DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 4/4/2024.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a pre-processing module and a post-processing module in claim 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-11, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over SERNA (US 20210326940 A1), and in further view of RAVINDRAN (US 2023/0385884 A1).
REGARDING CLAIM 1, SERNA discloses a method for sentiment analysis regarding a communication network of a communication service provider (CSP), the method comprising:
collecting, using a sentiment analysis system, a set of customer comments from one or more online platforms (Fig. 1 – “Message (text) 120”; Fig. 4 – “Verbal statements, telephone calls, conversations, …”; Par 51 – “The customer support request may include a message. It should be noted that the message may be derived from the customer support request. When the customer support request is communication by voice, the system may use natural language processing and/or any other suitable voice to text engine. In addition, the customer support request may include voice information associated with the customer support request.”; Par 52 – “The system may further include a processor. The processor may be configured, for each customer support request, to harvest a plurality of artifacts from social media account history and/or other third party data source information associated with a user associated with the device identification number. Each of the plurality of artifacts may include sentiment information relevant to the customer support request.”), wherein each customer comment comprises text (Par 40 – “Support requests, and historical communications-related thereto, in the form of email, Instant Messaging Service (IMS), phone calls, video chats, Twitter communications such as Tweets™, and other elements (e.g., response time, escalations, etc.) may be analyzed to define the sentiment of the interactions of an individual, group and/or entity towards one or more individuals, groups and/or entities and to provide a current snapshot thereof.”) [and is related to the communication network of the CSP];
generating, using a pre-processing module, a profile for each customer comment in the set of customer comments (Par 64 – “The processor may be further configured to build a current profile for the communication device. The current profile may be based on the plurality of artifacts and the historical information.”);
providing the text of each customer comment to a natural language processing (NLP) [neural network] (Par 47 – “It should be noted that the topic analysis could be used for many different type of topics, but that such information could preferably be mined from the customer support request using such utilities as the aforementioned libraries including, but not limited to, the Natural Language Toolkit library.”; Par 73 – “The message may be derived from the customer support request using natural language processing.”; Par 119 – “Polarity-based scoring scale 702 is shown in FIG. 7. In such a scoring scale, each support request is scored on a polar scale using linguistic scoring methodology. Linguistic scoring methodology may utilize various language scoring methods, such as natural language processing, computational linguistics and biometrics. For the purposes of this application, natural language processing should be understood to refer to Natural Language Processing (NLP) is a subfield of linguistics, computer science, information engineering and artificial intelligence concerned with the interactions between computers and human (natural) languages. In particular, NLP refers to how to program computers to process and analyze large amounts of natural language data.”);
generating, using the NLP [neural network], at least a sentiment classification (Par 119 – “Polarity-based scoring scale 702 is shown in FIG. 7. In such a scoring scale, each support request is scored on a polar scale using linguistic scoring methodology. Linguistic scoring methodology may utilize various language scoring methods, such as natural language processing, computational linguistics and biometrics.”; Par 122 – “It should be appreciated that a polarity-based scale may include two opposite emotions, whether positive and negative, happy and sad or any other suitable opposite emotions. Therefore, each support request scored on a polarity-based score may only be given a sentiment score based on the polarity of the support request. However, at times, in order to compensate for the shortcomings of the polarity-based scoring models, an artifact may be scored on multiple polarity-based scoring models, and, the results of the scoring models may be combined.”; Par 124 – “Vector 834 may be a vector generated from a support request.”; Par 125 – “The sentiment of the support request plotted as vector 834 may be shown in-between intelligent and promoted. It should be appreciated that the multi-dimensional scoring scale may be used to determine the sentiment of a support request—with or without sentiment adjustment associated with retrieved artifacts.”) and an issue classification for each customer comment based on the text of the customer comment (Par 45 – “For example, if the sentiment associated with a user has been determined to be happy (sentiment analysis) and the user is asking questions regarding financial instruments (topic analysis), it could be beneficial to route that customer to a new financial advisor so the new financial advisor could build up their client book with a happy user.”; Par 47 – “It should be noted that the topic analysis could be used for many different type of topics, but that such information could preferably be mined from the customer support request using such utilities as the aforementioned libraries including, but not limited to, the Natural Language Toolkit library.”);
