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 .
Specification
The disclosure is objected to because of the following informalities: In paragraph [0043], the example for selecting a target setting temperature has incorrect values for the first and second differences, and discloses that the second difference is smaller than the first difference, when the first difference should be smaller than the second difference.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines(“2019 PEG”).
Step 1: Independent claims 1 (A method for…), 8 (An electronic device …), and 15 (A non-transitory storage medium …) are directed towards a method, a machine, and a manufacture respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine, manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
determining whether the initial setting temperature meets production requirements based on preset conditions and the target feature data;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge if the initial setting temperature meets requirements based on preset conditions and data. This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a target setting temperature based on a set of factors. See MPEP 2106.04(a)(2)(III);
determining a target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a target setting temperature based on a set of factors. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A method for determining temperature of a reflow oven, the method comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving an initial setting temperature of each of at least one zone of the reflow oven;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g);
obtaining target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A method for determining temperature of a reflow oven, the method comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving an initial setting temperature of each of at least one zone of the reflow oven;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
obtaining target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 2
Step 2A, Prong 1: There are no additional abstract idea(s) recited in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the target feature data comprises a slope, a peak temperature, and a duration of each of the at least one zone of the reflow oven.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data performed by a generic machine learning model. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the target feature data comprises a slope, a peak temperature, and a duration of each of the at least one zone of the reflow oven.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 3
Step 2A, Prong 1: The claim recites, inter alia:
in response that the slope, the peak temperature, and the duration of each of the at least one zone of the reflow oven meet the preset conditions, determining that the initial setting temperature meets the production requirements.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge that the initial setting temperature meets requirements. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: There are no additional element(s) recited in this claim.
Step 2B: There are no additional element(s) recited in this claim.
Claim 4
Step 2A, Prong 1: The claim recites, inter alia:
calculating a loss value of the machine learning algorithm according to the historical feature data and the predictive feature data;
This limitation recites a mathematical concept of calculating a loss value. See MPEP 2106.04(a)(2)(I);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
obtaining a historical setting temperature of each of the at least one zone of the reflow oven and historical feature data corresponding to the historical setting temperature;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g);
obtaining predictive feature data by predicting the historical setting temperature through a machine learning algorithm;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
adjusting the machine learning algorithm according to the loss value until the loss value drops to a preset range; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
setting the adjusted machine learning algorithm as the predetermined machine learning model.
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
obtaining a historical setting temperature of each of the at least one zone of the reflow oven and historical feature data corresponding to the historical setting temperature;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
obtaining predictive feature data by predicting the historical setting temperature through a machine learning algorithm;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
adjusting the machine learning algorithm according to the loss value until the loss value drops to a preset range; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
setting the adjusted machine learning algorithm as the predetermined machine learning model.
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 5
Step 2A, Prong 1: The claim recites, inter alia:
calculating the loss value based on errors between the historical feature data and corresponding predictive feature data.
This limitation recites a mathematical concept of calculating a loss value based on errors. See MPEP 2106.04(a)(2)(I);
Step 2A, Prong 2: There are no additional element(s) recited in this claim.
Step 2B: There are no additional element(s) recited in this claim.
Claim 6
Step 2A, Prong 1: There are no additional abstract idea(s) recited in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
receiving data sent from a plurality of collection tools arranged at different locations of the each of the at least one zone of the reflow oven; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g);
obtaining the actual data by combining the data.
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
receiving data sent from a plurality of collection tools arranged at different locations of the each of the at least one zone of the reflow oven; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g);
obtaining the actual data by combining the data.
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 7
Step 2A, Prong 1: The claim recites, inter alia:
determining a central value of a range of a production indicator of the preset conditions; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a central value of a range. See MPEP 2106.04(a)(2)(III);
selecting the initial setting temperature corresponding to the target feature data with a smallest distance from the central value as the target setting temperature.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge which initial setting temperature has the smallest distance from the central value and decide to use that as the target setting temperature. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: There are no additional element(s) recited in this claim.
Step 2B: There are no additional element(s) recited in this claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-13, and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by The Optimal Solution of Reflow Oven Recipe based on Physics guided Machine Learning Model by Lai et al., hereafter Lai.
