DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This office action is in response to communication filed on 1/12/2026.
The instant application having application No. 18/228,540 filed on July 31, 2023, presents claims 1-19 for examination. The instant application does not have priority data.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 12, 2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/26/2026 was filed before the mailing date of the Non-Final Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1, 12, and 18 are amended, claims 1-19 are currently pending in the application.
Response to Amendment
Regarding 35 U.S.C. § 101 rejection: Amended claims are still abstract idea without significantly more. New grounds of 101 abstract idea rejections are presented in the office action below.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 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.
Claim Objections
Claims 6-7, and 8-9 are objected to because of the following informalities:
Claim 6, line 3, “outputing”, suggestion: -outputting-. Claim 7 is objected to for the same reason because it depends from claim 6.
Claim 8, line 1, “herein”, suggestion: -wherein-. Claim 9 is objected to for the same reason because it depends from claim 8.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1, This claim is within at least one of the four categories of patent eligible subject matter as it is directed to a system claim under Step 1.
Under Prong 1, Step 2A:
However, the limitations of claim 1,
“deriving feature information associated with application data and update data, wherein the application data comprises a plurality of code sections each having one or more code blocks and the update data defines a modification to at least one code block, wherein deriving the feature information comprises:
deriving connectivity feature information defining a connectivity of code blocks to a particular code section of the plurality of code sections that will be modified by update data;
deriving historical feature information defining a frequency of updates to the particular code section of the plurality of code sections that will be modified by the update data;
deriving activity feature information defining a number of times the particular code section of the plurality of code sections that will be modified by the update data is accessed in a time period;
combining the connectivity feature information, the historical feature information, and the activity feature information to form a multi-modal vector;
predicting, […], a failure probability for the update data that modifies the particular code section using the multi-modal vector;
initiating, responsive to the indication signifying the failure, an automated rollback procedure that includes:
identifying, using the connectivity feature information, one or more downstream code sections that depend from the particular code section;
reverting the one or more downstream code sections and the particular code section to a respective prior state that existed prior to application of the update data; and
generating and […] a rollback event log associated with the automated rollback procedure.”
as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. E.g. human can manually derive feature information as defined in the claim, can manually derive connectivity feature information as defined in the claim, can manually derive historical feature information as defined in the claim, can manually derive activity information as defined in the claim, can manually combine the derived information to form a multi-modal vector, can manually predict a failure probability for the update data as defined in the claim, can manually initiate an automated rollback procedure as defined in the claim, can manually identify downstream code sections as defined in the claim, can manually revert the downstream code sections and the particular code section to a respective prior state as defined in the claim, can manually generate a rollback event log as defined in the claim. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A.
Under Prong 2, Step 2A:
The judicial exception is not integrated into a practical application. The claim recites the following additional elements
“A system”, “one or more processors”, “a memory”, “a neural network comprising a plurality of recurrent neural network layers trained with a machine learning algorithm”;
“outputting an indication signifying a failure based on the failure probability satisfying a threshold;” and
“… storing in the memory a rollback event log associated with the automated rollback procedure”
These additional elements “A system”, “one or more processors”, “a memory”, and “a neural network comprising a plurality of recurrent neural network layers trained with a machine learning algorithm”; are cited as generic computer/program components, or merely as a tool to implement the identified abstract idea, do not integrate the judicial exception into a practical application. Refer to MPEP 2106.05(f). The “outputting …” limitation is insignificant extra-solution activity like transmitting data. Refer to MPEP 2106.05(g). The “storing …” limitation is insignificant extra-solution activity such as storing and retrieving information in memory, Refer to MPEP 2106.05(d) II.
Under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “A system”, “one or more processors”, “a memory”, and “a neural network comprising a plurality of recurrent neural network layers trained with a machine learning algorithm” are mere use of generic computer to implement the abstract idea, do not amount to significantly more than the judicial exception, thus, are not an inventive concept. The “outputting …” limitation is insignificant extra-solution activity like transmitting data which is recognized as well-understood, routine, conventional activity, see MPEP 2106.05(d) II, Symantec for receiving and transmitting data. The “storing …” limitation is insignificant extra-solution activity such as storing and retrieving information in memory which is recognized as well-understood, routine, conventional activity, Refer to MPEP 2106.05(d) II, Versata Dev. Group, Inc. v. SAP Am., Inc. for retrieving and storing data. Accordingly, even viewed as a whole, the claim does not appear to be patent eligible under 35 USC 101.
