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 .
Priority
Applicant claims the benefit of prior-filed Korean Patent Application No. KR10-2023-0163097, filed on November, 22, 2023, which is acknowledged.
Drawings
The drawings were received on 11/27/2023 These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on the following date(s): 11/27/2023 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are noted below where the generic place holder is in bold and the functional language is italicized:
Claim 1:
a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice;
an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model;
and a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question..
Claim 5:
a support system that generates a support model that produces a response corresponding to a question of a requester in relation to a building of a microservice;
a monitoring system that analyzes a root cause of abnormal operation of target software using a corresponding analysis tool calculated by simulating comparison software configured to correspond to the target software used by the requester and operated in a microservice environment;
and an Interface calculator that produces a user interface that displays the response to the question of the requester through the support model, wherein the support system comprises: a Knowledge storage that stores background information, which is information including knowledge for building the microservice;
an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model;
and a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question, and wherein the Interface calculator uses the support model to provide the requester with the response necessary in a process of determining the comparison software.
Claim 6:
wherein the monitoring system comprises: an Analysis tool calculator that calculates the corresponding analysis tool based on the comparison software configured of corresponding independent modules, which are independent modules classified by function to correspond to the target software;
and a Cause analyser that analyzes the root cause of the abnormal operation occurring in the target software using the corresponding analysis tool, wherein the Analysis tool calculator comprises:
a Module collector that collects the corresponding independent module from an external server; a Simulator that builds the comparison software using the corresponding independent modules and simulates the comparison software through a predetermined control method;
a Date collector that collects result information composed of log information, metric information, and trace information generated during a simulation process utilizing different data collection members;
and a Root analyser that calculates the corresponding analysis tool based on result information.
Claim 7:
wherein the Date collector uses a Fluentd data collection member for the log information, a Prometheus data collection member for the metric information, and a Jaeger data collection member for the trace information.
Claim 8:
wherein: the Analysis tool calculator further comprises a Comparison analyser that configures the comparison software by configuring the corresponding independent module identical to a target independent module, which is an independent module forming the target software, and when the Module collector fails to collect the corresponding independent module that matches the target independent module, the Comparison analyser configures the comparison software by comprising at least one corresponding independent module similar to the target independent module within the scope that a predetermined compensation condition is satisfied.
Claim 10:
storing, by a Knowledge storage, background information, which is information comprising knowledge for building a microservice;
changing, by an Information processor, the background information into response information, which is information in a question-and-answer format, using a change model;
and processing, by a tuner, a basic model, which is a large language model different from the change model, using a predetermined processing method and then training the same with the response information to generate a support model that produces a response to an arbitrary question.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-10 are 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.
Regarding claims 1, 5-8 and 10, the claim limitations noted in the claim interpretation section above invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification appears to mirror the claim language and fails to clearly link the structure, material, or acts to the function disclosed in the claim. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding, the dependent claims of claim 1, 5 and 8, the claims do not resolve the noted deficiencies and are appropriately rejected.
Regarding claim 9, the limitation “the same” in the claim. There is insufficient antecedent basis for this limitation in the claim. Specifically the term “the same is used in claim 8 and in claim 9 and it is unclear what the term refers to in claim 9. The applicant should clearly state the object or element the claim term refers back to, if it is the model in claim 8, or some other model or data element.
Regarding claim 6, the limitation “wherein the monitoring system comprises: an Analysis tool calculator that calculates the corresponding analysis tool based on the comparison software configured of corresponding independent modules, which are independent modules classified by function to correspond to the target software”, the claim language is incoherent and thus renders the claim indefinite. Specifically, the claim limitation discloses the phrase “an Analysis tool calculator that calculates the corresponding analysis tool based on the comparison software configured of corresponding independent modules” and its unclear how or what operations of a calculator are capable of calculating non-numerical or quantifiable elements (e.g. claimed corresponding analysis tool). Its is unclear how one of ordinary skill in the art would ascertain the intended scope of the claimed invention. Thus, the claim is rendered indefinite.
Examiner interprets any classification process as within the scope of the claim limitation.
Regarding the claims that depend on claims 6, the limitations do not resolve the noted deficiency and are thus appropriately rejected.
Regarding claim 8 and 9, the claims recite the term/phrase “the scope” there is insufficient antecedent basis for this limitation in the claim.
Regarding claim 9, the limitation “wherein, when the comparison software is configured to comprise at least one corresponding independent module similar to the target independent module within the scope that the predetermined compensation condition is satisfied, the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester”, the claim language is incoherent and thus renders the claim indefinite. Specifically, the claim limitation discloses the phrase “the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester” and its unclear how or what operations of a calculator are capable of calculating non-numerical or quantifiable elements (e.g. claimed an interface that displays information about analysis of the comparison software and provide the same to the requester). Its is unclear how one of ordinary skill in the art would ascertain the intended scope of the claimed invention. Thus, the claim is rendered indefinite.
Examiner interprets any display operation corresponding with analyzed data as within the scope of the claim limitation.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Claims 1, 5-8 and 10 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, in determining whether the specification describes how the claimed function is achieved it is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved. See MPEP § 2161.01, subsection I. The specification, in this cases appears to recite claim language does not appear to clearly link the noted generic place holder to a corresponding structure (e.g. hardware component and algorithm) for performing the claimed function.
Thus, the specification fails to meet the written description requirement under 35 USC 112-f claim interpretation.
