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 .
Specification
The use of the term OptaMLTM, SigTest, Seasim and DPOJET which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4, 9-11, 16 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites “a transmitter” and “the transmitter”, its unclear if “a transmitter” is a new or different transmitter from claim 1 and it is further unclear which transmitter “the transmitter” refers to if they are different. Further claim 2 recites “one or more of the multiple signals from the receiver comprises a performance indicator” it is unclear if this is the same or different performance indicator from claim 1 and makes it unclear what “the performance indicator” in claims 3 and 4 refers to.
Claims 3-4 are rejected based on their dependency on claim 2.
Claim 9 recites the limitation "the signal" on line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites “the signal” it is unclear which signal is being referred to as it could be any of the multiple signals from claim 1 or the “a signal” introduced in claim 9.
Claim 11 recites “generate a performance indicator from the signal” it is unclear if this is the same or a different step from claim 1 “generate a performance indicator from the multiple signals” and further unclear which signal “the signal” is referring to. It is also unclear if this is the same or different performance indicator from claim 1.
Claims 16 and 18 recite “training the one or more neural networks” (emphasis added). There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claim 1 belongs to a statutory category of a machine.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 14 recite(s) “generate a performance indicator from the multiple signals;” which are directed to mathematical concepts and/or mental processes when viewed in light of applicants specification, for example Par. 19 and 34, as they amount to making mental determinations.
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “A margin tester, comprising: one or more ports to allow the margin tester to connect to a device under test (DUT); a memory, the memory containing a margin tester signature;” and “one or more processors configured to execute code that causes the one or more processors to:” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “a transmitter; a receiver to receive signals from the DUT;”, “receive multiple signals from the receiver through the one or more ports;”, “send the performance indicator and the margin tester signature to one or more machine learning networks;”, and “receive a result from the one or more machine learning networks containing a performance measurement prediction for the DUT” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “A margin tester, comprising: one or more ports to allow the margin tester to connect to a device under test (DUT); a memory, the memory containing a margin tester signature;” and “one or more processors configured to execute code that causes the one or more processors to:” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “a transmitter; a receiver to receive signals from the DUT;”, “receive multiple signals from the receiver through the one or more ports;”, “send the performance indicator and the margin tester signature to one or more machine learning networks;”, and “receive a result from the one or more machine learning networks containing a performance measurement prediction for the DUT” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i or ii) and further the courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claims 4-6, 8, 11, 19 are considered to further describe the abstract idea cited above.
In claims 2-3, 9-10, 12-13, 15 17 and 20 the additional elements are not integrated into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and ” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i or ii) and further the courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
In claim 7, “build a tensor image of the performance indicator” is considered to further describe the abstract idea cited above. the additional elements are not integrated into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and ” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i or ii) and further the courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 16 recites the additional element(s) of using generic AI/ML technology, i.e. training the one or more neural networks, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the training the one or more neural networks merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of training the one or more neural networks to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
In claim 16 and similarly 18 the elements of “labeling the performance indicators for each margin tester with the margin tester signature and stress applied to the multiple signals before the multiple signals were transmitted” are considered to further describe the abstract idea cited above. The elements of “collecting performance indicators for a number of margin testers” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i or ii) and further the courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
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) 1-6, 9-11, 14-15, 17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rice (US 20220021492 A1).
In claim 1, Rice discloses a margin tester (Fig. 2, Par. 31 “provide in-situ and real time estimates of the performance margin”), comprising: one or more ports (Par. 35 “serial link or bus”) to allow the margin tester to connect to a device under test (DUT) (Par. 31 “transmission system”); a memory (Fig. 2, 216), the memory containing a margin tester signature (Par. 7 “an eye mask representing a state decision boundary of a receiver may both be represented as independent, two-dimensional probability density functions (PDFs)” Par. 12, “a two-dimensional surface with contours of a constant BER”); a transmitter (Fig. 2, 204); a receiver (Fig. 2, 208) to receive signals from the DUT (Par. 35 “a signal may be received at the receiver”); one or more processors (Fig. 2, 212) configured to execute code (Par. 51 “application programming stored in the memory and executed by the processor”) that causes the one or more processors to: receive multiple signals from the receiver through the one or more ports (Par. 22 “receive and/or output a raw or noisy time series signal or collection of signals”); generate a performance indicator from the multiple signals (Par. 4 “bit error rate” Par. 26 “numerically estimate BER”); send the performance indicator and the margin tester signature to one or more machine learning networks (Par. 33, 42 “Numerical estimate of BER using PDFs as described herein has higher information content than direct BER measurement which allows for in-situ and real time estimates of transmission system margin and applications in machine learning” ); and receive a result from the one or more machine learning networks containing a performance measurement prediction for the DUT (Par. 33 “Feedback may likewise be generated by embodiments of the present disclosure to optimize filters to maintain or improve an estimated BER”).
