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 .
Status of Claims
Claims 1, 10, 19 and 21 – 22 have been amended and are hereby entered.
Claims 4 - 7, 13 - 16 and 20 were cancelled, while claims 25 - 29 were added.
Claims 1 - 3, 8 - 12, 17 - 19 and 21 - 29 are pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 24 2025, January 23 2026 and February 27 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed December 29, 2025 have been fully considered but they are not persuasive.
Regarding the applicant's arguments for Double-Patenting Rejection in p. 12: Although applicant did amend the claims, but did not file an Electronic-Terminal Disclosure or e-td to obviate the Obviousness-type Double Patenting (ODP) rejection, the ODP will be withdrawn. Because based on Applicant’s arguments that were considered, the amendments are distinct of the claimed invention reflected in the instant claims 22, 29 and 36 from the referenced claims 22, 29 and 36 (form app 18/145 ,063) which were also amended.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 13-16: Applicant’s arguments directed to Step 2A prong 1 and the rest of the 101 analysis were considered. However, these arguments are not persuasive and the Examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 2 and Step 2B starting in p. 12: Applicant argues that the claims and its features are patent eligible because the claims when considered as a whole: “(A) are no longer directed to an abstract idea (namely, a mental process or a method of organizing human activity); and (B) contain additional elements that are sufficient to amount to significantly more than any alleged judicial exception” and (C) further points out to the “August 4 Memo” to the limitations cannot be performed by the human mind as it is “not equipped” because thousands of data are “simultaneously analyzed” and received, as claimed (see p. 15 from Remarks). However, the Examiner respectfully disagrees and find these arguments unpersuasive. Because of the following reasons:
For the abstract idea recitation, the pending claims are still directed to the abstract ideas of a certain method of organizing human activity and a mental process as the claims’ limitations are explicitly reciting (i.e. claims “described” or “set forth” in the claim language) the intended result of resolving “customer-encountered issues” as a “service request” that at least encompasses interactions related to advertising for recommending relevant portions of “knowledge base articles” that are used to resolve the issue as requested and/or involve marketing or sales activities or behaviors when obtaining/resolving “service requests”, as claimed and underlined above in the steps. But also, the limitations recite conducting a “single-layer search” to obtain portions of knowledge base articles and supplemental data through a supplemental search to rank/enumerate them based on the determination of a threshold being exceeded or not (e.g. in different instances as underlined) to achieve the intended result of resolving the “service request” using the “portion of the knowledge base articles, the ranked order, and the supplemental result” or “portion of the knowledge base articles and the ranked order” to determine which products from the “production information” (i.e. product information) enumerated are relevant which requires at least evaluation and judgement.
The claims and their additional elements identified of a non-transitory machine-readable medium (from claim 10) a processor (from claims 10 and 19); a memory (from claim 19); a response management system (RMS); a knowledge base article repository; a development lifecycle infrastructure metadata cache (from claims 1, 10 and 19) are not sufficient to amount to significantly more than the exception itself (e.g. there is no inventive concept). Because these additional elements are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). The computer and these element features mentioned above are used to perform the claim functions related to the two-part or “multi-part search strategy” to achieve the intended result of resolving service requests related to “customer-encountered issues”. The computer and the additional elements used are recited at a high level of generality and are performed generally to apply the abstract idea without placing any limits on how the functions are distinct from being applied by a generic computer used as a tool to execute them and without reciting each function to generally “apply it” to the computer. Thus, such broad recitation of the computer and additional elements that invokes the use of the computer as a tool to perform the abstract idea cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application.
Also, these claims are not reflecting any specific improvement in the computer functioning or in the technical field of issue management and resolution systems by quickly resolving service requests related to customer-encountered issues. The claims itself and/or the specifications are not meaningfully limited to go beyond generally linking the use of the judicial exception to a particular technological environment, and thus not transforms a claim into patent-eligible subject matter. Rather, the claims are “a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art” (see MPEP 2106.05(a)). Further, the claims are not sufficiently distinctive to reflect such improvement from generally applying the recited functions and features to computer components in order to use a computer as a tool. Thus, for the same reasons, these limitations do not serve to improve technology or the issue management and resolution systems to be eligible at Step 2B, qualify as “significantly more” and provide for an “inventive concept” based on the reasons stated above (See MPEP 2106.05(a & f)).
As for arguments regarding the limitations that cannot be performed by the human mind as it is “not equipped” because thousands of data are “simultaneously analyzed” and received, this is unpersuasive. Because the Examiner considered the broadest reasonable interpretation of the claim in light of the specification and determined the claimed invention to be described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed on a “generic computer” that is also “merely using a computer as a tool to perform the concept”. Meaning that the amendment for “obtaining service requests” that are “simultaneously processed by the data processing system in real-time” is analogous as to having many agents with their different computers (used as a tool and with the help of physical aid such as pen and paper) attending and analyzing these thousands of data – in other words, this step could be "performed by humans without a computer" (see MPEP 2106.04(a)(2)(III)(C)). Thus, the step of “obtaining service requests” can either be done with the help of physical aid such as pen and paper since the physical aid used does not negate the mental nature of the limitation(s), even when using other generic computer components to obtain such requests (see MPEP 2106.04(a)(2)(III)(B & C)). Finally, claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer"). See MPEP 2106.04(a)(2)(III).
Therefore, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding the applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 16 – 18: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive. Because Applicant is focusing on each prior art teaching, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different from the prior art teachings. Rather, the steps disclose a broader language that the prior art combination of Adrian and Gondalia, still reasonably satisfies and teaches in light of the broadest reasonable interpretation (BRI) of the claim language. Specifically, under BRI, Adrian still teaches the limitation directed to “real-time updating of a development lifecycle infrastructure metadata cache stored in a RMS”, as alleged by the Applicant. Because Adrian suggests this non-functional descriptive matter claimed for having multiple service requests being simultaneously processed by the system when the system 100 can track when content is used and at what phase of the service process a corresponding search was done” (see ¶0065; Adrian) as well as the system can receive “input from a number of sources” (see ¶0061; Adrian).
