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 .
Claims 1-20 are pending.
Claim Objections
Claim 19 is objected to because of the following informalities: the claim has two periods, though the first one is a typographical error of replacing an intended comma with a period. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 9, and 15 recite determining “an appropriate recipient each action items of the one or more action items”. This phrase is grammatically incorrect and appears to be missing at least one word, rendering the meaning of the phrase indefinite. For the purposes of examination, the limitation will be interpreted as reading “recipient for each of the action items”. Appropriate correction is required.
Claims 2-8, 10-14, and 16-20 are rejected for incorporating at least the issues of the claims from which they depend.
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 an abstract idea without significantly more. Representative claim 9 recites “generating structured customer feedback data from unstructured customer feedback data received over a predefined period of time; retrieve usage analytics and system logs associated with the structured customer feedback data; comparing, …, the usage analytics and the system logs for the predefined period of time with the structured customer feedback data for the predefined period of time to predict a probable customer issue, wherein the probable customer issue is identified based on both the structured customer feedback data and the usage analytics and the system logs for the predefined period of time; automatically generating one or more action items based on a prediction of the probable customer issue; and determining an appropriate recipient for each of the action items of the one or more action items”. Therefore, the claim as a whole is directed to “Customer Issue History Analysis”, which is an abstract idea because it is a method of organizing human activity, including commercial interactions (including business relations); managing personal behavior or relationships or interactions between people. “Customer Issue History Analysis” is considered to be is a method of organizing human activity because the reading of historical customer service data and analysis of the same in order to determine reoccurring issues or problems of customers, such as low stock of holiday favored items at a grocery store, or power or connectivity issues after a storm or during a construction project. The gathering of form or unstructured verbal customer complaints through various communication types to determine the issues that occur during particular time periods a day month of year is a human organized activity regular practiced by store managers, information technology professionals, and supply chain managers. As such, the claims are directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 9 recites the following additional element(s): using machine learning usage analytics data modeling. Claim 1 further recites memory; and one or more processors coupled to the memory, the one or more processors, and claim 15 further recites one or more non-transitory computer-readable media storing instructions thereon. These additional elements individually or in combination do not integrate the exception into a practical application. That is, the recitations of additional elements amount merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f). While the claims recite using some technology, the technology is recited it a high level of generality. The use of commercially available technology to perform the abstract idea does not integrate the abstract idea into a practical application. There is no solution of a technological problem or improvement of any technology. As such, those additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). 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. Claim 9 is directed to an abstract idea.
Claim 9 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element, individually or in combination, are merely being used to apply the abstract idea to a technological environment. As noted above, there is no solution of a technological problem or improvement of any technology. Rather, the use of machine learning usage analytics data modeling is used to give a technological environment for the performance of the abstract idea. Accordingly, claim 9 is ineligible.
Claims 1 and 15 recite substantially similar features to those recited in representative claim 9 and are ineligible based on substantially the same reasons.
Dependent claims 2-8, 10-14, and 16-20 merely further limit the abstract idea and are thereby considered to be ineligible.
Dependent claim 2 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of the unstructured customer feedback data comprises website customer feedback data, app store customer feedback data, call center transcripts, or live support chat transcripts, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 2 is also non-statutory subject matter.
Dependent claim 3 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of to merge the structured customer feedback data generated from the unstructured customer feedback data received over the predefined period of time, wherein the unstructured customer feedback data is received from a plurality of different customer feedback data sources, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 3 is also non-statutory subject matter.
Dependent claim 4 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of retrieve system usage logs data associated with the structured customer feedback data; and detect, …, at least one customer service issue within the system usage logs data associated with the structured customer feedback data, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 4 is also non-statutory subject matter.
Dependent claims 5 and 10 further limit the abstract idea of “Customer Issue History Analysis” by introducing the element of to retrieve usage analytics data and system usage logs data associated with the structured customer feedback data; and corroborate at least one trending customer issue based upon cross-relating the at least one trending customer issue with at least one of the usage analytics data and the system usage logs data associated with the structured customer feedback data, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 5 and 10 also non-statutory subject matter.
Dependent claims 6 and 11further limit the abstract idea of “Customer Issue History Analysis” by introducing the element of to automatically prioritize the at least one trending customer issue over a plurality of other detected trending customer issues based on corroboration of the at least one trending customer issue, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 6 and 11 also non-statutory subject matter.
Dependent claim 7 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of the at least one trending customer issue detected within a plurality of categories of customer issues comprises at least one customer problem-related issue detected within the plurality of categories of customer issues, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 7 is also non-statutory subject matter.
