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
The following is a Non-Final Office Action.
Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/20/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7, 14, and 20 each recites the limitation "upon being resolved the issue review chain is analyzed". There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7), the computer program product, the computer program product comprising a non-transitory tangible storage device (claims 8-14), and the computer system (claim 15-20) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims recite an abstract idea by the “mental process” by reciting steps that can be performed in the human mind (e.g., observation, evaluation, judgment, opinion) of evaluating an issue information, personnel histories to score, rank, and assign task. The examiner further notes the claims fall under “certain methods of organizing human activity.” Thus, the claim recites an abstract idea. (See MPEP 2106.04(a)(2)). Further, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 8, are: A computer program product, the computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising: performing Natural Language Processing (NLP) analysis of text within an issue, wherein the analyzed text is compared to each of a plurality of individual/team’s corpus of issues, the output being a match percentage for each individual/team to the issue; building a list of each individuals/team ranked by the match percentage; applying weights to each individual/team in the list, based on their corresponding recognition scores in their profiles in a profile database, and wherein the recognition scores indicate an ability to recognize correct reassignment with a degree of accuracy above a threshold; reordering the list based on the applied weights; and assigning the issue to the individual/team having a highest rank. Claims 1 and 15 recite substantially the same limitations as claim 8 and therefore subject to the same rationale.
With respect to Step 2A Prong Two of MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a computer program product, the computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, Natural Language Processing (NLP) analysis (recited at high level of generality) a profile database (recited at high level of generality), and computer system to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification describes in paragraphs [0015]-[0017] which describe high level computing environment) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a computer program product, the computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, Natural Language Processing (NLP) analysis (recited at high level of generality) a profile database (recited at high level of generality), and computer system to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification describes in paragraphs [0015]-[0017] which describe high level computing environment) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well (i.e., profile database and NLP analysis). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification describes in paragraphs [0015]-[0017] which describe high level computing environment) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification describes in paragraphs [0015]-[0017]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo), however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of mental process and certain method of organizing human activity, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nikolaos Anerousis (US 2012/0023044 A1, hereinafter “Anerousis”) in view of Dasgupta (US 2017/0221373 A1, hereinafter “Dasgupta”) in view of Paulo Cesar Pinto Calabria (US 11,271,829 B1, hereinafter “Calabria”).
Claim 1/8/15
Anerousis teaches:
A computer program product, the computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method ([0097] methods, apparatus (systems) and computer program products. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus), the method comprising: performing Natural Language Processing (NLP) analysis of text within an issue([0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content),
wherein the analyzed text is compared to each of a plurality of individual/team’s corpus of issues ([0038] a generative model is built for each expert group using the textual descriptions of the problems the group has solved previously), the output being a match percentage for each individual/team to the issue([0064] Given a new ticket t, the probability that expert group g.sub.i can resolve the ticket);
building a list of each individuals/team ranked by the match percentage ([0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content);
reordering the list based on the applied weights ([0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t));
and assigning the issue to the individual/team having a highest rank ([0077] The group with the highest rank is selected as the next possible resolver).
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
applying weights to each individual/team in the list, based on their corresponding recognition scores in their profiles in a profile database ([0025] identifies at least one performance characteristic associated with a particular resolver at one or more steps of the resolution at 140. [0026] Once a particular performance characteristic is identified, it is rated based on a variety of factors. weight the scoring of each performance characteristic based on known factors (e.g., the associated resolver's group, resolver's experience, etc.). [0032] may adjust or modify the overall resolver score based on the identified performance characteristic, where the system scores and tracks a resolver's score for multiple groups, while [0027] the system may utilize a database to store historical information relating to the resolver, and based on the historical information generate a continuously updated score),
and wherein the recognition scores indicate an ability to recognize correct reassignment ([0025] may also be interested in the resolver group knowledge (e.g., determining if the resolver should have reassigned the ticket, and if they should have, whether it was reassigned to the best possible group/resolver)).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to include applying weights to each individual/team in the list, based on their corresponding recognition scores in their profiles in a profile database and wherein the recognition scores indicate an ability to recognize correct reassignment as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Calabria teaches:
with a degree of accuracy above a threshold(Col. 5 line 64- Col. 6 lines 1-9 Here the database of (historical) tickets can be used to get all tickets, and for each group of skill set/levels calculate the probability. A probability (user selection) threshold can be pre-established in the proposed embodiment to consider such failure cases or not when considering a task solver (e.g., user). For example, if 5% of cases generated failures, then such skill set/levels can tolerate such a ticket. Otherwise, such skill set/levels for that ticket should not be considered, in this case, candidates from the list of users of operation 204 that fall into that category should be filtered out.);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis and Dasgupta with Calabria to include applying a degree of accuracy above a threshold as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. (col. 3 lines 56-58).
