DETAILED ACTION
This examination is in response to the communication filed on 04/05/2024. Claims 1-15 are currently pending, where claims1, 9, 14 and 15 are independent.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/05/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 9 objected to because of the following informalities:
The acronym (NLP) removed from line 5 of claim 1 and line 11 of claim 9 in the preliminary amendment filed 05/21/2024 should be added back to provide proper reference for the use of the acronym in the dependent claims. Appropriate correction is required.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1 and 9 recite “receiving a request text, representing the request for assistance from the user”, “processing the request to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text”, “accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or a type of request for assistance” and “selecting a solution text from the plurality of solution texts, dependently on the input vector, to make the solution text available to the user”
The limitations of “receiving…”, “processing…”, “accessing…”, and “selecting…” as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “a natural language processing engine” (claims 1 and 9 ), a “server computer” (claim 9), a “communication system” (claim 9), nothing in the claimed elements preclude the steps from practically being performed by a person receiving a request for service, processing the text of the request to determine the type or issue associated with the respect and then selecting from a list of problems/solutions a response to the received request.
This judicial exception is not integrated into a practical application because the additional elements of “a natural language processing engine”, a “server computer” (claim 9), and a “communication system” (claim 9) are all recited at a high-level of generality, and the Specification merely describes the use of a client or server computer with no reference to its structure. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. In addition, the added limitation of using “a natural language processing engine” is not recited with sufficient specificity as to provide any details about how the engine operates and the plain meaning of “processing” encompasses mental observations or evaluations, e.g., an person’s mental observation of evaluation as to the type of the request or the related issue. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claims 1 and 9 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a natural language processing engine”, a “server computer” (claim 9), a “communication system” (claim 9) amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Step 2B).
With respect to dependent claims 2-8 and 10-13, these claims are directed the features extracted from the request, the categorization of the request, and/or the algorithm used to selected solution text. These limitations also relate to the abstract idea of “mental processes.” That is nothing in the claimed elements preclude the steps from practically being performed by a person using the recited criteria when processing the received request. Further claims 3-5 recite the additional element of “a machine-learned model”. The machine-learned model is recited at a high level of generality i.e., generic computer functions This generic machine-learned model limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. ( Step 2A prong 2)
With respect to independent claims 14 and 15, the claims recite “receiving and validating login credentials entered by the user”, “response to a result of validating the login credentials, providing access information representing the user…and the type of machine which the assistance is being requested for”, “receiving the access information and request text”, “processing the request to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text”, “accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or a type of request for assistance” and “selecting a solution text from the plurality of solution texts, dependently on the input vector, to make the solution text available to the user”
The limitations of “receiving and validating…”, “providing…”, “receiving…”, “processing…”, “accessing…”, and “selecting…” as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “a natural language processing engine”, a “client computer”, and a “server computer”, nothing in the claimed elements preclude the steps from practically being performed by a person receiving a request for service, validating the user’s identity, processing the text of the request to determine the type or issue associated with the respect and then selecting from a list of problems/solutions a response to the received request.
This judicial exception is not integrated into a practical application because the additional elements of “a natural language processing engine”, a “client computer”, and a “server computer” are all recited at a high-level of generality, and the Specification merely describes the use of a client or server computer with no reference to its structure. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. In addition, the added limitation of using “a natural language processing engine” is not recited with sufficient specificity as to provide any details about how the engine operates and the plain meaning of “processing” encompasses mental observations or evaluations, e.g., an person’s mental observation of evaluation as to the type of the request or the related issue. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1, 2 and 6-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vukovic et al. (US 10,650,356 B2; herein “Vukovic”) cited in Applicant IDS filed on 04/05/2024.