Dependent claims 8 and 12 further limit the abstract idea of “Customer Issue History Analysis” by introducing the element of the at least one trending customer issue detected within the plurality of categories of customer issues comprises at least one most frequently occurring customer issue detected within the plurality of categories of customer issues, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 8 and 12 also non-statutory subject matter.
Dependent claim 13 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of the at least one trending customer issue detected within the plurality of categories of customer issues comprises at least one customer issue detected within the plurality of categories of customer issues that exceeds an historic baseline for the at least one customer issue, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 13 is also non-statutory subject matter.
Dependent claims 14 and 17 further limit the abstract idea of “Customer Issue History Analysis” by introducing the element of logging the at least one trending customer issue into a service ticketing system to generate a logged at least one trending customer issue, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 14 and 17 also non-statutory subject matter.
Dependent claim 18 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of automatically track a status of the logged at least one trending customer issue, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 18 is also non-statutory subject matter.
Dependent claim 19 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of receive status updates comprising examination and evaluation updates regarding resolution of the logged at least one trending customer issue, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 19 is also non-statutory subject matter.
Dependent claim 20 further limits the abstract idea of “Customer Issue History Analysis” by introducing the element of to mark the logged at least one trending customer issue closed when the logged at least one trending customer issue is resolved, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 20 is also non-statutory subject matter.
Dependent claims 2-8, 10-14, and 16-20 also do not integrated into a practical application. The dependent claim 4 recites using unsupervised machine learning system, and claim 17 recites a service ticketing system. This additional element merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system is merely being used to apply the abstract idea to a technological environment. That is, the claims provide no practical limits or improvements to any technology. Accordingly, dependent claims 2-8, 10-14, and 16-20 1are also ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 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.
Claims 1-4, 9 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 20170270416 to Sri et al.
With regards to claims 1, 9 and 15, Sri et al. teaches
memory; and one or more processors coupled to the memory (paragraphs [0008], “In another embodiment of the invention, an apparatus for building prediction models from customer Web logs is disclosed. The apparatus includes at least one processor and a memory. The memory has stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to receive a Web log comprising unstructured data and structured data corresponding to a customer's journey on a Website.”), the one or more processors configured to:
generate structured customer feedback data from unstructured customer feedback data received over a predefined period of time (paragraphs [0008], “The memory has stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to receive a Web log comprising unstructured data and structured data corresponding to a customer's journey on a Website. The apparatus generates using the Web log: (1) a plurality of unstructured variables from the unstructured data, and (2) a plurality of structured variables from the structured data.”);
retrieve usage analytics and system logs associated with the structured customer feedback data (paragraphs [0021], “The captured structured information (for example, type of device/browser, day/time information, etc.) along with session flags and Web page visit information is subjected to exploratory data analysis to identify distribution of variables that may be used for building a prediction model for the customers visiting the enterprise Website.”; paragraph [0030], “In at least one example embodiment, the channel interfaces are configured to receive up-to-date information related to the customer-enterprise interactions from the enterprise interaction channels. In some embodiments, the information may also be collated from the plurality of devices used by the customers. To that effect, the communication interface 308 may be in operative communication with various customer touch points, such as electronic devices associated with the customers, Websites visited by the customers, devices used by customer support representatives (for example, voice agents, chat agents, IVR systems, in-store agents, and the like) engaged by the customers, and the like.”);
compare, using machine learning usage analytics data modeling, the usage analytics and the system logs for the predefined period of time with the structured customer feedback data for the predefined period of time to predict a probable customer issue (paragraphs [0023], “More specifically, various embodiments of the invention disclosed herein present a text mining based approach for building prediction models for customers from Web logs. The text mining based approach for building a prediction model for a customer (or a set of customers) from Web logs involves two processing steps. In the first processing step, structured information is converted into textual form and concatenated with other unstructured data to generate an unstructured freeform session string, which serves as a text representative of the customer's journey on the Website. In the second processing step, features are derived from the session string using text categorization tools and the features are then provided to models based on intention prediction algorithms for facilitating building of prediction models configured to predict, for example customer intentions or any other