1, recites further limitations such as: The “generating a plurality of combined confidence scores by combining the first confidence scores with the second confidence scores and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generating step from practically being performed in the human mind. This limitation is a mental process.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 4:
Claim 4, which incorporates the rejection of claim 1, recites further limitations such as “a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the second confidence scores” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 5:
Claim 5, which incorporates the rejection of claim 1, recites further limitations such as “each of the patterns has an associated upper threshold value and an associated lower threshold value” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 7:
Claim 7, which incorporates the rejection of claim 1, recites an additional element: “processing a first subset of the subsets of the sensor data based on a first property of the plurality of properties” step is a generic computer component to apply an abstract idea under MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “processing a first subset of the subsets of the sensor data based on a first property of the plurality of properties” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8:
For Step 1, the claim is a computing device, so it does recite a statutory category of invention.
For Step 2, Prong 1:
The claim recites the limitation of “monitor a device.” The “monitor” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the monitor step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determine associations between subsets of the sensor data with the plurality of properties by processing the sensor data the sensor data with a combination of the first confidence scores and the second confidence scores.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “a mapping generated…, wherein the mapping associates a respective pattern of sensor patterns and a respective association of the association with a respective property of the plurality of properties.” The “mapping” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the “mapping” step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of” processing the sensor data with a combination
of first confidence scores and second confidence scores of a mapping generated
from a first classification model and a second classification model.” The “processing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the processing step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of” first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data. “The “determining” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of” “second classification model is trained based on a comparison of variables to property types of the sample data. “The “second classification model is trained based” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the processing step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “comparing the data values to pairs of threshold values, wherein each of the properties is associated with a respective one of the pairs of threshold values, and each of the sensor patterns corresponds to a different one of the plurality of properties.” The “comparing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the comparing step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “comparing variable data of the sensor data with the variable names of the second classification model.” The “comparing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the comparing step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determine that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “determine a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
For Step 2, Prong 2, the claim recites additional elements: computing device, communication device, receive sensor data, sensor, mapping system, asset management system, first classification model and second classification model,
The “computing device, mapping manager, asset manager, first classification model and second classification model” are generic computer components to apply an abstract idea under 2106.05(f).
The “receive sensor data” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The additional element of “sensor “is generally linked to the judicial exception and does not amount to an improvement in another technology.
Step 2B
Accordingly, these additional elements of “computing device, communication unit, mapping system, asset management system, first classification model and second classification model” do not amount to significantly more for the reasons set forth in step 2A above.
Under the Subject Matter Eligibility (SME), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receive sensor data” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
“computing device, communication unit, mapping system, asset management system, first classification model and second classification model” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10:
Claim 10, which incorporates the rejection of claim 9, recites further limitations such as “generating a plurality of combined confidence scores by combining the plurality of first
confidence scores with the plurality of second confidence scores, and wherein associations between the subsets of the subsets of the sensor data and the plurality of properties are further determined based on the plurality of combined confidence scores” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generating step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 11:
Claim 11, which incorporates the rejection of claim 9, recites further limitations such as “a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the second confidence scores” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 12:
Claim 12, which incorporates the rejection of claim 8, recites further limitations such as “each of the patterns has an associated upper threshold value and an associated lower threshold value” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim14:
Claim 14, which incorporates the rejection of claim 1, “to determine the functionality of the device” that are part of the abstract idea.
The claim recites an additional element: “process a first subset of the subsets of the sensor data based on the plurality of properties” step is a generic computer component to apply an abstract idea under MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “process a first subset of the subsets of the sensor data based on the plurality of properties” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15:
For Step 1, the claim is a computer-readable storage medium, so it does recite a statutory category of invention.
For Step 2, Prong 1:
The claim recites the limitation of “monitor a device.” The “monitor” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the monitor step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determine associations between subsets of the sensor data with the plurality of properties by processing the sensor data the sensor data with a combination of the first confidence scores and the second confidence scores.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “identify variable names associated with the plurality of properties.” The “identify” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identify step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “processing the sensor data with the mapping…, wherein the mapping associates a respective pattern of sensor patterns and a respective association of the association with a respective property of the plurality of properties.” The “processing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the processing step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “comparing variable data of the sensor data with the variable names of the second classification model.” The “comparing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the comparing step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “comparing variable data of the sensor data with the variable names of the second classification model.” The “comparing” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the comparing step from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “determine that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
The claim recites the limitation of “determine a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties.” The “determine” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. MPEP 2106.04(a)(2)(III)(C).
For Step 2, Prong 2, the claim recites additional elements: computer-readable storage medium, computer-readable program code , computer processors, computing device, communication device, receive sensor data from a sensor, “the first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data, wherein each of the sensor patterns corresponds to a different one of a plurality of properties,” “the second classification model is trained based on a comparison of sensor variables to property types of the sample data,” first classification model and second classification model.
The “computer-readable storage medium, computer-readable program code, computer processors, computing device, communication device and “a mapping generated from a first classification model and a second classification model, wherein the mapping associates a respective pattern of sensor patterns and a respective association of the association with a respective property of the plurality of properties.” are generic components to apply an abstract idea under MPEP 2106.05(f).
The “computer processors” are recited at a high level of generality, i.e., as generic processors performing a generic computer function of processing data. These generic processors limitation is no more than mere instructions to apply the exception using a generic computer component.
The “receive sensor data from a sensor” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The recited “the first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data, wherein each of the sensor patterns corresponds to a different one of a plurality of properties” is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05 (f).
The recited “the second classification model is trained based on a comparison of sensor variables to property types of the sample data” is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05 (f).
