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 .
DETAILED ACTION
The action is in response to claims dated 3/20/2026.
Claims pending in the case: 1-14
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.
Claim(s) 1-11, 13-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
evaluating the inference model based on the sample (This is evaluating data and is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.); and
modifying one or more parameters of the inference model based on the evaluation (This is modifying data based on information and is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
the apparatus comprising at least two receiver units configured to receive signals from user equipment in the communication network and a logical unit communicatively coupled to each of the at least two receiver units, the logical unit configured to receive a signal from each of the receiver units and output a sequence of data corresponding to a sequence of transmitted data (This is a recitation of generic components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
obtaining a sample from a training dataset, the training dataset comprising sequences of transmitted data values and corresponding signals received at respective receiver units (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
wherein the inference model comprises sub-models corresponding to each of the at least two receiver units and a sub-model corresponding to the logical unit (This is a recitation of generic components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).);
These claimed limitations therefore do not integrate the abstract idea into a practical application.
Independent claims 13 and 14 are similar in scope to claim 1 and therefore rejected using the same rational above.
The above independent claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
The dependent claims 2, 5-6, 9-10 pertain to model usage and update (This high level recitation of the machine learning model and neural network and training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)Hence these claims are rejected as being abstract;
The dependent claims 3-4, 7 recites standardized mathematical processes (This is series of mathematical calculations based on information that falls into the mathematical concepts group of abstract ideas.);
The dependent claims 8 pertain to a data type (This appears to be directed to the specification of data and a restriction to a particular type of data. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).);
The dependent claims 11 pertain to types of hardware (This is a high level recitation of generic components for applying a result of the abstract idea. The computer is used merely as a tool to implement an existing process. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
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.
Claim(s) 1-2, 4-6, 8-14 is/are rejected under 35 U.S.C. 103 as being unpatentable by OShea (US 20200343985).
Regarding claim 1, OShea teaches, A method for training an inference model for an apparatus in a communications network, the apparatus comprising at least two receiver units configured to receive signals from user equipment in the communication network and a logical unit communicatively coupled to each of the at least two receiver units, the logical unit configured to receive a signal from each of the receiver units and output a sequence of data corresponding to a sequence of transmitted data (OShea: Fig. 2A, [4, 32, 37, 60-61, 92, 130]: a communication network with machine learning model; [4]: “… receiver units into approximate networks …” Fig. 6, [129]: plurality of RUs ), the method comprising:
obtaining a sample from a training dataset, the training dataset comprising sequences of transmitted data values and corresponding signals received at respective receiver units (OShea: [72, 82]: training the model using received input signals);
evaluating the inference model based on the sample (OShea: [15, 95-97]: iterative training with error feedback and minimizing loss function); and
modifying one or more parameters of the inference model based on the evaluation (OShea: [96]: training the model to update parameters);
wherein the inference model comprises sub-models corresponding to each of the at least two receiver units and a sub-model corresponding to the logical unit (OShea: [79, 81]: shared configurations with multiple sub models);
Although OShea does not specifically use the words “logical unit”, OShea explains the various processing stages from receiving a signal to generating the output which reads on logical units processing the information. Thus the limitations as claimed are found to be obvious based on the teachings in OShea.
Regarding claim 2, OShea teaches the invention as claimed in claim 1 above and, wherein evaluating the inference model comprises evaluating a loss function based on an output of the inference model and the sequence transmitted data values of the sample (OShea: [13, 15, 68, 97]: iterative training with error feedback and minimizing loss function).
Regarding claim 4, OShea teaches the invention as claimed in claim 2 above and,
wherein modifying one or more parameters of the inference model comprises performing a stochastic gradient descent on the basis of the evaluation (OShea: [14, 97]: stochastic gradient descent).
Regarding claim 5, OShea teaches the invention as claimed in claim 2 above and, wherein the inference model comprises a neural network (OShea: [16, 64]: neural network).
Regarding claim 6, OShea teaches the invention as claimed in claim 5 above and, wherein each of the sub-models comprise comprises a neural network (OShea: [16, 64]: neural network).
