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 .
Claims 1-20 are presented for examination.
Response to Amendment
Applicant’s amendment has obviated most, but not all, of the objections to the drawings and claims and the rejections under 35 USC § 112. To the extent that an objection and/or rejection appears in both this Office action and the previous action, that objection and/or rejection is maintained. To the extent that the objection and/or rejection appears only in the previous Office action, that objection and/or rejection is withdrawn.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of the following informalities: the meaning of the abbreviation “TDECQ” should be spelled out and “feed-forward” should be “a feed-forward”. Claims 2-10 are objected to for dependency on claim 1.
Claims 4, 6, 14, and 16 are objected to because of the following informalities: “data is [has]” should be “data are [have]”. Claims 7-8 are objected to for dependency on claim 6 and claim 5 is objected to for dependency on claim 4.
Claims 9 and 19 are objected to because of the following informalities: “comprised of” should be “comprising”. Claims 10 and 20 are objected to for dependency on claims 9 and 19, respectively.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-10 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.
The term “optimal” in claim 1 is a relative term which renders the claim indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The word “optimal” is not defined in the specification, nor does the specification give any indication of what the criteria for optimality are, and Examiner is aware of no art-accepted definition of the term “optimal”.
All claims dependent on a claim rejected hereunder are also rejected for dependency on the rejected base 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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a test and measurement instrument comprising a processor; therefore, the claim is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
[S]cal[ing] the waveform data to fit within a magnitude range of the 3D tensor image: This limitation could encompass mentally scaling the data to fit within a range of a tensor. Scaling is also a mathematical concept.
[B]uild[ing] the 3D tensor image in accordance with the one or more inputs, wherein building the 3D tensor image comprises placing the waveform data in the 3D tensor image with one of time or frequency along a first axis, a number of rows along a second axis, and a magnitude along a third axis: This limitation could encompass building the 3D image by drawing it with a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the judicial exception is performed as part of a “test and measurement instrument, comprising: a port to allow the instrument to connect to a device under test (DUT) to receive waveform data; a connection to a pre-trained neural network; and one or more processors configured to execute code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim further recites “receiv[ing] one or more inputs about a three-dimensional (3D) tensor image; … send[ing] the 3D tensor image to the pre-trained neural network; and receiv[ing] a predictive result from the pre-trained neural network, the predictive result comprising one or more of optimal tuning parameters, TDECQ, or feed-forward equalizer (FFE) tap value.” These limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A prong 2, with the exception that the two receiving and sending limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As an ordered whole, the claim is directed to a mentally performable process of building a 3D tensor image. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “build[ing] three 3D tensor images, one 3D tensor image for each of a set of reference parameters”. This limitation could encompass drawing the three images with a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “plac[ing] each of the 3D tensor images on a different color channel of a red-green-blue color image.” This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim further recites that “the code that causes the one or more processors to build the 3D tensor image causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The placing limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis mirrors that of step 2A, prong 2.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] bar graphs of one or more operating parameters into the 3D tensor image.” This limitation could encompass drawing the bar graphs using a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the one or more processors are further configured to execute code that causes the one or more processors to” place the bar graphs. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the one or more processors are further configured to execute code that causes the one or more processors to” place the bar graphs. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “split the waveform data into multiple segments when the waveform data has more samples than an available width of the 3D tensor image; and place each one of the multiple segments in separate rows of a number of rows in the 3D tensor image, with time being along an x-axis as the first axis, the number of rows being along y-axis as the second axis, and magnitude of each segment being along a z-axis as the third axis.” These limitations could encompass mentally splitting the waveform data into segments and drawing those segments as rows of a 3D image using a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the code that causes the one or more processors to build the 3D tensor image comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the code that causes the one or more processors to build the 3D tensor image comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] each one of the multiple segments in a separate row spaced apart from rows containing others of the multiple segments