Regarding claim 1, Vukovic teaches a method for providing assistance for a user of a processing machine, comprising the following steps performed by a server computer (Fig. 9, Computer System/Server 912):
receiving a request text, representing the request for assistance from the user (Fig. 2A, step 205; Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 80 of in response to receiving computer system service data…” );
processing the request text through a natural language processing engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text (Col. 9, lines 9-20 teaches “…the natural language processing system 412 responds to electronic document submissions sent by client application 408…analyzes a received unstructured textual report (e.g., unstructured textual data 305, error reports 312, user input 309, emails 307, text messages 315…catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…” );
accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance (Col. 9, lines 9-20 teaches “…the natural language processing system 412…analyzes a received unstructured textual report (e.g., …catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…Fig. 8 steps 830 and 840 and col. 17, line 63 to col. 18, line 7 “…an operation 820 of identifying, by the second computer system, one or more computer system service tasks…an operation 830 of selecting, by the second computer system, a catalog among a plurality of catalogs…an operation 840 of generating…one or more suggestions based on the catalog and the one or more computer system service tasks…” );
selecting a solution text from the plurality of solution texts, dependently on the input vector, to make the selected solution text available to the user (Fig. 8, step 850 and Col. 18, lines 5-7 teaches “…displaying by the second computer system, the one or more suggestion on a display logically coupled to the computer system”).
Regarding claim 2, Vukovic teaches all of the elements of claim 1 (see detailed element listing above). In addition, Vukovic further teaches the database is provided with a reference dataset including, for each solution text, a respective solution vector, made up of values of the plurality of predetermined parameters extracted from that solution text by the NLP engine, the step of selecting being carried out dependently also on the reference dataset (Fig. 2A and col. 4, lines 28-48 and col. 6, lines 48-52 teaches “…the intelligent self-service delivery advisory further extracts relevant features and then, using a machine learning model trained using support vector machine (SVM), predicts the category and task associated with the change request” i.e., generates the feature set vectors and col. 8, lines 35-53 teaches “… a complete feature set…is utilized by statistical analyzer 350, using the method described herein (e.g., kMeans clustering), to determine correlations between features…of a particular computer system issue…and possible solutions…k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest means, serving as a protype of the cluster…statistical analyzer 350 generates suggestions 355…”).
Regarding claim 6, Vukovic teaches all of the elements of claim 1 (see detailed element listing above). In addition, Vukovic further teaches the plurality of predetermined parameters includes semantic metadata (Col. 9, lines 36-39 teaches “In an embodiment, natural language processor 414 performs various method and techniques for analyzing electronic documents (e.g., syntactic analysis, semantic analysis, etc.)…These modules includes, but are not limited to…a semantic relationship identifier 420…”).
Regarding claim 7, Vukovic teaches all of the elements of claim 1 (see detailed element listing above). In addition, Vukovic further teaches a step of receiving access information, representing the user who is requesting assistance and the type of machine which the assistance is being requested for, wherein the step of selecting a solution text is carried out also dependently on the access information (Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…” and col. 9, lines 21-23 teaches “…natural language processing system 412 analyzes a received unstructured textual report relating to the user skill level” the User skill level is interpreted as information representing who is requesting the assistance ).
Regarding claim 8, Vukovic teaches all of the elements of claim 7 (see detailed element listing above). In addition, Vukovic further teaches the selector generates from the input vector an enriched input vector that includes structured metadata derived from the access information (Col. 7, lines 16-20 teaches “…a data structuring module 320 includes, or is part of, a device for converting unstructured, raw data (e.g., textual data, images, videos, sound recordings, etc.) into structured data” See also Fig. 7, step 715 and col. 17, lines 3-11 ).