generating, using a post-processing module, one or more reports based on one or more of the sentiment classification and the issue classification of each customer comment (Fig. 4 – “support request 412”; Par 75 – “The foregoing are examples of analyses that an AI-bot may |use legacy customer support requests|[SD3], or other, information to tune a response to a current customer request. By forming a historical request profile, legacy information can be leveraged to more appropriately respond to current customer support requests. Additional examples of AI-bot responses are described in more detail below in the portion of the specification corresponding to FIGS. 15-22.”; Par 8 – “The customer support request may include a date of the customer support request, a time of receipt of the customer support request, a location of a communication device that was used to communicate the customer support request, a device identification number associated with the communication device and a message derivable from the customer support request.”; Par 144 – “Once the support requests have been received, the support requests may be parsed by parsing engine 1232 for date, time, location, name of requester and message content. Thereafter, response system 1234 may redirect the support request to either an employee in the support center 1210, auto-response system in the support center 1206 or a manager 1214.”; In other words, The support requests include the messages (i.e., comments) and other information (date, time, location, etc.) associated with the messages. The support requests are routed to a responsible person.); and
transmitting, using the sentiment analysis system, at least one report to a department of the CSP based on one or more of the sentiment classification or the issue classification (Par 45 –“For example, if the sentiment associated with a user has been determined to be happy (sentiment analysis) and the user is asking questions regarding financial instruments (topic analysis), it could be beneficial to route that customer to a new financial advisor so the new financial advisor could build up their client book with a happy user.”; Par 90 – “Finally, at step 206, the diagram shows routing the customer support request based on 1) a localized context and various request parameters associated with the request in combination with 2) the customer sentiment derived from social media artifacts.”; Par 144 – “Once the support requests have been received, the support requests may be parsed by parsing engine 1232 for date, time, location, name of requester and message content. Thereafter, response system 1234 may redirect the support request to either an employee in the support center 1210, auto-response system in the support center 1206 or a manager 1214.”).
SERNA does not explicitly teach the [square-bracketed] limitations.
RAVINDRAN discloses the [square-bracketed] limitations. RAVINDRAN discloses a method/system to identify issues from user comments comprising:
collecting, using a sentiment analysis system, a set of customer comments from one or more online platforms, wherein each customer comment comprises text (RAVINDRAN Par 89 – “Step 300 includes receiving natural language text generated by different sources of information. Receiving the natural language text may be performed as described with respect to step 200 of FIG. 2 . However, in step 300, many different sources of information are accessed, and the raw natural language text collated for processing.”) [and is related to the communication network of the CSP] (RAVINDRAN Par 78 – “Step 210 includes determining, based on the negative review, a technical issue with the target. For example, the target may be a software application. In this case, a technical issue with the software application may be determined. The determination may be performed by a computer technician but may also be performed automatically. For example, the negative review (and possibly other information, such as the category) may include an identified indication that the target (which is financial management software) is not communicating with a particular bank. The technical issue may be that the application programming interface of the financial management software is not properly configured to communicate with the bank's communication protocols. This technical problem may then be returned as the determined technical issue.”);
providing the text of each customer comment to a natural language processing (NLP) [neural network] (RAVINDRAN Par 83 – “As part of pre-processing, the method may also include vectorizing the natural language text to generate the first input to the MLM at step 202. Vectorizing may include inputting the natural language text to a third MLM, such as a bi-directional long short term memory neural network. Vectorizing also may include receiving, as output from the third MLM, a matrix of numbers representing both the natural language text and contexts of sentences in the natural language text.”);
