DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to Applicant Amendments and Remarks filed on 04/22/2026 for application number 18/330,990 filed on 06/07/2023, in which claims 1-28 were originally presented for examination.
Claim(s) 1, 9, 17 & 21 is/are currently amended, and no new or cancelled claims. Accordingly, claims 1-28 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for priority of provisional patent application No. 63,355,472 filed on 06/24/2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 11/07/2023 has/have been received and considered.
Examiner Notes
Examiner cites particular paragraphs (or columns and lines) in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. See MPEP §2111.01.
Response to Arguments
Arguments filed on 04/22/2026 have been fully considered and are addressed as follows:
Regarding the Claim Interpretation under 35 USC §112(f): Interpretation of claim(s) under 35 USC §112(f), is/are maintained, in view of Applicant’s Remarks. See page 7 of Remarks
Regarding the claim rejections under 35 USC §101: The rejections of claim(s) 1-28, for being directed to an abstract idea without significantly more, is/are maintained, as neither the amended base claims and/or applicant’s argument(s) filed on 04/22/2026 has/have overcome the rejection(s) as recited in the Non-Final Office Action mailed on 02/04/2026, and outlined below.
Examiner Notes that Applicant's amendment necessitated the new ground(s) of 101 rejection presented below.
Applicant asserts that:
“The Examiner characterizes the claims as directed to scaling layers of an ANN model using mathematical relationships. Applicant respectfully submits that the claims, read as a whole, do not recite a mental process or mathematical concept within the meaning of the 2019 PEG. … These operations are not performable in the human mind and are not directed to organizing human activity …
Accordingly, Step 2A, Prong One: the claims do not recite a judicial exception, and the eligibility analysis should conclude here”
(see Remarks pages 7-8; emphasis added)
The examiner respectfully disagrees. The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes”, because under Broadest Reasonable Interpretation (BRI), the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). Examiner notes that said operations; i.e. quantizing and/or scaling are reasonably performed in human mind and/or using a pen and paper.
Applicant asserts that:
“The published specification describes the quadapter as a structural module inserted into the ANN architecture that produces a concrete, transformed artifact
The specification further describes that the adapted ANN model preserves the initial model parameters of the first and second linear layers while the leamable quantization module parameter adapts to reduce quantization error …
Accordingly, Step 2A, Prong Two: the claims integrate any alleged exception into a practical application, and the rejection should be withdrawn”
(see Remarks pages 8-9; emphasis added)
The examiner respectfully disagrees. Examiner notes that the claim(s) is/are evaluated as a whole, it/they does/do not directedly or implicitly integrate the recited judicial exception into a practical application. See MPEP 2106.04(d)
Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant asserts that:
“The additional elements, viewed as an ordered combination, are not WURC. The specification describes the quadapter as a novel structural insertion …
Accordingly, the claims are eligible at Step 2B, and Applicant respectfully requests withdrawal of the rejection of claims 1-28 under 35 U.S.C. § 101”
(see Remarks pages 9-10; emphasis added)
The examiner respectfully disagrees. Examiner notes that the claim(s), as a whole, does/do not amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, add(s) an inventive concept to the claim(s). See MPEP 2106.05
Although the claim(s) is/are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
For at least the foregoing reasons, and the rejections outlined below, rejection(s) under 35 USC §101 is/are maintained.
Regarding the claim rejections under 35 USC §102(a)(1): Applicant’s arguments regarding the rejections of the claim(s) as being clearly anticipated by the prior art of Nagel (US-2020/0302299-A1) have been fully considered. However, those arguments are not persuasive.
Applicant asserts that:
“Independent claim 1 has been amended …
Applicant respectfully submits that Nagel does not disclose at least this element, and accordingly does not anticipate the amended independent claims.
Accordingly, Nagel fails to disclose all elements of independent claim 1. Therefore, independent claim 1 is patentably distinguishable over Nagel”
(see Remarks pages 10-12; emphasis added)
The examiner respectfully disagrees. Examiner notes that Applicant’s arguments are all focusing on new limitations added to the amended base claims 1, 9, 17 & 21 apparently to overcome the current anticipation rejection under §102(a)(1) as recited in the Non-Final Office Action mailed on 02/04/2026. Those arguments are rendered moot in light of the new grounds of rejection outlined below, which were necessitated by the applicant’s amendment. Although the examiner does not necessarily agree with the applicant arguments, and in the interest of concluding the prosecution, a new grounds for rejection(s) is/are introduced to teach some of the amended limitations as outlined in the prior art rejections below.
For at least the foregoing reasons, and the rejections outlined below, the prior art rejections are maintained.
