DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to amendment file on 5/22/2026, in which claims 1, 8, 15, and 22 was amended, and claims 1 – 28 was presented for examination.
3. Claims 1 – 28 are now pending in the application.
Response to Arguments
4. Applicant’s arguments with respect to claims 1 - 28 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Remarks
5. As per amended claim 1, applicants argues in substance in pages 8 – 10 that the claims are statutory under 35 U.S.C. 101 because the claims recites “selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and a selection threshold for each sample, the selection threshold for each sample being derived from a confidence output of the first ANN model for a corresponding sample” and argues that this function cannot be performed in human mind.
Examiner respectfully disagrees.
In response to applicants’ argument, Examiner respectfully responds that the limitation of “selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and a selection threshold for each sample” can actually be performed using pen and paper as explained in the detailed rejection. The process involves user manually comparing the values of first ANN and second ANN and selecting from the list of data, a sample data to be used to train the model. The claim further defines the criteria for determining the selection of the sample test data by manually comparing the first ANN model generated value to a specific value.
As per rejection of claims 22 – 28 under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth, applicants argues in substance in page 10 that the corresponding structure of the claims are disclosed in Fig.1, Fig.4, paragraph 29 – 30 and 149.
In response to applicants’ argument, Examiner respectfully responds the claims are directed to a special structure, however, the structure disclosed in Fig.1, Fig.4, paragraph 29 – 30 and 149 are generic structure.
Thus, the rejection is 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.
6.1 Claims 1 - 28 are directed are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1,
Step 1: Claim 1 recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The claim recites the limitation of
receiving, by a first artificial neural network (ANN) model and a second ANN model, a test data set including unlabeled data samples (Mathematical Process performed in human mind using a pen and paper (i.e. observation)).
the first ANN model being pretrained using a training data set, the second ANN model being an adapted model (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
generating, by the first ANN model, first estimated labels for the test data set (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
generating, by the second ANN model, second estimated labels for the test data set (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and a selection threshold for each sample (Mathematical Process performed in human mind using a pen and paper (i.e. evaluation)).
the selection threshold for each sample derived from a confidence output of the first ANN model for a corresponding sample (Mental Process performed in human mind using a pen and paper (i.e. judgement)).
and retraining the second ANN model based on the selected samples (Mathematical Process performed in human mind using a pen and paper (i.e. judgement)).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the additional elements of
selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and a selection threshold for each sample (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
the selection threshold for each sample derived from a confidence output of the first ANN model for a corresponding sample (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
and retraining the second ANN model based on the selected samples (the step is directed to evaluating information, which is understood to be significant extra-solution activity and is well understood, routine, and conventional activity of preparing data for presentation (MPEP 2106.05(d)(II)(i))))).
Although the additional element limits the identified judicial exceptions. The limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional element
selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and a selection threshold for each sample (this step is directed to evaluating information, which is understood to be significant extra-solution activity and is well understood, routine, and conventional activity of evaluating data).
the selection threshold for each sample derived from a confidence output of the first ANN model for a corresponding sample (this step is directed to evaluating information, which is understood to be significant extra-solution activity and is well understood, routine, and conventional activity of evaluating data).
and retraining the second ANN model based on the selected samples (the step is directed evaluating information, which is understood to be significant extra-solution activity, and is well understood, routine, and conventional activity of preparing data for presentation (MPEP 2106.05(d)(II)(i))))).
As explained above, the additional element are recited at a high level of generality. These elements amount to receiving, generating, and retraining are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The recitation of a computer to perform these limitations amounts to no more than mere instructions to apply the exception using a generic computer. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Thus, the claim is ineligible.
As per claim 2, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
receiving, by the retrained second ANN model, an input (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
processing, by the retrained second ANN model, the input to generate a feature representation of the input (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
and generating, by the retrained second ANN model, an inference relative to the input based on the feature representation (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
receiving, by the retrained second ANN model, an input (the step is directed to receiving information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
processing, by the retrained second ANN model, the input to generate a feature representation of the input (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
and generating, by the retrained second ANN model, an inference relative to the input based on the feature representation (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 3, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
determining first confidence values for the first estimated labels and second confidence values for the second estimated labels (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
and selecting samples of the test data set for which the second confidence values are greater than the first confidence values (Mathematical Process performed in human mind using a pen and a paper (i.e. evaluation)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
determining first confidence values for the first estimated labels and second confidence values for the second estimated labels (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
and selecting samples of the test data set for which the second confidence values are greater than the first confidence values (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 4, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
discarding unselected samples of the test data set (Mental Process performed in human mind using a pen and a paper (i.e. judgmental)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
discarding unselected samples of the test data set (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 5, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
in which the second estimated label is used as a pseudo label if corresponding test data comprises an unlabeled data sample (Mental Process performed in human mind using a pen and a paper (i.e. judgmental)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
in which the second estimated label is used as a pseudo label if corresponding test data comprises an unlabeled data sample (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 6, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
retraining the second ANN model using entropy minimization (Mathematical Process performed in human mind using a pen and a paper (i.e. judgmental)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
retraining the second ANN model using entropy minimization (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
As per claim 7, the rejection of claim 1 is incorporated.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated, the limitation of
in which the second ANN is retrained using only the selected samples (Mathematical Process performed in human mind using a pen and a paper (i.e. judgmental)).
