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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on April 23, 2025 and August 12, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
Fig. 4B is objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “492” has been used to designate both “SOFTMAX” and “LINEAR”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Fig. 6 is objected to as failing to comply with 37 CFR 1.84(p)(5) because it does not include the following reference sign mentioned in the description: 625. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2 and 15 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 2 depends on claim 1 and claim 15 depends on claim 14. Claims 2 and 15 recite wherein the prompt further comprises an indication of the obtained telemetry data.
Claims 1 and 14 recite:
obtaining a semantic event index associated with the execution environment, wherein the semantic event index includes: a definition for an event within the telemetry data;
generating a prompt for a generative machine learning model that includes natural language input and the semantic event index.
Under the broadest reasonable interpretation, a definition for an event within the telemetry data is interpreted as an indication of the obtained telemetry data. Accordingly, claims 2 and 15 fail to further limit the scope of claims 1 and 14 respectively.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the invention is directed at an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106.03, the claim recites A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations. The claim recites a machine. A machine is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106.04, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with a pen and paper like writing out text by hand, which is a mental process of observation/evaluation/judgement (MPEP 2106.04(a)(2))).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106.04, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
at least one processor; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
the set of operations comprising obtaining telemetry data corresponding to an execution environment; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
obtaining a semantic event index associated with the execution environment, wherein the semantic event index includes: a definition for an event within the telemetry data; and context information for the event; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
generating, using the generative machine learning model, model output for the telemetry data based on the prompt; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and providing an indication of the model output for the telemetry data. (i.e., the broadest reasonable interpretation of outputting a data instance is mere data outputting, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitations (IV), (V) and (VII), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (VI), under the broadest reasonable interpretation, merely recite steps that apply a generic generative model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Further, limitation (II) and (III), under the broadest reasonable interpretation, merely recite steps that apply generic computer components as a tool to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein the prompt further comprises an indication of the obtained telemetry data. Under the broadest reasonable interpretation, this limitation merely recites steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites:
wherein: the set of operations further comprises generating a set of embeddings based on the telemetry data; Under the broadest reasonable interpretation, this limitation recites choosing and writing down a set of numerical vectors which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes.
and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model. Under the broadest reasonable interpretation, this limitation recites steps of mere data transmission which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data transmission as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
For the above reasons, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein: the natural language input is received, from a computing device, as a request for model output; and the indication of the model output is provided, to the computing device, in response to the request. Under the broadest reasonable interpretation, this limitation recites steps of mere data gathering/outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)). Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 4 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites:
wherein: the request is a first request; the model output is first model output; and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data; Under the broadest reasonable interpretation, these limitations recite steps of mere data gathering/outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request and the semantic event index; Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic generative machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
and providing, in response to the second request, an indication of the second model output. Under the broadest reasonable interpretation, this limitation recites steps of mere data outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
For the above reasons, claim 5 does not solve the deficiencies of claim 4.
Regarding claim 6, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model. Under the broadest reasonable interpretation, the limitations recite selecting pre-written text which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 6 does not solve the deficiencies of claim 1.
Regarding claim 7, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. Under the broadest reasonable interpretation, the limitations merely recite steps that apply training to a generic generative machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 7 does not solve the deficiencies of claim 1.
Regarding claim 8, in step 1 of the 101 analysis set forth in MPEP 2106.03, the claim recites A method for processing telemetry data. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106.04, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
generating a prompt for a generative machine learning model that includes natural language input; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with a pen and paper like writing out text by hand, which is a mental process of observation/evaluation/judgement (MPEP 2106.04(a)(2))).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106.04, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
obtaining telemetry data corresponding to an execution environment; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
processing, using the generative machine learning model, telemetry data based on the prompt to generate model output, thereby interpreting the telemetry data using the generative machine learning model; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and providing an indication of the model output for the telemetry data. (i.e., the broadest reasonable interpretation of outputting a data instance is mere data outputting, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitations (II) and (IV), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (III), under the broadest reasonable interpretation, merely recite steps that apply a generic generative model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 9, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 9 recites wherein the prompt further comprises a semantic event index associated with the execution environment, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data. Under the broadest reasonable interpretation, this limitation merely recites steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 9 does not solve the deficiencies of claim 8.