generating, using the NLP [neural network] (RAVINDRAN Par 48 – “Other types of models may be used for the second machine learning model (120B). For example, a Multi-Layer Perceptron Model (MLP) may be used to categorize the negative reviews. A MLP is a fully connected class of feed forward artificial neural networks. However, other models may be used, like XGBoost, which is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. Recurrent neural networks can also be used to categorize the reviews.”), at least a sentiment classification (RAVINDRAN Par 90 – “Step 302 includes pre-processing the natural language text by cleaning and vectorizing the natural language text. Pre-processing, cleaning, and vectorizing may be performed as described with respect to step 202 of FIG. 2 . The pre-processing controller (128) of FIG. 1A may perform the cleaning and vectorizing of the natural language text.”; Par 83 – “Vectorizing may include inputting the natural language text to a third MLM, such as a bi-directional long short term memory neural network.”; Par 91 – “Step 304 includes extracting negative reviews from the natural language text by executing a first machine learning model (MLM). Extracting the negative reviews may be performed as described with respect to step 202 of FIG. 2 . Thus, for example, a first input to the first MLM is the natural language text and a first output of the first MLM is first probabilities that corresponding instances of the natural language text have negative sentiments.”) and an issue classification for each customer comment based on the text of the customer comment (RAVINDRAN Par 112 – “Next, during categorization (432), the negative reviews are categorized into two or more different categories. Categorization is described with respect to step 204 of FIG. 2 or step 306 of FIG. 3 . In other words, the negative reviews are sorted by category, such as by application type (e.g., whether the negative reviews relate to the enterprise system (400), the financial management web application (402), the tax preparation web application (404), and/or the online presence web application (406)) of FIG. 4A.”);
generating, using a post-processing module, one or more reports based on one or more of the sentiment classification and the issue classification of each customer comment (RAVINDRAN Par 77 – “Step 208 includes providing the name of the target and at least one category. Providing may be performed by transmitting the name of the target and the category into which the target falls to a software application for further processing. Providing may also be performed by displaying the name of the target and the category to a programmer or technician for review. Providing may also be performed by integrating the name of the target and the category into a dashboard or other GUI, that also displays other targets and categories, as shown in FIG. 4C through FIG. 4F.”); and
transmitting, using the sentiment analysis system, at least one report to a department of the CSP based on one or more of the sentiment classification or the issue classification (RAVINDRAN Par 77 – “Step 208 includes providing the name of the target and at least one category. Providing may be performed by transmitting the name of the target and the category into which the target falls to a software application for further processing. Providing may also be performed by displaying the name of the target and the category to a programmer or technician for review. Providing may also be performed by integrating the name of the target and the category into a dashboard or other GUI, that also displays other targets and categories, as shown in FIG. 4C through FIG. 4F.”)
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 method/system of SERNA to include comments regarding communication issues and neural networks, as taught by RAVINDRAN.
One of ordinary skill would have been motivated to include comments regarding communication issues and neural networks, in order to enable provide a variety of enterprise solutions with more accurate analyses.
REGARDING CLAIM 2, SERNA in view of RAVINDRAN discloses the method according to claim 1, wherein collecting the set of customer comments further comprises collecting a set of associated data for each customer comment (SERNA Par 42 – “This data may then be parsed and transformed into structured data which is then stored in a database. For the purposes of this disclosure, at least the following data points may be tracked: date, time, location username and message.”; Par 62 –“ The system may also include a processor. The processor may be configured to harvest, for each customer support request, a plurality of artifacts from social media account history and/or other third party data source information. The artifacts may be associated with a user. The user may be associated with the device identification number. Each of the plurality of artifacts may include sentiment information relevant to the customer support request.”; Par 64 – “The processor may be further configured to build a current profile for the communication device. The current profile may be based on the plurality of artifacts and the historical information.”), and wherein the profile generated for each customer comment includes the associated data for the customer comment (SERNA Par 177 – “FIG. 20 shows using microprocessor 2008 to convert historical sentiment value 2002, current sentiment value 2004 and message information 2006 into a current profile 2010.”).
REGARDING CLAIM 3, SERNA in view of RAVINDRAN discloses the method according to claim 1, wherein the one or more online platforms is one or more of an online marketplace, a web site, or a social media platform (SERNA Par 40 – “Support requests, and historical communications-related thereto, in the form of email, Instant Messaging Service (IMS), phone calls, video chats, Twitter communications such as Tweets™, and other elements (e.g., response time, escalations, etc.) may be analyzed to define the sentiment of the interactions of an individual, group and/or entity towards one or more individuals, groups and/or entities and to provide a current snapshot thereof.”; Par 91 – “FIG. 3 shows a more specific rendering of an illustrative flow diagram for a method associated with a customer support request routing system. In the diagram in FIG. 3, an API, such as Twitter™, receives a support request. This is shown at step 302.”; Par 144 – “API feed 1230 preferably acts as a conduit to receive support requests in the form of social media communications such as Tweets.”).