response variables related to the customer, such as for example, customer persona, likelihood of the customer to call, outcome of a chat (e.g. a sale/no-sale outcome, an escalation to a live agent outcome or a voice referral outcome, etc.).”), wherein the probable customer issue is identified based on both the structured customer feedback data and the usage analytics and the system logs for the predefined period of time (paragraphs [0043], “Moreover, in some scenarios, the time stamps of each Web page may also be used to compute a time spent on each Web page. For example, if a customer has visited a Web page ‘P1’ at time ‘T1’ and subsequently visited another Web page ‘P2’ at time ‘T2’, then the processor 302 may be configured to compute the time spent on Web page ‘P1’ as ‘T2−T1’ and store the time spent on each Web page as an unstructured variable as ‘Duration 1’ or ‘D1’. In some scenarios, the processor 302 may also create bins (or classification categories) to classify time spent on various pages. The bins may be generated automatically, or supported based on configurable partitions. For example, a bin may be created to classify all time-spent values between one-second to one-minute duration. Similarly, another bin may be created to classify all time-spent values between one-minute and five-minute durations, and so on and so forth.”);
automatically generate one or more action items based on a prediction of the probable customer issue (paragraphs [0080], “At operation 510 of the method 500, at least one prediction model is built by the processor using the plurality of features. The at least one prediction model is configured to facilitate prediction of at least one response variable. In an embodiment, predicting a response variable includes predicting one of an intention of the customer, a persona of the customer, a sentiment of the customer, a likelihood of the customer to call, an outcome of a chat offer to the customer, a net promoter score (NPS), a customer satisfaction score (CSAT), and a net experience score (NES) for the customer.”); and
determine an appropriate recipient each action items of the one or more action items (paragraphs [0062], “The built models may then be used to for generating predictions of all customers visiting the enterprise Website. In at least one example embodiment, the predictions for the customers may be used for improving chances of sale or providing an improved browsing experience to the customers. In an illustrative example, the processor 302 is configured to determine whether a chat option may be offered to a customer on the Website, or to which chat agent a chat interaction may be routed based on the predicted intention of the customer.”).
With regards to claim 2, Sri et al. teaches the unstructured customer feedback data comprises website customer feedback data, app store customer feedback data, call center transcripts, or live support chat transcripts (paragraphs [0060], “Also, ensembles of models may be used for text classification using a variety of voting schemes. As mentioned above, the response variable, for example an intention to be predicted/modeled, may relate to a purchase of a particular product, purchase of a number of products, a persona of the customer, a sentiment of the customer, a net promoter score (NPS), a customer satisfaction score (CSAT), a first call resolution (FCR), a predicted or an actual experience score for the customer, a customer's call or a chat interaction after a Web session, a queue, an agent's skill, a voice referral from a chat interaction, a chat transfer, an outcome of a following interaction with an interactive voice response (IVR) system, a voice call, a chat interaction, a degree of decidedness for sales, and the like. The classification algorithm (i.e. the prediction classifier) may be configured to assign weights to features based on their perceived respective contribution towards achieving a chosen response variable and compute an overall likelihood of occurrence of the response variable. The weights for the features may be chosen by an experienced user (for example, a field expert) or may be learnt by the apparatus 300 using machine learning by observing activity and subsequent response variable outcomes for a plurality of customers visiting the enterprise Website.”).
With regards to claim 3, Sri et al. teaches where the one or more processors are further configured to merge the structured customer feedback data generated from the unstructured customer feedback data received over the predefined period of time, wherein the unstructured customer feedback data is received from a plurality of different customer feedback data sources (paragraph [0007], “The method receives, by a processor, a Web log including unstructured data and structured data corresponding to a customer's journey on a Website. The method generates by the processor, using the Web log: (1) a plurality of unstructured variables from the unstructured data, and (2) a plurality of structured variables from the structured data. The method generates, by the processor, a session string by concatenating the plurality of unstructured variables and the plurality of structured variables. The session string configures a textual representation of the customer's journey on the Website.”; paragraph [0023], “The text mining based approach for building a prediction model for a customer (or a set of customers) from Web logs involves two processing steps. In the first processing step, structured information is converted into textual form and concatenated with other unstructured data to generate an unstructured freeform session string, which serves as a text representative of the customer's journey on the Website. In the second processing step, features are derived from the session string using text categorization tools and the features are then provided to models based on intention prediction algorithms for facilitating building of prediction models configured to predict, for example customer intentions or any other response variables related to the customer, such as for example, customer persona, likelihood of the customer to call, outcome of a chat (e.g. a sale/no-sale outcome, an escalation to a live agent outcome or a voice referral outcome, etc.).”).