Regarding claim 10, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 10 recites wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. Under the broadest reasonable interpretation, the limitations merely recite steps that apply training to a generic generative machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 10 does not solve the deficiencies of claim 8.
Regarding claim 11, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 11 recites wherein the prompt further comprises an indication of the obtained telemetry data. Under the broadest reasonable interpretation, this limitation merely recites steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 11 does not solve the deficiencies of claim 8.
Regarding claim 12, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 12 recites:
wherein: the method further comprises generating a set of embeddings based on the telemetry data; Under the broadest reasonable interpretation, this limitation recites calculating number vectors based on text which is interpreted as a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept.
processing the telemetry data using the generative machine learning model further comprises providing the set of embeddings for processing by the generative machine learning model. Under the broadest reasonable interpretation, this limitation recites steps of mere data transmission which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data transmission as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
For the above reasons, claim 12 does not solve the deficiencies of claim 8.
Regarding claim 13, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 13 recites wherein the natural language input is at least one of: received, from a computing device, as natural language user input by a user of the computing device; or obtained from a predefined set of conversational inputs. Under the broadest reasonable interpretation, this limitation recites steps of mere data gathering/outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)). Therefore, claim 13 does not solve the deficiencies of claim 8.
Regarding claim 14, in step 1 of the 101 analysis set forth in MPEP 2106.03, the claim recites A method for processing telemetry data. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106.04, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with a pen and paper like writing out text by hand, which is a mental process of observation/evaluation/judgement (MPEP 2106.04(a)(2))).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106.04, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
the method comprising: obtaining telemetry data corresponding to an execution environment; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
obtaining a semantic event index associated with the execution environment, wherein the semantic event index includes: a definition for an event within the telemetry data; and context information for the event; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
generating, using the generative machine learning model, model output for the telemetry data based on the prompt; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and providing an indication of the model output for the telemetry data. (i.e., the broadest reasonable interpretation of outputting a data instance is mere data outputting, which is an insignificant extra solution activity (MPEP 2106.05(d)(II))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitations (II), (III) and (V), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (IV), under the broadest reasonable interpretation, merely recite steps that apply a generic generative model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 15, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 15 recites wherein the prompt further comprises an indication of the obtained telemetry data. Under the broadest reasonable interpretation, this limitation merely recites steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 15 does not solve the deficiencies of claim 14.
Regarding claim 16, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 16 recites:
wherein: the method further comprises generating a set of embeddings based on the telemetry data; Under the broadest reasonable interpretation, this limitation recites calculating number vectors based on text which is interpreted as a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept.
processing the telemetry data using the generative machine learning model further comprises providing the set of embeddings for processing by the generative machine learning model. Under the broadest reasonable interpretation, this limitation recites steps of mere data transmission which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data transmission as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
For the above reasons, claim 16 does not solve the deficiencies of claim 14.
Regarding claim 17, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 17 recites wherein: the natural language input is received, from a computing device, as a request for model output; and the indication of the model output is provided, to the computing device, in response to the request. Under the broadest reasonable interpretation, this limitation recites steps of mere data gathering/outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)). Therefore, claim 17 does not solve the deficiencies of claim 14.
Regarding claim 18, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 18 recites:
wherein: the request is a first request; the model output is first model output; and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data; Under the broadest reasonable interpretation, this limitation recites steps of mere data gathering/outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request and the semantic event index; Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic generative machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
and providing, in response to the second request, an indication of the second model output. Under the broadest reasonable interpretation, this limitation recites steps of mere data outputting which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)).
For the above reasons, claim 18 does not solve the deficiencies of claim 14.
Regarding claim 19, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 19 recites wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model. Under the broadest reasonable interpretation, the limitations recite selecting pre-written text which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 19 does not solve the deficiencies of claim 14.