Claim 2/9/16
Anerousis further teaches:
The computer program product of claim 8, wherein the issue is assigned to the individual/team having the highest rank from the NLP analysis with no weight being applied, based on this being a first iteration and based on there being no previous issue review chain([0063] he Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). [0065] A routing algorithm for this model tries different candidate resolver groups in descending order of P(g.sub.i,t). [0067] When a new ticket t first enters the expert network, it is assigned to an initial group g.sub.init.Instead of calculating which group is likely to solve the problem).
Claim 3/10/17
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
The computer program product of claim 8, wherein the assigned individual/team manually reassigns the issue to another individual/team, based on the recognition score for making reassignments to the other individual/team […]([0021] the user may submit a problem ticket which is then issued to a first resolver. This first resolver examines the ticket to determine what type of ticket it is and what may be needed to resolve it. The first resolver may not have the skills required and may thus need to pass the open ticket along to a different resolver, perhaps in a different group. [0032] system may adjust or modify the overall resolver score based on the identified performance characteristic and cores and tracks a resolver's score for multiple groups (e.g., each group within the solution center). ).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to include the assigned individual/team manually reassigns the issue to another individual/team, based on the recognition score for making reassignments to the other individual/team as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Calabria teaches:
the other individual/team being above a threshold (Col. 5 line 64- Col. 6 lines 1-9 Here the database of (historical) tickets can be used to get all tickets, and for each group of skill set/levels calculate the probability. A probability (user selection) threshold can be pre-established in the proposed embodiment to consider such failure cases or not when considering a task solver (e.g., user). For example, if 5% of cases generated failures, then such skill set/levels can tolerate such a ticket. Otherwise, such skill set/levels for that ticket should not be considered, in this case, candidates from the list of users of operation 204 that fall into that category should be filtered out);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis and Dasgupta with Calabria to include reassignment matching criteria such as the other individual/team being above a threshold as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. (col. 3 lines 56-58).
Claim 4/11/18
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
The computer program product of claim 8, wherein the recognition score is weighted higher for manually reassigning the issue to another individual/team that resolved the issue([0028] it may be determined that the newly identified performance characteristic score is a positive score at 160; thus, it may increase the overall resolver score at 170 by some factor, while [0025] For example, a resolver may have attempted a solution, failed, and subsequently reassigned the ticket to a new resolver, wherein determining if the resolver should have reassigned the ticket, and if they should have, whether it was reassigned to the best possible group/resolver).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to include the recognition score is weighted higher for manually reassigning the issue to another individual/team that resolved the issue as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
Claim 5/12/19
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
The computer program product of claim 8, wherein the recognition score of the individual/team is weighted lower for manually reassigning the issue to another team that did not resolve the issue([0018] resolvers with poor domain knowledge or limited experience may wrongly transfer a ticket, properly assigned to their group, to a second group which is ill-equipped and/or unable to solve the issue (with its resolvers being weak resolvers). [0028] it may determine that the newly identified performance characteristic a negative score at 180, and thus may reduce the overall resolver score by some factor at 190. , while [0025] the at least one performance characteristic may be one of: resolver technical knowledge and resolver group knowledge. For example, a resolver may have attempted a solution, failed, and subsequently reassigned the ticket to a new resolver).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to include the recognition score of the individual/team is weighted lower for manually reassigning the issue to another team that did not resolve the issue as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
Claim 6/13
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
The computer program product of claim 8, wherein the recognition score is weighted lower for returning the issue for reassignment based on the individual/team not resolving the issue and not manually reassigning the issue([0028] may determine that the newly identified performance characteristic a negative score at 180, and thus may reduce the overall resolver score by some factor at 190. [0021] Thus, the user may submit a problem ticket which is then issued to a first resolver. This first resolver examines the ticket to determine what type of ticket it is and what may be needed to resolve it. It may be that the first resolver has the requisite skills necessary to resolve the ticket. Alternatively, the first resolver may not have the skills required and may thus need to pass the open ticket along to a different resolver, perhaps in a different group. [0025] For example, a resolver may have attempted a solution, failed, and subsequently reassigned the ticket to a new resolver).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to include the recognition score is weighted lower for returning the issue for reassignment based on the individual/team not resolving the issue and not manually reassigning the issue as part of the matching algorithm taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Anerousis in view of Dasgupta in view of Calabria, as applied in claims 1, 8, and 15, and further in view of Abdul Khader Jilani (US 10,438,212 B1, hereinafter “Jilani”).