Claim Rejections – 35 USC §101
35 USC §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.
Claim(s) 1-28 is/are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106 (III)
The determination of whether a claim recites patent ineligible subject matter is a two-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), See MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: See MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP 2106.05
Claim 1. A processor-implemented method, comprising:
receiving an artificial neural network (ANN) model, the ANN model having a plurality of channels of target activations [pre-solution activity/ particular technological environment or field of use without telling how it is accomplished]; and
incorporating a quantization module between a first linear layer of the ANN model and a second linear layer of the ANN model to generate an adapted ANN model configured for deployment on a resource-constrained device, wherein the quantization module is distinct from the first linear layer and the second linear layer and the quantization module [mathematical process/step]
scales a first set of weights and biases of the first linear layer based on a learnable quantization module parameter [mathematical process/step] and
scales a second set of weights of the second linear layer based on an inverse of the learnable quantization module parameter [mathematical process/step].
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method that is including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of (1) incorporating a quantization module between a first linear layer of the ANN model and a second linear layer of the ANN model to generate an adapted ANN model configured for deployment on a resource-constrained device, wherein the quantization module is distinct from the first linear layer and the second linear layer and the quantization module (2) scales a first set of weights and biases of the first linear layer based on a learnable quantization module parameter and (3) scales a second set of weights of the second linear layer based on an inverse of the learnable quantization module parameter.
The claim recite a method for scaling layers of an artificial neural network (ANN) model by incorporating quantization module and based on a learnable quantization module parameter and/or an inverse of the learnable quantization module parameter using mathematical techniques. In other words, the claimed method simply describes the concept of scaling set of weights and/or biases by using said parameter and/or its inverse through mathematical relationships. The incorporating and scaling merely employ(s) mathematical relationships to manipulate existing quantization module without limit to any use of the ANN model. This idea is similar to the basic concept of manipulating information using mathematical relationships (e.g., converting numerical representation in Benson or calculating parameters in Grams), which has been found by the courts to be an abstract idea. Therefore, the claim is directed to an abstract idea. These limitation(s), as drafted, is/are simple processes that, under its broadest reasonable interpretation, employ(s) mathematical relationships to manipulate existing quantization module but for the recitation of “at least one processor” and “memory” (in base claims 9 & 17). That is, other than reciting “processor”/ “memory” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the processor/ memory language, the claim encompasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of by a controller does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional step and/or element of receiving an artificial neural network (ANN) model, the ANN model having a plurality of channels of target activations. The receiving are recited at a high level of generality (i.e. as a general means of gathering data model for use in the incorporating and scaling steps), and amount to mere data gathering, which is a form of insignificant extra-solution activity.
The “artificial neural network (ANN) model … having a plurality of channels of target activations” element also recited at a high level of generality, and amounts to mere linking use of a judicial exception to a particular technological environment or field of use without telling how it is accomplished. Examiner notes, in base claims 9 & 17, the “at least one processor” and “memory” merely describes how to generally and merely automates the incorporating and scaling steps, therefore acting as a generic computer to perform the abstract idea and/ or “apply” the otherwise mental judgements using a generic or general-purpose processor, i.e. a computer. The processor/ memory system is recited at a high level of generality and is merely automates the incorporating and scaling steps.
Accordingly, even in combination, these additional elements/ steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving step(s), and the artificial neural network (ANN) model element(s) were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
MPEP 2106.05(d)(II), indicate that mere receiving or obtaining data and/or information related elements over a network, i.e., ANN model is/are a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). The “artificial neural network (ANN) model” element that has “a plurality of channels of target activations” also recited at a high level of generality, and amounts to mere linking use of a judicial exception to a particular technological environment or field of use without telling you how it is accomplished. The background section of Applicant’s Specification recites that the artificial neural network is a conventional neural networks, and the Specification does not provide any indication that the said artificial neural network (ANN) model is anything other than a conventional neural network model.
The processor/ memory of base claims 9 & 17 are merely describes how to generally and merely automates the incorporating & scaling steps, therefore acting as a generic computer to perform the abstract idea and/or “apply” the otherwise mathematical concept using a generic or general-purpose processor, i.e. a computer.
Accordingly, a conclusion that the receiving step(s), and the artificial neural network (ANN) model element(s) is/are well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible.
Independent apparatus, non-transitory computer-readable medium & other apparatus claims 9, 17 & 21, respectively, recite similar limitations performed by the method of claim 1. Therefore, claims 9, 17 & 21 are rejected under the same rationales used in the rejections of claim 1 as outlined above.