REGARDING CLAIM 5, SERNA in view of RAVINDRAN discloses the method according to claim 1, wherein the sentiment classification is one of positive, negative, or neutral (SERNA Par 122 – “It should be appreciated that a polarity-based scale may include two opposite emotions, whether positive and negative, happy and sad or any other suitable opposite emotions. Therefore, each support request scored on a polarity-based score may only be given a sentiment score based on the polarity of the support request.”).
REGARDING CLAIM 6, SERNA in view of RAVINDRAN discloses the method according to claim 1.
RAVINDRAN further discloses the method/system wherein the issue classification includes one or more categories of issues related to performance of the communication network of the CSP (RAVINDRAN Par 78 – “Step 210 includes determining, based on the negative review, a technical issue with the target. For example, the target may be a software application. In this case, a technical issue with the software application may be determined. The determination may be performed by a computer technician but may also be performed automatically. For example, the negative review (and possibly other information, such as the category) may include an identified indication that the target (which is financial management software) is not communicating with a particular bank. The technical issue may be that the application programming interface of the financial management software is not properly configured to communicate with the bank's communication protocols. This technical problem may then be returned as the determined technical issue.”).
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 method/system of SERNA to include comments regarding communication issues, as taught by RAVINDRAN.
One of ordinary skill would have been motivated to include comments regarding communication issues, in order to enable provide a variety of enterprise solutions with more accurate analyses.
REGARDING CLAIM 7, SERNA discloses a system for sentiment analysis regarding a communication network of a communication service provider (CSP), the system comprising:
an input (Fig. 4 –“Support Request”; Par 144 – “API feed 1230 preferably acts as a conduit to receive support requests in the form of social media communications such as Tweets.”) for receiving a set of customer comments collected from one or more online platforms (Fig. 1 – “Message (text) 120”; Fig. 4 – “Verbal statements, telephone calls, conversations, …”; Par 52 – “The system may further include a processor. The processor may be configured, for each customer support request, to harvest a plurality of artifacts from social media account history and/or other third party data source information associated with a user associated with the device identification number. Each of the plurality of artifacts may include sentiment information relevant to the customer support request.”), wherein each customer comment comprises text(Par 40 – “Support requests, and historical communications-related thereto, in the form of email, Instant Messaging Service (IMS), phone calls, video chats, Twitter communications such as Tweets™, and other elements (e.g., response time, escalations, etc.) may be analyzed to define the sentiment of the interactions of an individual, group and/or entity towards one or more individuals, groups and/or entities and to provide a current snapshot thereof.”) [and is related to the communication network of the CSP];
a pre-processing module coupled to the input and configured to generate a profile for each customer comment in the set of customer comments (Par 64 – “The processor may be further configured to build a current profile for the communication device. The current profile may be based on the plurality of artifacts and the historical information.”);
a natural language processing (NLP) [neural network] coupled to the pre-processing module and configured to generate at least a sentiment classification (Par 119 – “Polarity-based scoring scale 702 is shown in FIG. 7. In such a scoring scale, each support request is scored on a polar scale using linguistic scoring methodology. Linguistic scoring methodology may utilize various language scoring methods, such as natural language processing, computational linguistics and biometrics.”; Par 122 – “It should be appreciated that a polarity-based scale may include two opposite emotions, whether positive and negative, happy and sad or any other suitable opposite emotions. Therefore, each support request scored on a polarity-based score may only be given a sentiment score based on the polarity of the support request. However, at times, in order to compensate for the shortcomings of the polarity-based scoring models, an artifact may be scored on multiple polarity-based scoring models, and, the results of the scoring models may be combined.”; Par 124 – “Vector 834 may be a vector generated from a support request.”; Par 125 – “The sentiment of the support request plotted as vector 834 may be shown in-between intelligent and promoted. It should be appreciated that the multi-dimensional scoring scale may be used to determine the sentiment of a support request—with or without sentiment adjustment associated with retrieved artifacts.”) and an issue classification for each customer comment based on the text of the customer comment (Par 45 – “For example, if the sentiment associated with a user has been determined to be happy (sentiment analysis) and the user is asking questions regarding financial instruments (topic analysis), it could be beneficial to route that customer to a new financial advisor so the new financial advisor could build up their client book with a happy user.”; Par 47 – “It should be noted that the topic analysis could be used for many different type of topics, but that such information could preferably be mined from the customer support request using such utilities as the aforementioned libraries including, but not limited to, the Natural Language Toolkit library.”); and
