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 responsive to the Amendment filed on 6/18/2025.
Claims 1, 2, 4-6, 8-12 are pending in the case. Claim(s) 3 and 7 have been cancelled.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/18/2025 has been entered.
Response to Arguments
Applicant's following arguments with respect to claim(s) 1, 2, 4-6, 8 under 35 U.S.C. § 102 have been considered but are moot because the new ground of rejection does not rely on any reference or portion of reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument:
(a) “Claim 1 as amended recites in part:
"wherein the predetermined correlation is determined based on a correlation coefficient calculated between the variable forming the branch condition before updating and other explanatory variables"
Claim 5 has been similarly amended. Support for these amendments can be found at least in paragraph [0070] of the as-filed specification.
Applicant submits that the cited references, either alone or in combination, fail to disclose or suggest the above-noted features of claims 1 and 5.”
(b) “the amended claims clarify that the prediction rule calibration system 1s applicable to determining defects in various devices”.
Applicant's following arguments with respect to claim(s) 1, 2, 4-6, 8 under 35 U.S.C. § 102 have been considered but are not persuasive
Applicant argues
“Claim 1 as amended recites in part:"wherein the predetermined correlation is determined based on a correlation coefficient calculated between the variable forming the branch condition before updating and other explanatory variables"
Claim 5 has been similarly amended. Support for these amendments can be found at least in paragraph [0070] of the as-filed specification.
Applicant submits that the cited references, either alone or in combination, fail to disclose or suggest the above-noted features of claims 1 and 5.”
....
“Shimada does not teach or suggest determining a predetermined correlation based on a correlation coefficient calculated between variables, as recited in the amended claims”.
Examiner further notes that the claimed invention does not recite “high correlation”.
Examiner respectfully disagrees.
Shimada [40, 41, 49, 50-54] teaches based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after- limited branching conditions) the determined attribute (extracted variable) in the determined attribute branch,
Shimada [43-47] teaches based on determined branching conditions being the same (correlation coefficient of zero or one)- determined or branching attribute(s) may be added to candidate list, based on analysis of reducing entropy)
Therefore Shimada sufficiently teaches the broad meaning of wherein the predetermined correlation is determined based on a correlation coefficient calculated between the variable forming the branch condition before updating and other explanatory variables.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 101 rejection of claim(s) 1, 2, 4-6, 8 have been fully considered and are not persuasive.
Regarding claim 1, applicant argues
“Applicant submits that the amended claims are not directed to an abstract idea, but rather integrate any alleged abstract idea into a practical application that provides a technical solution for determining manufacturing defects using real sensor measurements. The claims now recite concrete technical steps for processing specific types of sensor data and generating a binary defect determination output. Specifically, the amended claims recite "wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective". As-Filed Specification, paragraph [0019]. This represents a transformation of raw sensor data into actionable defect determinations that cannot be practically performed in the human mind”.
Examiner respectfully disagrees. “processes ... data to output a binary determination of whether an item is defective or non-defective” constitutes a mental process.
Further, there is are no limitations regarding the sensor data (temperature, humidity, and pressure measurements) that indicate that they cannot be analyzed using mental processes.
Applicant further argues “Furthermore, the amended claims detail a specific technical process for converting learning models into simple prediction rules and modifying those rules through an iterative optimization process. As recited in the amended claims, "the threshold optimization unit iteratively changes the threshold of branch conditions to calculate a simple prediction rule with a high evaluation metric while satisfying the calibration information". As-Filed Specification, paragraphs [0060]-[0061]. This iterative technical process for optimizing prediction rule thresholds provides technological improvements through repeated refinement to achieve high evaluation metrics while maintaining calibration requirements. Such complex data processing and iterative optimization steps go beyond mere abstract mental processes.“
Examiner respectfully disagrees. Examiner notes that, as claimed, the optimization of prediction rule thresholds appear to be practically implementable in the human mind and is understood to be a recitation of a mental process. There is no indication in the claim or in applicant’s arguments as to why they could not be performed mentally. And iterations to perform optimization would also appear to be practically implementable in the human mind and is understood to be a recitation of a mental process.