Regarding claim 1, Lai teaches:
receiving an initial setting temperature of each of at least one zone of the reflow oven; ((Lai) Table I and Table II; Section II.A. paragraph 2, “The temperature of each zone was preset according to Table I and Table II for two rounds of measurement.” A preset temperature is an initial setting temperature.)
obtaining target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are target feature data. Predicting the board temperature peak is predicting the initial setting temperature.)
determining whether the initial setting temperature meets production requirements based on preset conditions and the target feature data; ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds” The criteria are preset conditions. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements; ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” Measured temperatures are actual data. ‘To compare with the results of the simulation’ means that it is in response to meeting production requirements. )
determining a target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data. ((Lai) Section IV. paragraph 7 below Table VIII, “The input (X1 - X4) of the data set with the lowest score was regarded as the optimal recipe of zone 4 to zone 7” Determining an optimal recipe is determining a target setting temperature.)
Regarding claim 2, Lai teaches the material disclosed in claim 1, and additionally teaches:
wherein the target feature data comprises a slope, a peak temperature, and a duration of each of the at least one zone of the reflow oven. ((Lai) Section III.A. Fig 8. Parameters for Y2, and Y3; Section IV. Table VIII. Parameter for row 2. Solder temperature peak and solder time above liquidus are peak temperature and duration respectively. Zone temperature slope is slope.)
Regarding claim 3, Lai teaches the material disclosed in claim 2, and additionally teaches:
wherein determining whether the initial setting temperature meets production requirements based on preset conditions and the target feature data further comprises:
in response that the slope, the peak temperature, and the duration of each of the at least one zone of the reflow oven meet the preset conditions, determining that the initial setting temperature meets the production requirements. ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds; Section IV. Table VIII. Parameter for row 2; Section II. Fig. 6 and 7, zone temperature slope is an inherent function of temperature divided by time. The criteria are preset conditions. Peak temperature of the central solder and solder time above liquidus are peak temperature and duration respectively. As zone temperature slope is an inherent function of temperature divided by time, having both temperature and time as preset conditions inherently uses slope as a preset condition. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
Regarding claim 4, Lai teaches the material disclosed in claim 1, and additionally teaches:
wherein before predicting the initial setting temperature through the predetermined machine learning model, the method further comprises:
obtaining a historical setting temperature of each of the at least one zone of the reflow oven and historical feature data corresponding to the historical setting temperature; ((Lai) Section III.A. Fig. 8. The temperature inputs are historical setting temperatures. )
obtaining predictive feature data by predicting the historical setting temperature through a machine learning algorithm; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are predictive feature data. Predicting the board temperature peak is predicting the historical setting temperature.)
calculating a loss value of the machine learning algorithm according to the historical feature data and the predictive feature data; ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals as loss values.)
adjusting the machine learning algorithm according to the loss value until the loss value drops to a preset range; and ((Lai) Section III.B. paragraph 1, “If the performance of ANN is unsatisfying on the original data, neurons amount should be increased. If the performance on the training set is good, but the test set performance is bad, which might indicate an overfitting issue, then reducing the neuron number can improve performance. If the training performance is poor, then the neuron number should increase, or the training data is not enough.” Changing the neuron amount is adjusting the machine learning algorithm. Performance is based on loss value.)
setting the adjusted machine learning algorithm as the predetermined machine learning model. ((Lai) Section III.B. paragraph 1, “The ANN architecture finally adopted has three layers totally with two hidden layers as shown in Figure 9.” Adopting an adjusted machine learning algorithm is setting the adjusted machine learning algorithm as the predetermined machine learning model.)
Regarding claim 5, Lai teaches the material disclosed in claim 4, and additionally teaches:
wherein calculating the loss value of the machine learning algorithm according to the historical feature data and the predictive feature data further comprises:
calculating the loss value based on errors between the historical feature data and corresponding predictive feature data. ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals between input and predictive output as loss values. Residuals are errors between the input, historical feature data, and the corresponding predictive feature data output.)
Regarding claim 6, Lai teaches the material disclosed in claim 1, and additionally teaches:
wherein obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature further comprises:
receiving data sent from a plurality of collection tools arranged at different locations of the each of the at least one zone of the reflow oven; and ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” )
obtaining the actual data by combining the data. ((Lai) Section II. Fig. 6 and Fig. 7, Fig. 6 and Fig. 7 show the measurement data for all the plurality of locations collected from.)
