DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim(s) 1 objected to because of the following informalities:
Claim 1 recites “train, based on the second plurality of error remediation recommendations, is a second artificial neural network”. The examiner believes this is a typo. A suggested amendment to the claim is “train, based on the second plurality of error remediation recommendations, [[is]] a second artificial neural network”. For examination purposes, the claim will be interpreted as suggested.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “provide, to the first artificial neural network, at least a portion of the first log entry; provide, to the second artificial neural network, at least a portion of the first log entry; receive, from the second artificial neural network and based on the at least a portion of the first log entry”. It is unclear if the “at least a portion of the first log entry” provided to the first artificial neural network and the second artificial neural network is the same portion or different portions. If the portions are different, then it is unclear which portion is referred to by “based on the at least a portion of the first log entry”. A suggested amendment to the claim is “provide, to the second artificial neural network, the at least a portion of the first log entry”. For examination purposes, the claim will be interpreted as suggested.
Claim(s) 3, 4, 7, 8, 10, 11, 27 fail to cure the deficiencies of their base claims and are rejected on the same grounds.
Claim 3 recites “further train, based on the indication whether the first error remediation recommendation fixed the first error, the first artificial neural network”. Claim 1 recites “an indication whether the first error remediation recommendation and/or the second error remediation recommendation fixed the first error”. Due to “and/or”, this limitation in claim 1 encompasses the alternative “an indication whether the first error remediation recommendation fixed the first error”, and it is unclear if this alternative is the same as or different than “an indication whether the first error remediation recommendation fixed the first error” in claim 3. If they are different, then it is unclear which indication is referred to by “based on the indication whether the first error remediation recommendation fixed the first error” in claim 3. A suggested amendment to the claim is to clarify that indications are different and which indication is referred to in claim 3. If the claims are amended to clarify that indications are the same, then claim 3 will be rejected under 112(d) for failing to further limit the subject matter of claim 1. For examination purposes, the claim will be interpreted as if the indications are different.
Claim(s) 13, 14, 22-24, 26 the method(s) implemented by the device(s) of claim(s) 1, 4, 3, 10, 11, 27, respectively, is/are rejected on the same grounds as claim(s) 1, 4, 3, 10, 11, 27, respectively.
Claim(s) 16, 18, 20, 21, 25, the media(s) that implement(s) the device(s) of claim(s) 1, 3, 10, 27, 11, respectively, is/are rejected on the same grounds as claim(s) 1, 3, 10, 27, 11, respectively.
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.
Claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more.
Claim(s) 1, 3, 4, 7, 8, 10, 11, 27 recite(s) devices, claim(s) 13, 14, 22-24, 26 recite(s) methods, and claim(s) 16, 18, 20, 21, 25 recite(s) non-transitory computer-readable media. Therefore, claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 fall(s) within a statutory category.
Claim 1 recites abstract ideas.
detect an error in an automated deployment system; process, using a first natural language processing (NLP) algorithm, a knowledgebase to identify a first plurality of error remediation recommendations; process, using a second NLP algorithm, a chat log to identify a second plurality of error remediation recommendations, wherein the chat log comprises a history of text messages; identify…a first log entry and a first computing device corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses parsing and analyzing human readable text ([0038], [0041], [0044], [0047], [0048], [0051], [0056]).
Claim 1 does not recite additional limitations that integrate the judicial exceptions into practical application.