Regarding claim 20, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 20 recites wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. Under the broadest reasonable interpretation, the limitations merely recite steps that apply training to a generic generative machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 20 does not solve the deficiencies of claim 14.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 8-9, 11, and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”).
Regarding claim 8, Egersdoerfer discloses:
A method for processing telemetry data, comprising: obtaining telemetry data corresponding to an execution environment; (Egersdoerfer, pg. 1 col 1, “We use OpenAI’s gpt-3.5-turbo model as our LLM and conduct evaluation on a dataset originating from the Lustre file system [A method for processing telemetry data, comprising: obtaining telemetry data corresponding to an execution environment;]”).
generating a prompt for a generative machine learning model that includes natural language input; (Egersdoerfer, Figure 2 (starting from “You will be given…”) and pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model [generating a prompt for a generative machine learning model that includes natural language input;]”).
processing, using the generative machine learning model, telemetry data based on the prompt to generate model output, thereby interpreting the telemetry data using the generative machine learning model; (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [processing, using the generative machine learning model, telemetry data based on the prompt to generate model output, thereby interpreting the telemetry data using the generative machine learning model;]”).
providing an indication of the model output for the telemetry data. (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [providing an indication of the model output for the telemetry data.]”).
Regarding claim 9, Egersdoerfer dicloses the method of claim 8. Egersdoerfer further discloses wherein the prompt further comprises a semantic event index associated with the execution environment, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data. (Egersdoerfer, Figure 4 (see “Historic Summary”) and pg. 1 col 1, “During Context Creation, we seek to maintain a useful memory of past logs (long-term context) to be used during the analysis of the current window of logs. This is done by requiring the LLM to constantly re-summarize the important system events described in historical logs. During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model (Examiner’s Comment: the examiner interprets the described summary of previous system events, and other context in the prompt, to be the “semantic event index”) [wherein the prompt further comprises a semantic event index associated with the execution environment, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data.]”).
Regarding claim 11, Egersdoerfer discloses the method of claim 8. Egersdoerfer further discloses wherein the prompt further comprises an indication of the obtained telemetry data. (Egersdoerfer, pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model (Examiner’s Comment: the examiner interprets the “immediate to-be-analyzed logs” to be “an indication of the obtained telemetry data”) [wherein the prompt further comprises an indication of the obtained telemetry data.]”).
Regarding claim 14, Egersdoerfer discloses:
A method for processing telemetry data, the method comprising: obtaining telemetry data corresponding to an execution environment; (Egersdoerfer, pg. 2 col 1, “We use OpenAI’s gpt-3.5-turbo model as our LLM and conduct evaluation on a dataset originating from the Lustre file system [the method comprising: obtaining telemetry data corresponding to an execution environment;]”).
obtaining a semantic event index associated with the execution environment, (Egersdoerfer, Figure 4 (see “Historic Summary”) and pg. 1 col 1, “During Context Creation, we seek to maintain a useful memory of past logs (long-term context) to be used during the analysis of the current window of logs. (Examiner’s Comment: the examiner interprets the described summary of previous system events, and other context in the prompt, to be the “semantic event index”) [obtaining a semantic event index associated with the execution environment,]”).
wherein the semantic event index includes: a definition for an event within the telemetry data; (Egersdoerfer, pg. 1 col 1, “This is done by requiring the LLM to constantly re-summarize the important system events described in historical logs. (Examiner’s Comment: the summaries of system events from historical logs contain descriptions of their meaning, the examiner interprets the summaries to be definitions for those events) [wherein the semantic event index includes: a definition for an event within the telemetry data;]”).
context information for the event; (Egersdoerfer, Figure 2 and pg. 2 col 1, “{file_system} represents the name of the file system from which the logs originate, [context information for the event;]”).
generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data; (Egersdoerfer, Figure 2 (starting from “You will be given…”) and pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model [generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data;]”).
generating, using the generative machine learning model, model output for the telemetry data based on the prompt; (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [generating, using the generative machine learning model, model output for the telemetry data based on the prompt;]”).
providing an indication of the model output for the telemetry data. (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [providing an indication of the model output for the telemetry data.]”).