Dependent claims 2-8, 10-16, 18-20 & 22-28 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application and amounts to mere input and/or output data manipulation. Therefore, dependent claims 2-8, 10-16, 18-20 & 22-28 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Thus, claims 1-28 are ineligible under 35 USC §101
Claim Rejections - 35 USC §102
In the event the determination of the status of the application as subject to AIA 35 USC §102 and §103 (or as subject to pre-AIA 35 USC §102 and §103) is incorrect, any correction of the statutory basis 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 USC §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, 8-14, 16-26 & 28 is/are rejected under 35 USC §102(a)(1) as being clearly anticipated by PG Pub. No. US-2020/0302299-A1 by Nagel et al. (hereinafter “Nagel”), which is found in the IDS submitted on 11/07/2023
As per claim 1, Nagel discloses a processor-implemented method (Nagel, in at least ¶¶3, 15, 33 & 54, discloses a method for performing quantization in neural networks), comprising:
receiving an artificial neural network (ANN) model, the ANN model having a plurality of channels of target activations (Nagel, in at least ¶¶3, 15, 33 & 54, discloses a method for performing quantization in neural networks, and to apply scaling in this manner to many channels/layers in the network); and
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308
542
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where
S [Wingdings font/0xE0] a diagonal matrix in which the element, and
Sii [Wingdings font/0xE0] a nonnegative scaling factor for channel i
Nagel’s specified equation(s) ¶54
incorporating a quantization module between a first linear layer of the ANN model and a second linear layer of the ANN model to generate an adapted ANN model configured for deployment on a resource-constrained device, wherein the quantization module is distinct from the first linear layer and the second linear layer and the quantization module scales a first set of weights and biases of the first linear layer based on a learnable quantization module parameter and scales a second set of weights of the second linear layer based on an inverse of the learnable quantization module parameter (Nagel, in at least ¶¶3, 15, 33 & 54, discloses a method for performing quantization in neural networks, including (1) cross-layer rescaling to reduce quantization errors due to layer weights that vary widely or include outliers, (2) equalizing the ranges of weight tensors or channel weights within a layer of a neural network by scaling each of the output channels weights by a corresponding scaling factor, and scaling the next layer's corresponding input channel weights by the inverse of the corresponding scaling factor, (3) techniques used to determine the corresponding scaling factor, including differential learning using Straight Through Estimator (STE) methods and a local or global loss, and/or by using a metric for the quantization error and a black box optimizer that minimizes the error metric with respect to the scaling parameters. Nagel further discloses determining that consecutive layers in the neural network have a linear relationship, and shift scaling factors from one layer to another to improve quantization performance, wherein the linear relationship between layers may be described generally including bias terms. See specified equation(s), which has/have reproduced above for convenience, providing a scaling for a first such layer followed by a rescaling using an inverse of the scaling factor for the subsequent layer. Nagel, in at least ¶¶14-16, 21 & 69-70, further discloses, enabling execution on target computing devices with a processor architecture having a bit-width smaller than the bit-width architecture of the training computing device, and using cross layer rescaling and quantization to transform the neural network into a form suitable for execution on a small bit-width computing device, wherein the term “computing device” is used herein to refer to any one or all of servers, personal computers, mobile devices, cellular telephones, smartphones, wherein many application development platforms and server computing devices utilize a large bit-width format (e.g., Floating point or FP32) for data, and neural networks are often trained in powerful servers using large bit-width values. On the other hand, resource constrained devices, such as IoT devices, smartphones [i.e., deployment on a resource-constrained device] and other mobile devices, may implement a small bit-width (e.g., INTS) architecture, and an increasing number of hardware manufactures have begun adding support for optimized eight-bit integer operations that better support neural networks and other systems that sequentially perform a large number of relatively simple computations).
As per claim 2, Nagel discloses the processor-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Nagel further discloses comprising scaling a target activation in each channel of the plurality of channels of target activations based on a learnable quantization parameter (Nagel, in at least ¶¶26-33, discloses activation re-quantization loss, wherein techniques used to determine the corresponding scaling factor, including differential learning using Straight Through Estimator (STE) methods and a local or global loss, and/or by using a metric for the quantization error and a black box optimizer that minimizes the error metric with respect to the scaling parameters).
As per claim 3, Nagel discloses the processor-implemented method of claim 2, accordingly, the rejection of claim 2 above is incorporated.
Nagel further discloses comprising determining the learnable quantization parameter based on a task loss of the adapted ANN model (Nagel, in at least Fig. 1B, and ¶¶26-33, discloses types of loss in the fixed-point quantized pipeline, e.g., input quantization loss, weight quantization loss, runtime saturation loss, activation re-quantization loss, and possible clipping loss for certain non-linear operations, wherein techniques used to determine the corresponding scaling factor, including differential learning using Straight Through Estimator (STE) methods and a local or global loss, and/or by using a metric for the quantization error and a black box optimizer that minimizes the error metric with respect to the scaling parameters).
