Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 5, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
The information disclosure statement (IDS) submitted on April 28, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
The information disclosure statement (IDS) submitted on June 11, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
The information disclosure statement (IDS) submitted on August 17, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 1-19 are rejeted under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding Independent Claim 1, the claim recites the following method steps:
receive a tabular dataset, the tabular dataset having one or more values that corrupt an artificial intelligence model and valid values;
encoding each valid value in an encoded dataset, the encoded dataset encoding each value to a particular feature and position of the tabular dataset, such that encoding removes the one or more values that corrupt the artificial intelligence model and encoded data sequences of the encoded dataset have a shorter sequence length;
decoding the encoded dataset to a decoded dataset.
It is the position of the Examiner that the method steps cited above are directed to an abstract mental process, as encoding a table of data into a compacted form by removing data unreadable by an AI model and then decoding the data requires no more than the type of observation, evaluation, judgement, and opinion that can practically be performed in the human mind or with the aid of pen and paper (see MPEP 2106.04(a)(2)(III)).
The additional computer element, such as a non-transitory computer-readable medium, fails to integrate the abstract idea into a practical application or provide significantly more because it is a generic computer element recited at a high level of generality and thus constitutes “apply it” language (see MPEP 2104.05(f)).
Regarding dependent Claims 2-7 and 9 merely define the dataset and are thus directed to the same abstract mental process set forthabove.
Regarding dependent Claim 8, the additional computer element, such as a transformer, fails to integrate the abstract idea into a practical application or provide significantly more because it is a generic computer element recited at a high level of generality and thus constitutes “apply it” language (see MPEP 2104.05(f)).
Regarding Independent Claim 10, the claim recites the following method steps:
encoding each data sequence with a sparse representation as a dimension tensor such that the sparse representation encodes a feature and corresponding position of each value of the data sequence, the sparse representation having a sequence length that is less than the sequence length of the data sequences.
It is the position of the Examiner that the method step cited above is directed to an abstract mental process, as encoding a table of data into a compacted form by removing data unreadable by an AI model requires no more than the type of observation, evaluation, judgement, and opinion that can practically be performed in the human mind or with the aid of pen and paper (see MPEP 2106.04(a)(2)(III)).
The additional method step, including “receiving input data having a plurality of data sequences forming a dataset, the dataset including defined values and undefined values, each data sequences having a sequence length” fails to integrate the abstract mental process into a practical application or provide significantly more because it constitues insignificant extra-solution activity (mere data gathering) (see MPEP 2104.05(g)(3)).
Regarding dependent Claims 11-18, the claims merely describe the dataset, and are thus directed to the same abstract mental process set forth above.
Regarding Independent Claim 19, the claim recites the following method steps:
reduce the tabular manufacturing data to sparse representations associated with each manufacturing product after removing the undefined values, each sparse representation representing a dimension tensor of one or more manufacturing properties.
It is the position of the Examiner that the method step cited above is directed to an abstract mental process, as encoding a table of data into a compacted form by removing data unreadable by an AI model requires no more than the type of observation, evaluation, judgement, and opinion that can practically be performed in the human mind or with the aid of pen and paper (see MPEP 2106.04(a)(2)(III)).
The additional method steps, including “receive tabular manufacturing data for a transformer, the tabular manufacturing data representing a plurality of manufacturing products (B) and manufacturing features (T) associated with each manufacturing product, the tabular manufacturing data having undefined values and defined values”, fails to integrate the abstract mental process into a practical application or provide significantly more because it constitues insignificant extra-solution activity (mere data gathering) (see MPEP 2104.05(g)(3)).
The additional computer elements, such as “a memory with instruction, and a processor operable to execute the instruction”, fail to integrate the abstract idea into a practical application or provide significantly more because it is a generic computer element recited at a high level of generality and thus constitutes “apply it” language (see MPEP 2104.05(f)).
Regarding dependent Claim 20, it is the position of the Examiner that the claim integrates the abstract mental process set forth above into a practical application and is thus not directed to an abstract idea without significantly more.
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.
Claims 1-9 are rejected under 35 U.S.C. 112(b) as failing to set forth the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Independent Claim 1, the phrase “such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Dependent Claims 2-9 are rejected as being dependent upon a rejected base claim.
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.
Claims 1, 2, 10, 11, and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maxwell (PG Pub. No. 2019/0370254 A1).