A computing device configured to…, the computing device comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the computing device to; by a computing device amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
an automated deployment system amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
provide automatic error remediation recommendations for the error; display, on a browser executing on the computing device, log data comprising a plurality of log entries, wherein each of the plurality of log entries corresponds to one or more computing devices managed via the automated deployment system; generate a first output, wherein the first output indicates the first error remediation recommendation, the first computing device, and a first URL corresponding to the first error remediation recommendation; generate a second output, wherein the second output indicates the second error remediation recommendation, the first computing device, and a second URL configured to open a text message service; and cause the first and second outputs to be displayed, in the browser executing on the computing device, where the first log entry is displayed amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
train, based on the first plurality of error remediation recommendations, a first artificial neural network to output an error remediation recommendation based on an input of a log entry indicating an error; train, based on the second plurality of error remediation recommendations is used to train a second artificial neural network to output an error remediation recommendation based on an input of a log entry indicating an error; provide, to the first artificial neural network, at least a portion of the first log entry; receive, from the first artificial neural network and based on the at least a portion of the first log entry, a first error remediation recommendation corresponding to the first error; provide, to the second artificial neural network, at least a portion of the first log entry; receive, from the second artificial neural network and based on the at least a portion of the first log entry, a second error remediation recommendation corresponding to the first error; train, based on the indication and for each artificial neural network, the first artificial neural network and the second artificial neural network amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
wherein: the first log entry is from the plurality of log entries, the first log entry is a thread status indicator from the first computing device in the one or more computing devices managed via the automated deployment system, and the first log entry indicates a first error on the first computing device amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g).
receive, based on user input, an indication whether the first error remediation recommendation and/or the second error remediation recommendation fixed the first error amounts to mere data gathering, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception, insignificant extra-solution activity, and generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(I)(A).
Claim 3 does not recite additional limitations that integrate the judicial exceptions into practical application.
receive, from a user, an indication whether the first error remediation recommendation fixed the first error amounts to mere data gathering, which is insignificant extra-solution activity. See MPEP 2106.05(g).
further train, based on the indication whether the first error remediation recommendation fixed the first error, the first artificial neural network amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception and insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 4 does not recite additional limitations that integrate the judicial exceptions into practical application.
further cause the computing device to process the knowledgebase by: modifying one or more weights of the one or more nodes of the first artificial neural network amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception. See MPEP 2106.05(I)(A).
Claim 7 does not recite additional limitations that integrate the judicial exceptions into practical application.
receive, via a browser extension operating on the browser executing on the computing device, the log data, wherein the log data comprises data in a Document Object Model (DOM) of a web page displayed by the browser on the computing device amounts to mere data gathering, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 8 does not recite additional limitations that integrate the judicial exceptions into practical application.
display log data from a file, wherein: the file comprises the log data, and wherein the log data is formatted in a serialized data format amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 10 does not recite additional limitations that integrate the judicial exceptions into practical application.
receive from the first computing device, output indicating a thread status, wherein the thread status indicates a status for a plurality of threads running on the first computing device; and wherein a thread, of the plurality of threads, corresponds to a computing process on the first computing device amounts to mere data gathering, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 11 refines recited abstract ideas.
identify the first log entry by: comparing the at least a portion of the first log entry to a regular expression corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses parsing and analyzing human readable text ([0038], [0041], [0044], [0047], [0048], [0051], [0056]).
The claim does not contain additional limitations that integrate the judicial exceptions into practical application and does not contain additional limitations that are sufficient to amount to significantly more than the judicial exceptions.
Claim 27 further recites abstract ideas.
identify, from the plurality of log entries, a second log entry indicating a second error corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses parsing and analyzing human readable text ([0038], [0041], [0044], [0047], [0048], [0051], [0056]).
Claim 27 does not recite additional limitations that integrate the judicial exceptions into practical application.
provide, to the first artificial neural network, at least a portion of the second log entry amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g).
receive, from the first artificial neural network, an indication that no error remediation recommendation corresponds to the second error amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
cause a third output to be displayed, in the browser executing on the computing device, wherein the third output indicates that no corresponding error remediation recommendation exists amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception and insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim(s) 13, 14, 22-24, 26 the method(s) implemented by the device(s) of claim(s) 1, 4, 3, 10, 11, 27, respectively, is/are rejected on the same grounds as claim(s) 1, 4, 3, 10, 11, 27, respectively.