Regarding claim 15, Egersdoerfer discloses the method of claim 14. Egersdoerfer further discloses wherein the prompt further comprises an indication of the obtained telemetry data. (Egersdoerfer, pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model (Examiner’s Comment: the examiner interprets the “immediate to-be-analyzed logs” to be “an indication of the obtained telemetry data”) [wherein the prompt further comprises an indication of the obtained telemetry data.]”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 7, 10, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Black, et al., US Pre-Grant Publication US20250217346A1 (“Black”).
Regarding claim 1, Egersdoerfer discloses:
the set of operations comprising: obtaining telemetry data corresponding to an execution environment; (Egersdoerfer, pg. 2 col 1, “We use OpenAI’s gpt-3.5-turbo model as our LLM and conduct evaluation on a dataset originating from the Lustre file system [the set of operations comprising: obtaining telemetry data corresponding to an execution environment;]”).
obtaining a semantic event index associated with the execution environment, (Egersdoerfer, Figure 4 (see “Historic Summary”) and pg. 1 col 1, “During Context Creation, we seek to maintain a useful memory of past logs (long-term context) to be used during the analysis of the current window of logs. (Examiner’s Comment: the examiner interprets the described summary of previous system events, and other context in the prompt, to be the “semantic event index”) [obtaining a semantic event index associated with the execution environment,]”).
wherein the semantic event index includes: a definition for an event within the telemetry data; (Egersdoerfer, Figure 4 (see “Historic Summary”) pg. 1 col 1, “This is done by requiring the LLM to constantly re-summarize the important system events described in historical logs. (Examiner’s Comment: the summaries of system events from historical logs contain descriptions of their meaning, the examiner interprets the summaries to be definitions for those events) [wherein the semantic event index includes: a definition for an event within the telemetry data;]”).
context information for the event; (Egersdoerfer, Figure 2 and pg. 2 col 1, “{file_system} represents the name of the file system from which the logs originate, [context information for the event;]”).
generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data; (Egersdoerfer, Figure 2 (starting from “You will be given…”) and pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model [generating a prompt for a generative machine learning model that includes natural language input and the semantic event index, thereby enabling the generative machine learning model to attach semantic meaning to one or more events of the telemetry data;]”).
generating, using the generative machine learning model, model output for the telemetry data based on the prompt; (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [generating, using the generative machine learning model, model output for the telemetry data based on the prompt;]”).
providing an indication of the model output for the telemetry data. (Egersdoerfer, Figure 4 and pg. 1 col 1-2, “prompt the model to create an output which includes a prediction of current system status and a detailed description of reasoning behind the prediction. [providing an indication of the model output for the telemetry data.]”).
While Egersdoerfer teaches a system for generating output based on telemetry data, Egersdoerfer does not explicitly teach:
A system comprising: at least one processor;
and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations,
However, Black teaches A system comprising: at least one processor; (Black, claim 10, “A system comprising: a memory; and at least one processing device, [A system comprising: at least one processor;]”).
Black also teaches and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, (Black, claim 10, “at least one processing device, coupled to the memory, configured to perform operations, [and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations,]”).
Egersdoerfer and Black are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer and Black to teach the above limitations. The motivation for doing so is that the prior art contained a base method, the use of machine learning for log data anaylsis of Egersdoerfer, upon which the claimed invention is an improvement. The claimed invention is an improvement on the base method because the added processor and memory allow the machine learning process to be run on a computer. The technique disclosed in Black, using a processor and memory to perform machine learning, applies to the base method because it performs machine learning for log data analysis. A person of ordinary skill in the art would have recognized that applying the known technique to the base method would have yielded the predictable result of running a machine learning process on a computer and resulted in an improved system. A processor and memory would have been expected to make it possible to perform the method on a computer and therefore usable in a production environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of Black to the machine learning method of Egersdoerfer. This would yield the predictable result of a machine learning system which can be run on a computer (See MPEP 2143).