Step 2B
Accordingly, these additional elements of “computer-readable storage medium, computer-readable program code , computer processors, computing device, communication device, “the first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data, wherein each of the sensor patterns corresponds to a different one of a plurality of properties,” “the second classification model is trained based on a comparison of sensor variables to property types of the sample data,” first classification model and second classification model” do not amount to significantly more for the reasons set forth in step 2A above.
Under the Subject Matter Eligibility (SME), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receive sensor data from a sensor” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
computer-readable storage medium, computer-readable program code , computer processors, computing device, communication device, “the first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data, wherein each of the sensor patterns corresponds to a different one of a plurality of properties,” “the second classification model is trained based on a comparison of sensor variables to property types of the sample data,” first classification model and second classification model” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17:
Claim 17, which incorporates the rejection of claim 16, recites further limitations such as “generate a plurality of combined confidence scores by combining the plurality of first
confidence scores with the plurality of second confidence scores, and wherein the
subsets of the subsets of the sensor data are associated with the plurality of properties
further based on plurality of combined confidence scores” limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generate step from practically being performed in the human mind. This limitation is a mental process.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 18:
Claim 18, which incorporates the rejection of claim 15, recites further limitations such as “a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the plurality of second confidence scores” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 19:
Claim 19, which incorporates the rejection of claim 15, recites an additional element:
“a first subset of the subsets of the sensor data is processed based on a first property of the plurality of properties” step is a generic computer component to apply an abstract idea under MPEP 2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “a first subset of the subsets of the sensor data is processed based on a first property of the plurality of properties”” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20:
Claim 20, which incorporates the rejection of claim 15, recites further limitations such as “each of the sensor patterns has an associated upper threshold value and an associated lower threshold value” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 21:
Claim 21, which incorporates the rejection of claim 1, recites an additional element:
“The first classification model and the second classification model are trained by a mapping generator of the computing system and output to a mapping manager of the computing system, and the associations between subsets of the sensor data and the plurality of properties are generated by the mapping manager and output to an asset manager of the computing system configured to determine the functionality of the device” is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05 (f).
Accordingly, this additional element of “the first classification model and the second classification model are trained by a mapping generator of the computing system and output to a mapping manager of the computing system, and the associations between subsets of the sensor data and the plurality of properties are generated by the mapping manager and output to an asset manager of the computing system configured to determine the functionality of the device” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
“The first classification model and the second classification model are trained by a mapping generator of the computing system and output to a mapping manager of the computing system, and the associations between subsets of the sensor data and the plurality of properties are generated by the mapping manager and output to an asset manager of the computing system configured to determine the functionality of the device” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 22:
Claim 22, which incorporates the rejection of claim 8, recites an additional element:
“a mapping generator configured to train the first classification model and the second classification model, wherein the mapping generator outputs the first classification model and the second classification model to the mapping generator, and wherein the mapping generator outputs the associations between subsets of the sensor data and the plurality of properties to the asset manager “is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05 (f).
Accordingly, this additional element of “a mapping generator configured to train the first classification model and the second classification model, wherein the mapping generator outputs the first classification model and the second classification model to the mapping generator, and wherein the mapping generator outputs the associations between subsets of the sensor data and the plurality of properties to the asset manager” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of
“a mapping generator configured to train the first classification model and the second classification model, wherein the mapping generator outputs the first classification model and the second classification model to the mapping generator, and wherein the mapping generator outputs the associations between subsets of the sensor data and the plurality of properties to the asset manager” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 7, 12, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Thampy (US 2019/0068627 A1, hereinafter referred to as Thampy), and further in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq).
As to claim 1, Wang538 teaches a method for mapping sensor data to a plurality of properties (paragraph [0060] Mapping of sensor data), the method comprising:
receiving, by a computing system, sensor data from a sensor configured to
monitor a device, wherein the sensor data is associated with the device and the
computing system is communicatively connected to the sensor (paragraph [0006], receive by the model selection pipeline, first historical sensor data of the first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of the any number of renewable energy assets);.
training a first classification model by determining patterns for each of the plurality of properties within data values of sample sensor data [by comparing the data values to pairs of threshold values wherein each of the properties is associated with a respective one of the pairs of threshold values], and each of the patterns corresponds to a different one of the plurality of properties (Abstract, wherein Examiner interprets “training a set of models to predict faults for each component using the patterns of events and historical sensor data” to teach the limitation; paragraphs [0004] Extracting patterns of events based on the feature matrix may comprise counting a number of event codes of events that occurred during a time interval using the feature matrix and sequence the event codes to include dynamics of events in a longitudinal time dimension; [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138], train a first model, using subsets 1-4 and evaluate the first model with subset 5; [0208], discover patterns among the event and alarm data in the longitudinal history ( e.g., patterns may be as simple as unique event code counts in a past time period such as a month, advanced time sequence patterns such as A->B->C, or complicated encoded event sequence vectors); [0215] The model training and testing module 534 may receive the patterns and/or the pattern matrix in addition to historical sensor data to cross validate failure prediction models and/or train a set of failure prediction models);
training a second classification model based on a comparison of variables to property types of the sample sensor data to identify associations between the variables and the plurality of properties (paragraphs [0060], mapping of sensor data
may be based on observability and take into account sensor dynamic range. For example, the sensor data related (Examiner interprets data related as “mapping”) to temperature, noise, and/or vibration is observed against the background of other sensor data readings, and the sensor dynamic range of each individual sensor or combination of sensors should be recognized.; [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138], train a second model, using subsets 2-5 and evaluate the second model with subset 1; wherein using the broadest interpretation, Examiner interprets combining the mapping of sensor data and train a second model, using subsets to teach the limitation); and
determining subsets of the sensor data with the plurality of properties by processing the sensor data with the first classification model and the second classification model (paragraphs [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138]-[0139] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance; wherein Examiner interprets the “cross-validation” to teach the “association”); and
determining a functionality of the device by processing the sensor data based on
the associations between the subsets and the plurality of properties (paragraphs [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138]-[0141] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance. The cross-validation module 520 may aggregate performance measures of any number of the fault prediction models created using the divided training data set and/or make selections of preferred fault prediction models to estimate the model's predictive performance; [0144], compares the predictions of each failure prediction model of a set of failure prediction models using historical sensor data to compare the results against ground truth ( e.g., known failures and known periods of time that the component (Examiner interprets “known failures and known periods of time that the component” as “functionality of the device” ) did not fail).;.