Step 2A Prong 2: the judicial exceptions are not integrated into a practical application. The claim recites additional elements of
in which the second ANN is retrained using only the selected samples (the step is directed to evaluating information, which is understood to be significant extra-solution activity, see MPEP 2106.05(g)).
The limitation recited at high level of generality and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions. Mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The claim is not patent eligible.
Claims 8 - 14 recites an apparatus, which is one of the four statutory categories of eligible matter.
As per other analysis, claims 8 - 14 are apparatus claim corresponding to method claims 1 — 7 respectively, thus the rationale discussed above regarding claims 1 - 7 are applied to claims 8 - 14 respectively.
Claims 15 - 21 recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
As per other analysis, claims 15 - 21 are apparatus claim corresponding to method claims 1 — 7 respectively, thus the rationale discussed above regarding claims 1 - 7 are applied to claims 15 - 21 respectively.
Claims 21 - 28 recites an apparatus (means for), which is one of the four statutory categories of eligible matter.
As per other analysis, claims 21 - 28 are apparatus (means for) claim corresponding to method claims 1 — 7 respectively, thus the rationale discussed above regarding claims 1 - 7 are applied to claims 21 - 28 respectively.
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.
6. Claims 21 – 28 limitation of “means for” 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.
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.
7. Claims 1 – 28 are rejected under 35 U.S.C. 103 as being unpatentable over Kuehnel et al (US 2021/0095995 A1), in view of Hall et al (WO 2021/195688 A1), and further in view of Zhao et al (CN 115511069 A).
As per claim 1, Kuehnel et al (US 2021/0095995 A1) discloses,
A processor-implemented method comprising: receiving, by a first artificial neural network (ANN) model and a second ANN model (para.[0040]; “Input data 2 are fed to both first artificial neural network 10 as well as to second artificial neural network”).
a test data set including unlabeled data samples (para.[0044]; “measured
data relating to the inertial sensor are established. ….. the measured data are subdivided into training data and test data”, where data related to the inertial sensor is analogous to “unlabeled data samples” as claimed).
the first ANN model being pretrained using a training data set (para.[0040]; “First artificial neural network 10 utilizes linear activation functions in order to generate a prediction yc” and para.[0045]; “inputting the test data into the trained first artificial neural network”).
the second ANN model being an adapted model (para.[0040]; “Second artificial neural network 20 utilizes non-linear activation functions in order to generate a prediction Yr”)
generating, by the first ANN model, first estimated labels for the test data set (NOTE: para.[0045]; “inputting the test data into the trained first artificial neural network in order to obtain a first output value of the first artificial neural network” and para.[0048]; “First and second artificial neural network 10, 20 generate their outputs y c and y F based the model parameters obtained during the training”).
generating, by the second ANN model, second estimated labels for the test data set (NOTE: para.[0048]; “First and second artificial neural network 10, 20 generate their outputs y c and y F based the model parameters obtained during the training”)
Kuehnel does not disclose selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels and retraining the second ANN model based on the selected samples.
However, Hall et al (WO 2021/195688 A1) in an analogous art discloses,
generating, by the first ANN model, first estimated labels for the test data set (para.[0093]; “training, for each training subset, a plurality (n) of Artificial Intelligence (Al) models on two or more …. then obtain a plurality of label (e.g. n x k) estimates for each sample in an unlabeled dataset”).
generating, by the second ANN model, second estimated labels for the test data set (para.[0093]; “training, for each training subset, a plurality (n) of Artificial Intelligence (Al) models on two or more …. then obtain a plurality of label (e.g. n x k) estimates for each sample in an unlabeled dataset”).
selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels (para.[0096]; “confidence can be estimated based on the difference between the label with the maximum number of successful predictions (say label A) and the second-best label (say label B)”).
and retraining the second ANN model based on the selected samples (NOTE: para.[0028]; “retraining the plurality of trained AI models using the cleansed dataset”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the system of Kuehnel to remove noise from data used for training artificial intelligent, thereby improve the quality of information produce.