Regarding claim 9, Vukovic teaches a maintenance system for providing assistance for a user of a processing machine, comprising a server computer (Fig. 9, Computer System/Server 912) and a database (Col. 10, lines 5-6 teaches “…the output of natural language processing system 412 populates a text index, a triple store, or a relational database…”) containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance (Col. 10, lines 60-66 teaches “…information corpus 426 is a storage mechanism that houses a standardized, consistent, clean and integrated list of features…information corpus 42 also stores, for each feature, a list of associated suggestions…” ), wherein the server computer
is configured to receive, through a communication system, a request text representing the request for assistance from the user, and access information representing the user who is requesting assistance and the type of machine which the assistance is being requested for (Fig. 2A, step 205; Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 80 of in response to receiving computer system service data…” and Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…”),
is programmed to process the request text through a natural language processing engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text (Col. 9, lines 9-20 teaches “…the natural language processing system 412 responds to electronic document submissions sent by client application 408…analyzes a received unstructured textual report (e.g., unstructured textual data 305, error reports 312, user input 309, emails 307, text messages 315…catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…”),
includes a selector programmed to select a solution text from the plurality of solution texts dependently on the input vector and on the access information (Fig. 8, step 850 and Col. 18, lines 5-7 teaches “…displaying by the second computer system, the one or more suggestion on a display logically coupled to the computer system”);
is configured to make the selected solution text available to the user (Fig. 8, step 850 and Col. 18, lines 5-7 teaches “…displaying by the second computer system, the one or more suggestion on a display logically coupled to the computer system”).
Regarding claim 10, Vukovic teaches all of the elements of claim 9 (see detailed element listing above). In addition, Vukovic further teaches the database is provided with a reference dataset including, for each solution text, a respective solution vector, made up of values of the plurality of predetermined parameters extracted from that solution text by the NLP engine, and wherein the selector is programmed to process the input vector based on the reference dataset (Fig. 2A and col. 4, lines 28-48 and col. 6, lines 48-52 teaches “…the intelligent self-service delivery advisory further extracts relevant features and then, using a machine learning model trained using support vector machine (SVM), predicts the category and task associated with the change request” i.e., generates the feature set vectors and col. 8, lines 35-53 teaches “… a complete feature set…is utilized by statistical analyzer 350, using the method described herein (e.g., kMeans clustering), to determine correlations between features…of a particular computer system issue…and possible solutions…k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest means, serving as a protype of the cluster…statistical analyzer 350 generates suggestions 355…”).
Regarding claim 11, Vukovic teaches all of the elements of claim 9 (see detailed element listing above). In addition, Vukovic further teaches the database includes a list of predetermined categories of possible requests from the user (Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 810 of … identifying, by a second computer system, a computer system service category among a plurality of computer system categories” See also Fig. 2A, step 210 “Identify Category (SVM));
the server computer is programmed to perform a step of categorizing, in which it processes the request text to derive therefrom a degree of matching the predetermined categories (Fig. 2A, step 210 “Identify Category (SVM)),
the selector is programmed to assign a confidence level to the selection (Col. 2, lines 56-62 teaches “…the groups of suggestions lists change categories…for each suggestion…and a confidence values from a column 140…for each suggestion”),
the server computer is also programmed to generate a message for the user, dependently on the result of the step of categorizing, if the confidence level on the selection text is less than a predetermined threshold value (Col. 2, lines 63-65 teaches “…the advisory offers suggestions, with certain confidence…” and col. 3, lines 1-10 teaches “…each suggestion will assigned a confidence value to show the hierarchical nature of the suggestions…a confidence value from column 140 is the likelihood that a suggestion will resolve the issue” ).
Regarding claim 12, Vukovic teaches all of the elements of claim 9 (see detailed element listing above). In addition, Vukovic further teaches the server computer is configured to receive, through the communication system, access information representing the user who is requesting assistance and the type of machine which the assistance is being requested for, and wherein the selector is programmed to select the solution text also dependently on the access information (Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…” and col. 9, lines 21-23 teaches “…natural language processing system 412 analyzes a received unstructured textual report relating to the user skill level” the User skill level is interpreted as information representing who is requesting the assistance).
