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 .
Claim Objections
Claim 14 is objected to because of the following informalities: Claim 14 depends on claim 6 and recites “the electronic device of claim 6”. However, claim 6 depends on claim 5 and further depends on claim 1 recite a method, not an electronic device. Appropriate correction is required. For examination purpose, the examiner will consider claim 14 as depends on claim 8, which recites an electronic device.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Regarding claim 1,
Step 1:
Claim 1 recites a method, one of the four statutory categories of patentable subject matter.
Step 2A, Prong I:
Claim 1 further recites the limitations of:
“selecting, for each resource element, a detector from a set of detectors based on non-normalized outputs of the NN” This limitation recites a mental process. A person can mentally select a detector from a set of detector for each resource element based on analyzing the result from a machine that employ a neural network. In particular, the non-normalized outputs of the NN are simply data value that can be mentally evaluated by a human’s mind to further select a detector for a resource element. Such evaluation and selection are considered to be a mental process.
Step 2A, Prong II:
Claim 1 recites the following additional elements:
“... an electronic device for detector selection”, “... by the electronic device, ...” These additional elements are a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
“receiving, ..., at an inference time, a signal from a transmitting device” This additional element recites an additional element of an insignificant extra-solution activity as identified in MPEP 2106.05(g) of mere data gathering, and does not provide integration into a practical application.
“extracting features from the received signal” This additional element recites an additional element of an insignificant extra-solution activity as identified in MPEP 2106.05(g) of selecting a particular data source or type of data to be manipulated, and does not provide integration into a practical application.
“inputting the extracted features to a neural network (NN), which is trained, at least in part, with a normalization function” This additional element recites an additional element of a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation recites the application of a black-box machine learning practice, wherein a neural network is trained by receiving input with a normalization function. The limitation does not receive any specific improved technique to train the neural network, any improvement in machine learning training algorithm, or improvement toward any computer elements.
Step 2B:
When considered individually or in combination, the additional limitations and elements of claim 1 does not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional elements of outlined in Step 2A performing functions as designed simply accomplishes execution of the abstract ideas.
The additional elements “... an electronic device for detector selection”, “... by the electronic device, ...” are a high-level recitation of generic computer components used as a tool, and does not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional element “receiving, ..., at an inference time, a signal from a transmitting device” further recites an additional element of a well-understood, routine, conventional activity as identified in MPEP 2106.05(d) of receiving or transmitting data over a network, and does not amount to significantly more than the judicial exception for the same reasons discussed above.
The additional element “extracting features from the received signal” further recites an additional element of a well-understood, routine, conventional activity as identified in MPEP 2106.05(d) of extracting data, and does not amount to significantly more than the judicial exception for the same reasons discussed above.
The additional element “inputting the extracted features to a neural network (NN), which is trained, at least in part, with a normalization function” recites an additional element of a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not amount to significantly more than the judicial exception for the same reasons discussed above.
In conclusions from above for the elements considered as a mental process, elements reciting high-level recitation of generic computer components used as a tool, and elements reciting a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f) are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claim is ineligible.
Therefore, additional limitations of claim 1 do not amount to significantly more than the judicial exception.
Thus, claim 1 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Therefore, claim 1 is not patent eligible.
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated.
“The method of claim 1, wherein the normalization function includes a softmax function” This element recites a mental process as well as a mathematical concept, because the normalization function which includes a softmax function is a mathematical concept, and a person can mentally or manually calculate such function.
Thus, claim 2 recites abstract ideas rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 3 depends on claim 1, thus the rejection of claim 1 is incorporated.
“The method of claim 1, wherein the NN includes an a multi-layer perceptron (MLP) network” This additional element recites an additional element of a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application or amount to significantly more than the judicial exception. The limitation recites the application of conventional machine learning configuration, which is the multi-layer perceptron (MLP) neural network, without providing specific detail of improvement toward the generation or configuration of the MPL NN or improvement toward any computer elements.
Thus, claim 3 recites additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
“The method of claim 1, wherein the outputs of the NN are arranged in an ascending order of detector complexity” This element recites a mental process, because a person can mentally the outputs of the NN are simply data value and a person can mentally or manually arrange the data value through comparison in an ascending order.