Regarding claim 2, Egersdoerfer in view of Black teaches the system of claim 1. Egersdoerfer further teaches wherein the prompt further comprises an indication of the obtained telemetry data. (Egersdoerfer, pg. 1 col 1, “During Active Analysis, we use the previously described summary, as well as the immediate to-be-analyzed logs, as input to prompt the model (Examiner’s Comment: the examiner interprets the “immediate to-be-analyzed logs” to be “an indication of the obtained telemetry data”) [wherein the prompt further comprises an indication of the obtained telemetry data.]”). The motivation to combine is the same as claim 1 above.
Regarding claim 7, Egersdoerfer in view of Black teaches the system of claim 1. Black further teaches wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. (Black, paragraph [0067], “In some embodiments, the second portion of training, including fine-tuning, may be unsupervised, supervised, reinforced, or any other type of training…In a non-limiting example associated with reinforcement learning, the outputs of the AI model 510 while training may be ranked by a user, according to a variety of factors, including accuracy, helpfulness, veracity, acceptability, or any other metric useful in the fine-tuning portion of training. [wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment.]”).
Egersdoerfer and Black are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer and Black to teach the above limitations. The motivation for doing so is that finetuning a model trains it to produce output that is more useful and relevant to users (cf. Black, paragraph [0067], “In this manner, the AI model 510 can learn to favor these and any other factors relevant to users when generating a response.”).
Regarding claim 10, Egersdoerfer teaches the method of claim 8. Egersdoerfer does not teach:
wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment.
However, Black teaches wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. (Black, paragraph [0066], “In some embodiments, the AI model 510 can then be further trained or fine-tuned on organizational data, including proprietary organizational data. The AI model 510 can also be further trained or fine-tuned on sample event logs 400, event log parser code, predefined fields 434, event log key 404-to-predefined field 434 mappings, or other data stored by the parser storage 120. [wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment.]”).
Egersdoerfer and Black are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer and Black to teach the above limitations. The motivation for doing so is that finetuning a model trains it to produce output that is more useful and relevant to users (cf. Black, paragraph [0067], “In this manner, the AI model 510 can learn to favor these and any other factors relevant to users when generating a response.”).
Regarding claim 20, Egersdoerfer teaches the method of claim 14. Egersdoerfer does not teach:
wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment.
However, Black teaches wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment. (Black, paragraph [0066], “In some embodiments, the AI model 510 can then be further trained or fine-tuned on organizational data, including proprietary organizational data. The AI model 510 can also be further trained or fine-tuned on sample event logs 400, event log parser code, predefined fields 434, event log key 404-to-predefined field 434 mappings, or other data stored by the parser storage 120. [wherein the generative machine learning model is finetuned to process telemetry data corresponding to the execution environment.]”).
Egersdoerfer and Black are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer and Black to teach the above limitations. The motivation for doing so is that finetuning a model trains it to produce output that is more useful and relevant to users (cf. Black, paragraph [0067], “In this manner, the AI model 510 can learn to favor these and any other factors relevant to users when generating a response.”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Black, et al., US Pre-Grant Publication US20250217346A1 (“Black”) and further in view of Le, et al., Non-Patent Literature “Log-based Anomaly Detection Without Log Parsing” (“Le”).
Regarding claim 3, Egersdoerfer in view of Black teaches the system of claim 1. Egersdoerfer in view of Black does not teach:
wherein: the set of operations further comprises generating a set of embeddings based on the telemetry data;
and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model.
However, Le teaches wherein: the set of operations further comprises generating a set of embeddings based on the telemetry data; (Le, pg. 7 col 2, “When a set of new log messages arrives, NeuralLog firstly conducts preprocessing. Then it transforms the new log messages into semantic vectors. [wherein: the set of operations further comprises generating a set of embeddings based on the telemetry data;]”).
Le also teaches and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model. (Le, pg. 7 col 2, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. [and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model.]”).
Egersdoerfer in view of Black and Le are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer in view of Black and Le to teach the above limitations. The motivation for doing so is that embeddings capture the semantic meaning of text and can be used as input on which a model bases its output. (cf. Le, pg. 7, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. Finally, the transformer-based model can predict whether this log sequence is anomalous or not.”).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Black, et al., US Pre-Grant Publication US20250217346A1 (“Black”) and further in view of Barros, US Pre-Grant Publication US12499144B2 (“Barros”).
