DETAILED ACTION
This action is in response to the amendment filed 11/20/2025. Claims 1, 3-7, 9-13, 15-19, 21-25, 27-30 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 25 is objected to because of the following informalities:
Regarding claim 25, “an data processing system” in line 1 should read “a data processing system”.
Appropriate correction is required.
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, 3-7, 9-13, 15-19, 21-25, 27-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a processor and is thus an article of manufacture, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
cause one or more data values…to be replaced by one or more invalid data values (This limitation could encompass a human mentally replacing a value in their head with an invalid value)
wherein the one or more invalid data values correspond to values that are unable to be generated (This limitation could encompass a human mentally replacing a data value in their head with a data value that’s unable to be generated)
detect the one or more invalid data values in data (This limitation could encompass a human mentally detecting invalid data values.)
cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations(This limitation could encompass a human mentally causing operations to be performed without considering the invalid data values.)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A processor, comprising: one or more circuits (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Generated by the one or more circuits during one or more operations of one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
of the one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 1 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
A processor, comprising: one or more circuits uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Generated by the one or more circuits during one or more operations of one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
of the one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites the same mental processes as in claim 1 and, therefore, recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 recites additional elements of
wherein the one or more circuits are further to cause a plurality of data values to be loaded into one or more registers, wherein the plurality of data values include one or more valid data values and the one or more invalid data values (This additional element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering(see MPEP 2106.05(g)).)
Therefore, Claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
These additional elements of Claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination, because
wherein the one or more circuits are further to cause a plurality of data values to be loaded into one or more registers, wherein the plurality of data values include one or more valid data values and the one or more invalid data values is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see 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)).
Therefore, Claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
utilize the plurality of data values from one or more registers to perform one or more operations in a sequence (This limitation could encompass a human mentally performing operations with the data values)
wherein individual operations of the sequence are able to identify the invalid data values (This limitation could encompass a human mentally identifying the invalid data values via the operations.)
Claim 4 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 recites additional elements of
wherein the one or more circuits are further to utilize data values (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
wherein the one or more circuits are further to utilize data values uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
wherein at least one individual operation of the one or more operations is enabled to propagate the invalid data values. (This limitation could encompass a human mentally propagating or replacing the data values.)
Claim 5 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 has no additional elements that would integrate the abstract idea into a practical application, and therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements to provide significantly more than the abstract idea itself, taken alone and in combination, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites the same mental processes as in claim 1 and, therefore, recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 recites additional elements of
wherein the one or more neural networks are to perform one or more convolutions using a data set, including the invalid data values, using only valid data values. (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
wherein the one or more neural networks are to perform one or more convolutions using a data set, including the invalid data values, using only valid data values uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Claim 7 recites a system, comprising one or more processors and is thus an apparatus, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
cause one or more data values…to be replaced by one or more invalid data values (This limitation could encompass a human mentally replacing a value in their head with an invalid value)
wherein the one or more invalid data values correspond to values that are unable to be generated (This limitation could encompass a human mentally replacing a data value in their head with a data value that’s unable to be generated)
detect the one or more invalid data values in data (This limitation could encompass a human mentally detecting invalid data values.)
cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations(This limitation could encompass a human mentally causing operations to be performed without considering the invalid data values.)
Claim 7 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
A system, comprising: one or more processors (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Generated by the one or more circuits during one or more operations of one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
of the one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 7 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
A system, comprising: one or more processors uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Generated by the one or more circuits during one or more operations of one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
of the one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 7 is subject-matter ineligible.
Regarding claim 9, claim 9 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 1:
Claim 13 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites
cause one or more data values…to be replaced by one or more invalid data values (This limitation could encompass a human mentally replacing a value in their head with an invalid value)
wherein the one or more invalid data values correspond to values that are unable to be generated (This limitation could encompass a human mentally replacing a data value in their head with a data value that’s unable to be generated)
detect the one or more invalid data values in data (This limitation could encompass a human mentally detecting invalid data values.)
cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations(This limitation could encompass a human mentally causing operations to be performed without considering the invalid data values.)
Claim 13 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 further recites additional elements of
Generated by the one or more circuits during one or more operations of one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
of the one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 13 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
Generated by the one or more circuits during one or more operations of one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
of the one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 13 is subject-matter ineligible.