The claims that depend on claims 1and 5 fail to resolve the noted deficiency, and thus appropriately rejected.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
information into response information, which is information in a question-and-answer format, (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
a Knowledge storage that stores background information, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Storing and retrieving information in memory)
(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception; and that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the noted additional limitation elements directed to insignificant solution activity, as noted above, the courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the background information is knowledge obtained by analyzing an architectural component of the microservice, . (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III and )
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
information collected from independent modules configuring the microservice (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
and is information that comprises knowledge of at least one of statistical analysis, correlation analysis, trend analysis, or seasonality analysis based on information collected(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the change model is ChatGPT. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the predetermined processing method is a method of quantizing 4 bits as a reference and processing the basic model using LoRA (Low-Rank Adaptation) technique. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising: a support system that generates a support model that (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).).
wherein the support system comprises: a Knowledge storage that stores background information, which is information including knowledge for building the microservice; … a basic model, which is a large language model different from the change model, using a predetermined processing method… (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
provide the requester with the response necessary in a process of determining the comparison software… (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the monitoring system comprises: an Analysis tool calculator that calculates (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
wherein the Analysis tool calculator comprises: a Module collector that collects the corresponding independent module from an external server; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
a Simulator that builds the comparison software using the corresponding independent modules and simulates the comparison software through a predetermined control method; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
a Date collector that collects result information composed of log information, metric information, and (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 7: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 6.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the Date collector uses a Fluentd data collection member for the log information, a Prometheus data collection member for the metric information, and a Jaeger data collection member for the trace information (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 8: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 6.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
wherein: the Analysis tool calculator further comprises a Comparison analyser that configures the comparison software by configuring the corresponding independent module identical to a target independent module, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 9: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 8.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein, when the comparison software is configured to comprise at least one corresponding independent module similar to the target independent module within the scope that the predetermined compensation condition is satisfied, the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester.(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 10: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
changing, (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising: storing, by a Knowledge storage, background information, which is information comprising knowledge for building a microservice; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Storing and retrieving information in memory)
(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
changing, by an Information processor, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception; and that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the noted additional limitation elements directed to insignificant solution activity, as noted above, the courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
As shown above, claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more” than the recited judicial exception. The claims are therefore directed to an abstract idea.
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 1-3 and 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Awadallah et al. (US 20250094827, hereinafter ‘Awa’).
Regarding independent claim 1, Awa teaches a support system, comprising: a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice; (in As depicted in Fig. 1 and [0111]…. The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples [a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice]...)
an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model; (in [0111] (A1) According to one aspect, a method (e.g., the process 1402) is described for training a machine-trained model. The method includes, in a training example-generating operation, generating a plurality of training examples, each training example being produced by: receiving (e.g., in block 1404) a system instruction (e.g., the system instruction 114) that requests a teacher language model (e.g., the teacher language model 104 or 106) to formulate responses to queries [an Information processor that changes the background information into response information] that describe final results and processes for producing the final results; receiving (e.g., in block 1406) a client instruction (e.g., the client instruction 116) that specifies a query; producing (e.g., in block 1408) a combined prompt (e.g., the prompt 112) that includes a combination of the system instruction and the client instruction; submitting (e.g., in block 1410) the combined prompt to the teacher language model. The teacher language model transforms the combined prompt into a teacher-model response (e.g., the teacher-model response 136) [which is information in a question-and-answer format, using a change model]. The teacher-model response describes a final result and a process for producing the final result. The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples [an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model]...)
and a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question. (in [0111] … The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples. In a training operation [a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method], the method includes training (e.g., in block 1416) parameters of a student language model [a basic model,] (e.g., the student language model 406) based on the training examples [and then trains the same with the response information to generate a support model that produces a response to an arbitrary question.]. And in [0113] (A3) According to some implementations of the methods of A1 or A2, the teacher language model is a different model than the student language model, the teacher language model having greater capabilities compared to the student language model, and/or the teacher language model consuming more resources compared to the student language model, and/or the teacher language model having a larger size than the student language model… [0120] (A10) According to some implementations of any of the methods of A1-A9, the training operation further includes submitting a student-model prompt to the student language model, and, in response, receiving a student-model response [generate a support model that produces a response to an arbitrary question support model as trained student model with updated parameter values]… The method further includes: generating a measure of loss that depends on a difference between the teacher-model response and the student-model response; and updating parameters of the student language model based on the loss. And in [0029] …FIG. 4 (described below) illustrates a second phase in which the training system 102 trains a student language model on the basis of the training examples produced in the first phase of operation [a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question]. Once full trained, the student language model constitutes a client language model for operating in a local system [to generate a support model that produces a response to an arbitrary question].)
Regarding claim 2, the rejection of claim 1 is incorporated and Awa in combination with NN teaches the support system of claim 1, wherein the background information is knowledge obtained by analyzing an architectural component of the microservice, and is information that comprises knowledge of at least one of statistical analysis, correlation analysis, trend analysis, or seasonality analysis based on information collected from independent modules configuring the microservice (in [0041] In some implementations, the data store 110 specifically stores a plurality of subsets of training queries associated with different categories. For example, the categories refer to different respective tasks and/or different ways of structuring the queries [wherein the background information is knowledge obtained by analyzing an architectural component of the microservice] and is information that comprises knowledge of at least one of . For each category of interest (“category-of-interest”), the example-generating system 108 randomly samples a category-specific amount of queries from the subset of queries associated with the category-of-interest.