Regarding claim 4, Egersdoerfer in view of Black teaches the system of claim 1. Egersdoerfer does not teach:
wherein: the natural language input is received, from a computing device, as a request for model output;
and the indication of the model output is provided, to the computing device, in response to the request.
However, Barros teaches herein: the natural language input is received, from a computing device, as a request for model output; (Barros, paragraph [0013], “The user query in natural language can be provided to a first LLM (which is an example of the aforementioned first generative model) locally at the computing device, to be processed using the first LLM. [wherein: the natural language input is received, from a computing device, as a request for model output;]”).
Barros also teaches and the indication of the model output is provided, to the computing device, in response to the request. (Barros, paragraph [0013], “the user query in natural language can be processed using the first LLM local to the computing device, to generate a first model output, where a first response (responsive to user query) can be derived from the first model output. [and the indication of the model output is provided, to the computing device, in response to the request.]”).
Egersdoerfer in view of Black and Barros are both in the same field of endeavor (i.e. generative machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egerdoerfer in view of Black and Barros to teach the above limitations. The motivation for doing so is that generative models (such as LLMs) are built to handle natural language requests and generate appropriate output in response. (cf. Barros, paragraph [0001], “large language models (LLM(s)) have been developed that can be used to process NL content and/or other input(s), to generate LLM output that reflects generative NL content and/or other generative content that is responsive to the input(s).”).
Regarding claim 5, Egersdoerfer in view of Black and Barros teach The system of claim 4,. Barros further teaches:
wherein: the request is a first request; (Barros, paragraph [0013], “The user query in natural language can be provided to a first LLM (which is an example of the aforementioned first generative model) locally at the computing device, to be processed using the first LLM. [wherein: the request is a first request;]”).
the model output is first model output; (Barros, paragraph [0013], “the user query in natural language can be processed using the first LLM local to the computing device, to generate a first model output [the model output is first model output;]”).
and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data; (Barros, paragraph [0015], “the user query can be provided to a second LLM (which is an example of the aforementioned second generative model) remote to the computing device, [and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data;]”).
generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request and the semantic event index; (Barros, paragraph [0015], “where the user query in natural language can be processed using the second LLM, to generate a second model output from which a second response is derived. [generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request]”, Egersdoerfer, pg. 2 col 1, “a useful memory of past logs (long-term context) to be used during the analysis of the current window of logs. [and the semantic event index;]”).
and providing, in response to the second request, an indication of the second model output. (“where the user query in natural language can be processed using the second LLM, to generate a second model output from which a second response is derived. [and providing, in response to the second request, an indication of the second model output.]”).
The motivation to combine is the same as claim 4 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Black, et al., US Pre-Grant Publication US20250217346A1 (“Black”) and further in view of MacNeil, et al., Non-Patent Literature “Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances” (“MacNeil”).
Regarding claim 6, Egersdoerfer in view of Black teaches The system of claim 1. Egersdoerfer in view of Black does not teach:
wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model.
However, MacNeil teaches wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model. (MacNeil, pg. 2 col 1, “A static prompt is a predefined prompt generated by experts through prompt engineering to achieve high quality responses from an LLM. As shown in Figure 1, static prompts can be hidden behind a button in a UI to send a predefined prompt to an LLM on behalf of the user. [wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model.]”).
Egersdoerfer in view of Black and MacNeil are both in the same field of endeavor (i.e. generative machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer in view of Black and MacNeil to teach the above limitations. The motivation for doing so is that predefined prompts allow users to query the generative model without prior experience or knowledge in prompt engineering. (cf. MacNeil, pg. 2 col 1, “This allows users to tap into best practices with minimal effort but at the cost of giving up control of prompt generation.”).
Claim 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Le, et al., Non-Patent Literature “Log-based Anomaly Detection Without Log Parsing” (“Le”).