Regarding claim 15, claim 15 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 16, claim 16 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 1:
Claim 19 recites a non-transitory machine-readable medium and is thus an article of manufacture, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites
cause one or more data values…to be replaced by one or more invalid data values (This limitation could encompass a human mentally replacing a value in their head with an invalid value)
wherein the one or more invalid data values correspond to values that are unable to be generated (This limitation could encompass a human mentally replacing a data value in their head with a data value that’s unable to be generated)
detect the one or more invalid data values in data (This limitation could encompass a human mentally detecting invalid data values.)
cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations(This limitation could encompass a human mentally causing operations to be performed without considering the invalid data values.)
Claim 19 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 further recites additional elements of
a non-transitory machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least: cause one or more data values to be replaced (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Generated by the one or more circuits during one or more operations of one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
of the one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 19 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
a non-transitory machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least: cause one or more data values to be replaced uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Generated by the one or more circuits during one or more operations of one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
of the one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 19 is subject-matter ineligible.
Regarding claim 21, claim 21 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 22, claim 22 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 23, claim 23 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 24, claim 24 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding Claim 25:
Subject Matter Eligibility Analysis Step 1:
Claim 25 recites a data processing system, comprising: one or more processors and is thus an apparatus, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 25 recites
cause one or more data values…to be replaced by one or more invalid data values (This limitation could encompass a human mentally replacing a value in their head with an invalid value)
wherein the one or more invalid data values correspond to values that are unable to be generated (This limitation could encompass a human mentally replacing a data value in their head with a data value that’s unable to be generated)
detect the one or more invalid data values in data (This limitation could encompass a human mentally detecting invalid data values.)
cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations(This limitation could encompass a human mentally causing operations to be performed without considering the invalid data values.)
Claim 25 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 25 further recites additional elements of
An data processing system, comprising: one or more processors to: cause one or more data values to be replaced (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Generated by the one or more circuits during one or more operations of one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
of the one or more neural networks (This additional element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 25 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of Claim 25 does not provide significantly more than the abstract idea itself, taken alone and in combination, because
An data processing system, comprising: one or more processors to: cause one or more data values to be replaced uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Generated by the one or more circuits during one or more operations of one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
of the one or more neural networks uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 25 is subject-matter ineligible.
Regarding claim 27, claim 27 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 28, claim 28 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 29, claim 29 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 30, claim 30 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1, 3-7, 9-13, 15-19, 21-25, 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Meyer (US 12039330 B1) (“Meyer”), in view of Shivarathri (US 12153596 B1) (“Shivar”).
Regarding claim 1, Meyer teaches
a processor, comprising one or more circuits (Meyer, page 27, column 12, lines 63-67, “Execution engine 700 can be part of a data processor (e.g. a data processing integrated circuit device such as a processor, a graphics processor, a digital signal processor, a tensor processor, a neural network accelerator, or other types of application specific integrated circuits)”),
cause one or more data values to be replaced by one or more invalid data values (Meyer, page 27, column 12, line 4-5 “the first top N values [data values] have been replaced by the minimum value [invalid data value]” where “the minimum value [invalid data value] can be a value representing negative infinity (e.g., when operating on signed floating-point numbers), zero (e.g., when operating on positive numbers), or a value representing the largest magnitude negative integer (e.g., when operating on signed integers), etc.” (page 29, col 15, line 33- 38)).
Meyer does not teach, but Shivar does teach
wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks (Shivar, page 12, column 11, lines 39-43, “The anomalous data [invalid data values] in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set [values that are unable to be generated], or the like” wherein “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65).);
detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.).;
cause the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation at 320. Thus, the extraction operation is performed without considering the invalid data.).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to have the invalid data correspond to values that are unable to be generated. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 3, Meyer in view of Shivar teach the processor of claim 1. Meyer further teaches
the one or more circuits are further to cause a plurality of data values to be loaded into one or more registers (Meyer, page 23, column 8, line 11-14, “the set of largest values are first loaded into the feedback registers of the pipeline”).
wherein the plurality of data values include one or more valid data values and the one or more invalid data values (Meyer, page 24, column 6, line 4-7, “Fig 3b at cycle 3, ALU stage-3… stores -[infinity] [invalid data value] in the feedback register. ALU stage-2... stores 21.1 [valid data value] in the feedback register”).
