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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered. Claims 1-15 are pending. Claims 1 and 10 are currently amended.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claims 1 and 11
Step 1 -- whether the claim falls within any statutory category. See MPEP 2106.03
Claim 1 is drawn to a method claim and claim 11 is drawn to a computer readable storage medium claim. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, the claim is directed to a method for processing data, comprising:
The limitations of “obtaining a group of data to be quantized for a machine learning model, wherein the group of data to be quantized are neural network parameters that comprise neurons or weights”, “quantizing the group of data to be quantized respectively through using a plurality of pairs of truncation thresholds to determine a plurality of groups of quantized data, wherein each pair of truncation thresholds in the plurality of pairs of truncation thresholds includes a truncation positive value and a truncation negative value that are symmetrical”, “obtaining an absolute value of each group of quantized data in the plurality of groups of quantized data and a first mean value of the absolute value obtained for every group of quantized data in the plurality of groups of quantized data”, “obtaining an absolute value of the group of data to be quantized and a second mean value of the absolute value obtained for the group of data to be quantized”, “selecting a pair of truncation thresholds from the plurality of pairs of truncation thresholds based on differences between the mean value and the second mean value obtained for each group in the plurality of groups of quantized data, wherein the selected pair of truncation thresholds for the group of data to be quantized corresponds to a smallest difference among the differences to reduce precision loss during quantization” and “quantizing the group of data to be quantized using the selected pair of truncation thresholds to obtain the group of quantized data.”
These limitations are directed towards the abstract idea of obtaining data, quantizing the data using pairs of truncation thresholds, and selecting a pair of truncation thresholds which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. “Obtaining” and “selecting” steps encompass observing a data set and performing an evaluation to identify data. “Quantizing” step encompasses reducing/evaluating data by using pairs of truncation thresholds. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
The limitations of “each pair of truncation thresholds includes a truncation positive value and a truncation negative value that are symmetrical” and “a difference between a mean value of an absolute value of each group of quantized data in the plurality of groups of quantized data and a mean value of an absolute value of the group of data to be quantized to quantize the group of data to be quantized” are directed towards the abstract idea of a mathematical relationship, specifically organizing information and manipulating information through mathematical correlations (see MPEP § 2106.04(a)(2), subsection I, A).
Independent claim 11 is a computer readable storage medium claim reciting similar limitations of claim 1 and is directed towards the abstract idea for similar reasons.
Step 2A Prong 2 -- whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding independent claim 1, as recited in steps “obtaining a group of data”, “quantizing the group of data”, “obtaining an absolute value of each group of quantized data”, “obtaining an absolute value of the group of data”, “selecting a pair of truncation thresholds” and “quantizing the group of data” are mere data gathering and evaluating recited at a high level of generality, and thus are insignificant extra-solution activity. Similarly, this claim recites additional element of “a neural network” and “a machine learning model”, which provides nothing more than mere instructions to implement an abstract idea on a generic computer. In addition, this claim recites “wherein the selected pair of truncation thresholds for the group of data to be quantized corresponds to a smallest difference among the differences to reduce precision loss during quantization”, which is the improvement in the abstract idea itself, but do not integrate the judicial exception into a practical application. Thus, the claim fails to integrate the exception into a practical application because it cannot provide an inventive concept. Claim 1 is ineligible.
Regarding independent claim 11 is drawn to a computer readable storage medium claim reciting similar limitations of claim 1 and is rejected under the same rationale. Claim 11 also recites additional elements of “computer readable storage medium”, which amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It recites a generic computer or generic computer components that merely act as a tool on which the method operates, and thus, fails to integrate the exception into a practical application because it cannot provide an inventive concept. Claims 11 is ineligible.
Regarding dependent claims 2-9 and 12-15
Claims 2-9 and 12-15 merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claims 1 and 11, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mathematical relationships and mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-9 and 12-15 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. § 101.
Step 1 -- whether the claim falls within any statutory category. See MPEP 2106.03
Claims 2-9 and 12-15 are drawn to method and computer readable storage medium claims, respectively. Therefore, each of these claims fall under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding claim 2, this claim recites limitation of “determining a maximum absolute value of all data in the group of data to be quantized” and “determining the pairs of truncation threshold based on the maximum absolute value”. These limitations are directed towards the abstract idea of a mathematical relationship and do not integrate the recited judicial exception into a practical application, besides, organizing information and manipulating information through mathematical correlations which can also perform manually by a human being.