Regarding Claim 1, Maxwell disclsoes a non-transitory computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions operable by a processor to:
convert a first dataset with missing data to a second dataset without missing data (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values) such as for an attention-based neural network, the instructions operable to perform the following functions:
receive a tabular dataset, the tabular dataset having one or more values that corrupt an artificial intelligence model and valid values (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values);
encoding each valid value in an encoded dataset, the encoded dataset encoding each value to a particular feature and position of the tabular dataset, such that encoding removes the one or more values that corrupt the artificial intelligence model and encoded data sequences of the encoded dataset have a shorter sequence length (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values); and
decoding the encoded dataset to a decoded dataset (see Maxwell, paragraph [0129], where the returned sparse vector rows are collected to a single machine, expanded into dense vectors (e..g, the sparse values are added back in), and transposed such that individuals are rows and the various sparse vector identifiers become columns).
Regarding Claim 2, Maxwell discloses the non-transitory computer readable medium of Claim 1, wherein each encoded data sequence corresponds to a row of the tabular dataset (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values).
Regarding Claim 10, Maxwell discloses a method of reducing a dataset with missing data, the method comprising:
receiving input data having a plurality of data sequences forming a dataset, the dataset including defined values and undefined values, each data sequences having a sequence length (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values); and
encoding each data sequence with a sparse representation as a dimension tensor such that the sparse representation encodes a feature and corresponding position of each value of the data sequence, the sparse representation having a sequence length that is less than the sequence length of the data sequences (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where triplet data structure 222 includes rows, data values, and the column position of the data value in a tuple).
Regarding Claim 11, Maxwell discloses the method of Claim 10, wherein the undefined values are representative of missing data (see Maxwell, paragraph [0079], where the value of the quantitative traint for the individual can be NULL (e.g., missing data).
Regarding Claim 13, Maxwell discloses method of Claim 10, wherein the input data is tabular data and each data sequences corresponds to a row (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3, 9, 12, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Maxwell as applied to Claims 1, 2, 10, 11, and 13 above, and further in view of Bobbitt (“Pandas: How to Use fillna() with Specific Columns”, Pandas: How to Use fillna() with Specific Columns, https://www.statology.org/pandas-fillna-specific-column/, June 10, 2022).
Regarding Claim 3, Maxwell discloses the non-transitory computer readable medium of Claim 1, wherein:
Maxwell does not disclose the tabular dataset is a numerical dataset and the one or more values that corrupt the artificial intelligence model are Not-a-Number values. Bobbitt discloses the tabular dataset is a numerical dataset and the one or more values that corrupt the artificial intelligence model are Not-a-Number values (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Regarding Claim 9, Maxwell discloses the non-transitory computer readable medium of Claim 1, further comprising:
Maxwell does not disclose substituting the one or more values that corrupt the artificial intelligence model with a dummy value to remove. Bobbitt discloses substituting the one or more values that corrupt the artificial intelligence model with a dummy value to remove (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Regarding Claim 12, Maxwell discloses the method of Claim 10, wherein:
Maxwell does not disclose the input data is numerical data and the undefined values are represented as Not-a-Number (NaN). Bobbitt discloses the input data is numerical data and the undefined values are represented as Not-a-Number (NaN) (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Regarding Claim 16, Maxwell discloses the method of Claim 10, further comprising:
Maxwell does not disclose imputing values for one or more undefined values. Bobbitt discloses imputing values for one or more undefined values (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros [it is the position of the Examiner that the broadest reasonable interpretation of imputing values encompasses substituting a constant value such as 0 for missing data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Regarding Claim 17, Maxwell discloses the method of Claim 10, wherein:
Maxwell does not disclose the undefined values are replaced with placeholder values. Bobbitt discloses the undefined values are replaced with placeholder values (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Regarding Claim 18, Maxwell in view of Bobbitt discloses the method of Claim 17, wherein:
Maxwell does not disclose the placeholder values are zero. Bobbitt discloses the placeholder values are zero (see Bobbitt, Example 1, for sample code using fill(na) to replace NaN values with zeros).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the inveniton to apply the NaN fill technique from Bobbitt to the N/A data cells in Maxwell as it amounts to simple substitution of one known element for another to obtain predictable results (see MPEP 2143(I)(B)).
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Maxwell as applied to Claims 1, 2, 10, 11, and 13 above, and further in view of Gould (PG Pub. No. 2015/0106341 A1).
Regarding Claim 4, Maxwell discloses the non-transitory computer readable medium of Claim 1, wherein:
Maxwell does not disclose the one or more values that corrupt an artificial intelligence model are present in an amount of at least 10% of all values in the tabular dataset. Gould discloses the one or more values that corrupt an artificial intelligence model are present in an amount of at least 10% of all values in the tabular dataset (see Gould, paragraph [0133], where Fig. 10 shows a flowchart for an example of a procedure 1000 for profiling a data set to test its quality before transforming and loading it into a data store … this would enable the business to detect ‘bad’ data (e..g, data with a percentage of invalid values higher than a threshold) [it is the position of the Examiner that setting the threshold disclosed in Gould at 10% constitutes routine optimization (see MPEP 2144.05(II))]).