by a predetermined number of rows based upon a size of internal neural network convolutional filters in the pre-trained neural network.” This limitation could encompass placing the segments in the rows based on the filter size using a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the code that causes the one or more processors to place each one of the multiple segments in separate rows further comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the code that causes the one or more processors to place each one of the multiple segments in separate rows further comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “ split[ting] the waveform data for each S-parameter into real and imaginary waveforms; and plac[ing] each of the real waveforms and each of the imaginary waveforms into separate rows of the 3D tensor image, with frequency being along an x-axis as the first axis, the number of rows being along y-axis as the second axis, and magnitude of each waveform being along a z-axis as the third axis.” These limitations could encompass mentally splitting the waveform data and drawing the waveforms on a 3D image with a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiv[ing] the waveform data, wherein the waveform data is S-parameter waveform data”. This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim further recites that “the code that causes the one or more processors to build the 3D tensor image comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis mirrors that of step 2A, prong 2.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] each of the real waveforms and the imaginary waveforms into separate rows spaced apart from others of the real and imaginary waveforms by a predetermined number of rows based upon a size of internal neural network convolutional filters in the pre-trained neural network.” This limitation could encompass drawing the waveforms on a 3D grid a certain number of rows away from others using a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the code that causes the one or more processors to place each of the real waveforms and each of the imaginary waveforms into separate rows comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the code that causes the one or more processors to place each of the real waveforms and each of the imaginary waveforms into separate rows comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] each of the real waveforms and the imaginary waveforms into separate rows with no spaces between rows.” This limitation could encompass placing the waveforms in separate rows using a pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the code that causes the one or more processors to place each of the real waveforms and each of the imaginary waveforms into separate rows comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the code that causes the one or more processors to place each of the real waveforms and each of the imaginary waveforms into separate rows comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] each repetition of the short pattern waveform in a row of the image to form a group of rows with no spacing between them, the 3D tensor image having time along an x-axis as the first axis, the number of rows along a y-axis as the second axis, and magnitude along a z-axis as the third axis.” This limitation could include drawing the tensor with a pen and paper by drawing the repetitions of the waveform in rows.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the code that causes the one or more processors to build the 3D tensor image comprises code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “captur[ing] multiple repetitions of a short pattern waveform [comprising] a portion of a waveform having a length equal to a pre-determined number of unit intervals, the short pattern waveform being identified by the one or more inputs about the 3D tensor image”. This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The capturing limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis mirrors that of step 2A, prong 2.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “plac[ing] each repetition of the at least one other short pattern waveform in at least one other group of rows with no spacing between the rows of the other group of rows, the spacing between groups of rows being based upon a size of internal neural network convolutional filters in the pre-trained neural network.” This limitation could encompass drawing the waveforms in rows of a 3D image using a pen and paper based on the neural network filter size.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “captur[ing] multiple repetitions of at least one other short pattern waveform”. This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim further recites that “the one or more processors are further configured to execute code that causes the one or more processors to [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A prong 2, with the exception that the capturing limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); 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 11-20
Step 1: The claims recite a method; therefore, they are directed to the statutory category of processes.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 1-10, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-10, respectively, except insofar as these claims do not recite what the predictive result is.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-10, respectively, except insofar as these claims do not recite what the predictive result is.
Claim Rejections - 35 USC § 103
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Chae et al. (US 20220399025) (“Chae”) in view of Absher et al. (US 20180074096) (“Absher”) and further in view of Cichocki, “Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions,” in arXiv preprint arXiv:1403.2048 (2014) (“Cichocki”) and Hayashi et al. (US 11467620) (“Hayashi”).