Regarding claim 8, Lai teaches:
An electronic device comprising:
a storage device;
at least one processor, wherein
the storage device stores one or more programs, which when executed by the at least one processor, cause the at least one processor to: ((Lai) Fig. 1.; II.A. paragraph 1, “As shown in Fig.1, two k-type thermocouples were embedded into the front and reverse sides of the PCB board. The other two thermocouples were embedded under the solder joints at the corner and the center of a lidded package[15]. All the thermocouples were bonded to the board using thermal epoxy. The thermal profiler with a thermal barrier cover was used to collect in-transit temperature data during reflow.”)
receive an initial setting temperature of each of at least one zone of the reflow oven; ((Lai) Table I and Table II; Section II.A. paragraph 2, “The temperature of each zone was preset according to Table I and Table II for two rounds of measurement.” A preset temperature is an initial setting temperature.)
obtain target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are target feature data. Predicting the board temperature peak is predicting the initial setting temperature.)
determine whether the initial setting temperature meets production requirements based on preset conditions and the target feature data; ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds” The criteria are preset conditions. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
obtain actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements, ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” Measured temperatures are actual data. ‘To compare with the results of the simulation’ means that it is in response to meeting production requirements. )
determine a target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data. ((Lai) Section IV. paragraph 7 below Table VIII, “The input (X1 - X4) of the data set with the lowest score was regarded as the optimal recipe of zone 4 to zone 7” Determining an optimal recipe is determining a target setting temperature.)
Regarding claim 9, Lai teaches the material disclosed in claim 8, and additionally teaches:
wherein the target feature data comprises a slope, a peak temperature, and a duration of each of the at least one zone of the reflow oven. ((Lai) Section III.A. Fig 8. Parameters for Y2, and Y3; Section IV. Table VIII. Parameter for row 2. Solder temperature peak and solder time above liquidus are peak temperature and duration respectively. Zone temperature slope is slope.)
Regarding claim 10, Lai teaches the material disclosed in claim 9, and additionally teaches:
wherein the at least one processor determines whether the initial setting temperature meets production requirements based on preset conditions and the target feature data by:
in response that the slope, the peak temperature, and the duration of each of the at least one zone of the reflow oven meet the preset conditions, determining that the initial setting temperature meets the production requirements. ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds; Section IV. Table VIII. Parameter for row 2; Section II. Fig. 6 and 7, zone temperature slope is an inherent function of temperature divided by time. The criteria are preset conditions. Peak temperature of the central solder and solder time above liquidus are peak temperature and duration respectively. As zone temperature slope is an inherent function of temperature divided by time, having both temperature and time as preset conditions inherently uses slope as a preset condition. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
Regarding claim 11, Lai teaches the material disclosed in claim 8, and additionally teaches:
wherein before the at least one processor predicts the initial setting temperature through the predetermined machine learning model, the at least one processor is further caused to:
obtain a historical setting temperature of each of the at least one zone of the reflow oven and historical feature data corresponding to the historical setting temperature; ((Lai) Section III.A. Fig. 8. The temperature inputs are historical setting temperatures. )
obtain predictive feature data by predicting the historical setting temperature through a machine learning algorithm; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are predictive feature data. Predicting the board temperature peak is predicting the historical setting temperature.)
calculate a loss value of the machine learning algorithm according to the historical feature data and the predictive feature data; ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals as loss values.)
adjust the machine learning algorithm according to the loss value until the loss value drops to a preset range; and ((Lai) Section III.B. paragraph 1, “If the performance of ANN is unsatisfying on the original data, neurons amount should be increased. If the performance on the training set is good, but the test set performance is bad, which might indicate an overfitting issue, then reducing the neuron number can improve performance. If the training performance is poor, then the neuron number should increase, or the training data is not enough.” Changing the neuron amount is adjusting the machine learning algorithm. Performance is based on loss value.)
set the adjusted machine learning algorithm as the predetermined machine learning model. ((Lai) Section III.B. paragraph 1, “The ANN architecture finally adopted has three layers totally with two hidden layers as shown in Figure 9.” Adopting an adjusted machine learning algorithm is setting the adjusted machine learning algorithm as the predetermined machine learning model.)
Regarding claim 12, Lai teaches the material disclosed in claim 11, and additionally teaches:
wherein the at least one processor calculates the loss value of the machine learning algorithm according to the historical feature data and the predictive feature data by:
calculating the loss value based on errors between the historical feature data and corresponding predictive feature data. ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals between input and predictive output as loss values. Residuals are errors between the input, historical feature data, and the corresponding predictive feature data output.)
Regarding claim 13, Lai teaches the material disclosed in claim 8, and additionally teaches:
wherein the at least one processor obtains actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature by:
receiving data sent from a plurality of collection tools arranged at different locations of the each of the at least one zone of the reflow oven; and ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” )
obtaining the actual data by combining the data. ((Lai) Section II. Fig. 6 and Fig. 7, Fig. 6 and Fig. 7 show the measurement data for all the plurality of locations collected from.)