Regarding claim 8, OShea teaches the invention as claimed in claim 7 above and, wherein the reference signal comprises a reference front haul signal (OShea: [112, 129]: front-haul signal).
Regarding claim 9, OShea teaches the invention as claimed in claim 1 above and, wherein evaluating the inference model comprises evaluating the sub-models corresponding to the at least two receiver units (OShea: [15, 95-97]: iterative training with error feedback and minimizing loss function); and
wherein modifying one or more parameters of the inference model based on the evaluation comprises modifying parameters of the sub-models corresponding to the at least two receiver units based on the evaluation of the respective sub- models (OShea: [96]: training the models to update parameters);
Although not specifically mentioned, it is obvious that the sub-models are trained in a similar manner.
Regarding claim 10, OShea teaches the invention as claimed in claim 1 above and, wherein evaluating the inference model comprises evaluating the sub-model corresponding to the logical unit (OShea: [15, 95-97]: iterative training with error feedback and minimizing loss function); and
wherein modifying one or more parameters of the inference model based on the evaluation comprises modifying parameters of the sub-model corresponding to the logical unit (OShea: [96]: training the models to update parameters);
Although not specifically mentioned, it is obvious that the sub-models are trained in a similar manner.
Regarding claim 11, OShea teaches the invention as claimed in claim 1 above and, wherein the at least two receiver units are distributed receiver units and the logical unit is a distributed unit in a distributed multiple input multiple output system (OShea: Fig. 6, [111, 129 135]: distributed system).
Regarding claim 12, OShea teaches, A method for an apparatus in a communications network, the apparatus comprising at least two receiver units configured to receive signals from user equipment UEs in the communication network and a logical unit communicatively coupled to the at least two receiver units, the logical unit to receive signals a signal from each of the receiver units and output a sequence of data corresponding to a sequence of transmitted data (OShea: Fig. 2A, [4, 32, 37, 60-61, 79, 92]: a communication network with machine learning model; Fig. 6, [124, 129]: plurality of RUs, user equipment), the method comprising:
receiving a signal at the at least two receiver units (OShea: Fig. 2A, [91-92, 129]: signal receivers); and
obtaining a sequence of data based on an output of an inference model that is trained to receive an input comprising a signal received at the at least two receiver units and outputs a sequence of data corresponding to a sequence of transmitted data from a UE (OShea: [94]: output data);
wherein the inference model comprises sub-models corresponding to each of the at least two receiver units and a sub-model corresponding to the logical unit (OShea: [79, 81]: shared configurations with multiple sub models).
Regarding Claim(s) 13-14, this/these claim(s) is/are similar in scope as claim(s) 1. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over OShea (US 20200343985) in view of OShea1 (US 11228379).
Regarding claim 3, OShea teaches the invention as claimed in claim 2 above but not, wherein the loss function comprises a cross entropy loss function of the output of the inference model and the sequence of transmitted bits;
OShea1 teaches, wherein the loss function comprises a cross entropy loss function of the output of the inference model and the sequence of transmitted bits (OShea1: col 5 lines 18-29: loss function may be cross-entropy);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of OShea and OShea1 because the arts pertain to machine learning in signal processing systems and the combination would enable using a cross-entropy loss function. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enables using a loss functional commonly used in the art for signal processing systems.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over OShea (US 20200343985) in view of Oshea2 (US 11334807).
Regarding claim 7, OShea teaches the invention as claimed in claim 6 above and , wherein the … a mean squared error function of an output of the sub-models of the at least two receiver units and a reference signal (OShea: [2, 26, 32, 97, 100]: mean-squared error approach for error correction);
OShea2 further teaches, wherein the loss function further comprises a mean squared error function (Oshea2: col 9 lines 57-66: loss function can include a mean-squared error loss function);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of OShea and Oshea2 because the arts pertain to machine learning in signal processing systems and the combination would enable using a loss function with mean squared error function. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enables using a loss functional commonly used in the art for signal processing systems.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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, Tamara Kyle can be reached at 571 272 4241. 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.
/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
Prosecution Insights