As for the arguments of “looking the data in the internet” taught by Gondalia, which is not satisfying the step of “performing a supplemental search of a development lifecycle infrastructure metadata cache stored within the RMS” and such search is not within the invention system for the “development lifecycle infrastructure metadata cache” is unpersuasive. Because Gondalia still teaches the performing function even though is differentiated for looking the data in the internet (see ¶0040; Gondalia). However, because Gondalia is combined with Adrian, Adrian teaches that its system can modify (iterate) “multi-factor context” based on “additional information provided by a customer service agent, by assignment of an associated ticket to a service, occurrence of related incidents (e.g., other similar tickets), and so forth” which are within the system (directed to the data stored within RMS system claimed; see ¶0025 and ¶0057; Adrian) and additional searches can be “performed iteratively as additional context information is added to a corresponding multi-factor context” to “improve and/or update the relevancy of the results 160 provided to an IT customer service agent for resolving a particular problem” (see ¶0060 and ¶0063; Adrian). As for the subsequent arguments, these arguments failed to comply with 37 CFR 1.111(b) as such arguments amount to general allegations that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from at least the Adrian and Gondalia references.
Therefore, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 3, 8 - 12, 17 - 19 and 21 - 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 19, the most representative claim of the independent claims set 1, 10 and 19, as follows:
At Step 1: Claim 1 – 3, 8 – 9, 21 – 24 and 29 falls under statutory category of a process, while claims 10 – 13, 17 – 19 and 25 - 28 are directed to a system, respectively.
At Step 2A Prong 1: Claim 19 (representative of claim 1 and 10) recites an abstract idea in the following limitations:
obtaining a service request for a customer-encountered issue, wherein multiple ones of the service requests are simultaneously processed…in real-time;
for each of the service requests: performing, to resolve a service request of the service requests directed to a customer-encountered issue of the customer-encountered issues, a multi- part search strategy comprising:
conducting a single-layer search based on the customer-encountered issues on all knowledge base articles contained…, the single-layer search comprising searching only within a body of the knowledge base articles without searching within metadata of each of the knowledge base articles to obtain a portion of the knowledge base articles;
obtaining a portion of knowledge base articles that are responsive to only a result of a single-layer search;
ranking the portion of the knowledge base articles to obtain a ranked order, each of the knowledge base articles having a metadata quality score that is based on a number of metadata types contained within the metadata of each respective one the knowledge base articles, and the ranking of the portion of the knowledge base articles being based at least on the metadata quality score of each knowledge base article among the portion of the knowledge bases articles; and
making a determination regarding whether the ranking order for the portion for the knowledge base articles indicates that the portion of the knowledge base articles have information content that exceeds a threshold;
in a first instance of the determination where the information content does not exceed the threshold:
performing a supplemental search of a development lifecycle infrastructure metadata cache stored…to obtain a supplemental result
resolving the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result; and
in a second instance of the determination where the information content exceeds the threshold:
resolving the service request using the portion of the knowledge base articles and the ranked order and
updating, in the real-time and while the multiple ones of the service requests are simultaneously processed also in the real-time, the development lifecycle infrastructure metadata cache stored in the RMS by:
analyzing developmental lifecycle infrastructure data of products associated with the entity to identify product information comprising at least version control of the products, code artifacts used in the products, tracking systems used to track issues present in the products, and communication systems used to create and maintain the products;
enumerating each of the product information within the developmental lifecycle infrastructure data;
determining to which ones of the products each of the production information that is enumerated is relevant; and
creating entries in the development lifecycle infrastructure metadata cache for each of the product information that is enumerated, each of the entries listing the ones of the products to which each of the production information that is enumerated is relevant.
Generally, these limitations describe a method and a system for resolving service requests related to customer-encountered issues by efficiently providing ranked knowledge base articles and supplemental data based on a threshold determination. As disclosed in the specification in ¶0016 and ¶0019, this invention “improve[s] ease of search and use of the articles” and “reduce the suffering of the customers subject to the customer-encountered issues through reduced time to resolution”. However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) are/is recited in claim 19 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in the steps of “obtaining a service request for a customer-encountered issue of the customer-encountered issues…”, “…performing, to resolve a service request of the service requests directed to a customer-encountered issue of the customer-encountered issues while reducing an aggregate quantity of computing resources expanded…to resolve the customer-encountered issues, a multi- part search strategy…” that on a first instance (wherein information content does not exceed the threshold): “resolving the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result;” or in a second instance (wherein information content exceeds the threshold): “resolving the service request using the portion of the knowledge base articles and the ranked order” and “…creating entries in the development lifecycle infrastructure metadata cache for each of the product information that is enumerated”. Because these steps are explicitly reciting the intended result that at least encompasses interactions related to advertising for recommending relevant portions of “knowledge base articles” that are used to resolve the issue as requested and/or involve marketing or sales activities or behaviors when obtaining/resolving “service requests” related to resolving customer-encountered issues for product support (see ¶0016 and ¶0039 from Applicant’s disclosure), as claimed and underlined above in the steps.