There is no indication of what is considered to be complex data processing.
Applicant further argues by reciting the Federal Circuit decision “SR/ Int’, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1376, 99 USPQ2d 1690, 1699 (Fed. Cir. 2011) (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that 'could not, as a practical matter, be performed entirely in a human's mind"). Similarly, the human mind 1s not equipped to process complex sensor data, perform iterative threshold optimizations, and generate binary defect determinations as recited in the amended claims.“
Examiner respectfully disagrees. Applicant’s claims recite “sensor data includes temperature, humidity, and pressure measurements”. Unlike the claims in the above recited decisions, the claims in the instant application do not provide any indication of “complex sensor data” as temperature, humidity and pressure measurements have broad meaning and could be integer measurements. Further, applicant does not provide any concrete argument as to what makes the claimed sensor data “complex”.
Applicant further argues “the amended claims provide a technical solution to a technical problem in the field of manufacturing defect detection....
the prediction rule calibration system and the prediction rule calibration method are widely applicable to the case of determining whether various devices or their components other than medical devices are a non-defective item or a defective item...”.
Examiner respectfully disagrees. As noted in the 101 rejection, generally linking the use of the judicial exception to a particular technological environment or field of use, does not make a claim patent eligible, and does not constitute a technological improvement - see MPEP 2106.05(h)).
Applicant further argues “The claimed invention improves upon conventional defect detection systems by providing a more accurate and efficient method of processing sensor data to determine manufacturing defects across a wide range of devices and components”.
Examiner respectfully disagrees. Applicant’s stated improvements are not directed towards hardware or the learning model and therefore do not constitute a technological improvement, but rather the stated improvements are directed towards the mental process.
Regarding claim(s) 2, 4-6, 8, applicant argues that the claims are allowable for reasons similar to those references with regards to claim 1.
For reasons similar to those discussed above with regards to claim 1, examiner asserts that claims 2, 4-6, 8 are ineligible under 35 USC 101.
The 35 U.S.C. § 101 rejection of claim(s) 1, 2, 4-6, 8 is respectfully maintained.
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, 2, 4-6, 8-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1, claims 1, 2, 4, 9-12 are directed towards a system comprising prediction rule simplification unit, branch condition search unit and threshold optimization unit, which are implemented as hardware/ processor(s) executing software modules to perform the instructions of the modules (i.e. a machine), and
claims 5, 6 and 8 are directed towards a method.
Thus, each of the claims falls within one of the four statutory categories of invention (i.e. process, machine, manufacture, or composition of matter).
Step 2A, prong one, claims 1 and 5, recite
modifies a prediction rule ... that outputs a predicted value of an object variable from a combination of values of variable learning data by a tree structure using an evaluation metric and a restriction,
processes ... data ... to output a binary determination of whether an item is defective or non-defective;
updates a part of a modified branch condition for prediction rule based on calibration information expressing a request for a prediction value or a specific branch condition; and
updates a part of a threshold of the modified prediction rule based on the calibration information, ....iteratively changes the threshold of branch conditions to calculate a simple prediction rule with a high evaluation metric while satisfying the calibration information;
wherein a section that is updated ... is limited to a branch condition specified by the calibration information,
wherein... a process of extracting a variable having a predetermined correlation with a variable forming a branch condition before updating is performed, wherein the predetermined correlation is determined based on a correlation coefficient calculated between the variable forming the branch condition before updating and other explanatory variables
wherein the branch condition that includes the extracted variable is set as a candidate of the branch condition, and
wherein the modified prediction rule is inserted into conditions requested by a user,
to determine whether... is a non-defective item or a defective item using the sensor data..
These steps of simplifying a prediction rule, updating part of a branch condition, repeatedly updating a part of a threshold of the simplified prediction rule, extracting a variable and setting a candidate appear to be practically implementable in the human mind and is understood to be a recitation of a mental process. (Examiner notes that iterations merely constitute repeating a process that is practically implementable in the mind).