Additionally in [0051] In operation (5), the example-generating system 108 produces a training example based on the teacher-model response 136, and stores the training example in the first set of training examples 130. The training example includes: the prompt 112 (including the system instruction 114 and the client instruction 116 that specifies a particular query) and the teacher-model response 136. … [0075] Example 4 of FIG. 10 represents another variation of Example 2. In this case, the training 102 revises the teacher-response R.sub.M1_T(1) in one or more improvement cycles, prior to comparison with the student-model response R.sub.M2_S... The notation “(1)” indications that this teacher-model response is an initial version of the response. In an improvement cycle 1002, the teacher language model MIT transforms a prompt (including a combination of a system instruction S.I.(n), the last-computed teacher-model response R.sub.M1_T(n), and any added guidance information 1004) to a revised teacher-model response R.sub.M1_(T(n+1) [and is information that comprises knowledge of at least one of … correlation analysis, trend analysis]. The notation “(n)” represents the nth iteration of the improvement cycle 1002 [correlation analysis, trend analysis as corelated improvement trend analysis at each change cycle for modifying training example responses], with n initially being 1. The added guidance information 1004 specifies any criteria by which the last-computed teacher-model response R.sub.M1_T(n) is to be evaluated in the current improvement cycle n. For instance, the added guidance information 1004 specifies a ground-truth answer and/or a general directive to amend the last-computed teacher-model response R.sub.M1_T(n) in a particular way …)
Regarding claim 3, the rejection of claim 1 is incorporated and Awa in combination with NN teaches the support system of claim 1, wherein the change model is ChatGPT. (in [0047] In some implementations, the first teacher language model 104 represents the ChatGPT language model [wherein the change model is ChatGPT] (also known as the GPT-3.5 (turbo) model),…)
Regarding independent claim 10, Awa teaches a support method, comprising: storing, by a Knowledge storage, background information, which is information comprising knowledge for building a microservice; (in As depicted in Fig. 1 and [0111]…. The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples [storing, by a Knowledge storage, background information, which is information comprising knowledge for building a microservice]...)
changing, by an Information processor, the background information into response information, which is information in a question-and-answer format, using a change model; (in [0111] (A1) According to one aspect, a method (e.g., the process 1402) is described for training a machine-trained model. The method includes, in a training example-generating operation, generating a plurality of training examples, each training example being produced by: receiving (e.g., in block 1404) a system instruction (e.g., the system instruction 114) that requests a teacher language model (e.g., the teacher language model 104 or 106) to formulate responses to queries [changing, by an Information processor, the background information into response information] that describe final results and processes for producing the final results; receiving (e.g., in block 1406) a client instruction (e.g., the client instruction 116) that specifies a query; producing (e.g., in block 1408) a combined prompt (e.g., the prompt 112) that includes a combination of the system instruction and the client instruction; submitting (e.g., in block 1410) the combined prompt to the teacher language model. The teacher language model transforms the combined prompt into a teacher-model response (e.g., the teacher-model response 136) which is information in a question-and-answer format, using a change model]. The teacher-model response describes a final result and a process for producing the final result. The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples [changing, by an Information processor, the background information into response information, which is information in a question-and-answer format, using a change model]...)
and processing, by a tuner, a basic model, which is a large language model different from the change model, using a predetermined processing method and then training the same with the response information to generate a support model that produces a response to an arbitrary question. (in [0111] … The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples. In a training operation [using a predetermined processing method and then training the same with the response information to generate a support model], the method includes training (e.g., in block 1416) parameters of a student language model [a basic model that is trained using the training examples] (e.g., the student language model 406) based on the training examples [using a predetermined processing method and then training the same with the response information to generate a support model]. And in [0113] (A3) According to some implementations of the methods of A1 or A2, the teacher language model is a different model than the student language model, the teacher language model having greater capabilities compared to the student language model, and/or the teacher language model consuming more resources compared to the student language model, and/or the teacher language model having a larger size than the student language model… [0120] (A10) According to some implementations of any of the methods of A1-A9, the training operation further includes submitting a student-model prompt to the student language model, and, in response, receiving a student-model response [and then training the same with the response information to generate a support model that produces a response to an arbitrary question where the support model is a trained student model with updated parameter values]… The method further includes: generating a measure of loss that depends on a difference between the teacher-model response and the student-model response; and updating parameters of the student language model based on the loss [and then training the same with the response information to generate a support model that produces a response to an arbitrary question]. And in [0029] …FIG. 4 (described below) illustrates a second phase in which the training system 102 trains a student language model on the basis of the training examples produced in the first phase of operation [processing, by a tuner, a basic model, which is a large language model different from the change model, using a predetermined processing method and then training the same with the response information to generate a support model that produces a response to an arbitrary question]. Once full trained, the student language model constitutes a client language model for operating in a local system [to generate a support model that produces a response to an arbitrary question].)
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Awa in view of Li et al. (NPL: LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models, hereinafter ‘Li’).
Regarding claim 4, the rejection of claim 1 is incorporated and Awa in combination with Li teaches the support system of claim 1, wherein the predetermined processing method is a method (in [0058] Other implementations of the training system 102 vary the above-described approach in different respective ways. In one variation, instead of training all of the parameters of the student language model 406, the training system 102 trains a delta-version (difference-version) of a base language model, where the parameters of the student language model 406 represent add-on parameters that are combined with the parameters of the base language model (at the time of inference or prior to the time of inference). There are different ways of producing add-on parameters, e.g., using adapters or add-on weight matrices. Background information on the general topic of training delta versions of machine-trained models [wherein the predetermined processing method is a method …] can be found at: Hu, at al., “LoRA: Low-Rank Adaptation of Large Language Models,” arXiv, arXiv:2106.09685v2 [cs.CL], Oct. 16, 2021, 26 pages [wherein the predetermined processing method is a method of quantizing 4 bits as a reference and processing the basic model using LoRA (Low-Rank Adaptation) technique], and Houlsby, et al., “Parameter-Efficient Transfer Learning for NLP,” arXiv, arXiv:1902.00751v2 [cs.LG], June 2019, 13 pages.)