On the other hand, at least the claim steps recite “conducting a single-layer search based on the customer-encountered issues on all knowledge base articles…”, “ranking the portion of the knowledge base articles to obtain a ranked order …”, “making a determination regarding whether the ranking order for the portion for the knowledge base articles indicates that the portion…have information content that exceeds a threshold”; in a first instance when threshold did not exceeded: “performing a supplemental search… to obtain a supplemental result” and “resolving the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result” and in a second instance when threshold exceeded: “resolving the service request using the portion of the knowledge base articles and the ranked order”, and in order to update “the development lifecycle infrastructure metadata cache”, “analyzing developmental lifecycle infrastructure data of products associated with the entity to identify product information…”, “enumerating each of the product information…” and “determining to which ones of the products each of the production information that is enumerated is relevant” . Thus, these steps fall under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because conducting a “single-layer search” to obtain portions of knowledge base articles and supplemental data through a supplemental search to rank them based on the determination of a threshold being exceeded or not (e.g. in different instances as underlined) requires evaluation and judgement. As for the subsequent steps at a second instance for analyzing and enumerating product information to determine which products are relevant requires evaluation and judgement as well. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps does not negate and further still reads in the mental nature of the limitation(s), when obtaining such information, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of “conducting a single-layer search” to “obtain the portion of the knowledge base articles” and “performing a supplemental search” to obtain supplemental results in order to resolve the service request (see MPEP 2106.04(a)(2)(III)(B & C)).
Step 2A Prong 2: For independent claims 1, 10 and 19, This judicial exception is not integrated into a practical application. Because the claims at steps and their additional feature element(s) of a non-transitory machine-readable medium (from claim 10) a processor (from claims 10 and 19); a memory (from claim 19); a data processing system, a response management system (RMS); a knowledge base article repository; a development lifecycle infrastructure metadata cache (from claims 1, 10 and 19). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). These element features including the computer are recited at a high level of generality and are performed generally to apply the abstract idea without placing any limits on how these steps are performed distinctively from generic computer components and without having each function to generally “apply it” to a computer. See MPEP 2106.05(f). Finally, “a development lifecycle infrastructure metadata cache” is merely recited as data being searched, received and/or transmitted that is stored within the RMS system.
As for the two steps of “obtaining” for “a service request” and “a portion of knowledge base articles”, and the two steps of “resolving the service request” using different types of data in each instance in the claims are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)).
Step 2B: For independent claims 1, 10 and 19, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a non-transitory machine-readable medium (from claim 10) a processor (from claims 10 and 19); a memory (from claim 19); a response management system (RMS); a data processing system, a knowledge base article repository; a development lifecycle infrastructure metadata cache (from claims 1, 10 and 19). These additional elements recited are invoking computers merely used as a tool to perform or “apply” the abstract idea(s) to the existing process of conducting searches to obtain relevant knowledge-based portions that are recommended/suggested and used to resolve a service request related to customer-encountered issues. Thus, amounting to no more than mere instructions to “apply” the exception using a generic computer component (MPEP 2106.05(f) and (f)(2)). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
For dependent claims 2-3, 8 - 9, 11-12, 17 - 18 and 21 – 29, these claims cover or fall under the same abstract ideas of a certain method of organizing human activity and a mental processes. They describe additional limitations steps of:
Claims 2-3, 8 - 9, 11-12, 17 - 18 and 21 – 29: further describes the abstract idea of the method for “managing customer-encountered issues” and further describes the use of terms of an identified product for performing supplemental searches and the type of data and identifiers that the “development lifecycle infrastructure metadata cache” and the “code artifacts” comprises of, respectively. The types of scores for the ranking score calculation and how to resolve the service request are also further described to order “the one of the knowledge base articles” and prompt a “service agent to review” the data portions, respectively. Finally, the consumption requirement for the customer-encountered issues and how these are resolved by reducing the quantity of computing resources are further described as well as the type of metadata in the cache for the development process of the products. Thus, these limitations encompass commercial interactions related to advertising for recommending relevant portions of “knowledge base articles” that are used to resolve the issue as requested and/or involve marketing or sales activities or behaviors when obtaining/resolving “service requests” (i.e. providing product support services) as well as the steps are at least encompassing evaluation and judgment for the ranking scores being calculated and providing relevant results.
Step 2A Prong 2 and Step 2B: For dependent claim 21, this claim recites the additional element(s) of a storage of the data processing system which is also recited to be merely used as a tool to perform the abstract idea to store the customer-encountered issues. Thus, it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)) Therefore, these claim limitations amount to no more than mere instructions to apply the exception using generic computer components and or computing technologies (e.g. that are merely deployed to be used as a tool; see MPEP 2106.05 (f)).
Finally, the additional elements previously mentioned above, are nothing more than descriptive language about the elements that define the abstract idea, and these claims remain rejected under 101 as well.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 - 3, 8 - 13, 17 - 19 and 21 - 29 are rejected under 35 U.S.C. 103 as being unpatentable over Adrian (U.S. Pub No. 20170178145 A1) in view of Gondalia (U.S. Pub No. 20190050319 A1).
Regarding claims 1, 10 and 19:
Adrian teaches:
a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing remediation of customer-encountered issues, obtained from customers of an entity, the operations comprising: (In ¶0084 - 85: teaches “a computer program product tangibly embodied on a non-transitory computer-readable storage medium can include executable code” that can be configured to “receive input corresponding with the IT customer service issue; determine, based at least in part on the input” wherein such input is “corresponding with the IT customer service issue” that derives from “open IT customer service tickets associated with the customer” as well as other “customer context information” such as “a description of the issue in a customer service ticket” (see ¶0013 and ¶0022 – 23), in order to “display, in a graphical user interface, a ranked list of a subset of the plurality of resources, the ranked list being ordered based on the respective relevancy scores of the subset of the resources”. Refer to ¶0088 for details of the processors and their memory.)
obtaining service requests comprising the customer-encountered issues, wherein multiple ones of the service requests are simultaneously processed by the data processing system in real-time; (In ¶0061; Figs. 2 – 3; Fig. 5 (510): teaches that “at block 510, receiving input corresponding with an IT customer service issue” which can include “input from a number of sources” such as “include information about an IT customer service issue IT that is entered by a customer service agent in the agent interface 310, selection of a resource from the result 160, context information from other sources, etc.”. Refer ¶0065 wherein the “the system 100 can track when content is used and at what phase of the service process a corresponding search was done” which suggests the non-functional descriptive matter claimed for having multiple service requests being simultaneously processed by the system. Another example is in ¶0043 wherein “the subset of the plurality of resources included in the results 160 can be one or more of an open customer service ticket, a closed customer service ticket, and/or a work order a change request, such as repository may be included in an IT incidents/tickets/work orders 154.”)