Step 2A, prong two:
Claim 1 recites prediction rule simplification unit, branch condition search unit and threshold optimization unit which are implemented as hardware/ processor(s) executing software modules to perform the instructions of the modules. The units are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Claims 1 and 5, also recite
a prediction rule of a learning model wherein the learning model processes ...data (This limitation is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)),
sensor data includes temperature, humidity, and pressure measurements (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation), and
a device or component ... using sensor data, wherein the device includes medical devices and various other devices (These limitations appear to be directed to the specification of the field (different devices) that the prediction rule is used in and a specification of the type of data (sensor data) to be used, and is understood to be a field of use limitation" See MPEP 2106.05(h)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B
Claim 1 recites prediction rule simplification unit, branch condition search unit and threshold optimization unit which are implemented as hardware/ processor(s) executing software modules to perform the instructions of the modules. The units are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Claims 1 and 5, also recite
a prediction rule of a learning model wherein the learning model processes ...data (This limitation is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)),
sensor data includes temperature, humidity, and pressure measurements (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation), and
a device or component ... using sensor data, wherein the device includes medical devices and various other devices (These limitations appear to be directed to the specification of the field (different devices) that the prediction rule is used in and a specification of the type of data (sensor data) to be used, and is understood to be a field of use limitation" See MPEP 2106.05(h)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore the claim limitations, taken either alone or in combination, fail to provide an inventive concept. Thus the claims are not patent eligible.
Step 2A, Prong 1 Dependent Claims
Regarding claims 2 and 6, these claims recite wherein... a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (these steps appear to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Regarding claims 4 and 8, these claims recite wherein in the branch condition search unit and the threshold optimization unit, a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (these steps appear to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Regarding claim 8, this claim recites wherein, in the modifying of the prediction rule and the updating of the part of the threshold, a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (these steps appear to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Regarding claim 9, this claim recites processing the sensor measurements ... to extract features for defect classification (these steps appear to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Regarding claim 11, this claim recites wherein the threshold optimization unit iteratively modifies thresholds of branch conditions in the prediction rule to achieve high evaluation metrics while maintaining calibration requirements through repeated refinement (these steps appear to be practically implementable in the human mind, and repeating the steps, and is understood to be a recitation of a mental process).
Regarding claim 12, this claim recites wherein the branch condition search unit limits branch condition candidates to those including variables having the predetermined correlation with the variable forming the branch condition before updating, where the correlation is determined based on a correlation coefficient between the variable forming the branch condition before updating and other explanatory variables (these steps appear to be practically implementable in the human mind, and repeating the steps, and is understood to be a recitation of a mental process).
Step 2A, Prong 2 Dependent Claims
Regarding claim 9, this claim recites wherein the sensor data comprises temperature, humidity, and pressure sensor measurements acquired during manufacture of the medical device (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation),
wherein the learning model generates a binary defect determination output by processing the sensor measurements through multiple neural network layers to extract features for defect classification (This limitation recites a learning model with multiple neural network layers capable of feature extraction recited at a high level of generality and amounts to no more than using a learning model as a tool to perform an abstract idea, which is not indicative of integration into a practical application, and is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)).
Regarding claim 10, this claim recites wherein the calibration information comprises expert knowledge inputted as requests for prediction values or specific prediction rules that modify thresholds and branch conditions of the simplified prediction rule (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation),
Step 2B, Dependent Claims
Regarding claim 9, this claim recites wherein the sensor data comprises temperature, humidity, and pressure sensor measurements acquired during manufacture of the medical device (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation),
wherein the learning model generates a binary defect determination output by processing the sensor measurements through multiple neural network layers to extract features for defect classification (This limitation recites a learning model with multiple neural network layers capable of feature extraction recited at a high level of generality and amounts to no more than using a learning model as a tool to perform an abstract idea, which is not indicative of integration into a practical application, and is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)).