Maxwell discloes treating invalid data. Gould discloses detecting threshold amounts of invalid data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Gould for the benefit of proifling data before processing (see Gould, Abstract).
Regarding Claim 5, Maxwell discloses the non-transitory computer readable medium of Claim 1, wherein:
Maxwell does not disclose the one or more values that corrupt an artificial intelligence model are present in an amount of 10 to 50% of all values in the tabular dataset. Gould discloses the one or more values that corrupt an artificial intelligence model are present in an amount of 10 to 50% of all values in the tabular dataset (see Gould, paragraph [0133], where Fig. 10 shows a flowchart for an example of a procedure 1000 for profiling a data set to test its quality before transforming and loading it into a data store … this would enable the business to detect ‘bad’ data (e..g, data with a percentage of invalid values higher than a threshold) [it is the position of the Examiner that setting the threshold disclosed in Gould between 10% and 50% constitutes routine optimization (see MPEP 2144.05(II))]).
Maxwell discloes treating invalid data. Gould discloses detecting threshold amounts of invalid data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Gould for the benefit of proifling data before processing (see Gould, Abstract).
Claims 6, 14, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Maxwell as applied to Claims 1, 2, 10, 11, and 13 above, and further in view of Weiss (PG Pub. No. 2020/0013156 A1).
Regarding Claim 6, Maxwell discloses the non-transitory computer readable medium of Claim 1, wherein:
Maxwell does not disclose the tabular dataset is represented by (B, S), and a sparse representation of the tabular dataset is represented by (B, S, T) where B corresponds to a batch, S corresponds to a manufacturing feature, and T corresponds to a sequence length dimension that is less than or equal to S. Maxwell in view of Weiss discloses the tabular dataset is represented by (B, S), and a sparse representation of the tabular dataset is represented by (B, S, T) where B corresponds to a batch, S corresponds to a manufacturing feature (see Weiss, paragraph [0014], where during during or after production of a first assembly unit at an assembly line, the computer system can ingest timeseries and/or georeferenced) manufacturing data of different types for this assembly unit, such as: timestamped ambient data; assembly technician and station operator identifiers; component supplier and batch identifiers; component test data; screw driver torques; adhesive types and application conditions; finishing processes; assembly order; line equipment settings and timestamped use data; etc), and T corresponds to a sequence length dimension that is less than or equal to S (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Weiss as applying the techniques in Maxwell to the manufacturing data in Weiss constitutes using a known technique to improve similar devices (methods, or products) in the same way (see MPEP 2143(I)(C)).
Regarding Claim 14, Maxwell discloses the method of Claim 10, wherein:
Maxwell does not disclose the input data is manufacturing data. Weiss discloses the input data is manufacturing data (see Weiss, paragraph [0014], where during during or after production of a first assembly unit at an assembly line, the computer system can ingest timeseries and/or georeferenced) manufacturing data of different types for this assembly unit, such as: timestamped ambient data; assembly technician and station operator identifiers; component supplier and batch identifiers; component test data; screw driver torques; adhesive types and application conditions; finishing processes; assembly order; line equipment settings and timestamped use data; etc).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Weiss as applying the techniques in Maxwell to the manufacturing data in Weiss constitutes using a known technique to improve similar devices (methods, or products) in the same way (see MPEP 2143(I)(C)).
Regarding Claim 15, Maxwell discloses the method of Claim 10, wherein:
Maxwell does not disclose receiving the input data and encoding the data sequences are performed as pre-processing steps such that each sparse representation is stored and used for training. Weiss discloses receiving the input data and encoding the data sequences are performed as pre-processing steps such that each sparse representation is stored and used for training (see Weiss, paragraph [0069], where the computer system can automatically: aggregate a set of visual features and non-visual manufacturing data exhibiting high temporal and spatial proximity to occurrence of the defect in a particular assembly unit based on the feature map; repeat this process for other assembly units exhibiting and not exhibiting the defect; and then implement artificial intelligence, machine learning, regression, statistical analysis, and/or other methods and techniques to calculate correlations between these visual and non-visual features and the defect across this population of assembly units).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Weiss as applying the techniques in Maxwell to the manufacturing data in Weiss constitutes using a known technique to improve similar devices (methods, or products) in the same way (see MPEP 2143(I)(C)).