Regarding claim 13, Vukovic teaches all of the elements of claim 9 (see detailed element listing above). In addition, Vukovic further teaches the server computer is programmed to generate a reaction request and is configured to receive a reaction signal representing an evaluation by the user of the selected solution text (Col. 6, lines 7-11 teaches “…the intelligent self-service delivery advisory further includes an operation 260 of receiving context and history analysis, wherein the history of the user’s selections and input for previous computer system issues are analyzed to provide input for the initial suggestions”);
the selector includes a machine-learned model programmed to self-learn on the basis of the reaction signal (Col. 6 , lines 3-6 teaches “…the intelligent self-service deliver advisory can learn from the interaction with the user and user choices to update state and personalize recommendation for the future (and improve recommendation to other similar tasks and user roles)”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 non-obviousness.
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 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Vukovic as applied to claim 2 above, and further in view of Srivastava et al. (US 2018/0260760 A1; herein “Srivastava”) cited in Applicant IDS filed 04/05/2024.
Regarding claim 3, Vukovic teaches all of the elements of claim 2 (see detailed element listing above). In addition, Vukovic further teaches the step of selecting is carried out by a selector including a machine-learned model, the method further comprising
a step of learning by the machine-learned model responsive to a reaction signal received by the server computer (Col. 6, lines 7-11 teaches “…the intelligent self-service delivery advisory further includes an operation 260 of receiving context and history analysis, wherein the history of the user’s selections and input for previous computer system issues are analyzed to provide input for the initial suggestions”),
Vukovic fails to explicitly disclose the reaction signal representing an evaluation by the user of the selected solution text
Srivastava teaches an automated ticket resolution system and method that includes, inter alia, classifying, using a data model, the ticket data into a ticket type, generating, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the projection, and selecting from the set of recommended resolutions, a particular resolution based on a set of selection criteria (Srivastava, Abstract). In addition, Srivastava further teaches the reaction signal representing an evaluation by the user of the selected solution text (¶[0019] teaches “…the cloud platform may monitor the project and/or receive feedback about the particular resolution to determine an effectiveness level of the resolution…This may allow subsequent requests for historical ticket data (e.g., to update the ticket data model) to access the ticket and the particular resolution associated with the ticket”).
Vukovic differs from the claimed invention, as defined in claim 3, in that Vukovic fails to disclose incorporating user feedback regarding previously selected solutions as model fine-tuning parameter. Fine-tuning machine-learning models based on user feedback representing the effectiveness of past model performance is known in the art as evidenced by Srivastava. Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to have modified the intelligent self-service delivery advisor system taught by Vukovic to include user feedback regarding effectiveness of past model suggestions as taught by Srivastava as it merely constitutes the combination of known processes to achieve the predictable result of fine-tuning the suggestion selection machine learning model based on the effectiveness of past performance.
Regarding claim 4, the combination of Vukovic and Srivastava teaches all of the elements of claim 3 (see detailed element listing above). In addition, Vukovic further teaches the database includes a list of predetermined categories of possible requests from the user (Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 810 of … identifying, by a second computer system, a computer system service category among a plurality of computer system categories” See also Fig. 2A, step 210 “Identify Category (SVM));
the server computer performs a step of categorizing, in which it processes the request text to derive therefrom a degree of matching the predetermined categories, the machine-learned model assigns a confidence level to the selection, if the confidence level on the selection text is less than a predetermined threshold value or if the reaction signal is negative, the server computer performs a step of generating a message for the user dependently on the result of the step of categorizing (Col. 2, lines 56-62 teaches “…the groups of suggestions lists change categories…for each suggestion…and a confidence values from a column 140…for each suggestion” and Col. 2, lines 63-65 teaches “…the advisory offers suggestions, with certain confidence…” and col. 3, lines 1-10 teaches “…each suggestion will assigned a confidence value to show the hierarchical nature of the suggestions…a confidence value from column 140 is the likelihood that a suggestion will resolve the issue”).
Regarding claim 5, the combination of Vukovic and Srivastava teaches all of the elements of claim 4 (see detailed element listing above). In addition, Srivastava further teaches categorization is performed by the NLP engine, the NLP engine being also trained to derive from it the degree of matching the predetermined categories of the request texts (“¶[0038] teaches “In some implementations, automatic ticket classification module 270 may apply a text similarity technique”).