Regarding claim 4, Meyer in view of Shivar teaches the processor of claim 3 (and thus the rejection of claim 3 is incorporated). Meyer also teaches
wherein the one or more circuits are further to utilize the plurality of data values from the one or more registers to perform one or more operations in a sequence (Meyer, page 22, column 2, line 16, “Each ALU stage [one or more circuits] can perform various arithmetic operations on a set of inputs [plurality of data values] and provides the result to the next ALU stage” where “[30.0, 21.2, 33.1, -2.5, 31.0, 40.2, 17.3, 40.2] represent[] an input tensor [plurality of data values]” (Meyer, page 24, column 5, lines 21-22) and “each ALU [circuit] is configured to select the feedback value [data value] from the feedback register” (Meyer, page 24, column 5, lines 52-53).
Meyer does not teach, but Shivar does teach
wherein individual operations of the sequence are able to identify the invalid data values (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” [process of determining that the data value is of the wrong type = sequence of operations]).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to be able to identify invalid data values. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 5, Meyer in view of Shivar teaches the processor of claim 4 (and thus the rejection of claim 4 is incorporated). Meyer further teaches
wherein at least one individual operation of the one or more operations is enabled to propagate the invalid data values (Meyer, page 24, column 5, lines 35-37, “the feedback registers of all ALU stages are initialized [one or more operations] to a minimum value [invalid data values] prior to streaming [propagating] the input values into the pipeline”).
Meyer does not teach, but Shivar does teach
wherein at least one individual operation of the one or more operations is enabled… replace the invalid data values (Shivar, abstract, “a correction model is applied to the anomalous data [invalid data] to generate corrected data [replace invalid data values] based on the identified context”).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to be able to replace invalid data values. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 6, Meyer in view of Shivar teaches the processor of claim 1 (and thus the rejection of claim 1 is incorporated). Meyer further teaches
wherein the one or more neural networks are to perform one or more convolutions (Meyer, page 34, col 25, line 22-24 “For example, for a convolutional neural network, convolutions from multiple channels can be summed to produce an output activation for a single channel.”)
Meyer does not teach, but Shivar does teach
using a data set, including the invalid data values, using only valid data values (Shivar, page 12, col 11, lines 36 - 43 “the identified context is used by the system to determine data in the input data set that is expected [valid data values] and data that is not expected, or anomalous [invalid data values], as described herein. The anomalous data in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set, or the like” wherein “a correction model is applied to the anomalous data to generate corrected data [using only valid data values]”(Shivar, 12, column 11, lines 48-49)).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to be able to replace invalid data values. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 7, Meyer teaches
a system, comprising: one or more processor (Meyer, page 27, column 12, lines 63-67, “Execution engine 700 can be part of a data processor (e.g. a data processing integrated circuit device such as a processor, a graphics processor, a digital signal processor, a tensor processor, a neural network accelerator, or other types of application specific integrated circuits)”),
cause one or more data values to be replaced by one or more invalid data values (Meyer, page 27, column 12, line 4-5 “the first top N values [data values] have been replaced by the minimum value [invalid data value]” where “the minimum value [invalid data value] can be a value representing negative infinity (e.g., when operating on signed floating-point numbers), zero (e.g., when operating on positive numbers), or a value representing the largest magnitude negative integer (e.g., when operating on signed integers), etc.” (page 29, col 15, line 33- 38)).
Meyer does not teach, but Shivar does teach
wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks (Shivar, page 12, column 11, lines 39-43, “The anomalous data [invalid data values] in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set [values that are unable to be generated], or the like” wherein “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65).);
detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.).;
cause the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation at 320. Thus, the extraction operation is performed without considering the invalid data.).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to have the invalid data correspond to values that are unable to be generated. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 9, claim 9 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 13, Meyer teaches
a method, comprising: causing one or more data values to be replaced by one or more invalid data values (Meyer, page 27, column 12, line 4-5 “the first top N values [data values] have been replaced by the minimum value [invalid data value]” where “the minimum value [invalid data value] can be a value representing negative infinity (e.g., when operating on signed floating-point numbers), zero (e.g., when operating on positive numbers), or a value representing the largest magnitude negative integer (e.g., when operating on signed integers), etc.” (page 29, col 15, line 33- 38)).