Regarding Claim 19, Maxwell discloses a system to encode production data with missing data, the method comprising:
a memory with instruction (see Maxwell, paragraph [0053], where computer program instructions may also be stored in a computer-readable memory), and a processor (see Maxwell, paragraph [0169], where present methods and systems can be operational with numerous other general purpose or special purpose computing system environments) operable to execute the instruction to:
receive tabular data having undefined values and defined values (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values); and
reduce the tabular manufacturing data to sparse representations associated with each manufacturing product after removing the undefined values, each sparse representation representing a dimension tensor of one or more manufacturing properties (see Maxwell, paragraph [0106], where FIG. 4 illustrates an example quantitative trait matrix 202, a triplet data structure 222 derived therefrom, and an example sparse vector-based quantitative trait matrix 212 generated from the triplet data structure 222; see also Fig. 4, where trait matrix 202 contains N/A values and triplet data structure 222 derived therefrom does not contain N/A values).
Maxwell does not disclose the recevied tabular data is manufacturing data for a transformer, the tabular manufacturing data representing a plurality of manufacturing products (B) and manufacturing features (T) associated with each manufacturing product. Weiss discloses the recevied tabular data is manufacturing data for a transformer, the tabular manufacturing data representing a plurality of manufacturing products (B) and manufacturing features (T) associated with each manufacturing product (see Weiss, paragraph [0014], where during during or after production of a first assembly unit at an assembly line, the computer system can ingest timeseries and/or georeferenced) manufacturing data of different types for this assembly unit, such as: timestamped ambient data; assembly technician and station operator identifiers; component supplier and batch identifiers; component test data; screw driver torques; adhesive types and application conditions; finishing processes; assembly order; line equipment settings and timestamped use data; etc [it is the position of the Examiner that claim limitation ‘for a transformer’ constitutes intended use and thus receives no patentable weight]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Weiss as applying the techniques in Maxwell to the manufacturing data in Weiss constitutes using a known technique to improve similar devices (methods, or products) in the same way (see MPEP 2143(I)(C)).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Maxwell and Weiss as applied to Claims 6, 14, 15, and 19 above, and further in view of Onnx (“OneHot – 11”, Onnx 1.15.0 documentation, 2023).
Regarding Claim 7, Maxwell in view of Weiss discloses the non-transitory computer readable medium of Claim 6, wherein:
Maxwell does not disclose each data sequence is encoded with a dimension tensor of ones and zeros represented by (T, S). Onnx discloses each data sequence is encoded with a dimension tensor of ones and zeros represented by (T, S) (see Onnx, Summary, where OneHot produces a one-hot tensor based on inputs. The locations represented by the index values in the ‘indices’ input tensor will have ‘on_value’ and the other locations will have ‘off_value’ in the output tensor).
Maxwell and Onnx are directed to data manipulation, therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Onnx as it constitutes combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Maxwell as applied to Claims 1, 2, 10, 11, and 13 above, and further in view of Kranski (PG Pub. No. 2023/0109398 A1).
Regarding Claim 8, Maxwell discloses the non-transitory computer-readable medium of Claim 1, further comprising:
Maxwell does not disclose feeding the decoded dataset to a transformer. Kranski discloses feeding the decoded dataset to a transformer (see Kranski, paragraph [0051], where features may be extracted from sensor data (e.g., with … a transformer model).
Maxwell discloses preparing the dataset for a tool (see Maxwell, paragraph [0129]). Kranski describes a transformer receiving datasets (See Kranski, paragraph [0051]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Kranski as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Maxwell and Weiss as applied to Claims 6, 14, 15, and 19 above, and further in view of Kranski.
Regarding Claim 20, Maxwell in view of Weiss discloses the system of Claim 19, further comprising:
Maxwell does not disclose:
passing the sparse representations of the tabular manufacturing data to the transformer;
determining an actuation signal from output of the transformer; and
controlling an actuator using the actuation signal.
Kranski discloses:
passing the sparse representations of the tabular manufacturing data to the transformer (see Kranski, paragraph [0051], where features may be extracted from sensor data (e.g., with … a transformer model);
determining an actuation signal from output of the transformer (see Kranski, paragraph [0051], where embodiments herein to reduce processing complexity to a degree that supports near real-time (e.g., multiple times per second, such as 10, 20 or 30 or more) sequences of state determination to control model outputs that control robot actuators … features may be extracted from sensor data (e.g., with … a transformer model); and
controlling an actuator using the actuation signal (see Kranski, paragraph [0051], where embodiments herein to reduce processing complexity to a degree that supports near real-time (e.g., multiple times per second, such as 10, 20 or 30 or more) sequences of state determination to control model outputs that control robot actuators … features may be extracted from sensor data (e.g., with … a transformer model).
Maxwell discloses preparing the dataset for a tool (see Maxwell, paragraph [0129]). Kranski describes a transformer receiving datasets (See Kranski, paragraph [0051]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Maxwell with Kranski as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
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/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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