Li teaches the processing system including quantization techniques as claimed wherein the predetermined processing method is a method of quantizing 4 bits as a reference, in pg. 2, 1st full para.: To mitigate the extensive storage requirements of pre-trained models, quantization serves as a
pivotal compression technique (Zafrir et al., 2019; Shen et al., 2020; Bai et al., 2022; Dettmers et al.,
2022), converting high-precision numerical values into a discrete set of values. Typically, model
parameters, originally stored in a 16-bit float format, are transformed into a 4-bit integer format
through quantization [wherein the predetermined processing method is a method of quantizing 4 bits as a reference], resulting in a substantial 75% reduction in storage overhead. Additionally, to
facilitate the adaptation of quantized pre-trained models to downstream tasks efficiently, Low-Rank
Adaptation (LoRA) is a viable approach (Hu et al., 2021). This technique is a parameter-efficient
fine-tuning method traditionally applied to high-precision pre-trained models. It is predicated on
the hypothesis that the differences between fully fine-tuned weights and pre-trained weights exhibit
low-rank properties… And in Pg. 7: Sec. Quantization methods: We apply two quantization methods to demonstrate LoftQ is compatible with different quantization functions: NF4 and its 2-bit variant NF2 are quantization methods used in QLoRA (Dettmers et al., 2023). They assume that the high-precision values are drawn from a Gaussian distribution and map these values to discrete slots that have equal probability. We perform 2-bit and 4-bit quantization on all models [wherein the predetermined processing method is a method of quantizing 4 bits as a reference], achieving compression ratios of 25-30% and15-20% at the 4-bit and 2-bit levels, respectively…)
Li and Awa are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for implementing a data processing system using language machine learning algorithms as disclosed by Li with the method of developing information retrieval and processing techniques for network accessible applications using machine learning models disclosed by Awa.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Li and Awa above. Doing so allows for processing and modeling information using language models to using teaches that help alleviate the discrepancy between the quantized and full-precision model and significantly improve the generalization in downstream tasks, (Li, Abstract).
Claims 5-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Awadallah et al. (US 20250094827, hereinafter ‘Awa’) in view of Kholodkov et al. (US 20250147754, hereinafter ‘Kov’).
Regarding independent claim 5, Awa teaches an integrated operating system, comprising: a support system that generates a support model that produces a response corresponding to a question of a requester in relation to a building of a microservice; (in [0036] The training system 102 trains the client language model using a teacher-student approach [an integrated operating system, comprising: a support system that generates a support model that produces a response corresponding to a question of a requester in relation to a building of a microservice]. The training system 102 differs from prior applications of this approach, in part, by using explanation tuning. Explanation tuning composes a prompt that includes two parts: a system instruction and a client instruction. The client instruction expresses a query. The system instruction requests a language model to formulate responses to queries [a response corresponding to a question of a requester in relation to a building of a microservice] in a detailed and expansive manner… [0050] Assume that at the current point in time represented by FIG. 1, the prompt 112 is sent to the first teacher language model 104. In response, in operation (4), the first teacher language model 106 generates a teacher-model response 136. The teacher-model response 136 explains the logic by which the teacher-model response 136 is derivable.)
and an interface calculator that produces a user interface that displays the response to the question of the requester through the support model, (As depicted in Fig.3 and in [0052] Advancing to FIG. 3, this figure shows an example of a prompt 302 and a teacher-model response 304 [and an interface calculator that produces a user interface that displays the response to the question of the requester through the support model] produced by the second teacher language model 106, based on the prompt 302. The prompt 302 includes a system instruction 306 that corresponds to system instruction No. 7 shown in FIG. 2:…)
wherein the support system comprises: a Knowledge storage that stores background information, which is information including knowledge for building the microservice; (in As depicted in Fig. 1 and [0111]…. The method further includes storing (e.g., in block 1412) a training example in a data store (e.g., the data store 134) that includes the combined prompt and the teacher-model response, the data store storing the plurality of training examples [wherein the support system comprises: a Knowledge storage that stores background information, which is information including knowledge for building the microservice]...)
The remaining limitations are similar to those in claim 1 and thus rejected under the same rationale.
While Awa teaches the data processing system using machine learning models and techniques. Awa does not expressly disclose the application of machine learning system as a software debugging application.
Kov expressly teaches the application of machine learning system as a software debugging application: a monitoring system that analyzes a root cause of abnormal operation of target software using a corresponding analysis tool calculated by simulating comparison software configured to correspond to the target software used by the requester and operated in a microservice environment; in [0045] The process 600 includes an operation 612 of providing the prompt as an input to the language model 126 and an operation 614 of receiving the root cause failure analysis from the language model 126. [a monitoring system that analyzes a root cause of abnormal operation of target software …] The prompt includes the category of root cause predicted by the graphical language model 128. [0048] The process 640 includes an operation 642 of obtaining logs that include information associated with a problem with software [target software] and an operation 644 of analyzing the logs to generate a knowledge graph 130 based on the logs [using a corresponding analysis tool calculated by simulating comparison software configured to correspond to the target software used by the requester and operated in a microservice environment]. The logs may be the build logs 134 discussed in the preceding examples that include information indicative of a root cause of a build problem. The logs include information output as the software is being executed and include information indicative of a root cause of a runtime error that occurred while the software was being executed by the application services platform 110. The knowledge graph 130 [simulating comparison software] identifies the relationship between various entities in the logs. The knowledge graph generation unit 132 generates the knowledge graph 130 based on the logs.
