DETAILED ACTION
This Office Action is in response to the Preliminary Amendment filed on 1 July 2024.
Claims 1-20 are presented for examination.
Claims 1-2 and 5-14 are amended.
Claims 15-20 are new.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 14 June 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent Application No. FR 2113724, filed on 16 December 2021.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Determining Pre-Coding Weights in Artificial Neural Networks.
Claim Objections
Claims 1-2, 10-14 and 17 are objected to because of the following informalities:
Claim 1, line 3 recites the limitation “being”. For clarity and consistency, please amend to remove “being”. Please apply to claims 2 and 11-12.
Claim 10, line 3 recites "...by implementing the determination method according to claim 1." It is suggested to write out the method of claim 1 instead of writing, "…by implementing the determination method according to claim 1".
Claim 10, line 4 recites the limitation “to be”. For clarity and consistency, please amend and remove “to be”. Please apply to claims 2 and 11-13.
Claim 13, lines 2-3 recites the limitation "...according to claim 11." It is suggested to write out the method of claim 1 instead of writing, "…according to claim 11".
Claim 14, line 2 recites the pronoun “that”. For clarity and consistency, please amend and remove “that”.
Claim 17, line 2 has a typographical error, a comma, at the end of line 2.
Appropriate correction is required.
Dependent claims 3-9, 15-16 and 18-20 are also objected to since they are dependent upon the objected claims set forth above.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a module” in claim 12 and “a module” in claim 13.
Figure 11 and paragraph 267
[0267] As shown in FIG. 11, the terminal equipment 1100 may further include: a communication module 1130, an input unit 1140, a display 1150 and a power source 1160. The functions of said components are similar to relevant arts, which are not repeated here. It's worth noting that the terminal equipment 1100 does not have to include all the components shown in FIG. 11, said components are not indispensable. Moreover, the terminal equipment 1100 may also include components not shown in FIG. 11, relevant arts can be referred to.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 10-11 and 13-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al (US 2020/0374863 A1), hereinafter Lin.
Regarding Claim 1, Lin discloses a method for determining pre-coding weights (see Figure 4, step 414 and paragraph 93; a method for determining/obtained pre-coding weights/beam directions) based on a position vector representing the position of a communication terminal included in a communication system (see Figure 4, step 412 and paragraphs 91-93; based on a position vector representing the position/location of a communication terminal/UE included in a communication system/wireless communication network), said method being implemented using an artificial neural network (see paragraphs 33 and 93; said method being implemented using an artificial neural network/machine learning (ML) module), the method comprising:
determining the pre-coding weights using the artificial neural network receiving the position vector as an input (see Figure 4, step 414 and paragraph 93; determining the pre-coding weights/(beam directions) using the artificial neural network/(machine learning (ML) module) receiving the position vector/(UE location information) as an input/input), and
outputting the pre-coding weights (see Figure 4, step 414 and paragraph 93; outputting/output the pre-coding weights/beam directions).
Regarding Claim 10, Lin discloses a transmission method comprising:
determining the pre-coding weights by implementing the determination method (see Figure 4, step 414 and paragraph 93; determining the pre-coding weights/beam directions) by implementing the determination method according to claim 1,
pre-coding data to be transmitted using the determined pre-coding weights (see Figure 4, step 414 and paragraph 93; pre-coding data/(antenna beam directions) to be transmitted/output using the determined pre-coding weights/beam directions), and
transmitting the pre-coded data (see Figure 4, step 414 and paragraph 93; transmitting/output the pre-coded data/antenna beam directions).
Regarding Claim 11, Lin discloses a device for determining pre-coding weights (see Figure 4 and paragraph 93; a device/(ML modules) for determining pre-coding weights/beam directions) based on a position vector representing the position of a communication terminal included in a communication system (see Figure 4, step 412 and paragraphs 91-93; based on a position vector representing the position/location of a communication terminal/UE included in a communication system/wireless communication network), the device being adapted to determine the pre-coding weights using an artificial neural network receiving the position vector as an input (see Figure 4, step 414 and paragraph 93; the device/(ML modules) being adapted to determine the pre-coding weights/(beam directions) using an artificial neural network/(machine learning (ML) module) receiving the position vector/(UE location information) as an input/input) and outputting the pre-coding weights (see Figure 4, step 414 and paragraph 93; and outputting/output the pre-coding weights/beam directions).