As per claim 4, Nagel discloses the processor-implemented method of claim 2, accordingly, the rejection of claim 1 above is incorporated.
Nagel further discloses in which the learnable quantization parameter and the learnable quantization module parameter are jointly determined (Nagel, in at ¶¶26-33, discloses performing post-training quantization of weights and activations of a trained neural network model without asymmetric min-max quantization. A computing device may be configured to map large bit-width (e.g., FP32, etc.) weights and activations to small bit-width (e.g., INTS) representations).
As per claim 5, Nagel discloses the processor-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Nagel further discloses comprising re-training the adapted ANN model using a quantization-aware training process (Nagel, in at ¶¶26-33, discloses performing post-training quantization of weights and activations of a trained neural network model).
As per claim 6, Nagel discloses the processor-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Nagel further discloses in which the ANN model comprises a transformer neural network model (Nagel, in at Fig. 5, and ¶¶14, 24 & 69-70, discloses trained neural networks are transformed to lower precision through a process known as quantization so that the weight tensors within the neural network. Nagel further discloses using cross layer rescaling and quantization to transform the neural network into a form suitable for execution on a small bit-width computing device).
As per claim 8, Nagel discloses the processor-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Nagel further discloses comprising operating the adapted ANN model to generate an inference based on the learnable quantization module parameter (Nagel, in at ¶¶17, 23, 25, 37, 40, 50 & 72, discloses process that controls a function of a computing device or generates a neural network inference, wherein neural network quantization techniques are used to reduce size, memory access, and computation requirements of neural network inference by using small bit-width values (e.g., INTS values) in the weights and activations of a neural network model. Nagel further discloses the output layer 204 includes a node 242 that operates on the inputs augmented with the weight factors to produce an estimated value 244 as output or neural network inference).
Claim Rejections - 35 USC §103
In the event the determination of the status of the application as subject to AIA 35 USC §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis 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 USC §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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claim(s) 7, 15 & 27 is/are rejected under 35 USC §103 as being unpatentable over Nagel (US-2020/0302299-A1) in view of IEEE Publication with DOI: 10.1109/CAC53003.2021.9728246 to Li et al. (hereinafter “Li”), which are both found in the IDS submitted on 11/07/2023
As per claim 7, Nagel discloses the processor-implemented method of claim 6, accordingly, the rejection of claim 6 above is incorporated.
While Nagel clearly discloses to transform the neural network, it is silent on claim 7 limitations. Li, in at least Page 7282 that is was old and well known at the time of filing in the art of neural network systems, teaches in which the transformer neural network model comprises one of a bi-directional encoder representations from transformers (BERT), a robustly optimized BERT approach (RoBERTa)-based transformer, an XLNet-based transformer, a Transformer-XL-based transformer, or a generative pre-trained transformer (GPT) (Li, , in at Page 7282, discloses the quantization of BERT, being an effective Transformer-based model that can handle various downstream NLP tasks after being pretrained on a large dataset and finetuned on specific tasks).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify Nagel in view of Li with a reasonable expectation of success, as both/ all inventions are directed to the same field of endeavor – neural network systems - and the combination would handle various downstream tasks with no severe performance drop (see at least Li’s ¶¶2782).
As per claims 9-16, the claims is/are directed towards apparatus that recite(s) similar limitations performed by the method(s) of claim(s) 1-8. The cited portions of Nagel & Li used in the rejection(s) of claim(s) 1-8 discloses/ teaches the same apparatus limitations of claim(s) 9-16. Therefore, claim 9-14 & 16 is/are rejected under the same rationales used in the rejections of claims 1-8 as outlined above.
As per claims 17-20, the claims is/are directed towards non-transitory computer-readable medium(s) that recite(s) similar limitations performed by the method(s) of claim(s) 1-3 & 8. The cited portions of Nagel used in the rejection(s) of claim(s) 1-3 & 8 discloses the same apparatus limitations of claim(s) 17-20. Therefore, claim 17-20 is/are rejected under the same rationales used in the rejections of claims 1-3 & 8 as outlined above.
As per claims 21-28, the claims is/are directed towards apparatus that recite(s) similar limitations performed by the method(s) of claim(s) 1-8. The cited portions of Nagel and/or Li used in the rejection(s) of claim(s) 1-8 discloses/ teaches the same apparatus limitations of claim(s) 21-28. Therefore, claim 21-28 is/are rejected under the same rationales used in the rejections of claims 1-8 as outlined above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See previously mailed PTO-892 form.
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 extension fee 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 Tarek Elarabi whose telephone number is (313)446-4911. The examiner can normally be reached on Monday thru Thursday; 6:00 AM - 4:00 PM EST.
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/Tarek Elarabi/Primary Examiner, Art Unit 3661
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