generating, via the computing system, a mapping from the first classification model and the second classification model, wherein the mapping associates a respective one of the patterns and a respective one of the associations with a respective property of the plurality of properties (paragraphs [0138]-[0139] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance; wherein Examiner interprets the “cross-validation” to teach the “association”);
However, Wang538 fails to explicitly teach:
[training a first classification model by] determining patterns [for each of the plurality of properties within data values of sample sensor data] by comparing the data values to pairs of threshold values wherein each of the properties is associated with a respective one of the pairs of threshold values;
determining first confidence scores by comparing values of the sensor data with the patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties; and
determining second confidence scores by comparing variable data of the sensor data with the variables of the second classification model;
determining that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores; and
determining, via the computing system, associations between subsets of the sensor data with the plurality of properties by processing the sensor data with combinations of the first confidence scores and the second confidence scores.
Thampy, in combination with Wang538, teaches:
[training a first classification model by] determining patterns [for each of the plurality of properties within data values of sample sensor data] by comparing the data values to pairs of threshold values each of the properties is associated with a respective one of the pairs of threshold values (paragraphs [0292]-[0294] Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof.
Examiner interpretation of upper and lower threshold values is based on paragraph [0035] of the original disclosure(specification);
[0303]-[0304] Classification of patterns may be determined based on one or more trained models; [0306] models to be trained for classifying patterns; [0307], classifying a pattern, one or more models may be prepared at step 1816 to improve training of models).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Wang538 to add upper and lower threshold values to the system of Wang538, as taught by Thampy above. The modification would have been obvious because one of ordinary skill would be motivated to have thresholds for each subset may be defined based on previous patterns or behavior with respect to an activity or use of a service by a user in an organization, as suggested by Thampy ([0293]).
However, Wang538 and Thampy fail to explicitly teach:
determining first confidence scores by comparing values of the sensor data with the patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties;
determining second confidence scores by comparing variable data of the sensor data with the variables of the second classification model;
determining that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores; and
determining, via the computing system, associations between subsets of the sensor data with the plurality of properties by processing the sensor data with combinations of the first confidence scores and the second confidence scores.
Tariq, in combination with Wang538 and Thampy, teaches:
determining first confidence scores by comparing values of the sensor data with the patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties (paragraph [0054] FIG. 2B illustrates example portions of the image (i.e., cells in the image) and their associated regions of interest (RO Is) with respect to a classification of "car." FIG. 2B illustrates the ROis as bounding boxes, although it is understood that the region of the image representing an object may be otherwise indicated (e.g., by a mask). An ML model, as discussed herein, may determine ROI 204' for portion 204 (e.g., cell 204), ROI 206' for portion 206, and
ROI 208' for portion 208. In some instances, the ML model may determine a first confidence score in association with ROI 204', a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'. A confidence score may indicate a probability that the associated ROI accurately represents a region of the image that represents an object (here, a car). Each of the ROIs 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; wherein Examiner interprets the ROIs 204', 206', and 208' as patterns of the ML model (interpreted as a first classification model);): determining that the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores (paragraphs [0024]-[0025], training the ML model to produce better Rois (ROis identifying where the object is in the image more accurately) and/or more accurate confidence scores (e.g., producing a score closer to O for an ROI that does not contain an object and/or a score closer to 1 for an ROI that does indicate a salient object) and to reduce the compute time to achieve ROis of such an accuracy;
[0054] the ML model may determine a first confidence score in association with ROI 204', a second confidence score in association with ROI 206'(i.e., computer analysis and patterns)), and a third confidence score in association with ROI 208'. A confidence score may indicate a probability that the associated ROI accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110, respectively);
determining, via the computing system, associations between subsets of the sensor data with the plurality of properties by processing the sensor data with combinations of the first confidence scores and the second confidence scores. (paragraphs [0054] The ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e., computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the ROIs 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]- [0063], FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generated.)
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system Wang538 and Thampy to add first confidence scores and second confidence scores to the combination system Wang538 and Thampy, as taught by Tariq above. The modification would have been obvious because one of ordinary skill would be motivated to use techniques for training the ML model and how to train the ML model so that the ML model will generate ROIs and/or confidence scores that are more accurate, as suggested by Tariq ([0056]).
As to claim 5, which incorporates the rejection of claim 1, Wang538 fails to explicitly teach wherein each of the patterns has an associated upper threshold value and an associated lower threshold value.
However, Thampy, in combination with Wang538, teaches wherein each of the patterns has an associated upper threshold value and an associated lower threshold value (paragraphs [0293]-[0294] …Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof….each subset may be defined by a threshold (e.g., upper and lower thresholds)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Wang538 to add upper and lower threshold values to the system of Wang538, as taught by Thampy, above. The modification would have been obvious because one of ordinary skill would be motivated to have thresholds for each subset may be defined based on previous patterns or behavior with respect to an activity or use of a service by an organization, as suggested by Thampy, ([0293]).