Thus, claim 4 recites abstract ideas rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 5 depends on claim 4, thus the rejection of claim 4 is incorporated.
“The method of claim 4, wherein selecting the detector from the set of detectors based on the outputs of the NN comprises selecting a higher complexity detector than predicted based on a maximum value of the outputs” This element recites a mental process as well as a mathematical concept, the computing of a maximum value of the outputs is considered to be a mathematical concept, and subsequent data evaluation via comparison and data selection is considered to be a mental process.
Thus, claim 5 recites abstract ideas rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 6 depends on claim 5, thus the rejection of claim 5 is incorporated.
“Comparing a value of a predicted detector with a combined value of the higher complexity detector and a predetermined margin; and selecting the higher complexity detector based on the comparison” This element recites a mental process, because a person can mentally compare and select a data value based on a predetermined margin.
Thus, claim 6 recites abstract ideas rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 7 depends on claim 6 thus the rejection of claim 6 is incorporated.
“The method of claim 6, wherein the predetermined margin is calculated during training based on a comparison of block error rate (BLER) differences to a tolerable BLER loss” This element recites a mental process as well as a mathematical concept. The calculation of block error rate (BLER) differences to a tolerable BLER loss is considered to be a mathematical concept, which can be mentally performed by a human’s mind and further comparison evaluation is a mental process.
Thus, claim 7 recites abstract ideas rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Regarding claim 8 which recites a machine, one of the four statutory categories of patentable subject matter.
Claim 8 recites the following additional elements: “a transceiver”, “a processor” These additional elements are a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
Claim 8 is further rejected under the same rationale as claim 1. The applicant is further directed to the rejection of claim 1 above, because claim 8 recites similar limitation and processing steps.
Regarding claim 9 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 9 is further rejected under the same rationale as claim 2. The applicant is further directed to the rejection of claim 2 above, because the claims recite similar limitation and processing steps.
Regarding claim 10 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 10 is further rejected under the same rationale as claim 3. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 11 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 11 is further rejected under the same rationale as claim 4. The applicant is further directed to the rejection of claim 4 above, because the claims recite similar limitation and processing steps.
Regarding claim 12 depends on claim 11 thus the rejection of claim 11 is incorporated. Claim 12 is further rejected under the same rationale as claim 5. The applicant is further directed to the rejection of claim 5 above, because the claims recite similar limitation and processing steps.
Regarding claim 13 depends on claim 12 thus the rejection of claim 12 is incorporated. Claim 13 is further rejected under the same rationale as claim 6. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 14 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 14 is further rejected under the same rationale as claim 7. The applicant is further directed to the rejection of claim 7 above, because the claims recite similar limitation and processing steps.
Regarding claim 15 which recites a machine, one of the four statutory categories of patentable subject matter.
Claim 15 recites the following additional elements: “A non-transitory computer readable medium that stores instructions, which when executed by an electronic device, control the electronic device ...” This additional element is a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
Claim 15 is further rejected under the same rationale as claim 1. The applicant is further directed to the rejection of claim 1 above, because claim 15 recites similar limitation and processing steps.
Regarding claim 16 depends on claim 15 thus the rejection of claim 15 is incorporated. Claim 16 is further rejected under the same rationale as claim 3. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 17 depends on claim 15 thus the rejection of claim 15 is incorporated. Claim 17 is further rejected under the same rationale as claim 4. The applicant is further directed to the rejection of claim 4 above, because the claims recite similar limitation and processing steps.
Regarding claim 18 depends on claim 17 thus the rejection of claim 17 is incorporated. Claim 18 is further rejected under the same rationale as claim 5. The applicant is further directed to the rejection of claim 5 above, because the claims recite similar limitation and processing steps.
Regarding claim 19 depends on claim 18 thus the rejection of claim 18 is incorporated. Claim 19 is further rejected under the same rationale as claim 6. The applicant is further directed to the rejection of claim 6 above, because the claims recite similar limitation and processing steps.