Regarding claim 10, this claim recites wherein the calibration information comprises expert knowledge inputted as requests for prediction values or specific prediction rules that modify thresholds and branch conditions of the simplified prediction rule (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation),
Accordingly, these additional elements in the dependent claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore the claim limitations, taken either alone or in combination, fail to provide an inventive concept. Thus the claims are not patent eligible.
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.
Claims 1, 2, 4-6, 8-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 5, each recite “a high evaluation metric” and
claim 11 recites “high evaluation metrics”.
The term “high” in claims 1 and 5 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention, rendering claims 1 and 5 indefinite.
Claims 1 and 5, each recite “medical devices and various other devices”. It is unclear as to what the scope of “various other devices” since neither the claims nor the specify what devices fall under “various other devices”. Therefore, it is unclear as to what the metes and bounds of the claims are, rendering claims 1 and 5 indefinite.
Claim(s) 2, 4, 6, 8-12 are dependent on claims 1 and 5, do not contain claim limitations that cure the indefiniteness of claim(s) 1 and 5, and therefore are also indefinite under 35 U.S.C. 112(b).
Claim 11 recites “while maintaining calibration requirements through repeated refinement”. It is unclear what is being “refined” and what constitutes “refinement”, rendering the claim indefinite.
For examination purposes the examiner has interpreted “****” to be “****”.
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.
Claims 1, 2, 4-6, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Shimada (US 20120084236 A1), in view of Lillo (US 20220284261 A1), Noone (US 20200166909 A1) and Chen (US 10929762 B1).
Regarding claim 1, Shimada teaches a prediction rule calibration system comprising: a processor coupled to a memory storing instructions for the processor to function as: a prediction rule simplification unit...; a branch condition search unit... and a threshold optimization unit ... (Shimada [93-104] device with processor executes functions to perform operations),
a prediction rule simplification unit that modifies a prediction rule of a learning model outputs a predicted value...by a tree structure using an evaluation metric and a restriction (Shimada [3] processes may be for machine learning model(s), Shimada [40, 41, 49, 50-52, 59, 60] based on rule stipulations (attribute and constraints), rule may be expanded to determine attribute set to apply condition to, model may use a decision tree to predict causes and decisions);
a branch condition search unit that updates a part of a modified branch condition for prediction rule based on calibration information expressing a request for a specific branch condition (Shimada [38,-0, 41, 49, 50-52, 59, 60] based on stipulations and attribute set, branching attribute is determined); and
a threshold optimization unit that updates a part of a threshold of the modified prediction rule based on the calibration information, wherein the threshold optimization unit iteratively changes the threshold of branch conditions to calculate a simple prediction rule with a high evaluation metric while satisfying the calibration information (Shimada [51-54] based on updates- constraints may be deleted, Shimada [46, 47] based on determined branching conditions and analysis of reducing entropy- branching attribute(s) may be updated to reduce entropy, Shimada [40, 41, 46, 47, 49, 50-54, (See 40-57 as a whole)] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after, ) the determined attribute in the determined attribute branch, based on determined branching conditions and analysis of reducing entropy- determined or branching attribute(s) may be updated to reduce entropy, based on analysis of reducing entropy (which would increase the inverse metric- i.e. information content) for candidate list, attributes may be updated to reduce entropy, tree is evaluated recursively over various nodes)),
wherein a section that is updated in the branch condition search unit is limited to a branch condition specified by the calibration information, wherein, in the branch condition search unit, a process of extracting a variable having a predetermined correlation with a variable forming a branch condition before updating is performed, wherein the predetermined correlation is determined based on a correlation coefficient calculated between the variable forming the branch condition before updating and other explanatory variables, wherein the branch condition that includes the extracted variable is set as a candidate of the branch condition (Shimada [40, 41, 49, 50-54] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after- limited branching conditions) the determined attribute (extracted variable) in the determined attribute branch, Shimada [43-47] based on determined branching conditions being the same (correlation coefficient of zero or one)- determined or branching attribute(s) may be added to candidate list, based on analysis of reducing entropy),
wherein the modified prediction rule is inserted into conditions requested by a user (Shimada [38, 39, 71] modified rules are output as decision tree).