for each of the service requests: performing, to resolve a service request of the service requests directed to a customer-encountered issue of the customer-encountered issues, a multi- part search strategy comprising: (In ¶0021: teaches that the “systems and techniques described herein are implemented using a multi-factor context to search a set of available resources to proactively identify resources that are potentially relevant to resolving an IT customer service issue associated with (characterized by) the multi-factor context” wherein such “multi-factor context can be dynamically updated throughout the process of resolving the associated IT customer service issue” and can be “updated (e.g., modified, etc.), the relevant resources identified can change as well by conducting additional (iterative) searches of the set of available resources” which is directed to performing a multi-part search. Further, in ¶0053, in the system, “a description of the problem entered by the customer service agent can reduce the scope of an associated search (and make that search more efficient)” which satisfies the reduction of an aggregate quantity of computing resources expanded by the data processing system to resolve the customer-encountered issues.)
conducting a single-layer search based on the customer-encountered issues on all knowledge base articles contained within a knowledge base article repository stored within the RMS, the single-layer search comprising searching only within a body of the knowledge base articles without searching within metadata of each of the knowledge base articles to obtain a portion of the knowledge base articles; (In ¶0065 - 66; Fig. 5 (520 – 530): teaches under the broadest reasonable interpretation (BRI), that “depending on the particular resource being search by the relevance-based search engine 140, different types of searches can be performed”, one of them can be “full text searches can be performed on resources included in the IT incidents/tickets/work order repository 154 and on knowledge articles 152”. Also, the system allows to choose which “resources in the search content 150 can be configured to make them more relevant for a particular multi-factor context”, such as “including certain keywords in metadata or other fields of the resource” (see ¶0065) which is directed to the knowledge base articles’ body as underlined. Thus, this prior art suggests that this type of configuration can be refrained or not chosen at all, in accordance to the example given in ¶0052 – 54 from Applicant disclosure. Examiner notes that the prior art disclosure, although limits the “multi-factor context” to “two or more” factors, such as “one or more of customer context information; environment context information; and customer service process context information”, the system’s “relevance-based search engine” based on these factors can be performed for “two or more respective resources of the plurality of resources and perform a full text search of at least one resource of the plurality of resources” which is directed to conducting a single-layer search as underlined (see ¶0077). Refer to ¶0038 for an example of search restrictions.)
obtaining the portion of knowledge base articles that are responsive to only a result of a single-layer search; (In ¶0053 – 54; Fig. 5 (520 – 530): teaches an example wherein “customer service agent types in information such as “Allen Allbrook is having a problem with his email. He cannot connect to the email server when he is working in the San Jose office” the following actions can take place” wherein a “multi-factor context can be generated where the included context information can indicate that Allen Allbrook is the customer, his email service is affected, his email application may be the product affected and his location is San Jose”. Afterwards, the “relevance-based search engine 140 can then use this multi-factor context to search the search content 150 to identify relevant resources (e.g., the results 160) for resolving the email issue” wherein the resources can be “knowledge articles”. Refer to ¶0061 – 0062 for more details about steps 520 and 530.)
ranking the portion of the knowledge base articles to obtain a ranked order, each of the knowledge base articles having a metadata quality score that is based on a number of metadata types contained within the metadata of each respective one the knowledge base articles, and the ranking of the portion of the knowledge base articles being based at least on the metadata quality score of each knowledge base article among the portion of the knowledge bases articles; and (In ¶0067; Fig. 5 (530 – 540): teaches that at “block 530, the relevance-based search engine 140 can assign relevancy scores to each of the searched resources in the search content 150 and select a subset of the those search resources (based on their respective relevancy scores) to present as the results 160 for a given multi-factor context” which can be displayed as “a ranked list of a subset of the plurality of resources (e.g., the results 160)” and “ordered based on the respective relevancy scores of the subset of the resources” at block 540 (see ¶0073). Moreover, “relevancy scores can be assigned and/or calculated based on a number of factors” (see ¶0068) such as “a frequency of occurrence of given terms in a multi-factor context across searched resources”, “a number of times a search term from a multi-factor context appears in a given resource” and/or “the length of text included in a searched resource” (see ¶0068 – 70). But also, in ¶0071 such factors can be considered for “assigning a relevancy score for a given searched resource” with “multiple data fields (e.g., such as fields in a database object)”, “relevancy scoring can be performed for every field of the resource” which can be aggregated for the given resource which is directed to metadata quality scores based on the number of metadata types. Finally, “other techniques can be used to influence/weight relevancy scores that are assigned to searched resources” such as “certain fields of a given resource can be assigned relevancy weights that can impact an aggregate relevancy score for the given resource” (see ¶0072). Thus, “searches against certain fields of a given resource can be ranked higher than relevancy scores for other fields of that resource” (see ¶0072), which is directed to ranking the portion of the knowledge base articles based on based at least on the metadata quality score of each knowledge base article, in accordance to the different types of metadata from an enumerated list disclosed in ¶0058 from Applicant specs)
in a second instance of the determination where the information content exceeds the threshold: resolving the service request using the portion of the knowledge base articles and the ranked order, and (In ¶0054; Fig. 5 (530 – 540): teaches that the “relevance-based search engine 140 can then use this multi-factor context to search the search content 150 to identify relevant resources (e.g., the results 160) for resolving the email issue” with “resources” that can “include knowledge articles”. The “multi-factor context” can be used to assign “a respective relevancy score to each of the plurality of resources that are search in the search content 150 and provide the results 160 (which can be a ranked list of a subset of the plurality of resources that is determined to be relevant to the particular IT customer service issue associated with the multi-factor context) to the UI 110 for display and/or selection” (see ¶0041). These “relevancy score” assignments can include factors such as “a frequency of occurrence of given terms in a multi-factor context across searched resources of the search content 150”, “a number of times a search term from a multi-factor context appears in a given resource”, “the length of text included in a searched resource”, among other “techniques” that can “be used to influence/weight relevancy scores that are assigned to searched resources” (see ¶0068 – 72) which are directed to comparing content exceeding a threshold under BRI to provide portion of the knowledge base articles and the ranked order. Refer to ¶0067 for block 530 details.)