a post-processing module coupled to the NLP neural network and configured to generate one or more reports based on one or more of the sentiment classification and the issue classification of each customer comment (Fig. 4 – “support request 412”; Par 75 – “The foregoing are examples of analyses that an AI-bot may |use legacy customer support requests|[SD3], or other, information to tune a response to a current customer request. By forming a historical request profile, legacy information can be leveraged to more appropriately respond to current customer support requests. Additional examples of AI-bot responses are described in more detail below in the portion of the specification corresponding to FIGS. 15-22.”; Par 8 – “The customer support request may include a date of the customer support request, a time of receipt of the customer support request, a location of a communication device that was used to communicate the customer support request, a device identification number associated with the communication device and a message derivable from the customer support request.”; Par 144 – “Once the support requests have been received, the support requests may be parsed by parsing engine 1232 for date, time, location, name of requester and message content. Thereafter, response system 1234 may redirect the support request to either an employee in the support center 1210, auto-response system in the support center 1206 or a manager 1214.”; In other words, The support requests include the messages (i.e., comments) and other information (date, time, location, etc.) associated with the messages. The support requests are routed to a responsible person.).
SERNA does not explicitly teach the [square-bracketed] limitations.
RAVINDRAN discloses the [square-bracketed] limitations. RAVINDRAN discloses a method/system to identify issues from user comments comprising:
an input for receiving a set of customer comments collected from one or more online platforms, wherein each customer comment comprises text (RAVINDRAN Par 89 – “Step 300 includes receiving natural language text generated by different sources of information. Receiving the natural language text may be performed as described with respect to step 200 of FIG. 2 . However, in step 300, many different sources of information are accessed, and the raw natural language text collated for processing.”) [and is related to the communication network of the CSP] (RAVINDRAN Par 78 – “Step 210 includes determining, based on the negative review, a technical issue with the target. For example, the target may be a software application. In this case, a technical issue with the software application may be determined. The determination may be performed by a computer technician but may also be performed automatically. For example, the negative review (and possibly other information, such as the category) may include an identified indication that the target (which is financial management software) is not communicating with a particular bank. The technical issue may be that the application programming interface of the financial management software is not properly configured to communicate with the bank's communication protocols. This technical problem may then be returned as the determined technical issue.”);
a natural language processing (NLP) [neural network] coupled to the pre-processing module (RAVINDRAN Par 83 – “As part of pre-processing, the method may also include vectorizing the natural language text to generate the first input to the MLM at step 202. Vectorizing may include inputting the natural language text to a third MLM, such as a bi-directional long short term memory neural network. Vectorizing also may include receiving, as output from the third MLM, a matrix of numbers representing both the natural language text and contexts of sentences in the natural language text.”; Par 48 – “Other types of models may be used for the second machine learning model (120B). For example, a Multi-Layer Perceptron Model (MLP) may be used to categorize the negative reviews. A MLP is a fully connected class of feed forward artificial neural networks. However, other models may be used, like XGBoost, which is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. Recurrent neural networks can also be used to categorize the reviews.”) and configured to generate at least a sentiment classification (RAVINDRAN Par 90 – “Step 302 includes pre-processing the natural language text by cleaning and vectorizing the natural language text. Pre-processing, cleaning, and vectorizing may be performed as described with respect to step 202 of FIG. 2 . The pre-processing controller (128) of FIG. 1A may perform the cleaning and vectorizing of the natural language text.”; Par 83 – “Vectorizing may include inputting the natural language text to a third MLM, such as a bi-directional long short term memory neural network.”; Par 91 – “Step 304 includes extracting negative reviews from the natural language text by executing a first machine learning model (MLM). Extracting the negative reviews may be performed as described with respect to step 202 of FIG. 2 . Thus, for example, a first input to the first MLM is the natural language text and a first output of the first MLM is first probabilities that corresponding instances of the natural language text have negative sentiments.”) and an issue classification for each customer comment based on the text of the customer comment (RAVINDRAN Par 112 – “Next, during categorization (432), the negative reviews are categorized into two or more different categories. Categorization is described with respect to step 204 of FIG. 2 or step 306 of FIG. 3 . In other words, the negative reviews are sorted by category, such as by application type (e.g., whether the negative reviews relate to the enterprise system (400), the financial management web application (402), the tax preparation web application (404), and/or the online presence web application (406)) of FIG. 4A.”); and
a post-processing module coupled to the NLP neural network and configured to generate one or more reports based on one or more of the sentiment classification and the issue classification of each customer comment (RAVINDRAN Par 77 – “Step 208 includes providing the name of the target and at least one category. Providing may be performed by transmitting the name of the target and the category into which the target falls to a software application for further processing. Providing may also be performed by displaying the name of the target and the category to a programmer or technician for review. Providing may also be performed by integrating the name of the target and the category into a dashboard or other GUI, that also displays other targets and categories, as shown in FIG. 4C through FIG. 4F.”).