As to claim 7, which incorporates the rejection of claim 1, Wang538 teaches:
processing a first subset of the subsets of the sensor data based on a first property of the plurality of properties (paragraphs [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0131], data processing module 522; [0138], train a first model, using subsets 1-4 and evaluate the first model with subset 5. Subsequently, the component failure prediction system 104 may train a second model, using subsets 2-5 and evaluate the second model with subset 1. This process can continue until five models are created with different subsets of data and evaluated by a different subset of the data.).
As to claim 8, Wang538 teaches a computing device comprising:
a communication device communicatively connected with a sensor and configured to receive sensor data from the sensor, wherein the sensor is configured to monitor a device, wherein the sensor data is associated with the device (paragraphs [0007] SCADA systems that monitor any number of wind turbines, the event and alarm data being generated during a first period of time, receiving by the model selection pipeline, historical wind turbine component failure data and wind turbine asset data from the one or more SCADA systems);
a mapping system configured to:
wherein the mapping associates a respective pattern of sensor patterns and a respective association of the association with a respective property of the plurality of properties (paragraphs [0138]-[0139] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance; wherein Examiner interprets the “cross-validation” to teach the “association”);
wherein the first classification model is trained by determining patterns
for each of the plurality of properties within values of sample sensor data
[by comparing the data values to pairs of threshold values, wherein each of the properties is associated with a respective one of the pairs of threshold values], and each of the sensor patterns corresponds to a different one of the plurality of properties (Abstract, wherein Examiner interprets “training a set of models to predict faults for each component using the patterns of events and historical sensor data” to teach the limitation; paragraphs [0004] Extracting patterns of events based on the feature matrix may comprise counting a number of event codes of events that occurred during a time interval using the feature matrix and sequence the event codes to include dynamics of events in a longitudinal time dimension; [0054] The ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e., computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the ROIs 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]- [0063], FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generate; [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138], train a first model, using subsets 1-4 and evaluate the first model with subset 5), and the second classification model is trained based on a comparison of variables to property types of the sample sensor data to identify variable names associated with the plurality of properties (paragraphs [0060]..mapping of sensor data [0138], train a second model, using subsets 2-5 and evaluate the second model with subset 1; wherein using the broadest interpretation, Examiner interprets combining the mapping of sensor data and train a second model, using subsets to teach the limitation); and
an asset management system configured to determine a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties (paragraphs [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138]-[0141] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance. The cross-validation module 520 may aggregate performance measures of any number of the fault prediction models created using the divided training data set and/or make selections of preferred fault prediction models to estimate the model's predictive performance; [0144], compares the predictions of each failure prediction model of a set of failure prediction models using historical sensor data to compare the results against ground truth ( e.g., known failures and known periods of time that the component (Examiner interprets “known failures and known periods of time that the component” as “functionality of the device” ) did not fail)),
However, Wang538 fails to explicitly teach:
comparing the data values to pairs of threshold values, wherein each of the properties is associated with a respective one of the pairs of threshold values;
determine associations between subsets of the sensor data and a plurality of properties by processing the sensor data with a combination of first confidence scores and second confidence scores of a mapping generated from a first classification model and a second classification model, and wherein the first confidence scores are determined by comparing values of the sensor data with the sensor patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties and the second confidence scores are determined by comparing variable data of the sensor data with the variable names of the second classification model; and
determine that the first classification model and the second classification
model are accurate based on the first confidence scores and the second
confidence scores.
Thampy, in combination with Wang538, teaches:
comparing the data values to pairs of threshold values, wherein each of the properties is associated with a respective one of the pairs of threshold values (paragraphs [0293]-[0294] Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof.).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Wang538 to add upper and lower threshold values to the system of Wang538, as taught by Thampy above. The modification would have been obvious because one of ordinary skill would be motivated to have thresholds for each subset may be defined based on previous patterns or behavior with respect to an activity or use of a service by a user in an organization, as suggested by Thampy ([0293]).
However, Wang538 and Thampy fail to explicitly teach:
determine associations between subsets of the sensor data and a plurality of properties by processing the sensor data with a combination of first confidence scores and second confidence scores of a mapping generated from a first classification model and a second classification model, and wherein the first confidence scores are determined by comparing values of the sensor data with the sensor patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties and the second confidence scores are determined by comparing variable data of the sensor data with the variable names of the second classification model; and
determine that the first classification model and the second classification
model are accurate based on the first confidence scores and the second
confidence scores.
Tariq, in combination with Wang538 and Thampy, teaches:
determine associations between subsets of the sensor data and a plurality of properties by processing the sensor data with a combination of first confidence scores and second confidence scores of a mapping generated from a first classification model and a second classification model (paragraphs [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e., computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]-[0063] FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generated), and
wherein the first confidence scores are determined by comparing values of the sensor data with the sensor patterns of the first classification model (paragraphs [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), wherein the first confidence scores include a respective confidence score for associated with each of the properties (paragraph [0054] The ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), and the second confidence scores are determined by comparing variable data of the sensor data with the variable names of the second classification model (paragraphs [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e. computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]-[0063]. FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generated); and
determine that the first classification model and the second classification
model are accurate based on the first confidence scores and the second
confidence scores (paragraphs [0024]-[0025], training the ML model to produce better Rois (ROis identifying where the object is in the image more accurately) and/or more accurate confidence scores (e.g., producing a score closer to O for an ROI that does not contain an object and/or a score closer to 1 for an ROI that does indicate a salient object) and to reduce the compute time to achieve ROis of such an accuracy; [0054] the ML model may determine a first confidence score in association with ROI 204', a second confidence score in association with ROI 206'(i.e., computer analysis and patterns)), and a third confidence score in association with ROI 208'. A confidence score may indicate a probability that the associated ROI accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110, respectively).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system Wang538 and Thampy to add first confidence scores and second confidence scores to the combination system Wang538 and Thampy, as taught by Tariq above. The modification would have been obvious because one of ordinary skill would be motivated to use techniques for training the ML model and how to train the ML model so that the ML model will generate ROIs and/or confidence scores that are more accurate, as suggested by Tariq ([0056]).