Regarding claim 1, Chae discloses “[an] … instrument, comprising: …
a connection to a pre-trained neural network (Chae Fig. 1 shows that the person background image and speech audio signal are input to encoders and a decoder [i.e., there is a connection to a machine learning network]; see also paragraphs 33-34 (indicating that the device may be implemented by a CNN and describing the training of the models)); and
one or more processors configured to execute code (Chae Fig. 9 shows processor 14 connected via a bus to computer readable storage medium 16 storing a program [code] 20) that causes the one or more processors to:
receive one or more inputs about a three-dimensional (3D) tensor image (first encoder extracts an image feature vector [inputs about a 3D tensor image] from portions of a person background image, except for portions related to speech – Chae, paragraph 37; see also Figs. 1 (showing that the output of the first encoder is input to the decoder), 3 (showing that the image feature vector A is in the form of a 3D tensor));
scale … waveform data to fit within a magnitude range of the 3D tensor image (combiner may transform [scale] a voice feature vector B into a vector having the same form as an image feature vector A [i.e., to fit within the size/magnitude range of the dimensions of A] by copying the voice feature vector B by the height of the image feature vector A in the height direction and copying the voice feature vector B by the width of the image feature vector A in the width direction – Chae, paragraph 56; see also paragraph 66 (disclosing that the resulting voice feature vector is a 3D tensor), Fig. 1 (showing that the raw speech audio signal is a waveform));
build the 3D tensor image in accordance with the one or more inputs (combiner may generate [build] a combined vector [tensor image] by multiplying the reshaped voice feature vector B for each height and width of the image feature vector A [inputs] – Chae, paragraph 67; see also Fig. 3 (showing that the combined tensor is 3D));
send the 3D tensor image to the pre-trained neural network (decoder [pre-trained neural network] may reconstruct the speech video of a person using the combined vector output from the combiner [3D tensor image] as an input – Chae, paragraph 42); and
receive a predictive result from the pre-trained neural network (decoder [pre-trained neural network] may generate a speech video by performing deconvolution on the combined vector followed by up-sampling, and the decoder may compare the generated speech video with an original speech video [i.e., the generated speech video is a prediction of what the original speech video looks and sounds like] – Chae, paragraphs 43-44) ….”
Chae appears not to disclose explicitly the further limitations of the claim. However, Absher discloses “[a] test and measurement instrument, comprising:
a port to allow the instrument to connect to a device under test (DUT) to receive waveform data (Absher Fig. 1 discloses an oscilloscope [test and measurement instrument] containing a port that connects to a DUT; paragraph 5 discloses that the oscilloscope uses machine learning to classify incoming waveforms [i.e., the oscilloscope receives waveform data]) ….”
Absher and the instant application both relate to device testing and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chae to employ the system in a test and measurement instrument that receives waveform data from a DUT via a port, as disclosed by Absher, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the user to gain insights into the workings of the DUT that can be used for downstream tasks. See Absher, paragraph 2.
Neither Absher nor Chae appears to disclose explicitly the further limitations of the claim. However, Cichocki discloses that “building the 3D tensor … comprises placing the … data in the 3D tensor … with one of time or frequency along a first axis, a number of rows along a second axis, and a magnitude along a third axis (higher-order tensor can be interpreted as a multiway array; the order of a tensor is the number of its “modes”, “ways”, or “dimensions” [axes], which can include space, time [which may be regarded as a magnitude along a third axis], frequency [first axis], trials, classes [number of rows along a second axis], and dictionaries – Cichocki, sec. II, first paragraph) ….”
Cichocki and the instant application both relate to tensor manipulation and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Absher and Chae to organize the tensor along three axes including a frequency, a number of rows, and a magnitude, as disclosed by Cichocki, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow a user to visualize the tensor more easily, thereby rendering it more intelligible to the user. See Cichocki, sec. II, first paragraph.
Neither Absher, Chae, nor Cichocki appears to disclose explicitly the further limitations of the claim. However, Hayashi discloses that “the predictive result compris[es] one or more of optimal tuning parameters, TDECQ, or feed-forward equalizer (FFE) tap value (in an ASIC associated with generating and transmitting data packets, clock uncertainties may be within a predicted margin that the tuned phases may accommodate [predicted margin = optimal tuning parameter] – Hayashi, col. 7, ll. 44-45 and col. 8, ll. 29-40).”
Hayashi and the instant application both relate to modeling of designs under test and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Absher, Chae, and Cichocki to predict an optimal tuning parameter, as disclosed by Hayashi, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would cause the device under test to be more properly tuned, thereby increasing its reliability. See Hayashi, col. 8, ll. 29-40.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Absher and further in view of Cichocki.
Claim 11 is a method claim corresponding to instrument claim 1 and is rejected for the same reasons as given in the rejection of that claim, except insofar as claim 11 (a) recites that the machine learning model is “pretrained,” which is taught by Chae (decoder is a machine learning model that is trained [pretrained] to reconstruct a portion covered with a mask of the image feature vector output from the first encoder on the basis of the voice feature vector output from the second encoder – Chae, paragraph 42), and (b) fails to recite that that “the predictive result compris[es] one or more of optimal tuning parameters, TDECQ, or feed-forward equalizer (FFE) tap value.”