Claim 13 does not recite additional limitations that integrate the judicial exceptions into practical application.
A computer-implemented method amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception. See MPEP 2106.05(I)(A).
Claim(s) 16, 18, 20, 21, 25, the media(s) that implement(s) the device(s) of claim(s) 1, 3, 10, 27, 11, respectively, is/are rejected on the same grounds as claim(s) 1, 3, 10, 27, 11, respectively.
Claim 16 does not recite additional limitations that integrate the judicial exceptions into practical application.
One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause a computing device to perform steps amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception. See MPEP 2106.05(I)(A).
For at least the reasons provided above, claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 are not patent eligible.
Allowable Subject Matter
Claims 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
None of the prior art of record, either alone or when combined, teaches or suggests process, using a first natural language processing (NLP) algorithm, a knowledgebase to identify a first plurality of error remediation recommendations; process, using a second NLP algorithm, a chat log to identify a second plurality of error remediation recommendations, wherein the chat log comprises a history of text messages; train, based on the first plurality of error remediation recommendations, a first artificial neural network to output an error remediation recommendation based on an input of a log entry indicating an error; train, based on the second plurality of error remediation recommendations is used to train a second artificial neural network to output an error remediation recommendation based on an input of a log entry indicating an error; provide, to the first artificial neural network, at least a portion of the first log entry; receive, from the first artificial neural network and based on the at least a portion of the first log entry, a first error remediation recommendation corresponding to the first error; provide, to the second artificial neural network, at least a portion of the first log entry as recited in claim(s) 1, 13, 16.
Response to Arguments
Applicant's arguments, see pg. 11, filed 12/22/2025, with respect to the 112(b) rejection(s) of claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 have been fully considered but they are not persuasive. The previous issues have not been addressed. See above rejections.
Applicant's arguments, see pg. 11-14, filed 12/22/2025, with respect to the 101 rejection(s) of claim(s) 1, 3, 4, 7, 8, 10, 11, 13, 14, 16, 18, 20-27 have been fully considered but they are not persuasive.
On pg. 12-13, Applicant argues:
“For example, claim 1 does not "set forth or describe" the abstract idea of "data analysis" because the claims relate to identifying and predicting error remediations across automated deployed systems. Although the Office has stated that "determining an error involves reading human-readable text and determining that the text has the word 'ERROR,' which is something that can be done in the human mind," Applicant notes that the Specification was describing only one example of a potential error indicator, while other error indicators may not be obvious to the human eye. Applicant further notes even if a human is able to identify a log entry indicating an error, the human being may not be practically able to "identify[] ... a first log entry and a first computing device," where the "first computing device" is where the error occurred, from the first log entry alone.
Furthermore, even assuming that a human being would recognize an error indicator, the claims are not directed to the alleged abstract idea of recognizing an error indicator. Instead, claim 1 recites further steps to identify the origin device for the error-"the first computing device”-and uses the first log entry as input to "receive, from the [first/second] artificial neural network, a [first/second] error remediation recommendation.
The claim then further recites, "based on user input, whether the first error remediation recommendation and/or second error remediation recommendation fixed the first error" and "train[ing], based on the indication and for each artificial neural network, the first artificial neural network and the second artificial neural network."
The USPTO has also indicated that the Office should "distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further analysis)." August 4th Memo at 3. As noted in the August 4th Memo, "'training the neural network' ... does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols." August 4th Memo at 3. Amended claim 1 recites several steps for training and updating an artificial neural network and does not recite any mathematical algorithms or other judicial exceptions. Neither do these limitations have any analogue to any arrangement of human beings, nor can they be practically performed in the human mind. Under the August 4th Memo, amended claim 1 is patentable under Step 2A-Prong One of Alice."