Adrian teaches that the system can modify (iterate) “multi-factor context” based on “additional information provided by a customer service agent, by assignment of an associated ticket to a service, occurrence of related incidents (e.g., other similar tickets), and so forth” which are within the system (directed to the data stored within RMS system claimed; see ¶0025 and ¶0057; Adrian) and additional searches can be “performed iteratively as additional context information is added to a corresponding multi-factor context” to “improve and/or update the relevancy of the results 160 provided to an IT customer service agent for resolving a particular problem” (see ¶0060 and ¶0063; Adrian). However, Adrian does not explicitly teach the abilities of making a determination in different instances as to whether the ranking order for the portion for the knowledge base articles indicates that the portion of the knowledge base articles have information content that exceeds a specific threshold; perform a supplemental search while having development lifecycle infrastructure metadata cache storing related product development data; resolve the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result to resolve the service request and during a second instance when information content exceeds the threshold, update the development lifecycle infrastructure metadata cache stored in the system by analyzing and identifying product information, enumerating them and determining relevancy of each product information to create entries that list the products and their relevant information. Thus, Gondalia teaches:
making a determination regarding whether the ranking order for the portion for the knowledge base articles indicates that the portion of the knowledge base articles have information content that exceeds a threshold; (In ¶0040; Fig. 5 (510 – 512 and 522): teaches “A scorer threshold module scores (510) the matching records based on the relevancy of each record to the provided software instructions and determine (512) the number of matched records that meet a threshold”. Thus, “when the number of scored matched records that meet the threshold exceed a configurable amount, the matched record that meet the threshold are returned (522) to the developer and/or stored in a reporting database to be processed at a later time and the process ends”. Refer to ¶0037 for more “scorer threshold module” details.)
in a first instance of the determination where the information content does not exceed the threshold: performing a supplemental search of a development lifecycle infrastructure metadata cache stored within the RMS to obtain a supplemental result; and (In ¶0040; Fig. 3 (340 and 338); Fig. 4 (400, 438 and 436); Fig. 5 (510 – 512 and 514): teaches that “when the number of scored matched records that meet the threshold does not meet the configurable amount, a custom search module performs (514) an Internet search for potential matches (e.g., code snippets, vulnerabilities, vulnerability fixes)” directed to the knowledge base articles claimed, in accordance to the example given in ¶0073 from Applicant disclosure. Refer to ¶0037 – 39 for more details about “custom search module”, “custom analyzer module” and “custom ranker module” as well as the provided results “along with any records found in rules database” and in the “indexer database 436, which indexes of the results found for faster searching” which is directed to the development lifecycle infrastructure metadata cache under the BRI. As for the stored information related to a development process of respective products and the development process indicating how the products were developed are satisfied under BRI, in the example provided in ¶0029 – 30. Wherein a “developer 310 checks in a code snippet in a source code repository through integrated development environment 320” for “an application and/or system development life-cycle” (see ¶0003). Further, a “code separator submodule 332 parses and separates the checked code snippet based on, for example, annotations or code markers into code blocks” that are directed to the development process indicating how the products were developed, in accordance to ¶0023 – 24, ¶0026 and ¶0039 from Applicant specs. Refer to ¶0027 wherein the system can “review generation history and provide analysis to relevant business and technical units within a respective enterprise” and “additionally, developer 220 may use reporting dashboard 246 to review current code implementation and/or code generation history for particular categories of development”. Examiner notes that whether the supplemental search is performed based on internet sources or metadata cache, as claimed and supported by the Adrian reference, such non-functional descriptive matter distinction does not hold patentable weight.)
resolving the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result; and (In ¶0040; Fig. 5 (522): teaches that “the matches records that meet the threshold from both the Internet search and the rules database are returned (524) to the developer”. Refer to ¶0039 wherein results “along with any records found in rules database” are provided.)
in a second instance…updating, in the real-time and while the multiple ones of the service requests are simultaneously processed also in the real-time, the development lifecycle infrastructure metadata cache stored in the RMS by: (In ¶0026: teaches that “Based on the selection of a particular code snippet by developer 210, the confidence updater module 237 persists the selection in reporting database 242 and within the machine learning component 239 for future use.” See ¶0021 wherein “code generated through the integrated development environment 122 may be updated within reporting component 128 to allow the reporting component 128 to cater any future requirements from the code generation engine 124.”)
analyzing developmental lifecycle infrastructure data of products associated with the entity to identify product information comprising at least version control of the products, code artifacts used in the products, tracking systems used to track issues present in the products, and communication systems used to create and maintain the products; (In ¶0035; Fig. 4 (438); Fig. 5 (508): teaches that the “Pattern detection module 424 queries the rules database 438 for potential matches (e.g., code snippets, vulnerabilities, vulnerability fixes) for the request based on the NLP output.” Refer to ¶0040 also for more details and refer to ¶0020 wherein a “security tool 126 provides support for security scanning of application and systems that are live in production or testing environment to detect anomalies or problems” which is directed to tracking systems.)