… and wherein the Interface calculator uses the support model to provide the requester with the response necessary in a process of determining the comparison software., in [0022] The language model 126 is a machine learning model trained to generate textual content in response to natural language prompts [… and wherein the Interface calculator uses the support model to provide the requester with the response necessary in a process of determining the comparison software] input by a user via the native application 114 or via the browser application 112. The language model 126 is implemented using a large language model (LLM), in some implementations. Examples of such models include but are not limited to a Generative Pre-trained Transformer 3 (GPT-3), or GPT-4 model… The language model 126 is used to generate root cause analysis information that predicts the cause of problems associated with software builds [the response necessary in a process of determining the comparison software]. As discussed in detail in the examples which follow, the language model 126 works in conjunction with the graphical language model 128 to generate the root cause analysis predictions based on the build logs 134…
Kov and Awa are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for implementing a data processing system using language machine learning algorithms in software development applications as disclosed by Kov with the method of developing information retrieval and processing techniques for network accessible applications using machine learning models disclosed by Awa.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Kov and Awa above. Doing so allows for processing and modeling information using language models to generate correct root cause analysis and avoid hallucinations, (Awa, 0022).
Regarding claim 6, the rejection of claim 5 is incorporated and Awa in combination with Kov further teaches the integrated operating system of claim 5, wherein the monitoring system comprises: an Analysis tool calculator tcomprises: a Module collector that collects the corresponding independent module from an external server; (in [0044] In operation (3), the training system 102 submits the prompt 112 to one of the teacher language models (104, 106) in a teacher system 118. In some implementations, the teacher system 118 implements the teacher language models (104, 106) using teacher-system resources 120 [wherein the monitoring system comprises: an Analysis tool calculator t] (e.g., memory resources and processing resources). In some implementations, the teacher system 118 specifically includes one or more servers [from an external server] that the example-generating system 108 interacts with via a computer network 122 (e.g., the Internet) using an application programming interface (API) 124.)
Kov further teaches the integrated operating system of claim 5, wherein the monitoring system comprises: an Analysis tool calculator that calculates the corresponding analysis tool based on the comparison software configured of corresponding independent modules, which are independent modules classified by function to correspond to the target software; (in [0027] The signature extraction unit 138 provides the signature generated for the knowledge graph 130 [based on the comparison software configured of corresponding independent modules] as an input to the graphical language model 128 and the graphical language model 128 outputs a predicted category of root cause failure [wherein the monitoring system comprises: an Analysis tool calculator that calculates the corresponding analysis tool…]. In some instances, a new type of root cause failure may occur that has not yet been labeled. The graphical language model 128 outputs an indication that the root cause problem could not be classified, and the request processing unit 122 can generate a request for a human user to review the build log information 134, the signature information extracted by the signature extraction unit 138, and/or other information that may be relevant for assessing whether a new category of root cause failure has been detected [which are independent modules classified by function to correspond to the target software]… )
and a Cause analyser that analyzes the root cause of the abnormal operation occurring in the target software using the corresponding analysis tool, (in [0016] Systems and methods for using artificial intelligence (AI) for root cause analysis are described herein. These techniques provide a technical solution to the problems associated with current solutions that attempt to utilize an LLM to analyze the logs associated with a software build or execution to attempt to identify a root cause of a problem with the software [and a Cause analyser that analyzes the root cause of the abnormal operation occurring in the target software using the corresponding analysis tool,] or the software build by implementing a multi-model approach. The multi-model approach provided herein provides a flexible and durable solution for automating root cause analysis by using two different types of AI models… [0019] ... The web-enabled application enables the authorized user to view the build logs 134 associated with a build and root cause analysis predictions made by the graphical language model 128 [and a Cause analyser that analyzes the root cause of the abnormal operation occurring in the target software using the corresponding analysis tool] and the language model 126… )
wherein the Analysis tool calculator comprises: a Module collector that collects the corresponding independent module from an external server; (in [0021] The request processing unit 122 is configured to receive requests [wherein the Analysis tool calculator comprises: a Module collector that collects the corresponding independent module from an external server] from the native application 114 of the client device 105 and/or the web application 190 of the application services platform 110… The request processing unit 122 also coordinates communication and exchange of data among components of the application services platform 110 [from an external server] as discussed in the examples which follow. And in [0067] The machine 800 may include processors 810, memory 830, and I/O components 850, which may be communicatively coupled via, for example, a bus 802 0 [from an external server]... Although FIG. 8 shows multiple processors, the machine 800 may include a single processor with a single core, a single processor with multiple cores (for example, a multi-core processor), multiple processors each with a single core, multiple processors each with multiple cores, or any combination thereof. In some examples, the machine 800 may include multiple processors distributed among multiple machines [from an external server]. )
a Simulator that builds the comparison software using the corresponding independent modules and simulates the comparison software through a predetermined control method; (in [0045] The process 600 includes an operation 612 of providing the prompt as an input to the language model 126 and an operation 614 of receiving the root cause failure analysis from the language model 126. The prompt includes the category of root cause predicted by the graphical language model 128. [0048] The process 640 includes an operation 642 [a Simulator that builds the comparison software using the corresponding independent modules] of obtaining logs that include information associated with a problem with software and an operation 644 of analyzing the logs to generate a knowledge graph 130 based on the log. The logs may be the build logs 134 discussed in the preceding examples that include information indicative of a root cause of a build problem. The logs include information output as the software is being executed and include information indicative of a root cause of a runtime error that occurred while the software was being executed by the application services platform 110. The knowledge graph 130 [and simulates the comparison software through a predetermined control method] identifies the relationship between various entities in the logs. The knowledge graph generation unit 132 generates the knowledge graph 130 based on the logs.)