As to claim 12, which incorporates the rejection of claim 8, Wang538 fails to explicitly teach wherein each of the patterns has an associated upper threshold value and an associated lower threshold value.
However, Thampy, in combination with Wang538, teaches wherein each of the patterns has an associated upper threshold value and an associated lower threshold value (paragraphs [0293]-[0294] Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof….each subset may be defined by a threshold (e.g., upper and lower thresholds)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Wang538 to add upper and lower threshold values to the combination system of Wang538, as taught by Thampy, above. The modification would have been obvious because one of ordinary skill would be motivated to have thresholds for each subset may be defined based on previous patterns or behavior with respect to an activity or use of a service by an organization, as suggested by Thampy, ([0293]).
As to claim 14, which incorporates the rejection of claim 8, Wang538 teaches:
processing a first subset of the subsets of the sensor data based on a first property of the plurality of properties (paragraphs [0131], data processing module 522; [0138] train a first model, using subsets 1-4 and evaluate the first model with subset 5. Subsequently, the component failure prediction system 104 may train a second model, using subsets 2-5 and evaluate the second model with subset 1. This process can continue until five models are created with different subsets of data and evaluated by a different subset of the data.)
As to claim 20, which incorporates the rejection of claim 15, Wang538 fails to explicitly teach wherein each of the patterns has an associated upper threshold value and an associated lower threshold value.
However, Thampy, in combination with Wang538, teaches wherein each of the patterns has an associated upper threshold value and an associated lower threshold value (paragraphs [0293]-[0294] Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof each subset may be defined by a threshold (e.g., upper and lower thresholds)).
Claims 15, 19 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq).
As to claim 15, Wang538 teaches a computer program product for mapping sensor data to a property (paragraph [0060] Mapping of sensor data), the computer program product comprising:
a computer-readable storage medium having computer-readable program code embodied therewith (paragraph [0003], nontransitory computer readable medium comprises executable instructions. The executable instructions are executable by one or more processors), the computer-readable program code executable by one or more computer processors to:
receive, at a communication device, sensor data from a plurality of sensors configured to monitor a device, wherein the sensor data is associated with the device, and the communication device is communicatively connected with the sensor (paragraph [0006], receive by the model selection pipeline, first historical sensor data of the first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of the any number of renewable energy assets);
wherein the mapping associates a respective pattern of sensor patterns and a respective association of the association with a respective property of the plurality of properties (paragraphs [0138]-[0139] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance; wherein Examiner interprets the “cross-validation” to teach the “association”), wherein the first classification model is trained by determining patterns for each of the plurality of properties within values of sample sensor data by comparing the data values to pairs of threshold values , wherein each of the properties is associated with a respective one of the pairs of threshold values, and each of the patterns corresponds to a different one of the plurality of properties (paragraphs [0004] Extracting patterns of events based on the feature matrix may comprise counting a number of event codes of events that occurred during a time interval using the feature matrix and sequence the event codes to include dynamics of events in a longitudinal time dimension; [0054] The ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e., computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the ROIs 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]- [0063], FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generate; [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138], train a first model, using subsets 1-4 and evaluate the first model with subset 5), and the second classification model is trained based on a comparison of sensor variables to property types of the sample sensor data to identify variable names associated with the plurality of properties (paragraphs [0060], mapping of sensor data [0138], train a second model, using subsets 2-5 and evaluate the second model with subset 1; wherein using the broadest interpretation, Examiner interprets combining the mapping of sensor data and train a second model, using subsets to teach the limitation); and
determine a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties (paragraphs [0133] These features may encapsulate central properties of a data set and represent the data set and a low dimensional space that facilitates learning; [0138]-[0141] The cross-validation module 520 may train new models using different permutations of four subsets, leaving a different subset for evaluation/testing to find preferred or optimal parameter and establish cross-validation performance. The cross-validation module 520 may aggregate performance measures of any number of the fault prediction models created using the divided training data set and/or make selections of preferred fault prediction models to estimate the model's predictive performance; [0144], compares the predictions of each failure prediction model of a set of failure prediction models using historical sensor data to compare the results against ground truth ( e.g., known failures and known periods of time that the component (Examiner interprets “known failures and known periods of time that the component” as “functionality of the device” ) did not fail)),
However, Wang538 fails to explicitly teach:
determine associations between subsets of the sensor data and a plurality of properties by processing the sensor data with a combination of first confidence scores and second confidence scores of a mapping generated from a first classification model and a second classification model, and wherein the first confidence scores are determined by comparing values of the sensor data with the sensor patterns of the first classification model, wherein the first confidence scores include a respective confidence score for associated with each of the properties and the second confidence scores are determined by comparing variable data of the sensor data with the variable names of the second classification model; and
determine that the first classification model and the second classification
model are accurate based on the first confidence scores and the second
confidence scores.