In claim 2, Rice further discloses wherein the DUT comprises a transmitter (Par. 4 “data is transmitted over a channel or data link” Par. 9 “transmitter”), and one or more of the multiple signals from the receiver comprises a performance indicator (Par. 40 “parameters of one or more digital signals used by a receiver may be adjusted based on eye mask analysis” Par. 41 “Based on analysis of a signal as described herein, a computer system may be configured to automatically adjust digital filter parameters based on the determined bit error rate”) generated from reception of a transmission from the transmitter (Par. 22 “receive and/or output a raw or noisy time series signal or collection of signals”).
In claim 3, Rise discloses all of claim 2. Rice further discloses wherein the code that causes the one or more processors to send the performance indicator and the margin tester signature to one or more machine learning networks comprises code that causes the one or more processors to send parameters and settings for the transmitter to the one or more machine learning networks (Par. 41-43 “machine learning system may be used to automatically adjust digital filter parameters based on signal analysis. For example, a transmitted signal may be sent through a changing environment. Digital filters can be adapted, using machine learning, to continually optimize performance of the transmission link”).
In claim 4, Rise discloses all of claim 2. Rice further discloses wherein the code that causes the one or more processors to generate the performance indicator comprises code that causes the one or more processors to generate bit error ratio (BER) contours (Fig. 4, 418).
In claim 5, Rice further discloses wherein the margin tester signature comprises BER contours unique to the margin tester (Par. 21 “An over-laid eye mask PDF 106 may be used to define a performance limit of the receiver” See Fig. 1, examiner considers the contours shown to be said BER contours).
In claim 6, Rice further discloses wherein the performance measurement prediction comprises one or either a pass/fail prediction, or one or more measurements (Par. 32 “A measured eye diagram PDF has much higher information content than a conventional BER measurement”).
In claim 9, Rice further discloses wherein the DUT being tested comprises the receiver (Par. 8 “components of the serial link, i.e., transmitter, media, and receiver”) and the signal received from the DUT comprise a signal received by the receiver (Par. 35 “a signal may be received at the receiver”).
In claim 10, Rise discloses all of claim 9. Rice further discloses wherein the signal received by the receiver is generated by the margin tester transmitter (Par. 8, all the components of the serial link, i.e., transmitter, Par. 35 “a signal may be received at the receiver”).
In claim 11, Rise discloses all of claim 9. Rice further discloses wherein the code that causes the one or more processors to generate a performance indicator from the signal comprises code that causes the one or more processors to generate BER contour scans of the signal (Par. 21 “An over-laid eye mask PDF 106 may be used to define a performance limit of the receiver” See Fig. 1, examiner considers the contours shown to be said BER contours).
In claim 14, Rice discloses a method, comprising: receiving multiple signals from a device under test (DUT) (Par. 35 “a signal may be received at the receiver”) at a receiver (Fig. 2, 208) of a margin tester (Fig. 2, Par. 31 “provide in-situ and real time estimates of the performance margin”), the margin tester having a margin tester signature (Par. 7 “an eye mask representing a state decision boundary of a receiver may both be represented as independent, two-dimensional probability density functions (PDFs)” Par. 12, “a two-dimensional surface with contours of a constant BER”); a transmitter (Fig. 2, 204); generating a performance indicator from the multiple signals (Par. 4 “bit error rate” Par. 26 “numerically estimate BER”); sending the performance indicator and the margin tester signature to one or more machine learning networks (Par. 33, 42 “Numerical estimate of BER using PDFs as described herein has higher information content than direct BER measurement which allows for in-situ and real time estimates of transmission system margin and applications in machine learning”); and receiving a result from the one or more machine networks containing a performance measurement prediction for the DUT (Par. 33 “Feedback may likewise be generated by embodiments of the present disclosure to optimize filters to maintain or improve an estimated BER”).
In claim 15, Rice further discloses wherein the DUT comprises a transmitter (Par. 4 “data is transmitted over a channel or data link” Par. 9 “transmitter”).
In claim 17, Rice further discloses wherein the DUT wherein the DUT comprises a receiver (Par. 8 “components of the serial link, i.e., transmitter, media, and receiver”).
In claim 19, Rice further discloses wherein the performance measurement prediction comprises one or either a pass/fail prediction, or one or more measurement predictions for the DUT (Par. 32 “A measured eye diagram PDF has much higher information content than a conventional BER measurement”).
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.
Claim(s) 7-8, 12-13, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rice in view of Hamedi (US 20200210764 A1).
In claim 7, Rice does not explicitly disclose to build a tensor image of the performance indicator and send the tensor image to the machine learning network (Emphasis added).