Regarding claim 20 depends on claim 19 thus the rejection of claim 19 is incorporated. Claim 20 is further rejected under the same rationale as claim 7. The applicant is further directed to the rejection of claim 7 above, because the claims recite similar limitation and processing steps.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhari et.al (NPL: Reliable and Low-Complexity MIMO Detector Selection using Neural Network) in view of Waters et.al (US 20080137762 A1)
Regarding claim 1,
PNG
media_image1.png
227
858
media_image1.png
Greyscale
Chaudhari teaches a part of the limitation “receiving, ..., at an inference time, a signal” ()
Chaudhari teaches the limitation “extracting features from the received signal” (and Fig. 2: Online detector selection, Page 1 section I-1) “In order to select the detector before evaluating any candidate detector, we train a MLP network to select the detector using features derived from instantaneous channel in the RE, received signal and noise variance”, Page 2 section II “The 2 × 1 received signal vector in the nth RE is given by yn”, and Page 3 section III-A-2) “Feature identification: The input features are generated using yn,Hn,σ2 ...” Chaudhari discloses a method for a reliable and low-complexity MIMO detector selection. Within the disclosure, Chaudhari discloses the method comprises receiving signals and derive/extract features from the received signal. The signals are represented by the symbol yn, and according to the Fig. 2 and Fig. 3, the signals is inputted through a feature extraction to extract feature from the received signal, thereby corresponds to the claimed process of extracting features from the received signal.)
Chaudhari teaches the limitation “inputting the extracted features to a neural network (NN), which is trained, at least in part, with a normalization function” (Fig. 2: Online detector selection, and Page 4 section III-B “We consider a 3-layer MLP network as shown in Fig.5 with 3 input features identified earlier, ... Finally, the MLP output is given by applying softmax ... The MLP network is trained offline using the training dataset” Chaudhari discloses after feature extraction, the input features are inputted through the MLP neural network, where in the MLP neural network is trained offline using the training data set. The output of the MLP network is given by applying softmax, which is a normalization function as understood by a person ordinary skill in the art. Thus, Chaudhari teaches a MLP neural network that receive input features and output that is applied with a softmax normalization function, thereby corresponds to the claimed process of inputting the extracted features to a neural network (NN), which is trained, at least in part, with a normalization function.)
Chaudhari teaches the limitation “selecting, for each resource element (RE), a detector from a set of detectors based on non-normalized outputs of the NN” (Page 6 section III-D Fig.3 “In order to reduce the complexity of online detector selection, we incorporate reliable detector selection in MLP re training. For re-training, we generate new labels ζn for each RE ... The MLP is re-trained ... Note that the softmax and reliable detector selection blocks are not required in online selection after MLP re-training.”)
PNG
media_image2.png
229
842
media_image2.png
Greyscale
(Chaudhari discloses the detector selection is incorporated in MLP training, in which the result of the re-trained MLP is used to select detector for resource element. As previously discloses at Fig. 2 above, the MLP output is applied with softmax normalization function and a reliable detector selection block that compare the NN output with a margin to ensure the detector selection is reliable. However, at later retraining step of the MLP NN as disclosed in Fig. 3, the output of the retrained MLP network can be directly process to obtain detector selection without performing softmax normalization and reliable assurance process anymore. Thus, the output of the retrained MLP is non-normalized outputs, thereby the retraining process of the MLP to select a detector by Chaudhari teaches the selection of a detector for each resource element (RE) based on non-normalized outputs of the NN, as claimed.)