Meyer does not teach, but Shivar does teach
wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks (Shivar, page 12, column 11, lines 39-43, “The anomalous data [invalid data values] in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set [values that are unable to be generated], or the like” wherein “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65).);
detecting the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.).;
causing the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation at 320. Thus, the extraction operation is performed without considering the invalid data.).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to have the invalid data correspond to values that are unable to be generated. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 15, claim 15 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 16, claim 16 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 19, Meyer teaches
A non-transitory machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors cause the one or more processors to at least: (Meyer, page 37, column 31, lines 1-13, “Computer-readable storage media are physical mediums that are capable of storing data in a format that can be read by a device such as the host processor 1572. Computer-readable storage media can be non-transitory. Non-transitory computer-readable media can retain the data stored thereon when no power is applied to the media….In various examples the data stored on computer-readable storage media can include program instructions”),
cause one or more data values to be replaced by one or more invalid data values (Meyer, page 27, column 12, line 4-5 “the first top N values [data values] have been replaced by the minimum value [invalid data value]” where “the minimum value [invalid data value] can be a value representing negative infinity (e.g., when operating on signed floating-point numbers), zero (e.g., when operating on positive numbers), or a value representing the largest magnitude negative integer (e.g., when operating on signed integers), etc.” (page 29, col 15, line 33- 38)).
Meyer does not teach, but Shivar does teach
wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks (Shivar, page 12, column 11, lines 39-43, “The anomalous data [invalid data values] in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set [values that are unable to be generated], or the like” wherein “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65).);
detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.).;
cause the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation at 320. Thus, the extraction operation is performed without considering the invalid data.).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to have the invalid data correspond to values that are unable to be generated. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 21, claim 21 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 22, claim 22 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 23, claim 23 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 24, claim 24 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 25, Meyer teaches
An data processing system, comprising one or more processors (Meyer, page 27, column 12, lines 63-67, “Execution engine 700 can be part of a data processor (e.g. a data processing integrated circuit device such as a processor, a graphics processor, a digital signal processor, a tensor processor, a neural network accelerator, or other types of application specific integrated circuits)”),
cause one or more data values to be replaced by one or more invalid data values (Meyer, page 27, column 12, line 4-5 “the first top N values [data values] have been replaced by the minimum value [invalid data value]” where “the minimum value [invalid data value] can be a value representing negative infinity (e.g., when operating on signed floating-point numbers), zero (e.g., when operating on positive numbers), or a value representing the largest magnitude negative integer (e.g., when operating on signed integers), etc.” (page 29, col 15, line 33- 38)).
Meyer does not teach, but Shivar does teach
wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks (Shivar, page 12, column 11, lines 39-43, “The anomalous data [invalid data values] in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set [values that are unable to be generated], or the like” wherein “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65).);
detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.).;
cause the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation at 320. Thus, the extraction operation is performed without considering the invalid data.).
Meyer and Shivar are considered analogous because they are in the same field of machine learning, and they are both data processing systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to have the invalid data correspond to values that are unable to be generated. Doing so would “enable[] the system to be flexible in what types and/or formats of data it is configured to receive and to automatically respond to detected issues with the data, avoiding manual review which may impede the data processing” (Shivar, page 8, column 3, line 4-7).
Regarding claim 27, claim 27 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 28, claim 28 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 29, claim 29 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 30, claim 30 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Response to Arguments
On page 8, Applicant argues:
Meyer does not teach "to cause one or more data values, to be used by one or more neural networks, to be replaced by one or more invalid data values," as recited in original claim 1. Meyer describes "a modified tensor that replaces the top N values of the original input tensor with a minimum value." Meyer, col. 11, line 66 to col. 12, line 1. However, far from being an "invalid" data value, the minimum value is a necessary and required value used in the comparison process described in Meyer. For example, as described for FIGs. 3A-D, Meyers initializes feedback registers to the minimum value and then performs a MIN function on each tensor value and the current feedback register value to identify the N largest tensor values. After an initial N largest values have been identified, those values can be replaced with the minimum value and the process repeated to identify the next N largest values in the tensor, as described in the cited portions of Meyer. The minimum value described in the cited portions of Meyer must be a valid data value for the MIN function to perform. Thus, far from causing one or more data values, to be used by one or more neural networks, to be replaced by one or more invalid data values, Meyer requires that a valid minimum value be used. Given the minimum value in Meyer must be understood as a minimum by the MIN function in Meyer for correct operation, the replacement value in Meyer cannot be an "invalid" data value as recited in Applicant's claim. In response to Applicant's previous argument, the Office Action on p.18 alleges the minimum value of negative infinity in Meyer is a invalid value. However, this interpretation is demonstrably incorrect. As shown above, the negative infinity value is explicitly generated and operated on in Meyer as valid data.