Neither Kuehnel et al (US 2021/0095995 A1) nor , Hall et al (WO 2021/195688 A1) specifically disclose a selection threshold for each sample, the selection threshold for each sample derived from a confidence output of the first ANN model for a corresponding sample.
However, Zhao et al (CN 115511069 A) in an analogous art discloses,
selecting samples of the test data set based on a confidence difference between the first estimated labels and the second estimated labels (pg.6 lines 8 – 10; “obtain a total loss function value of the second neural network based on the output value of the first neural network, the output value of the second neural network, the target value corresponding to the training data sample, and the weight distribution of the first neural network”).
and a selection threshold for each sample, the selection threshold for each sample derived from a confidence output of the first ANN model for a corresponding sample (pg.9 lines 8 – 10; “training sample data through the first neural network and the second neural network, respectively, and then calculate the total loss function value based on the inference calculation results of the two neural networks, the real target values corresponding to the training data samples, and the weight distribution of the first neural network”).
and retraining the second ANN model based on the selected samples (pg.9 lines 15 – 16; “retraining the second neural network through the training data samples”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate the process of performing pruning operation of first neural network to obtain second neural network of the system of Zhao into estimation of confidence between labels generated by different artificial intelligence models of Hall to improve the accuracy of the second neural network on data processing.
As per claim 2, the rejection of claim 1 is incorporated and further Kuehnel et al (US 2021/0095995 A1) discloses,
further comprising: receiving, by the retrained second ANN model, an input (para.[0047]; “second artificial neural network 20 is trained again in step S65 using the training data”).
processing, by the retrained second ANN model, the input to generate a feature representation of the input, and generating, by the retrained second ANN model, an inference relative to the input based on the feature representation (para.[0047]; “A second output accuracy value is established based on a comparison result between the third output value and the test data …….second artificial neural network 20 is trained again in step S65 using the training data”).
As per claim 3, the rejection of claim 1 is incorporated and further Hall et al (WO 2021/195688 A1) discloses,
further comprising: determining first confidence values for the first estimated labels and second confidence values for the second estimated labels (para.[0096]; “confidence can be estimated based on the difference between the label with the maximum number of successful predictions (say label A) and the second-best label (say label B)”).
and selecting samples of the test data set for which the second confidence values are greater than the first confidence values (para.[0028]; “retraining the plurality of trained AI models using the cleansed dataset”).
. Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the Kuehnel to remove noise from data used to train artificial intelligence, thereby improving the quality of information generated.
As per claim 4, the rejection of claim 3 is incorporated and further Hall et al (WO 2021/195688 A1) discloses,
further comprising discarding unselected samples of the test data set (para.[0024]; “removing or relabeling samples in the training dataset which are consistently wrongly predicted by comparing the predictions with a consistency threshold”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the Kuehnel to remove noise from data used to train artificial intelligence, thereby improving the quality of information generated.
As per claim 5, the rejection of claim 3 is incorporated and further Hall et al (WO 2021/195688 A1) discloses,
in which the second estimated label is used as a pseudo label if corresponding test data comprises an unlabeled data sample (para.[0036]; “assigning a label for each sample in the unlabeled dataset by using a voting strategy to combine the plurality of estimated labels for the sample”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the Kuehnel to remove noise from data used to train artificial intelligence, thereby improving the quality of information generated.
As per claim 6, the rejection of claim 1 is incorporated and further Hall et al (WO 2021/195688 A1) discloses,
further comprising retraining the second ANN model using entropy minimization (para.[0028]; “retraining the plurality of trained AI models using the cleansed dataset”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the Kuehnel to remove noise from data used to train artificial intelligence, thereby improving the quality of information generated.
As per claim 7, the rejection of claim 1 is incorporated and further Hall et al (WO 2021/195688 A1) discloses,
in which the second ANN is retrained using only the selected samples (para.[0028]; “retraining the plurality of trained AI models using the cleansed dataset”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate estimation of confidence between labels generated by different artificial intelligence models of the system of Hall into training of data using different artificial intelligence of the system of Kuehnel to remove noise from data used to train artificial intelligence, thereby improving the quality of information generated.
Claims 8 – 14 are apparatus claim corresponding to method claims 1- 7 respectively, and rejected under the same reason set forth in connection to the rejection of claims 1 = 7 respectively above.
Claims 15 - 21 are non-transitory computer-readable medium claim corresponding to method claims 1 - 7 respectively, and rejected under the same reason set forth in connection to the rejection of claims 1 - 7 respectively above.
Claims 22 - 28 are apparatus (means for) claim corresponding to method claims 1- 7 respectively, and rejected under the same reason set forth in connection to the rejection of claims 1 - 7 respectively above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUGUSTINE KUNLE OBISESAN whose telephone number is (571)272-2020. The examiner can normally be reached 9:00am - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
8/10/2026
Prosecution Insights