Regarding claim 15, Lai teaches:
A non-transitory storage medium having instructions stored thereon, when the instructions are executed by a processor of an electronic device, the processor is caused to perform a method, wherein the method comprises: ((Lai) Fig. 1.; II.A. paragraph 1, “As shown in Fig.1, two k-type thermocouples were embedded into the front and reverse sides of the PCB board. The other two thermocouples were embedded under the solder joints at the corner and the center of a lidded package[15]. All the thermocouples were bonded to the board using thermal epoxy. The thermal profiler with a thermal barrier cover was used to collect in-transit temperature data during reflow.”)
receiving an initial setting temperature of each of at least one zone of the reflow oven; ((Lai) Table I and Table II; Section II.A. paragraph 2, “The temperature of each zone was preset according to Table I and Table II for two rounds of measurement.” A preset temperature is an initial setting temperature.)
obtaining target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are target feature data. Predicting the board temperature peak is predicting the initial setting temperature.)
determining whether the initial setting temperature meets production requirements based on preset conditions and the target feature data; ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds” The criteria are preset conditions. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements; ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” Measured temperatures are actual data. ‘To compare with the results of the simulation’ means that it is in response to meeting production requirements. )
determining a target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data. ((Lai) Section IV. paragraph 7 below Table VIII, “The input (X1 - X4) of the data set with the lowest score was regarded as the optimal recipe of zone 4 to zone 7” Determining an optimal recipe is determining a target setting temperature.)
Regarding claim 16, Lai teaches the material disclosed in claim 15, and additionally teaches:
wherein the target feature data comprises a slope, a peak temperature, and a duration of each of the at least one zone of the reflow oven. ((Lai) Section III.A. Fig 8. Parameters for Y2, and Y3; Section IV. Table VIII. Parameter for row 2. Solder temperature peak and solder time above liquidus are peak temperature and duration respectively. Zone temperature slope is slope.)
Regarding claim 17, Lai teaches the material disclosed in claim 16, and additionally teaches:
wherein determining whether the initial setting temperature meets production requirements based on preset conditions and the target feature data further comprises:
in response that the slope, the peak temperature, and the duration of each of the at least one zone of the reflow oven meet the preset conditions, determining that the initial setting temperature meets the production requirements. ((Lai) Section IV. paragraph 3 below Fig. 10, “Two criteria were used to eliminate the unqualified data. The peak temperature of the central solder should reach 230℃, meanwhile, its time above liquidus should reach 60 seconds; Section IV. Table VIII. Parameter for row 2; Section II. Fig. 6 and 7, zone temperature slope is an inherent function of temperature divided by time. The criteria are preset conditions. Peak temperature of the central solder and solder time above liquidus are peak temperature and duration respectively. As zone temperature slope is an inherent function of temperature divided by time, having both temperature and time as preset conditions inherently uses slope as a preset condition. Eliminating unqualified data is determining whether the initial setting temperature meets production requirements.)
Regarding claim 18, Lai teaches the material disclosed in claim 15, and additionally teaches:
wherein before predicting the initial setting temperature through the predetermined machine learning model, the method further comprises:
obtaining a historical setting temperature of each of the at least one zone of the reflow oven and historical feature data corresponding to the historical setting temperature; ((Lai) Section III.A. Fig. 8. The temperature inputs are historical setting temperatures. )
obtaining predictive feature data by predicting the historical setting temperature through a machine learning algorithm; ((Lai) Section III.A. Fig 8. Parameters for Y1, Y2, and Y3. Solder temperature peak and solder time above liquidus are predictive feature data. Predicting the board temperature peak is predicting the historical setting temperature.)
calculating a loss value of the machine learning algorithm according to the historical feature data and the predictive feature data; ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals as loss values.)
adjusting the machine learning algorithm according to the loss value until the loss value drops to a preset range; and ((Lai) Section III.B. paragraph 1, “If the performance of ANN is unsatisfying on the original data, neurons amount should be increased. If the performance on the training set is good, but the test set performance is bad, which might indicate an overfitting issue, then reducing the neuron number can improve performance. If the training performance is poor, then the neuron number should increase, or the training data is not enough.” Changing the neuron amount is adjusting the machine learning algorithm. Performance is based on loss value.)
setting the adjusted machine learning algorithm as the predetermined machine learning model. ((Lai) Section III.B. paragraph 1, “The ANN architecture finally adopted has three layers totally with two hidden layers as shown in Figure 9.” Adopting an adjusted machine learning algorithm is setting the adjusted machine learning algorithm as the predetermined machine learning model.)