Regarding the Applicant’s argument that the prior art of record does not teach “to cause one or more data values, to be used by one or more neural networks, to be replaced by one or more invalid data values”, the Examiner respectfully disagrees. Specifically, the Examiner respectfully notes that paragraph 0046 of the instant specification states “In at least one embodiment, a special or invalid value in this context refers to a value that would not be produced by an operation to be performed, or determined to be a valid input value for an operation to be performed. In at least one embodiment, this might include a value such as "-0" (or another value that may be a "not a number" (NaN) value and that would not be produced by a typical operation, such as a value considered to be a numeric data type that can be interpreted as a value that is undefined or unrepresentable.” Based on this interpretation of what an “invalid data value” is, Meyer’s negative infinity maps to invalid data value regardless if it is the minimum by the MIN function. Just as described in the specification, Meyer’s negative infinity is a value that is not produced by a typical operation. Negative infinity is also a value that is not a number and undefined. Examiner further notes that the value of negative infinity is not produced by an operation to be performed, but rather, replaces the top N values with negative infinity in order to find the next top N values (Meyer, page 27, column 11, line 62 – column 12, line 6). In doing so, negative infinity is not the output in this case of an operation as outlined by the specification. Negative infinity is used as input. Because Meyer replaces the first top N values with a minimum value such as negative infinity, Meyer teaches “to cause one or more data values, to be used by one or more neural networks, to be replaced by one or more invalid data values.”
On page 9, Applicant argues:
The Office Action refers to Shivarathri's discussion of "anomalous data in the input data set may include incorrect data types of data entries, empty data fields of data entries, extra data fields in the data set, missing data fields in the data set, or the like." However, none of these examples of anomalous data in Shivarathri represent invalid data values that are unable to be generated by the one or more circuits during one or more operations. Just because data is anomalous (unexpected), or missing, does not mean the data has an actual value that is invalid and cannot be generated by the one or more circuits during one or more operations. Moreover, the "anomalous data" in Shivarathri is part of the original input data, not replacement values. Accordingly, Shivarathri has no relevance to cause one or more data values to be replaced by one or more invalid data values, wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of the one or more neural networks. In response to Applicant's previous argument, the Office Action on p.19 alleges the anomalous data in Shivarathri is somehow invalid. However, this interpretation has no basis in the actual teachings of the reference. Anomalous data is data that is unexpected, but that is not the same as invalid. While unexpected, the anomalous data can most certainly be generated in Shivarathri. Nothing in Shivarathri equates anomalous data to invalid data. Moreover, the anomalous data in Shivarathri is original data, not replacement data (a point ignored in the Office Action).
Regarding the Applicant’s argument that the prior art of record does not teach claim 1, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that the anomalous data, such as the empty data values in Shivarathri map to values that are unable to be generated by the one or more circuits during one or more operations. Since the data is anomalous, the data is not correct and therefore invalid (Shivar, page 9, column 5, lines 56-61, “Further, anomalous data 114 ma include anomalies such as metadata values that are unexpected (e.g., typos in metadata labels or field names of the data set) or otherwise found to be incorrect.”). Examiner further respectfully notes that Shivarathri discloses operations of storing and extracting data via a system or circuit (Shivarathri, page 8, column 4, lines 11-19). In both operations, data is not generated since the operation of storing and extracting data is storing and reading data. Additionally, in both of these operations, the data is not changed nor is data generated. Examiner notes that claim 1 is taught by a combination of Meyer and Shivarathri. Therefore, since Meyer teaches replacing data values with invalid data values and Shivarathri teaches that invalid data values are unable to be generated, Meyer in view of Shivarathri teach claim 1 (see also 103 rejection). Examiner recommends expanding upon what kind of operations are performed as well as expanding upon the meaning of “unable to be generated”.
On pages 9-10, Applicant argues:
For multiple reasons, as presented above, Meyer in view of Shivarathri does not teach to cause one or more data values to be replaced by one or more invalid data values, wherein the one or more invalid data values correspond to values that are unable to be generated by the one or more circuits during one or more operations of one or more neural networks. Thus, even without amendment, the claims distinguish over Meyer and Shivarathri. However, in the interest of compact prosecution, Applicant has further amended claim 1 to recite to "detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks; and cause the one or more operations to be performed without considering the one or more invalid data values." Neither Meyer nor Shivarathri describe causing one or more operations of a neural network to be performed without considering detected invalid data values that replaced previous data values. Similar remarks apply with respect to Applicant's other independent claims. Accordingly, withdrawal of the§ 103 rejection is respectfully requested.