Regarding Claim 13, Lin discloses a transmission device comprising:
the device for determining the pre-coding weights (see Figure 4, step 414 and paragraph 93; for determining the pre-coding weights/beam directions) according to claim 11 (as set forth in claim 11),
a module configured to pre-code to be transmitted using the determined pre-coding weights (see Figure 4, step 414 and paragraph 93; configured to pre-code data/(antenna beam directions) to be transmitted/output using the determined pre-coding weights/beam directions), and
a module configured to transmit the pre-coded data (see Figure 4, step 414 and paragraph 93; configured to transmit/output the pre-coded data/antenna beam directions).
Regarding Claim 14, Lin discloses a non-transitory computer-readable medium on which is stored a computer program comprising instructions executable by a processor that when executed by the processor (see paragraphs 185-186; a non-transitory/non-transitory computer-readable medium/(computer-readable medium) on which is stored/store a computer program/programming comprising instructions/instructions executable/executable by a processor/processor that when executed/executable by the processor/processor), cause the processor (see paragraphs 185-186) and adapted to implement the method according to claim 1 (as set forth in claim 1 above).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Tancik et al (“Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains”) [provided in the IDS dated 6/14/2024], hereinafter Tancik.
Regarding Claim 2, Although Lin discloses the method as set forth above,
Lin does not explicitly disclose “wherein the artificial neural network is implemented using a plurality of frequency vectors, each of the plurality of frequency vectors comprising frequency components, the frequency components of the plurality of frequency vectors of said plurality being distributed according to a predetermined distribution, the method also comprising: applying the position vector to the input of a first part of the artificial neural network, in order to produce intermediate data at an output of said first part, said intermediate data being respectively obtained, for each given one the plurality of frequency vectors, by application of a trigonometric function to result of a scalar product between the position vector and the given frequency vector”. However, Tancik discloses the method, wherein the artificial neural network is implemented using a plurality of frequency vectors (see page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; wherein the artificial neural network/(multilayer perceptron (MLP)) is implemented using a plurality of frequency vectors/Fourier features), each of the plurality of frequency vectors comprising frequency components (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; each of the plurality of frequency vectors/(Fourier features, B in the vector equation) comprising frequency components/mapping input coordinates v), the frequency components of the plurality of frequency vectors of said plurality being distributed according to a predetermined distribution (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; the frequency components/(mapping input coordinates v) of the plurality of frequency vectors/(Fourier features) of said plurality/(Fourier features) being distributed/distribution according to a predetermined distribution/Isotropic distribution), the method also comprising:
applying the position vector to the input of a first part of the artificial neural network (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; applying the position vector/(input coordinates) to the input/input of a first part/(Fourier features) of the artificial neural network/multilayer perceptron (MLP)), in order to produce intermediate data at an output of said first part (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; in order to produce intermediate data at an output/(sum of the equation y(v) of page 2) of said first part/Fourier features), said intermediate data being respectively obtained (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; said intermediate data being respectively obtained/sum of the equation y(v) of page 2), for each given one the plurality of frequency vectors (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; for each given one the plurality of frequency vectors/rows of b in the equation on page 2 are frequency vectors), by application of a trigonometric function to result of a scalar product between the position vector and the given frequency vector (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; by application of a trigonometric function/(y(v) = [a1 cos(2Πb1v), sin (2Πb1v)], trigonometric functions sine) to result/(the result of y(v)=) of a scalar product/(multiplying the 2 vectors in the trigonometric equation above) between the position vector/(input coordinates) and the given frequency vector/rows of b in the equation on page 2 are frequency vectors).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “wherein the artificial neural network is implemented using a plurality of frequency vectors, each of the plurality of frequency vectors comprising frequency components, the frequency components of the plurality of frequency vectors of said plurality being distributed according to a predetermined distribution, the method also comprising: applying the position vector to the input of a first part of the artificial neural network, in order to produce intermediate data at an output of said first part, said intermediate data being respectively obtained, for each given one the plurality of frequency vectors, by application of a trigonometric function to result of a scalar product between the position vector and the given frequency vector” as taught by Tancik in the system of Lin to demonstrate that a random Fourier feature mapping with an appropriately chosen scan dramatically improves the performance of coordinate-based MLPs (see page 2, second dot of Tancik).