Tariq, in combination with Wang538, teaches:
determine associations between subsets of the sensor data and a plurality of properties by processing the sensor data with a combination of first confidence scores and second confidence scores of a mapping generated from a first classification model and a second classification model (paragraphs [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e., computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]-[0063] FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generated), and
wherein the first confidence scores are determined by comparing values of the sensor data with the sensor patterns of the first classification model (paragraphs [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), wherein the first confidence scores include a respective confidence score for associated with each of the properties (paragraph [0054] The ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), and the second confidence scores are determined by comparing variable data of the sensor data with the variable names of the second classification model (paragraph [0054], the ML model may determine a first confidence score in association with ROI 204' (i.e. computer analysis and patterns)), a second confidence score in association with ROI 206', and a third confidence score in association with ROI 208'(i.e. computer analysis and patterns)). A confidence score may indicate a probability that the associated ROI (i.e., computer analysis and patterns)) accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110; [0058]-[0063]. FIGS. 4A-4D illustrate example ROIs, example confidence scores associated with the ROIs, and portions of the image for which the ROIs were generated); and
determine that the first classification model and the second classification
model are accurate based on the first confidence scores and the second
confidence scores (paragraphs [0024]-[0025], training the ML model to produce better Rois (ROis identifying where the object is in the image more accurately) and/or more accurate confidence scores (e.g., producing a score closer to O for an ROI that does not contain an object and/or a score closer to 1 for an ROI that does indicate a salient object) and to reduce the compute time to achieve ROis of such an accuracy; [0054] the ML model may determine a first confidence score in association with ROI 204', a second confidence score in association with ROI 206'(i.e., computer analysis and patterns)), and a third confidence score in association with ROI 208'. A confidence score may indicate a probability that the associated ROI accurately represents a region of the image that represents an object (here, a car). Each of the RO Is 204', 206', and 208' identifies different regions of the image that represent different objects, i.e., vehicles 106, 108, and 110, respectively).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system Wang538 and Thampy to add first confidence scores and second confidence scores to the combination system Wang538 and Thampy, as taught by Tariq above. The modification would have been obvious because one of ordinary skill would be motivated to use techniques for training the ML model and how to train the ML model so that the ML model will generate ROIs and/or confidence scores that are more accurate, as suggested by Tariq ([0056]).
As to claim 19, which incorporates the rejection of claim 15, Wang538 teaches wherein a first subset of the subsets of the sensor data is processed based on a first property of the plurality of properties (paragraphs [0131], data processing module 522; [0138], train a first model, using subsets 1-4 and evaluate the first model with subset 5. Subsequently, the component failure prediction system 104 may train a second model, using subsets 2-5 and evaluate the second model with subset 1. This process can continue until five models are created with different subsets of data and evaluated by a different subset of the data.)
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Thampy (US 2019/0068627 A1, hereinafter referred to as Thampy), and further in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq), and Skipper et al. (US 2011/0081073 A1, hereinafter referred to as Skipper).
As to claim 3, which incorporates the rejection of claim 1, Wang538, Thampy and Tariq fail to explicitly teach:
generating a plurality of combined confidence scores by combining the plurality of first confidence scores with the second confidence scores, and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores.
However, Skipper, in combination with Wang538, Thampy and Tariq, teaches:
generating a plurality of combined confidence scores by combining the plurality of first confidence scores with the second confidence scores (paragraph [0040]
the fusion schemes combine confidence scores of different algorithm response planes in a manner that is at least partially multiplicative in nature.), and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores (paragraphs [0025]- [0026] metrics that predict the degree of dependency between detection algorithms and provide a correlation with the area under the receiver-operator characteristic curve may be employed to select candidate detection algorithms for use in an ensemble classifier; [0061] Roadside imagery may be captured under all illumination conditions using a vehicle-mounted multimodal imaging system, i.e., a system that combines multiple sensors for capturing images (such as electro-optic, short-wave infrared, midwave infrared and long-wave infrared), sound, temperature, and the like. Additionally, the system may comprise sensors suited for a given ambient lighting condition (e.g., dawn, daylight, dusk and night). The sensor data may be evaluated with heuristic data to produce ground truth data for generating/validating an ensemble classifier. Once generated, the ensemble classifier may be stored on memory communicably coupled with a processor and the sensors and operate to detect IED's).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538, Thampy and Tariq to add combined confidence scores to the combination system of Wang538, Thampy and Tariq, as taught by Skipper, above. The modification would have been obvious because one of ordinary skill would be motivated to ranking the best performing algorithms, as suggested by Skipper ([0041).
As to claim 10, which incorporates the rejection of claim 8, Wang538, Thampy and Tariq fail to explicitly teach:
generate a plurality of combined confidence scores by combining the first confidence scores with the second confidence scores, and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores.
However, Skipper, in combination with Wang538 and Tariq, teaches:
generate a plurality of combined confidence scores by combining the first confidence scores with the second confidence scores, and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores (paragraph [0040]..the fusion schemes combine confidence scores of different algorithm response planes in a manner that is at least partially multiplicative in nature.), and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores (paragraphs [0025]- [0026] …metrics that predict the degree of dependency between detection algorithms and provide a correlation with the area under the receiver-operator characteristic curve may be employed to select candidate detection algorithms for use in an ensemble classifier; [0061] Roadside imagery may be captured under all illumination conditions using a vehicle-mounted multimodal imaging system, i.e., a system that combines multiple sensors for capturing images (such as electro-optic, short-wave infrared, midwave infrared and long-wave infrared), sound, temperature, and the like. Additionally, the system may comprise sensors suited for a given ambient lighting condition (e.g., dawn, daylight, dusk and night). The sensor data may be evaluated with heuristic data to produce ground truth data for generating/validating an ensemble classifier. Once generated, the ensemble classifier may be stored on memory communicably coupled with a processor and the sensors and operate to detect IED's).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538, Thampy and Tariq to add combined confidence scores to the combination system of Wang538, Thampy and Tariq, as taught by Skipper, above. The modification would have been obvious because one of ordinary skill would be motivated to ranking the best performing algorithms, as suggested by Skipper ([0041).
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Thampy (US 2019/0068627 A1, hereinafter referred to as Thampy), and further in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq), and Wang et al. (US 2018/0216942 A1, hereinafter referred to as Wang942).
As to claim 4, which incorporates the rejection of claim 1, Wang538, Thampy and Tariq fail to explicitly teach wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the of second confidence scores.