a Date collector that collects result information composed of log information, metric information, and trace information generated during a simulation process utilizing different data collection members; (in [0020] The application services platform 110 [a Date collector that collects result information composed of] includes a request processing unit 122, a prompt construction unit 124, a language model 126, graphical language model 128, knowledge graph 130, knowledge graph generation unit 132, build logs 134, a build-related prompt datastore 136, a build-related prompt datastore 136, a signature extraction unit 138, the web application 190, and moderation services 168… [0023] The graphical language model 128 is a machine learning model trained to predict a class of problems that has occurred based on a graphical representation of information extracted from the build logs 134 [log information]. In contrast with the language model 126 which analyzes textual inputs to generate content, the graphical language model 128 is trained to analyze a graphical input [metric information] and to output a predicted root cause failure type based on the graphical input. The graphical language model 128 is trained to recognize various patterns or signatures [metric information, and trace information generated during a simulation process utilizing different data collection members] in the graphical input that represent various failure types [ … utilizing different data collection members] that may be encountered in the build logs 134. A technical benefit of the graphical language model 128 is that the graphical language model 128 is a SGLM that can be trained on a very small number of samples and has a significant yield of predictive power to weight ratio.)
and a Root analyser that calculates the corresponding analysis tool based on result information. (in [0038] The prompt submission unit 406 submits the formatted prompt to the language model 126. The language model 126 analyzes the prompt and generates a response based on the prompt. The response to the prompt includes a root cause failure analysis [a Root analyser that calculates the corresponding analysis tool based on result information] that identifies the root cause of the problem experienced during the build or during the runtime of the software… The moderation services 168 generates a blocked content notification in response to determining that the generated content includes potentially objectionable or offensive content, and the notification is provided to the native application 114 or the web application 190 so that the notification can be presented to the user on the client device 105. The root cause failure analysis is presented to an administrator to analyze whether the content generated by the language model 126 includes potentially objectionable or offensive content… [0039] FIG. 5 is a diagram of example user interface 505 of an application that implements the techniques described herein. The example user interface 505 enables a user to view the root cause failure analysis generated according to the techniques herein. The user interface 505 also includes controls that enables the user to search for the root cause failure analysis [a Root analyser that calculates the corresponding analysis tool based on result information] for previous build problems [based on result information] and/or software build problems [based on result information] that may assist the user in analyzing a current problem...)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Kov and Awa for the same reasons disclosed above.
Regarding claim 8, the rejection of claim 6 is incorporated and Kov further teaches the integrated operating system of claim 6, wherein: the Analysis tool calculator further comprises a Comparison analyser that configures the comparison software by configuring the corresponding independent module identical to a target independent module, which is an independent module forming the target software, (in [0045] The process 600 includes an operation 612 of providing the prompt as an input to the language model 126 and an operation 614 of receiving the root cause failure analysis from the language model 126. The prompt includes the category of root cause predicted by the graphical language model 128. [0048] The process 640 includes an operation 642 of obtaining logs that include information associated with a problem with software [which is an independent module forming the target software] and an operation 644 of analyzing [wherein: the Analysis tool calculator further comprises a Comparison analyser that configures the comparison software by configuring the corresponding independent module identical to a target independent module, which is an independent module forming the target software] the logs to generate a knowledge graph 130 [configures the comparison software by configuring the corresponding independent module identical to a target independent module] based on the logs. The logs may be the build logs 134 discussed in the preceding examples that include information indicative of a root cause of a build problem. The logs include information output as the software is being executed and include information indicative of a root cause of a runtime error that occurred while the software was being executed by the application services platform 110. The knowledge graph 130 [configures the comparison software by configuring the corresponding independent module identical to a target independent module] identifies the relationship between various entities in the logs. The knowledge graph generation unit 132 generates the knowledge graph 130 based on the logs…; And in [0058] In some examples, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. …)
and when the Module collector fails to collect the corresponding independent module that matches the target independent module, the Comparison analyser configures the comparison software by comprising at least one corresponding independent module similar to the target independent module within the scope that a predetermined compensation condition is satisfied. (in [0027] The signature extraction unit 138 provides the signature generated for the knowledge graph 130 as an input to the graphical language model 128 and the graphical language model 128 outputs a predicted category of root cause failure. In some instances, a new type of root cause failure may occur that has not yet been labeled. The graphical language model 128 outputs an indication that the root cause problem could not be classified, and the request processing unit 122 can generate a request for a human user to review the build log information 134, the signature information extracted by the signature extraction unit 138, and/or other information that may be relevant for assessing whether a new category of root cause failure has been detected [when the Module collector fails to collect the corresponding independent module that matches the target independent module, …]. The human user can review the information provided by the request processing unit 122 via a user interface provided by the native application 114 and/or the web application 190. The human user may determine that a new category of root cause failure has been identified and input a label for that root cause failure. The native application 114 and/or the web application 190 generates new training data for the graphical language model 128 to fine-tune the training of the model to recognize the new category of root cause failure [he Comparison analyser configures the comparison software by comprising at least one corresponding independent module similar to the target independent module within the scope that a predetermined compensation condition is satisfied]. Otherwise, if the human user determines that the root cause failure belongs to an existing category of root cause failure, the native application 114 and/or the web application 190 can generate training data for the graphical language model 128 to fine-tune the training of the graphical language model 128 to recognize that category of root cause failure in the future […a predetermined compensation condition is satisfied]. )