Regarding claim 3, this claim recites “determining a first … search order”, “quantizing …positive value”, and “determining… to be quantized.” These limitations are directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion. “Determining” steps encompass observing a data set and performing an evaluation to identify data while Quantizing” step encompasses reducing/evaluating data by using pairs of truncation thresholds. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
Regarding claim 4, this claim recites “incrementing the current search order”, “determining a second truncation … search order”, “quantizing the group of data …positive value”, and “determining a second difference … quantized.” These limitations are similar with the limitations cited in claim 3 and directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion. “Incrementing” step is directed towards the abstract idea of a mathematical relationship, specifically organizing information and manipulating information through mathematical correlations. “Determining” steps encompass observing a data set and performing an evaluation to identify data. “Quantizing” step encompasses reducing/evaluating data by using pairs of truncation thresholds. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
Regarding claim 5, this claim recites “determining, … absolute value” and “selecting a pair … truncation thresholds.” These limitations are directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion. “Determining” and “selecting” steps encompass observing a data set and performing an evaluation to identify data. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
Regarding claim 6, this claim recites “determining a truncation search range…thresholds”, “determining a plurality of new pairs … search range”, “quantizing the group … quantized data” and “selecting a new pair ..quantized data”. These limitations are directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion. Determining” and “selecting” steps encompass observing a data set and performing an evaluation to identify data. “Quantizing” step encompasses reducing/evaluating data by using pairs of truncation thresholds. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
Regarding claim 7, this claim recites “determining a maximum…to be quantized”, “determining … maximum absolute value” and “quantizing …quantized data.” This limitation is directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion. “Determining” steps encompass observing a data set and performing an evaluation to identify data. “Quantizing” step encompasses reducing/evaluating data by using pairs of truncation thresholds. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
Regarding claim 8, this claim recites “executing the following actions … is met”, “selecting the pair … truncation thresholds”, “determining whether a difference … predetermined threshold”, “stopping the iterative execution … the predetermined threshold”, and “redetermining the three pairs … truncation thresholds.” These limitations are directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion.
Regarding claim 9, this claim recites “quantize the group of data … obtain quantized data”, and “setting a value … positive value”, and “setting a value that is less … negative value”. These limitations are directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion.
Regarding claims 12-15, the claims 12-15 recites similar limitations of claims 2-5, respectively. Therefore, they are analyzed similar to claims 2-5 above.
Step 2A Prong 2 -- whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding claims 2-8, the claims as a whole do not integrate the judicial exception into a practical application because there are no additional elements recited in the claims beyond the judicial exception. Thus, the claims fail to integrate the exception into a practical application and do not provide an inventive concept. The claims 2-8 are ineligible.
Regarding claim 9, the claim recites additional step “inputting the obtained quantized data to the neural network model for processing”, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. The “neural network model for processing” is at best the equivalent of merely adding the words “apply it” to the judicial exception and cannot provide an inventive concept. Claim 9 is ineligible.
Regarding claims 12-15, the claims recites additional step of “computer storage medium”, which is best the equivalent of merely adding the words “apply it” to the judicial exception cannot provide an inventive concept. Claims 12-15 are ineligible.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. (Jung), US Patent Application Publication No. US 2019/0347550 A1, in view of Yu et al. (Yu), NPL “Neural network generating and image processing method and device, platform and electronic device”, published on 2021-03-23, but filed on 2019-01-24, Document ID, CN-109800865-B, pages: 26.
As to independent claim 1, Jung discloses a method for processing data, comprising:
obtaining a group of data to be quantized for a machine learning model, wherein the group of data to be quantized are neural network parameters that comprise neuron or weights (Abstract and paragraph [0010]: input an input activation map (group of data) into a current layer included in the neural network, output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a firs representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter):
quantizing the group of data to be quantized respectively through using a plurality of pairs of truncation thresholds to determine a plurality of groups of quantized data, (Abstract: output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a first representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter; paragraph [0006]: the activation quantization parameter may include a first threshold and a second threshold related to the output activation map, wherein the first threshold may indicate an upper limit of an activation map section with respect to the output activation map, and the second threshold indicates a lower limit of the activation map section);
obtaining an absolute value of each group of quantized data in the plurality of groups of quantized data and a first mean value of the absolute value obtained for every group of quantized data in the plurality of groups of quantized data (paragraph [0007]: the activation quantization parameter may include a first median value and a first difference value with respect to the output activation map, wherein the first difference value indicates a half of a difference between a first threshold and a second threshold, and the first median value indicates a middle value of the first threshold and the second threshold, wherein the first threshold indicates an upper limit of an activation map section with respect to the output activation map, and the second threshold indicates a lower limit of the activation map section);
obtaining an absolute value of the group of data to be quantized and a second mean value of the absolute value obtained for the group of data to be quantized (paragraphs [0156]-[0157]: the distribution state of the weight is represented with a median value of the section and in interval of the section, instead of the upper limit and the lower limit of the section. Here, a second median value and a second difference value of an absolute value of a weight of a current layer are used, wherein the second difference value indicates a half of a difference between the third threshold and the fourth threshold, and the second median value indicates a middle of the third threshold and the fourth threshold);
selecting a pair of truncation thresholds from the plurality of pairs of truncation
thresholds based on differences between the first mean value and the second mean value obtained for each group in the plurality of groups of quantized data (paragraph [0153]: the weight quantization parameter (for weight) may include a third threshold and a fourth threshold (a pair of thresholds) of an absolute value of a weight of each of a plurality of layers, and an upper limit of a weight section of a weight represented with the first representation bit number is referred to as the third threshold, and a lower limit of the weight section is referred to as the fourth threshold; paragraph [0155]: the activation quantization parameter (for neurons) includes a first threshold and a second threshold (pair of thresholds) of the activation map of each of the plurality of layers, and an upper limit of a section of the activation map represented with the second representation bit number is referred to as the first threshold, and a lower limit of the section of the activation map is referred to as the second threshold. Thus, the first threshold and second threshold are different from third threshold and fourth threshold).