Regarding claim 12, Egersdoerfer teaches the method of claim 8. However, Egersdoerfer does not teach:
wherein: the method further comprises generating a set of embeddings based on the telemetry data;
and processing the telemetry data further comprises providing the set of embeddings for processing by the generative machine learning model.
However, Le teaches herein: the method further comprises generating a set of embeddings based on the telemetry data; (Le, pg. 7 col 2, “When a set of new log messages arrives, NeuralLog firstly conducts preprocessing. Then it transforms the new log messages into semantic vectors. [wherein: the method further comprises generating a set of embeddings based on the telemetry data;]”).
Le also teaches and processing the telemetry data further comprises providing the set of embeddings for processing by the generative machine learning model. (Le, pg. 7 col 2, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. [and processing the telemetry data further comprises providing the set of embeddings for processing by the generative machine learning model.]”).
Egersdoerfer and Le are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in art before the effective filing date of the claimed invention to combine Egerdoerfer and Le to teach the above limitations. The motivation for doing so is that embeddings capture the semantic meaning of text and can be used as input on which a model bases its output. (cf. Le, pg. 7, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. Finally, the transformer-based model can predict whether this log sequence is anomalous or not.”).
Regarding claim 16, Egersdoerfer teaches the method of claim 14. Egersdoerfer does not teach:
wherein: the method further comprises generating a set of embeddings based on the telemetry data;
and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model. (Le, pg. 7 col 2, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. [and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model.]”).
However, Le teaches wherein: the method further comprises generating a set of embeddings based on the telemetry data; (Le, pg. 7 col 2, “When a set of new log messages arrives, NeuralLog firstly conducts preprocessing. Then it transforms the new log messages into semantic vectors. [wherein: the method further comprises generating a set of embeddings based on the telemetry data;]”).
Le also teaches and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model. (Le, pg. 7 col 2, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. [and generating the model output further comprises providing the set of embeddings for processing by the generative machine learning model.]”).
Egersdoerfer and Le are both in the same field of endeavor (i.e. telemetry data analysis using machine learning). It would have been obvious for a person having ordinary skill in art before the effective filing date of the claimed invention to combine Egerdoerfer and Le to teach the above limitations. The motivation for doing so is that embeddings capture the semantic meaning of text and can be used as input on which a model bases its output. (cf. Le, pg. 7, “The log sequence, represented as a list of semantic vectors, is fed into the trained model. Finally, the transformer-based model can predict whether this log sequence is anomalous or not.”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Barros, US Pre-Grant Publication US12499144B2 (“Barros”).
Regarding claim 13, Egersdoerfer teaches the method of claim 8. Barros further teaches:
wherein the natural language input is at least one of: received, from a computing device, as natural language user input by a user of the computing device; (Barros, paragraph [0013], “The user query in natural language can be provided to a first LLM (which is an example of the aforementioned first generative model) locally at the computing device, to be processed using the first LLM. [wherein the natural language input is at least one of: received, from a computing device, as natural language user input by a user of the computing device;]”).
Egersdoerfer and Barros are both in the same field of endeavor (i.e. generative machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egerdoerfer and Barros to teach the above limitations. The motivation for doing so is that generative models (such as LLMs) are built to handle natural language requests and generate appropriate output in response. (cf. Barros, paragraph [0001], “large language models (LLM(s)) have been developed that can be used to process NL content and/or other input(s), to generate LLM output that reflects generative NL content and/or other generative content that is responsive to the input(s).”).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of Barros, US Pre-Grant Publication US12499144B2 (“Barros”).
Regarding claim 17, Egersdoerfer teaches the method of claim 14. Barros further teaches:
wherein: the natural language input is received, from a computing device, as a request for model output; (Barros, paragraph [0013], “The user query in natural language can be provided to a first LLM (which is an example of the aforementioned first generative model) locally at the computing device, to be processed using the first LLM. [wherein: the natural language input is received, from a computing device, as a request for model output;]”).
and the indication of the model output is provided, to the computing device, in response to the request. (Barros, paragraph [0013], “the user query in natural language can be processed using the first LLM local to the computing device, to generate a first model output, where a first response (responsive to user query) can be derived from the first model output. [and the indication of the model output is provided, to the computing device, in response to the request.]”).