Hamedi teaches to build a tensor image (Par. 52 “feature sets can be mathematically represented as tensors”) of the performance indicator and send the tensor image to the machine learning network (Par. 52 “machine learning mode”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to modify Rice to build a tensor image of the performance indicator and send the tensor image to the machine learning network based on the teachings of Hamedi in order to allow smaller feature tensors to be given more significance than their small size would suggest for purposes of training the model (Hamedi Par. 52) thus improving the accuracy of the machine learning network.
In claim 8, Rice in view of Hamedi disclose all of claim 7. Modified Rice further discloses wherein the tensor image includes one or more of configuration parameters for one or more channels of the DUT, environmental parameters, and measurement results (Par. 28 “equalization filters to environment changes”, Par. 33 “in-situ measurements” Par. 4 “channel”).
In claim 12, Rice does not explicitly disclose to send the performance indicator and the margin tester signature to one or more external neural networks (Emphasis added).
Hamedi teaches to send the performance indicator and the margin tester signature to one or more external neural networks (Par. 4 “neural network” See Fig. 1, 114 examiner notes the machine learning model is on a server external from the client computing platform).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice to send the performance indicator and the margin tester signature to one or more external neural networks based on the teachings of Hamedi as it is simply substituting one known element for another for a predictable result.
In claim 13, Rice further discloses to send the performance indicator and the margin tester signature to one or more machine learning networks residing on the margin tester (Par. 33, 42 “Numerical estimate of BER using PDFs as described herein has higher information content than direct BER measurement which allows for in-situ and real time estimates of transmission system margin and applications in machine learning”).
Rice does not explicitly disclose to send the performance indicator and the margin tester signature to one or more neural networks (Emphasis added).
Hamedi teaches to send the performance indicator and the margin tester signature to one or more neural networks (Par. 4 “neural network” See Fig. 1, 114 examiner notes the machine learning model is on a server external from the client computing platform).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice to send the performance indicator and the margin tester signature to one or more neural networks based on the teachings of Hamedi as it is simply substituting one known element for another for a predictable result.
In claim 16, Rice discloses all of claim 15. Rice further discloses the training comprising: collecting performance indicators for a number of margin testers (Par. 33 “transmission systems”, Par. 40 “parameters of one or more digital signals used by a receiver may be adjusted based on eye mask analysis”); and labeling the performance indicators for each margin tester with the margin tester signature (Par. 34 “a numerical estimate of BER of a signal from dynamic eye diagram PDFs and any receiver eye masks represented as PDFs”) and one or more of configuration of a transmitter channel, environment parameters, and raw measurement results (Par. 28 “equalization filters to environment changes”, Par. 22 “output a raw or noisy time series signal or collection of signals” Par. 4 “channel”).
Rice does not explicitly disclose training the one or more neural networks (emphasis added).
Hamedi teaches training to one or more neural networks (Par. 4 “neural network”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice to use one or more neural networks based on the teachings of Hamedi to improve the accuracy of the model's performance (Par. 51).
In claim 20, Rice further discloses sending the performance indicator and the margin tester signature to either one of one or more neural networks external to the margin tester, or one or machine learning networks residing in the margin tester (Par. 33, 42 “Numerical estimate of BER using PDFs as described herein has higher information content than direct BER measurement which allows for in-situ and real time estimates of transmission system margin and applications in machine learning”).
Rice does not explicitly disclose to send the performance indicator and the margin tester signature to one or more neural networks (Emphasis added).
Hamedi teaches to send the performance indicator and the margin tester signature to one or more neural networks (Par. 4 “neural network” See Fig. 1, 114 examiner notes the machine learning model is on a server external from the client computing platform).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice to send the performance indicator and the margin tester signature to one or more neural networks based on the teachings of Hamedi as it is simply substituting one known element for another for a predictable result.
Claim(s) 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rice in view of Hamedi in further view of Waschura (US 20060069971 A1)
In claim 18, Rice discloses all of claim 17. Rice further discloses collecting performance indicators for a number of margin testers (Par. 33 “transmission systems”, Par. 40 “parameters of one or more digital signals used by a receiver may be adjusted based on eye mask analysis”); and labeling the performance indicators for each margin tester with the margin tester signature (Par. 34 “a numerical estimate of BER of a signal from dynamic eye diagram PDFs and any receiver eye masks represented as PDFs”).
Rice does not explicitly disclose training the one or more neural networks (emphasis added).
Hamedi teaches training to one or more neural networks (Par. 4 “neural network”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice to use one or more neural networks based on the teachings of Hamedi to improve the accuracy of the model's performance (Par. 51).
Rice does not explicitly disclose stress applied to the multiple signals before the multiple signals were transmitted.
Waschura teaches stress applied to the multiple signals before the multiple signals were transmitted (Par. 6 “creating a stressed input”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to further modify Rice by having stress applied to the multiple signals before the multiple signals were transmitted based on the teachings of Waschura in order to see whether the receiver is operating error-free (Waschura Par. 6) thus improving the accuracy of the method.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.J.B/Examiner, Art Unit 2857
/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