enumerating each of the product information within the developmental lifecycle infrastructure data; determining to which ones of the products each of the production information that is enumerated is relevant; and (In ¶0040; Fig. 5 (512, 518 and 520 – 522): teaches that “a custom ranker module scores (518) each of the search results based on the relevancy of each result to the provided software instructions and compares (520) the scores to the threshold” and “when the number of scored results meets the configurable amount, an intelligent indexer module persists (522) the results that meet the configurable threshold in an indexer data base” which is an example of determining and enumerating the product information based on relevancy.)
creating entries in the development lifecycle infrastructure metadata cache for each of the product information that is enumerated, each of the entries listing the ones of the products to which each of the production information that is enumerated is relevant. (In ¶0040: teaches that “matches records that meet the threshold from both the Internet search and the rules database are returned (524) to the developer and/or stored in a reporting database to be processed at a later time and the process ends” which is an example of creating entries of the results enumerated based on relevancy. See ¶0031 – 32 for more details of the returned “suggestions for updates to each code block to address the respective vulnerabilities to fix suggester module 336, which persist the update suggestion with each corresponding vulnerability and code block in reporting database 342” and the use of “reporting dashboard 346 for current scan results and suggested code updates.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Adrian to provide the abilities of making a determination in different instances as to whether the ranking order for the portion for the knowledge base articles indicates that the portion of the knowledge base articles have information content that exceeds a specific threshold; perform a supplemental search while having development lifecycle infrastructure metadata cache storing related product development data; resolve the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result to resolve the service request and during a second instance when information content exceeds the threshold, update the development lifecycle infrastructure metadata cache stored in the system by analyzing and identifying product information, enumerating them and determining relevancy of each product information to create entries that list the products and their relevant information, as taught by Gondalia in order to “promptly responding to these business changes with an appropriate and robust solution” (¶0002; Gondalia) and identify both “applicable code snippets” that “may be generated” and existing vulnerabilities and relevant fixes” (¶0016; Gondalia) which would be “obvious to try” to increase the results accuracy when attempting to solve the specific software/hardware issue requested.
Regarding claims 2, 11 and 25:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claims 1, 10 and 19, respectively.
Adrian does teach the system searching based on search terms (see ¶0052 – 54 and ¶0069 – 70; Adrian). Adrian does not explicitly teach the ability of using a set of terms used to obtain portion of the knowledge base articles, specifically when performing the supplemental search. Thus, Gondalia further teaches:
wherein the supplemental search is performed using a set of terms used to obtain the portion of the knowledge base articles. (In ¶0035; Fig. 5 (508 and 514): teaches that “NLP module performs (506) NLP on the identified information based on machine learning in which keywords (tags) and patterns are identified and examined and then used to improve the understanding of the information”. Then, “The pattern detection module queries (508) a rules database for potential matches (e.g., code snippets, vulnerabilities, vulnerability fixes)”. Refer to ¶0025 for more details regarding the identification of “relevant keywords (tags) in the data” when querying the “rules database” for potential matches, similar when performing “additional Internet search for potential matches” at step 514 (see ¶0040).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Adrian to provide the ability of using a set of terms used to obtain portion of the knowledge base articles specifically when performing the supplemental search, as taught by Gondalia in order to “promptly responding to these business changes with an appropriate and robust solution” (¶0002; Gondalia) and identify both “applicable code snippets” that “may be generated” and existing vulnerabilities and relevant fixes” (¶0016; Gondalia) which would be “obvious to try” to look to the keywords that identify the knowledge articles and increase the results accuracy when attempting to solve the specific software/hardware issue requested.
Regarding claims 3, 12 and 26:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claims 2, 11 and 25, respectively.
Adrian further teaches:
wherein the set of terms comprises an identity of a product, from among the products, that is associated with the customer-encountered issue. (In ¶0068: teaches that the system takes into account various “multi-factor context across searched resources of the search content 150” such as “frequency of occurrence of given terms”. Wherein this “context information” and its terms can include “a name of a customer associated with the IT customer service issue, a location of the customer, products and/or services associated with (e.g., subscribed to by) the customer” under BRI (see ¶0035 and ¶0083) in accordance to ¶0039 and ¶0078 from applicant specifications. Refer to ¶0054 for an example.)
Regarding claims 8, 17 and 27:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claims 1, 10 and 19, respectively.
Adrian further teaches:
wherein ranking the portion of the knowledge base articles further comprises: for the one of the knowledge base articles of the portion of the knowledge base articles: calculating a ranking score for the one of the knowledge base articles based on the metadata quality score for the one of the knowledge base articles, (In ¶0028 – 29: teaches that “based on respective relevance scores of searched resources, which can be determined (assigned, etc.)” using different approaches such as taking into account “knowledge article ratings, customer service survey ratings, etc. Said another way, relevance of a particular resource (e.g., article, person, group, etc.) can be determined based on such ratings” (see ¶0028) wherein “a ranked set of relevant resources (e.g., articles, web search results, service request templates, identification of groups or individuals, etc.) can be provided to an IT customer service professional working on resolving a given IT customer service issue” and “the ranked set of relevant resources can be determined by a relevance-based search that is based on a multi-factor context corresponding with the IT customer service issue”, in accordance to ¶0053 and ¶0055 – 57 from applicant specs.)
a use rate score for the one of the knowledge base articles, (In ¶0072: teaches that “respective usage counts for resources of the search content 150 can be used to weight relevancy scores for particular multi-factor contexts”. Refer to ¶0075 for another example.)
and an accuracy score for one of the knowledge base articles; and (In ¶0056: teaches that “tracked information can be used to weight the relevancy score of the selected result 160 for resolving future similar problems”. Wherein “some implementations, after resolution of the problem, the customer service agent can be automatically prompted to author a knowledge article documenting the actions taken to resolve the problem” and that “knowledge article could then be provided as a relevant result for customer service agents that are working on resolving similar problems in the future” which is directed to taking this action into account for the accuracy score of the knowledge base articles, in accordance to ¶0013 and ¶0060 from applicant specs. Refer to ¶0028 wherein a “multi-factor, relevance-based search” can be performed to “identify groups and/or individuals who have worked on indexing the particular database type in the past (e.g., from resolved customer support tickets), or to identify individuals that have authored knowledge articles on indexing databases of that type”.)
ordering the one of the knowledge base articles with respect to other knowledge base articles of the portion of the knowledge base articles based on the ranking score and ranking score of the other knowledge base articles. (In ¶0041: teaches that the “ranked list of the results 160 can be ordered based on the respective relevancy scores of the subset of the resources” as described previously per each type of score claimed.)