Regarding claim 9, the rejection of claim 9 is incorporated and Kov further teaches the integrated operating system of claim 8, wherein, when the comparison software is configured to comprise at least one corresponding independent module similar to the target independent module within the scope that the predetermined compensation condition is satisfied, the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester. (in [0043] The process 600 includes an operation 608 of providing the signature of the candidate root cause to a graphical language model 128 to obtain a prediction of a category of root cause failure [wherein, when the comparison software is configured to comprise at least one corresponding independent module similar to the target independent module within the scope that the predetermined compensation condition is satisfied]. The graphical language model 128 is trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause. [0044] The process 600 includes an operation 610 of constructing a prompt for a language model using a prompt construction unit 124, the prompt instructing the language model 126 [the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester] to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause predicted by the graphical language model 128… [0046] The process 600 includes an operation 616 of performing one or more actions in response to receiving the root cause failure analysis. The one or more actions may include causing the native application 114 and/or the web application 190 to present the root cause analysis on a user interface [the same to the requester] of the application [the Interface calculator uses the support model to calculate an interface that displays information about analysis of the comparison software and provide the same to the requester]. The one or more actions may also include storing the natural language prompt [the same to the requester] and the root cause analysis in the build-related datastore 136 as discussed above.)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Awadallah et al. (US 20250094827, hereinafter ‘Awa’) in view of Kholodkov et al. (US 20250147754, hereinafter ‘Kov’) in further view of Fortkort et al. (US 20240395388, hereinafter ‘Fort’).
Regarding claim 7, the rejection of claim 6 is incorporated and Kov further teaches the integrated operating system of claim 6, wherein the Date collector uses a . (in [0020] The application services platform 110 [wherein the Date collector uses a information] includes a request processing unit 122, a prompt construction unit 124, a language model 126, graphical language model 128, knowledge graph 130, knowledge graph generation unit 132, build logs 134, a build-related prompt datastore 136, a build-related prompt datastore 136, a signature extraction unit 138, the web application 190, and moderation services 168… [0023] The graphical language model 128 is a machine learning model trained to predict a class of problems that has occurred based on a graphical representation of information extracted from the build logs 134 [the log information]. In contrast with the language model 126 which analyzes textual inputs to generate content, the graphical language model 128 is trained to analyze a graphical input [metric information] and to output a predicted root cause failure type based on the graphical input. The graphical language model 128 is trained to recognize various patterns or signatures [the metric information, and … the trace information ] in the graphical input that represent various failure types that may be encountered in the build logs 134…; And in [0058] In some examples, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules [wherein the Date collector uses a ]. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment [wherein the Date collector uses a ] or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by, and/or among, multiple computers (as examples of machines including processors), …)
Kov teaches the data processing elements/components of a computing environment.
Fort expressly teaches the claimed labels, in [0488] The distributed tracing and logging module 1237 traces requests across service boundaries and centralized logging to understand behavior and to troubleshoot any issues… The distributed tracing and logging module 1237 may incorporate or leverage various tools for this purpose. These include, without limitation, tools such as Jaeger or Zipkin for distributed tracing, or Elasticsearch, Logstash, Kibana (ELK stack) or Fluentd for logging [wherein the Date collector uses a Fluentd data collection member for the log information, ]… [0504] In the CD phase, tools such as Kubernetes or Docker Swarm are utilized to pull the latest container image from the registry and deploy it to the appropriate environment (staging or production)… This helps to maintain consistency across different deployment environments. Once deployed, the applications may be monitored using tools such as, for example, Prometheus [wherein the Date collector uses ] or ELK Stack…
Fort, Kov and Awa are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for implementing a data processing using machine learning systems/techniques in distributed computing environments as disclosed by Fort with the method of developing information retrieval and processing techniques for network accessible applications using machine learning models collectively disclosed by Kov and Awa.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Fort, Kov and Awa above. Doing so allows for processing and modeling information in computing environments using cloud computing tools that help collect logs and metrics data from applications running on containers to detect any issues and enable quick troubleshooting, (Fort, 0504).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh (NPL: Dynamic Rank Assignment in LoRA Fine-Tuning for Large Language Models): teaches When considering a quantized model, which is a common scenario, it is essential to acknowledge that in such models, different types of parameters are typically quantized with varying precision. Let’s assume a q-bit quantization scheme. The majority of parameters will be quantized from 16-bits to q-bits (often q = 4 or occasionally q = 8), while all normalization layers, including embedding and unembedding (Nnorms) are upscaled to32-bits (4 bytes).