With respect to claim 12, This claim is within at least one of the four categories of patent eligible subject matter as it is directed to a method claim under Step 1.
This claim recites a method that is disclosed in claim 1 and therefore recites the same abstract idea as claim 1, please see the office action analysis regarding claim 1.
Claim 12 does not recite any additional element that is not recited in claim 1.
With respect to claims 2 and 13, “wherein deriving the feature information further comprises: determining a size of the particular code section that will be modified by the update data and outputting at least the size of the particular code section that will be modified by the update data as complexity feature information and the neural network is additionally trained using the complexity feature information.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine a size of the particular code section as defined in the claim; the “outputting …” limitation is insignificant extra-solution activity like transmitting data which is recognized as well-understood, routine, conventional activity, see MPEP 2106.05(d). Training the neural network using the complexity feature information can be performed by human manually.
With respect to claims 3 and 14, “wherein deriving the feature information further comprises: determining a trust score for the update data and output at least a trust score for the update data as social feature information wherein the trust score includes at least a number of other successful code section changes made by a person that created the update data and, wherein the neural network is additionally trained using the trust score.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine a trust score for the update data as defined in the claim; the “output …” limitation is insignificant extra-solution activity like transmitting data which is recognized as well-understood, routine, conventional activity, see MPEP 2106.05(d). Training the neural network using the trust score can be performed by human manually.
With respect to claims 4 and 15, “wherein the trust score further includes a tenure of the person that created the update data.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually calculate the trust score including consideration of a tenure of the person that created the update data.
With respect to claims 5 and 16, “wherein the trust score includes a score for rank or title within an organization for the person that created the update data.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually calculate the trust score including consideration of rank or title of the person that created the update data.
With respect to claims 6 and 17, “wherein deriving the feature information further comprises: determining a sentiment of code comments in the particular code section that will be modified by the update data and outputing at least a score for sentiment as comment feature information wherein the neural network is additionally trained using the score for sentiment.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine the sentiment of code comments as defined by the claim language; the “outputting …” limitation is insignificant extra-solution activity like transmitting data which is recognized as well-understood, routine, conventional activity, see MPEP 2106.05(d). Training the neural network using the score for sentiment can be performed by human manually.
With respect to claims 7 and 18, “further comprising a sentiment analysis neural network trained with a machine learning algorithm to determine sentiment from text strings in code comments.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. That is, other than reciting “a sentiment analysis neural network trained with a machine learning algorithm”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a sentiment analysis neural network trained with a machine learning algorithm” language, “determine” in the context of this claim encompasses the user manually determine sentiment from text strings in the comments of code. “a sentiment analysis neural network trained with a machine learning algorithm”; is cited as generic computer/program component, or merely as a tool to implement the identified abstract idea, does not integrate the judicial exception into a practical application, and is not an inventive concept. Refer to MPEP 2106.05(f).
With respect to claims 8 and 19, “herein deriving the feature information further comprises determining a number of failures the particular code section that will be modified by the update data has experienced due to past updates and output the number of failures the particular code section that will be modified by the update data has experienced due to past updates as failure feature information wherein the neural network is additionally trained using the failure feature information.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine a number of failures as defined by the claim; the “output …” limitation is insignificant extra-solution activity like transmitting data which is recognized as well-understood, routine, conventional activity, see MPEP 2106.05(d). Training the neural network using the failure feature information can be performed by human manually.
With respect to claim 9, “wherein the failure feature information further includes a number of reverts to original code.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine whether the failure feature information further includes a number of reverts to original code.
With respect to claim 10, “wherein the particular code section includes an entire file.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine whether the code section includes an entire file.
With respect to claim 11, “wherein the code section includes multiple files.” The claim as drafted, is function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. e.g. human can manually determine whether the code section includes multiple files.
Response to Arguments
Applicant's arguments against 101 abstract idea rejections filed 1/12/2026 have been fully considered but they are not persuasive.
At p10 second from last paragraph of the Remarks, Applicant argued that “claim 1, as amended, includes additional elements that demonstrate that claim 1, as a whole, improves upon computational efficiency in computing systems with respect to performing updates to complex code bases in software development environments. …”
Examiner respectfully disagrees, because the improved computational efficiency is the benefit of using a computer including machine learning as a tool, no technology is improved, i.e. no hardware or software technology is improved. Providing warnings about risks involved with a particular update to software to developers is mental process because human can predict and output outcomes of events based on information from prior events.