The Examiner respectfully disagrees. A human would be able to perform data analysis to identify error origins by parsing human-readable text in logs to identify a first log entry and a first computing device. An example of human-readable text is provided in the specification is in [0048]: “error,”“computer ID 3,”, where “error” identifies the error and “computer ID 3” identifies the device. Furthermore, the specification explicit states that this is a process that can be performed manually by a user in [0047]. Therefore, the claim recites abstract ideas. Training and updating the neural network were not indicated as abstract ideas.
On pg. 13, Applicant argues:
“At minimum, the limitations discussed above integrate the alleged abstract idea into a practical application because they enable a computing device to identify an error, the error origin, and present error remediations for said error at the origin. The claim also recites steps to improve the predictions of the artificial neural networks used with the identified error. As seen in MCRO, these limitations enable a computer beyond the ordinary capabilities. McRO, Inc. v. Bandai Namco Games America, Inc., 837 F. 3d 1299, 1316 (Fed. Cir. 2016) (finding claims patent-eligible because they recited a specific, limited method for improving computer animation compared to existing manual techniques).
This is also supported by the August 4th Memo, which directs the Office to allow claims that "reflect[s] an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception." August 4th Memo at 4. Or, in other words, "a technological solution to a technological problem." Here, claim 1 recites a technical solution of identifying an error and potential remediations for that error across an automated deployment system. Although a human could, with great difficulty, achieve the same goals, claim l's incorporation of specific structural requirements makes claim 1 integrated into a practical application, similar to McRO. 837 F. 3d at 1316. The claims are directed to improving detection of errors logged across an automated deployment system, which the computer was not previously capable of.”
The Examiner respectfully disagrees. Identifying the error along with the origin and presenting a remediation to the error to the user does not provide a technical solution to a technical problem such as an error, and instead amounts to abstract ideas with mere data output. Utilizing artificial neural networks with the identified error amounts to utilizing a computer in its ordinary capacity with abstract ideas, and improves performance of abstract ideas rather than the artificial neural networks. Improving detection of errors, which the specification and the applicant’s arguments indicate can be done manually, improves the abstract idea and does not provide an improvement to the functioning of a computer or to another technology or technical field. Therefore, the claim does not integrate the abstract ideas into practical application.
On pg. 13-14, Applicant argues:
“Finally, even assuming that claim 1 is directed at a judicial exception and does not integrate the alleged judicial exception into a practical application (which Applicant disputes), claim 1 recites significantly more than the alleged judicial exception. Claim 1 recites elements to improve the functioning of a computer and an automated deployment system by identifying errors occurring within the system, similar to DDR Holdings and BASCOM. DDR Holdings, LLC v. Hotels. com, L.P., 773 F. 3d 1245, 1258-59 (Fed. Cir. 2014); BASCOM Global Internet v. AT&T Mobility LLC, 827 F. 3d 1341, 1350-51 (Fed. Cir. 2016). Similar to BASCOM, claim 1 recites an arrangement of artificial neural networks to determine error remediation recommendations in a specific technical setting; here, an automated deployment system, compared to BASCOM's filtering system. 827 F. 3d at 1350. In other words, claim l's recitation of artificial neural networks, error identification from an automated deployment system, determination of error remediation recommendations, and re-training based on an implemented error remediation recommendation is a "specific, discrete implementation" of error identification. BASCOM, F. 3d at 1350.”
The Examiner respectfully disagrees. The recited artificial neural networks do not have specific, discrete implementation and are generic artificial neural networks trained on a specified type of data. Error identification as claimed amounts to abstract ideas. The claims are only generally linked to an automated deployment system and are not tied to that environment. Determination of error remediation recommendations using an artificial neural network and updating the artificial neural network based on the result amounts to using an artificial neural network in its ordinary capacity. Therefore, the claim does not amount to significantly more than the abstract ideas.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALBERT LI whose telephone number is (571)272-5721. The examiner can normally be reached M-F 7:00AM-3:00PM PT.
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/A.L./Examiner, Art Unit 2113
/MARC DUNCAN/Primary Examiner, Art Unit 2113