Regarding Claim 12, Lin discloses the determination device,
implement the artificial neural network in order to determine the pre-coding weights using the artificial neural network (see Figure 4, step 414 and paragraph 93; determine the pre-coding weights/(beam directions) using the artificial neural network/(machine learning (ML) module) and outputting the pre-coding weights (see Figure 4, step 414 and paragraph 93; and outputting/output the pre-coding weights/beam directions).
Although Lin discloses determine the pre-coding weights using the artificial neural network as set forth above,
Lin does not explicitly disclose “wherein the artificial neural network is adapted to use a plurality of frequency vectors, each of the plurality of frequency vectors comprising frequency components, the frequency components of the plurality of frequency vectors being distributed according to a predetermined distribution, wherein the device further comprises: a module configured to apply the position vector to the input of a first part of the artificial neural network, in order to produce intermediate data at an output of said first part, said intermediate data being respectively obtained, for each given one of the plurality of frequency vectors, by application of a trigonometric function to a result of a scalar product between the position vector and the given frequency vector, and a module configured to implement the artificial neural network in order to determine the pre-coding weights using a second part of the artificial neural network receiving the intermediate data as an input and outputting the pre-coding weights”.
However, Tancik discloses the determination device, wherein the artificial neural network is adapted to use a plurality of frequency vectors (see page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; wherein the artificial neural network/(multilayer perceptron (MLP)) is adapted to use a plurality of frequency vectors/Fourier features), each of the plurality of frequency vectors comprising frequency components (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; each of the plurality of frequency vectors/(Fourier features, B in the vector equation) comprising frequency components/mapping input coordinates v), the frequency components of the plurality of frequency vectors being distributed according to a predetermined distribution (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; the frequency components/(mapping input coordinates v) of the plurality of frequency vectors/(Fourier features) being distributed/distribution according to a predetermined distribution/Isotropic distribution), wherein the device further comprises:
a module configured to apply the position vector to the input of a first part of the artificial neural network (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; a module configured to apply the position vector/(input coordinates) to the input/input of a first part/(Fourier features) of the artificial neural network/multilayer perceptron (MLP)), in order to produce intermediate data at an output of said first part (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; in order to produce intermediate data at an output/(sum of the equation y(v) of page 2) of said first part/Fourier features), said intermediate data being respectively obtained (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; said intermediate data being respectively obtained/sum of the equation y(v) of page 2), for each given one of the plurality of frequency vectors (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; for each given one the plurality of frequency vectors/rows of b in the equation on page 2 are frequency vectors), by application of a trigonometric function to a result of a scalar product between the position vector and the given frequency vector (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; by application of a trigonometric function/(y(v) = [a1 cos(2Πb1v), sin (2Πb1v)], trigonometric functions sine) to a result/(the result of y(v)=) of a scalar product/(multiplying the 2 vectors in the trigonometric equation above) between the position vector/(input coordinates) and the given frequency vector/rows of b in the equation on page 2 are frequency vectors), and
a module configured to implement the artificial neural network in order to determine using a second part of the artificial neural network receiving the intermediate data as an input and outputting (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel; a module/MLP configured to implement the artificial neural network/(other layers of coordinate based multilayer perceptron (MLP)) in order to determine using a second part/(other layers of multilayer perceptron (MLP) neural network) of the artificial neural network/(multilayer perceptron (MLP) receiving the intermediate data/(y(v) equation) as an input and outputting/output).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “wherein the artificial neural network is adapted to use a plurality of frequency vectors, each of the plurality of frequency vectors comprising frequency components, the frequency components of the plurality of frequency vectors being distributed according to a predetermined distribution, wherein the device further comprises: a module configured to apply the position vector to the input of a first part of the artificial neural network, in order to produce intermediate data at an output of said first part, said intermediate data being respectively obtained, for each given one of the plurality of frequency vectors, by application of a trigonometric function to a result of a scalar product between the position vector and the given frequency vector, and a module configured to implement the artificial neural network in order to determine the pre-coding weights using a second part of the artificial neural network receiving the intermediate data as an input and outputting the pre-coding weights” as taught by Tancik in the system of Lin to demonstrate that a random Fourier feature mapping with an appropriately chosen scan dramatically improves the performance of coordinate-based MLPs (see page 2, second dot of Tancik).