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Absher, Cichocki, and Hayashi and further in view of Giner et al. (US 20210279880) (“Giner”).
Regarding claim 2, the rejection of claim 1 is incorporated. Chae further discloses “3D tensor images”, as shown above in the rejection of claim 1.
Neither Chae, Cichocki, Hayashi, nor Absher appears to disclose explicitly the further limitations of the claim. However, Giner discloses that “the code that causes the one or more processors to build the … tensor image causes the one or more processors to:
build three … tensor images, one … tensor image for each of a set of reference parameters (data format used in natural images is data that are “seen” in 2D but in reality are a 3D tensor of dimensions WxHxC, where the 3D tensor channel C can be three channels standing for the color channels in the RGB image [each tensor image being a 2D tensor corresponding to reference parameters associated with red, blue, and green, respectively] – Giner, paragraph 85); and
place each of the … tensor images on a different color channel of a red-green-blue color image (data format used in natural images is data that are “seen” in 2D but in reality are a 3D tensor of dimensions WxHxC, where the 3D tensor channel C can be three channels standing for the color channels in the RGB image [each tensor image being a 2D tensor corresponding to reference parameters associated with red, blue, and green, respectively] – Giner, paragraph 85).”
Giner and the instant application both relate to building tensors for machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Chae, Cichocki, Hayashi, and Absher to form three channels of tensors, as disclosed by Giner, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the computation to be distributed across color channels, thereby reducing the computational workload on any one set of hardware. See Giner, paragraph 85.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Absher and Cichocki and further in view of Giner.
Claim 12 is a method claim corresponding to instrument claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Absher, Cichocki, and Hayashi and further in view of Ojo et al. (US 20230297625) (“Ojo”).
Regarding claim 3, the rejection of claim 1 is incorporated. Chae further discloses a “3D tensor image”, as shown in the rejection of claim 1.
Neither Chae, Hayashi, Cichocki, nor Absher appears to disclose explicitly the further limitations of the claim. However, Ojo discloses that “the one or more processors are further configured to execute code that causes the one or more processors to place bar graphs of one or more operating parameters into the … image (data visualization [image] of one or more data attributes [operating parameters] can include a bar chart showing different data values for data attributes displayed according to various visual configuration parameters – Ojo, paragraph 46).”
Ojo and the instant application both relate to visualization of machine learning results and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Chae, Cichocki, Hayashi, and Absher to create a bar chart of the operating parameters, as disclosed by Ojo, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the user to understand the data more effectively. See Ojo, paragraph 46.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Absher and Cichocki and further in view of Ojo.
Claim 13 is a method claim corresponding to instrument claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Response to Arguments
Applicant's arguments filed July 30, 2026 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the introduction of a new ground of rejection, not persuasive.
Applicant first argues that the claims as amended are eligible under 35 USC § 101 because the analysis allegedly gives insufficient weight to the ordered combination, which allegedly is directed to an improvement in the technical field of test and measurement instrument operation, as shown in paragraph 58 of the specification. Remarks at 11-13. However, even assuming arguendo that the specification discloses an improvement to the technical field of test and measurement instrument operation and the interaction of such instruments with neural networks, which Examiner does not concede, this alleged improvement is not reflected in the claim language itself. “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology.” MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)). Insofar as Applicant relies on the building of the image itself as providing the alleged improvement, this cannot, of itself, provide an improvement to technology because it is part of the abstract idea. MPEP § 2106.05(I) (“An inventive concept ‘cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.’”) (citations omitted). And insofar as Applicant relies on the sending of this image to the pre-trained neural network as allegedly providing the improvement, that limitation also does not reflect an improvement to technology because it is directed to mere data output, which is insignificant extra-solution activity, as well as to the well-understood, routine, and conventional activity of transmitting data over a network. The independent claims as a whole are directed to the abstract idea of making predictions using 3D tensor images and merely invoke pre-trained neural networks as a tool to perform this abstract idea.
Applicant’s arguments with respect to the art rejection, Remarks at 13-14, are moot by virtue of the use of newly cited references Cichocki and Hayashi to teach the disputed limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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, Kamran Afshar, can be reached at 571-272-7796. 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.
/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
Prosecution Insights