Vukovic differs from the claimed invention, as defined in claim 5, in that Vukovic fails to disclose categorizing the service request text utilizing a natural language processing engine that utilizes a degree of matching algorithm. Categorizing service requests based on a degree of matching algorithm, e.g., text similarity technique is known in the art as evidenced by Srivastava. Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to have modified the intelligent self-service delivery advisor system taught by Vukovic to include utilize a text similarity technique as taught by Srivastava as it merely constitutes the substitution of known processes to achieve the predictable result of classifying the text of the service requests based it similarity with predefined categories.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vukovic further in view of Mackie et al. (US 2023/0006907 A1; herein “Mackie”).
Regarding claim 14, Vukovic teaches a method for obtaining assistance for a processing machine, the method comprising the following steps, performed by a client computer (Fig. 4, element 400) accessible by a user of the machine and configured to exchange information with a server computer (Fig. 4, element 412) through a communication system (Fig. 4, element 415) :
providing access information representing the user who is requesting assistance and the type of machine which the assistance is being requested for (Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…” and col. 9, lines 21-23 teaches “…natural language processing system 412 analyzes a received unstructured textual report relating to the user skill level” the User skill level is interpreted as information representing who is requesting the assistance);
enabling the user to enter a request text, representing the request for assistance from the user (Col. 3, lines 28-38 teaches “…the dynamic user interface 100 will provide information on why a selection is not available…The advisor then prompts the user for additional information…” and Fig. 2A, step 205; Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 80 of in response to receiving computer system service data…” );
via the client computer (the “or” makes this limitation optional) or the server computer, processing the request text through a natural language processing engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text (Col. 9, lines 9-20 teaches “…the natural language processing system 412 responds to electronic document submissions sent by client application 408…analyzes a received unstructured textual report (e.g., unstructured textual data 305, error reports 312, user input 309, emails 307, text messages 315…catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…”);
via the client computer (the “or” makes this limitation optional) or the server computer, accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance (Col. 9, lines 9-20 teaches “…the natural language processing system 412…analyzes a received unstructured textual report (e.g., …catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…Fig. 8 steps 830 and 840 and col. 17, line 63 to col. 18, line 7 “…an operation 820 of identifying, by the second computer system, one or more computer system service tasks…an operation 830 of selecting, by the second computer system, a catalog among a plurality of catalogs…an operation 840 of generating…one or more suggestions based on the catalog and the one or more computer system service tasks…”);
via the client computer (the “or” makes this limitation optional) or the server computer, selecting a solution text from the plurality of solution texts dependently on the input vector and on the access information (col. 17, line 63 to col. 18, line 7 “…an operation 840 of generating…one or more suggestions based on the catalog and the one or more computer system service tasks…” );
making the selected solution text available to the user (Fig. 8, step 850 and Col. 18, lines 5-7 teaches “…displaying by the second computer system, the one or more suggestion on a display logically coupled to the computer system”).
Vukovic fails to disclose receiving and validating login credentials entered by the user or that the step of providing access information and enabling the user to enter a request test are responsive to a result of validating the login credentials.
Mackie disclose a user interface which enables “a user to submit user authentication credentials for verification, and if successfully verified, began an authenticated session associated with a particular application” (Mackie, ¶[0068]). Therefore, Mackie teaches receiving and validating login credentials entered by the user and responsive to a result of validating the login credentials allowing access to an application.
Vukovic differs from the claimed invention, as defined by claim 14, in that Vukovic fails to disclose that the user interface includes means for receiving and validating login credentials prior at allowing user access to the intelligent self-service delivery advisor. User interfaces which provide means for receiving and validating login credentials prior to allowing access to an application are known in the art as evidenced by Mackie. Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to have modified the dynamic user interface taught by Vukovic to include means for validating user login credentials prior to allowing access to the intelligent self-service delivery advisor as it merely constitutes the combination of known processes to achieve the predictable result to preventing unauthorized access to the intelligent self-service delivery advisor.