Chaudhari does not teach the transmitting device as well as the electronic device aspect within the limitation “receiving, by the electronic device, at an inference time, a signal from a transmitting device”. However, Waters teaches the devices aspect within the limitation (paragraph 34 “MIMO-OFDM receiver 104 is configured to receive radio frequency signals transmitted by MIMO-OFDM transmitter 102. MIMO-OFDM receiver 104 may, in general, be a fixed or portable wireless device, a cellular phone, a personal digital assistant, a wireless modem card, or any other device configured to transmit on a MIMO-OFDM wireless network. MIMO-OFDM receiver 104 includes one or more antennas 108 for receiving transmitted radio frequency signals.” Water discloses transmitting signals from a transmitter, which corresponds to the signal from the transmitting device as claimed, and receiving signals via a receiver, which corresponds to the receiving of the signal by the electronic device, as claimed. Such process of transmitting and receiving signal may be performed in view of the online detector selection after MLP re-training disclosed by Chaudhari above, which corresponds to the claimed condition “at an inference time”, because Chaudhari discloses using the retrained MLP to process the received signals, thus indicating use of the already re-trained MLP during detector selection, which corresponds to the “at an inference time”.)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teaching of the method for MIMO detection and selection for each resource element by Chaudhari with the teaching of employing a system that contain receiver and transmitter to receive and transmit signal while perform detector selection based on MIMO detection performance while maintaining efficient use of the receiver computational resources by Waters. The motivation to do so is referred to in Waters’s disclosure (paragraph 34 “Rather than applying a pre-selected detector to each sub-carrier, receiver 104 preferably includes an embodiment of the invention disclosed herein to tailor the complexity of the detector applied to each sub-carrier in accordance with the quality of the received signal. Receiver 104 may thus apply more complex detectors to lower quality sub-carriers and less complex detectors to higher quality sub-carriers resulting in efficient use of the receiver 104 computational resources while attaining optimal detection performance.” Waters discloses suing receiver and transmitter to receive and transmit signals, wherein the user may further tailor the complexity of the detector applied to each sub-carrier in accordance with the quality of the received signal to assign corresponding detectors resulting in efficient use of the receiver computational resources. A person of ordinary skill in the art would have been able to modify the method by Chaudhari to be implemented within a receiver and transmitter device as taught by Waters and further tailor the complexity of each detector during detector selection to maintain efficient use of computational resources while attaining each detector optimal performance, thus improve the teaching of MIMO detector selection by Chaudhari.)
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated.
Chaudhari teaches the limitation “The method of claim 1, wherein the normalization function includes a softmax function” (Page 4 section III-B “We consider a 3-layer MLP network as shown in Fig.5 with 3 input features identified earlier, ... Finally, the MLP output is given by applying softmax ... The MLP network is trained offline using the training dataset” Chaudhari discloses the MLP network output is given by applying softmax, wherein the softmax is a normalization function as understood by one of ordinary skill in the art, thereby teaches the claimed normalization function includes a softmax function.)
Regarding claim 3 depends on claim 1, thus the rejection of claim 1 is incorporated.
Chaudhari teaches the limitation “The method of claim 1, wherein the NN includes an a multi-layer perceptron (MLP) network” (Page 4 section III-B “We consider a 3-layer MLP network as shown in Fig.5 with 3 input features identified earlier, ... Finally, the MLP output is given by applying softmax ... The MLP network is trained offline using the training dataset” Chaudhari discloses the neural network which is trained to perform the detector selection is the MLP network, thereby teaches the NN includes a multi-layer perceptron (MLP) network, as claimed.)
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
Chaudhari teaches the limitation “The method of claim 1, wherein the outputs of the NN are arranged in an ascending order of detector complexity” (Page 2 section II “The detectors are indexed in ascending order of complexity as follows: Z ={1:MMSE,2:ICR-16,3 : ICR-32, 4 : ICR-64,5 : DR-ML}” Chaudhari discloses the MLP network outputs correspond to candidate detectors, and the detectors are indexed in an ascending order of complexity from the lowest to the highest. Thus, the outputs of the MLP network are arranged according to the ascending order of detector complexity, thereby teaches or at least suggests the outputs of the NN are arranged in an ascending order of detector complexity, as claimed.)
Regarding claim 5 depends on claim 4, thus the rejection of claim 4 is incorporated.
Chaudhari teaches the limitation “The method of claim 4, wherein selecting the detector from the set of detectors based on the outputs of the NN comprises selecting a higher complexity detector than predicted based on a maximum value of the outputs” (Fig. 7, Page 2 section II “The detectors are indexed in ascending order of complexity as follows: Z ={1:MMSE,2:ICR-16,3 : ICR-32, 4 : ICR-64,5 : DR-ML}”, and Page 5 section III-C “A naive way to select the detector is based on the maximum value among the MLP outputs ... However, our objective is to select a reliable detector to generate LLR ... If there is an underestimation error, then a low complexity detector will be used for the RE that requires high complexity detector based on the channel conditions ... we use margin δd for selecting detector-d instead of detector-(d + 1)” Chaudhari discloses that the detector predicted based on the maximum MLP output may underestimate the required detector complexity. Therefore, the reliable detector selection adjusts the initial prediction to select a detector having higher complexity when the initial selection is insufficient, which is configured via equation 13 and illustrated via Fig. 7, wherein the detector 1 is obtained based on the prediction with a maximum value of the outputs, but the detector 2 is selected via the reliable detector selection, and the detector 2 has higher complexity, thus teaches or at least suggests the process of selecting the detector from the set of detectors based on the outputs of the NN comprises selecting a higher complexity detector than predicted based on a maximum value of the outputs, as claimed.)