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 method/system of SERNA to include comments regarding communication issues and neural networks, as taught by RAVINDRAN.
One of ordinary skill would have been motivated to include comments regarding communication issues and neural networks, in order to enable provide a variety of enterprise solutions with more accurate analyses.
REGARDING CLAIM 8, SERNA in view of RAVINDRAN discloses the system according to claim 7, wherein the system for sentiment analysis system is further configured to transmit at least one report to a department of the CSP based on one or more of the sentiment classification or the issue classification (SERNA Par 45 –“For example, if the sentiment associated with a user has been determined to be happy (sentiment analysis) and the user is asking questions regarding financial instruments (topic analysis), it could be beneficial to route that customer to a new financial advisor so the new financial advisor could build up their client book with a happy user.”; Par 90 – “Finally, at step 206, the diagram shows routing the customer support request based on 1) a localized context and various request parameters associated with the request in combination with 2) the customer sentiment derived from social media artifacts.”; Par 144 – “Once the support requests have been received, the support requests may be parsed by parsing engine 1232 for date, time, location, name of requester and message content. Thereafter, response system 1234 may redirect the support request to either an employee in the support center 1210, auto-response system in the support center 1206 or a manager 1214.”; RAVINDRAN also teaches the limitations: Par 77 – “Step 208 includes providing the name of the target and at least one category. Providing may be performed by transmitting the name of the target and the category into which the target falls to a software application for further processing. Providing may also be performed by displaying the name of the target and the category to a programmer or technician for review. Providing may also be performed by integrating the name of the target and the category into a dashboard or other GUI, that also displays other targets and categories, as shown in FIG. 4C through FIG. 4F.”).
REGARDING CLAIM 9, SERNA in view of RAVINDRAN discloses the system according to claim 8, wherein the department of the CSP is one or more of network support, customer support, product development, marketing, or billing (SERNA Par 144 – “Thereafter, response system 1234 may redirect the support request to either an employee in the support center 1210, auto-response system in the support center 1206 or a manager 1214.”).
REGARDING CLAIM 10, SERNA in view of RAVINDRAN discloses the system according to claim 7, wherein the set of customer comments includes associated data collected from the one or more online platforms for each customer comment (SERNA Par 42 – “This data may then be parsed and transformed into structured data which is then stored in a database. For the purposes of this disclosure, at least the following data points may be tracked: date, time, location username and message.”; Par 62 –“ The system may also include a processor. The processor may be configured to harvest, for each customer support request, a plurality of artifacts from social media account history and/or other third party data source information. The artifacts may be associated with a user. The user may be associated with the device identification number. Each of the plurality of artifacts may include sentiment information relevant to the customer support request.”; Par 64 – “The processor may be further configured to build a current profile for the communication device. The current profile may be based on the plurality of artifacts and the historical information.”), and wherein the profile generated for each customer comment includes the associated data for the customer comment (SERNA Par 177 – “FIG. 20 shows using microprocessor 2008 to convert historical sentiment value 2002, current sentiment value 2004 and message information 2006 into a current profile 2010.”).