Regarding claim 15, Vukovic teaches a method for providing assistance for a user of a processing machine, the method comprising the following steps:
, providing access information representing the user who is requesting assistance and the type of machine which the assistance is being requested for (Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…” and col. 9, lines 21-23 teaches “…natural language processing system 412 analyzes a received unstructured textual report relating to the user skill level” the User skill level is interpreted as information representing who is requesting the assistance);
enabling the user to enter a request text, representing the request for assistance from the user (Col. 3, lines 28-38 teaches “…the dynamic user interface 100 will provide information on why a selection is not available…The advisor then prompts the user for additional information…” and Fig. 2A, step 205; Fig. 8, step 810 and col. 17, lines 58-62 teaches “…an operation 80 of in response to receiving computer system service data…”);
via a server computer (Fig. 6, host device 621 and Fig. 9, Server 912), receiving the access information and the request text (Col. 8, lines 32-39 teaches “…a complete feature set (e.g., set of all feature related to a computer system issue, computer system, or user skill level) is utilized by statistical analyzer 350…” and col. 9, lines 21-23 teaches “…natural language processing system 412 analyzes a received unstructured textual report relating to the user skill level” the User skill level is interpreted as information representing who is requesting the assistance);
processing the request text through a natural language processing engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text (Col. 9, lines 9-20 teaches “…the natural language processing system 412 responds to electronic document submissions sent by client application 408…analyzes a received unstructured textual report (e.g., unstructured textual data 305, error reports 312, user input 309, emails 307, text messages 315…catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…”);
accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance (Col. 9, lines 9-20 teaches “…the natural language processing system 412…analyzes a received unstructured textual report (e.g., …catalogs 340, computer system information 345, etc.) to identify a feature or feature set…and one or more suggestions (e.g., how to resolve the service issue)”; Fig. 8, step 820 and col. 17, lines 58-62 teaches ““…an operation 80 of in response to receiving computer system service data, identifying, by a second computer system, a computer system service category among a plurality of computer system categories…Fig. 8 steps 830 and 840 and col. 17, line 63 to col. 18, line 7 “…an operation 820 of identifying, by the second computer system, one or more computer system service tasks…an operation 830 of selecting, by the second computer system, a catalog among a plurality of catalogs…an operation 840 of generating…one or more suggestions based on the catalog and the one or more computer system service tasks…”);
selecting a solution text from the plurality of solution texts, dependently on the input vector and on the access information, to make the selected solution text available to the user (col. 17, line 63 to col. 18, line 7 “…an operation 840 of generating…one or more suggestions based on the catalog and the one or more computer system service tasks…” ).
Vukovic fails to disclose via a client computer accessible to a user of the machine, receiving and validating login credentials entered by the user or that the step of providing access information and enabling the user to enter a request test are responsive to a result of validating the login credentials.
Mackie disclose a user interface which enables “a user to submit user authentication credentials for verification, and if successfully verified, began an authenticated session associated with a particular application” (Mackie, ¶[0068]). Therefore, Mackie teaches receiving and validating login credentials entered by the user and responsive to a result of validating the login credentials allowing access to an application.
Vukovic differs from the claimed invention, as defined by claim 15, in that Vukovic fails to disclose that the user interface includes means for receiving and validating login credentials prior at allowing user access to the intelligent self-service delivery advisor. User interfaces which provide means for receiving and validating login credentials prior to allowing access to an application are known in the art as evidenced by Mackie. Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to have modified the dynamic user interface taught by Vukovic to include means for validating user login credentials prior to allowing access to the intelligent self-service delivery advisor as it merely constitutes the combination of known processes to achieve the predictable result to preventing unauthorized access to the intelligent self-service delivery advisor.
Conclusion
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/PENNY L CAUDLE/Examiner, Art Unit 2657