Jung discloses in Figures 11A-11B and paragraphs [0151], [0163], [0214] that the weight might have a maximum value and a minimum value and may be distributed intensively in a predetermined range between the maximum and the minimum value. Figures 11A-11B shows weight can be -a, a and/or -b, b. Thus, Jung would imply “wherein each pair of truncation thresholds in the plurality of pairs of truncation thresholds includes a truncation positive value and a truncation negative value that are symmetrical”. Jung, however, does not disclose wherein the selected pair of truncation thresholds for the group of data to be quantized corresponds to a smallest difference among the differences to reduce precision loss during quantization and quantizing the group of data to be quantized using the selected pair of truncation thresholds to obtain the group of quantized data.
In the same field of endeavor, Yu discloses determining the network parameter of each network unit in the at least one network unit in the energy average value of 1 norm (1 norm as vector norm, can represent the sum of vector element absolute value (Yu, page 12)), the average value of the distribution of the energy average value as the value of the network parameter, and determining a dynamic threshold value based on the average value of the distribution of the value of the network parameter (each group of data) and the set parameter (group of data), wherein the dynamic threshold value comprises a positive threshold value and a negative threshold value (Yu, page 5). Yu further discloses due to the floating point model with the same network structure, (network parameter is floating point of neural network), if the precision is the same or similar, the network parameter range and the energy average ratio is smaller; the fixed point model (network parameter is the neural network with fix point number) precision loss is smaller, and the precision is higher, and adjusting the value of the network parameter based on the dynamic threshold so that the ratio of the range of the network parameter and the energy average value is reduced, for example, the distribution of the network floating point parameter is limited in a certain range by means of dynamic threshold truncation, so as to improve the precision of the neural network (Yu, page 11). Yu further disclose obtaining the positive threshold value and the negative threshold value van be calculated based on the formula:
Threshold = k*mean (abs (weight)), wherein the threshold represents a dynamic threshold value, the positive threshold value in the dynamic threshold value is threshold, the negative threshold value is -threshold, threshold value interval is [-threshold, threshold), weight represents the floating point parameter of one network unit, abs represents the absolute value of the floating point parameter, mean represents the average after summing all network floating point parameters, mean (abs (weight)) can be generalized as calculating the neural network in any network unit of the floating point parameter of 1 norm of energy average value, k is the set parameter (Yu, page 12). Yu further disclose replacing the value of the network parameter of at least one network unit with the value exceeding the dynamic threshold value as the dynamic threshold value (Yu, pages 12-13). Yu further disclose reducing the precision loss from the floating point number to the fixed point number improves the precision of the neural network after the fixed point adjustment (Yu, page 17).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Jung to include wherein the selected pair of truncation thresholds for the group of data to be quantized corresponds to a smallest difference among the differences to reduce precision loss during quantization and quantizing the group of data to be quantized using the selected pair of truncation thresholds to obtain the group of quantized data, as taught by Yu for the purpose of reducing the precision loss from the floating point number to the fixed point number (Yu, page 17).
As to dependent claim 2, Jung discloses wherein determining the plurality of groups of quantized data includes:
determining a maximum absolute value of all data in the group of data to be quantized (paragraphs [0151], [0163], [0214]); and
determining the plurality of pairs of truncation thresholds based on the maximum absolute value (paragraphs [0151], [0163], [0214]).
As to dependent claim 3, Jung discloses wherein determining the plurality of groups of quantized data includes:
determining a first truncation positive value based on the maximum absolute value, a predetermined total number of searches, and a current search order (paragraphs [0151], [0214]);
quantizing the group of data to be quantized through using a first pair of truncation thresholds to determine a first group of quantized data, wherein the first pair of truncation thresholds includes a first truncation positive value and a first truncation negative value that is opposite to the first positive value (paragraph [0216]); and
determining a first difference between a mean value of an absolute value of the first group of quantized data and the mean value of the absolute value of the group of data to be quantized (paragraphs [0222], [0223]).