With regards to claim 4, Sri et al. teaches the one or more processors are further configured to: retrieve system usage logs data associated with the structured customer feedback data (paragraphs [0021], “The captured structured information (for example, type of device/browser, day/time information, etc.) along with session flags and Web page visit information is subjected to exploratory data analysis to identify distribution of variables that may be used for building a prediction model for the customers visiting the enterprise Website.”); and
detect, using unsupervised machine learning system, at least one customer service issue within the system usage logs data associated with the structured customer feedback data (paragraphs [0023], “In the first processing step, structured information is converted into textual form and concatenated with other unstructured data to generate an unstructured freeform session string, which serves as a text representative of the customer's journey on the Website. In the second processing step, features are derived from the session string using text categorization tools and the features are then provided to models based on intention prediction algorithms for facilitating building of prediction models configured to predict, for example customer intentions or any other response variables related to the customer, such as for example, customer persona, likelihood of the customer to call, outcome of a chat (e.g. a sale/no-sale outcome, an escalation to a live agent outcome or a voice referral outcome, etc.).”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 5-8, 10-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20170270416 to Sri et al. as applied to claims 1-4, 9 and 15 above, and further in view of U.S. Patent Application Publication No. 2019/0220695 to Nefedov.
With regards to claims 5 and 10, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches the one or more processors are further configured to: retrieve usage analytics data and system usage logs data associated with the structured customer feedback data (paragraphs [0011], “Often documents and files are generated in simple form as unstructured documents. Tagging of data is used to enhance files (unstructured files or to further enhance structured files) and data structures may be created to link data and documents containing data with resources to provide enhanced services. For example, Thomson Reuters' Text Metadata Services group (“TMS”) is one exemplary IE-based solution provider offering text analytics software used to “tag,” or categorize, unstructured information and to extract facts about people, organizations, places or other details from documents.”); and
corroborate at least one trending customer issue based upon cross-relating the at least one trending customer issue with at least one of the usage analytics data and the system usage logs data associated with the structured customer feedback data (paragraphs [0025], “elements related to data elements or fields or database targets. The PCSS may further comprise a discovery engine adapted to extract and tag keyword data to allow analyst-type users to classify and navigate over historical data records and/or known solution records to quickly identify trends related to user inquiries, and adapted to provide cross-mapping or/and cross-learning using mapping extracted taxonomies from different topical domains associated with historical data records and/or known solution records. The tagging engine may tag inquiry data based on a set of topics, and the clustering engine may be adapted to cluster cases based at least in part on topics to generate a set of clusters adapted for use by product manager-type users to identify trends or product/service related issues over time.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claims 6 and 11, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches the one or more processors are further configured to automatically prioritize the at least one trending customer issue over a plurality of other detected trending customer issues based on corroboration of the at least one trending customer issue (paragraphs [0057], “In one instance, documents/cases analytics in product customer support system. A Discovery Engine 6 is used to discover dominating problems and/or trends by clustering cases to help product managers locate and fix problems with products. In this example the Discovery Engine 6 is based on unsupervised clustering, but supervised clustering or extending existing clustering are also manners of operation. Automatic tagging for clusters done by the Cluster Tagging Engine 7 (“CTE”) helps analysts classify and navigate over cases. In this example the CTE 7 is configured to use features engineering and unsupervised clustering; automatic topic tagging for a set of documents/cases; and creation of new tags for topics documents if the reported problems are not in the knowledge database.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claim 7, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches the at least one trending customer issue detected within a plurality of categories of customer issues comprises at least one customer problem-related issue detected within the plurality of categories of customer issues (paragraphs [0061], “In one manner of operation, the Discovery engine 6 is used to extract and tag data to discover new trends and problems in a product by analyzing customer support databases PKD 2 to help product managers to locate and fix problems in such products. The Cluster Tagging Engine 7 automatically tags keywords for clusters to help analysts to classify and navigate over cases, for instance: based on already classified cases from a knowledge database 2 or/and, creation of new tags if the reported problems are not in the knowledge database, and cross-content learning.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claims 8 and 12, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches the at least one trending customer issue detected within the plurality of categories of customer issues comprises at least one most frequently occurring customer issue detected within the plurality of categories of customer issues (paragraphs [0057], “A Discovery Engine 6 is used to discover dominating problems and/or trends by clustering cases to help product managers locate and fix problems with products. In this example the Discovery Engine 6 is based on unsupervised clustering, but supervised clustering or extending existing clustering are also manners of operation. Automatic tagging for clusters done by the Cluster Tagging Engine 7 (“CTE”) helps analysts classify and navigate over cases. In this example the CTE 7 is configured to use features engineering and unsupervised clustering; automatic topic tagging for a set of documents/cases; and creation of new tags for topics documents if the reported problems are not in the knowledge database.