Egersdoerfer and Barros are both in the same field of endeavor (i.e. generative machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egerdoerfer and Barros to teach the above limitations. The motivation for doing so is that generative models (such as LLMs) are built to handle natural language requests and generate appropriate output in response. (cf. Barros, paragraph [0001], “large language models (LLM(s)) have been developed that can be used to process NL content and/or other input(s), to generate LLM output that reflects generative NL content and/or other generative content that is responsive to the input(s).”).
Regarding claim 18, Egersdoerfer in view of Barros teaches the method of claim 17, Barros further teaches:
wherein: the request is a first request; (Barros, paragraph [0013], “The user query in natural language can be provided to a first LLM (which is an example of the aforementioned first generative model) locally at the computing device, to be processed using the first LLM. [wherein: the request is a first request;]”).
the model output is first model output; (Barros, paragraph [0013], “the user query in natural language can be processed using the first LLM local to the computing device, to generate a first model output [the model output is first model output;]”).
and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data; (Barros, paragraph [0015], “the user query can be provided to a second LLM (which is an example of the aforementioned second generative model) remote to the computing device, [and the set of operations further comprises: receiving, from the computing device, a second request for model output relating to the telemetry data;]”).
generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request and the semantic event index; (Barros, paragraph [0015], “where the user query in natural language can be processed using the second LLM, to generate a second model output from which a second response is derived. [generating, using the generative machine learning model, second model output for the telemetry data based on natural language input of the second request]”, Egersdoerfer, pg. 2 col 1, “a useful memory of past logs (long-term context) to be used during the analysis of the current window of logs. [and the semantic event index;]”).
and providing, in response to the second request, an indication of the second model output. (Barros, paragraph [0015], “where the user query in natural language can be processed using the second LLM, to generate a second model output from which a second response is derived. [and providing, in response to the second request, an indication of the second model output.]”).
The motivation to combine is the same as claim 17 above.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Egersdoerfer, et al., Non-Patent Literature “Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs” (“Egersdoerfer”) in view of MacNeil, et al., Non-Patent Literature “Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances” (“MacNeil”).
Regarding claim 19, Egersdoerfer teaches the method of claim 14. MacNeil further teaches wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model. (MacNeil, pg. 2 col 1, “A static prompt is a predefined prompt generated by experts through prompt engineering to achieve high quality responses from an LLM. As shown in Figure 1, static prompts can be hidden behind a button in a UI to send a predefined prompt to an LLM on behalf of the user. [wherein the natural language input is selected from a predefined set of conversational inputs to programmatically process the telemetry data using the generative machine learning model.]”).
Egersdoerfer and MacNeil are both in the same field of endeavor (i.e. generative machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Egersdoerfer and MacNeil to teach the above limitations. The motivation for doing so is that predefined prompts allow users to query the generative model without prior experience or knowledge in prompt engineering. (cf. MacNeil, pg. 2 col 1, “This allows users to tap into best practices with minimal effort but at the cost of giving up control of prompt generation.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu, et al., “LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis” discloses an online, interpretable log analysis approach employing LLMs and advanced prompting strategies such as self-prompting, chain-of-thought prompting, and in-context prompts.
Qi, et al., “LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection” discloses a framework for log-based anomaly detection using ChatGPT that improves the interpretability of output compared to other frameworks.
Jiao, et al., “NETWORK DEVICE SYSTEM LOGGING SUMMARIZATION BASED ON LOW-RANK ADAPTATION AND CONTRASTIVE LEARNING” discloses automatic generation of summarized text from syslog message sequences. Uses LLMs trained with contrastive learning and fine-tuned using Low-Rank Adaption.
Block, et al., “Summary Cycles: Exploring the Impact of Prompt Engineering on Large Language Models’ Interaction with Log Information” discloses recursive summarization of user interaction logs using ChatGPT.
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/D.E.B./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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