Regarding Claim 15, Although Lin discloses the method as set forth above,
Lin does not explicitly disclose “wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution”. However, Tancik discloses the method,
wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution (see Figure 1 and page 2, paragraph under Figure 1 write up and Section 4 Fourier Features for a Tunable Stationary Neural Tangent Kernel and page 3, Approximating deep networks with kernel regression; wherein the frequency components/(mapping input coordinates v) are randomly distributed/(b in the equation is a random n x n kernel (Gram) matrix) within a range of spatial frequencies/(control the bandwidth of the NTK) according to a normal distribution/Gaussian distribution N).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution” as taught by Tancik in the system of Lin to demonstrate that a random Fourier feature mapping with an appropriately chosen scan dramatically improves the performance of coordinate-based MLPs (see page 2, second dot of Tancik).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Schlemper et al (WO 2023038910 A1), hereinafter Schlemper.
Regarding Claim 4, Although Lin discloses the method as set forth above,
Lin does not explicitly disclose “wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution”.
However, Schlemper discloses the method, wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution (see paragraphs 85, 94 and 100; wherein the frequency components are randomly/random distributed within a range of spatial frequencies/(spatial frequency data 302) according to a normal distribution/Gaussian (see provisional 63/241,238; page 1, columns 1-2).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “wherein the frequency components are randomly distributed within a range of spatial frequencies according to a normal distribution” as taught by Schlemper in the system of Lin to provide a method for training a machine-learning model for image reconstruction (see page 1, paragraph 3 of Schlemper).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Tancik, and further in view of Sipperley et al (US 2021/0148802A1), hereinafter Sipperley.
Regarding Claim 17, Although the combination of Lin and Tancik discloses the method as set forth above,
The combination of Lin and Tancik does not explicitly disclose “training the artificial neural network in such a way as to determine second weighting coefficients respectively associated with nodes of the second part of the artificial neural network”.
However, Sipperley discloses the method, further comprising, prior to applying the position vector to the input of the first part of the artificial neural network:
training the artificial neural network in such a way as to determine second weighting coefficients respectively associated with nodes of the second part of the artificial neural network (see Figure 2, step 205 and paragraph 37; training the artificial neural network/(artificial neural network (ANN)) in such a way as to determine second weighting coefficients/(set of coefficient weights for each layer of the ANN) respectively associated with nodes of the second part/(internal layers) of the artificial neural network/ artificial neural network).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “training the artificial neural network in such a way as to determine second weighting coefficients respectively associated with nodes of the second part of the artificial neural network” as taught by Sipperley in the combined system of Lin and Tancik to provide systems for acquiring and characterizing flow fields using a processing stage that incorporates machine learning (see page 1, paragraph 2 of Sipperley).
Allowable Subject Matter
Claims 3, 5-9, 16 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beyene et al (US 2020/0125959 A1) discloses Autoencoder Neural Network For Signal Integrity Analysis Of Interconnect Systems. Specifically, see Figure 9, step 914 and paragraphs 48.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATRESA A McCALLUM whose telephone number is (571)270-5385. The examiner can normally be reached M-F 7:00am-4pm.
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/L.A.M/Examiner, Art Unit 2469
/JACKIE ZUNIGA ABAD/Primary Examiner, Art Unit 2469
Prosecution Insights