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claim 13, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches the at least one trending customer issue detected within the plurality of categories of customer issues comprises at least one customer issue detected within the plurality of categories of customer issues that exceeds an historic baseline for the at least one customer issue (paragraph [0025], “The PCSS may further comprise a discovery engine adapted to extract and tag keyword data to allow analyst-type users to classify and navigate over historical data records and/or known solution records to quickly identify trends related to user inquiries, and adapted to provide cross-mapping or/and cross-learning using mapping extracted taxonomies from different topical domains associated with historical data records and/or known solution records. The tagging engine may tag inquiry data based on a set of topics, and the clustering engine may be adapted to cluster cases based at least in part on topics to generate a set of clusters adapted for use by product manager-type users to identify trends or product/service related issues over time.”; paragraphs [0057], “A Discovery Engine 6 is used to discover dominating problems and/or trends by clustering cases to help product managers locate and fix problems with products. In this example the Discovery Engine 6 is based on unsupervised clustering, but supervised clustering or extending existing clustering are also manners of operation. Automatic tagging for clusters done by the Cluster Tagging Engine 7 (“CTE”) helps analysts classify and navigate over cases. In this example the CTE 7 is configured to use features engineering and unsupervised clustering; automatic topic tagging for a set of documents/cases; and creation of new tags for topics documents if the reported problems are not in the knowledge database.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claims 14 and 17, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches teaches logging the at least one trending customer issue into a service ticketing system to generate a logged at least one trending customer issue (paragraphs [], “”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claim 18, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches automatically track a status of the logged at least one trending customer issue (paragraphs [0057], “In this manner the PCSS 3/PKD 2 provides for automatic taxonomy building for a selected set of documents. The CTE 7 may also be configured to provide cross-content learning and soft clustering as well as semi-supervised learning to use already classified cases from an existing product knowledge database. The PCSS 3/PKD 2 may be configured to enable self-service for customers. For example, the PCSS 3 may be used to find similar cases reported before, such as using Discovery Engine 5 and Search Engine 8. The PCSS 3/PKD 2 may also be configured to recommend a possible solution based on cases resolved before Recommendation Engine 9 soft-clustering.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claim 19, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches receive status updates comprising examination and evaluation updates regarding resolution of the logged at least one trending customer issue (paragraphs [0057], “In this manner the PCSS 3/PKD 2 provides for automatic taxonomy building for a selected set of documents. The CTE 7 may also be configured to provide cross-content learning and soft clustering as well as semi-supervised learning to use already classified cases from an existing product knowledge database. The PCSS 3/PKD 2 may be configured to enable self-service for customers. For example, the PCSS 3 may be used to find similar cases reported before, such as using Discovery Engine 5 and Search Engine 8. The PCSS 3/PKD 2 may also be configured to recommend a possible solution based on cases resolved before Recommendation Engine 9 soft-clustering.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
With regards to claim 20, Sri et al. fails to explicitly teach trending customer issue. However Nefedov teaches to mark the logged at least one trending customer issue closed when the logged at least one trending customer issue is resolved (paragraphs [0057], “In this manner the PCSS 3/PKD 2 provides for automatic taxonomy building for a selected set of documents. The CTE 7 may also be configured to provide cross-content learning and soft clustering as well as semi-supervised learning to use already classified cases from an existing product knowledge database. The PCSS 3/PKD 2 may be configured to enable self-service for customers. For example, the PCSS 3 may be used to find similar cases reported before, such as using Discovery Engine 5 and Search Engine 8. The PCSS 3/PKD 2 may also be configured to recommend a possible solution based on cases resolved before Recommendation Engine 9 soft-clustering.”).
This part of Nefedov is applicable to the system of Sri et al. as they both share characteristics and capabilities, namely, they are directed to the analysis of customer service issues. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sri et al. to include the trending issue identifications as taught by Nefedov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sri et al. in order to help product managers locate and fix problems with products (see paragraph [0057] of Nefedov).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 20200380074 to Li et al. discusses trending issue identification in text streams. In one embodiment, a method for improving resolution of a trending issue identified in a set of text streams includes presenting a user interface of an application that is being executed by a computing device. The method also includes receiving a notification including the trending issue that has been identified in the set of text streams based at least in part on textual analysis performed on the set of text streams, and presenting the trending issue on the user interface of the application to enable an action to be performed to resolve the trending issue.
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/J.D.S./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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