However, Wang942, in combination with of Wang538, Nguyen and Tariq, teaches wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the plurality of first confidence scores and a highest ranked one of the plurality of second confidence scores (paragraphs [0036]-[0037], first confidence is considered highest if the collected real-time pose is consistent with a previous real-time pose. The second confidence score is considered highest if the subset of the first localization map data is complete; [0050]-[0051] …determine confidence scores for collected localization data; [0054], highest confidence score based on the confidence scores determined by analysis module… [0078], highest ranking.).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538, Thampy and Tariq to add highest ranked confidence scores to the combination system of Wang538, Thampy and Tariq, as taught by Wang942, above. The modification would have been obvious because one of ordinary skill would be motivated to have select candidates with highest confidence score, as suggested by Wang942, ([0021]).
As to claim 11, which incorporates the rejection of claim 8, of Wang538, Thampy and Tariq fail to explicitly teach wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the of second confidence scores.
However, Wang942, in combination with of Wang538, Thampy and Tariq, teaches wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the plurality of first confidence scores and a highest ranked one of the plurality of second confidence scores (see paragraphs [0036]-[0037], first confidence is considered highest if the collected real-time pose is consistent with a previous real-time pose. The second confidence score is considered highest if the subset of the first localization map data is complete; [0050]-[0051], determine confidence scores for collected localization data; [0054], highest confidence score based on the confidence scores determined by analysis module; [0078], highest ranking.).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538, Thampy and Tariq to add highest ranked confidence scores to the combination system of Wang538, Thampy and Tariq, as taught by Wang942, above. The modification would have been obvious because one of ordinary skill would be motivated to have select candidates with highest confidence score, as suggested by Wang942, ([0021]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq), and further in view of Skipper et al. (US 2011/0081073 A1, hereinafter referred to as Skipper).
As to claim 17, which incorporates the rejection of claim 15, Wang538 and Tariq fail to explicitly teach:
generate a plurality of combined confidence scores by combining the first confidence scores with the second confidence scores, and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores.
However, Skipper, in combination with Wang538 and Tariq, teaches:
generate a plurality of combined confidence scores by combining the first confidence scores with the second confidence scores, and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores (paragraph [0040]..the fusion schemes combine confidence scores of different algorithm response planes in a manner that is at least partially multiplicative in nature.), and wherein the subsets of the subsets of the sensor data are associated with the plurality of properties further based on plurality of combined confidence scores (paragraphs [0025]- [0026], metrics that predict the degree of dependency between detection algorithms and provide a correlation with the area under the receiver-operator characteristic curve may be employed to select candidate detection algorithms for use in an ensemble classifier; [0061] Roadside imagery may be captured under all illumination conditions using a vehicle-mounted multimodal imaging system, i.e., a system that combines multiple sensors for capturing images (such as electro-optic, short-wave infrared, midwave infrared and long-wave infrared), sound, temperature, and the like. Additionally, the system may comprise sensors suited for a given ambient lighting condition (e.g., dawn, daylight, dusk and night). The sensor data may be evaluated with heuristic data to produce ground truth data for generating/validating an ensemble classifier. Once generated, the ensemble classifier may be stored on memory communicably coupled with a processor and the sensors and operate to detect IED's).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538 and Tariq to add combined confidence scores to the combination system of Wang538 and Tariq, as taught by Skipper, above. The modification would have been obvious because one of ordinary skill would be motivated to ranking the best performing algorithms, as suggested by Skipper ([0041).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210538 A1, hereinafter referred to as Wang538), in view of Tariq et al. (US 2019/0392268 A1, hereinafter referred to as Tariq), and further in view of Wang et al. (US 2018/0216942 A1, hereinafter referred to as Wang942).
As to claim 18, which incorporates the rejection of claim 15, of Wang538 and Tariq fail to explicitly teach wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the first confidence scores and a highest ranked one of the of second confidence scores.
However, Wang942, in combination with of Wang538 and Tariq, teaches wherein a first property of the plurality of properties is associated with at least one of a highest ranked one of the plurality of first confidence scores and a highest ranked one of the plurality of second confidence scores (see paragraphs [0036]-[0037], first confidence is considered highest if the collected real-time pose is consistent with a previous real-time pose. The second confidence score is considered highest if the subset of the first localization map data is complete; [0050]-[0051], determine confidence scores for collected localization data; [0054], highest confidence score based on the confidence scores determined by analysis module… [0078], highest ranking.).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Wang538 and Tariq to add highest ranked confidence scores to the combination system of Wang538 and Tariq, as taught by Wang942, above. The modification would have been obvious because one of ordinary skill would be motivated to have select candidates with highest confidence score, as suggested by Wang942, ([0021]).
Response to Applicant’s arguments
Applicant's arguments on file on 07/17/2025 with respect to the 101 and 103 rejections of claims 1,3-5, 7-8, 10-12 and 14-15 and 17-22 and the claim interpretation 112(f) have been considered. Those arguments are not persuasive for the 101 and 103
REMARKS
Claim Rejections under 35 USC § 101
Applicant appears to assert that Example 40 of the Subject Matter Eligibility Examples dated January 7, 2019, indicates that claim 1 is patent eligible as claim 1 as a whole integrates a mental process into a practical application. Specifically, in the provided example it is indicated that while each of the collecting steps of claim 1 when analyzed individually may be viewed as mere pre-solution or post-solution activity, claim 1 taken as a whole is directed to a particular improvement in collecting traffic data. Further, it is indicated that the additional elements of claim provide a specific improvement over prior system, resulting in improved network monitoring. Similarly, amended claim 1 of the present application improves the assignment of sensor data to properties to determine the functionality of a corresponding device, improving the functionality of the corresponding computers system.