Regarding claims 9, 18 and 28:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claims 8, 17 and 27, respectively.
Adrian does not explicitly teach the ability of prompting a service agent to review the portion of knowledge base articles in an order based on ranked order, and in context of the supplemental result. However, Gondalia further teaches:
wherein resolving the service request using the portion of the knowledge base articles, the ranked order, and the supplemental result comprises: prompting a service agent to review the portion of the knowledge base articles in an order specified by the ranked order, and in context of the supplemental result. (In ¶0032; Fig. 3 (338, 339 and 344); Fig. 4: teaches that an “Analyst/Administrator 350 may employ reporting engine 244 to review scanning and suggested update history and provide analysis to relevant business and technical units within a respective enterprise”. Wherein the “Analyst/Administrator 350 may be a quality assurance administrator that may use the reports provided from reporting engine 344 to test and check code quality” and additionally, a “developer 320 may use reporting dashboard 346 for current scan results and suggested code updates”. Refer to ¶0027 for more information about the analyst/administrator and developer reviewing “generation history” and “current code implementation and/or code generation history for particular categories of development”, respectively. Finally, refer to ¶0033 for “reporting engine 344” details which output and display the results run by the “Machine learning component 400” including search results such as “results along with any records found in rules database 438” wherein these results are returned and scored by relevance as well (see ¶0039 – 40).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Adrian to provide the ability of prompting a service agent to review the portion of knowledge base articles in an order based on ranked order, and in context of the supplemental result, as taught by Gondalia in order to “promptly responding to these business changes with an appropriate and robust solution” (¶0002; Gondalia) and identify both “applicable code snippets” that “may be generated” and existing vulnerabilities and relevant fixes” (¶0016; Gondalia) which would be “obvious to try” to test the codes found in the knowledge articles and additional information obtained to efficiently and accurately solve the specific software/hardware issue requested.
Regarding claim 13:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 12.
Adrian further teaches:
wherein the development lifecycle infrastructure metadata cache comprises metadata regarding a version control system used to maintain the product. (In ¶0026: teaches this descriptive subject matter as locating “resources that are internal to an IT support center's computing resources, as well as resources that external to the IT support center”. But also, the system can also “search the search content 150 (e.g., a plurality of resources)” that can be “located on a second computing device, such as on an external website, a technology website, in an external database, etc.” (see ¶0040) and these resources can be “one or more of an open customer service ticket, a closed customer service ticket, and/or a work order a change request, such as may be included in an IT incidents/tickets/work orders repository 154” (see ¶0043) which are directed to metadata related to a version control system used to maintain the product, in accordance to ¶0077 -78 from applicant specs.)
Regarding claim 21:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 1.
Adrian further teaches:
wherein the customer-encountered issues are stored in a storage of the data processing system and a processing of each of the customer-encountered issues requires consumption of a first quantity of limited computing resources of the processor and the storage, and a first number of the customer-encountered issues is resolved per a unit of time based on the first quantity without using the multi-part search strategy. (In ¶0040; Fig. 1 (154): teaches under the broadest reasonable interpretation (BRI), that the system’s “relevance-based search engine 140 can be implemented on the first computing device” which further includes “search content 150” (see Fig. 1) with “IT incidents/tickets/work orders repository 154” to store issues (see ¶0043). Also, the system allows the search to be configured in a “ranked list of a subset of the plurality of resources” (see ¶0041 and ¶0054) which is directed to resolving the customer-encountered issues without using the multi-part search strategy or only using one strategy such as using a portion of the knowledge base articles and the ranked order, in accordance to ¶0099 from applicant specs. Refer to ¶0042 – 43 and ¶0045 for more details about the “multi-form search” and its different configurations to limit the amount and type of search results provided and index the resources in internal databases to efficiently resolve more issues in less time.)
Regarding claim 22:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 1.
Adrian does not explicitly teach the development lifecycle infrastructure metadata cache with metadata that includes revision histories and commit messages used during the development process of the products. Thus, Gondalia further teaches:
wherein the development lifecycle infrastructure metadata cache further comprises metadata regarding the development process of the products associated with the customer-encountered issues, the metadata comprising revision histories and commit messages used during the development process of the products. (In ¶0027; Fig. 3 (340 and 338); Fig. 4 (400, 438 and 436): teaches that the system’s “Analyst/Administrator 250 may employ reporting engine 244 to review generation history and provide analysis to relevant business and technical units within a respective enterprise” and “Additionally, developer 220 may use reporting dashboard 246 to review current code implementation and/or code generation history for particular categories of development” which were retrieved and fetched from the “reporting database 242” and subsequently these records directed to the metadata cache claimed are used by the “machine learning component 400” and its “indexer database 436, and rules database 348” for faster search (see ¶0039).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Adrian to provide the development lifecycle infrastructure metadata cache with metadata that includes revision histories and commit messages used during the development process of the products, as taught by Gondalia in order to “promptly responding to these business changes with an appropriate and robust solution” (¶0002; Gondalia) and quickly identify both “applicable code snippets” that “may be generated” and existing vulnerabilities and relevant fixes” (¶0016; Gondalia) which would be “obvious to try” to increase the results accuracy when attempting to solve the specific software/hardware issue requested.
Regarding claim 23:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 1.