As to dependent claim 4, Jung discloses wherein determining the plurality of groups of quantized data includes:
incrementing the current search order (paragraphs [0137], [0139]);
determining a second truncation positive value based on the maximum absolute value, the predetermined total number of searches, and the current search order (paragraphs [0151], [0152]);
quantizing the group of data to be quantized through using a second pair of truncation thresholds to determine a second group of quantized data, wherein the second pair of truncation thresholds includes a second truncation positive value and a second truncation negative value that is opposite to the second truncation positive value (paragraph [0216]); and
determining a second difference between a mean value of an absolute value of the second group of quantized data and the mean value of the absolute value of the group of data to be quantized (paragraph [0013]).
As to dependent claim 5, Jung discloses wherein selecting the pair of truncation thresholds from the plurality of pairs of truncation threshold includes:
determining, from the plurality of groups of quantized data, a group of quantized data that has a smallest difference with the group of data to be quantized in terms of mean value of absolute value (paragraphs [0216], [0222]); and
selecting a pair of truncation thresholds corresponding to the group of quantized data from the plurality of pairs of truncation thresholds (paragraphs [0018]-[0019]).
As to dependent claim 6, Jung discloses determining a truncation search range associated with the selected pair of truncation thresholds;
determining a plurality of new pairs of truncation thresholds within the truncation search range (paragraphs [0088], [0099], [0151]);
quantizing the group of data to be quantized respectively through using the plurality of new pairs of truncation thresholds to determine a plurality of new groups of quantized data (paragraphs [0019], [0153], [0155]); and
selecting a new pair of truncation thresholds from the plurality of new pairs of truncation thresholds based on a difference between the mean value of the absolute value of the group of data to be quantized and a mean value of an absolute value of each group of the plurality of new groups of quantized data (paragraphs [0013], [0019]).
As to dependent claim 7, Jung discloses wherein quantizing the group of data to be quantized respectively through using the plurality of pairs of truncation thresholds to determine the plurality of groups of quantized data includes: determining a maximum absolute value of all data in the group of data to be quantized;
determining three pairs of truncation thresholds based on the maximum absolute value, wherein among the three pairs of truncation thresholds, a first pair of truncation thresholds includes a half of the maximum absolute value and an opposite of the half, and a second pair of truncation thresholds includes three-quarters of the maximum absolute value and an opposite of the three-quarters, and a third pair of truncation thresholds includes the maximum absolute value and an opposite of the maximum absolute value (paragraphs [0013], [0019]); and
quantizing the group of data to be quantized respectively through using the three pairs of truncation thresholds to determine three groups of quantized data (paragraph [0019]).
As to dependent claim 8, Jung discloses wherein selecting the pair of truncation thresholds from the plurality of pairs of truncation thresholds includes:
executing the following actions iteratively until a stop condition is met (paragraphs [0130], [0136], [0139]):
selecting the pair of truncation thresholds from the three pairs of truncation thresholds (paragraphs [0145], [0153]);
determining whether a difference corresponding to the selected pair of truncation thresholds is less than a predetermined threshold (paragraphs [0130], [0136], [0145]);
stopping the iterative execution of the actions in response to the difference being less than the predetermined threshold (paragraphs [0130], [0136], [0145]); and
redetermining the three pairs of truncation thresholds in response to the difference being greater than the predetermined threshold based on the selected pair of truncation thresholds (paragraph [0259]).
As to dependent claim 9, Jung discloses wherein the group of data to be quantized is a group of floating-point numbers in a neural network model, and the method further includes:
quantize the group of data to be quantized using the selected pair of truncation thresholds to obtain quantized data, wherein the group of data to be quantized includes: setting a value that is greater than the truncation positive value in the group of data to be quantized as the truncation positive value, and setting a value that is less than the truncation negative value in the group of data to be quantized as the truncation negative value; and inputting the obtained quantized data to the neural network model for processing (paragraph [0019], [0118], [0136], [0145]).
Claims 10 and (11-15) are apparatus and medium claims, respectively. Claim 10 contains similar limitations of claim 1. Claims 11-15 contain similar limitations of claims 1-5, respectively. Therefore, claims 10-15 are rejected under the same rationale.
Response to Arguments
Applicant’s arguments and amendments filed on 01/20/2026 have been fully considered but they are not deemed fully persuasive. Applicant’s arguments with respect to claims 1-15 have been considered but are moot in view of the new ground(s) of rejection as explained here below, necessitated by Applicant’s substantial amendment (i.e., wherein the selected pair of truncation thresholds for the group of data to be quantized corresponds to a smallest difference among the differences to reduce precision loss during quantization and quantizing the group of data to be quantized using the selected pair of truncation thresholds to obtain the group of quantized data) to the claims which significantly affected the scope thereof. Please see the rejection above with newly cited prior art Yu).
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145