Regarding claim 6 depends on claim 5, thus the rejection of claim 5 is incorporated.
Chaudhari teaches the limitation “The method of claim 5, wherein selecting the higher complexity detector than predicted based on the maximum value of the outputs comprises: Comparing a value of a predicted detector with a combined value of the higher complexity detector and a predetermined margin; and selecting the higher complexity detector based on the comparison” (Page 5 section III-C equation 13 Fig.6 “A naive way to select the detector is based on the maximum value among the MLP outputs ... However, our objective is to select a reliable detector to generate LLR ... If there is an underestimation error, then a low complexity detector will be used for the RE that requires high complexity detector based on the channel conditions ... we use margin δd for selecting detector-d instead of detector-(d + 1).A reliable detector is selected as – equation 13”, and Fig. 7. Chaudhari discloses that a naïve detector prediction is made based on the maximum value among the MLP outputs. However, Chaudhari explains that this prediction may underestimate the detector complexity required for a resource element, such that a low-complexity detector may be predicted when a higher-complexity detector is required. To address this underestimation, Chaudhari performs reliable detector selection by comparing the output value associated with the predicted detector with the output value associated with the next higher-complexity detector in view of a predetermined margin. In particular, Equation 13 applies the condition that the difference between the predicted-detector output and the next-detector output exceeds the margin, which corresponds or at least suggest the comparing of the predicted-detector output with the combined value of the next-detector output and the margin. Fig. 7 illustrates that, although the maximum MLP output initially predicts detector 1, the reliable detector selection instead selects detector 2 based on this margin comparison. Because Chaudhari indexes the detectors in ascending order of complexity, detector 2 has a higher complexity than detector 1. Thus, Chaudhari teaches or at least suggests comparing a value of a predicted detector with a combined value of a higher-complexity detector and a predetermined margin, and selecting the higher-complexity detector based on the comparison, as claimed.)
Regarding claim 7 depends on claim 6, thus the rejection of claim 6 is incorporated.
Chaudhari teaches the limitation “The method of claim 6, wherein the predetermined margin is calculated during training based on a comparison of block error rate (BLER) differences to a tolerable BLER loss” (Page 2 section II-A “The objective of this work is to select low-complexity detectors zn, n = 1, 2, ..., R for each RE in the transport block while keeping the BLER close to the most complex DR-ML detector. Let us define PeDRML as the BLER when all REs use the DR-ML detector, Pe(z) as the BLER when the n-th RE uses the selected detector zn ∈ Z. Then, the objective can be mathematically described as – equation 5”, and Page 5 section III-C“If there is an underestimation error, then a low complexity detector will be used for the RE that requires high complexity detector based on the channel conditions. This can increase the BLER after decoding. To limit the underestimation error below a threshold γ, we use margin δd”. Chaudhari further discloses that the detector-selection objective is to keep the BLER of the selected detectors close to the BLER obtained using the most complex DR-ML detector. Equation 5 compares the BLER difference between the selected-detector configuration and the DR-ML detector with a small positive tolerance. Thus, the BLER difference corresponds to the claimed BLER differences, and the small positive tolerance corresponds to the tolerable BLER loss. Because the margins are computed offline after MLP training and are used to implement reliable detector selection consistent with this BLER-loss constraint, Chaudhari teaches or at least suggests that the predetermined margin is calculated during training based on the comparison of BLER differences to a tolerable BLER loss.)