Regarding the Applicant’s argument that the prior art of record does not teach claim 1, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that a combination of Meyer and Shivarathri teach detect the one or more invalid data values in data provided to the one or more operations of the one or more neural networks (Shivar, page 9, column 5, lines 44-51, “Identification of the anomalous data [invalid data] 114 may be performed on the input data set 106 based on the context metadata 108 and/or the associated context profile 110 of the input data set 106. For instance, if a data value in the data set 106 is a string type and the context metadata 108 and/or context profile 110 indicates that the data value is expected to be an integer type, the data value may be determined to be anomalous data 114” where “input data sets 106 may be obtained from various data sources, such as customers or other entities that have been provided access to the system 100. Alternatively, or additionally, the system [one or more circuits] 100 may include a 15 buffer data store in which input data sets 106 are stored prior to processing and from which the extraction layer 102 obtains the input data sets 106 based on the configuration of the extraction layer 102 [one or more operations]” (Shivar, page 8, column 4, lines 11 - 19) and “the training of the correction model 116 includes machine learning techniques that use, for instance, a trained regressor such as a random decision forest, a directed acyclic graph, a support vector machine, a convolutional neural network or other neural network” (Shivar, page 9, column 6, lines 37-41) and where “the system 100 may be configured to enable the auditing or reviewing of the performance of the correction model 116 as it operates to improve its performance” (Shivar, page 9, column 6, lines 62-65). Examiner notes that detecting the one or more invalid data is determining whether the data value is anomalous. Examiner further notes that the input data in which the invalid data value is detected is provided to the operation of extraction. Examiner notes that the operation of extraction uses the one or more neural network within the process.) and cause the one or more operations to be performed without considering the one or more invalid data values (Shivar, page 4, Figure 3,
PNG
media_image1.png
1016
652
media_image1.png
Greyscale
Examiner notes that the anomalous data or invalid data is corrected before the extraction operation. Thus, the extraction operation is performed without considering the invalid data.). Examiner respectfully points the applicant to the above 103 rejection.
On page 10, Applicant argues:
As noted at, e.g., para. [0049] of Applicant's specification the technique as claimed provides an improvement to computer technology to minimize storage requirements, increase speed of computation, and save power. Accordingly, Applicant's amended claims are patent eligible at least on this basis as an improvement in computer technology.
Moreover, the claims are amended to recite a specific "ordered combination" of features to replacing data values to be used by one or more neural networks by invalid data values that are unable to be generated by the one or more circuits during one or more operations, detect the one or more invalid data values in data provided to the one or more operations of the neural network, and cause the one or more operations to be performed without considering the one or more invalid data values. Such an "ordered combination" provides "significantly more" than a mere abstract idea.
Accordingly, the pending claims are patent eligible for numerous reasons. Applicant also draws the Examiner's attention to the recent precedential PTAB Decision Ex Parte Desjardins et al., Appeal 2024-000567 (ARP Sept. 26, 2025) (precedential).
Regarding the Applicant’s argument that claims 1, 3-7, 9-13, 15-19, 21-25, 27-30 are patent eligible, Examiner respectfully disagrees. Specifically, Examiner respectfully notes that the clarifying details in paragraph 0049 of the instant specification are not explicitly claimed. Additionally, Examiner respectfully notes that “to cause one or more data values…to be replaced by one or more invalid data values” is a mental process since a human can replace data values in the mind. “Wherein the one or more invalid data values correspond to values that are unable to be generated” is also a mental process since a human can replace a data value with a data value that cannot be generated. “Detect the one or more invalid data values in data” is a mental process since a human can mentally detect invalid data values. “Cause the one or more operations to be performed without considering the one or more invalid data values provided to the one or more operations” is a mental process since a human can mentally cause operations to be performed without considering the invalid data values. “A processor, comprising: one or more circuits”, “generated by the one or more circuits during one or more operations of one or more neural networks”, and “of the one or more neural networks” recite a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)). Since those two limitations use a computer as a tool to perform the abstract idea, they cannot provide significantly more (see MPEP 2106.05(f)). Examiner further respectfully notes that the recent precedential PTAB decision Ex Parte Desjardins et al. does not change the analysis under 101. Therefore claim 1 maintains its rejection under 101. Claims 7, 13, 19, and 25 mirror claim 1 and also maintain their rejection under 101. Since independent claims 1, 7, 13, 19, and 25 maintain their rejections, their dependent claims 3-6, 9-12, 15-18, 21-24, and 27-30 also maintain their rejection under 101 for at least the reasons that the independent claims were rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R HAEFNER whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 7:15 am - 5:15 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571) 431-0762. 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.
/K.R.H./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148