Claim 7/14/20
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Dasgupta teaches:
The computer program product of claim 8, wherein upon being resolved the issue review chain is analyzed, the analysis comprising: updating the recognition scores of the profiles in the profile database ([0027] may adjust the determined score to the overall resolver score (e.g., a historical score based on an aggregate of all previous actions carried out by the resolver at 150). For example, an embodiment may utilize a database to store historical information relating to the resolver, and based on the historical information generate a continuously updated score).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis with Dasgupta to upon being resolved the issue review chain is analyzed, the analysis comprising: updating the recognition scores of the profiles in the profile database as part of the matching system taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. ([0025]).
While Anerousis teaches in [0021] It is assumed that a problem ticket 108 (e.g., record or log) is generated by the ticket processing engine 106 in response to the problem message 104 received from the customer 102. [0034] A ticket includes three components: (1) a problem category to which the ticket belongs, e.g., a WINDOWS problem or a DB2 problem, which is identified when the ticket is generated; (2) the ticket content, i.e., a textual description of the problem symptoms; [0043] The Resolution Model is a multi-class text classifier, which considers only ticket content. [0063] The Ranked Resolver algorithm is designed exclusively for the Resolution Model (RM). Expert groups are ranked based on the probability that they can resolve the ticket according to the ticket content. [0077] Once M is updated, the algorithm re-ranks the groups according to Equation (18) for each visited group in R(t) and the group with the highest rank is selected as the next possible resolver. Anerousis does not explicitly teach the following. However, analogues reference Jilani teaches:
and updating the text corpus used for NLP analysis(col. 9 lines 40-45 business relevant terminology can be added to the database and corpus of knowledge. The system provides the ability to update the corpus that is used in the natural language processing. In the updating and maintaining of this corpus, multiple synonyms may be stored).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Anerousis, Dasgupta, and Calabria with Jilani to updating the text corpus used for NLP analysis as part of the matching system taught in Anerousis, because it will aid in monitoring and assigning the best possible group/resolver to resolve the ticket which will result in customer satisfactory. (col. 3 lines 20-38).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Behnaz Arzani (US 20210224676 A1): relate to incident routing in a cloud environment. In an example, cloud provider teams utilize a scout framework to build a team-specific scout based on that team's expertise. In examples, an incident is detected and a description is sent to each team-specific scout. Each team-specific scout uses the incident description and the scout specifications provided by the team to identify, access, and process monitoring data from cloud components relevant to the incident. Each team-specific scout utilizes one or more machine learning models to evaluate the monitoring data and generate an incident-classification prediction about whether the team is responsible for resolving the incident.
Zachary Fry (US 20190347599 A1): The system may be configured to receive a support ticket. Metadata information corresponding to the support ticket may be obtained by the system. The metadata information may indicate at least one component associated with the support ticket. Based on the metadata information corresponding to the support ticket and respective attributes associated with the first entity, the system may be configured to assign the support ticket to at least a first entity. Assigning the support ticket to at least the first entity may include providing a list of one or more entities that are eligible for assignment, determining a user selection of the first entity through an interface, and storing a record of the support ticket being assigned to the first entity.
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/REHAM K ABOUZAHRA/Examiner, Art Unit 3625
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