At p10 last paragraph of the Remarks, Applicant argued that “While the tracing tool is useful in providing a clear view of a service architecture and interconnection it does little to warn developers that an update to a particular section of code will cause the system to fail.”
Examiner respectfully disagrees, as explained in 24, Providing warnings to developers is mental process because human can predict and output outcomes of events based on information from prior events.
At p11 first paragraph of the Remarks, Applicant argued that “… These steps integrate the predictive output of the neural network into a remediation workflow thereby signifying integration into a practical application. That is, the claimed techniques not only predict a failure probability using neural network techniques, but the claimed techniques also automate corrective actions across multiple interdependent software components using the neural network prediction. The series of automated, system-level operations associated with the automated rollback procedure go beyond what could be done in the human mind, on paper, or with conventional methods and function in a manner to recognize complex patterns and relationships among disparate types of data enabling highly accurate, real-time predictions not feasible through manual inspection or conventional tracing tools. …”
Examiner respectfully disagrees, because, as set forth in the office action above, the limitations, "initiating, responsive to the indication signifying the failure, an automated rollback procedure" that includes "identifying, using the connectivity feature information, one or more downstream code sections that depend from the particular code section; reverting the one or more downstream code sections and the particular code section to a respective prior state that existed prior to application of the update data; and generating … a rollback event log associated with the automated rollback procedure." recite the abstract idea of a mental process because human can manually initiate an automated rollback procedure as defined in the claim, can manually identify downstream code sections as defined in the claim, can manually revert the downstream code sections and the particular code section to a respective prior state as defined in the claim, and can manually generate a rollback event log as defined in the claim. The “storing in the memory …” limitation is insignificant extra-solution activity such as storing and retrieving information in memory which is recognized as well-understood, routine, conventional activity, Refer to MPEP 2106.05(d) II. Even viewed as a whole, the claims do not integrate the judicial exception into a practical application, and do not provide inventive concept. Accordingly, even viewed as a whole, the claims do not appear to be patent eligible under 35 USC 101.
At p11 first paragraph of the Remarks, Applicant further argued that “… Thus, the claimed techniques do not recite purely mental processes but instead recite concrete enhancements and practical applications of computer functionality and it is well-settled that such claims are patent eligible. See, e.g., Enfish, LLC v. Microsoft Corp., 822 F.3d 1327. See also McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F. 3d 1299.”
Examiner respectfully disagrees, because, Enfish and McRo cases are not applicable. Regarding Enfish, its self-referential data table was a data table of the memory controller of the computer itself and thus the improvement was to the computer itself. McRO improved existing technological process because the claimed rules enable the automation of specific animation tasks that previously could not be automated. The instant claims do not recite any feature similar to Enfish or McRO.
At p11 second paragraph of the Remarks, Applicant argued that “For at least the foregoing reasons, it is clear that the claims include additional elements and, as a whole, integrates the alleged abstract ideas into a practical application by clearly improving a technology and a technical field. The present claims, as amended, recite a specific technological improvement related to software development and testing. Thus, Applicant respectfully submits that the claims are not directed towards an abstract idea or any other judicial exception and amount to significantly more than just an abstract idea and any other judicial exception and thus are patent eligible.”
Examiner respectfully disagrees, because, as set forth in the office action, and as explained above, the claims do not affect computer functionality or software technology, i.e. the computer functions the same as it would before the instant application. The additional elements are either merely used as a tool to implement the identified abstract idea, or insignificant extra-solution activity like transmitting and storing data which are recognized as well-understood, routine, conventional activity; and therefore, do not integrate the judicial exception into a practical application, i.e. do not improve any technology, nor do they constitute an inventive concept.
At p11 last paragraph of the Remarks, Applicant argued that “Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C.§ 101 and allowance of the pending claims”
Examiner respectfully disagrees, because, as explained above, and as set forth in the office action above, the claims as a whole do not appear to be patent eligible under 35 USC 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For example Matos et al. US 12436865 B2, teaches Natural Language Processing Engine For Automated Detection Of Source Code Discrepancies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Zengpu Wei whose telephone number is 571-270-1302. The examiner can normally be reached on Monday to Friday from 8:00AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bradley Teets, can be reached on 571-272-3338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZENGPU WEI/Examiner, Art Unit 2197
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