Claim 11 is similar to claim 3; thus, it is rejected under the same rationale.
REGARDING CLAIM 13, SERNA in view of RAVINDRAN discloses the system according to claim 7.
RAVINDRAN discloses a method/system to identify issues from user comments, wherein the NLP neural network is an artificial neural network (RAVINDRAN Par 48 – “Other types of models may be used for the second machine learning model (120B). For example, a Multi-Layer Perceptron Model (MLP) may be used to categorize the negative reviews. A MLP is a fully connected class of feed forward artificial neural networks. However, other models may be used, like XGBoost, which is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. Recurrent neural networks can also be used to categorize the reviews.”; Par 83 – “As part of pre-processing, the method may also include vectorizing the natural language text to generate the first input to the MLM at step 202. Vectorizing may include inputting the natural language text to a third MLM, such as a bi-directional long short term memory neural network. Vectorizing also may include receiving, as output from the third MLM, a matrix of numbers representing both the natural language text and contexts of sentences in the natural language text.”).
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 method/system of SERNA to include neural networks, as taught by RAVINDRAN.
One of ordinary skill would have been motivated to include comments regarding neural networks, in order to enable provide a variety of enterprise solutions with more accurate analyses.
REGARDING CLAIM 14, SERNA in view of RAVINDRAN discloses a non-transitory, computer readable medium storing instructions that, when executed by one or more electronic processors, perform a set of functions, the set of functions comprising: performing the steps of claim 1; thus, it is rejected under the same rationale.
Claim 15 is similar to claim 2; thus, it is rejected under the same rationale.
REGARDING CLAIM 16, SERNA in view of RAVINDRAN discloses the non-transitory computer-readable medium according to claim 15, wherein the associated data includes one or more of a location, an IP address, a device used to post the customer comment, a user ID, a date of posting the customer comment, or a time of posting the customer comment (SERNA Par 42 – “This data may then be parsed and transformed into structured data which is then stored in a database. For the purposes of this disclosure, at least the following data points may be tracked: date, time, location username and message.”; Par 62 –“ The system may also include a processor. The processor may be configured to harvest, for each customer support request, a plurality of artifacts from social media account history and/or other third party data source information. The artifacts may be associated with a user. The user may be associated with the device identification number. Each of the plurality of artifacts may include sentiment information relevant to the customer support request.”; Par 64 – “The processor may be further configured to build a current profile for the communication device. The current profile may be based on the plurality of artifacts and the historical information.”).
Claim 17 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 19 is similar to claim 5; thus, it is rejected under the same rationale.
Claim 20 is similar to claim 6; thus, it is rejected under the same rationale.
Claims 4, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over SERNA (US 20210326940 A1) in view of RAVINDRAN (US 2023/0385884 A1), and in further view of MOUNIER (US 2021/0390563 A1).
REGARDING CLAIM 4, SERNA in view of RAVINDRAN discloses the method according to claim 1, further comprising generating, using the NLP neural network, a predicted location for at least one customer comment of the set of customer comments (SERNA Par 144 – “API feed 1230 preferably acts as a conduit to receive support requests in the form of social media communications such as Tweets. Once the support requests have been received, the support requests may be parsed by parsing engine 1232 for date, time, location, name of requester and message content.”) [based on the text of the at least one customer comment].
SERNA in view of RAVINDRAN does not explicitly teach the [square-bracketed] limitations.
MOUNIER disclose a method/system for analyzing customer comments comprising: generating, using the NLP neural network, a predicted location for at least one customer comment of the set of customer comments [based on the text of the at least one customer comment] (MOUNIER Claim 2 – “using natural language processing to extract organization names, people names and locations from the text, and categorizing opinions in the text, and determining if the text is positive, negative or neutral toward a topic.”).
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 method/system of SERNA in view of RAVINDRAN to include extracting location information from text, as taught by MOUNIER.
One of ordinary skill would have been motivated to include extracting location information from text in order to efficiently extract relevant information associated with a user with the given data.
Claim 12 is similar to claim 4; thus, it is rejected under the same rationale.
Claim 18 is similar to claim 4; thus, it is rejected under the same rationale.
Conclusion
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655