In view of the analysis applied to claim 1 of example 40 of the Subject Matter
Eligibility Examples dated January 7, 2019, assuming arguendo that the training steps
and determining steps recited above can be viewed as merely being mental processes,
when taken as a whole, the combination of the steps with the other elements of amended claim 1 improve the functionality of the corresponding computers system by allowing the computer system to automatically assign properties to sensor data to allow the computer system to determine the functionality of a monitored device. Thus, amended claims 1, 8, and 15 reflect the improvement described in paragraphs 0001 and 0012 of the filed application.
Assuming arguendo that claim 1 is directed to an abstract idea, the additional elements in at least the above disclosed steps, when considered in combination, integrate the abstract idea into a practical application because the amended claim improves the
functionality of a computer system and the corresponding technical field of monitoring the functionality of devices by automatically mapping and assigning sensor data to a meter (property). Thus, in view of the analysis of Example 40 of the Subject Matter Eligibility Examples dated January 7, 2019, the amended claim 1 as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception, and amended claims 1, 8 and 15 are eligible.
In view of the above statements, amended claims 1, 8, and 18 are directed to patent eligible subject matter. Accordingly, the Applicant requests that the 101 rejection
of amended claims 1, 8, and 15, and claims 3-5, 7, 10-12, 14, and 17-22 depending
therefrom, be withdrawn.
Examiner's response:
Examiner respectfully disagrees. The analysis of claim 1 in example 40, “each of the collecting steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data an then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The claim as a whole integrates the mental process into a practical application.
On the other hand, in the instant claim1, the claim as a whole does not integrate the mental process into a practical application. It is important to note that in order for a claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally cannot be said to improve computer technology. MPEP 2106.05(a). As noted in this action, to show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method.
The claim does recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)).
The claimed “monitor” is an observation or evaluation based on a device.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person monitors a device. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
The claimed “comparing” is an observation or evaluation based the data values to pairs of threshold values to identify patterns for each of the plurality of properties, wherein each of the properties is associated with a respective one of the pairs of threshold values, and each of the patterns corresponds to a different one of the plurality of properties.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person comparing values. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
The claimed “identify” is a mathematical concept based on associations between the variables and the plurality of properties.
The claimed “generating” is an observation or evaluation based on a mapping from the first classification model and the second classification model, wherein the mapping associates a respective one of the patterns and a respective one of the associations with a respective property of the plurality of properties.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person maps classification. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “determining first confidence scores” is an observation or evaluation based comparing values of the sensor data with the patterns of the first classification model,
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines a score. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
The claimed “determining second confidence scores” is an observation or evaluation based on comparing variable data of the sensor data with the variables of the second classification model.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines a score. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
The claimed “determining” is an observation or evaluation based on the first classification model and the second classification model are accurate based on the first confidence scores and the second confidence scores.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines an accuracy based on a score. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
The claimed “determining” is an observation or evaluation based on associations between subsets of the sensor data with the plurality of properties by processing the sensor data the sensor data with combinations of the first confidence scores and the second confidence.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines an association between variables. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “determining” is an observation or evaluation based on a functionality of the device by processing the sensor data based on the associations between the subsets and the plurality of properties.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines an association between subsets and properties. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The “receiving sensor data from a sensor” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The newly added claim features do not improve the functionality of a computer or any technology.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The additional elements of 1, 8, and 15 do not amount to significantly more than the judicial exception and do not improve any computer functionalities or technology. Therefore, the additional elements do link to the abstract idea.
For at least these reasons, Examiner respectfully maintains the rejections of all claims.
No further arguments are presented for the dependent claims. Examiner respectfully maintains the rejection under 35 U.S.C. § 101 of amended independent claims 1, 8, and 15, and their claims 1,3-5, 7-8, 10-12 and 14-15 and 17-25.
Claim Interpretation
The claim interpretation 112(f) is withdrawn.
Claim Rejections - 35 U.S.C. § 103
Argument (pages 13-14)
Applicant appears to assert that The cited references do not teach or suggest amended claim 1. However, Wang538 fails to teach or suggest the first classification model is trained by determining patterns for each of the properties within data values of sample sensor data by comparing the data values to pairs of threshold values, and that each of the properties is associated with a respective one of the pairs of threshold values, as is recited by amended claim 1.
Examiner's response:
Examiner respectfully disagrees. Wang538 teaches “training a set of models to predict faults for each component using the patterns of events and historical sensor data (Abstract). He further teaches “extracting patterns of events” ([0004]
Thampy (new ground(s) of rejection) teaches the comparing the data values to pairs of threshold values” limitation as shown in the rejection above.
Thampy, in combination with Wang538, teaches wherein each of the patterns has an associated upper threshold value and an associated lower threshold value (paragraphs [0293]-[0294] …Each subset for an attribute may be defined by an upper bound threshold value (e.g., a threshold for a highest value) for the attribute, a lower bound threshold (e.g., a threshold for a lowest value) for the attribute, or a combination thereof….each subset may be defined by a threshold (e.g., upper and lower thresholds)).
Argument (page 15)
Applicant appears to assert that Wang538 in view of Tariq fails to teach or suggest
amended claim 1, and amended claim 8 and 15 that recite similar elements as amended claim 1. Accordingly, the Applicant requests that the 103 rejection of amended claims 1, 8, and 15, and claims 7, 14, 19 depending therefrom, be withdrawn.
Examiner's response:
Examiner respectfully disagrees. Wang538 in view of Thampy and further in view of Tariq teach amended claim 1, and amended claim 8 and 15 that recite similar elements as amended claim 1. Therefore, Examiner maintains the 103 rejection of amended claims 1, 8, and 15, and their dependent claims.
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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/ABABACAR SECK/Examiner, Art Unit 2122
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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