Adrian further teaches:
wherein each of the metadata types is assigned a point value, and each of the metadata types corresponds to a respective metadata field among metadata fields contained within the metadata. (In ¶0071 – 72; Fig. 5 (540): teaches that the system allows “other techniques” that can be “used to influence/weight relevancy scores that are assigned to searched resources” such as “certain fields of a given resource can be assigned relevancy weights that can impact an aggregate relevancy score for the given resource” directed to the metadata quality score of each knowledge base article. More specifically, the point value was interpreted as a numerical value or each “relevancy score” or value given to each factor (e.g. “a frequency of occurrence of given terms”, “a number of times a search term from a multi-factor context appears” and/or “the length of text included in a searched resource”; see ¶0068 – 70) of a “given searched resource” with “multiple data fields (e.g., such as fields in a database object)” and the “relevancy scoring” given to “every field of the resource”, in accordance to the example given in ¶0057 from Applicant disclosure.)
Regarding claim 24:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 23.
Adrian further teaches:
wherein the metadata quality score is a point value sum of all of the metadata fields contained within the metadata of each respective one the knowledge base articles (In ¶0071 – 72; Fig. 5 (540): teaches that different factors can be considered for “assigning a relevancy score for a given searched resource” with “multiple data fields (e.g., such as fields in a database object)”, “relevancy scoring can be performed for every field of the resource” which can be aggregated for the given resource which is directed to metadata quality scores based on the number of metadata types that have a point value sum. Also, “other techniques can be used to influence/weight relevancy scores that are assigned to searched resources” such as “certain fields of a given resource can be assigned relevancy weights that can impact an aggregate relevancy score for the given resource” (see ¶0072).)
Regarding claim 29:
The combination of Adrian and Gondalia, as shown in the rejection above, discloses the limitations of claim 1.
Adrian further teaches:
further comprising: updating first metadata of a first knowledge base article of the knowledge base articles based on a first customer-encountered issue of the customer encountered issues to obtain an updated first metadata for the first knowledge base article by: (In ¶0060; Fig. 1 (130, 150 and 157); Fig. 5 (520): teaches that “in FIG. 5, the method 500 can be performed iteratively as additional context information is added to a corresponding multi-factor context. Such an approach can continually improve and/or update the relevancy of the results 160 provided to an IT customer service agent for resolving a particular problem as more detailed context information is obtained (e.g., as the resolution process progresses).” Refer to ¶0039 wherein “the content configuration engine 130 can be configured to receive configuration information (e.g., from a system administrator) for a plurality of resources (e.g., search content 150)” and can “also be used to produce (generate, create, build, etc.) and/or manage custom configured content 157, such as databases, database objects, repositories, etc., that can be included in the search content 150.”)
adding at least one word associated with the first customer-encountered issue into the first metadata, the at least one word being identical to one of the words used by a customer of the customers to initiate a first service request associated with the first customer-encountered issue, and the at least one word does not already appear within an actual content of the first knowledge base article (In ¶0038; Fig. 1 (130, 150 and 157): teaches that in “the system 100, the content configuration engine 130 can be configured to allow a user (e.g., a system administrator) to configure which content should be searched based on a given multi-factor context” such as restricting the “resource searching to only those resources that are appropriate for that context (e.g., service request templates, open customer service tickets, etc.)” which is directed to adding customer words used in the first service request (since multi-factor context include keywords or terms from “IT customer service tickets”; see ¶0034 – 37) and that does not already appear within the actual content of the first knowledge base article. Refer to ¶0065 for different factors such as “usage counts for resources of the search content 150” or “including certain keywords in metadata or other fields of the resource” for making resources “more relevant for a particular multi-factor context”, which are used as a basis for “customer service problem resolution process context (process context).” See ¶0068 – 70 and ¶0072 for more examples as well as “Other techniques can be used to influence/weight relevancy scores that are assigned to searched resources”.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
McReynolds (U.S. Pub No. 20240211961 A1) is pertinent because it “relate generally to issue management. More particularly, embodiments disclosed herein relate to systems and methods to manage issues using knowledge base metadata.”
Feng (U.S. Pub No. 20220269794 A1) is pertinent because it “describes examples of systems configured to match content, such as files and file snippets, using a scalable knowledge base.”
Srinivasa (U.S. Pub No. 20110219360 A1) is pertinent because the “software debugging recommendation technique embodiments described herein facilitate the software debugging procedure by automating the search for similar issues from the past. In general, this entails creating a database of characterized software bug descriptions and providing software debugging recommendations from the database in response to a query.”
Nastacio (U.S. Pub No. 20090222436 A1) is pertinent because it “provides a method for matching problems occurring in computer systems or networks with possible solutions contained in knowledge bases.”
Grabarnik (U.S. Pub No. 20090313202 A1) is pertinent because it “relate generally to automated systems and methods for search-based problem determination and resolution for complex systems and, in particular, automated search-based problem determination and resolution systems in which domain-specific data models for a class of complex systems, which are representative of structural relationships of entities within the class of complex systems, are utilized to provide enhanced domain-specific content searching and search results ranking for problem determination and resolution for complex systems.”
Prasad (U.S. Pub No. 20170132210 A1) is pertinent because it “relates to providing semantics based technical support, and more particularly to providing semantics based technical support based on available knowledge sources, prior code fixes and similarity of technical support issues.”
Podgorny (U.S. Pub No. 20170124184 A1) is pertinent because it is “a method and system for personalizing a user experience in a customer support system by improving topic identification of search query terms in a customer support system, at least partially based on contextual information related to the search query terms, to improve the likelihood of customer satisfaction with the customer support system.”
Potharaju (U.S. Pub No. 20160239487 A1) is pertinent because it is about “techniques and systems to detect and identify documents with solutions (e.g., fixes) to software issues (e.g., errors, misconfigurations, issues related to performance, security, reliability, system management, etc.) from a database (e.g., a knowledge base (KB) or other information repository) based on information associated with the software issues.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626