Regarding claim 19, Lai teaches the material disclosed in claim 18, and additionally teaches:
wherein calculating the loss value of the machine learning algorithm according to the historical feature data and the predictive feature data further comprises:
calculating the loss value based on errors between the historical feature data and corresponding predictive feature data. ((Lai) Section III.B. paragraph 1, “The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was used to map the input data and to the output.” The Levenberg-Marquardt algorithm calculates residuals between input and predictive output as loss values. Residuals are errors between the input, historical feature data, and the corresponding predictive feature data output.)
Regarding claim 20, Lai teaches the material disclosed in claim 15, and additionally teaches:
wherein obtaining actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature further comprises:
receiving data sent from a plurality of collection tools arranged at different locations of the each of the at least one zone of the reflow oven; and ((Lai) Section II.C. paragraph 1, “The measured temperatures of PCB and solder joints located at the center and corner were collected to compare with the results of the simulation as shown in Fig.6 and Fig. 7.” )
obtaining the actual data by combining the data. ((Lai) Section II. Fig. 6 and Fig. 7, Fig. 6 and Fig. 7 show the measurement data for all the plurality of locations collected from.)
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lai in view of US 6799712 B1 by Austen et al., hereafter Austen.
Regarding claim 7, Lai teaches the material disclosed in claim 1.
Lai does not explicitly disclose, but together with Austen does teach:
wherein determining the target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data further comprises:
determining a central value of a range of a production indicator of the preset conditions; and ((Austen) Column 7 lines 32-35, “In a specific embodiment, the oven fit (OF) for each profile is obtained by summing the absolute values of the ratios of the misfit (MF) of each phase to the length of the ideal phase applied to the oven” The length of the ideal phase is a central value of a range of a production indicator of the preset conditions.)
selecting the initial setting temperature corresponding to the target feature data with a smallest distance from the central value as the target setting temperature. ((Austen) Column 7 lines 41-43, and lines 59-60, “wherein the misfit of a phase is defined as the difference between the length of the phase after, alignment and the length of the ideal phase applied to the oven … In either case, the profiles are ranked according to their oven fit and the profile with the lowest oven fit value is selected as the target profile. The misfit is used in the oven fit value and is a distance from the central value. )
Austen and Lai are in the same area of invention, that being conveyor oven (reflow oven) optimal process setting determination.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date to have substituted the comparison to an ideal value selection method for determining optimal setting of temperatures, as taught by Austen, into the selection method for determining optimal setting of temperatures as taught by Lai in order to improve alignment of each of the phases of the oven. This simple substitution would produce the predictable result of the invention disclosed in claim 7.
Regarding claim 14, Lai teaches the material disclosed in claim 8.
Lai does not explicitly disclose, but together with Austen does teach:
wherein determining the target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data further comprises:
determining a central value of a range of a production indicator of the preset conditions; and ((Austen) Column 7 lines 32-35, “In a specific embodiment, the oven fit (OF) for each profile is obtained by summing the absolute values of the ratios of the misfit (MF) of each phase to the length of the ideal phase applied to the oven” The length of the ideal phase is a central value of a range of a production indicator of the preset conditions.)
selecting the initial setting temperature corresponding to the target feature data with a smallest distance from the central value as the target setting temperature. ((Austen) Column 7 lines 41-43, and lines 59-60, “wherein the misfit of a phase is defined as the difference between the length of the phase after, alignment and the length of the ideal phase applied to the oven … In either case, the profiles are ranked according to their oven fit and the profile with the lowest oven fit value is selected as the target profile. The misfit is used in the oven fit value and is a distance from the central value. )
Austen and Lai are in the same area of invention, that being conveyor oven (reflow oven) optimal process setting determination.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date to have substituted the comparison to an ideal value selection method for determining optimal setting of temperatures, as taught by Austen, into the selection method for determining optimal setting of temperatures as taught by Lai in order to improve alignment of each of the phases of the oven. This simple substitution would produce the predictable result of the invention disclosed in claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to reflow ovens, machine learning on reflow ovens, and determining optimal settings on reflow ovens.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN H LAI whose telephone number is (571)272-8628. The examiner can normally be reached Monday - Friday 7:30am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 5712524241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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D. H. L.
Examiner
Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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