Yuan et al. (US 20250156653): teaches a support system, comprising: a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice; (in As depicted in Figs. 2 & 3 and in[0039] Similarly, system 100 can use background knowledge as metadata to extract query 116. For example, a query can precede query 116, such that the two queries can be sequential and query 116 can be based on the preceding query. In this scenario, background knowledge can represent a developed memory of system 100, such that system 100 can identify a real intention behind query 116 based on historical query records…)
an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model; (in [0050] Constructing a semantic retrieval system can be based on simCSE and a tagging system…Semantic retrieval can comprise performing a search (e.g., by semantic retrieval component 108) within a certain scope using query statements, filtering (e.g., by semantic retrieval component 108) using tags (if available), and performing a semantic search (e.g., by semantic retrieval component 108). For example, the semantic retrieval system can search for online knowledge 204 using the query statements, wherein online knowledge 204 can comprise geographic location 206, background knowledge 208, external knowledge 210, etc. Retrieval results generated by the semantic retrieval system can be a collection of candidate sets in units of sentences, that is, a sentence collection. For the sentence collection in the retrieval results, an algorithm based on SPO structure extraction can be used to extract (e.g., by information extraction component 110) a main structure of each sentence. SPO extraction can be very fast (i.e., at the ms level). Next, the extracted SPO structures can be organized (e.g., by information extraction component 110) into a virtual graph in the form of triplets that can be used for a subsequent step of graph structure search [an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model]..)
and a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question. (in [0053] A CRF model can be used (e.g., by filtering component 112) to additionally filter the output generated by LLM 202 to generate a final response from LLM 202. When a knowledge archive accessible to LLM 202 reaches a certain scale, a first level knowledge graph can be constructed (e.g., by information extraction component 110) based on the knowledge archive, which can play a role of a mem-cache in a memory database. If a query can be answered based on the first level knowledge graph, process 200 can prioritize answering the query based on information in the first level knowledge graph. LLM 202 [a tuner that processes a basic model, which is a large language model different from the change model] combined with the semantic retrieval system and CRF can represent enhanced LLM 214 that can be an enhanced version of LLM 202 based on in-context learning [and then trains the same with the response information to generate a support model that produces a response to an arbitrary question] (at 214) combined with online knowledge 204 [the response information]. Enhanced LLM 214 can be used in products such as customer service robots. For example, customer service robot 212 can be an intelligent robot driven by enhanced LLM 214 that can make restaurant suggestions [that produces a response to an arbitrary question] (e.g., “How about chicken curry? I have some recommendations.”). As such, trustworthiness of an output of LLM 202 can be improved by in-context learning combined with online knowledge. Additional aspects of the various embodiments herein are described in greater detail with reference to subsequent figures.)
Bayless et al. (US 20250111192): teaches a support system, comprising: a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice; (in As depicted in Fig. 1 and [0019] This disclosure describes techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users. There have been recent advances in generative AI, particularly around chatbots, code generators (and other domain-specific LLMs), and similar models and services that generate text using LLMs... According to the techniques described herein, the knowledge-graph system builds the knowledge graph using answers produced by an LLM such that the knowledge graph [a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice] grows each time a novel query is answered by the LLM. The chatbot will continue to use the LLM to answer novel queries, but the chatbot may harness the knowledge graph to answer repeat questions to gain various efficiencies over LLM-backed chatbots. For example, the knowledge-graph system may easily debug or otherwise improve the answers in the knowledge graphs, store provenance information in the knowledge graphs for answers [a Knowledge storage that stores background information, which is information comprising knowledge for building a microservice], and augment the knowledge graphs using other data sources…)
an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model; (in [0038] The chatbot system 110 may provide the formal-language query 128 to the knowledge-graph system 104. Thus, the chatbot system 110 may answer user 106 queries by working in conjunction with the knowledge-graph system 104 that may generate, build, or augment a knowledge graph 122 at least partly using answers from an LLM 118 of an AI system 116. When the chatbot system 110 receives the query 128 from a user 106, the knowledge-graph system 104 [using a change model] may, at 3, initially attempt to determine an answer 134 to the query 128 using the knowledge graph 122 [an Information processor that changes the background information into response information, which is information in a question-and-answer format, using a change model]. A knowledge-graph component 120 may include a query evaluation engine evaluates the formal-language query 128 using the knowledge graph 122 to determine if the knowledge graph 122 returns results for the particular query (e.g., answers)... [0041] …. Additionally, the knowledge-graph system 104 [using a change model]] may be able to improve, or augment, curated domain-specific knowledge graphs [changes the background information into response information, which is information in a question-and-answer format] by adding general-purpose knowledge to those knowledge graphs 122 through prompt-engineering with the LLMs 118.)
and a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method and then trains the same with the response information to generate a support model that produces a response to an arbitrary question. (As depicted in Fig. 1 and in [0045] Generative AI can be used to generate text that resembles human-like responses to prompts. Transformers are very effective in training the models used generate text, often referred to as Large Language Models (LLMs 118) [a tuner that processes a basic model, which is a large language model different from the change model, using a predetermined processing method]. LLMs 118 are trained on large sets or corpuses of text data to generate human-like textual responses to prompts [then trains the same with the response information to generate a support model that produces a response to an arbitrary question]. LLMs 118 are generally trained in two stages, pre-training and fine-tuning. During the pre-training stage, LLMs 118 are trained on massive datasets of unlabeled text data (or “unsupervised learning”) where transformers allow the LLMs 118 to process and learn the patterns and relationships between words. During the fine-tuning stage, the LLMs 118 can be fine-tuned for specific tasks or prompts [trains the same with the response information], such as summarizing content, answering questions, and text completion. There are generalized LLMs 118 that have been trained on sets of text data [trains the same with the response information] describing all types of content (e.g., data obtained from crawlers that scrape the public Internet). There are also specialized LLMs 118 that have been trained on specialized sets of data [trains the same with the response information] that are specific to a particular type of content, such as travel or shopping.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWATOSIN ALABI whose telephone number is (571)272-0516. The examiner can normally be reached Monday-Friday, 8:00am-5:00pm EST..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
Prosecution Insights