Regarding claim 8
Waters teaches the limitations “a transceiver”, “a processor” paragraph 34 “MIMO-OFDM receiver 104 is configured to receive radio frequency signals transmitted by MIMO-OFDM transmitter 102. MIMO-OFDM receiver 104 may, in general, be a fixed or portable wireless device, a cellular phone, a personal digital assistant, a wireless modem card, or any other device configured to transmit on a MIMO-OFDM wireless network. MIMO-OFDM receiver 104 includes one or more antennas 108 for receiving transmitted radio frequency signals”, and paragraph 42 “MIMO detector 208 and its sub-systems (i.e. detection means, detector assignment means, list metric determination means, list assignment means, etc.) may be implemented using, for example, a digital signal processor, or other processor”. Waters discloses transmitting signals from a transmitter, and receiving signals via a receiver, which corresponds to the transceiver, as claimed. Waters further discloses using processor to implement the MIMO detector selection embodiments, thus corresponds to the processor as claimed.
Claim 8 is further rejected under the same rationale as claim 1. The applicant is further directed to the rejection of claim 1 above, because the claims recite similar limitation and processing steps.
Regarding claim 9 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 9 is further rejected under the same rationale as claim 2. The applicant is further directed to the rejection of claim 2 above, because the claims recite similar limitation and processing steps.
Regarding claim 10 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 10 is further rejected under the same rationale as claim 3. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 11 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 11 is further rejected under the same rationale as claim 4. The applicant is further directed to the rejection of claim 4 above, because the claims recite similar limitation and processing steps.
Regarding claim 12 depends on claim 11 thus the rejection of claim 11 is incorporated. Claim 12 is further rejected under the same rationale as claim 5. The applicant is further directed to the rejection of claim 5 above, because the claims recite similar limitation and processing steps.
Regarding claim 13 depends on claim 12 thus the rejection of claim 12 is incorporated. Claim 13 is further rejected under the same rationale as claim 6. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 14 depends on claim 8 thus the rejection of claim 8 is incorporated. Claim 14 is further rejected under the same rationale as claim 7. The applicant is further directed to the rejection of claim 7 above, because the claims recite similar limitation and processing steps.
Regarding claim 15,
Waters teaches the limitations “A non-transitory computer readable medium that stores instructions, which when executed by an electronic device, control the electronic device ...” (paragraph 9 “Further, the term “software” includes any executable code capable of running on a processor, regardless of the media used to store the software. Thus, code stored in non-volatile memory, and sometimes referred to as “embedded firmware,” is included within the definition of software”, and paragraph 42 “MIMO detector 208 and its sub-systems (i.e. detection means, detector assignment means, list metric determination means, list assignment means, etc.) may be implemented using, for example, a digital signal processor, or other processor”. Waters discloses the system of the MIMO transmitter/receiver implemented as a software includes any executable code capable of running on a processor stored in non-volatile memory, thereby teaches or at least suggests the non-transitory computer readable medium that stores instructions, which when executed by an electronic device, as claimed.)
Claim 15 is further rejected under the same rationale as claim 1. The applicant is further directed to the rejection of claim 1 above, because the claims recite similar limitation and processing steps.
Regarding claim 16 depends on claim 15 thus the rejection of claim 15 is incorporated. Claim 16 is further rejected under the same rationale as claim 3. The applicant is further directed to the rejection of claim 3 above, because the claims recite similar limitation and processing steps.
Regarding claim 17 depends on claim 15 thus the rejection of claim 15 is incorporated. Claim 17 is further rejected under the same rationale as claim 4. The applicant is further directed to the rejection of claim 4 above, because the claims recite similar limitation and processing steps.
Regarding claim 18 depends on claim 17 thus the rejection of claim 17 is incorporated. Claim 18 is further rejected under the same rationale as claim 5. The applicant is further directed to the rejection of claim 5 above, because the claims recite similar limitation and processing steps.
Regarding claim 19 depends on claim 18 thus the rejection of claim 18 is incorporated. Claim 19 is further rejected under the same rationale as claim 6. The applicant is further directed to the rejection of claim 6 above, because the claims recite similar limitation and processing steps.
Regarding claim 20 depends on claim 19 thus the rejection of claim 19 is incorporated. Claim 20 is further rejected under the same rationale as claim 7. The applicant is further directed to the rejection of claim 7 above, because the claims recite similar limitation and processing steps.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY TU DIEP whose telephone number is (703)756-1738. The examiner can normally be reached M-F 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DUY T DIEP/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
Prosecution Insights