Shimada does not specifically teach a predicted value of an object variable from a combination of values of variable learning data... , wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective; to determine whether a device or component is a non-defective item or a defective item using the sensor data, wherein the device includes medical devices and various other devices.
However Lillo teaches a learning model that outputs a predicted value of an object variable from a combination of values of variable learning data by a tree structure (Lillo [18, 19, 27] model may be a decision tree to determine classification score-which is probability of proper classification (predicted value), Lillo [87, 88] classification score (predicted value) is determined from input vector for data points and from training vectors (an object variable from a combination of values of variable learning data)).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Lillo of a learning model that outputs a predicted value of an object variable from a combination of values of variable learning data by a tree structure, into the invention suggested by Shimada,
so that the prediction rule simplification unit that modifies a prediction rule of a learning model outputs a predicted value...by a tree structure using an evaluation metric and a restriction of Shimada,
is a prediction rule simplification unit that modifies a prediction rule of a learning model that outputs a predicted value of an object variable from a combination of values of variable learning data by a tree structure using an evaluation metric and a restriction;
since both inventions are directed towards decision trees that may generate a predicted value, and incorporating the teaching of Lillo into the invention suggested by Shimada would provide the added advantage of allowing multi-dimensional variables to be predicted using the decision tree, and the combination would perform with a reasonable expectation of success (Lillo [18, 19, 27, 87, 88).
Shimada and Lillo do not specifically teach wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective; to determine whether a device or component is a non-defective item or a defective item using the sensor data, wherein the device includes medical devices and various other devices
However Noone teaches conditions ... to determine whether a device or component is a non-defective item or a defective item using ... sensor data, wherein the device includes medical devices and various other devices (Noone [39-41, 245, 272] decision tree may have branching conditions to determine whether there is a defect, defect may be determined based in sensor information and for medical equipment and equipment in other industries).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Noone of conditions ... to determine whether a device or component is a non-defective item or a defective item using ... sensor data, wherein the device includes medical devices and various other devices, into the invention suggested by Shimada and Lillo,
so that the modified prediction rule ... inserted into conditions requested by a user of Shimada and Lillo,
results in modified prediction rule is inserted into conditions requested by a user to determine whether a device or component is a non-defective item or a defective item using ... sensor data, wherein the device includes medical devices and various other devices;
since both inventions are directed towards decision trees that may generate a predicted value, and incorporating the teaching of Noone into the invention suggested by Shimada and Lillo would provide the added advantage of allowing modification of prediction rules used to determine defects in a variety of devices, and the combination would perform with a reasonable expectation of success (Noone [39, 40, 41, 245]).
Shimada, Lillo and Noone do not specifically teach wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective.
However Chen teaches wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective (Chen Col 4, line 48- Col 8, line 8, sensor data may include temperature, humidity, and pressure measurements, Chen Background, Col 26, line 55- Col 27, line 8, machine learning model may be used to determine whether or not item is defective, Chen Col 22, lines 18-34, model may use a tree).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Chen of wherein the learning model processes sensor data includes temperature, humidity, and pressure measurements to output a binary determination of whether an item is defective or non-defective, into the invention suggested by Shimada, Lillo and Noone; since both inventions are directed towards using sensor data to make a determination as to predict whether an item is defective, and incorporating the teaching of Chen into the invention suggested by Shimada, Lillo and Noone would provide the added advantage of allowing specific sensor information (i.e. temperature, humidity, and pressure measurements to predict whether an item is defective, and the combination would perform with a reasonable expectation of success (Chen Col 4, line 48- Col 8, line 8, Background, Col 26, line 55- Col 27, line 8, Col 22, lines 18-34).
Regarding claim 2, Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Shimada further teaches wherein, in the branch condition search unit and the threshold optimization unit, a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (Shimada [40, 41, 49, 50-54] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after) the determined attribute in the determined attribute branch).
Regarding claim 4 Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Shimada further teaches wherein, in the branch condition search unit and the threshold optimization unit, a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (Shimada [40, 41, 49, 50-54] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after) the determined attribute in the determined attribute branch).
Claim 5 is directed towards a method performing steps similar in scope to the instructions executed by the system of claim 1, and is rejected under the same rationale.
Claim(s) 6, is/are dependent on claim 5 above, is/are directed towards a method performing steps similar in scope to the instructions executed by the system of claim(s) 2, and is/are rejected under the same rationale.
Regarding claim 8, Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 5 above. Shimada further teaches wherein, in the modifying of the prediction rule and the updating of the part of the threshold, a section in which the modified prediction rule is updated is limited to a lower subtree of a branch condition specified by the calibration information (Shimada [40, 41, 49, 50-54] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after) the determined attribute in the determined attribute branch).
Regarding claim 9, Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Claim 1 further teaches wherein the sensor data includes temperature, humidity, and pressure measurements.
Shimada does not specifically teach ...sensor measurements acquired during manufacture of the medical device, and wherein the learning model generates a binary defect determination output by processing the sensor measurements through multiple neural network layers to extract features for defect classification
However Noone teaches sensor measurements acquired during manufacture of the medical device (Noone 3, 32] sensor measurements may be acquired during fabrication), and
wherein the learning model generates a binary defect determination output by processing the sensor measurements through multiple neural network layers to extract features for defect classification (Noone [3, 39, 250-252, 219, 222-224] determination is made whether or not fabricated item meets specified quality, determination may be made with multiple neural network layers that extract features for defect classification).
Regarding claim 10, Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Shimada does not specifically teach wherein the learning model is converted into a simplified prediction rule, and wherein the calibration information comprises expert knowledge inputted as requests for prediction values or specific prediction rules that modify thresholds and branch conditions of the simplified prediction rule.
However Lillo teaches wherein the learning model is converted into a simplified prediction rule (Lillo [18, 27] model may be a decision tree or random forest that is trained).
Further, Noone teaches wherein the calibration information comprises expert knowledge inputted as requests for prediction values or specific prediction rules that modify thresholds and branch conditions of the simplified prediction rule (Noone [245] model may be decision tree-based expert system that uses knowledge base with if-then rules).
Regarding claim 11 Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Shimada further teaches wherein the threshold optimization unit iteratively modifies thresholds of branch conditions in the prediction rule to achieve high evaluation metrics while maintaining calibration requirements through repeated refinement (Shimada [51-54] based on updates- constraints may be deleted, Shimada [46, 47] based on determined branching conditions and analysis of reducing entropy- branching attribute(s) may be updated to reduce entropy, Shimada [40, 41, 46, 47, 49, 50-54, (See 40-57 as a whole)] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after, ) the determined attribute in the determined attribute branch, based on determined branching conditions and analysis of reducing entropy- determined or branching attribute(s) may be updated to reduce entropy, based on analysis of reducing entropy (which would increase the inverse metric- i.e. information content) for candidate list, attributes may be updated to reduce entropy, tree is evaluated recursively over various nodes)).
Regarding claim 12, Shimada, Lillo, Noone and Chen teach the invention as claimed in claim 1 above. Shimada further teaches wherein the branch condition search unit limits branch condition candidates to those including variables having the predetermined correlation with the variable forming the branch condition before updating, where the correlation is determined based on a correlation coefficient between the variable forming the branch condition before updating and other explanatory variables (Shimada [40, 41, 49, 50-54] based on determined attribute and constraint, stipulations may be applied to nodes above or below (before and after- limited branching conditions) the determined attribute (extracted variable) in the determined attribute branch, Shimada [43-47] based on determined branching conditions being the same (correlation coefficient of zero or one)- determined or branching attribute(s) may be added to candidate list, based on analysis of reducing entropy).
Conclusion
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146